system

Abstract

Provide a system. [Solution] A way for NPOs to input the amount of funding they wish to raise and how it will be used, A way for companies to input the messages they want to send to society, A means for evaluating the affinity between the NPO and the company based on the input fundraising amount, purpose, and message; A means for proposing suitable companies to the NPO based on the evaluation; A means for proposing appropriate NPO corporations to companies based on the evaluation; A means for notifying the NPO and the company of the content of the proposal; A system including:

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] This invention aims to solve the problem that many NPOs, which operate with a small number of staff, have difficulty expanding the scale of their activities due to a lack of resources for fundraising, as well as the problem that companies making donations lack information to select appropriate recipients, resulting in donations being concentrated on certain well-known NPO organizations and diluting the company's message of social contribution. [Means for solving the problem]

[0005] According to the present invention, a system is provided which enables efficient and effective matching of fundraising and donations between NPO corporations and companies by providing a means for NPO corporations to input the amount of fundraising they wish to raise and its use, a means for companies to input the message they wish to send to society, a means for evaluating the affinity between NPO corporations and companies based on the input amount of fundraising they wish to raise, the use of the funds, and the message, a means for suggesting appropriate companies to NPO corporations based on the evaluation, a means for suggesting appropriate NPO corporations to companies based on the evaluation, and a means for notifying the NPO corporations and companies of the content of the proposals.

[0006] An "NPO" is a corporation that is established primarily for the purpose of contributing to society and is operated on a non-profit basis.

[0007] The "funding requirement" is the amount of money an NPO needs to carry out a specific project or activity.

[0008] "Purpose of fundraising" refers to the specific purpose for which the funds raised by the NPO will be used.

[0009] An "enterprise" is a legal entity that provides products or services for profit.

[0010] The "message that a company wants to send to society" is the content that shows what values ​​and intentions the company wants to express to society through donations.

[0011] "Affinity" refers to the degree to which common values ​​and goals are aligned between an NPO and a company.

[0012] "Generative AI" is artificial intelligence that automatically generates appropriate suggestions based on input data.

[0013] A "database" is a collection of information about NPOs and companies that is centrally managed and can be searched and updated.

[0014] "Proposal details" refers to information including the specific conditions and details of fundraising requested by the NPO and donations made by companies.

[0015] "Matching" is the process of establishing the right combination of fundraising and donation recipients between NPOs and companies. [Brief explanation of the drawings]

[0016] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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, a 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), and an APU (Accelerated Processing Unit).

[0020] 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.

[0021] 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.

[0022] 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), Bluetooth (registered trademark), etc.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0028] 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.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This system is a matching system that uses generative AI to support effective fundraising and donation destination selection between NPOs and companies. The specific configuration for implementing the system of the present invention is as follows.

[0038] Data Entry Phase

[0039] Data entry for nonprofit organizations

[0040] 1. The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[0041] 2. The terminal sends the login information to the server, and the server performs user authentication.

[0042] 3. After logging in, the user enters the amount of funds they wish to raise and the specific purpose of the funds (e.g., "1 million yen" or "urban greening project") in the form that appears.

[0043] 4. The terminal sends the input amount of funds desired to be raised and the purpose to the server.

[0044] Corporate Data Entry

[0045] 5. The user (company representative) uses the terminal to access the web application in the same way and log in.

[0046] 6. The terminal sends the login information to the server, and the server performs user authentication.

[0047] 7. After logging in, the user enters the message they want to convey to society (e.g., "I'm interested in environmental protection") into the form that appears.

[0048] 8. The terminal sends the entered message to the server.

[0049] Data Processing Phase

[0050] 9. The server stores the information sent by NPOs and companies in a database.

[0051] 10. Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[0052] Matching Phase

[0053] 11. The server searches the company data in the database based on the NPO's desired conditions.

[0054] 12. The server uses the generative AI to calculate the affinity score between the NPO and the company, taking into account factors such as purpose, amount, and social contribution theme.

[0055] 13. The server filters companies with high affinity scores and generates recommendations.

[0056] Proposal Generation Phase

[0057] Proposals for NPOs

[0058] 14. The server uses the generation AI to generate specific proposals for the NPO. Example: "Company X can provide 1 million yen for urban greening projects."

[0059] 15. The server sends the generated proposal to the device.

[0060] 16. The terminal displays the proposal to the user (NPO representative).

[0061] Proposals for companies

[0062] 17. The server uses AI to generate specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[0063] 18. The server sends the generated donation proposal to the terminal.

[0064] 19. The terminal displays the donation proposal to the user (company representative).

[0065] Matching establishment phase

[0066] 20. The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected company list.

[0067] 21. The terminal sends the selection information to the server.

[0068] 22. The server notifies the company representative of the procurement request.

[0069] 23. The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[0070] 24. The terminal sends the selection information to the server.

[0071] 25. The server notifies the NPO representative of the donation decision.

[0072] 26. Once both parties agree, the server will consider the match to be successful and update the information in the database.

[0073] 27. The server sends a notification of successful matching to both the NPO and the company.

[0074] 28. The device will display a match notification to each user.

[0075] Specific examples

[0076] As a concrete example, if the nonprofit organization "Environmental Protection Association" wants to raise 1 million yen to use for an "urban greening project," it first accesses a web application from a terminal and inputs the amount it wants to raise and the purpose of the fundraising. Similarly, a company "Ecotech Co., Ltd." inputs a message saying that it is "interested in environmental protection."

[0077] The server uses a generative AI to calculate highly compatible combinations based on the information from both parties, and notifies the NPO "Environmental Protection Association" that "Ecotech Co., Ltd. can donate to urban greening projects." At the same time, it suggests to the company "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen."

[0078] Both parties confirm the proposal, the NPO sends a procurement request to the company, and when the company decides to make a donation, a match is made and a notification is sent to both parties. In this way, the system of the present invention enables efficient and effective fundraising and donations.

[0079] The processing flow will be explained below.

[0080] Step 1:

[0081] The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[0082] Step 2:

[0083] The terminal sends the login information to the server.

[0084] Step 3:

[0085] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[0086] Step 4:

[0087] The user confirms that the authentication was successful and then enters the amount of funding they wish to raise (e.g., "1 million yen") and its specific purpose (e.g., "urban greening project") into the form.

[0088] Step 5:

[0089] The terminal transmits the input desired fundraising amount and purpose to the server.

[0090] Step 6:

[0091] Similarly, the user (company representative) accesses the web application from a terminal and logs in by entering their ID and password.

[0092] Step 7:

[0093] The terminal sends the login information to the server.

[0094] Step 8:

[0095] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[0096] Step 9:

[0097] The user confirms that the authentication was successful and then enters the message he or she wants to convey to society (e.g., "I'm interested in environmental protection") into the form.

[0098] Step 10:

[0099] The terminal transmits the input message to the server.

[0100] Step 11:

[0101] The server stores the information sent by NPOs and companies in a database.

[0102] Step 12:

[0103] Based on the stored information, the server uses generative AI to perform preprocessing, cleaning and normalizing the input data.

[0104] Step 13:

[0105] The server searches for company information in the database based on the NPO's desired criteria.

[0106] Step 14:

[0107] The server uses a generative AI to calculate the affinity score between NPOs and companies, taking into account factors such as purpose, amount, and social contribution theme.

[0108] Step 15:

[0109] The server filters companies with high affinity scores and creates a list.

[0110] Step 16:

[0111] The server uses a generative AI to generate specific proposals for NPOs.

[0112] Step 17:

[0113] The server transmits the generated proposal to the terminal.

[0114] Step 18:

[0115] The terminal displays the proposal contents to the user (NPO representative).

[0116] Step 19:

[0117] The server also uses generative AI to generate specific donation proposals for companies.

[0118] Step 20:

[0119] The server transmits the generated donation proposal to the terminal.

[0120] Step 21:

[0121] The terminal displays the donation proposal to the user (company representative).

[0122] Step 22:

[0123] The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected list of companies.

[0124] Step 23:

[0125] The terminal transmits the selection information to the server.

[0126] Step 24:

[0127] The server notifies the company representative of the procurement request.

[0128] Step 25:

[0129] The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[0130] Step 26:

[0131] The terminal transmits the selection information to the server.

[0132] Step 27:

[0133] The server notifies the NPO representative of the donation decision.

[0134] Step 28:

[0135] Once both parties agree, the server determines that a match has been made and updates the information in the database.

[0136] Step 29:

[0137] The server sends a notification of a successful match to both the NPO and the company.

[0138] Step 30:

[0139] The terminal displays a match notification to each user.

[0140] Example 1

[0141] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0142] When nonprofit organizations raise funds, the process of finding the right entity is extremely time-consuming and inefficient. This increases the time and effort spent on fundraising, making it difficult to focus on their core activities. It is also often difficult for organizations to find nonprofits that are suitable for their social contribution activities, preventing them from making effective donations. This creates a problem of mismatches between the fundraising and donation processes.

[0143] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0144] In this invention, the server includes: a means for a nonprofit organization to input the amount of fundraising it wishes to receive and its use; a means for a business entity to input a message it wishes to send to society; a means for evaluating the affinity between the nonprofit organization and business entities based on the input amount of fundraising it wishes to receive, its use, and the message; a means for suggesting suitable business entities to the nonprofit organization based on the evaluation; a means for suggesting suitable nonprofit organizations to business entities based on the evaluation; a means for notifying the nonprofit organization and business entities of the content of the proposal; a means for notifying the business entity of a procurement request made by the nonprofit organization; a means for notifying the nonprofit organization of a donation decision made by the business entity; and a means for notifying the nonprofit organization and business entities of a match based on an agreement between the two parties. This enables efficient matching between nonprofit organizations and business entities, facilitating the fundraising and donation processes.

[0145] A "non-profit organization" is an organization that operates for the public benefit and does not seek profit.

[0146] "Business Entity" means a legal entity or individual engaged in commercial activities.

[0147] "Funding Suggestion" refers to the amount of funding a nonprofit organization needs for a particular activity or project.

[0148] "Use" indicates the purpose for which the funds obtained through fundraising will be used.

[0149] A "message" refers to the values ​​and initiatives that a business wants to convey to society.

[0150] A "compatibility assessment" is a process that uses numerical values ​​and indicators to evaluate the compatibility between a nonprofit organization and a business entity.

[0151] "Proposal" means a proposal for collaboration with a non-profit organization or entity generated based on an affinity assessment.

[0152] "Procurement Request" means a request by a Nonprofit Organization for funding from a particular entity.

[0153] A "donation decision" is the process by which an entity decides to provide funding to a particular nonprofit organization.

[0154] A "match" is when a funding agreement is reached between a nonprofit organization and a business entity.

[0155] "Notification" means the act of communicating information, such as proposals and agreements, to nonprofit organizations and entities.

[0156] "Generative AI" refers to artificial intelligence systems that generate natural language and perform data analysis.

[0157] This invention is a matching system that uses generative AI to support efficient fundraising and donation destination selection between nonprofit organizations and business entities. Specific embodiments for implementing the invention are described below.

[0158] Hardware and software used

[0159] This system consists of a server, a terminal, and a user. The server is equipped with a database and a generative AI model, and the terminal implements a web application that provides an interface for user operation. The main software used is a web browser (e.g., GOOGLE CHROME (registered trademark), Firefox), an HTTP server (e.g., Apache (registered trademark), Nginx), a database management system (e.g., MySQL (registered trademark), PostgreSQL), and an AI model execution environment (e.g., TENSORFLOW (registered trademark), PyTorch).

[0160] Data Entry Phase

[0161] Nonprofit data entry

[0162] 1. The user (non-profit organization employee) accesses the system's web application using a web browser and logs in by entering their ID and password. The terminal sends the login information to the server, which then authenticates the user.

[0163] 2. After successful authentication, the user inputs the amount of funds they wish to raise and the specific purpose of the funds. The terminal then sends this information to the server.

[0164] Business entity data entry

[0165] 3. The user (business entity employee) similarly accesses the web application, enters login information, and is authenticated by the server.

[0166] 4. After successful authentication, the user enters the message they wish to send to society. The device then sends this information to the server.

[0167] Data Processing Phase

[0168] 5. The server stores the information submitted by nonprofit organizations and businesses in a database, and the generative AI model cleans and normalizes the data based on the stored information.

[0169] Matching Phase

[0170] 6. The server searches the entity data based on the nonprofit organization's desired criteria, calculates affinity scores using the generative AI model, filters entities with high affinity scores, and generates proposals.

[0171] Proposal Generation Phase

[0172] Proposals for nonprofit organizations

[0173] 7. The server uses the generative AI model to generate specific proposals for the nonprofit organization and sends them to the device, which then displays them to the user.

[0174] Proposals to business entities

[0175] 8. The server also uses the generative AI model to generate specific donation suggestions for the business entity and sends them to the device, which then displays them to the user.

[0176] Matching establishment phase

[0177] 9. The user (non-profit organization representative) reviews the proposal and selects the entity to request procurement from the list of selected entities. The selection information is sent to the server and notified to the entity representative.

[0178] 10. The user (business operator) reviews the donation proposal and selects the nonprofit organization to which the donation will be made. The selection information is sent to the server and notified to the nonprofit organization operator.

[0179] 11. The server will then determine if the match is successful and update the information in the database. A notification of the match will be sent to both the nonprofit organization and the business entity, and the device will display it to the user.

[0180] Specific examples

[0181] As a concrete example, if the nonprofit organization "Environmental Protection Association" wishes to raise 1 million yen to use for an "urban greening project," the user accesses a web application from their device and inputs the amount they wish to raise and the purpose of the donation. Similarly, the business entity "Ecotech Co., Ltd." inputs a message saying, "I'm interested in environmental protection." The server uses generative AI based on the information of both parties to calculate highly compatible pairs and notifies the nonprofit organization "Environmental Protection Association" that "Ecotech Co., Ltd." can donate to the urban greening project." At the same time, it proposes to the business entity "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen." Both parties confirm the proposal, and the nonprofit organization sends a procurement request to the business entity. When the business entity decides to donate, a match is made and notifications are sent to both parties.

[0182] Example of prompt input to a generative AI model

[0183] Nonprofit data entry prompt example:

[0184] The non-profit organization Environmental Protection Association is seeking 1 million yen for an urban greening project.

[0185] Example of a prompt for business entity message input:

[0186] Ecotech Co., Ltd. cares about protecting the environment.

[0187] The generative AI model calculates an affinity score based on these inputs and generates specific suggestions.

[0188] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0189] Step 1:

[0190] The user (non-profit organization employee) accesses the system's web application using a web browser and logs in by entering their ID and password. Input data: ID and password. Output data: authentication request. The specific operation is to access the login page and enter their ID and password in the designated form.

[0191] Step 2:

[0192] The terminal sends the login information entered by the user to the server. Input data: ID and password. Output data: HTTP POST request. Specifically, when the user clicks the "Login" button, the login information is sent to the server.

[0193] Step 3:

[0194] The server authenticates the received login information, and if the user has entered it correctly, it generates an authentication token and returns it to the terminal. Input data: ID and password. Output data: authentication token. Specifically, the server searches the database for the corresponding user information, and if there is a match, it generates an authentication token and returns it as a response.

[0195] Step 4:

[0196] After logging in, the user enters the desired amount of funding and the specific purpose in the form that appears. Input data: desired amount of funding, purpose of funding. Output data: request to send input data. Specifically, the user enters the specific amount and purpose in the "desired amount of funding" and "purpose" fields, respectively, and presses the "Submit" button.

[0197] Step 5:

[0198] The terminal sends the input desired fundraising amount and purpose to the server. Input data: desired fundraising amount, purpose of fundraising. Output data: HTTP POST request. Specifically, the terminal sends data to the server using an HTTP POST request.

[0199] Step 6:

[0200] The user (business person) similarly accesses the web application and logs in. Input data: ID and password. Output data: authentication request. Specifically, the business person accesses the login page in the same way as the nonprofit organization person and enters their ID and password in the designated form.

[0201] Step 7:

[0202] The terminal sends the login information of the business entity employee to the server. Input data: ID and password. Output data: HTTP POST request. Specifically, when the user clicks the "Login" button, the information is sent to the server.

[0203] Step 8:

[0204] The server authenticates the received login information, and if the business person is successfully authenticated, it generates an authentication token and returns it to the terminal. Input data: ID and password. Output data: authentication token. The specific operation is the same as the authentication process for nonprofit organization personnel.

[0205] Step 9:

[0206] After logging in, the user enters the message that the company wants to send to society in the form that appears. Input data: Message. Output data: Message sending request. The specific operation is that the business person in charge enters the specific content in the "Message" field and presses the "Send" button.

[0207] Step 10:

[0208] The terminal sends the input message to the server. Input data: message. Output data: HTTP POST request. In concrete terms, the terminal sends the transmitted message to the server as an HTTP POST request.

[0209] Step 11:

[0210] The server stores the information submitted by nonprofits and businesses in a database. Input data: fundraising amount, purpose, message. Output data: new record in the database. Specifically, the server connects to the database, inserts a new record, and saves the user's input data.

[0211] Step 12:

[0212] The server performs preprocessing using a generative AI model based on the stored information, cleaning and normalizing the input data. Input data: desired fundraising amount, purpose, message. Output data: cleaned and normalized data. Specifically, the server calls the generative AI model and formats the input data.

[0213] Step 13:

[0214] The server searches the database for entity data based on the nonprofit organization's desired criteria. Input data: cleaned and normalized data. Output data: relevant entity data. Specifically, the server uses an SQL query to extract entity data that matches the criteria.

[0215] Step 14:

[0216] The server uses the generative AI model to calculate the affinity score between the nonprofit organization and the business entity. Input data: relevant business entity data. Output data: affinity score. Specifically, the server inputs the extracted data into the generative AI model to calculate the affinity score.

[0217] Step 15:

[0218] The server filters out businesses with high affinity scores and generates proposals. Input data: affinity score. Output data: proposals. Specifically, it selects businesses with high scores and automatically generates proposals using a generative AI model.

[0219] Step 16:

[0220] The server sends the generated proposal to the terminal. Input data: proposal. Output data: HTTP response. Specifically, the server sends the generated proposal to the terminal as an HTTP response.

[0221] Step 17:

[0222] The terminal displays the proposal to the user (nonprofit organization representative). Input data: HTTP response. Output data: Display of proposal. The specific operation is that the proposal is displayed in the browser.

[0223] Step 18:

[0224] The server uses the generative AI model to generate specific donation proposals for businesses and send them to the terminal. Input data: Proposal content from nonprofit organizations. Output data: HTTP response with donation proposal content. The specific operation is to automatically generate a donation proposal using the generative AI model and send it as a response to the terminal.

[0225] Step 19:

[0226] The terminal displays the donation proposal content to the user (business entity representative). Input data: HTTP response. Output data: Display of donation proposal content. Specifically, the donation proposal content is displayed in the browser.

[0227] Step 20:

[0228] The user (non-profit organization representative) reviews the proposal content and selects the business entity to request procurement from the list of selected entities. Input data: Proposal content. Output data: Procurement request information. Specifically, the non-profit organization representative selects the target business entity from the list and presses the "Procurement request" button.

[0229] Step 21:

[0230] The terminal sends the selection information to the server. Input data: Procurement request information. Output data: HTTP POST request. Specifically, the terminal sends the selection information to the server as an HTTP POST request.

[0231] Step 22:

[0232] The server notifies the business entity person of the procurement request. Input data: Procurement request information. Output data: Notification. Specifically, the server sends an email or in-app notification of the procurement request to the business entity person.

[0233] Step 23:

[0234] The user (business entity representative) checks the donation proposal details and selects the nonprofit organization to which the donation will be made. Input data: donation proposal details. Output data: donation recipient selection information. Specifically, the business entity representative selects the nonprofit organization to which the donation will be made and presses the "Donation Confirmation" button.

[0235] Step 24:

[0236] The terminal sends the selection information to the server. Input data: donation destination selection information. Output data: HTTP POST request. Specifically, the terminal sends the selection information to the server as an HTTP POST request.

[0237] Step 25:

[0238] The server notifies the nonprofit organization's representative of the donation decision. Input data: donation destination selection information. Output data: notification. Specifically, the server sends a donation decision notification to the nonprofit organization's representative.

[0239] Step 26:

[0240] The server determines that a match has been made based on the agreement between the two parties and updates the information in the database. Input data: Adjustment information. Output data: Updated information indicating that a match has been made. Specifically, the server updates the status field in the database to "match made."

[0241] Step 27:

[0242] The server sends a notification of successful matching to both the nonprofit organization and the business. Input data: Matching update information. Output data: Notification. Specifically, the server notifies both parties of successful matching via email or in-app notification.

[0243] Step 28:

[0244] The device displays a match-making notification to each user. Input data: Notification. Output data: Display of match-making notification. Specifically, a notification is displayed in the device browser.

[0245] (Application example 1)

[0246] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0247] Existing systems for matching NPOs and corporations are problematic in that they are inefficient in fundraising and donation recipient selection. In particular, there is a lack of appropriate means for supporting collaboration between security service providers and NPOs. The present invention aims to solve these problems and provide an effective system for achieving optimal matching between NPOs and corporations.

[0248] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0249] In this invention, the server includes a means for NPOs to input the amount of fundraising they wish to raise and its use, a means for companies to input the message they wish to convey to society, and a means for evaluating the affinity between NPOs and companies based on the input amount of fundraising, use, and message. This allows security service providers to efficiently select NPOs for fundraising and donation recipients. Furthermore, evaluation results are notified and displayed through an application installed on a smartphone, smart glasses, or head-mounted display, enabling effective matching in real time.

[0250] An "NPO corporation" is an organization whose primary purpose is to engage in non-profit activities and to promote the general public's interests.

[0251] "Funding Suggestion" is the amount of money a nonprofit organization needs for a specific project or activity.

[0252] "Use" refers to the specific ways in which the NPO plans to use the funds.

[0253] A "business" is an organization that provides goods and services and operates to pursue profits.

[0254] A "message" is an expression that shows the philosophy or purpose that a company wants to communicate to society.

[0255] "Affinity" indicates the degree of agreement between the goals and objectives of an NPO and a company.

[0256] "Evaluation" is the process of quantitatively or qualitatively determining the affinity between an NPO and a company.

[0257] "Security services" are the practices and technologies used to provide physical or digital security.

[0258] A "server" is a computer system that provides data and services over a network.

[0259] A "smartphone" is a mobile device that has advanced computing power and Internet connectivity in addition to telephone functions.

[0260] "Smart glasses" are wearable devices equipped with a display to enhance visual information.

[0261] A "head-mounted display" is a display device that projects images directly into the field of vision.

[0262] An "application" is software designed to perform a specific function.

[0263] "Generative AI" is a system that uses artificial intelligence technology to generate new data and information.

[0264] "Notification" is the act of informing the user of important information or results.

[0265] "Display" is the act of visually providing information using a display or device.

[0266] This invention is a system for supporting effective fundraising and donation destination selection between NPOs and companies, and is particularly equipped with a function to promote cooperation with companies that provide security services.

[0267] System Program Overview

[0268] The system includes the following main features:

[0269] 1. A function that allows NPOs to input the amount of funds they wish to raise and the purpose for which they wish to raise funds.

[0270] 2. A function that allows companies to input messages they want to send to society.

[0271] 3. A generative AI function that evaluates the affinity between NPOs and companies based on input data.

[0272] 4. A function to generate and notify appropriate proposals to NPOs and companies based on the evaluation results.

[0273] 5. An application function that is installed on a smartphone, smart glasses, or head-mounted display and displays the evaluation results in real time.

[0274] Hardware and Software Configuration

[0275] The server provides the computational resources for storing data and performing affinity evaluation using the generated AI. Specifically, it utilizes cloud services (e.g., Amazon Web Services or Google Cloud Platform).

[0276] Users input data and receive notifications and assessment results using smartphones, smart glasses, or head-mounted displays, all of which are equipped with mobile operating systems such as iOS or Android.

[0277] Use data processing libraries (e.g., Pandas or NumPy in Python) for data cleaning and preprocessing.

[0278] Affinity assessment uses a generative AI model (e.g., GPT-3® from OpenAI®).

[0279] Processing flow details

[0280] 1. Data entry phase:

[0281] The user (NPO representative) uses a device such as a smartphone to input the amount of funds they wish to raise and the specific use of the funds into the web application.

[0282] Similarly, company representatives can use devices such as smartphones to input messages they wish to send to society.

[0283] 2. Data Processing Phase:

[0284] The server receives the entered data and stores it in a database.

[0285] Data cleaning and preprocessing is performed using Python data processing libraries.

[0286] 3. Matching Phase:

[0287] The server uses a generative AI model to evaluate the affinity between nonprofits and businesses, calculating an affinity score based on the input fundraising amount, purpose, and message.

[0288] 4. Proposal generation phase:

[0289] Proposals are generated based on the affinity assessment. Specifically, NPOs are notified of companies that can donate, and companies are notified of NPOs that need support.

[0290] Specific examples

[0291] As a concrete example, the following prompt sentences can be used to input the generative AI model to achieve effective matching:

[0292] Example prompt sentence:

[0293] The NPO "Children's Safety Association" wants to raise 500,000 yen to be used for "Local Child Protection Programs." The company "Security Solutions Inc." wants to support "Child Safety Initiatives." Calculate the affinity scores for these two organizations and generate the best matching proposal.

[0294] Based on these prompts, the generative AI model calculates an affinity score between the NPO and the company and notifies appropriate suggestions, thereby realizing effective cooperation between the security service company and the NPO.

[0295] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0296] Step 1:

[0297] The user (NPO employee) accesses the web application using a device such as a smartphone or PC and enters login information. The device sends the entered login information to the server, which then authenticates the user. After authentication, the user enters the amount of funding they wish to raise and its purpose into a web form. The input data generated is the "desired amount of funding" and "purpose." The device then sends this input data to the server.

[0298] Step 2:

[0299] A user (company representative) similarly accesses a web application using a device such as a smartphone or PC and enters login information. The device sends the entered login information to the server, which then authenticates the user. After authentication, the user enters the message they wish to send to society into a web form. A "message" is generated as input data. The device then sends this input data to the server.

[0300] Step 3:

[0301] The server stores the received input data ("desired fundraising amount," "purpose," and "message") in a database. When storing the data, it performs data cleaning to maintain data integrity. Specifically, the database uses the Python Pandas library to remove spaces and standardize the format. The output of this process is well-organized data stored in the database.

[0302] Step 4:

[0303] The server uses a generative AI model to evaluate the affinity between NPOs and companies based on the data stored in the database. Specifically, it sends the input data to a generative AI (e.g., OpenAI's GPT-3) and calculates an affinity score. The server generates a score for each combination of NPO and company and selects the combination with the highest score. An "affinity score" is generated as the output.

[0304] Step 5:

[0305] The server generates specific proposals for NPOs and businesses based on the affinity scores. Using a generative AI model, the server automatically generates prompts to inform NPOs of businesses that can donate to them, and to inform businesses of NPOs that need their support. For example, the server generates the following prompts:

[0306] The NPO "Children's Safety Association" wants to raise 500,000 yen to be used for "Local Child Protection Programs." The company "Security Solutions Inc." wants to support "Child Safety Initiatives." Calculate the affinity scores for these two organizations and generate the best matching proposal.

[0307] As an output, a "proposal" is generated.

[0308] Step 6:

[0309] The server notifies the generated proposal contents to the terminals of the corresponding NPO and company personnel. The notified proposal contents are displayed on the terminal screen, allowing the user to confirm the proposal contents. A "notification of proposal contents" is generated as an output.

[0310] Step 7:

[0311] The user (NPO representative) checks the proposal details and selects an appropriate company from among the proposed companies. The selection details are sent to the server via the terminal. The server receives this selection information and notifies the company representative of the procurement request. The "Procurement Request Details" is generated as the output.

[0312] Step 8:

[0313] The user (company representative) receives a notification of the procurement request and selects the NPO to which the donation will be made. The selection is sent to the server via the terminal. The server receives this selection information and notifies the NPO representative of the decision to donate. A "donation decision notification" is generated as the output.

[0314] Step 9:

[0315] Once both parties agree, the server determines that the match has been made and updates the information in the database. It then sends a notification of the match to both the NPO and the company. The device displays the notification to each user. A "matching successful notification" is generated as an output.

[0316] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0317] This system is a matching system that uses generative AI and an emotion engine to support effective fundraising and donation destination selection between NPOs and companies. The specific configuration for implementing the system of the present invention is as follows.

[0318] Data Entry Phase

[0319] Data entry for nonprofit organizations

[0320] 1. The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[0321] 2. The terminal sends the login information to the server, and the server performs user authentication.

[0322] 3. After logging in, the user enters the amount of funds they wish to raise and the specific purpose of the funds (e.g., "1 million yen" or "urban greening project") in the form that appears.

[0323] 4. The terminal sends the input amount of funds desired to be raised and the purpose to the server.

[0324] Corporate Data Entry

[0325] 5. The user (company representative) uses the terminal to access the web application in the same way and log in.

[0326] 6. The terminal sends the login information to the server, and the server performs user authentication.

[0327] 7. After logging in, the user enters the message they want to convey to society (e.g., "I'm interested in environmental protection") into the form that appears.

[0328] 8. The terminal sends the entered message to the server.

[0329] Data Processing Phase

[0330] 9. The server stores the information sent by NPOs and companies in a database.

[0331] 10. Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[0332] Matching Phase

[0333] 11. The server searches for company information in the database based on the NPO's desired criteria.

[0334] 12. The emotion engine recognizes and analyzes user emotions from input information from NPOs and companies.

[0335] 13. The server uses generative AI to calculate the affinity score between the NPO and the company, taking into account the purpose, amount, and social contribution theme as well as the perceived sentiment.

[0336] 14. The server filters companies with high affinity scores and creates a list.

[0337] Proposal Generation Phase

[0338] Proposals for NPOs

[0339] 15. The server generates specific proposals for NPOs based on the results of the generative AI and emotion engine. Example: "Company X can provide 1 million yen for urban greening projects."

[0340] 16. The server sends the generated proposal to the device.

[0341] 17. The terminal displays the proposal to the user (NPO representative).

[0342] Proposals for companies

[0343] 18. Based on the results of the generative AI and emotion engine, the server also generates specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[0344] 19. The server sends the generated donation proposal to the terminal.

[0345] 20. The terminal displays the donation proposal to the user (company representative).

[0346] Matching establishment phase

[0347] 21. The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected company list.

[0348] 22. The terminal sends the selection information to the server.

[0349] 23. The server notifies the company representative of the procurement request.

[0350] 24. The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[0351] 25. The terminal sends the selection information to the server.

[0352] 26. The server notifies the NPO representative of the donation decision.

[0353] 27. Once both parties agree, the server will consider the match to be successful and update the information in the database.

[0354] 28. The server sends a notification of successful matching to both the NPO and the company.

[0355] 29. The device will display a match notification to each user.

[0356] Specific examples

[0357] For example, if the nonprofit organization "Environmental Protection Association" wants to raise 1 million yen to use for an "urban greening project," the person in charge can access the web application from a terminal and enter the amount they want to raise and the purpose of the donation. Similarly, the company "Ecotech Co., Ltd." can enter a message saying that they are "interested in environmental protection."

[0358] The server uses a generative AI to evaluate affinity based on the information from both parties, and then adjusts the affinity score by analyzing the user's emotions with an emotion engine. As a result, the server notifies the NPO "Environmental Protection Association" that "Ecotech Co., Ltd. is available to donate to urban greening projects." At the same time, it suggests to the company "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen."

[0359] Both parties confirm the proposal, the NPO sends a procurement request to the company, and when the company decides to make a donation, a match is made and a notification is sent to both parties. In this way, the system of the present invention enables efficient and effective fundraising and donations.

[0360] The processing flow will be explained below.

[0361] Step 1:

[0362] The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[0363] Step 2:

[0364] The terminal sends the login information to the server.

[0365] Step 3:

[0366] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[0367] Step 4:

[0368] The user confirms that the authentication was successful and then enters the amount of funding they wish to raise (e.g., "1 million yen") and its specific purpose (e.g., "urban greening project") into the form.

[0369] Step 5:

[0370] The terminal transmits the input desired fundraising amount and purpose to the server.

[0371] Step 6:

[0372] Similarly, the user (company representative) accesses the web application from a terminal and logs in by entering their ID and password.

[0373] Step 7:

[0374] The terminal sends the login information to the server.

[0375] Step 8:

[0376] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[0377] Step 9:

[0378] The user confirms that the authentication was successful and then enters the message he or she wants to convey to society (e.g., "I'm interested in environmental protection") into the form.

[0379] Step 10:

[0380] The terminal transmits the input message to the server.

[0381] Step 11:

[0382] The server stores the information sent by NPOs and companies in a database.

[0383] Step 12:

[0384] The server uses an emotion engine to recognize the user's emotions from the input information of NPOs and companies and record them in a database.

[0385] Step 13:

[0386] Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[0387] Step 14:

[0388] The server searches for company information in the database based on the NPO's desired criteria.

[0389] Step 15:

[0390] The server uses generative AI to calculate the affinity score between NPOs and companies, taking into account the purpose, amount, and social contribution theme, as well as perceived emotions.

[0391] Step 16:

[0392] The server filters companies with high affinity scores and creates a list.

[0393] Step 17:

[0394] The server generates specific proposals for NPOs based on the results of the generative AI and emotion engine. Example: "Company X can provide 1 million yen for urban greening projects."

[0395] Step 18:

[0396] The server transmits the generated proposal to the terminal.

[0397] Step 19:

[0398] The terminal displays the proposal contents to the user (NPO representative).

[0399] Step 20:

[0400] Based on the results of the generative AI and emotion engine, the server also generates specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[0401] Step 21:

[0402] The server transmits the generated donation proposal to the terminal.

[0403] Step 22:

[0404] The terminal displays the donation proposal to the user (company representative).

[0405] Step 23:

[0406] The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected list of companies.

[0407] Step 24:

[0408] The terminal transmits the selection information to the server.

[0409] Step 25:

[0410] The server notifies the company representative of the procurement request.

[0411] Step 26:

[0412] The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[0413] Step 27:

[0414] The terminal transmits the selection information to the server.

[0415] Step 28:

[0416] The server notifies the NPO representative of the donation decision.

[0417] Step 29:

[0418] Once both parties agree, the server determines that a match has been made and updates the information in the database.

[0419] Step 30:

[0420] The server sends a notification of a successful match to both the NPO and the company.

[0421] Step 31:

[0422] The terminal displays a match notification to each user.

[0423] Example 2

[0424] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0425] Until now, it has been difficult to efficiently and effectively raise funds and select donation recipients between NPOs and companies, and while a two-way affinity assessment is considered particularly important, achieving this has been problematic as it requires a lot of time and effort.Therefore, there has been a need for a system that can efficiently assess the affinity between NPOs and companies, make optimal proposals to both parties, and promptly notify them of these proposals.

[0426] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0427] In this invention, the server includes: a means for an NPO to input the amount of fundraising it wishes to receive and its use; a means for a company to input a message it wishes to send to society; a means for evaluating the affinity between the NPO and companies based on the input amount of fundraising it wishes to receive, its use, and the message; a means for suggesting suitable companies to the NPO based on the evaluation; a means for suggesting suitable NPOs to companies based on the evaluation; a means for calculating an affinity score using a generative AI and an emotion engine; and a means for notifying the NPO and companies of the content of the suggestions. This enables quick and effective fundraising and donation destination selection between NPOs and companies.

[0428] An "NPO corporation" refers to a corporation that engages in public interest activities for non-profit purposes.

[0429] "Desired fundraising amount" refers to the amount of funds an NPO needs for its projects and activities.

[0430] "Use" refers to the specific purpose that indicates what projects or activities the NPO will use the funds it raises for.

[0431] "Business" refers to an organization that provides goods or services for profit.

[0432] A "message" refers to information that expresses the intentions and interests that a company wants to convey to society.

[0433] "Affinity" refers to the degree of agreement between the goals and values ​​of an NPO and a company.

[0434] "Generative AI" refers to artificial intelligence technology that automatically analyzes data and derives specific patterns and outcomes.

[0435] An "emotion engine" refers to technology that analyzes a user's emotions and generates appropriate suggestions based on those emotions.

[0436] "Affinity score" refers to the evaluation result that quantifies the affinity between an NPO and a company.

[0437] "Proposal content" refers to specific proposal information for NPOs and companies created based on generative AI and emotion engines.

[0438] "Notification" refers to the act of communicating important information, such as proposal details, to NPOs and companies.

[0439] "Database" refers to a system for systematically storing and managing data collected from NPOs and companies.

[0440] This invention is a matching system for effective and efficient fundraising and donation destination selection between NPOs and companies. This system uses generative AI and an emotion engine to evaluate affinity and make optimal proposals.

[0441] Data Entry Phase

[0442] Data entry for nonprofit organizations

[0443] The user (NPO employee) uses a terminal to access the web application and log in by entering their ID and password. The terminal sends the login information to the server, which then authenticates the user. After logging in, the user enters the amount of funding they wish to raise and its specific purpose (e.g., "1 million yen" or "urban greening project"), and the terminal then sends the information they entered to the server.

[0444] Corporate Data Entry

[0445] Similarly, users (company personnel) use their terminals to access and log in to a web application. The terminals send the login information to the server, which then authenticates the user. After logging in, the user enters a message they want to convey to society (e.g., "I'm interested in environmental protection"), and the terminals send the message to the server.

[0446] Data Processing Phase

[0447] The server stores the information sent by NPOs and companies in a database. Next, the server uses generative AI to perform preprocessing, cleaning the input data (removing noise and filling in missing data) and normalizing it (standardizing the data format).

[0448] Matching Phase

[0449] The server searches for company information in a database based on the NPO's desired criteria. The emotion engine recognizes and analyzes user emotions from the input information about the NPO and company. The server uses generative AI to calculate an affinity score, taking into account the desired fundraising amount, purpose, message content, and emotion score. The server filters companies with high affinity scores and creates a list.

[0450] Proposal Generation Phase

[0451] Proposals for NPOs

[0452] The server generates a specific proposal for the NPO based on the results of the generation AI and emotion engine. For example, "Company X can provide 1 million yen for a greening project in an urban area." The server sends the generated proposal to the terminal, which then displays it to the user (NPO representative).

[0453] Proposals for companies

[0454] Based on the results of the generation AI and the emotion engine, the server also generates specific donation proposals for companies. For example, "NPO Y is seeking a donation of 1 million yen." The server sends the generated donation proposal to the terminal, which then displays it to the user (company representative).

[0455] Matching establishment phase

[0456] The user (NPO representative) reviews the proposal and selects a company to request procurement from the list of selected companies. The device sends the selection information to the server, which notifies the company representative of the procurement request. The user (company representative) reviews the donation proposal and selects the NPO to donate to. The device sends the selection information to the server, which notifies the NPO representative of the decision to donate. Once both parties agree, a match is made and the server updates the information in the database. The server sends a notification of the match to both the NPO and the company, and the device displays a notification of the match to each user.

[0457] Specific examples

[0458] For example, if an NPO needs 1 million yen for an "urban greening project," the person in charge accesses the web application from their device, logs in, and enters the desired fundraising amount and purpose. The company enters a message saying, "I'm interested in environmental protection." The server uses generative AI to evaluate affinity based on the information of both parties, and the emotion engine analyzes the user's emotions to adjust the affinity score. As a result, the server notifies the NPO that "the company can donate to the urban greening project" and suggests to the company that "the NPO is seeking a donation of 1 million yen." Both parties confirm the proposal, and the NPO sends a procurement request to the company. When the company decides to donate, a match is made and notifications are sent to both parties. In this way, the system of the present invention enables efficient and effective fundraising and donation destination selection.

[0459] Examples of standard prompts

[0460] "If an NPO needs to raise 1 million yen to fund a greening project in an urban area, please suggest what kind of company would be suitable."

[0461] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0462] Step 1:

[0463] The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password. The terminal sends the login information to the server, which then authenticates the user. If the login is successful, the server returns an authentication success message to the terminal and displays an input form for the user. Input: ID, password. Output: Authentication success message, input form.

[0464] Step 2:

[0465] The user (NPO representative) inputs the amount of funding they wish to raise and its specific purpose. The device sends this information to the server. The server temporarily stores the received data and prepares to connect to the database. Input: Desired amount of funding, purpose. Output: Temporarily stores input data.

[0466] Step 3:

[0467] The user (company representative) uses a terminal to access the web application, logs in by entering their ID and password in the same way, and enters a message. The terminal sends this information to the server. The server temporarily stores the received data and checks the consistency of the NPO's data with the company's data. Input: ID, password, message. Output: Temporarily stores input data, checks data consistency.

[0468] Step 4:

[0469] The server stores all data sent by NPOs and companies in a database. After storing, the server performs preprocessing (data cleaning and normalization) using generative AI. This process removes noise from the data and fills in missing data. Input: All stored data. Output: Cleaned and normalized data.

[0470] Step 5:

[0471] The server searches for company information based on the NPO data and creates an initial list of matching candidates. A high-speed search algorithm is used for this. Input: NPO's desired conditions. Output: Matching candidate list.

[0472] Step 6:

[0473] The emotion engine recognizes and analyzes user emotions from input information from NPOs and companies. For example, it distinguishes between positive and negative emotions and assigns an emotion score to each. Input: Input information from NPOs and companies. Output: Emotion score.

[0474] Step 7:

[0475] The server uses generative AI to calculate affinity scores between NPOs and companies. This calculation takes into account the desired fundraising amount, purpose, message content, and sentiment score. Based on the calculation results, it filters companies with high affinity scores and updates the list. Input: Cleaned and normalized data, sentiment score. Output: Filtered company list.

[0476] Step 8:

[0477] The server uses the generative AI and emotion engine to generate specific proposals for NPOs. Example: "Company X can provide 1 million yen for urban greening projects." The generated proposals are sent to the device, which displays them to the user (NPO representative). Input: Filtered company list, emotion score. Output: Proposal generation, proposal display.

[0478] Step 9:

[0479] The server also generates specific donation suggestions for companies based on the results of the generation AI and emotion engine. Example: "NPO is seeking a donation of 1 million yen." The generated donation suggestions are sent to the terminal, which displays them to the user (company representative). Input: Filtered company list, emotion score. Output: Generation of donation suggestions, display of donation suggestions.

[0480] Step 10:

[0481] The user (NPO representative) checks the proposal details and selects a company to request procurement from a list of companies. The terminal sends the selection information to the server, and the server notifies the company representative of the procurement request. Input: Proposal details. Output: Sending selection information, notification of procurement request.

[0482] Step 11:

[0483] The user (company representative) checks the donation proposal details and selects the NPO to which the donation will be made. The device sends the selection information to the server, and the server notifies the NPO representative of the decision to donate. Input: Donation proposal details. Output: Sending selection information, notification of donation decision.

[0484] Step 12:

[0485] Once both parties agree, the server will consider the match to be successful and update the information in the database. The server will then send a notification of the successful match to both the NPO and the company, and the device will display a notification of the successful match to each user. Input: Agreement information between the two parties. Output: Update the database, send and display a notification of the successful match.

[0486] (Application example 2)

[0487] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0488] Conventional matching systems for donations and fundraising do not adequately assess the affinity between NPOs and companies, resulting in inappropriate matching. Furthermore, it is difficult to realize advertising campaigns through social contribution activities, and collaboration between the two parties is inefficient. As a result, NPOs do not receive sufficient support, and companies lose opportunities to effectively communicate their social contribution activities. The purpose of this invention is to solve these problems and realize effective matching between NPOs and companies and advertising campaigns.

[0489] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for an NPO to input the amount of fundraising it wishes to receive and its purpose; a means for a company to input a message it wishes to convey to society; a means for evaluating the affinity between the NPO and companies based on the input amount of fundraising it wishes to receive, its purpose, and its message; a means for suggesting suitable companies to the NPO based on the evaluation; a means for suggesting suitable NPOs to companies based on the evaluation; a means for notifying the NPO and companies of the proposals; a means for providing advertising space based on the evaluation; a means for an advertiser to input a budget and message for an advertising campaign and store the input in a database; and a means for evaluating and suggesting the affinity between advertisers and advertising space providers using a generative AI and an emotion engine. This enables efficient and effective matching between NPOs and companies and the creation of advertising campaigns.

[0490] An "NPO corporation" is a corporation that carries out non-profit activities and whose primary purpose is to contribute to society.

[0491] "Funding Suggestion" refers to the amount of funding a nonprofit organization needs for a particular project or activity.

[0492] "Purpose" refers to the specific purpose and activities for which the funds raised will be used.

[0493] "Enterprise" refers to a legal entity or organization engaged in commercial activities for profit.

[0494] A "message" refers to the philosophy, opinions, and information that a company wants to communicate to society.

[0495] "Affinity" refers to the degree of agreement between the goals and values ​​of an NPO and a company.

[0496] "Evaluation" refers to the act of quantifying or qualitatively judging affinity based on information about the NPO and the company.

[0497] A "proposal" is an action that involves making an appropriate match based on the evaluation and notifying the NPO or company.

[0498] "Notification" refers to the act of communicating the contents and results of a proposal to NPOs and companies.

[0499] An "advertising space" refers to the space or medium for displaying advertisements, and refers to the advertising space provided by NPOs.

[0500] "Advertiser" refers to a company or individual that places an advertisement.

[0501] "Advertising campaign" refers to a series of advertising activities undertaken to achieve a specific advertising objective.

[0502] A "database" is a system for storing, searching, and managing information in an organized manner.

[0503] "Generative AI" refers to artificial intelligence technology that automatically generates specific information or suggestions from input data.

[0504] An "emotion engine" is a technology that analyzes emotions from text and data and provides the results.

[0505] The system of this invention supports effective fundraising and advertising campaign matching between NPOs and companies, and includes the following components: Hardware components include a server, terminals, and databases, while software components include a generative AI model, an emotion engine, and a cloud platform.

[0506] Hardware Configuration

[0507] 1. Server:

[0508] It mainly processes data and executes matching algorithms.

[0509] Google Cloud Platform is used as the cloud platform.

[0510] 2. Terminal:

[0511] Used by nonprofit and corporate users to enter data and review proposals.

[0512] This includes smartphones, tablets, and PCs.

[0513] 3. Database:

[0514] Firebase Realtime Database is used for data storage, saving access data and matching information.

[0515] Software Configuration

[0516] 1. Generative AI model:

[0517] We use OpenAI's GPT-3 to perform affinity evaluation and generate proposals.

[0518] 2. Emotion Engine:

[0519] The Microsoft (registered trademark) Azure (registered trademark) Emotion API is used to analyze emotions from input data.

[0520] Data Entry and Processing

[0521] Users input the amount of funds their NPO wishes to raise and how it will be used, the budget for their company's advertising campaign, and the message they wish to convey to society via their devices. This input data is then stored in a database via the server.

[0522] Affinity evaluation

[0523] The server calculates an affinity score using a generative AI and an emotion engine based on the information of NPOs and companies stored in a database. Specifically, the generative AI cleans and normalizes the input data, and the emotion engine analyzes emotions from the text data to evaluate the affinity between the two.

[0524] Proposal generation and notification

[0525] Based on the results of the affinity assessment, the server generates and notifies NPOs and companies of their optimal matching proposals. Users can view these proposals on their devices.

[0526] Specific examples

[0527] For example, if an NPO wants to raise 1 million yen for an "urban greening project," and a company wants to run an "environmental protection advertising campaign," the server uses generative AI and an emotion engine to evaluate the affinity between the NPO's project and the company's campaign content, and notifies both parties of appropriate proposals.

[0528] Prompt Sentence Examples

[0529] The emotional message of a certain nonprofit environmental organization is, "Greening is the first step in protecting the urban environment. Your support will help preserve the greenery for future generations." Find a company message that matches this message.

[0530] The campaign message of a certain technology company is, "We are taking action today for a sustainable future, to leave green cities for future generations." Find a message from a nonprofit organization that matches this message.

[0531] As described above, the system of the present invention can support efficient and effective matching between NPOs and companies and the realization of advertising campaigns.

[0532] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0533] Step 1:

[0534] Data entry for NPO personnel

[0535] The user (NPO representative) accesses the web application using a terminal and logs in.

[0536] The terminal sends the login information to the server, and the server performs user authentication.

[0537] Once authentication is successful, the user is taken to a form where they can enter the amount they wish to raise and the specific purpose of the funds.

[0538] This data (amount of funding desired and purpose) is sent from the terminal to the server and stored in a database.

[0539] Input: Desired amount of funding, purpose

[0540] Output: Fundraising requests of nonprofit organizations stored in a database

[0541] Step 2:

[0542] Data entry by company personnel

[0543] The user (company employee) similarly accesses the web application using a terminal and logs in.

[0544] The terminal sends the login information to the server, and the server performs user authentication.

[0545] Once authentication is successful, the user is taken to a form where they can enter the budget for their advertising campaign and the message they wish to send to society.

[0546] This data (advertising campaign budget, message) is sent from the device to the server and stored in a database.

[0547] Input: Advertising campaign budget, message you want to convey to society

[0548] Output: Company advertising data stored in a database

[0549] Step 3:

[0550] Cleaning and normalizing data

[0551] The server retrieves data sent by NPOs and companies from a database.

[0552] A generative AI model (OpenAI's GPT-3) is used to clean and normalize the input data.

[0553] Data cleaning refers to correcting incorrect or missing values, and normalization refers to standardizing the data format.

[0554] Input: NPO and company input data stored in the database

[0555] Output: Normalized and cleaned data

[0556] Step 4:

[0557] Calculating affinity scores

[0558] The server uses generative AI (OpenAI's GPT-3) to calculate an affinity score between the nonprofit organization's purpose and the company's advertising message.

[0559] Using an emotion engine (Microsoft Azure Emotion API), emotions are analyzed from text data (purpose and message) and reflected in the score.

[0560] Input: normalized data, text data

[0561] Output: Affinity score between NPOs and companies

[0562] Step 5:

[0563] Proposal generation

[0564] The server generates optimal matching proposals for both NPOs and companies based on the affinity scores.

[0565] At this time, the proposal will also include information about the provision of advertising space and specific details of the company's advertising campaign.

[0566] Input: affinity score

[0567] Output: Proposal content (for NPOs and companies)

[0568] Step 6:

[0569] Notification of proposal details

[0570] The server notifies the generated proposals to NPO and corporate users.

[0571] Users (NPO representatives and corporate representatives) can check the proposal contents from their terminals.

[0572] Input: Proposal

[0573] Output: The suggestion sent to the user

[0574] Step 7:

[0575] Match confirmation and notification

[0576] The users (NPO representative and company representative) review the proposal, and if both parties agree, they notify the server via their terminal.

[0577] The server confirms the agreement, updates the database to indicate that the match has been made, and sends a notification to both parties.

[0578] Input: User consent notice

[0579] Output: Notification of successful match

[0580] Through these steps, the system helps to efficiently match NPOs and companies and realize effective advertising campaigns.

[0581] 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.

[0582] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0583] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0584] [Second embodiment]

[0585] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0586] 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.

[0587] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0588] 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.

[0589] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0590] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0591] 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.

[0592] 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.

[0593] 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 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.

[0594] 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.

[0595] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0596] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0597] This system is a matching system that uses generative AI to support effective fundraising and donation destination selection between NPOs and companies. The specific configuration for implementing the system of the present invention is as follows.

[0598] Data Entry Phase

[0599] Data entry for nonprofit organizations

[0600] 1. The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[0601] 2. The terminal sends the login information to the server, and the server performs user authentication.

[0602] 3. After logging in, the user enters the amount of funds they wish to raise and the specific purpose of the funds (e.g., "1 million yen" or "urban greening project") in the form that appears.

[0603] 4. The terminal sends the input amount of funds desired to be raised and the purpose to the server.

[0604] Corporate Data Entry

[0605] 5. The user (company representative) uses the terminal to access the web application in the same way and log in.

[0606] 6. The terminal sends the login information to the server, and the server performs user authentication.

[0607] 7. After logging in, the user enters the message they want to convey to society (e.g., "I'm interested in environmental protection") into the form that appears.

[0608] 8. The terminal sends the entered message to the server.

[0609] Data Processing Phase

[0610] 9. The server stores the information sent by NPOs and companies in a database.

[0611] 10. Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[0612] Matching Phase

[0613] 11. The server searches the company data in the database based on the NPO's desired conditions.

[0614] 12. The server uses the generative AI to calculate the affinity score between the NPO and the company, taking into account factors such as purpose, amount, and social contribution theme.

[0615] 13. The server filters companies with high affinity scores and generates recommendations.

[0616] Proposal Generation Phase

[0617] Proposals for NPOs

[0618] 14. The server uses the generation AI to generate specific proposals for the NPO. Example: "Company X can provide 1 million yen for urban greening projects."

[0619] 15. The server sends the generated proposal to the device.

[0620] 16. The terminal displays the proposal to the user (NPO representative).

[0621] Proposals for companies

[0622] 17. The server uses AI to generate specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[0623] 18. The server sends the generated donation proposal to the terminal.

[0624] 19. The terminal displays the donation proposal to the user (company representative).

[0625] Matching establishment phase

[0626] 20. The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected company list.

[0627] 21. The terminal sends the selection information to the server.

[0628] 22. The server notifies the company representative of the procurement request.

[0629] 23. The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[0630] 24. The terminal sends the selection information to the server.

[0631] 25. The server notifies the NPO representative of the donation decision.

[0632] 26. Once both parties agree, the server will consider the match to be successful and update the information in the database.

[0633] 27. The server sends a notification of successful matching to both the NPO and the company.

[0634] 28. The device will display a match notification to each user.

[0635] Specific examples

[0636] As a concrete example, if the nonprofit organization "Environmental Protection Association" wants to raise 1 million yen to use for an "urban greening project," it first accesses a web application from a terminal and inputs the amount it wants to raise and the purpose of the fundraising. Similarly, a company "Ecotech Co., Ltd." inputs a message saying that it is "interested in environmental protection."

[0637] The server uses a generative AI to calculate highly compatible combinations based on the information from both parties, and notifies the NPO "Environmental Protection Association" that "Ecotech Co., Ltd. can donate to urban greening projects." At the same time, it suggests to the company "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen."

[0638] Both parties confirm the proposal, the NPO sends a procurement request to the company, and when the company decides to make a donation, a match is made and a notification is sent to both parties. In this way, the system of the present invention enables efficient and effective fundraising and donations.

[0639] The processing flow will be explained below.

[0640] Step 1:

[0641] The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[0642] Step 2:

[0643] The terminal sends the login information to the server.

[0644] Step 3:

[0645] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[0646] Step 4:

[0647] The user confirms that the authentication was successful and then enters the amount of funding they wish to raise (e.g., "1 million yen") and its specific purpose (e.g., "urban greening project") into the form.

[0648] Step 5:

[0649] The terminal transmits the input desired fundraising amount and purpose to the server.

[0650] Step 6:

[0651] Similarly, the user (company representative) accesses the web application from a terminal and logs in by entering their ID and password.

[0652] Step 7:

[0653] The terminal sends the login information to the server.

[0654] Step 8:

[0655] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[0656] Step 9:

[0657] The user confirms that the authentication was successful and then enters the message he or she wants to convey to society (e.g., "I'm interested in environmental protection") into the form.

[0658] Step 10:

[0659] The terminal transmits the input message to the server.

[0660] Step 11:

[0661] The server stores the information sent by NPOs and companies in a database.

[0662] Step 12:

[0663] Based on the stored information, the server uses generative AI to perform preprocessing, cleaning and normalizing the input data.

[0664] Step 13:

[0665] The server searches for company information in the database based on the NPO's desired criteria.

[0666] Step 14:

[0667] The server uses a generative AI to calculate the affinity score between NPOs and companies, taking into account factors such as purpose, amount, and social contribution theme.

[0668] Step 15:

[0669] The server filters companies with high affinity scores and creates a list.

[0670] Step 16:

[0671] The server uses a generative AI to generate specific proposals for NPOs.

[0672] Step 17:

[0673] The server transmits the generated proposal to the terminal.

[0674] Step 18:

[0675] The terminal displays the proposal contents to the user (NPO representative).

[0676] Step 19:

[0677] The server also uses generative AI to generate specific donation proposals for companies.

[0678] Step 20:

[0679] The server transmits the generated donation proposal to the terminal.

[0680] Step 21:

[0681] The terminal displays the donation proposal to the user (company representative).

[0682] Step 22:

[0683] The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected list of companies.

[0684] Step 23:

[0685] The terminal transmits the selection information to the server.

[0686] Step 24:

[0687] The server notifies the company representative of the procurement request.

[0688] Step 25:

[0689] The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[0690] Step 26:

[0691] The terminal transmits the selection information to the server.

[0692] Step 27:

[0693] The server notifies the NPO representative of the donation decision.

[0694] Step 28:

[0695] Once both parties agree, the server determines that a match has been made and updates the information in the database.

[0696] Step 29:

[0697] The server sends a notification of a successful match to both the NPO and the company.

[0698] Step 30:

[0699] The terminal displays a match notification to each user.

[0700] Example 1

[0701] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0702] When nonprofit organizations raise funds, the process of finding the right entity is extremely time-consuming and inefficient. This increases the time and effort spent on fundraising, making it difficult to focus on their core activities. It is also often difficult for organizations to find nonprofits that are suitable for their social contribution activities, preventing them from making effective donations. This creates a problem of mismatches between the fundraising and donation processes.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0704] In this invention, the server includes: a means for a nonprofit organization to input the amount of fundraising it wishes to receive and its use; a means for a business entity to input a message it wishes to send to society; a means for evaluating the affinity between the nonprofit organization and business entities based on the input amount of fundraising it wishes to receive, its use, and the message; a means for suggesting suitable business entities to the nonprofit organization based on the evaluation; a means for suggesting suitable nonprofit organizations to business entities based on the evaluation; a means for notifying the nonprofit organization and business entities of the content of the proposal; a means for notifying the business entity of a procurement request made by the nonprofit organization; a means for notifying the nonprofit organization of a donation decision made by the business entity; and a means for notifying the nonprofit organization and business entities of a match based on an agreement between the two parties. This enables efficient matching between nonprofit organizations and business entities, facilitating the fundraising and donation processes.

[0705] A "non-profit organization" is an organization that operates for the public benefit and does not seek profit.

[0706] "Business Entity" means a legal entity or individual engaged in commercial activities.

[0707] "Funding Suggestion" refers to the amount of funding a nonprofit organization needs for a particular activity or project.

[0708] "Use" indicates the purpose for which the funds obtained through fundraising will be used.

[0709] A "message" refers to the values ​​and initiatives that a business wants to convey to society.

[0710] A "compatibility assessment" is a process that uses numerical values ​​and indicators to evaluate the compatibility between a nonprofit organization and a business entity.

[0711] "Proposal" means a proposal for collaboration with a non-profit organization or entity generated based on an affinity assessment.

[0712] "Procurement Request" means a request by a Nonprofit Organization for funding from a particular entity.

[0713] A "donation decision" is the process by which an entity decides to provide funding to a particular nonprofit organization.

[0714] A "match" is when a funding agreement is reached between a nonprofit organization and a business entity.

[0715] "Notification" means the act of communicating information, such as proposals and agreements, to nonprofit organizations and entities.

[0716] "Generative AI" refers to artificial intelligence systems that generate natural language and perform data analysis.

[0717] This invention is a matching system that uses generative AI to support efficient fundraising and donation destination selection between nonprofit organizations and business entities. Specific embodiments for implementing the invention are described below.

[0718] Hardware and software used

[0719] This system consists of a server, a terminal, and a user. The server has a database and a generative AI model, and the terminal implements a web application that provides an interface for users to operate it. The main software used is a web browser (e.g., Google Chrome, Firefox), an HTTP server (e.g., Apache, Nginx), a database management system (e.g., MySQL, PostgreSQL), and an AI model execution environment (e.g., TensorFlow, PyTorch).

[0720] Data Entry Phase

[0721] Nonprofit data entry

[0722] 1. The user (non-profit organization employee) accesses the system's web application using a web browser and logs in by entering their ID and password. The terminal sends the login information to the server, which then authenticates the user.

[0723] 2. After successful authentication, the user inputs the amount of funds they wish to raise and the specific purpose of the funds. The terminal then sends this information to the server.

[0724] Business entity data entry

[0725] 3. The user (business entity employee) similarly accesses the web application, enters login information, and is authenticated by the server.

[0726] 4. After successful authentication, the user enters the message they wish to send to society. The device then sends this information to the server.

[0727] Data Processing Phase

[0728] 5. The server stores the information submitted by nonprofit organizations and businesses in a database, and the generative AI model cleans and normalizes the data based on the stored information.

[0729] Matching Phase

[0730] 6. The server searches the entity data based on the nonprofit organization's desired criteria, calculates affinity scores using the generative AI model, filters entities with high affinity scores, and generates proposals.

[0731] Proposal Generation Phase

[0732] Proposals for nonprofit organizations

[0733] 7. The server uses the generative AI model to generate specific proposals for the nonprofit organization and sends them to the device, which then displays them to the user.

[0734] Proposals to business entities

[0735] 8. The server also uses the generative AI model to generate specific donation suggestions for the business entity and sends them to the device, which then displays them to the user.

[0736] Matching establishment phase

[0737] 9. The user (non-profit organization representative) reviews the proposal and selects the entity to request procurement from the list of selected entities. The selection information is sent to the server and notified to the entity representative.

[0738] 10. The user (business operator) reviews the donation proposal and selects the nonprofit organization to which the donation will be made. The selection information is sent to the server and notified to the nonprofit organization operator.

[0739] 11. The server will then determine if the match is successful and update the information in the database. A notification of the match will be sent to both the nonprofit organization and the business entity, and the device will display it to the user.

[0740] Specific examples

[0741] As a concrete example, if the nonprofit organization "Environmental Protection Association" wishes to raise 1 million yen to use for an "urban greening project," the user accesses a web application from their device and inputs the amount they wish to raise and the purpose of the donation. Similarly, the business entity "Ecotech Co., Ltd." inputs a message saying, "I'm interested in environmental protection." The server uses generative AI based on the information of both parties to calculate highly compatible pairs and notifies the nonprofit organization "Environmental Protection Association" that "Ecotech Co., Ltd." can donate to the urban greening project." At the same time, it proposes to the business entity "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen." Both parties confirm the proposal, and the nonprofit organization sends a procurement request to the business entity. When the business entity decides to donate, a match is made and notifications are sent to both parties.

[0742] Example of prompt input to a generative AI model

[0743] Nonprofit data entry prompt example:

[0744] The non-profit organization Environmental Protection Association is seeking 1 million yen for an urban greening project.

[0745] Example of a prompt for business entity message input:

[0746] Ecotech Co., Ltd. cares about protecting the environment.

[0747] The generative AI model calculates an affinity score based on these inputs and generates specific suggestions.

[0748] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0749] Step 1:

[0750] The user (non-profit organization employee) accesses the system's web application using a web browser and logs in by entering their ID and password. Input data: ID and password. Output data: authentication request. The specific operation is to access the login page and enter their ID and password in the designated form.

[0751] Step 2:

[0752] The terminal sends the login information entered by the user to the server. Input data: ID and password. Output data: HTTP POST request. Specifically, when the user clicks the "Login" button, the login information is sent to the server.

[0753] Step 3:

[0754] The server authenticates the received login information, and if the user has entered it correctly, it generates an authentication token and returns it to the terminal. Input data: ID and password. Output data: authentication token. Specifically, the server searches the database for the corresponding user information, and if there is a match, it generates an authentication token and returns it as a response.

[0755] Step 4:

[0756] After logging in, the user enters the desired amount of funding and the specific purpose in the form that appears. Input data: desired amount of funding, purpose of funding. Output data: request to send input data. Specifically, the user enters the specific amount and purpose in the "desired amount of funding" and "purpose" fields, respectively, and presses the "Submit" button.

[0757] Step 5:

[0758] The terminal sends the input desired fundraising amount and purpose to the server. Input data: desired fundraising amount, purpose of fundraising. Output data: HTTP POST request. Specifically, the terminal sends data to the server using an HTTP POST request.

[0759] Step 6:

[0760] The user (business person) similarly accesses the web application and logs in. Input data: ID and password. Output data: authentication request. Specifically, the business person accesses the login page in the same way as the nonprofit organization person and enters their ID and password in the designated form.

[0761] Step 7:

[0762] The terminal sends the login information of the business entity employee to the server. Input data: ID and password. Output data: HTTP POST request. Specifically, when the user clicks the "Login" button, the information is sent to the server.

[0763] Step 8:

[0764] The server authenticates the received login information, and if the business person is successfully authenticated, it generates an authentication token and returns it to the terminal. Input data: ID and password. Output data: authentication token. The specific operation is the same as the authentication process for nonprofit organization personnel.

[0765] Step 9:

[0766] After logging in, the user enters the message that the company wants to send to society in the form that appears. Input data: Message. Output data: Message sending request. The specific operation is that the business person in charge enters the specific content in the "Message" field and presses the "Send" button.

[0767] Step 10:

[0768] The terminal sends the input message to the server. Input data: message. Output data: HTTP POST request. In concrete terms, the terminal sends the transmitted message to the server as an HTTP POST request.

[0769] Step 11:

[0770] The server stores the information submitted by nonprofits and businesses in a database. Input data: fundraising amount, purpose, message. Output data: new record in the database. Specifically, the server connects to the database, inserts a new record, and saves the user's input data.

[0771] Step 12:

[0772] The server performs preprocessing using a generative AI model based on the stored information, cleaning and normalizing the input data. Input data: desired fundraising amount, purpose, message. Output data: cleaned and normalized data. Specifically, the server calls the generative AI model and formats the input data.

[0773] Step 13:

[0774] The server searches the database for entity data based on the nonprofit organization's desired criteria. Input data: cleaned and normalized data. Output data: relevant entity data. Specifically, the server uses an SQL query to extract entity data that matches the criteria.

[0775] Step 14:

[0776] The server uses the generative AI model to calculate the affinity score between the nonprofit organization and the business entity. Input data: relevant business entity data. Output data: affinity score. Specifically, the server inputs the extracted data into the generative AI model to calculate the affinity score.

[0777] Step 15:

[0778] The server filters out businesses with high affinity scores and generates proposals. Input data: affinity score. Output data: proposals. Specifically, it selects businesses with high scores and automatically generates proposals using a generative AI model.

[0779] Step 16:

[0780] The server sends the generated proposal to the terminal. Input data: proposal. Output data: HTTP response. Specifically, the server sends the generated proposal to the terminal as an HTTP response.

[0781] Step 17:

[0782] The terminal displays the proposal to the user (nonprofit organization representative). Input data: HTTP response. Output data: Display of proposal. The specific operation is that the proposal is displayed in the browser.

[0783] Step 18:

[0784] The server uses the generative AI model to generate specific donation proposals for businesses and send them to the terminal. Input data: Proposal content from nonprofit organizations. Output data: HTTP response with donation proposal content. The specific operation is to automatically generate a donation proposal using the generative AI model and send it as a response to the terminal.

[0785] Step 19:

[0786] The terminal displays the donation proposal content to the user (business entity representative). Input data: HTTP response. Output data: Display of donation proposal content. Specifically, the donation proposal content is displayed in the browser.

[0787] Step 20:

[0788] The user (non-profit organization representative) reviews the proposal content and selects the business entity to request procurement from the list of selected entities. Input data: Proposal content. Output data: Procurement request information. Specifically, the non-profit organization representative selects the target business entity from the list and presses the "Procurement request" button.

[0789] Step 21:

[0790] The terminal sends the selection information to the server. Input data: Procurement request information. Output data: HTTP POST request. Specifically, the terminal sends the selection information to the server as an HTTP POST request.

[0791] Step 22:

[0792] The server notifies the business entity person of the procurement request. Input data: Procurement request information. Output data: Notification. Specifically, the server sends an email or in-app notification of the procurement request to the business entity person.

[0793] Step 23:

[0794] The user (business entity representative) checks the donation proposal details and selects the nonprofit organization to which the donation will be made. Input data: donation proposal details. Output data: donation recipient selection information. Specifically, the business entity representative selects the nonprofit organization to which the donation will be made and presses the "Donation Confirmation" button.

[0795] Step 24:

[0796] The terminal sends the selection information to the server. Input data: donation destination selection information. Output data: HTTP POST request. Specifically, the terminal sends the selection information to the server as an HTTP POST request.

[0797] Step 25:

[0798] The server notifies the nonprofit organization's representative of the donation decision. Input data: donation destination selection information. Output data: notification. Specifically, the server sends a donation decision notification to the nonprofit organization's representative.

[0799] Step 26:

[0800] The server determines that a match has been made based on the agreement between the two parties and updates the information in the database. Input data: Adjustment information. Output data: Updated information indicating that a match has been made. Specifically, the server updates the status field in the database to "match made."

[0801] Step 27:

[0802] The server sends a notification of successful matching to both the nonprofit organization and the business. Input data: Matching update information. Output data: Notification. Specifically, the server notifies both parties of successful matching via email or in-app notification.

[0803] Step 28:

[0804] The device displays a match-making notification to each user. Input data: Notification. Output data: Display of match-making notification. Specifically, a notification is displayed in the device browser.

[0805] (Application example 1)

[0806] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0807] Existing systems for matching NPOs and corporations are problematic in that they are inefficient in fundraising and donation recipient selection. In particular, there is a lack of appropriate means for supporting collaboration between security service providers and NPOs. The present invention aims to solve these problems and provide an effective system for achieving optimal matching between NPOs and corporations.

[0808] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0809] In this invention, the server includes a means for NPOs to input the amount of fundraising they wish to raise and its use, a means for companies to input the message they wish to convey to society, and a means for evaluating the affinity between NPOs and companies based on the input amount of fundraising, use, and message. This allows security service providers to efficiently select NPOs for fundraising and donation recipients. Furthermore, evaluation results are notified and displayed through an application installed on a smartphone, smart glasses, or head-mounted display, enabling effective matching in real time.

[0810] An "NPO corporation" is an organization whose primary purpose is to engage in non-profit activities and to promote the general public's interests.

[0811] "Funding Suggestion" is the amount of money a nonprofit organization needs for a specific project or activity.

[0812] "Use" refers to the specific ways in which the NPO plans to use the funds.

[0813] A "business" is an organization that provides goods and services and operates to pursue profits.

[0814] A "message" is an expression that shows the philosophy or purpose that a company wants to communicate to society.

[0815] "Affinity" indicates the degree of agreement between the goals and objectives of an NPO and a company.

[0816] "Evaluation" is the process of quantitatively or qualitatively determining the affinity between an NPO and a company.

[0817] "Security services" are the practices and technologies used to provide physical or digital security.

[0818] A "server" is a computer system that provides data and services over a network.

[0819] A "smartphone" is a mobile device that has advanced computing power and Internet connectivity in addition to telephone functions.

[0820] "Smart glasses" are wearable devices equipped with a display to enhance visual information.

[0821] A "head-mounted display" is a display device that projects images directly into the field of vision.

[0822] An "application" is software designed to perform a specific function.

[0823] "Generative AI" is a system that uses artificial intelligence technology to generate new data and information.

[0824] "Notification" is the act of informing the user of important information or results.

[0825] "Display" is the act of visually providing information using a display or device.

[0826] This invention is a system for supporting effective fundraising and donation destination selection between NPOs and companies, and is particularly equipped with a function to promote cooperation with companies that provide security services.

[0827] System Program Overview

[0828] The system includes the following main features:

[0829] 1. A function that allows NPOs to input the amount of funds they wish to raise and the purpose for which they wish to raise funds.

[0830] 2. A function that allows companies to input messages they want to send to society.

[0831] 3. A generative AI function that evaluates the affinity between NPOs and companies based on input data.

[0832] 4. A function to generate and notify appropriate proposals to NPOs and companies based on the evaluation results.

[0833] 5. An application function that is installed on a smartphone, smart glasses, or head-mounted display and displays the evaluation results in real time.

[0834] Hardware and Software Configuration

[0835] The server provides the computing resources for storing data and performing affinity evaluation using the generated AI. Specifically, it uses cloud services (e.g., Amazon Web Services and Google Cloud Platform).

[0836] Users input data and receive notifications and assessment results using smartphones, smart glasses, or head-mounted displays, all of which run mobile operating systems such as iOS or Android.

[0837] Use data processing libraries (e.g., Pandas or NumPy in Python) for data cleaning and preprocessing.

[0838] Affinity assessment uses a generative AI model (e.g., OpenAI's GPT-3).

[0839] Processing flow details

[0840] 1. Data entry phase:

[0841] The user (NPO representative) uses a device such as a smartphone to input the amount of funds they wish to raise and the specific use of the funds into the web application.

[0842] Similarly, company representatives can use devices such as smartphones to input messages they wish to send to society.

[0843] 2. Data Processing Phase:

[0844] The server receives the entered data and stores it in a database.

[0845] Data cleaning and preprocessing is performed using Python data processing libraries.

[0846] 3. Matching Phase:

[0847] The server uses a generative AI model to evaluate the affinity between nonprofits and businesses, calculating an affinity score based on the input fundraising amount, purpose, and message.

[0848] 4. Proposal generation phase:

[0849] Proposals are generated based on the affinity assessment. Specifically, NPOs are notified of companies that can donate, and companies are notified of NPOs that need support.

[0850] Specific examples

[0851] As a concrete example, the following prompt sentences can be used to input the generative AI model to achieve effective matching:

[0852] Example prompt sentence:

[0853] The NPO "Children's Safety Association" wants to raise 500,000 yen to be used for "Local Child Protection Programs." The company "Security Solutions Inc." wants to support "Child Safety Initiatives." Calculate the affinity scores for these two organizations and generate the best matching proposal.

[0854] Based on these prompts, the generative AI model calculates an affinity score between the NPO and the company and notifies appropriate suggestions, thereby realizing effective cooperation between the security service company and the NPO.

[0855] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0856] Step 1:

[0857] The user (NPO employee) accesses the web application using a device such as a smartphone or PC and enters login information. The device sends the entered login information to the server, which then authenticates the user. After authentication, the user enters the amount of funding they wish to raise and its purpose into a web form. The input data generated is the "desired amount of funding" and "purpose." The device then sends this input data to the server.

[0858] Step 2:

[0859] A user (company representative) similarly accesses a web application using a device such as a smartphone or PC and enters login information. The device sends the entered login information to the server, which then authenticates the user. After authentication, the user enters the message they wish to send to society into a web form. A "message" is generated as input data. The device then sends this input data to the server.

[0860] Step 3:

[0861] The server stores the received input data ("desired fundraising amount," "purpose," and "message") in a database. When storing the data, it performs data cleaning to maintain data integrity. Specifically, the database uses the Python Pandas library to remove spaces and standardize the format. The output of this process is well-organized data stored in the database.

[0862] Step 4:

[0863] The server uses a generative AI model to evaluate the affinity between NPOs and companies based on the data stored in the database. Specifically, it sends the input data to a generative AI (e.g., OpenAI's GPT-3) and calculates an affinity score. The server generates a score for each combination of NPO and company and selects the combination with the highest score. An "affinity score" is generated as the output.

[0864] Step 5:

[0865] The server generates specific proposals for NPOs and businesses based on the affinity scores. Using a generative AI model, the server automatically generates prompts to inform NPOs of businesses that can donate to them, and to inform businesses of NPOs that need their support. For example, the server generates the following prompts:

[0866] The NPO "Children's Safety Association" wants to raise 500,000 yen to be used for "Local Child Protection Programs." The company "Security Solutions Inc." wants to support "Child Safety Initiatives." Calculate the affinity scores for these two organizations and generate the best matching proposal.

[0867] As an output, a "proposal" is generated.

[0868] Step 6:

[0869] The server notifies the generated proposal contents to the terminals of the corresponding NPO and company personnel. The notified proposal contents are displayed on the terminal screen, allowing the user to confirm the proposal contents. A "notification of proposal contents" is generated as an output.

[0870] Step 7:

[0871] The user (NPO representative) checks the proposal details and selects an appropriate company from among the proposed companies. The selection details are sent to the server via the terminal. The server receives this selection information and notifies the company representative of the procurement request. The "Procurement Request Details" is generated as the output.

[0872] Step 8:

[0873] The user (company representative) receives a notification of the procurement request and selects the NPO to which the donation will be made. The selection is sent to the server via the terminal. The server receives this selection information and notifies the NPO representative of the decision to donate. A "donation decision notification" is generated as the output.

[0874] Step 9:

[0875] Once both parties agree, the server determines that the match has been made and updates the information in the database. It then sends a notification of the match to both the NPO and the company. The device displays the notification to each user. A "matching successful notification" is generated as an output.

[0876] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0877] This system is a matching system that uses generative AI and an emotion engine to support effective fundraising and donation destination selection between NPOs and companies. The specific configuration for implementing the system of the present invention is as follows.

[0878] Data Entry Phase

[0879] Data entry for nonprofit organizations

[0880] 1. The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[0881] 2. The terminal sends the login information to the server, and the server performs user authentication.

[0882] 3. After logging in, the user enters the amount of funds they wish to raise and the specific purpose of the funds (e.g., "1 million yen" or "urban greening project") in the form that appears.

[0883] 4. The terminal sends the input amount of funds desired to be raised and the purpose to the server.

[0884] Corporate Data Entry

[0885] 5. The user (company representative) uses the terminal to access the web application in the same way and log in.

[0886] 6. The terminal sends the login information to the server, and the server performs user authentication.

[0887] 7. After logging in, the user enters the message they want to convey to society (e.g., "I'm interested in environmental protection") into the form that appears.

[0888] 8. The terminal sends the entered message to the server.

[0889] Data Processing Phase

[0890] 9. The server stores the information sent by NPOs and companies in a database.

[0891] 10. Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[0892] Matching Phase

[0893] 11. The server searches for company information in the database based on the NPO's desired criteria.

[0894] 12. The emotion engine recognizes and analyzes user emotions from input information from NPOs and companies.

[0895] 13. The server uses generative AI to calculate the affinity score between the NPO and the company, taking into account the purpose, amount, and social contribution theme as well as the perceived sentiment.

[0896] 14. The server filters companies with high affinity scores and creates a list.

[0897] Proposal Generation Phase

[0898] Proposals for NPOs

[0899] 15. The server generates specific proposals for NPOs based on the results of the generative AI and emotion engine. Example: "Company X can provide 1 million yen for urban greening projects."

[0900] 16. The server sends the generated proposal to the device.

[0901] 17. The terminal displays the proposal to the user (NPO representative).

[0902] Proposals for companies

[0903] 18. Based on the results of the generative AI and emotion engine, the server also generates specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[0904] 19. The server sends the generated donation proposal to the terminal.

[0905] 20. The terminal displays the donation proposal to the user (company representative).

[0906] Matching establishment phase

[0907] 21. The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected company list.

[0908] 22. The terminal sends the selection information to the server.

[0909] 23. The server notifies the company representative of the procurement request.

[0910] 24. The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[0911] 25. The terminal sends the selection information to the server.

[0912] 26. The server notifies the NPO representative of the donation decision.

[0913] 27. Once both parties agree, the server will consider the match to be successful and update the information in the database.

[0914] 28. The server sends a notification of successful matching to both the NPO and the company.

[0915] 29. The device will display a match notification to each user.

[0916] Specific examples

[0917] For example, if the nonprofit organization "Environmental Protection Association" wants to raise 1 million yen to use for an "urban greening project," the person in charge can access the web application from a terminal and enter the amount they want to raise and the purpose of the donation. Similarly, the company "Ecotech Co., Ltd." can enter a message saying that they are "interested in environmental protection."

[0918] The server uses a generative AI to evaluate affinity based on the information from both parties, and then adjusts the affinity score by analyzing the user's emotions with an emotion engine. As a result, the server notifies the NPO "Environmental Protection Association" that "Ecotech Co., Ltd. is available to donate to urban greening projects." At the same time, it suggests to the company "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen."

[0919] Both parties confirm the proposal, the NPO sends a procurement request to the company, and when the company decides to make a donation, a match is made and a notification is sent to both parties. In this way, the system of the present invention enables efficient and effective fundraising and donations.

[0920] The processing flow will be explained below.

[0921] Step 1:

[0922] The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[0923] Step 2:

[0924] The terminal sends the login information to the server.

[0925] Step 3:

[0926] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[0927] Step 4:

[0928] The user confirms that the authentication was successful and then enters the amount of funding they wish to raise (e.g., "1 million yen") and its specific purpose (e.g., "urban greening project") into the form.

[0929] Step 5:

[0930] The terminal transmits the input desired fundraising amount and purpose to the server.

[0931] Step 6:

[0932] Similarly, the user (company representative) accesses the web application from a terminal and logs in by entering their ID and password.

[0933] Step 7:

[0934] The terminal sends the login information to the server.

[0935] Step 8:

[0936] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[0937] Step 9:

[0938] The user confirms that the authentication was successful and then enters the message he or she wants to convey to society (e.g., "I'm interested in environmental protection") into the form.

[0939] Step 10:

[0940] The terminal transmits the input message to the server.

[0941] Step 11:

[0942] The server stores the information sent by NPOs and companies in a database.

[0943] Step 12:

[0944] The server uses an emotion engine to recognize the user's emotions from the input information of NPOs and companies and record them in a database.

[0945] Step 13:

[0946] Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[0947] Step 14:

[0948] The server searches for company information in the database based on the NPO's desired criteria.

[0949] Step 15:

[0950] The server uses generative AI to calculate the affinity score between NPOs and companies, taking into account the purpose, amount, and social contribution theme, as well as perceived emotions.

[0951] Step 16:

[0952] The server filters companies with high affinity scores and creates a list.

[0953] Step 17:

[0954] The server generates specific proposals for NPOs based on the results of the generative AI and emotion engine. Example: "Company X can provide 1 million yen for urban greening projects."

[0955] Step 18:

[0956] The server transmits the generated proposal to the terminal.

[0957] Step 19:

[0958] The terminal displays the proposal contents to the user (NPO representative).

[0959] Step 20:

[0960] Based on the results of the generative AI and emotion engine, the server also generates specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[0961] Step 21:

[0962] The server transmits the generated donation proposal to the terminal.

[0963] Step 22:

[0964] The terminal displays the donation proposal to the user (company representative).

[0965] Step 23:

[0966] The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected list of companies.

[0967] Step 24:

[0968] The terminal transmits the selection information to the server.

[0969] Step 25:

[0970] The server notifies the company representative of the procurement request.

[0971] Step 26:

[0972] The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[0973] Step 27:

[0974] The terminal transmits the selection information to the server.

[0975] Step 28:

[0976] The server notifies the NPO representative of the donation decision.

[0977] Step 29:

[0978] Once both parties agree, the server determines that a match has been made and updates the information in the database.

[0979] Step 30:

[0980] The server sends a notification of a successful match to both the NPO and the company.

[0981] Step 31:

[0982] The terminal displays a match notification to each user.

[0983] Example 2

[0984] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0985] Until now, it has been difficult to efficiently and effectively raise funds and select donation recipients between NPOs and companies, and while a two-way affinity assessment is considered particularly important, achieving this has been problematic as it requires a lot of time and effort.Therefore, there has been a need for a system that can efficiently assess the affinity between NPOs and companies, make optimal proposals to both parties, and promptly notify them of these proposals.

[0986] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0987] In this invention, the server includes: a means for an NPO to input the amount of fundraising it wishes to receive and its use; a means for a company to input a message it wishes to send to society; a means for evaluating the affinity between the NPO and companies based on the input amount of fundraising it wishes to receive, its use, and the message; a means for suggesting suitable companies to the NPO based on the evaluation; a means for suggesting suitable NPOs to companies based on the evaluation; a means for calculating an affinity score using a generative AI and an emotion engine; and a means for notifying the NPO and companies of the content of the suggestions. This enables quick and effective fundraising and donation destination selection between NPOs and companies.

[0988] An "NPO corporation" refers to a corporation that engages in public interest activities for non-profit purposes.

[0989] "Desired fundraising amount" refers to the amount of funds an NPO needs for its projects and activities.

[0990] "Use" refers to the specific purpose that indicates what projects or activities the NPO will use the funds it raises for.

[0991] "Business" refers to an organization that provides goods or services for profit.

[0992] A "message" refers to information that expresses the intentions and interests that a company wants to convey to society.

[0993] "Affinity" refers to the degree of agreement between the goals and values ​​of an NPO and a company.

[0994] "Generative AI" refers to artificial intelligence technology that automatically analyzes data and derives specific patterns and outcomes.

[0995] An "emotion engine" refers to technology that analyzes a user's emotions and generates appropriate suggestions based on those emotions.

[0996] "Affinity score" refers to the evaluation result that quantifies the affinity between an NPO and a company.

[0997] "Proposal content" refers to specific proposal information for NPOs and companies created based on generative AI and emotion engines.

[0998] "Notification" refers to the act of communicating important information, such as proposal details, to NPOs and companies.

[0999] "Database" refers to a system for systematically storing and managing data collected from NPOs and companies.

[1000] This invention is a matching system for effective and efficient fundraising and donation destination selection between NPOs and companies. This system uses generative AI and an emotion engine to evaluate affinity and make optimal proposals.

[1001] Data Entry Phase

[1002] Data entry for nonprofit organizations

[1003] The user (NPO employee) uses a terminal to access the web application and log in by entering their ID and password. The terminal sends the login information to the server, which then authenticates the user. After logging in, the user enters the amount of funding they wish to raise and its specific purpose (e.g., "1 million yen" or "urban greening project"), and the terminal then sends the information they entered to the server.

[1004] Corporate Data Entry

[1005] Similarly, users (company personnel) use their terminals to access and log in to a web application. The terminals send the login information to the server, which then authenticates the user. After logging in, the user enters a message they want to convey to society (e.g., "I'm interested in environmental protection"), and the terminals send the message to the server.

[1006] Data Processing Phase

[1007] The server stores the information sent by NPOs and companies in a database. Next, the server uses generative AI to perform preprocessing, cleaning the input data (removing noise and filling in missing data) and normalizing it (standardizing the data format).

[1008] Matching Phase

[1009] The server searches for company information in a database based on the NPO's desired criteria. The emotion engine recognizes and analyzes user emotions from the input information about the NPO and company. The server uses generative AI to calculate an affinity score, taking into account the desired fundraising amount, purpose, message content, and emotion score. The server filters companies with high affinity scores and creates a list.

[1010] Proposal Generation Phase

[1011] Proposals for NPOs

[1012] The server generates a specific proposal for the NPO based on the results of the generation AI and emotion engine. For example, "Company X can provide 1 million yen for a greening project in an urban area." The server sends the generated proposal to the terminal, which then displays it to the user (NPO representative).

[1013] Proposals for companies

[1014] Based on the results of the generation AI and the emotion engine, the server also generates specific donation proposals for companies. For example, "NPO Y is seeking a donation of 1 million yen." The server sends the generated donation proposal to the terminal, which then displays it to the user (company representative).

[1015] Matching establishment phase

[1016] The user (NPO representative) reviews the proposal and selects a company to request procurement from the list of selected companies. The device sends the selection information to the server, which notifies the company representative of the procurement request. The user (company representative) reviews the donation proposal and selects the NPO to donate to. The device sends the selection information to the server, which notifies the NPO representative of the decision to donate. Once both parties agree, a match is made and the server updates the information in the database. The server sends a notification of the match to both the NPO and the company, and the device displays a notification of the match to each user.

[1017] Specific examples

[1018] For example, if an NPO needs 1 million yen for an "urban greening project," the person in charge accesses the web application from their device, logs in, and enters the desired fundraising amount and purpose. The company enters a message saying, "I'm interested in environmental protection." The server uses generative AI to evaluate affinity based on the information of both parties, and the emotion engine analyzes the user's emotions to adjust the affinity score. As a result, the server notifies the NPO that "the company can donate to the urban greening project" and suggests to the company that "the NPO is seeking a donation of 1 million yen." Both parties confirm the proposal, and the NPO sends a procurement request to the company. When the company decides to donate, a match is made and notifications are sent to both parties. In this way, the system of the present invention enables efficient and effective fundraising and donation destination selection.

[1019] Examples of standard prompts

[1020] "If an NPO needs to raise 1 million yen to fund a greening project in an urban area, please suggest what kind of company would be suitable."

[1021] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1022] Step 1:

[1023] The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password. The terminal sends the login information to the server, which then authenticates the user. If the login is successful, the server returns an authentication success message to the terminal and displays an input form for the user. Input: ID, password. Output: Authentication success message, input form.

[1024] Step 2:

[1025] The user (NPO representative) inputs the amount of funding they wish to raise and its specific purpose. The device sends this information to the server. The server temporarily stores the received data and prepares to connect to the database. Input: Desired amount of funding, purpose. Output: Temporarily stores input data.

[1026] Step 3:

[1027] The user (company representative) uses a terminal to access the web application, logs in by entering their ID and password in the same way, and enters a message. The terminal sends this information to the server. The server temporarily stores the received data and checks the consistency of the NPO's data with the company's data. Input: ID, password, message. Output: Temporarily stores input data, checks data consistency.

[1028] Step 4:

[1029] The server stores all data sent by NPOs and companies in a database. After storing, the server performs preprocessing (data cleaning and normalization) using generative AI. This process removes noise from the data and fills in missing data. Input: All stored data. Output: Cleaned and normalized data.

[1030] Step 5:

[1031] The server searches for company information based on the NPO data and creates an initial list of matching candidates. A high-speed search algorithm is used for this. Input: NPO's desired conditions. Output: Matching candidate list.

[1032] Step 6:

[1033] The emotion engine recognizes and analyzes user emotions from input information from NPOs and companies. For example, it distinguishes between positive and negative emotions and assigns an emotion score to each. Input: Input information from NPOs and companies. Output: Emotion score.

[1034] Step 7:

[1035] The server uses generative AI to calculate affinity scores between NPOs and companies. This calculation takes into account the desired fundraising amount, purpose, message content, and sentiment score. Based on the calculation results, it filters companies with high affinity scores and updates the list. Input: Cleaned and normalized data, sentiment score. Output: Filtered company list.

[1036] Step 8:

[1037] The server uses the generative AI and emotion engine to generate specific proposals for NPOs. Example: "Company X can provide 1 million yen for urban greening projects." The generated proposals are sent to the device, which displays them to the user (NPO representative). Input: Filtered company list, emotion score. Output: Proposal generation, proposal display.

[1038] Step 9:

[1039] The server also generates specific donation suggestions for companies based on the results of the generation AI and emotion engine. Example: "NPO is seeking a donation of 1 million yen." The generated donation suggestions are sent to the terminal, which displays them to the user (company representative). Input: Filtered company list, emotion score. Output: Generation of donation suggestions, display of donation suggestions.

[1040] Step 10:

[1041] The user (NPO representative) checks the proposal details and selects a company to request procurement from a list of companies. The terminal sends the selection information to the server, and the server notifies the company representative of the procurement request. Input: Proposal details. Output: Sending selection information, notification of procurement request.

[1042] Step 11:

[1043] The user (company representative) checks the donation proposal details and selects the NPO to which the donation will be made. The device sends the selection information to the server, and the server notifies the NPO representative of the decision to donate. Input: Donation proposal details. Output: Sending selection information, notification of donation decision.

[1044] Step 12:

[1045] Once both parties agree, the server will consider the match to be successful and update the information in the database. The server will then send a notification of the successful match to both the NPO and the company, and the device will display a notification of the successful match to each user. Input: Agreement information between the two parties. Output: Update the database, send and display a notification of the successful match.

[1046] (Application example 2)

[1047] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1048] Conventional matching systems for donations and fundraising do not adequately assess the affinity between NPOs and companies, resulting in inappropriate matching. Furthermore, it is difficult to realize advertising campaigns through social contribution activities, and collaboration between the two parties is inefficient. As a result, NPOs do not receive sufficient support, and companies lose opportunities to effectively communicate their social contribution activities. The purpose of this invention is to solve these problems and realize effective matching between NPOs and companies and advertising campaigns.

[1049] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for an NPO to input the amount of fundraising it wishes to receive and its purpose; a means for a company to input a message it wishes to convey to society; a means for evaluating the affinity between the NPO and companies based on the input amount of fundraising it wishes to receive, its purpose, and its message; a means for suggesting suitable companies to the NPO based on the evaluation; a means for suggesting suitable NPOs to companies based on the evaluation; a means for notifying the NPO and companies of the proposals; a means for providing advertising space based on the evaluation; a means for an advertiser to input a budget and message for an advertising campaign and store the input in a database; and a means for evaluating and suggesting the affinity between advertisers and advertising space providers using a generative AI and an emotion engine. This enables efficient and effective matching between NPOs and companies and the creation of advertising campaigns.

[1050] An "NPO corporation" is a corporation that carries out non-profit activities and whose primary purpose is to contribute to society.

[1051] "Funding Suggestion" refers to the amount of funding a nonprofit organization needs for a particular project or activity.

[1052] "Purpose" refers to the specific purpose and activities for which the funds raised will be used.

[1053] "Enterprise" refers to a legal entity or organization engaged in commercial activities for profit.

[1054] A "message" refers to the philosophy, opinions, and information that a company wants to communicate to society.

[1055] "Affinity" refers to the degree of agreement between the goals and values ​​of an NPO and a company.

[1056] "Evaluation" refers to the act of quantifying or qualitatively judging affinity based on information about the NPO and the company.

[1057] A "proposal" is an action that involves making an appropriate match based on the evaluation and notifying the NPO or company.

[1058] "Notification" refers to the act of communicating the contents and results of a proposal to NPOs and companies.

[1059] An "advertising space" refers to the space or medium for displaying advertisements, and refers to the advertising space provided by NPOs.

[1060] "Advertiser" refers to a company or individual that places an advertisement.

[1061] "Advertising campaign" refers to a series of advertising activities undertaken to achieve a specific advertising objective.

[1062] A "database" is a system for storing, searching, and managing information in an organized manner.

[1063] "Generative AI" refers to artificial intelligence technology that automatically generates specific information or suggestions from input data.

[1064] An "emotion engine" is a technology that analyzes emotions from text and data and provides the results.

[1065] The system of this invention supports effective fundraising and advertising campaign matching between NPOs and companies, and includes the following components: Hardware components include a server, terminals, and databases, while software components include a generative AI model, an emotion engine, and a cloud platform.

[1066] Hardware Configuration

[1067] 1. Server:

[1068] It mainly processes data and executes matching algorithms.

[1069] Google Cloud Platform is used as the cloud platform.

[1070] 2. Terminal:

[1071] Used by nonprofit and corporate users to enter data and review proposals.

[1072] This includes smartphones, tablets, and PCs.

[1073] 3. Database:

[1074] Firebase Realtime Database is used for data storage, saving access data and matching information.

[1075] Software Configuration

[1076] 1. Generative AI model:

[1077] We use OpenAI's GPT-3 to perform affinity evaluation and generate proposals.

[1078] 2. Emotion Engine:

[1079] Uses the Microsoft Azure Emotion API to analyze emotions from input data.

[1080] Data Entry and Processing

[1081] Users input the amount of funds their NPO wishes to raise and how it will be used, the budget for their company's advertising campaign, and the message they wish to convey to society via their devices. This input data is then stored in a database via the server.

[1082] Affinity evaluation

[1083] The server calculates an affinity score using a generative AI and an emotion engine based on the information of NPOs and companies stored in a database. Specifically, the generative AI cleans and normalizes the input data, and the emotion engine analyzes emotions from the text data to evaluate the affinity between the two.

[1084] Proposal generation and notification

[1085] Based on the results of the affinity assessment, the server generates and notifies NPOs and companies of their optimal matching proposals. Users can view these proposals on their devices.

[1086] Specific examples

[1087] For example, if an NPO wants to raise 1 million yen for an "urban greening project," and a company wants to run an "environmental protection advertising campaign," the server uses generative AI and an emotion engine to evaluate the affinity between the NPO's project and the company's campaign content, and notifies both parties of appropriate proposals.

[1088] Prompt Sentence Examples

[1089] The emotional message of a certain nonprofit environmental organization is, "Greening is the first step in protecting the urban environment. Your support will help preserve the greenery for future generations." Find a company message that matches this message.

[1090] The campaign message of a certain technology company is, "We are taking action today for a sustainable future, to leave green cities for future generations." Find a message from a nonprofit organization that matches this message.

[1091] As described above, the system of the present invention can support efficient and effective matching between NPOs and companies and the realization of advertising campaigns.

[1092] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1093] Step 1:

[1094] Data entry for NPO personnel

[1095] The user (NPO representative) accesses the web application using a terminal and logs in.

[1096] The terminal sends the login information to the server, and the server performs user authentication.

[1097] Once authentication is successful, the user is taken to a form where they can enter the amount they wish to raise and the specific purpose of the funds.

[1098] This data (amount of funding desired and purpose) is sent from the terminal to the server and stored in a database.

[1099] Input: Desired amount of funding, purpose

[1100] Output: Fundraising requests of nonprofit organizations stored in a database

[1101] Step 2:

[1102] Data entry by company personnel

[1103] The user (company employee) similarly accesses the web application using a terminal and logs in.

[1104] The terminal sends the login information to the server, and the server performs user authentication.

[1105] Once authentication is successful, the user is taken to a form where they can enter the budget for their advertising campaign and the message they wish to send to society.

[1106] This data (advertising campaign budget, message) is sent from the device to the server and stored in a database.

[1107] Input: Advertising campaign budget, message you want to convey to society

[1108] Output: Company advertising data stored in a database

[1109] Step 3:

[1110] Cleaning and normalizing data

[1111] The server retrieves data sent by NPOs and companies from a database.

[1112] A generative AI model (OpenAI's GPT-3) is used to clean and normalize the input data.

[1113] Data cleaning refers to correcting incorrect or missing values, and normalization refers to standardizing the data format.

[1114] Input: NPO and company input data stored in the database

[1115] Output: Normalized and cleaned data

[1116] Step 4:

[1117] Calculating affinity scores

[1118] The server uses generative AI (OpenAI's GPT-3) to calculate an affinity score between the nonprofit organization's purpose and the company's advertising message.

[1119] Using an emotion engine (Microsoft Azure Emotion API), emotions are analyzed from text data (purpose and message) and reflected in the score.

[1120] Input: normalized data, text data

[1121] Output: Affinity score between NPOs and companies

[1122] Step 5:

[1123] Proposal generation

[1124] The server generates optimal matching proposals for both NPOs and companies based on the affinity scores.

[1125] At this time, the proposal will also include information about the provision of advertising space and specific details of the company's advertising campaign.

[1126] Input: affinity score

[1127] Output: Proposal content (for NPOs and companies)

[1128] Step 6:

[1129] Notification of proposal details

[1130] The server notifies the generated proposals to NPO and corporate users.

[1131] Users (NPO representatives and corporate representatives) can check the proposal contents from their terminals.

[1132] Input: Proposal

[1133] Output: The suggestion sent to the user

[1134] Step 7:

[1135] Match confirmation and notification

[1136] The users (NPO representative and company representative) review the proposal, and if both parties agree, they notify the server via their terminal.

[1137] The server confirms the agreement, updates the database to indicate that the match has been made, and sends a notification to both parties.

[1138] Input: User consent notice

[1139] Output: Notification of successful match

[1140] Through these steps, the system helps to efficiently match NPOs and companies and realize effective advertising campaigns.

[1141] 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.

[1142] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1143] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1144] [Third embodiment]

[1145] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1148] 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.

[1149] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1150] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1151] 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.

[1152] 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.

[1153] 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 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.

[1154] 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.

[1155] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1156] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1157] This system is a matching system that uses generative AI to support effective fundraising and donation destination selection between NPOs and companies. The specific configuration for implementing the system of the present invention is as follows.

[1158] Data Entry Phase

[1159] Data entry for nonprofit organizations

[1160] 1. The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[1161] 2. The terminal sends the login information to the server, and the server performs user authentication.

[1162] 3. After logging in, the user enters the amount of funds they wish to raise and the specific purpose of the funds (e.g., "1 million yen" or "urban greening project") in the form that appears.

[1163] 4. The terminal sends the input amount of funds desired to be raised and the purpose to the server.

[1164] Corporate Data Entry

[1165] 5. The user (company representative) uses the terminal to access the web application in the same way and log in.

[1166] 6. The terminal sends the login information to the server, and the server performs user authentication.

[1167] 7. After logging in, the user enters the message they want to convey to society (e.g., "I'm interested in environmental protection") into the form that appears.

[1168] 8. The terminal sends the entered message to the server.

[1169] Data Processing Phase

[1170] 9. The server stores the information sent by NPOs and companies in a database.

[1171] 10. Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[1172] Matching Phase

[1173] 11. The server searches the company data in the database based on the NPO's desired conditions.

[1174] 12. The server uses the generative AI to calculate the affinity score between the NPO and the company, taking into account factors such as purpose, amount, and social contribution theme.

[1175] 13. The server filters companies with high affinity scores and generates recommendations.

[1176] Proposal Generation Phase

[1177] Proposals for NPOs

[1178] 14. The server uses the generation AI to generate specific proposals for the NPO. Example: "Company X can provide 1 million yen for urban greening projects."

[1179] 15. The server sends the generated proposal to the device.

[1180] 16. The terminal displays the proposal to the user (NPO representative).

[1181] Proposals for companies

[1182] 17. The server uses AI to generate specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[1183] 18. The server sends the generated donation proposal to the terminal.

[1184] 19. The terminal displays the donation proposal to the user (company representative).

[1185] Matching establishment phase

[1186] 20. The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected company list.

[1187] 21. The terminal sends the selection information to the server.

[1188] 22. The server notifies the company representative of the procurement request.

[1189] 23. The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[1190] 24. The terminal sends the selection information to the server.

[1191] 25. The server notifies the NPO representative of the donation decision.

[1192] 26. Once both parties agree, the server will consider the match to be successful and update the information in the database.

[1193] 27. The server sends a notification of successful matching to both the NPO and the company.

[1194] 28. The device will display a match notification to each user.

[1195] Specific examples

[1196] As a concrete example, if the nonprofit organization "Environmental Protection Association" wants to raise 1 million yen to use for an "urban greening project," it first accesses a web application from a terminal and inputs the amount it wants to raise and the purpose of the fundraising. Similarly, a company "Ecotech Co., Ltd." inputs a message saying that it is "interested in environmental protection."

[1197] The server uses a generative AI to calculate highly compatible combinations based on the information from both parties, and notifies the NPO "Environmental Protection Association" that "Ecotech Co., Ltd. can donate to urban greening projects." At the same time, it suggests to the company "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen."

[1198] Both parties confirm the proposal, the NPO sends a procurement request to the company, and when the company decides to make a donation, a match is made and a notification is sent to both parties. In this way, the system of the present invention enables efficient and effective fundraising and donations.

[1199] The processing flow will be explained below.

[1200] Step 1:

[1201] The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[1202] Step 2:

[1203] The terminal sends the login information to the server.

[1204] Step 3:

[1205] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[1206] Step 4:

[1207] The user confirms that the authentication was successful and then enters the amount of funding they wish to raise (e.g., "1 million yen") and its specific purpose (e.g., "urban greening project") into the form.

[1208] Step 5:

[1209] The terminal transmits the input desired fundraising amount and purpose to the server.

[1210] Step 6:

[1211] Similarly, the user (company representative) accesses the web application from a terminal and logs in by entering their ID and password.

[1212] Step 7:

[1213] The terminal sends the login information to the server.

[1214] Step 8:

[1215] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[1216] Step 9:

[1217] The user confirms that the authentication was successful and then enters the message he or she wants to convey to society (e.g., "I'm interested in environmental protection") into the form.

[1218] Step 10:

[1219] The terminal transmits the input message to the server.

[1220] Step 11:

[1221] The server stores the information sent by NPOs and companies in a database.

[1222] Step 12:

[1223] Based on the stored information, the server uses generative AI to perform preprocessing, cleaning and normalizing the input data.

[1224] Step 13:

[1225] The server searches for company information in the database based on the NPO's desired criteria.

[1226] Step 14:

[1227] The server uses a generative AI to calculate the affinity score between NPOs and companies, taking into account factors such as purpose, amount, and social contribution theme.

[1228] Step 15:

[1229] The server filters companies with high affinity scores and creates a list.

[1230] Step 16:

[1231] The server uses a generative AI to generate specific proposals for NPOs.

[1232] Step 17:

[1233] The server transmits the generated proposal to the terminal.

[1234] Step 18:

[1235] The terminal displays the proposal contents to the user (NPO representative).

[1236] Step 19:

[1237] The server also uses generative AI to generate specific donation proposals for companies.

[1238] Step 20:

[1239] The server transmits the generated donation proposal to the terminal.

[1240] Step 21:

[1241] The terminal displays the donation proposal to the user (company representative).

[1242] Step 22:

[1243] The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected list of companies.

[1244] Step 23:

[1245] The terminal transmits the selection information to the server.

[1246] Step 24:

[1247] The server notifies the company representative of the procurement request.

[1248] Step 25:

[1249] The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[1250] Step 26:

[1251] The terminal transmits the selection information to the server.

[1252] Step 27:

[1253] The server notifies the NPO representative of the donation decision.

[1254] Step 28:

[1255] Once both parties agree, the server determines that a match has been made and updates the information in the database.

[1256] Step 29:

[1257] The server sends a notification of a successful match to both the NPO and the company.

[1258] Step 30:

[1259] The terminal displays a match notification to each user.

[1260] Example 1

[1261] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1262] When nonprofit organizations raise funds, the process of finding the right entity is extremely time-consuming and inefficient. This increases the time and effort spent on fundraising, making it difficult to focus on their core activities. It is also often difficult for organizations to find nonprofits that are suitable for their social contribution activities, preventing them from making effective donations. This creates a problem of mismatches between the fundraising and donation processes.

[1263] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1264] In this invention, the server includes: a means for a nonprofit organization to input the amount of fundraising it wishes to receive and its use; a means for a business entity to input a message it wishes to send to society; a means for evaluating the affinity between the nonprofit organization and business entities based on the input amount of fundraising it wishes to receive, its use, and the message; a means for suggesting suitable business entities to the nonprofit organization based on the evaluation; a means for suggesting suitable nonprofit organizations to business entities based on the evaluation; a means for notifying the nonprofit organization and business entities of the content of the proposal; a means for notifying the business entity of a procurement request made by the nonprofit organization; a means for notifying the nonprofit organization of a donation decision made by the business entity; and a means for notifying the nonprofit organization and business entities of a match based on an agreement between the two parties. This enables efficient matching between nonprofit organizations and business entities, facilitating the fundraising and donation processes.

[1265] A "non-profit organization" is an organization that operates for the public benefit and does not seek profit.

[1266] "Business Entity" means a legal entity or individual engaged in commercial activities.

[1267] "Funding Suggestion" refers to the amount of funding a nonprofit organization needs for a particular activity or project.

[1268] "Use" indicates the purpose for which the funds obtained through fundraising will be used.

[1269] A "message" refers to the values ​​and initiatives that a business wants to convey to society.

[1270] A "compatibility assessment" is a process that uses numerical values ​​and indicators to evaluate the compatibility between a nonprofit organization and a business entity.

[1271] "Proposal" means a proposal for collaboration with a non-profit organization or entity generated based on an affinity assessment.

[1272] "Procurement Request" means a request by a Nonprofit Organization for funding from a particular entity.

[1273] A "donation decision" is the process by which an entity decides to provide funding to a particular nonprofit organization.

[1274] A "match" is when a funding agreement is reached between a nonprofit organization and a business entity.

[1275] "Notification" means the act of communicating information, such as proposals and agreements, to nonprofit organizations and entities.

[1276] "Generative AI" refers to artificial intelligence systems that generate natural language and perform data analysis.

[1277] This invention is a matching system that uses generative AI to support efficient fundraising and donation destination selection between nonprofit organizations and business entities. Specific embodiments for implementing the invention are described below.

[1278] Hardware and software used

[1279] This system consists of a server, a terminal, and a user. The server has a database and a generative AI model, and the terminal implements a web application that provides an interface for users to operate it. The main software used is a web browser (e.g., Google Chrome, Firefox), an HTTP server (e.g., Apache, Nginx), a database management system (e.g., MySQL, PostgreSQL), and an AI model execution environment (e.g., TensorFlow, PyTorch).

[1280] Data Entry Phase

[1281] Nonprofit data entry

[1282] 1. The user (non-profit organization employee) accesses the system's web application using a web browser and logs in by entering their ID and password. The terminal sends the login information to the server, which then authenticates the user.

[1283] 2. After successful authentication, the user inputs the amount of funds they wish to raise and the specific purpose of the funds. The terminal then sends this information to the server.

[1284] Business entity data entry

[1285] 3. The user (business entity employee) similarly accesses the web application, enters login information, and is authenticated by the server.

[1286] 4. After successful authentication, the user enters the message they wish to send to society. The device then sends this information to the server.

[1287] Data Processing Phase

[1288] 5. The server stores the information submitted by nonprofit organizations and businesses in a database, and the generative AI model cleans and normalizes the data based on the stored information.

[1289] Matching Phase

[1290] 6. The server searches the entity data based on the nonprofit organization's desired criteria, calculates affinity scores using the generative AI model, filters entities with high affinity scores, and generates proposals.

[1291] Proposal Generation Phase

[1292] Proposals for nonprofit organizations

[1293] 7. The server uses the generative AI model to generate specific proposals for the nonprofit organization and sends them to the device, which then displays them to the user.

[1294] Proposals to business entities

[1295] 8. The server also uses the generative AI model to generate specific donation suggestions for the business entity and sends them to the device, which then displays them to the user.

[1296] Matching establishment phase

[1297] 9. The user (non-profit organization representative) reviews the proposal and selects the entity to request procurement from the list of selected entities. The selection information is sent to the server and notified to the entity representative.

[1298] 10. The user (business operator) reviews the donation proposal and selects the nonprofit organization to which the donation will be made. The selection information is sent to the server and notified to the nonprofit organization operator.

[1299] 11. The server will then determine if the match is successful and update the information in the database. A notification of the match will be sent to both the nonprofit organization and the business entity, and the device will display it to the user.

[1300] Specific examples

[1301] As a concrete example, if the nonprofit organization "Environmental Protection Association" wishes to raise 1 million yen to use for an "urban greening project," the user accesses a web application from their device and inputs the amount they wish to raise and the purpose of the donation. Similarly, the business entity "Ecotech Co., Ltd." inputs a message saying, "I'm interested in environmental protection." The server uses generative AI based on the information of both parties to calculate highly compatible pairs and notifies the nonprofit organization "Environmental Protection Association" that "Ecotech Co., Ltd." can donate to the urban greening project." At the same time, it proposes to the business entity "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen." Both parties confirm the proposal, and the nonprofit organization sends a procurement request to the business entity. When the business entity decides to donate, a match is made and notifications are sent to both parties.

[1302] Example of prompt input to a generative AI model

[1303] Nonprofit data entry prompt example:

[1304] The non-profit organization Environmental Protection Association is seeking 1 million yen for an urban greening project.

[1305] Example of a prompt for business entity message input:

[1306] Ecotech Co., Ltd. cares about protecting the environment.

[1307] The generative AI model calculates an affinity score based on these inputs and generates specific suggestions.

[1308] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1309] Step 1:

[1310] The user (non-profit organization employee) accesses the system's web application using a web browser and logs in by entering their ID and password. Input data: ID and password. Output data: authentication request. The specific operation is to access the login page and enter their ID and password in the designated form.

[1311] Step 2:

[1312] The terminal sends the login information entered by the user to the server. Input data: ID and password. Output data: HTTP POST request. Specifically, when the user clicks the "Login" button, the login information is sent to the server.

[1313] Step 3:

[1314] The server authenticates the received login information, and if the user has entered it correctly, it generates an authentication token and returns it to the terminal. Input data: ID and password. Output data: authentication token. Specifically, the server searches the database for the corresponding user information, and if there is a match, it generates an authentication token and returns it as a response.

[1315] Step 4:

[1316] After logging in, the user enters the desired amount of funding and the specific purpose in the form that appears. Input data: desired amount of funding, purpose of funding. Output data: request to send input data. Specifically, the user enters the specific amount and purpose in the "desired amount of funding" and "purpose" fields, respectively, and presses the "Submit" button.

[1317] Step 5:

[1318] The terminal sends the input desired fundraising amount and purpose to the server. Input data: desired fundraising amount, purpose of fundraising. Output data: HTTP POST request. Specifically, the terminal sends data to the server using an HTTP POST request.

[1319] Step 6:

[1320] The user (business person) similarly accesses the web application and logs in. Input data: ID and password. Output data: authentication request. Specifically, the business person accesses the login page in the same way as the nonprofit organization person and enters their ID and password in the designated form.

[1321] Step 7:

[1322] The terminal sends the login information of the business entity employee to the server. Input data: ID and password. Output data: HTTP POST request. Specifically, when the user clicks the "Login" button, the information is sent to the server.

[1323] Step 8:

[1324] The server authenticates the received login information, and if the business person is successfully authenticated, it generates an authentication token and returns it to the terminal. Input data: ID and password. Output data: authentication token. The specific operation is the same as the authentication process for nonprofit organization personnel.

[1325] Step 9:

[1326] After logging in, the user enters the message that the company wants to send to society in the form that appears. Input data: Message. Output data: Message sending request. The specific operation is that the business person in charge enters the specific content in the "Message" field and presses the "Send" button.

[1327] Step 10:

[1328] The terminal sends the input message to the server. Input data: message. Output data: HTTP POST request. In concrete terms, the terminal sends the transmitted message to the server as an HTTP POST request.

[1329] Step 11:

[1330] The server stores the information submitted by nonprofits and businesses in a database. Input data: fundraising amount, purpose, message. Output data: new record in the database. Specifically, the server connects to the database, inserts a new record, and saves the user's input data.

[1331] Step 12:

[1332] The server performs preprocessing using a generative AI model based on the stored information, cleaning and normalizing the input data. Input data: desired fundraising amount, purpose, message. Output data: cleaned and normalized data. Specifically, the server calls the generative AI model and formats the input data.

[1333] Step 13:

[1334] The server searches the database for entity data based on the nonprofit organization's desired criteria. Input data: cleaned and normalized data. Output data: relevant entity data. Specifically, the server uses an SQL query to extract entity data that matches the criteria.

[1335] Step 14:

[1336] The server uses the generative AI model to calculate the affinity score between the nonprofit organization and the business entity. Input data: relevant business entity data. Output data: affinity score. Specifically, the server inputs the extracted data into the generative AI model to calculate the affinity score.

[1337] Step 15:

[1338] The server filters out businesses with high affinity scores and generates proposals. Input data: affinity score. Output data: proposals. Specifically, it selects businesses with high scores and automatically generates proposals using a generative AI model.

[1339] Step 16:

[1340] The server sends the generated proposal to the terminal. Input data: proposal. Output data: HTTP response. Specifically, the server sends the generated proposal to the terminal as an HTTP response.

[1341] Step 17:

[1342] The terminal displays the proposal to the user (nonprofit organization representative). Input data: HTTP response. Output data: Display of proposal. The specific operation is that the proposal is displayed in the browser.

[1343] Step 18:

[1344] The server uses the generative AI model to generate specific donation proposals for businesses and send them to the terminal. Input data: Proposal content from nonprofit organizations. Output data: HTTP response with donation proposal content. The specific operation is to automatically generate a donation proposal using the generative AI model and send it as a response to the terminal.

[1345] Step 19:

[1346] The terminal displays the donation proposal content to the user (business entity representative). Input data: HTTP response. Output data: Display of donation proposal content. Specifically, the donation proposal content is displayed in the browser.

[1347] Step 20:

[1348] The user (non-profit organization representative) reviews the proposal content and selects the business entity to request procurement from the list of selected entities. Input data: Proposal content. Output data: Procurement request information. Specifically, the non-profit organization representative selects the target business entity from the list and presses the "Procurement request" button.

[1349] Step 21:

[1350] The terminal sends the selection information to the server. Input data: Procurement request information. Output data: HTTP POST request. Specifically, the terminal sends the selection information to the server as an HTTP POST request.

[1351] Step 22:

[1352] The server notifies the business entity person of the procurement request. Input data: Procurement request information. Output data: Notification. Specifically, the server sends an email or in-app notification of the procurement request to the business entity person.

[1353] Step 23:

[1354] The user (business entity representative) checks the donation proposal details and selects the nonprofit organization to which the donation will be made. Input data: donation proposal details. Output data: donation recipient selection information. Specifically, the business entity representative selects the nonprofit organization to which the donation will be made and presses the "Donation Confirmation" button.

[1355] Step 24:

[1356] The terminal sends the selection information to the server. Input data: donation destination selection information. Output data: HTTP POST request. Specifically, the terminal sends the selection information to the server as an HTTP POST request.

[1357] Step 25:

[1358] The server notifies the nonprofit organization's representative of the donation decision. Input data: donation destination selection information. Output data: notification. Specifically, the server sends a donation decision notification to the nonprofit organization's representative.

[1359] Step 26:

[1360] The server determines that a match has been made based on the agreement between the two parties and updates the information in the database. Input data: Adjustment information. Output data: Updated information indicating that a match has been made. Specifically, the server updates the status field in the database to "match made."

[1361] Step 27:

[1362] The server sends a notification of successful matching to both the nonprofit organization and the business. Input data: Matching update information. Output data: Notification. Specifically, the server notifies both parties of successful matching via email or in-app notification.

[1363] Step 28:

[1364] The device displays a match-making notification to each user. Input data: Notification. Output data: Display of match-making notification. Specifically, a notification is displayed in the device browser.

[1365] (Application example 1)

[1366] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1367] Existing systems for matching NPOs and corporations are problematic in that they are inefficient in fundraising and donation recipient selection. In particular, there is a lack of appropriate means for supporting collaboration between security service providers and NPOs. The present invention aims to solve these problems and provide an effective system for achieving optimal matching between NPOs and corporations.

[1368] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1369] In this invention, the server includes a means for NPOs to input the amount of fundraising they wish to raise and its use, a means for companies to input the message they wish to convey to society, and a means for evaluating the affinity between NPOs and companies based on the input amount of fundraising, use, and message. This allows security service providers to efficiently select NPOs for fundraising and donation recipients. Furthermore, evaluation results are notified and displayed through an application installed on a smartphone, smart glasses, or head-mounted display, enabling effective matching in real time.

[1370] An "NPO corporation" is an organization whose primary purpose is to engage in non-profit activities and to promote the general public's interests.

[1371] "Funding Suggestion" is the amount of money a nonprofit organization needs for a specific project or activity.

[1372] "Use" refers to the specific ways in which the NPO plans to use the funds.

[1373] A "business" is an organization that provides goods and services and operates to pursue profits.

[1374] A "message" is an expression that shows the philosophy or purpose that a company wants to communicate to society.

[1375] "Affinity" indicates the degree of agreement between the goals and objectives of an NPO and a company.

[1376] "Evaluation" is the process of quantitatively or qualitatively determining the affinity between an NPO and a company.

[1377] "Security services" are the practices and technologies used to provide physical or digital security.

[1378] A "server" is a computer system that provides data and services over a network.

[1379] A "smartphone" is a mobile device that has advanced computing power and Internet connectivity in addition to telephone functions.

[1380] "Smart glasses" are wearable devices equipped with a display to enhance visual information.

[1381] A "head-mounted display" is a display device that projects images directly into the field of vision.

[1382] An "application" is software designed to perform a specific function.

[1383] "Generative AI" is a system that uses artificial intelligence technology to generate new data and information.

[1384] "Notification" is the act of informing the user of important information or results.

[1385] "Display" is the act of visually providing information using a display or device.

[1386] This invention is a system for supporting effective fundraising and donation destination selection between NPOs and companies, and is particularly equipped with a function to promote cooperation with companies that provide security services.

[1387] System Program Overview

[1388] The system includes the following main features:

[1389] 1. A function that allows NPOs to input the amount of funds they wish to raise and the purpose for which they wish to raise funds.

[1390] 2. A function that allows companies to input messages they want to send to society.

[1391] 3. A generative AI function that evaluates the affinity between NPOs and companies based on input data.

[1392] 4. A function to generate and notify appropriate proposals to NPOs and companies based on the evaluation results.

[1393] 5. An application function that is installed on a smartphone, smart glasses, or head-mounted display and displays the evaluation results in real time.

[1394] Hardware and Software Configuration

[1395] The server provides the computing resources for storing data and performing affinity evaluation using the generated AI. Specifically, it uses cloud services (e.g., Amazon Web Services and Google Cloud Platform).

[1396] Users input data and receive notifications and assessment results using smartphones, smart glasses, or head-mounted displays, all of which run mobile operating systems such as iOS or Android.

[1397] Use data processing libraries (e.g., Pandas or NumPy in Python) for data cleaning and preprocessing.

[1398] Affinity assessment uses a generative AI model (e.g., OpenAI's GPT-3).

[1399] Processing flow details

[1400] 1. Data entry phase:

[1401] The user (NPO representative) uses a device such as a smartphone to input the amount of funds they wish to raise and the specific use of the funds into the web application.

[1402] Similarly, company representatives can use devices such as smartphones to input messages they wish to send to society.

[1403] 2. Data Processing Phase:

[1404] The server receives the entered data and stores it in a database.

[1405] Data cleaning and preprocessing is performed using Python data processing libraries.

[1406] 3. Matching Phase:

[1407] The server uses a generative AI model to evaluate the affinity between nonprofits and businesses, calculating an affinity score based on the input fundraising amount, purpose, and message.

[1408] 4. Proposal generation phase:

[1409] Proposals are generated based on the affinity assessment. Specifically, NPOs are notified of companies that can donate, and companies are notified of NPOs that need support.

[1410] Specific examples

[1411] As a concrete example, the following prompt sentences can be used to input the generative AI model to achieve effective matching:

[1412] Example prompt sentence:

[1413] The NPO "Children's Safety Association" wants to raise 500,000 yen to be used for "Local Child Protection Programs." The company "Security Solutions Inc." wants to support "Child Safety Initiatives." Calculate the affinity scores for these two organizations and generate the best matching proposal.

[1414] Based on these prompts, the generative AI model calculates an affinity score between the NPO and the company and notifies appropriate suggestions, thereby realizing effective cooperation between the security service company and the NPO.

[1415] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1416] Step 1:

[1417] The user (NPO employee) accesses the web application using a device such as a smartphone or PC and enters login information. The device sends the entered login information to the server, which then authenticates the user. After authentication, the user enters the amount of funding they wish to raise and its purpose into a web form. The input data generated is the "desired amount of funding" and "purpose." The device then sends this input data to the server.

[1418] Step 2:

[1419] A user (company representative) similarly accesses a web application using a device such as a smartphone or PC and enters login information. The device sends the entered login information to the server, which then authenticates the user. After authentication, the user enters the message they wish to send to society into a web form. A "message" is generated as input data. The device then sends this input data to the server.

[1420] Step 3:

[1421] The server stores the received input data ("desired fundraising amount," "purpose," and "message") in a database. When storing the data, it performs data cleaning to maintain data integrity. Specifically, the database uses the Python Pandas library to remove spaces and standardize the format. The output of this process is well-organized data stored in the database.

[1422] Step 4:

[1423] The server uses a generative AI model to evaluate the affinity between NPOs and companies based on the data stored in the database. Specifically, it sends the input data to a generative AI (e.g., OpenAI's GPT-3) and calculates an affinity score. The server generates a score for each combination of NPO and company and selects the combination with the highest score. An "affinity score" is generated as the output.

[1424] Step 5:

[1425] The server generates specific proposals for NPOs and businesses based on the affinity scores. Using a generative AI model, the server automatically generates prompts to inform NPOs of businesses that can donate to them, and to inform businesses of NPOs that need their support. For example, the server generates the following prompts:

[1426] The NPO "Children's Safety Association" wants to raise 500,000 yen to be used for "Local Child Protection Programs." The company "Security Solutions Inc." wants to support "Child Safety Initiatives." Calculate the affinity scores for these two organizations and generate the best matching proposal.

[1427] As an output, a "proposal" is generated.

[1428] Step 6:

[1429] The server notifies the generated proposal contents to the terminals of the corresponding NPO and company personnel. The notified proposal contents are displayed on the terminal screen, allowing the user to confirm the proposal contents. A "notification of proposal contents" is generated as an output.

[1430] Step 7:

[1431] The user (NPO representative) checks the proposal details and selects an appropriate company from among the proposed companies. The selection details are sent to the server via the terminal. The server receives this selection information and notifies the company representative of the procurement request. The "Procurement Request Details" is generated as the output.

[1432] Step 8:

[1433] The user (company representative) receives a notification of the procurement request and selects the NPO to which the donation will be made. The selection is sent to the server via the terminal. The server receives this selection information and notifies the NPO representative of the decision to donate. A "donation decision notification" is generated as the output.

[1434] Step 9:

[1435] Once both parties agree, the server determines that the match has been made and updates the information in the database. It then sends a notification of the match to both the NPO and the company. The device displays the notification to each user. A "matching successful notification" is generated as an output.

[1436] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1437] This system is a matching system that uses generative AI and an emotion engine to support effective fundraising and donation destination selection between NPOs and companies. The specific configuration for implementing the system of the present invention is as follows.

[1438] Data Entry Phase

[1439] Data entry for nonprofit organizations

[1440] 1. The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[1441] 2. The terminal sends the login information to the server, and the server performs user authentication.

[1442] 3. After logging in, the user enters the amount of funds they wish to raise and the specific purpose of the funds (e.g., "1 million yen" or "urban greening project") in the form that appears.

[1443] 4. The terminal sends the input amount of funds desired to be raised and the purpose to the server.

[1444] Corporate Data Entry

[1445] 5. The user (company representative) uses the terminal to access the web application in the same way and log in.

[1446] 6. The terminal sends the login information to the server, and the server performs user authentication.

[1447] 7. After logging in, the user enters the message they want to convey to society (e.g., "I'm interested in environmental protection") into the form that appears.

[1448] 8. The terminal sends the entered message to the server.

[1449] Data Processing Phase

[1450] 9. The server stores the information sent by NPOs and companies in a database.

[1451] 10. Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[1452] Matching Phase

[1453] 11. The server searches for company information in the database based on the NPO's desired criteria.

[1454] 12. The emotion engine recognizes and analyzes user emotions from input information from NPOs and companies.

[1455] 13. The server uses generative AI to calculate the affinity score between the NPO and the company, taking into account the purpose, amount, and social contribution theme as well as the perceived sentiment.

[1456] 14. The server filters companies with high affinity scores and creates a list.

[1457] Proposal Generation Phase

[1458] Proposals for NPOs

[1459] 15. The server generates specific proposals for NPOs based on the results of the generative AI and emotion engine. Example: "Company X can provide 1 million yen for urban greening projects."

[1460] 16. The server sends the generated proposal to the device.

[1461] 17. The terminal displays the proposal to the user (NPO representative).

[1462] Proposals for companies

[1463] 18. Based on the results of the generative AI and emotion engine, the server also generates specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[1464] 19. The server sends the generated donation proposal to the terminal.

[1465] 20. The terminal displays the donation proposal to the user (company representative).

[1466] Matching establishment phase

[1467] 21. The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected company list.

[1468] 22. The terminal sends the selection information to the server.

[1469] 23. The server notifies the company representative of the procurement request.

[1470] 24. The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[1471] 25. The terminal sends the selection information to the server.

[1472] 26. The server notifies the NPO representative of the donation decision.

[1473] 27. Once both parties agree, the server will consider the match to be successful and update the information in the database.

[1474] 28. The server sends a notification of successful matching to both the NPO and the company.

[1475] 29. The device will display a match notification to each user.

[1476] Specific examples

[1477] For example, if the nonprofit organization "Environmental Protection Association" wants to raise 1 million yen to use for an "urban greening project," the person in charge can access the web application from a terminal and enter the amount they want to raise and the purpose of the donation. Similarly, the company "Ecotech Co., Ltd." can enter a message saying that they are "interested in environmental protection."

[1478] The server uses a generative AI to evaluate affinity based on the information from both parties, and then adjusts the affinity score by analyzing the user's emotions with an emotion engine. As a result, the server notifies the NPO "Environmental Protection Association" that "Ecotech Co., Ltd. is available to donate to urban greening projects." At the same time, it suggests to the company "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen."

[1479] Both parties confirm the proposal, the NPO sends a procurement request to the company, and when the company decides to make a donation, a match is made and a notification is sent to both parties. In this way, the system of the present invention enables efficient and effective fundraising and donations.

[1480] The processing flow will be explained below.

[1481] Step 1:

[1482] The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[1483] Step 2:

[1484] The terminal sends the login information to the server.

[1485] Step 3:

[1486] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[1487] Step 4:

[1488] The user confirms that the authentication was successful and then enters the amount of funding they wish to raise (e.g., "1 million yen") and its specific purpose (e.g., "urban greening project") into the form.

[1489] Step 5:

[1490] The terminal transmits the input desired fundraising amount and purpose to the server.

[1491] Step 6:

[1492] Similarly, the user (company representative) accesses the web application from a terminal and logs in by entering their ID and password.

[1493] Step 7:

[1494] The terminal sends the login information to the server.

[1495] Step 8:

[1496] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[1497] Step 9:

[1498] The user confirms that the authentication was successful and then enters the message he or she wants to convey to society (e.g., "I'm interested in environmental protection") into the form.

[1499] Step 10:

[1500] The terminal transmits the input message to the server.

[1501] Step 11:

[1502] The server stores the information sent by NPOs and companies in a database.

[1503] Step 12:

[1504] The server uses an emotion engine to recognize the user's emotions from the input information of NPOs and companies and record them in a database.

[1505] Step 13:

[1506] Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[1507] Step 14:

[1508] The server searches for company information in the database based on the NPO's desired criteria.

[1509] Step 15:

[1510] The server uses generative AI to calculate the affinity score between NPOs and companies, taking into account the purpose, amount, and social contribution theme, as well as perceived emotions.

[1511] Step 16:

[1512] The server filters companies with high affinity scores and creates a list.

[1513] Step 17:

[1514] The server generates specific proposals for NPOs based on the results of the generative AI and emotion engine. Example: "Company X can provide 1 million yen for urban greening projects."

[1515] Step 18:

[1516] The server transmits the generated proposal to the terminal.

[1517] Step 19:

[1518] The terminal displays the proposal contents to the user (NPO representative).

[1519] Step 20:

[1520] Based on the results of the generative AI and emotion engine, the server also generates specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[1521] Step 21:

[1522] The server transmits the generated donation proposal to the terminal.

[1523] Step 22:

[1524] The terminal displays the donation proposal to the user (company representative).

[1525] Step 23:

[1526] The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected list of companies.

[1527] Step 24:

[1528] The terminal transmits the selection information to the server.

[1529] Step 25:

[1530] The server notifies the company representative of the procurement request.

[1531] Step 26:

[1532] The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[1533] Step 27:

[1534] The terminal transmits the selection information to the server.

[1535] Step 28:

[1536] The server notifies the NPO representative of the donation decision.

[1537] Step 29:

[1538] Once both parties agree, the server determines that a match has been made and updates the information in the database.

[1539] Step 30:

[1540] The server sends a notification of a successful match to both the NPO and the company.

[1541] Step 31:

[1542] The terminal displays a match notification to each user.

[1543] Example 2

[1544] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1545] Until now, it has been difficult to efficiently and effectively raise funds and select donation recipients between NPOs and companies, and while a two-way affinity assessment is considered particularly important, achieving this has been problematic as it requires a lot of time and effort.Therefore, there has been a need for a system that can efficiently assess the affinity between NPOs and companies, make optimal proposals to both parties, and promptly notify them of these proposals.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1547] In this invention, the server includes: a means for an NPO to input the amount of fundraising it wishes to receive and its use; a means for a company to input a message it wishes to send to society; a means for evaluating the affinity between the NPO and companies based on the input amount of fundraising it wishes to receive, its use, and the message; a means for suggesting suitable companies to the NPO based on the evaluation; a means for suggesting suitable NPOs to companies based on the evaluation; a means for calculating an affinity score using a generative AI and an emotion engine; and a means for notifying the NPO and companies of the content of the suggestions. This enables quick and effective fundraising and donation destination selection between NPOs and companies.

[1548] An "NPO corporation" refers to a corporation that engages in public interest activities for non-profit purposes.

[1549] "Desired fundraising amount" refers to the amount of funds an NPO needs for its projects and activities.

[1550] "Use" refers to the specific purpose that indicates what projects or activities the NPO will use the funds it raises for.

[1551] "Business" refers to an organization that provides goods or services for profit.

[1552] A "message" refers to information that expresses the intentions and interests that a company wants to convey to society.

[1553] "Affinity" refers to the degree of agreement between the goals and values ​​of an NPO and a company.

[1554] "Generative AI" refers to artificial intelligence technology that automatically analyzes data and derives specific patterns and outcomes.

[1555] An "emotion engine" refers to technology that analyzes a user's emotions and generates appropriate suggestions based on those emotions.

[1556] "Affinity score" refers to the evaluation result that quantifies the affinity between an NPO and a company.

[1557] "Proposal content" refers to specific proposal information for NPOs and companies created based on generative AI and emotion engines.

[1558] "Notification" refers to the act of communicating important information, such as proposal details, to NPOs and companies.

[1559] "Database" refers to a system for systematically storing and managing data collected from NPOs and companies.

[1560] This invention is a matching system for effective and efficient fundraising and donation destination selection between NPOs and companies. This system uses generative AI and an emotion engine to evaluate affinity and make optimal proposals.

[1561] Data Entry Phase

[1562] Data entry for nonprofit organizations

[1563] The user (NPO employee) uses a terminal to access the web application and log in by entering their ID and password. The terminal sends the login information to the server, which then authenticates the user. After logging in, the user enters the amount of funding they wish to raise and its specific purpose (e.g., "1 million yen" or "urban greening project"), and the terminal then sends the information they entered to the server.

[1564] Corporate Data Entry

[1565] Similarly, users (company personnel) use their terminals to access and log in to a web application. The terminals send the login information to the server, which then authenticates the user. After logging in, the user enters a message they want to convey to society (e.g., "I'm interested in environmental protection"), and the terminals send the message to the server.

[1566] Data Processing Phase

[1567] The server stores the information sent by NPOs and companies in a database. Next, the server uses generative AI to perform preprocessing, cleaning the input data (removing noise and filling in missing data) and normalizing it (standardizing the data format).

[1568] Matching Phase

[1569] The server searches for company information in a database based on the NPO's desired criteria. The emotion engine recognizes and analyzes user emotions from the input information about the NPO and company. The server uses generative AI to calculate an affinity score, taking into account the desired fundraising amount, purpose, message content, and emotion score. The server filters companies with high affinity scores and creates a list.

[1570] Proposal Generation Phase

[1571] Proposals for NPOs

[1572] The server generates a specific proposal for the NPO based on the results of the generation AI and emotion engine. For example, "Company X can provide 1 million yen for a greening project in an urban area." The server sends the generated proposal to the terminal, which then displays it to the user (NPO representative).

[1573] Proposals for companies

[1574] Based on the results of the generation AI and the emotion engine, the server also generates specific donation proposals for companies. For example, "NPO Y is seeking a donation of 1 million yen." The server sends the generated donation proposal to the terminal, which then displays it to the user (company representative).

[1575] Matching establishment phase

[1576] The user (NPO representative) reviews the proposal and selects a company to request procurement from the list of selected companies. The device sends the selection information to the server, which notifies the company representative of the procurement request. The user (company representative) reviews the donation proposal and selects the NPO to donate to. The device sends the selection information to the server, which notifies the NPO representative of the decision to donate. Once both parties agree, a match is made and the server updates the information in the database. The server sends a notification of the match to both the NPO and the company, and the device displays a notification of the match to each user.

[1577] Specific examples

[1578] For example, if an NPO needs 1 million yen for an "urban greening project," the person in charge accesses the web application from their device, logs in, and enters the desired fundraising amount and purpose. The company enters a message saying, "I'm interested in environmental protection." The server uses generative AI to evaluate affinity based on the information of both parties, and the emotion engine analyzes the user's emotions to adjust the affinity score. As a result, the server notifies the NPO that "the company can donate to the urban greening project" and suggests to the company that "the NPO is seeking a donation of 1 million yen." Both parties confirm the proposal, and the NPO sends a procurement request to the company. When the company decides to donate, a match is made and notifications are sent to both parties. In this way, the system of the present invention enables efficient and effective fundraising and donation destination selection.

[1579] Examples of standard prompts

[1580] "If an NPO needs to raise 1 million yen to fund a greening project in an urban area, please suggest what kind of company would be suitable."

[1581] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1582] Step 1:

[1583] The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password. The terminal sends the login information to the server, which then authenticates the user. If the login is successful, the server returns an authentication success message to the terminal and displays an input form for the user. Input: ID, password. Output: Authentication success message, input form.

[1584] Step 2:

[1585] The user (NPO representative) inputs the amount of funding they wish to raise and its specific purpose. The device sends this information to the server. The server temporarily stores the received data and prepares to connect to the database. Input: Desired amount of funding, purpose. Output: Temporarily stores input data.

[1586] Step 3:

[1587] The user (company representative) uses a terminal to access the web application, logs in by entering their ID and password in the same way, and enters a message. The terminal sends this information to the server. The server temporarily stores the received data and checks the consistency of the NPO's data with the company's data. Input: ID, password, message. Output: Temporarily stores input data, checks data consistency.

[1588] Step 4:

[1589] The server stores all data sent by NPOs and companies in a database. After storing, the server performs preprocessing (data cleaning and normalization) using generative AI. This process removes noise from the data and fills in missing data. Input: All stored data. Output: Cleaned and normalized data.

[1590] Step 5:

[1591] The server searches for company information based on the NPO data and creates an initial list of matching candidates. A high-speed search algorithm is used for this. Input: NPO's desired conditions. Output: Matching candidate list.

[1592] Step 6:

[1593] The emotion engine recognizes and analyzes user emotions from input information from NPOs and companies. For example, it distinguishes between positive and negative emotions and assigns an emotion score to each. Input: Input information from NPOs and companies. Output: Emotion score.

[1594] Step 7:

[1595] The server uses generative AI to calculate affinity scores between NPOs and companies. This calculation takes into account the desired fundraising amount, purpose, message content, and sentiment score. Based on the calculation results, it filters companies with high affinity scores and updates the list. Input: Cleaned and normalized data, sentiment score. Output: Filtered company list.

[1596] Step 8:

[1597] The server uses the generative AI and emotion engine to generate specific proposals for NPOs. Example: "Company X can provide 1 million yen for urban greening projects." The generated proposals are sent to the device, which displays them to the user (NPO representative). Input: Filtered company list, emotion score. Output: Proposal generation, proposal display.

[1598] Step 9:

[1599] The server also generates specific donation suggestions for companies based on the results of the generation AI and emotion engine. Example: "NPO is seeking a donation of 1 million yen." The generated donation suggestions are sent to the terminal, which displays them to the user (company representative). Input: Filtered company list, emotion score. Output: Generation of donation suggestions, display of donation suggestions.

[1600] Step 10:

[1601] The user (NPO representative) checks the proposal details and selects a company to request procurement from a list of companies. The terminal sends the selection information to the server, and the server notifies the company representative of the procurement request. Input: Proposal details. Output: Sending selection information, notification of procurement request.

[1602] Step 11:

[1603] The user (company representative) checks the donation proposal details and selects the NPO to which the donation will be made. The device sends the selection information to the server, and the server notifies the NPO representative of the decision to donate. Input: Donation proposal details. Output: Sending selection information, notification of donation decision.

[1604] Step 12:

[1605] Once both parties agree, the server will consider the match to be successful and update the information in the database. The server will then send a notification of the successful match to both the NPO and the company, and the device will display a notification of the successful match to each user. Input: Agreement information between the two parties. Output: Update the database, send and display a notification of the successful match.

[1606] (Application example 2)

[1607] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1608] Conventional matching systems for donations and fundraising do not adequately assess the affinity between NPOs and companies, resulting in inappropriate matching. Furthermore, it is difficult to realize advertising campaigns through social contribution activities, and collaboration between the two parties is inefficient. As a result, NPOs do not receive sufficient support, and companies lose opportunities to effectively communicate their social contribution activities. The purpose of this invention is to solve these problems and realize effective matching between NPOs and companies and advertising campaigns.

[1609] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for an NPO to input the amount of fundraising it wishes to receive and its purpose; a means for a company to input a message it wishes to convey to society; a means for evaluating the affinity between the NPO and companies based on the input amount of fundraising it wishes to receive, its purpose, and its message; a means for suggesting suitable companies to the NPO based on the evaluation; a means for suggesting suitable NPOs to companies based on the evaluation; a means for notifying the NPO and companies of the proposals; a means for providing advertising space based on the evaluation; a means for an advertiser to input a budget and message for an advertising campaign and store the input in a database; and a means for evaluating and suggesting the affinity between advertisers and advertising space providers using a generative AI and an emotion engine. This enables efficient and effective matching between NPOs and companies and the creation of advertising campaigns.

[1610] An "NPO corporation" is a corporation that carries out non-profit activities and whose primary purpose is to contribute to society.

[1611] "Funding Suggestion" refers to the amount of funding a nonprofit organization needs for a particular project or activity.

[1612] "Purpose" refers to the specific purpose and activities for which the funds raised will be used.

[1613] "Enterprise" refers to a legal entity or organization engaged in commercial activities for profit.

[1614] A "message" refers to the philosophy, opinions, and information that a company wants to communicate to society.

[1615] "Affinity" refers to the degree of agreement between the goals and values ​​of an NPO and a company.

[1616] "Evaluation" refers to the act of quantifying or qualitatively judging affinity based on information about the NPO and the company.

[1617] A "proposal" is an action that involves making an appropriate match based on the evaluation and notifying the NPO or company.

[1618] "Notification" refers to the act of communicating the contents and results of a proposal to NPOs and companies.

[1619] An "advertising space" refers to the space or medium for displaying advertisements, and refers to the advertising space provided by NPOs.

[1620] "Advertiser" refers to a company or individual that places an advertisement.

[1621] "Advertising campaign" refers to a series of advertising activities undertaken to achieve a specific advertising objective.

[1622] A "database" is a system for storing, searching, and managing information in an organized manner.

[1623] "Generative AI" refers to artificial intelligence technology that automatically generates specific information or suggestions from input data.

[1624] An "emotion engine" is a technology that analyzes emotions from text and data and provides the results.

[1625] The system of this invention supports effective fundraising and advertising campaign matching between NPOs and companies, and includes the following components: Hardware components include a server, terminals, and databases, while software components include a generative AI model, an emotion engine, and a cloud platform.

[1626] Hardware Configuration

[1627] 1. Server:

[1628] It mainly processes data and executes matching algorithms.

[1629] Google Cloud Platform is used as the cloud platform.

[1630] 2. Terminal:

[1631] Used by nonprofit and corporate users to enter data and review proposals.

[1632] This includes smartphones, tablets, and PCs.

[1633] 3. Database:

[1634] Firebase Realtime Database is used for data storage, saving access data and matching information.

[1635] Software Configuration

[1636] 1. Generative AI model:

[1637] We use OpenAI's GPT-3 to perform affinity evaluation and generate proposals.

[1638] 2. Emotion Engine:

[1639] Uses the Microsoft Azure Emotion API to analyze emotions from input data.

[1640] Data Entry and Processing

[1641] Users input the amount of funds their NPO wishes to raise and how it will be used, the budget for their company's advertising campaign, and the message they wish to convey to society via their devices. This input data is then stored in a database via the server.

[1642] Affinity evaluation

[1643] The server calculates an affinity score using a generative AI and an emotion engine based on the information of NPOs and companies stored in a database. Specifically, the generative AI cleans and normalizes the input data, and the emotion engine analyzes emotions from the text data to evaluate the affinity between the two.

[1644] Proposal generation and notification

[1645] Based on the results of the affinity assessment, the server generates and notifies NPOs and companies of their optimal matching proposals. Users can view these proposals on their devices.

[1646] Specific examples

[1647] For example, if an NPO wants to raise 1 million yen for an "urban greening project," and a company wants to run an "environmental protection advertising campaign," the server uses generative AI and an emotion engine to evaluate the affinity between the NPO's project and the company's campaign content, and notifies both parties of appropriate proposals.

[1648] Prompt Sentence Examples

[1649] The emotional message of a certain nonprofit environmental organization is, "Greening is the first step in protecting the urban environment. Your support will help preserve the greenery for future generations." Find a company message that matches this message.

[1650] The campaign message of a certain technology company is, "We are taking action today for a sustainable future, to leave green cities for future generations." Find a message from a nonprofit organization that matches this message.

[1651] As described above, the system of the present invention can support efficient and effective matching between NPOs and companies and the realization of advertising campaigns.

[1652] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1653] Step 1:

[1654] Data entry for NPO personnel

[1655] The user (NPO representative) accesses the web application using a terminal and logs in.

[1656] The terminal sends the login information to the server, and the server performs user authentication.

[1657] Once authentication is successful, the user is taken to a form where they can enter the amount they wish to raise and the specific purpose of the funds.

[1658] This data (amount of funding desired and purpose) is sent from the terminal to the server and stored in a database.

[1659] Input: Desired amount of funding, purpose

[1660] Output: Fundraising requests of nonprofit organizations stored in a database

[1661] Step 2:

[1662] Data entry by company personnel

[1663] The user (company employee) similarly accesses the web application using a terminal and logs in.

[1664] The terminal sends the login information to the server, and the server performs user authentication.

[1665] Once authentication is successful, the user is taken to a form where they can enter the budget for their advertising campaign and the message they wish to send to society.

[1666] This data (advertising campaign budget, message) is sent from the device to the server and stored in a database.

[1667] Input: Advertising campaign budget, message you want to convey to society

[1668] Output: Company advertising data stored in a database

[1669] Step 3:

[1670] Cleaning and normalizing data

[1671] The server retrieves data sent by NPOs and companies from a database.

[1672] A generative AI model (OpenAI's GPT-3) is used to clean and normalize the input data.

[1673] Data cleaning refers to correcting incorrect or missing values, and normalization refers to standardizing the data format.

[1674] Input: NPO and company input data stored in the database

[1675] Output: Normalized and cleaned data

[1676] Step 4:

[1677] Calculating affinity scores

[1678] The server uses generative AI (OpenAI's GPT-3) to calculate an affinity score between the nonprofit organization's purpose and the company's advertising message.

[1679] Using an emotion engine (Microsoft Azure Emotion API), emotions are analyzed from text data (purpose and message) and reflected in the score.

[1680] Input: normalized data, text data

[1681] Output: Affinity score between NPOs and companies

[1682] Step 5:

[1683] Proposal generation

[1684] The server generates optimal matching proposals for both NPOs and companies based on the affinity scores.

[1685] At this time, the proposal will also include information about the provision of advertising space and specific details of the company's advertising campaign.

[1686] Input: affinity score

[1687] Output: Proposal content (for NPOs and companies)

[1688] Step 6:

[1689] Notification of proposal details

[1690] The server notifies the generated proposals to NPO and corporate users.

[1691] Users (NPO representatives and corporate representatives) can check the proposal contents from their terminals.

[1692] Input: Proposal

[1693] Output: The suggestion sent to the user

[1694] Step 7:

[1695] Match confirmation and notification

[1696] The users (NPO representative and company representative) review the proposal, and if both parties agree, they notify the server via their terminal.

[1697] The server confirms the agreement, updates the database to indicate that the match has been made, and sends a notification to both parties.

[1698] Input: User consent notice

[1699] Output: Notification of successful match

[1700] Through these steps, the system helps to efficiently match NPOs and companies and realize effective advertising campaigns.

[1701] 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.

[1702] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1703] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1704] [Fourth embodiment]

[1705] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1706] 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.

[1707] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1708] 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.

[1709] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1710] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1711] 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.

[1712] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1713] 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.

[1714] 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 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.

[1715] 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.

[1716] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1717] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1718] This system is a matching system that uses generative AI to support effective fundraising and donation destination selection between NPOs and companies. The specific configuration for implementing the system of the present invention is as follows.

[1719] Data Entry Phase

[1720] Data entry for nonprofit organizations

[1721] 1. The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[1722] 2. The terminal sends the login information to the server, and the server performs user authentication.

[1723] 3. After logging in, the user enters the amount of funds they wish to raise and the specific purpose of the funds (e.g., "1 million yen" or "urban greening project") in the form that appears.

[1724] 4. The terminal sends the input amount of funds desired to be raised and the purpose to the server.

[1725] Corporate Data Entry

[1726] 5. The user (company representative) uses the terminal to access the web application in the same way and log in.

[1727] 6. The terminal sends the login information to the server, and the server performs user authentication.

[1728] 7. After logging in, the user enters the message they want to convey to society (e.g., "I'm interested in environmental protection") into the form that appears.

[1729] 8. The terminal sends the entered message to the server.

[1730] Data Processing Phase

[1731] 9. The server stores the information sent by NPOs and companies in a database.

[1732] 10. Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[1733] Matching Phase

[1734] 11. The server searches the company data in the database based on the NPO's desired conditions.

[1735] 12. The server uses the generative AI to calculate the affinity score between the NPO and the company, taking into account factors such as purpose, amount, and social contribution theme.

[1736] 13. The server filters companies with high affinity scores and generates recommendations.

[1737] Proposal Generation Phase

[1738] Proposals for NPOs

[1739] 14. The server uses the generation AI to generate specific proposals for the NPO. Example: "Company X can provide 1 million yen for urban greening projects."

[1740] 15. The server sends the generated proposal to the device.

[1741] 16. The terminal displays the proposal to the user (NPO representative).

[1742] Proposals for companies

[1743] 17. The server uses AI to generate specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[1744] 18. The server sends the generated donation proposal to the terminal.

[1745] 19. The terminal displays the donation proposal to the user (company representative).

[1746] Matching establishment phase

[1747] 20. The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected company list.

[1748] 21. The terminal sends the selection information to the server.

[1749] 22. The server notifies the company representative of the procurement request.

[1750] 23. The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[1751] 24. The terminal sends the selection information to the server.

[1752] 25. The server notifies the NPO representative of the donation decision.

[1753] 26. Once both parties agree, the server will consider the match to be successful and update the information in the database.

[1754] 27. The server sends a notification of successful matching to both the NPO and the company.

[1755] 28. The device will display a match notification to each user.

[1756] Specific examples

[1757] As a concrete example, if the nonprofit organization "Environmental Protection Association" wants to raise 1 million yen to use for an "urban greening project," it first accesses a web application from a terminal and inputs the amount it wants to raise and the purpose of the fundraising. Similarly, a company "Ecotech Co., Ltd." inputs a message saying that it is "interested in environmental protection."

[1758] The server uses a generative AI to calculate highly compatible combinations based on the information from both parties, and notifies the NPO "Environmental Protection Association" that "Ecotech Co., Ltd. can donate to urban greening projects." At the same time, it suggests to the company "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen."

[1759] Both parties confirm the proposal, the NPO sends a procurement request to the company, and when the company decides to make a donation, a match is made and a notification is sent to both parties. In this way, the system of the present invention enables efficient and effective fundraising and donations.

[1760] The processing flow will be explained below.

[1761] Step 1:

[1762] The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[1763] Step 2:

[1764] The terminal sends the login information to the server.

[1765] Step 3:

[1766] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[1767] Step 4:

[1768] The user confirms that the authentication was successful and then enters the amount of funding they wish to raise (e.g., "1 million yen") and its specific purpose (e.g., "urban greening project") into the form.

[1769] Step 5:

[1770] The terminal transmits the input desired fundraising amount and purpose to the server.

[1771] Step 6:

[1772] Similarly, the user (company representative) accesses the web application from a terminal and logs in by entering their ID and password.

[1773] Step 7:

[1774] The terminal sends the login information to the server.

[1775] Step 8:

[1776] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[1777] Step 9:

[1778] The user confirms that the authentication was successful and then enters the message he or she wants to convey to society (e.g., "I'm interested in environmental protection") into the form.

[1779] Step 10:

[1780] The terminal transmits the input message to the server.

[1781] Step 11:

[1782] The server stores the information sent by NPOs and companies in a database.

[1783] Step 12:

[1784] Based on the stored information, the server uses generative AI to perform preprocessing, cleaning and normalizing the input data.

[1785] Step 13:

[1786] The server searches for company information in the database based on the NPO's desired criteria.

[1787] Step 14:

[1788] The server uses a generative AI to calculate the affinity score between NPOs and companies, taking into account factors such as purpose, amount, and social contribution theme.

[1789] Step 15:

[1790] The server filters companies with high affinity scores and creates a list.

[1791] Step 16:

[1792] The server uses a generative AI to generate specific proposals for NPOs.

[1793] Step 17:

[1794] The server transmits the generated proposal to the terminal.

[1795] Step 18:

[1796] The terminal displays the proposal contents to the user (NPO representative).

[1797] Step 19:

[1798] The server also uses generative AI to generate specific donation proposals for companies.

[1799] Step 20:

[1800] The server transmits the generated donation proposal to the terminal.

[1801] Step 21:

[1802] The terminal displays the donation proposal to the user (company representative).

[1803] Step 22:

[1804] The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected list of companies.

[1805] Step 23:

[1806] The terminal transmits the selection information to the server.

[1807] Step 24:

[1808] The server notifies the company representative of the procurement request.

[1809] Step 25:

[1810] The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[1811] Step 26:

[1812] The terminal transmits the selection information to the server.

[1813] Step 27:

[1814] The server notifies the NPO representative of the donation decision.

[1815] Step 28:

[1816] Once both parties agree, the server determines that a match has been made and updates the information in the database.

[1817] Step 29:

[1818] The server sends a notification of a successful match to both the NPO and the company.

[1819] Step 30:

[1820] The terminal displays a match notification to each user.

[1821] Example 1

[1822] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1823] When nonprofit organizations raise funds, the process of finding the right entity is extremely time-consuming and inefficient. This increases the time and effort spent on fundraising, making it difficult to focus on their core activities. It is also often difficult for organizations to find nonprofits that are suitable for their social contribution activities, preventing them from making effective donations. This creates a problem of mismatches between the fundraising and donation processes.

[1824] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1825] In this invention, the server includes: a means for a nonprofit organization to input the amount of fundraising it wishes to receive and its use; a means for a business entity to input a message it wishes to send to society; a means for evaluating the affinity between the nonprofit organization and business entities based on the input amount of fundraising it wishes to receive, its use, and the message; a means for suggesting suitable business entities to the nonprofit organization based on the evaluation; a means for suggesting suitable nonprofit organizations to business entities based on the evaluation; a means for notifying the nonprofit organization and business entities of the content of the proposal; a means for notifying the business entity of a procurement request made by the nonprofit organization; a means for notifying the nonprofit organization of a donation decision made by the business entity; and a means for notifying the nonprofit organization and business entities of a match based on an agreement between the two parties. This enables efficient matching between nonprofit organizations and business entities, facilitating the fundraising and donation processes.

[1826] A "non-profit organization" is an organization that operates for the public benefit and does not seek profit.

[1827] "Business Entity" means a legal entity or individual engaged in commercial activities.

[1828] "Funding Suggestion" refers to the amount of funding a nonprofit organization needs for a particular activity or project.

[1829] "Use" indicates the purpose for which the funds obtained through fundraising will be used.

[1830] A "message" refers to the values ​​and initiatives that a business wants to convey to society.

[1831] A "compatibility assessment" is a process that uses numerical values ​​and indicators to evaluate the compatibility between a nonprofit organization and a business entity.

[1832] "Proposal" means a proposal for collaboration with a non-profit organization or entity generated based on an affinity assessment.

[1833] "Procurement Request" means a request by a Nonprofit Organization for funding from a particular entity.

[1834] A "donation decision" is the process by which an entity decides to provide funding to a particular nonprofit organization.

[1835] A "match" is when a funding agreement is reached between a nonprofit organization and a business entity.

[1836] "Notification" means the act of communicating information, such as proposals and agreements, to nonprofit organizations and entities.

[1837] "Generative AI" refers to artificial intelligence systems that generate natural language and perform data analysis.

[1838] This invention is a matching system that uses generative AI to support efficient fundraising and donation destination selection between nonprofit organizations and business entities. Specific embodiments for implementing the invention are described below.

[1839] Hardware and software used

[1840] This system consists of a server, a terminal, and a user. The server has a database and a generative AI model, and the terminal implements a web application that provides an interface for users to operate it. The main software used is a web browser (e.g., Google Chrome, Firefox), an HTTP server (e.g., Apache, Nginx), a database management system (e.g., MySQL, PostgreSQL), and an AI model execution environment (e.g., TensorFlow, PyTorch).

[1841] Data Entry Phase

[1842] Nonprofit data entry

[1843] 1. The user (non-profit organization employee) accesses the system's web application using a web browser and logs in by entering their ID and password. The terminal sends the login information to the server, which then authenticates the user.

[1844] 2. After successful authentication, the user inputs the amount of funds they wish to raise and the specific purpose of the funds. The terminal then sends this information to the server.

[1845] Business entity data entry

[1846] 3. The user (business entity employee) similarly accesses the web application, enters login information, and is authenticated by the server.

[1847] 4. After successful authentication, the user enters the message they wish to send to society. The device then sends this information to the server.

[1848] Data Processing Phase

[1849] 5. The server stores the information submitted by nonprofit organizations and businesses in a database, and the generative AI model cleans and normalizes the data based on the stored information.

[1850] Matching Phase

[1851] 6. The server searches the entity data based on the nonprofit organization's desired criteria, calculates affinity scores using the generative AI model, filters entities with high affinity scores, and generates proposals.

[1852] Proposal Generation Phase

[1853] Proposals for nonprofit organizations

[1854] 7. The server uses the generative AI model to generate specific proposals for the nonprofit organization and sends them to the device, which then displays them to the user.

[1855] Proposals to business entities

[1856] 8. The server also uses the generative AI model to generate specific donation suggestions for the business entity and sends them to the device, which then displays them to the user.

[1857] Matching establishment phase

[1858] 9. The user (non-profit organization representative) reviews the proposal and selects the entity to request procurement from the list of selected entities. The selection information is sent to the server and notified to the entity representative.

[1859] 10. The user (business operator) reviews the donation proposal and selects the nonprofit organization to which the donation will be made. The selection information is sent to the server and notified to the nonprofit organization operator.

[1860] 11. The server will then determine if the match is successful and update the information in the database. A notification of the match will be sent to both the nonprofit organization and the business entity, and the device will display it to the user.

[1861] Specific examples

[1862] As a concrete example, if the nonprofit organization "Environmental Protection Association" wishes to raise 1 million yen to use for an "urban greening project," the user accesses a web application from their device and inputs the amount they wish to raise and the purpose of the donation. Similarly, the business entity "Ecotech Co., Ltd." inputs a message saying, "I'm interested in environmental protection." The server uses generative AI based on the information of both parties to calculate highly compatible pairs and notifies the nonprofit organization "Environmental Protection Association" that "Ecotech Co., Ltd." can donate to the urban greening project." At the same time, it proposes to the business entity "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen." Both parties confirm the proposal, and the nonprofit organization sends a procurement request to the business entity. When the business entity decides to donate, a match is made and notifications are sent to both parties.

[1863] Example of prompt input to a generative AI model

[1864] Nonprofit data entry prompt example:

[1865] The non-profit organization Environmental Protection Association is seeking 1 million yen for an urban greening project.

[1866] Example of a prompt for business entity message input:

[1867] Ecotech Co., Ltd. cares about protecting the environment.

[1868] The generative AI model calculates an affinity score based on these inputs and generates specific suggestions.

[1869] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1870] Step 1:

[1871] The user (non-profit organization employee) accesses the system's web application using a web browser and logs in by entering their ID and password. Input data: ID and password. Output data: authentication request. The specific operation is to access the login page and enter their ID and password in the designated form.

[1872] Step 2:

[1873] The terminal sends the login information entered by the user to the server. Input data: ID and password. Output data: HTTP POST request. Specifically, when the user clicks the "Login" button, the login information is sent to the server.

[1874] Step 3:

[1875] The server authenticates the received login information, and if the user has entered it correctly, it generates an authentication token and returns it to the terminal. Input data: ID and password. Output data: authentication token. Specifically, the server searches the database for the corresponding user information, and if there is a match, it generates an authentication token and returns it as a response.

[1876] Step 4:

[1877] After logging in, the user enters the desired amount of funding and the specific purpose in the form that appears. Input data: desired amount of funding, purpose of funding. Output data: request to send input data. Specifically, the user enters the specific amount and purpose in the "desired amount of funding" and "purpose" fields, respectively, and presses the "Submit" button.

[1878] Step 5:

[1879] The terminal sends the input desired fundraising amount and purpose to the server. Input data: desired fundraising amount, purpose of fundraising. Output data: HTTP POST request. Specifically, the terminal sends data to the server using an HTTP POST request.

[1880] Step 6:

[1881] The user (business person) similarly accesses the web application and logs in. Input data: ID and password. Output data: authentication request. Specifically, the business person accesses the login page in the same way as the nonprofit organization person and enters their ID and password in the designated form.

[1882] Step 7:

[1883] The terminal sends the login information of the business entity employee to the server. Input data: ID and password. Output data: HTTP POST request. Specifically, when the user clicks the "Login" button, the information is sent to the server.

[1884] Step 8:

[1885] The server authenticates the received login information, and if the business person is successfully authenticated, it generates an authentication token and returns it to the terminal. Input data: ID and password. Output data: authentication token. The specific operation is the same as the authentication process for nonprofit organization personnel.

[1886] Step 9:

[1887] After logging in, the user enters the message that the company wants to send to society in the form that appears. Input data: Message. Output data: Message sending request. The specific operation is that the business person in charge enters the specific content in the "Message" field and presses the "Send" button.

[1888] Step 10:

[1889] The terminal sends the input message to the server. Input data: message. Output data: HTTP POST request. In concrete terms, the terminal sends the transmitted message to the server as an HTTP POST request.

[1890] Step 11:

[1891] The server stores the information submitted by nonprofits and businesses in a database. Input data: fundraising amount, purpose, message. Output data: new record in the database. Specifically, the server connects to the database, inserts a new record, and saves the user's input data.

[1892] Step 12:

[1893] The server performs preprocessing using a generative AI model based on the stored information, cleaning and normalizing the input data. Input data: desired fundraising amount, purpose, message. Output data: cleaned and normalized data. Specifically, the server calls the generative AI model and formats the input data.

[1894] Step 13:

[1895] The server searches the database for entity data based on the nonprofit organization's desired criteria. Input data: cleaned and normalized data. Output data: relevant entity data. Specifically, the server uses an SQL query to extract entity data that matches the criteria.

[1896] Step 14:

[1897] The server uses the generative AI model to calculate the affinity score between the nonprofit organization and the business entity. Input data: relevant business entity data. Output data: affinity score. Specifically, the server inputs the extracted data into the generative AI model to calculate the affinity score.

[1898] Step 15:

[1899] The server filters out businesses with high affinity scores and generates proposals. Input data: affinity score. Output data: proposals. Specifically, it selects businesses with high scores and automatically generates proposals using a generative AI model.

[1900] Step 16:

[1901] The server sends the generated proposal to the terminal. Input data: proposal. Output data: HTTP response. Specifically, the server sends the generated proposal to the terminal as an HTTP response.

[1902] Step 17:

[1903] The terminal displays the proposal to the user (nonprofit organization representative). Input data: HTTP response. Output data: Display of proposal. The specific operation is that the proposal is displayed in the browser.

[1904] Step 18:

[1905] The server uses the generative AI model to generate specific donation proposals for businesses and send them to the terminal. Input data: Proposal content from nonprofit organizations. Output data: HTTP response with donation proposal content. The specific operation is to automatically generate a donation proposal using the generative AI model and send it as a response to the terminal.

[1906] Step 19:

[1907] The terminal displays the donation proposal content to the user (business entity representative). Input data: HTTP response. Output data: Display of donation proposal content. Specifically, the donation proposal content is displayed in the browser.

[1908] Step 20:

[1909] The user (non-profit organization representative) reviews the proposal content and selects the business entity to request procurement from the list of selected entities. Input data: Proposal content. Output data: Procurement request information. Specifically, the non-profit organization representative selects the target business entity from the list and presses the "Procurement request" button.

[1910] Step 21:

[1911] The terminal sends the selection information to the server. Input data: Procurement request information. Output data: HTTP POST request. Specifically, the terminal sends the selection information to the server as an HTTP POST request.

[1912] Step 22:

[1913] The server notifies the business entity person of the procurement request. Input data: Procurement request information. Output data: Notification. Specifically, the server sends an email or in-app notification of the procurement request to the business entity person.

[1914] Step 23:

[1915] The user (business entity representative) checks the donation proposal details and selects the nonprofit organization to which the donation will be made. Input data: donation proposal details. Output data: donation recipient selection information. Specifically, the business entity representative selects the nonprofit organization to which the donation will be made and presses the "Donation Confirmation" button.

[1916] Step 24:

[1917] The terminal sends the selection information to the server. Input data: donation destination selection information. Output data: HTTP POST request. Specifically, the terminal sends the selection information to the server as an HTTP POST request.

[1918] Step 25:

[1919] The server notifies the nonprofit organization's representative of the donation decision. Input data: donation destination selection information. Output data: notification. Specifically, the server sends a donation decision notification to the nonprofit organization's representative.

[1920] Step 26:

[1921] The server determines that a match has been made based on the agreement between the two parties and updates the information in the database. Input data: Adjustment information. Output data: Updated information indicating that a match has been made. Specifically, the server updates the status field in the database to "match made."

[1922] Step 27:

[1923] The server sends a notification of successful matching to both the nonprofit organization and the business. Input data: Matching update information. Output data: Notification. Specifically, the server notifies both parties of successful matching via email or in-app notification.

[1924] Step 28:

[1925] The device displays a match-making notification to each user. Input data: Notification. Output data: Display of match-making notification. Specifically, a notification is displayed in the device browser.

[1926] (Application example 1)

[1927] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1928] Existing systems for matching NPOs and corporations are problematic in that they are inefficient in fundraising and donation recipient selection. In particular, there is a lack of appropriate means for supporting collaboration between security service providers and NPOs. The present invention aims to solve these problems and provide an effective system for achieving optimal matching between NPOs and corporations.

[1929] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1930] In this invention, the server includes a means for NPOs to input the amount of fundraising they wish to raise and its use, a means for companies to input the message they wish to convey to society, and a means for evaluating the affinity between NPOs and companies based on the input amount of fundraising, use, and message. This allows security service providers to efficiently select NPOs for fundraising and donation recipients. Furthermore, evaluation results are notified and displayed through an application installed on a smartphone, smart glasses, or head-mounted display, enabling effective matching in real time.

[1931] An "NPO corporation" is an organization whose primary purpose is to engage in non-profit activities and to promote the general public's interests.

[1932] "Funding Suggestion" is the amount of money a nonprofit organization needs for a specific project or activity.

[1933] "Use" refers to the specific ways in which the NPO plans to use the funds.

[1934] A "business" is an organization that provides goods and services and operates to pursue profits.

[1935] A "message" is an expression that shows the philosophy or purpose that a company wants to communicate to society.

[1936] "Affinity" indicates the degree of agreement between the goals and objectives of an NPO and a company.

[1937] "Evaluation" is the process of quantitatively or qualitatively determining the affinity between an NPO and a company.

[1938] "Security services" are the practices and technologies used to provide physical or digital security.

[1939] A "server" is a computer system that provides data and services over a network.

[1940] A "smartphone" is a mobile device that has advanced computing power and Internet connectivity in addition to telephone functions.

[1941] "Smart glasses" are wearable devices equipped with a display to enhance visual information.

[1942] A "head-mounted display" is a display device that projects images directly into the field of vision.

[1943] An "application" is software designed to perform a specific function.

[1944] "Generative AI" is a system that uses artificial intelligence technology to generate new data and information.

[1945] "Notification" is the act of informing the user of important information or results.

[1946] "Display" is the act of visually providing information using a display or device.

[1947] This invention is a system for supporting effective fundraising and donation destination selection between NPOs and companies, and is particularly equipped with a function to promote cooperation with companies that provide security services.

[1948] System Program Overview

[1949] The system includes the following main features:

[1950] 1. A function that allows NPOs to input the amount of funds they wish to raise and the purpose for which they wish to raise funds.

[1951] 2. A function that allows companies to input messages they want to send to society.

[1952] 3. A generative AI function that evaluates the affinity between NPOs and companies based on input data.

[1953] 4. A function to generate and notify appropriate proposals to NPOs and companies based on the evaluation results.

[1954] 5. An application function that is installed on a smartphone, smart glasses, or head-mounted display and displays the evaluation results in real time.

[1955] Hardware and Software Configuration

[1956] The server provides the computing resources for storing data and performing affinity evaluation using the generated AI. Specifically, it uses cloud services (e.g., Amazon Web Services and Google Cloud Platform).

[1957] Users input data and receive notifications and assessment results using smartphones, smart glasses, or head-mounted displays, all of which run mobile operating systems such as iOS or Android.

[1958] Use data processing libraries (e.g., Pandas or NumPy in Python) for data cleaning and preprocessing.

[1959] Affinity assessment uses a generative AI model (e.g., OpenAI's GPT-3).

[1960] Processing flow details

[1961] 1. Data entry phase:

[1962] The user (NPO representative) uses a device such as a smartphone to input the amount of funds they wish to raise and the specific use of the funds into the web application.

[1963] Similarly, company representatives can use devices such as smartphones to input messages they wish to send to society.

[1964] 2. Data Processing Phase:

[1965] The server receives the entered data and stores it in a database.

[1966] Data cleaning and preprocessing is performed using Python data processing libraries.

[1967] 3. Matching Phase:

[1968] The server uses a generative AI model to evaluate the affinity between nonprofits and businesses, calculating an affinity score based on the input fundraising amount, purpose, and message.

[1969] 4. Proposal generation phase:

[1970] Proposals are generated based on the affinity assessment. Specifically, NPOs are notified of companies that can donate, and companies are notified of NPOs that need support.

[1971] Specific examples

[1972] As a concrete example, the following prompt sentences can be used to input the generative AI model to achieve effective matching:

[1973] Example prompt sentence:

[1974] The NPO "Children's Safety Association" wants to raise 500,000 yen to be used for "Local Child Protection Programs." The company "Security Solutions Inc." wants to support "Child Safety Initiatives." Calculate the affinity scores for these two organizations and generate the best matching proposal.

[1975] Based on these prompts, the generative AI model calculates an affinity score between the NPO and the company and notifies appropriate suggestions, thereby realizing effective cooperation between the security service company and the NPO.

[1976] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1977] Step 1:

[1978] The user (NPO employee) accesses the web application using a device such as a smartphone or PC and enters login information. The device sends the entered login information to the server, which then authenticates the user. After authentication, the user enters the amount of funding they wish to raise and its purpose into a web form. The input data generated is the "desired amount of funding" and "purpose." The device then sends this input data to the server.

[1979] Step 2:

[1980] A user (company representative) similarly accesses a web application using a device such as a smartphone or PC and enters login information. The device sends the entered login information to the server, which then authenticates the user. After authentication, the user enters the message they wish to send to society into a web form. A "message" is generated as input data. The device then sends this input data to the server.

[1981] Step 3:

[1982] The server stores the received input data ("desired fundraising amount," "purpose," and "message") in a database. When storing the data, it performs data cleaning to maintain data integrity. Specifically, the database uses the Python Pandas library to remove spaces and standardize the format. The output of this process is well-organized data stored in the database.

[1983] Step 4:

[1984] The server uses a generative AI model to evaluate the affinity between NPOs and companies based on the data stored in the database. Specifically, it sends the input data to a generative AI (e.g., OpenAI's GPT-3) and calculates an affinity score. The server generates a score for each combination of NPO and company and selects the combination with the highest score. An "affinity score" is generated as the output.

[1985] Step 5:

[1986] The server generates specific proposals for NPOs and businesses based on the affinity scores. Using a generative AI model, the server automatically generates prompts to inform NPOs of businesses that can donate to them, and to inform businesses of NPOs that need their support. For example, the server generates the following prompts:

[1987] The NPO "Children's Safety Association" wants to raise 500,000 yen to be used for "Local Child Protection Programs." The company "Security Solutions Inc." wants to support "Child Safety Initiatives." Calculate the affinity scores for these two organizations and generate the best matching proposal.

[1988] As an output, a "proposal" is generated.

[1989] Step 6:

[1990] The server notifies the generated proposal contents to the terminals of the corresponding NPO and company personnel. The notified proposal contents are displayed on the terminal screen, allowing the user to confirm the proposal contents. A "notification of proposal contents" is generated as an output.

[1991] Step 7:

[1992] The user (NPO representative) checks the proposal details and selects an appropriate company from among the proposed companies. The selection details are sent to the server via the terminal. The server receives this selection information and notifies the company representative of the procurement request. The "Procurement Request Details" is generated as the output.

[1993] Step 8:

[1994] The user (company representative) receives a notification of the procurement request and selects the NPO to which the donation will be made. The selection is sent to the server via the terminal. The server receives this selection information and notifies the NPO representative of the decision to donate. A "donation decision notification" is generated as the output.

[1995] Step 9:

[1996] Once both parties agree, the server determines that the match has been made and updates the information in the database. It then sends a notification of the match to both the NPO and the company. The device displays the notification to each user. A "matching successful notification" is generated as an output.

[1997] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1998] This system is a matching system that uses generative AI and an emotion engine to support effective fundraising and donation destination selection between NPOs and companies. The specific configuration for implementing the system of the present invention is as follows.

[1999] Data Entry Phase

[2000] Data entry for nonprofit organizations

[2001] 1. The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[2002] 2. The terminal sends the login information to the server, and the server performs user authentication.

[2003] 3. After logging in, the user enters the amount of funds they wish to raise and the specific purpose of the funds (e.g., "1 million yen" or "urban greening project") in the form that appears.

[2004] 4. The terminal sends the input amount of funds desired to be raised and the purpose to the server.

[2005] Corporate Data Entry

[2006] 5. The user (company representative) uses the terminal to access the web application in the same way and log in.

[2007] 6. The terminal sends the login information to the server, and the server performs user authentication.

[2008] 7. After logging in, the user enters the message they want to convey to society (e.g., "I'm interested in environmental protection") into the form that appears.

[2009] 8. The terminal sends the entered message to the server.

[2010] Data Processing Phase

[2011] 9. The server stores the information sent by NPOs and companies in a database.

[2012] 10. Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[2013] Matching Phase

[2014] 11. The server searches for company information in the database based on the NPO's desired criteria.

[2015] 12. The emotion engine recognizes and analyzes user emotions from input information from NPOs and companies.

[2016] 13. The server uses generative AI to calculate the affinity score between the NPO and the company, taking into account the purpose, amount, and social contribution theme as well as the perceived sentiment.

[2017] 14. The server filters companies with high affinity scores and creates a list.

[2018] Proposal Generation Phase

[2019] Proposals for NPOs

[2020] 15. The server generates specific proposals for NPOs based on the results of the generative AI and emotion engine. Example: "Company X can provide 1 million yen for urban greening projects."

[2021] 16. The server sends the generated proposal to the device.

[2022] 17. The terminal displays the proposal to the user (NPO representative).

[2023] Proposals for companies

[2024] 18. Based on the results of the generative AI and emotion engine, the server also generates specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[2025] 19. The server sends the generated donation proposal to the terminal.

[2026] 20. The terminal displays the donation proposal to the user (company representative).

[2027] Matching establishment phase

[2028] 21. The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected company list.

[2029] 22. The terminal sends the selection information to the server.

[2030] 23. The server notifies the company representative of the procurement request.

[2031] 24. The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[2032] 25. The terminal sends the selection information to the server.

[2033] 26. The server notifies the NPO representative of the donation decision.

[2034] 27. Once both parties agree, the server will consider the match to be successful and update the information in the database.

[2035] 28. The server sends a notification of successful matching to both the NPO and the company.

[2036] 29. The device will display a match notification to each user.

[2037] Specific examples

[2038] For example, if the nonprofit organization "Environmental Protection Association" wants to raise 1 million yen to use for an "urban greening project," the person in charge can access the web application from a terminal and enter the amount they want to raise and the purpose of the donation. Similarly, the company "Ecotech Co., Ltd." can enter a message saying that they are "interested in environmental protection."

[2039] The server uses a generative AI to evaluate affinity based on the information from both parties, and then adjusts the affinity score by analyzing the user's emotions with an emotion engine. As a result, the server notifies the NPO "Environmental Protection Association" that "Ecotech Co., Ltd. is available to donate to urban greening projects." At the same time, it suggests to the company "Ecotech Co., Ltd." that "The Environmental Protection Association is seeking a donation of 1 million yen."

[2040] Both parties confirm the proposal, the NPO sends a procurement request to the company, and when the company decides to make a donation, a match is made and a notification is sent to both parties. In this way, the system of the present invention enables efficient and effective fundraising and donations.

[2041] The processing flow will be explained below.

[2042] Step 1:

[2043] The user (NPO employee) uses a terminal to access the web application and logs in by entering their ID and password.

[2044] Step 2:

[2045] The terminal sends the login information to the server.

[2046] Step 3:

[2047] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[2048] Step 4:

[2049] The user confirms that the authentication was successful and then enters the amount of funding they wish to raise (e.g., "1 million yen") and its specific purpose (e.g., "urban greening project") into the form.

[2050] Step 5:

[2051] The terminal transmits the input desired fundraising amount and purpose to the server.

[2052] Step 6:

[2053] Similarly, the user (company representative) accesses the web application from a terminal and logs in by entering their ID and password.

[2054] Step 7:

[2055] The terminal sends the login information to the server.

[2056] Step 8:

[2057] The server performs user authentication based on the received login information, and if authentication is successful, returns the authentication result to the terminal.

[2058] Step 9:

[2059] The user confirms that the authentication was successful and then enters the message he or she wants to convey to society (e.g., "I'm interested in environmental protection") into the form.

[2060] Step 10:

[2061] The terminal transmits the input message to the server.

[2062] Step 11:

[2063] The server stores the information sent by NPOs and companies in a database.

[2064] Step 12:

[2065] The server uses an emotion engine to recognize the user's emotions from the input information of NPOs and companies and record them in a database.

[2066] Step 13:

[2067] Based on the stored information, the server performs preprocessing using generative AI to clean and normalize the input data.

[2068] Step 14:

[2069] The server searches for company information in the database based on the NPO's desired criteria.

[2070] Step 15:

[2071] The server uses generative AI to calculate the affinity score between NPOs and companies, taking into account the purpose, amount, and social contribution theme, as well as perceived emotions.

[2072] Step 16:

[2073] The server filters companies with high affinity scores and creates a list.

[2074] Step 17:

[2075] The server generates specific proposals for NPOs based on the results of the generative AI and emotion engine. Example: "Company X can provide 1 million yen for urban greening projects."

[2076] Step 18:

[2077] The server transmits the generated proposal to the terminal.

[2078] Step 19:

[2079] The terminal displays the proposal contents to the user (NPO representative).

[2080] Step 20:

[2081] Based on the results of the generative AI and emotion engine, the server also generates specific donation proposals for companies. Example: "NPO Y is seeking a donation of 1 million yen."

[2082] Step 21:

[2083] The server transmits the generated donation proposal to the terminal.

[2084] Step 22:

[2085] The terminal displays the donation proposal to the user (company representative).

[2086] Step 23:

[2087] The user (NPO representative) reviews the proposal and selects a company to request procurement from the selected list of companies.

[2088] Step 24:

[2089] The terminal transmits the selection information to the server.

[2090] Step 25:

[2091] The server notifies the company representative of the procurement request.

[2092] Step 26:

[2093] The user (company representative) checks the donation proposal and selects the NPO to which the donation will be made.

[2094] Step 27:

[2095] The terminal transmits the selection information to the server.

[2096] Step 28:

[2097] The server notifies the NPO representative of the donation decision.

[2098] Step 29:

[2099] Once both parties agree, the server determines that a match has been made and updates the information in the database.

[2100] Step 30:

[2101] The server sends a notification of a successful match to both the NPO and the company.

[2102] Step 31:

[2103] The terminal displays a match notification to each user.

[2104] Example 2

[2105] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2106] Until now, it has been difficult to efficiently and effectively raise fu...

Claims

1. A means for NPOs to input the amount of funds they wish to raise and the purpose of the funds; A way for companies to input the messages they want to send to society, A means for evaluating the affinity between the NPO and the company based on the input fundraising amount, purpose, and message; A means for proposing suitable companies to the NPO corporation based on the evaluation; A means for proposing appropriate NPO corporations to companies based on the evaluation; A means for notifying the NPO corporation and the company of the content of the proposal; A system including:

2. The system of claim 1 further comprising means for storing data on nonprofit corporations and businesses in a database.

3. The system of claim 1 , wherein the affinity evaluation uses generated AI.

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