System

Abstract

A system is provided.SOLUTION: A system for automatically generating a grant application document utilizing a generation AI, the system comprising: means for uploading prior submission documents; means for analyzing the uploaded submission documents to extract key information; means for inputting new project information; the generation AI generating a draft of the grant application document based on the new project information and the extracted key information; means for displaying the generated draft to a user and inputting amendments; and means for storing and transmitting a final version of the grant application document to a submission destination.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Preparing grant application documents requires a lot of time and effort, placing a significant burden on small and medium-sized enterprises and nonprofit organizations. Furthermore, if the quality of grant proposal documents is low, they are less likely to be accepted, leading to problems such as a lack of funding hindering project progress. Therefore, there is a need for a method to efficiently prepare high-quality grant application documents with minimal effort, thereby improving the grant acceptance rate. [Means for solving the problem]

[0005] The automatic grant application document creation system using a generation AI according to the present invention includes the following means: a means for uploading past proposal documents, a means for analyzing the uploaded proposal documents to extract key information, a means for inputting new project information, a means for the generation AI to generate a draft grant application document based on the new project information and the extracted key information, a means for displaying the generated draft to a user and allowing the user to input corrections, and a means for saving the final grant application document and sending it to the recipient. This system enables users to create high-quality grant proposal documents with minimal effort, thereby improving the grant acceptance rate.

[0006] "Generative AI" is an artificial intelligence technology that uses natural language processing to automatically generate sentences.

[0007] A "grant application document" is a proposal document submitted to obtain a grant, and is a document that describes the project's purpose, content, budget, etc.

[0008] A "system" is a computer-based mechanism in which multiple components work together to achieve a specific function.

[0009] "Analysis" is the process of examining uploaded materials and entered data in detail and extracting the necessary information.

[0010] "Project information" is the details of a newly set project, including the project name, objectives, content, budget, schedule, and the like.

[0011] A "draft" is the first version of a grant application created by the generative AI, before it has been reviewed and revised by a user.

[0012] "Revision" is a process in which the user checks the generated draft content and corrects or adds to the content as necessary.

[0013] "Uploading" is the act of a user sending a file or data from their own terminal to a server.

[0014] "Submission" refers to the act of sending the final version of the grant application documents to the grant-providing agency or recipient. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] Specific embodiments for carrying out the present invention are described below.

[0037] System Overview

[0038] The system of the present invention automates the creation of grant application documents using generative AI. The system extracts necessary information from past proposal documents and generates high-quality draft grant application documents based on new project information entered by the user. The system includes a server, a terminal, and generative AI.

[0039] Program processing overview

[0040] User Registration and Login

[0041] A user accesses the system's website and creates a new account. The device prompts the user to enter the required information (such as name, email address, and password) and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information to access the system.

[0042] Initial Setup and Upload Information

[0043] Users upload past grant proposals and related materials. The device sends these materials to the server, which analyzes them, extracts key information (project name, summary, budget, etc.), and stores it in a database.

[0044] Preparation of grant application documents

[0045] A user inputs project information to create a new grant proposal. The device sends this input information to a server. The server uses generative AI to generate a draft grant proposal based on the input information and past materials. The generative AI generates answers to questions and automatically generates high-quality sections of the proposal. The server returns the generated draft to the user.

[0046] Review and revise the draft

[0047] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final proposal document and stores it in a database.

[0048] Submitting a grant application

[0049] The user reviews the final proposal and submits the grant. The server verifies the information of the grant recipients and provides a mechanism to automatically submit the proposal (if necessary).

[0050] Specific examples

[0051] Project Manager at a Nonprofit Organization

[0052] A project manager at a nonprofit organization applying for a grant for a new environmental project follows these steps:

[0053] 1. User registration and login: The project manager creates a new account and logs in.

[0054] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[0055] 3. Creating grant proposals: Enter details about a new project (such as the name, purpose, and budget) and have the Generative AI generate a draft proposal. For example, in response to a question like, "What is the social significance of this project?", the Generative AI might respond with, "This project aims to raise environmental awareness in the local community and contribute to achieving the Sustainable Development Goals."

[0056] 4. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[0057] 5. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[0058] Through this system, project managers can create high-quality grant proposal documents with minimal effort and efficiently obtain grant funding.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user accesses the system's website, enters the required information (such as name, email address, and password) on the new registration screen, and clicks the "Register" button.

[0062] Step 2:

[0063] The device sends the entered information to the server, which receives it, stores it in a database, generates an authentication token, and returns it to the device.

[0064] Step 3:

[0065] The user uses the authentication token returned from the server to log in. The terminal sends the login information to the server and receives the authentication result.

[0066] Step 4:

[0067] If the user authentication is successful, the server returns information for displaying a dashboard screen to the terminal, which then displays the dashboard screen to the user.

[0068] Step 5:

[0069] Users can click the "Upload Documents" button on the dashboard, select and upload past grant proposal documents.

[0070] Step 6:

[0071] The device sends the selected file to the server, which parses it, extracts key information such as the project name, summary, and budget information, and stores it in a database.

[0072] Step 7:

[0073] Users click the "Create a new proposal" button from the dashboard and enter information about the new project (such as name, purpose, budget, and schedule).

[0074] Step 8:

[0075] The device sends the input information to a server, which then compares it with data from past proposal documents and queries the AI ​​to generate a draft of the grant application.

[0076] Step 9:

[0077] The generative AI generates optimal answers to requested questions (e.g., "What is the social significance of this project?") and fills in each section of the proposal.

[0078] Step 10:

[0079] The server stores the generated draft in a database and returns the proposal draft to the terminal, which displays the draft to the user.

[0080] Step 11:

[0081] The user checks the displayed draft, enters any corrections, and submits it. The terminal then transmits the corrections to the server.

[0082] Step 12:

[0083] The server reflects the received corrections in the draft and stores the updated draft in the database again. The terminal displays the updated draft again to the user.

[0084] Step 13:

[0085] The user checks the final version of the proposal document and clicks the "Submit" button. The terminal displays information on the recipient and any additional documents required.

[0086] Step 14:

[0087] The user uploads any additional documents required and clicks the "Final Submission" button. The terminal sends all information to the server.

[0088] Step 15:

[0089] The server packages the proposal documents and additional documents based on the format of the submission destination and executes the submission process. If the submission is successful, the server sends a confirmation notice to the user via an in-system notification or email.

[0090] Example 1

[0091] 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."

[0092] The traditional grant application document preparation process is time-consuming, labor-intensive, and largely manual, making it inefficient. Furthermore, creating high-quality applications requires experience and skill, which presents a barrier to many applicants. Furthermore, analyzing proposal documents and extracting key information is time-consuming, creating a need for rapid information organization.

[0093] 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.

[0094] In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft grant application document based on the new project information and the extracted key information, means for displaying the generated draft to a user and for inputting corrections, means for saving the final grant application document and transmitting it to a submission destination, means for generating an authentication token and performing user authentication, means for analyzing the uploaded materials using an OCR tool or natural language processing technology and identifying key information, and means for the generation AI to generate prompt sentences and generate high-quality document sections based on the prompt sentences, thereby enabling users to efficiently create high-quality grant application documents with little effort and submit them quickly.

[0095] "Generative AI" is a technology that uses artificial intelligence to generate text and data.

[0096] "Grant application" means an official document containing the information required for the purpose of receiving a grant.

[0097] A "proposal document" is a document that was previously created for the purpose of applying for a grant or other purpose, and contains project details and plans.

[0098] "Upload" refers to the operation of sending data from a terminal to a server.

[0099] "Analysis" is the process of examining data in detail and extracting useful information from it.

[0100] "Key Information" refers to the most important and required information items in a grant application.

[0101] "New project information" is detailed information about a new project that the user is about to start.

[0102] A "draft" is a rough draft or rough draft created before the final version.

[0103] "User" refers to any individual or organization that uses this system.

[0104] An "authentication token" is a unique credential used to authenticate a user.

[0105] An "OCR tool" is a tool that uses optical character recognition technology to read text in an image and convert it into digital data.

[0106] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language.

[0107] A "prompt sentence" is an input sentence that causes a generation AI to generate text for a specific purpose.

[0108] "Submission Destination" refers to the destination or institution to which grant applications are submitted.

[0109] The following describes an embodiment of the present invention.

[0110] System Configuration

[0111] The system of the present invention utilizes generative AI to automate the creation of grant application documents. The system includes a server, a terminal, and generative AI. The server can be a cloud server, and the terminal can be a PC or smartphone. The generative AI model uses the latest AI technology based on natural language processing (NLP).

[0112] User Registration and Login

[0113] A user accesses the system's website and creates a new account. The device prompts the user for necessary information, such as name, email address, and password, and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information.

[0114] Initial Setup and Upload Information

[0115] Users upload past grant proposals and related materials. The terminal provides a drag-and-drop upload interface. Users select and upload files. The terminal then sends the uploaded files to the server, which analyzes the materials using OCR tools and natural language processing technology, extracting key information (project name, summary, budget, etc.) and storing it in a database.

[0116] Preparation of grant application documents

[0117] The user enters new project information. The device displays a form for entering details such as the project name, purpose, and budget. After the user enters and submits the information, the device sends this data to the server. The server then uses the received data to generate a prompt using the generation AI.

[0118] As a specific example, it generates a prompt such as, "What is the social significance of this project?" Based on this prompt, the generation AI generates an answer such as, "This project aims to raise environmental awareness in the local community and contributes to achieving the Sustainable Development Goals." The generated draft is sent back to the user's device via the server.

[0119] Review and revise the draft

[0120] The user can check the generated draft and enter any necessary corrections. The terminal provides a display interface for the draft and allows the user to edit any section. When the user enters the corrections and clicks Done, the terminal sends the correction information to the server. The server generates a final version reflecting the received corrections and stores it in the database.

[0121] Submitting a grant application

[0122] The user reviews the final version of the grant application documents and clicks the submit button. The terminal displays the final version and sends a submission instruction to the server. The server verifies the information of the designated recipients and electronically transmits the proposal documents. After the application is completed, the server generates a confirmation notice and sends it to the user's terminal.

[0123] Specific examples

[0124] When a project manager at a nonprofit organization applies for a grant for a new environmental protection project, they follow these steps: First, they register and log in, then upload a previous successful grant proposal. After entering the details of the new project, they let the generative AI generate a draft proposal. They review the generated draft and make any necessary revisions to finalize it. Finally, they submit it to the grant recipient and receive a confirmation notification.

[0125] This allows users to efficiently create high-quality grant application documents with minimal effort and submit them quickly.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1: User Registration and Login

[0128] input

[0129] The user enters their name, email address, and password.

[0130] Processing and Data Manipulation

[0131] The device sends the entered information to the server, which stores the information in a database and generates an authentication token.

[0132] output

[0133] The server returns an authentication token to the terminal, and the user logs in using the authentication token.

[0134] Specific actions

[0135] When a user visits a website and creates a new account, the device displays a form for them to fill out. After completing the form, the device sends the information to the server, which stores the information and generates an authentication token that is returned to the device. The user then logs in using this token.

[0136] Step 2: Initial setup and upload information

[0137] input

[0138] Users upload past grant proposals.

[0139] Processing and Data Manipulation

[0140] The device sends the uploaded documents to a server, which analyzes them using OCR tools and natural language processing technology to extract key information.

[0141] output

[0142] The server stores the extracted information in a database.

[0143] Specific actions

[0144] Users upload documents using a drag-and-drop method, and the device sends the files to the server, which then uses OCR tools and natural language processing technology to extract information such as project name, summary, and budget from the documents and stores it in a database.

[0145] Step 3: Compile your grant application

[0146] input

[0147] The user enters the new project information.

[0148] Processing and Data Manipulation

[0149] The device sends the input data to the server, which generates a prompt based on the received data and passes it to the generation AI, which then generates a draft based on the prompt.

[0150] output

[0151] The server returns the generated draft to the terminal.

[0152] Specific actions

[0153] When a user inputs and submits new project information, the device sends this data to the server, which generates a prompt (e.g., "What is the social significance of this project?") and passes it to the generation AI. The generation AI creates a draft based on this prompt, and the server returns the draft to the device.

[0154] Step 4: Review and revise the draft

[0155] input

[0156] The user checks the generated draft and enters any corrections.

[0157] Processing and Data Manipulation

[0158] The device sends the modifications to the server, which then generates the final version incorporating the modifications.

[0159] output

[0160] The server generates the final version and stores it in a database.

[0161] Specific actions

[0162] The user reviews the draft and corrects any sections that need to be corrected. The device sends the corrections to the server, which then reflects them and generates the final version, which is then stored in the database.

[0163] Step 5: Submit your grant application

[0164] input

[0165] The user checks the final version of the proposal document and submits it.

[0166] Processing and Data Manipulation

[0167] The terminal sends the final version as a submission instruction to the server, which then checks the information of the designated application destination and electronically transmits the proposal documents.

[0168] output

[0169] The server generates an acknowledgement and sends it to the user's terminal.

[0170] Specific actions

[0171] The user checks the final version and clicks the submit button. The terminal sends a submission instruction to the server, which then electronically transmits the proposal documents to the submission destination. After the application is completed, the server generates a confirmation notice and sends it to the user's terminal.

[0172] (Application example 1)

[0173] 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."

[0174] In the past, creating grant application documents and managing inventory in brick-and-mortar stores was often done manually, hindering efficiency and requiring time and effort. Furthermore, determining the appropriate inventory levels and product placement during inventory management relied on experience and intuition, making it prone to errors and difficult to achieve optimal results. It was necessary to solve these problems and achieve more efficient and accurate creation of grant application documents and inventory management.

[0175] 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.

[0176] In this invention, the server includes a means for uploading past proposal documents, a means for analyzing the uploaded proposal documents to extract key information, and a means for inputting new project information, which enables the generation AI to generate draft grant application documents and optimal inventory quantities and product placement proposals for inventory management based on past data.

[0177] The "means for uploading past proposal documents" is an interface for a user to import electronic data of proposal documents previously created into the system.

[0178] "Means for analyzing uploaded proposal documents and extracting key information" refers to an algorithm that automatically analyzes imported proposal documents and populates a database with key information (e.g., project name, summary, budget, schedule, etc.).

[0179] The "means for inputting new project information" is a UI (user interface) that allows a user to input information about a new project that is being planned into the system.

[0180] "Means for generative AI to generate draft grant application documents" refers to a function in which AI automatically creates an initial draft of a grant application document based on input new project information and information extracted from past proposal documents.

[0181] "Means for displaying the generated draft to the user and allowing the user to input corrections" refers to a function that displays the draft created by the generation AI in a user interface and allows the user to make corrections to it.

[0182] The "means for saving the final version of the grant application documents and sending them to the recipient" is a function that saves the final version of the grant application documents that the user has revised in a database and sends them electronically to the recipient as needed.

[0183] "A means of using generative AI to analyze past inventory data and sales data and propose optimal inventory quantities and product placement" is a function that analyzes past inventory data and sales data, and uses AI to calculate the ideal inventory quantity and product placement method, and proposes this to the user.

[0184] Overall system configuration

[0185] This invention is a system that utilizes generative AI to streamline the creation of grant application documents and inventory management for physical stores. The system includes a server, a terminal, generative AI, and a database. The server manages user registration, information upload, draft generation by generative AI, and the storage and submission of the final version.

[0186] User Registration and Login

[0187] A user accesses the system using a device (e.g., smart glasses) and creates a new account. The user enters their name, email address, and password, which are sent to the server. The server stores the information in a database and generates an authentication token, which is sent back to the device. The user then logs into the system using the authentication information.

[0188] Initial Setup and Upload Information

[0189] Users upload past proposal documents, inventory data, and sales data from their devices to the server. The server analyzes the uploaded data, extracts key information, and stores it in a database. This makes it easier for users to receive data-based proposals in the future.

[0190] Preparation of grant application documents

[0191] The user inputs new project information. The device sends this information to the server, which then uses a generative AI to generate a draft application document. The generative AI generates optimal answers to questions based on past proposal documents and the new project information, resulting in a high-quality draft.

[0192] Review and revise the draft

[0193] The user reviews the draft sent from the server on their device and makes any necessary corrections. The corrected information is sent back to the server, and the final version is generated. The final version is saved in the database and can be viewed by the user.

[0194] Submitting a grant application

[0195] Once the user has reviewed the final version of the grant application, it is automatically sent to the submission destination via the server. Once the application has been submitted successfully, the user is notified.

[0196] Optimizing inventory management

[0197] A user wearing smart glasses scans inventory in a physical store and sends the information to a server. The server analyzes past inventory and sales data and uses a generative AI to suggest optimal inventory levels and product placement. Specifically, a barcode reader is used to obtain product information, and the generative AI makes suggestions for inventory replenishment and placement based on past data.

[0198] Hardware and software used

[0199] Hardware: Smart glasses (e.g. Google Glass, Microsoft HoloLens), built-in barcode reader (or external device Bluetooth connection)

[0200] Software: Server-side uses Python, Django, Flask, etc. Databases use PostgreSQL and MySQL. Generative AI models use GPT-4, TensorFlow, and PyTorch. Communication uses WebSocket and REST API.

[0201] Examples of concrete examples and prompts

[0202] When a user scans inventory information using smart glasses and a barcode reader, the following prompt is sent to the generation AI:

[0203] Example prompt sentence:

[0204] Product name: Product A

[0205] Current stock: 10

[0206] Average sales: 30 / week

[0207] Please suggest a refill.

[0208] Based on this prompt, the AI ​​generator will suggest an appropriate replenishment amount, such as, "The recommended replenishment amount for your inventory is 20 units. This will ensure you have enough stock for next week's sales."

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] The user puts on the smart glasses and starts the system. They scan the QR code to register as a user. The user enters their name, email address, and password using voice input or the touchpad, and this information is sent from the device to the server. The server stores the received information in a database, generates an authentication token, and sends it back to the device. The input data is user information, and the output is an authentication token.

[0212] Step 2:

[0213] As an initial setting, the user uploads past proposal documents, inventory data, and sales data to the server via their terminal. The server analyzes the uploaded data, extracts key information, and stores it in a database. Specifically, it extracts project names, summaries, budgets, schedules, etc. from proposal documents. The input data are proposal documents and past data, and the output is the extracted key information stored in a database.

[0214] Step 3:

[0215] The user inputs new project information. The user inputs the project name, objectives, budget, etc. through the smart glasses, and this information is sent from the device to the server. The server combines this input information with past information, stores it in a database, and prepares the data to be input to the generative AI. The input data is the new project information, and the output is the input data for the generative AI.

[0216] Step 4:

[0217] The server uses generative AI to generate a draft grant application. The generative AI model (e.g., GPT-4) generates optimal answers to questions based on data from past proposal documents and new project information, and automatically generates each section of the grant application. Specifically, in response to the question, "What is the social significance of this project?", the server generates the answer, "This project aims to raise environmental awareness in the local community and contribute to achieving the Sustainable Development Goals." The input data is the new project information and past data, and the output is the draft grant application.

[0218] Step 5:

[0219] The generated draft is sent to the terminal, where the user reviews the draft through the smart glasses and makes any necessary corrections. The user inputs the corrections using voice input or the touchpad, and the terminal sends the corrections to the server. The server receives the corrections, generates the final grant application document, and stores it in a database. The input data are the user's corrections, and the output is the final grant application document.

[0220] Step 6:

[0221] The user reviews the final grant application and automatically sends it to the recipient through the server. The server verifies the recipient's information and sends the application in the appropriate format. Once the submission is complete, the server notifies the user. The input data is the final grant application and the output is a notification that the submission is complete.

[0222] Step 7:

[0223] A user uses smart glasses to browse the shelves in a store and scans inventory information using a barcode reader. The device then sends the acquired product information to a server. The input data is barcode information, and the output is updated inventory information.

[0224] Step 8:

[0225] The server combines and analyzes the acquired inventory information with past inventory and sales data, and uses generation AI to generate optimal inventory quantity and product placement proposals. For example, the generation AI might suggest, "Product A is running low on stock. Replenishment is required." The input data is inventory scan information and past data, and the output is a stock replenishment proposal.

[0226] Step 9:

[0227] The generated inventory management proposal is sent to the terminal, and the user can check the proposal through the smart glasses. If the user accepts the proposal, inventory replenishment and product allocation are carried out based on it. The input data is the inventory proposal content, and the output is the execution of inventory management.

[0228] 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.

[0229] Specific embodiments for carrying out the present invention are described below.

[0230] System Overview

[0231] The system of the present invention automates the creation of grant application documents using generative AI and incorporates an emotion engine that recognizes user emotions. The system extracts necessary information from past proposal documents and generates high-quality draft grant application documents based on new project information entered by the user. The system also uses the emotion engine to analyze user emotions and incorporates that feedback into the generated draft. The system includes a server, a terminal, generative AI, and the emotion engine.

[0232] Program processing overview

[0233] User Registration and Login

[0234] A user accesses the system's website and creates a new account. The device prompts the user to enter the required information (such as name, email address, and password) and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information to access the system.

[0235] Initial Setup and Upload Information

[0236] Users upload past grant proposals and related materials. The device sends these materials to the server, which analyzes them, extracts key information (project name, summary, budget, etc.), and stores it in a database.

[0237] Emotion Engine Operation

[0238] The emotion engine recognizes the user's emotions by detecting micro-expressions, tone of voice, and typing speed. The device sends this emotion data to the server, which then feeds it back to the AI ​​and takes it into account when generating the draft.

[0239] Preparation of grant application documents

[0240] A user inputs project information to create a new grant proposal. The device sends this input information to a server. The server uses generative AI to generate a draft grant proposal based on the input information and past materials. The generative AI takes emotional data into account and adjusts the tone and style of the text to match the user's intentions and emotions. The server returns the generated draft to the user.

[0241] Review and revise the draft

[0242] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final proposal document and stores it in a database.

[0243] Submitting a grant application

[0244] The user reviews the final proposal and submits the grant. The server verifies the information of the grant recipients and provides a mechanism to automatically submit the proposal (if necessary).

[0245] Specific examples

[0246] Project Manager at a Nonprofit Organization

[0247] A project manager at a nonprofit organization applying for a grant for a new environmental project follows these steps:

[0248] 1. User registration and login: The project manager creates a new account and logs in.

[0249] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[0250] 3. Emotion engine operation: While the project manager is entering information about a new project, the emotion engine recognizes the user's emotions by detecting their micro-expressions and typing speed. For example, if the user is nervous, it will detect this and provide feedback to the generation AI.

[0251] 4. Grant proposal creation: Enter details about a new project and let the generative AI generate a draft proposal. The generative AI will adjust the tone and style of the text appropriately based on emotional data. For example, if a user is passionate about the question, "What is the social significance of this project?", the AI ​​will generate an answer such as, "This project aims to dramatically increase environmental awareness in the local community and promote significant change."

[0252] 5. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[0253] 6. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[0254] This system allows users to create high-quality grant proposal documents with minimal effort and efficiently obtain grants. In addition, by combining it with an emotion engine, proposal documents can be generated that are more suited to the user.

[0255] The processing flow will be explained below.

[0256] Step 1:

[0257] The user accesses the system's website, enters the required information (such as name, email address, and password) on the new registration screen, and clicks the "Register" button.

[0258] Step 2:

[0259] The device sends the entered information to the server, which receives it, stores it in a database, generates an authentication token, and returns it to the device.

[0260] Step 3:

[0261] The user uses the authentication token returned from the server to log in. The terminal sends the login information to the server and receives the authentication result.

[0262] Step 4:

[0263] If the user authentication is successful, the server returns information for displaying a dashboard screen to the terminal, which then displays the dashboard screen to the user.

[0264] Step 5:

[0265] Users can click the "Upload Documents" button on the dashboard, select and upload past grant proposal documents.

[0266] Step 6:

[0267] The device sends the selected file to the server, which parses it, extracts key information such as the project name, summary, and budget information, and stores it in a database.

[0268] Step 7:

[0269] Users click the "Create a new proposal" button from the dashboard and enter information about the new project (such as name, purpose, budget, and schedule).

[0270] Step 8:

[0271] The device sends the input information to a server, which then compares it with data from past proposal documents and queries the AI ​​to generate a draft of the grant application.

[0272] Step 9:

[0273] The emotion engine detects micro-expressions and typing speed as the user enters new project information, and the device transmits the emotion data to the server.

[0274] Step 10:

[0275] The server feeds emotional data back to the AI ​​generator, which takes this into account when generating drafts, and the AI ​​then generates text in a tone and style that matches the user's emotions.

[0276] Step 11:

[0277] The generative AI generates answers to the requested questions and fills in each section of the grant application. The server stores the generated draft in a database and returns the draft proposal to the device.

[0278] Step 12:

[0279] The user checks the displayed draft, enters any corrections, and submits it. The terminal then transmits the corrections to the server.

[0280] Step 13:

[0281] The server reflects the received corrections in the draft and stores the updated draft in the database again. The terminal displays the updated draft again to the user.

[0282] Step 14:

[0283] The user checks the final version of the proposal document and clicks the "Submit" button. The terminal displays information on the recipient and any additional documents required.

[0284] Step 15:

[0285] The user uploads any additional documents required and clicks the "Final Submission" button. The terminal sends all information to the server.

[0286] Step 16:

[0287] The server packages the proposal documents and additional documents based on the format of the submission destination and executes the submission process. If the submission is successful, the server sends a confirmation notice to the user via an in-system notification or email.

[0288] Example 2

[0289] 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."

[0290] Creating grant proposals is a complex and time-consuming task, especially when extracting information from past proposals and combining it with new project information. Furthermore, if the tone and style of a grant proposal doesn't reflect the user's emotions, its quality and persuasiveness will be affected. There is a need for a system that automates this process and generates high-quality proposals that are both efficient and emotionally sensitive.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft of grant application documents based on the new project information and the extracted key information, means for detecting a user's emotions and analyzing the data, means for feeding back the detected emotion data to the generation AI and reflecting it in the tone and style of the generated draft, means for displaying the generated draft to the user and allowing the user to input corrections, and means for saving the final grant application documents and transmitting them to the submission destination. This improves the efficiency of grant application document creation and enables the generation of high-quality application documents that are adapted to the user's emotions.

[0292] 1. "Generative AI" is a system that uses artificial intelligence to generate natural language and create new documents and texts.

[0293] 2. "Grant application" means an official document submitted to obtain a grant for a specific project or activity.

[0294] 3. "Server" means a computer system that provides services to other devices on a network.

[0295] 4. "Terminal" means an electronic device with a user interface for inputting, displaying, and transmitting information.

[0296] 5. "User" means a person or individual who operates the system.

[0297] 6. "Emotion Engine" means software or a system for detecting and analyzing a user's emotions.

[0298] 7. "Draft" means an early manuscript or draft of a grant application.

[0299] 8. "Project Information" means detailed data and details relating to a particular project.

[0300] 9. "Key Information" means the essential data required to prepare a grant application.

[0301] 10. "Feedback" is the process of re-inputting results or reactions into a system or process.

[0302] System Overview

[0303] This invention is a system that automatically generates grant application documents using generative AI. This system extracts key information from past proposal documents and combines it with new project information to generate high-quality grant application documents. In addition, by incorporating an emotion engine that recognizes the user's emotions, the system can reflect the user's emotions in the generated draft. This system includes a server, a terminal, generative AI, and an emotion engine.

[0304] User Registration and Login

[0305] A user accesses the system's website and creates a new account. The device receives necessary information from the user, such as name, email address, and password, and sends it to the server. The server stores the received information in a database, generates an authentication token, and sends it to the device. The user then logs in to the system using this authentication information.

[0306] Initial Setup and Upload Information

[0307] Users upload previously created grant proposals and related materials. The device then sends these materials to the server, which analyzes the received materials, extracts key information such as the project name, summary, and budget, and stores it in a database.

[0308] Emotion Engine Operation

[0309] When a user inputs new information, the emotion engine recognizes the user's emotions by detecting micro-expressions, tone of voice, and input speed through the device. The device then sends this emotion data to the server, which then feeds it back to the generation AI, which takes this into account when generating the draft.

[0310] Preparation of grant application documents

[0311] A user inputs project information to create a new grant proposal. The device sends this information to a server. The server uses a generative AI to generate a draft of the grant proposal based on the input information and previous documents. The generative AI takes emotional data into account and adjusts the tone and style of the text to match the user's intentions and emotions. The server returns the generated draft to the user.

[0312] Review and revise the draft

[0313] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final grant application document and stores it in a database.

[0314] Submitting a grant application

[0315] The user checks the final grant application documents and submits them to the grant recipient. The server checks the recipient information and automatically sends the proposal documents to the recipient. The server then sends the user a confirmation that submission is complete.

[0316] Specific examples

[0317] Project Manager at a Nonprofit Organization

[0318] A project manager at a nonprofit organization might apply for a grant for a new environmental protection project by following these steps:

[0319] 1. User registration and login: The project manager creates a new account and logs into the system.

[0320] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[0321] 3. Emotion engine operation: While the project manager is entering new project information, the emotion engine recognizes emotions by detecting micro-expressions, typing speed, etc. For example, if the user is nervous, it will detect this and provide feedback to the generation AI.

[0322] 4. Grant proposal creation: Enter details about a new project and let the generative AI generate a draft proposal. The generative AI will adjust the tone and style of the writing based on emotional data. For example, if a user writes passionately in response to the question, "What is the social significance of this project?", the AI ​​will generate an answer such as, "This project aims to dramatically increase environmental awareness in the local community and promote significant change."

[0323] 5. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[0324] 6. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[0325] Through these steps, users can create high-quality grant proposals with minimal effort and efficiently obtain grants. In addition, by combining the emotion engine, proposals can be generated that are more tailored to the user.

[0326] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0327] Step 1:

[0328] A user visits the system's website and creates a new account.

[0329] Input: Name, email address, password, etc.

[0330] Output: Authentication token

[0331] Specific operation: The terminal receives information entered by the user and sends it to the server. The server stores the received information in a database, generates an authentication token, and sends it to the terminal. The user uses this token to log in to the system.

[0332] Step 2:

[0333] Users upload past grant proposals and related materials.

[0334] Input: Past proposal documents, related materials

[0335] Output: Main information (project name, summary, budget, etc.)

[0336] Specific operation: The terminal sends the documents uploaded by the user to the server. The server analyzes the received documents, extracts key information such as the project name, summary, and budget, and stores it in a database.

[0337] Step 3:

[0338] The emotion engine works when a user enters new project information.

[0339] Input: New project information, user emotional data (micro-expressions, tone of voice, typing speed, etc.)

[0340] Output: Parsed emotion data

[0341] Specific operation: The device detects the user's facial expressions, tone of voice, input speed, etc. while inputting, and collects them as emotional data. The device then sends the collected emotional data to the server, which then feeds it back to the generation AI.

[0342] Step 4:

[0343] The server uses the generative AI to generate a draft of the grant application.

[0344] Input: New project information, information extracted from historical documents, analyzed sentiment data

[0345] Output: Draft grant application

[0346] Specific operation: The server uses generative AI to generate a draft of a grant application document based on new project information, information extracted from past documents, and emotional data. The generative AI adjusts the tone and style of the text, taking the emotional data into account. The server then sends the generated draft to the device and displays it to the user.

[0347] Step 5:

[0348] The user reviews the generated draft and enters any corrections.

[0349] Input: Correction

[0350] Output: Final grant application

[0351] Specific operation: The terminal sends the corrections entered by the user to the server, which then generates a final version of the grant application document that reflects the corrections and stores it in the database.

[0352] Step 6:

[0353] The server sends the final grant application to the submission destination.

[0354] Input: Final grant application documents and submission information

[0355] Output: Notification of submission completion

[0356] Specific operation: The server automatically sends the proposal documents to the recipient based on the final grant application documents and the recipient information. Once the submission is complete, a confirmation notification of the submission is sent to the user's device.

[0357] (Application example 2)

[0358] 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."

[0359] In food delivery services, the challenge is to recognize in real time the dissatisfaction and anxiety that many users feel regarding their orders and delivery status, and to respond quickly and accurately. Conventional systems lack a means of accurately grasping users' emotions, making it difficult to provide appropriate customer support. Furthermore, the inability to accurately analyze and reflect user feedback makes it difficult to improve customer satisfaction.

[0360] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0361] In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft of a grant application document based on the new project information and the extracted key information, means for displaying the generated draft to a user and for inputting corrections, means for saving the final grant application document and transmitting it to the submission destination, means for analyzing a user's facial expression, tone of voice, and input speed to extract emotional data, and means for feeding the extracted emotional data back to the generation AI and adjusting the tone and style of the document. This enables the user's emotions to be analyzed in real time and the generation AI to provide an appropriate response.

[0362] "Generative AI" is artificial intelligence that automatically generates optimal answers and suggestions from a variety of data.

[0363] A "grant proposal" is a formal document requesting funding for a specific project.

[0364] "Emotion data" is information that indicates the emotional state of the user, analyzed from facial expressions, tone of voice, input speed, and the like.

[0365] "Key information" refers to important data extracted from the proposal documents, such as the project name, summary, budget, and schedule.

[0366] A "draft" is an early stage grant application document created by the generative AI, which will become the final version after being revised by the user.

[0367] "Means for uploading" is a function that allows users to send past proposal documents to the system.

[0368] "Means of analyzing and extracting key information" is a function that automatically identifies important data from uploaded proposal documents.

[0369] The "means for inputting new project information" is a function for inputting detailed information about a project that the user is currently working on.

[0370] "Means by which the generative AI generates a draft grant application document" refers to the function by which the generative AI creates the initial stage of the document based on user input and extracted key information.

[0371] The "means for displaying to the user and inputting corrections" is a function for showing the generated draft to the user and reflecting necessary changes.

[0372] The "means for saving the final version of the grant application document" is a function for storing the final version of the document that reflects the user's modifications.

[0373] The "means for sending to the submission destination" is a function for sending the generated final version of the grant application documents to the specified destination.

[0374] The "means for analyzing the user's facial expression" is a function that uses a camera to identify the user's facial expression and generate emotion data.

[0375] The "means for analyzing voice tone" is a function that uses a microphone to analyze the tone of the user's voice and generate emotion data.

[0376] The "means for analyzing input speed" is a function that measures the input speed of the user and generates emotion data from it.

[0377] "Means of providing feedback to the generation AI" is a function that instructs the generation AI to make adjustments based on the extracted emotional data.

[0378] "Means to adjust the tone and style of a document" refers to the ability of generative AI to optimize the expression of a document based on emotional data.

[0379] The present invention, when applied to a smartphone application, is described in detail below. This invention is intended to analyze user emotions in real time in a food delivery service and provide appropriate responses using generative AI.

[0380] System Configuration

[0381] The system includes the following main components:

[0382] 1. User device: A smartphone (compatible with iOS or Android) uses the camera and microphone to collect user emotion data.

[0383] 2. Server: Hosts data processing and generative AI.

[0384] 3. Generative AI: Generates documents and responses based on user emotional data and input information.

[0385] 4. Emotion engine: Generates emotion data by analyzing facial expressions, voice tone, and typing speed.

[0386] Hardware and software used

[0387] Face recognition library: OpenCV (open source)

[0388] Speech recognition: Google Cloud Speech-to-Text

[0389] Generative AI: OpenAI GPT-3 (using API)

[0390] Data Processing Overview

[0391] 1. User Registration and Login

[0392] A user downloads a smartphone application and creates a new account. They enter the required information (name, email address, password, etc.) and send it to the server. The server stores this information in a database, generates authentication information, and returns it to the user.

[0393] 2. Collecting Emotional Data

[0394] When a user uses the application, the smartphone's camera and microphone are used to collect the user's facial expressions and voice. The emotion engine analyzes this data and generates emotion data.

[0395] 3. Response generation using generative AI

[0396] The server then feeds the emotion data obtained from the emotion engine back to the AI ​​generator to generate an appropriate response. For example, if the user is dissatisfied, the AI ​​generator will provide an apology message or a discount coupon for the next order.

[0397] 4. View and edit the draft

[0398] The generated drafts and responses are displayed to the user, who can make corrections as needed, and the final corrections are sent to the server and saved as the final version.

[0399] Specific examples

[0400] For example, if a user is complaining about a late delivery, use a prompt like this:

[0401] "Generate an appropriate apology message if a customer is upset about a delayed delivery."

[0402] Based on this prompt, the Generative AI would generate a response like this:

[0403] "We sincerely apologize for the delay in delivery. As a token of our apology, we will provide you with a discount coupon for your next purchase."

[0404] This makes it possible to analyze user emotions in real time and provide appropriate responses. The system of the present invention improves the user experience and significantly improves the efficiency of customer support.

[0405] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0406] Step 1:

[0407] User Registration and Login

[0408] The user launches the smartphone application and creates a new account. The required information (name, email address, password, etc.) is entered and sent from the device to the server. The server stores this information in a database, generates authentication information, and returns it to the user. When logging in, the authentication information entered by the user is sent to the server for authentication.

[0409] Input: User information (name, email address, password)

[0410] Output: Credentials

[0411] Step 2:

[0412] Collecting Emotional Data

[0413] When a user uses the application, facial expressions and voice are collected using the smartphone's camera and microphone. The device then transmits this data in real time to the emotion engine, which analyzes the collected data and identifies the user's emotions.

[0414] Input: User's facial expression video and voice data

[0415] Output: Emotion data (e.g., joy, anger, sadness)

[0416] Step 3:

[0417] Response generation by generative AI

[0418] The device sends emotional data and user input information to the server. The server then feeds the emotional data back to the AI ​​generator, which then generates an appropriate response or suggestion. For example, if the user is dissatisfied, the AI ​​generator generates an apology message.

[0419] Input: Emotion data, user input information

[0420] Output: Generated response (e.g., apology message)

[0421] Step 4:

[0422] View and modify drafts

[0423] The terminal displays the generated responses and suggestions to the user, who can then make any necessary corrections and send them back to the server, which then generates a final version incorporating the final corrections and stores it in a database.

[0424] Input: Generated response, user modifications

[0425] Output: Final version after corrections are reflected

[0426] Step 5:

[0427] Final communication and storage

[0428] The server saves the final document and, if necessary, sends it to the submission destination, and notifies the user that the submission is complete.

[0429] Input: Final document

[0430] Output: Submission confirmation notification

[0431] These steps enable real-time analysis of user sentiment and use of generative AI to provide appropriate responses, thereby improving the quality of customer support for food delivery services.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] [Second embodiment]

[0436] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] In the smart glasses 214, 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.

[0447] 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."

[0448] Specific embodiments for carrying out the present invention are described below.

[0449] System Overview

[0450] The system of the present invention automates the creation of grant application documents using generative AI. The system extracts necessary information from past proposal documents and generates high-quality draft grant application documents based on new project information entered by the user. The system includes a server, a terminal, and generative AI.

[0451] Program processing overview

[0452] User Registration and Login

[0453] A user accesses the system's website and creates a new account. The device prompts the user to enter the required information (such as name, email address, and password) and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information to access the system.

[0454] Initial Setup and Upload Information

[0455] Users upload past grant proposals and related materials. The device sends these materials to the server, which analyzes them, extracts key information (project name, summary, budget, etc.), and stores it in a database.

[0456] Preparation of grant application documents

[0457] A user inputs project information to create a new grant proposal. The device sends this input information to a server. The server uses generative AI to generate a draft grant proposal based on the input information and past materials. The generative AI generates answers to questions and automatically generates high-quality sections of the proposal. The server returns the generated draft to the user.

[0458] Review and revise the draft

[0459] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final proposal document and stores it in a database.

[0460] Submitting a grant application

[0461] The user reviews the final proposal and submits the grant. The server verifies the information of the grant recipients and provides a mechanism to automatically submit the proposal (if necessary).

[0462] Specific examples

[0463] Project Manager at a Nonprofit Organization

[0464] A project manager at a nonprofit organization applying for a grant for a new environmental project follows these steps:

[0465] 1. User registration and login: The project manager creates a new account and logs in.

[0466] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[0467] 3. Creating grant proposals: Enter details about a new project (such as the name, purpose, and budget) and have the Generative AI generate a draft proposal. For example, in response to a question like, "What is the social significance of this project?", the Generative AI might respond with, "This project aims to raise environmental awareness in the local community and contribute to achieving the Sustainable Development Goals."

[0468] 4. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[0469] 5. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[0470] Through this system, project managers can create high-quality grant proposal documents with minimal effort and efficiently obtain grant funding.

[0471] The processing flow will be explained below.

[0472] Step 1:

[0473] The user accesses the system's website, enters the required information (such as name, email address, and password) on the new registration screen, and clicks the "Register" button.

[0474] Step 2:

[0475] The device sends the entered information to the server, which receives it, stores it in a database, generates an authentication token, and returns it to the device.

[0476] Step 3:

[0477] The user uses the authentication token returned from the server to log in. The terminal sends the login information to the server and receives the authentication result.

[0478] Step 4:

[0479] If the user authentication is successful, the server returns information for displaying a dashboard screen to the terminal, which then displays the dashboard screen to the user.

[0480] Step 5:

[0481] Users can click the "Upload Documents" button on the dashboard, select and upload past grant proposal documents.

[0482] Step 6:

[0483] The device sends the selected file to the server, which parses it, extracts key information such as the project name, summary, and budget information, and stores it in a database.

[0484] Step 7:

[0485] Users click the "Create a new proposal" button from the dashboard and enter information about the new project (such as name, purpose, budget, and schedule).

[0486] Step 8:

[0487] The device sends the input information to a server, which then compares it with data from past proposal documents and queries the AI ​​to generate a draft of the grant application.

[0488] Step 9:

[0489] The generative AI generates optimal answers to requested questions (e.g., "What is the social significance of this project?") and fills in each section of the proposal.

[0490] Step 10:

[0491] The server stores the generated draft in a database and returns the proposal draft to the terminal, which displays the draft to the user.

[0492] Step 11:

[0493] The user checks the displayed draft, enters any corrections, and submits it. The terminal then transmits the corrections to the server.

[0494] Step 12:

[0495] The server reflects the received corrections in the draft and stores the updated draft in the database again. The terminal displays the updated draft again to the user.

[0496] Step 13:

[0497] The user checks the final version of the proposal document and clicks the "Submit" button. The terminal displays information on the recipient and any additional documents required.

[0498] Step 14:

[0499] The user uploads any additional documents required and clicks the "Final Submission" button. The terminal sends all information to the server.

[0500] Step 15:

[0501] The server packages the proposal documents and additional documents based on the format of the submission destination and executes the submission process. If the submission is successful, the server sends a confirmation notice to the user via an in-system notification or email.

[0502] Example 1

[0503] 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."

[0504] The traditional grant application document preparation process is time-consuming, labor-intensive, and largely manual, making it inefficient. Furthermore, creating high-quality applications requires experience and skill, which presents a barrier to many applicants. Furthermore, analyzing proposal documents and extracting key information is time-consuming, creating a need for rapid information organization.

[0505] 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.

[0506] In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft grant application document based on the new project information and the extracted key information, means for displaying the generated draft to a user and for inputting corrections, means for saving the final grant application document and transmitting it to a submission destination, means for generating an authentication token and performing user authentication, means for analyzing the uploaded materials using an OCR tool or natural language processing technology and identifying key information, and means for the generation AI to generate prompt sentences and generate high-quality document sections based on the prompt sentences, thereby enabling users to efficiently create high-quality grant application documents with little effort and submit them quickly.

[0507] "Generative AI" is a technology that uses artificial intelligence to generate text and data.

[0508] "Grant application" means an official document containing the information required for the purpose of receiving a grant.

[0509] A "proposal document" is a document that was previously created for the purpose of applying for a grant or other purpose, and contains project details and plans.

[0510] "Upload" refers to the operation of sending data from a terminal to a server.

[0511] "Analysis" is the process of examining data in detail and extracting useful information from it.

[0512] "Key Information" refers to the most important and required information items in a grant application.

[0513] "New project information" is detailed information about a new project that the user is about to start.

[0514] A "draft" is a rough draft or rough draft created before the final version.

[0515] "User" refers to any individual or organization that uses this system.

[0516] An "authentication token" is a unique credential used to authenticate a user.

[0517] An "OCR tool" is a tool that uses optical character recognition technology to read text in an image and convert it into digital data.

[0518] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language.

[0519] A "prompt sentence" is an input sentence that causes a generation AI to generate text for a specific purpose.

[0520] "Submission Destination" refers to the destination or institution to which grant applications are submitted.

[0521] The following describes an embodiment of the present invention.

[0522] System Configuration

[0523] The system of the present invention utilizes generative AI to automate the creation of grant application documents. The system includes a server, a terminal, and generative AI. The server can be a cloud server, and the terminal can be a PC or smartphone. The generative AI model uses the latest AI technology based on natural language processing (NLP).

[0524] User Registration and Login

[0525] A user accesses the system's website and creates a new account. The device prompts the user for necessary information, such as name, email address, and password, and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information.

[0526] Initial Setup and Upload Information

[0527] Users upload past grant proposals and related materials. The terminal provides a drag-and-drop upload interface. Users select and upload files. The terminal then sends the uploaded files to the server, which analyzes the materials using OCR tools and natural language processing technology, extracting key information (project name, summary, budget, etc.) and storing it in a database.

[0528] Preparation of grant application documents

[0529] The user enters new project information. The device displays a form for entering details such as the project name, purpose, and budget. After the user enters and submits the information, the device sends this data to the server. The server then uses the received data to generate a prompt using the generation AI.

[0530] As a specific example, it generates a prompt such as, "What is the social significance of this project?" Based on this prompt, the generation AI generates an answer such as, "This project aims to raise environmental awareness in the local community and contributes to achieving the Sustainable Development Goals." The generated draft is sent back to the user's device via the server.

[0531] Review and revise the draft

[0532] The user can check the generated draft and enter any necessary corrections. The terminal provides a display interface for the draft and allows the user to edit any section. When the user enters the corrections and clicks Done, the terminal sends the correction information to the server. The server generates a final version reflecting the received corrections and stores it in the database.

[0533] Submitting a grant application

[0534] The user reviews the final version of the grant application documents and clicks the submit button. The terminal displays the final version and sends a submission instruction to the server. The server verifies the information of the designated recipients and electronically transmits the proposal documents. After the application is completed, the server generates a confirmation notice and sends it to the user's terminal.

[0535] Specific examples

[0536] When a project manager at a nonprofit organization applies for a grant for a new environmental protection project, they follow these steps: First, they register and log in, then upload a previous successful grant proposal. After entering the details of the new project, they let the generative AI generate a draft proposal. They review the generated draft and make any necessary revisions to finalize it. Finally, they submit it to the grant recipient and receive a confirmation notification.

[0537] This allows users to efficiently create high-quality grant application documents with minimal effort and submit them quickly.

[0538] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0539] Step 1: User Registration and Login

[0540] input

[0541] The user enters their name, email address, and password.

[0542] Processing and Data Manipulation

[0543] The device sends the entered information to the server, which stores the information in a database and generates an authentication token.

[0544] output

[0545] The server returns an authentication token to the terminal, and the user logs in using the authentication token.

[0546] Specific actions

[0547] When a user visits a website and creates a new account, the device displays a form for them to fill out. After completing the form, the device sends the information to the server, which stores the information and generates an authentication token that is returned to the device. The user then logs in using this token.

[0548] Step 2: Initial setup and upload information

[0549] input

[0550] Users upload past grant proposals.

[0551] Processing and Data Manipulation

[0552] The device sends the uploaded documents to a server, which analyzes them using OCR tools and natural language processing technology to extract key information.

[0553] output

[0554] The server stores the extracted information in a database.

[0555] Specific actions

[0556] Users upload documents using a drag-and-drop method, and the device sends the files to the server, which then uses OCR tools and natural language processing technology to extract information such as project name, summary, and budget from the documents and stores it in a database.

[0557] Step 3: Compile your grant application

[0558] input

[0559] The user enters the new project information.

[0560] Processing and Data Manipulation

[0561] The device sends the input data to the server, which generates a prompt based on the received data and passes it to the generation AI, which then generates a draft based on the prompt.

[0562] output

[0563] The server returns the generated draft to the terminal.

[0564] Specific actions

[0565] When a user inputs and submits new project information, the device sends this data to the server, which generates a prompt (e.g., "What is the social significance of this project?") and passes it to the generation AI. The generation AI creates a draft based on this prompt, and the server returns the draft to the device.

[0566] Step 4: Review and revise the draft

[0567] input

[0568] The user checks the generated draft and enters any corrections.

[0569] Processing and Data Manipulation

[0570] The device sends the modifications to the server, which then generates the final version incorporating the modifications.

[0571] output

[0572] The server generates the final version and stores it in a database.

[0573] Specific actions

[0574] The user reviews the draft and corrects any sections that need to be corrected. The device sends the corrections to the server, which then reflects them and generates the final version, which is then stored in the database.

[0575] Step 5: Submit your grant application

[0576] input

[0577] The user checks the final version of the proposal document and submits it.

[0578] Processing and Data Manipulation

[0579] The terminal sends the final version as a submission instruction to the server, which then checks the information of the designated application destination and electronically transmits the proposal documents.

[0580] output

[0581] The server generates an acknowledgement and sends it to the user's terminal.

[0582] Specific actions

[0583] The user checks the final version and clicks the submit button. The terminal sends a submission instruction to the server, which then electronically transmits the proposal documents to the submission destination. After the application is completed, the server generates a confirmation notice and sends it to the user's terminal.

[0584] (Application example 1)

[0585] 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."

[0586] In the past, creating grant application documents and managing inventory in brick-and-mortar stores was often done manually, hindering efficiency and requiring time and effort. Furthermore, determining the appropriate inventory levels and product placement during inventory management relied on experience and intuition, making it prone to errors and difficult to achieve optimal results. It was necessary to solve these problems and achieve more efficient and accurate creation of grant application documents and inventory management.

[0587] 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.

[0588] In this invention, the server includes a means for uploading past proposal documents, a means for analyzing the uploaded proposal documents to extract key information, and a means for inputting new project information, which enables the generation AI to generate draft grant application documents and optimal inventory quantities and product placement proposals for inventory management based on past data.

[0589] The "means for uploading past proposal documents" is an interface for a user to import electronic data of proposal documents previously created into the system.

[0590] "Means for analyzing uploaded proposal documents and extracting key information" refers to an algorithm that automatically analyzes imported proposal documents and populates a database with key information (e.g., project name, summary, budget, schedule, etc.).

[0591] The "means for inputting new project information" is a UI (user interface) that allows a user to input information about a new project that is being planned into the system.

[0592] "Means for generative AI to generate draft grant application documents" refers to a function in which AI automatically creates an initial draft of a grant application document based on input new project information and information extracted from past proposal documents.

[0593] "Means for displaying the generated draft to the user and allowing the user to input corrections" refers to a function that displays the draft created by the generation AI in a user interface and allows the user to make corrections to it.

[0594] The "means for saving the final version of the grant application documents and sending them to the recipient" is a function that saves the final version of the grant application documents that the user has revised in a database and sends them electronically to the recipient as needed.

[0595] "A means of using generative AI to analyze past inventory data and sales data and propose optimal inventory quantities and product placement" is a function that analyzes past inventory data and sales data, and uses AI to calculate the ideal inventory quantity and product placement method, and proposes this to the user.

[0596] Overall system configuration

[0597] This invention is a system that utilizes generative AI to streamline the creation of grant application documents and inventory management for physical stores. The system includes a server, a terminal, generative AI, and a database. The server manages user registration, information upload, draft generation by generative AI, and the storage and submission of the final version.

[0598] User Registration and Login

[0599] A user accesses the system using a device (e.g., smart glasses) and creates a new account. The user enters their name, email address, and password, which are sent to the server. The server stores the information in a database and generates an authentication token, which is sent back to the device. The user then logs into the system using the authentication information.

[0600] Initial Setup and Upload Information

[0601] Users upload past proposal documents, inventory data, and sales data from their devices to the server. The server analyzes the uploaded data, extracts key information, and stores it in a database. This makes it easier for users to receive data-based proposals in the future.

[0602] Preparation of grant application documents

[0603] The user inputs new project information. The device sends this information to the server, which then uses a generative AI to generate a draft application document. The generative AI generates optimal answers to questions based on past proposal documents and the new project information, resulting in a high-quality draft.

[0604] Review and revise the draft

[0605] The user reviews the draft sent from the server on their device and makes any necessary corrections. The corrected information is sent back to the server, and the final version is generated. The final version is saved in the database and can be viewed by the user.

[0606] Submitting a grant application

[0607] Once the user has reviewed the final version of the grant application, it is automatically sent to the submission destination via the server. Once the application has been submitted successfully, the user is notified.

[0608] Optimizing inventory management

[0609] A user wearing smart glasses scans inventory in a physical store and sends the information to a server. The server analyzes past inventory and sales data and uses a generative AI to suggest optimal inventory levels and product placement. Specifically, a barcode reader is used to obtain product information, and the generative AI makes suggestions for inventory replenishment and placement based on past data.

[0610] Hardware and software used

[0611] Hardware: Smart glasses (e.g. Google Glass, Microsoft HoloLens), built-in barcode reader (or external device Bluetooth connection)

[0612] Software: Server-side uses Python, Django, Flask, etc. Databases use PostgreSQL and MySQL. Generative AI models use GPT-4, TensorFlow, and PyTorch. Communication uses WebSocket and REST API.

[0613] Examples of concrete examples and prompts

[0614] When a user scans inventory information using smart glasses and a barcode reader, the following prompt is sent to the generation AI:

[0615] Example prompt sentence:

[0616] Product name: Product A

[0617] Current stock: 10

[0618] Average sales: 30 / week

[0619] Please suggest a refill.

[0620] Based on this prompt, the AI ​​generator will suggest an appropriate replenishment amount, such as, "The recommended replenishment amount for your inventory is 20 units. This will ensure you have enough stock for next week's sales."

[0621] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0622] Step 1:

[0623] The user puts on the smart glasses and starts the system. They scan the QR code to register as a user. The user enters their name, email address, and password using voice input or the touchpad, and this information is sent from the device to the server. The server stores the received information in a database, generates an authentication token, and sends it back to the device. The input data is user information, and the output is an authentication token.

[0624] Step 2:

[0625] As an initial setting, the user uploads past proposal documents, inventory data, and sales data to the server via their terminal. The server analyzes the uploaded data, extracts key information, and stores it in a database. Specifically, it extracts project names, summaries, budgets, schedules, etc. from proposal documents. The input data are proposal documents and past data, and the output is the extracted key information stored in a database.

[0626] Step 3:

[0627] The user inputs new project information. The user inputs the project name, objectives, budget, etc. through the smart glasses, and this information is sent from the device to the server. The server combines this input information with past information, stores it in a database, and prepares the data to be input to the generative AI. The input data is the new project information, and the output is the input data for the generative AI.

[0628] Step 4:

[0629] The server uses generative AI to generate a draft grant application. The generative AI model (e.g., GPT-4) generates optimal answers to questions based on data from past proposal documents and new project information, and automatically generates each section of the grant application. Specifically, in response to the question, "What is the social significance of this project?", the server generates the answer, "This project aims to raise environmental awareness in the local community and contribute to achieving the Sustainable Development Goals." The input data is the new project information and past data, and the output is the draft grant application.

[0630] Step 5:

[0631] The generated draft is sent to the terminal, where the user reviews the draft through the smart glasses and makes any necessary corrections. The user inputs the corrections using voice input or the touchpad, and the terminal sends the corrections to the server. The server receives the corrections, generates the final grant application document, and stores it in a database. The input data are the user's corrections, and the output is the final grant application document.

[0632] Step 6:

[0633] The user reviews the final grant application and automatically sends it to the recipient through the server. The server verifies the recipient's information and sends the application in the appropriate format. Once the submission is complete, the server notifies the user. The input data is the final grant application and the output is a notification that the submission is complete.

[0634] Step 7:

[0635] A user uses smart glasses to browse the shelves in a store and scans inventory information using a barcode reader. The device then sends the acquired product information to a server. The input data is barcode information, and the output is updated inventory information.

[0636] Step 8:

[0637] The server combines and analyzes the acquired inventory information with past inventory and sales data, and uses generation AI to generate optimal inventory quantity and product placement proposals. For example, the generation AI might suggest, "Product A is running low on stock. Replenishment is required." The input data is inventory scan information and past data, and the output is a stock replenishment proposal.

[0638] Step 9:

[0639] The generated inventory management proposal is sent to the terminal, and the user can check the proposal through the smart glasses. If the user accepts the proposal, inventory replenishment and product allocation are carried out based on it. The input data is the inventory proposal content, and the output is the execution of inventory management.

[0640] 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.

[0641] Specific embodiments for carrying out the present invention are described below.

[0642] System Overview

[0643] The system of the present invention automates the creation of grant application documents using generative AI and incorporates an emotion engine that recognizes user emotions. The system extracts necessary information from past proposal documents and generates high-quality draft grant application documents based on new project information entered by the user. The system also uses the emotion engine to analyze user emotions and incorporates that feedback into the generated draft. The system includes a server, a terminal, generative AI, and the emotion engine.

[0644] Program processing overview

[0645] User Registration and Login

[0646] A user accesses the system's website and creates a new account. The device prompts the user to enter the required information (such as name, email address, and password) and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information to access the system.

[0647] Initial Setup and Upload Information

[0648] Users upload past grant proposals and related materials. The device sends these materials to the server, which analyzes them, extracts key information (project name, summary, budget, etc.), and stores it in a database.

[0649] Emotion Engine Operation

[0650] The emotion engine recognizes the user's emotions by detecting micro-expressions, tone of voice, and typing speed. The device sends this emotion data to the server, which then feeds it back to the AI ​​and takes it into account when generating the draft.

[0651] Preparation of grant application documents

[0652] A user inputs project information to create a new grant proposal. The device sends this input information to a server. The server uses generative AI to generate a draft grant proposal based on the input information and past materials. The generative AI takes emotional data into account and adjusts the tone and style of the text to match the user's intentions and emotions. The server returns the generated draft to the user.

[0653] Review and revise the draft

[0654] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final proposal document and stores it in a database.

[0655] Submitting a grant application

[0656] The user reviews the final proposal and submits the grant. The server verifies the information of the grant recipients and provides a mechanism to automatically submit the proposal (if necessary).

[0657] Specific examples

[0658] Project Manager at a Nonprofit Organization

[0659] A project manager at a nonprofit organization applying for a grant for a new environmental project follows these steps:

[0660] 1. User registration and login: The project manager creates a new account and logs in.

[0661] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[0662] 3. Emotion engine operation: While the project manager is entering information about a new project, the emotion engine recognizes the user's emotions by detecting their micro-expressions and typing speed. For example, if the user is nervous, it will detect this and provide feedback to the generation AI.

[0663] 4. Grant proposal creation: Enter details about a new project and let the generative AI generate a draft proposal. The generative AI will adjust the tone and style of the text appropriately based on emotional data. For example, if a user is passionate about the question, "What is the social significance of this project?", the AI ​​will generate an answer such as, "This project aims to dramatically increase environmental awareness in the local community and promote significant change."

[0664] 5. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[0665] 6. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[0666] This system allows users to create high-quality grant proposal documents with minimal effort and efficiently obtain grants. In addition, by combining it with an emotion engine, proposal documents can be generated that are more suited to the user.

[0667] The processing flow will be explained below.

[0668] Step 1:

[0669] The user accesses the system's website, enters the required information (such as name, email address, and password) on the new registration screen, and clicks the "Register" button.

[0670] Step 2:

[0671] The device sends the entered information to the server, which receives it, stores it in a database, generates an authentication token, and returns it to the device.

[0672] Step 3:

[0673] The user uses the authentication token returned from the server to log in. The terminal sends the login information to the server and receives the authentication result.

[0674] Step 4:

[0675] If the user authentication is successful, the server returns information for displaying a dashboard screen to the terminal, which then displays the dashboard screen to the user.

[0676] Step 5:

[0677] Users can click the "Upload Documents" button on the dashboard, select and upload past grant proposal documents.

[0678] Step 6:

[0679] The device sends the selected file to the server, which parses it, extracts key information such as the project name, summary, and budget information, and stores it in a database.

[0680] Step 7:

[0681] Users click the "Create a new proposal" button from the dashboard and enter information about the new project (such as name, purpose, budget, and schedule).

[0682] Step 8:

[0683] The device sends the input information to a server, which then compares it with data from past proposal documents and queries the AI ​​to generate a draft of the grant application.

[0684] Step 9:

[0685] The emotion engine detects micro-expressions and typing speed as the user enters new project information, and the device transmits the emotion data to the server.

[0686] Step 10:

[0687] The server feeds emotional data back to the AI ​​generator, which takes this into account when generating drafts, and the AI ​​then generates text in a tone and style that matches the user's emotions.

[0688] Step 11:

[0689] The generative AI generates answers to the requested questions and fills in each section of the grant application. The server stores the generated draft in a database and returns the draft proposal to the device.

[0690] Step 12:

[0691] The user checks the displayed draft, enters any corrections, and submits it. The terminal then transmits the corrections to the server.

[0692] Step 13:

[0693] The server reflects the received corrections in the draft and stores the updated draft in the database again. The terminal displays the updated draft again to the user.

[0694] Step 14:

[0695] The user checks the final version of the proposal document and clicks the "Submit" button. The terminal displays information on the recipient and any additional documents required.

[0696] Step 15:

[0697] The user uploads any additional documents required and clicks the "Final Submission" button. The terminal sends all information to the server.

[0698] Step 16:

[0699] The server packages the proposal documents and additional documents based on the format of the submission destination and executes the submission process. If the submission is successful, the server sends a confirmation notice to the user via an in-system notification or email.

[0700] Example 2

[0701] 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."

[0702] Creating grant proposals is a complex and time-consuming task, especially when extracting information from past proposals and combining it with new project information. Furthermore, if the tone and style of a grant proposal doesn't reflect the user's emotions, its quality and persuasiveness will be affected. There is a need for a system that automates this process and generates high-quality proposals that are both efficient and emotionally sensitive.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft of grant application documents based on the new project information and the extracted key information, means for detecting a user's emotions and analyzing the data, means for feeding back the detected emotion data to the generation AI and reflecting it in the tone and style of the generated draft, means for displaying the generated draft to the user and allowing the user to input corrections, and means for saving the final grant application documents and transmitting them to the submission destination. This improves the efficiency of grant application document creation and enables the generation of high-quality application documents that are adapted to the user's emotions.

[0704] 1. "Generative AI" is a system that uses artificial intelligence to generate natural language and create new documents and texts.

[0705] 2. "Grant application" means an official document submitted to obtain a grant for a specific project or activity.

[0706] 3. "Server" means a computer system that provides services to other devices on a network.

[0707] 4. "Terminal" means an electronic device with a user interface for inputting, displaying, and transmitting information.

[0708] 5. "User" means a person or individual who operates the system.

[0709] 6. "Emotion Engine" means software or a system for detecting and analyzing a user's emotions.

[0710] 7. "Draft" means an early manuscript or draft of a grant application.

[0711] 8. "Project Information" means detailed data and details relating to a particular project.

[0712] 9. "Key Information" means the essential data required to prepare a grant application.

[0713] 10. "Feedback" is the process of re-inputting results or reactions into a system or process.

[0714] System Overview

[0715] This invention is a system that automatically generates grant application documents using generative AI. This system extracts key information from past proposal documents and combines it with new project information to generate high-quality grant application documents. In addition, by incorporating an emotion engine that recognizes the user's emotions, the system can reflect the user's emotions in the generated draft. This system includes a server, a terminal, generative AI, and an emotion engine.

[0716] User Registration and Login

[0717] A user accesses the system's website and creates a new account. The device receives necessary information from the user, such as name, email address, and password, and sends it to the server. The server stores the received information in a database, generates an authentication token, and sends it to the device. The user then logs in to the system using this authentication information.

[0718] Initial Setup and Upload Information

[0719] Users upload previously created grant proposals and related materials. The device then sends these materials to the server, which analyzes the received materials, extracts key information such as the project name, summary, and budget, and stores it in a database.

[0720] Emotion Engine Operation

[0721] When a user inputs new information, the emotion engine recognizes the user's emotions by detecting micro-expressions, tone of voice, and input speed through the device. The device then sends this emotion data to the server, which then feeds it back to the generation AI, which takes this into account when generating the draft.

[0722] Preparation of grant application documents

[0723] A user inputs project information to create a new grant proposal. The device sends this information to a server. The server uses a generative AI to generate a draft of the grant proposal based on the input information and previous documents. The generative AI takes emotional data into account and adjusts the tone and style of the text to match the user's intentions and emotions. The server returns the generated draft to the user.

[0724] Review and revise the draft

[0725] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final grant application document and stores it in a database.

[0726] Submitting a grant application

[0727] The user checks the final grant application documents and submits them to the grant recipient. The server checks the recipient information and automatically sends the proposal documents to the recipient. The server then sends the user a confirmation that submission is complete.

[0728] Specific examples

[0729] Project Manager at a Nonprofit Organization

[0730] A project manager at a nonprofit organization might apply for a grant for a new environmental protection project by following these steps:

[0731] 1. User registration and login: The project manager creates a new account and logs into the system.

[0732] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[0733] 3. Emotion engine operation: While the project manager is entering new project information, the emotion engine recognizes emotions by detecting micro-expressions, typing speed, etc. For example, if the user is nervous, it will detect this and provide feedback to the generation AI.

[0734] 4. Grant proposal creation: Enter details about a new project and let the generative AI generate a draft proposal. The generative AI will adjust the tone and style of the writing based on emotional data. For example, if a user writes passionately in response to the question, "What is the social significance of this project?", the AI ​​will generate an answer such as, "This project aims to dramatically increase environmental awareness in the local community and promote significant change."

[0735] 5. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[0736] 6. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[0737] Through these steps, users can create high-quality grant proposals with minimal effort and efficiently obtain grants. In addition, by combining the emotion engine, proposals can be generated that are more tailored to the user.

[0738] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0739] Step 1:

[0740] A user visits the system's website and creates a new account.

[0741] Input: Name, email address, password, etc.

[0742] Output: Authentication token

[0743] Specific operation: The terminal receives information entered by the user and sends it to the server. The server stores the received information in a database, generates an authentication token, and sends it to the terminal. The user uses this token to log in to the system.

[0744] Step 2:

[0745] Users upload past grant proposals and related materials.

[0746] Input: Past proposal documents, related materials

[0747] Output: Main information (project name, summary, budget, etc.)

[0748] Specific operation: The terminal sends the documents uploaded by the user to the server. The server analyzes the received documents, extracts key information such as the project name, summary, and budget, and stores it in a database.

[0749] Step 3:

[0750] The emotion engine works when a user enters new project information.

[0751] Input: New project information, user emotional data (micro-expressions, tone of voice, typing speed, etc.)

[0752] Output: Parsed emotion data

[0753] Specific operation: The device detects the user's facial expressions, tone of voice, input speed, etc. while inputting, and collects them as emotional data. The device then sends the collected emotional data to the server, which then feeds it back to the generation AI.

[0754] Step 4:

[0755] The server uses the generative AI to generate a draft of the grant application.

[0756] Input: New project information, information extracted from historical documents, analyzed sentiment data

[0757] Output: Draft grant application

[0758] Specific operation: The server uses generative AI to generate a draft of a grant application document based on new project information, information extracted from past documents, and emotional data. The generative AI adjusts the tone and style of the text, taking the emotional data into account. The server then sends the generated draft to the device and displays it to the user.

[0759] Step 5:

[0760] The user reviews the generated draft and enters any corrections.

[0761] Input: Correction

[0762] Output: Final grant application

[0763] Specific operation: The terminal sends the corrections entered by the user to the server, which then generates a final version of the grant application document that reflects the corrections and stores it in the database.

[0764] Step 6:

[0765] The server sends the final grant application to the submission destination.

[0766] Input: Final grant application documents and submission information

[0767] Output: Notification of submission completion

[0768] Specific operation: The server automatically sends the proposal documents to the recipient based on the final grant application documents and the recipient information. Once the submission is complete, a confirmation notification of the submission is sent to the user's device.

[0769] (Application example 2)

[0770] 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."

[0771] In food delivery services, the challenge is to recognize in real time the dissatisfaction and anxiety that many users feel regarding their orders and delivery status, and to respond quickly and accurately. Conventional systems lack a means of accurately grasping users' emotions, making it difficult to provide appropriate customer support. Furthermore, the inability to accurately analyze and reflect user feedback makes it difficult to improve customer satisfaction.

[0772] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0773] In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft of a grant application document based on the new project information and the extracted key information, means for displaying the generated draft to a user and for inputting corrections, means for saving the final grant application document and transmitting it to the submission destination, means for analyzing a user's facial expression, tone of voice, and input speed to extract emotional data, and means for feeding the extracted emotional data back to the generation AI and adjusting the tone and style of the document. This enables the user's emotions to be analyzed in real time and the generation AI to provide an appropriate response.

[0774] "Generative AI" is artificial intelligence that automatically generates optimal answers and suggestions from a variety of data.

[0775] A "grant proposal" is a formal document requesting funding for a specific project.

[0776] "Emotion data" is information that indicates the emotional state of the user, analyzed from facial expressions, tone of voice, input speed, and the like.

[0777] "Key information" refers to important data extracted from the proposal documents, such as the project name, summary, budget, and schedule.

[0778] A "draft" is an early stage grant application document created by the generative AI, which will become the final version after being revised by the user.

[0779] "Means for uploading" is a function that allows users to send past proposal documents to the system.

[0780] "Means of analyzing and extracting key information" is a function that automatically identifies important data from uploaded proposal documents.

[0781] The "means for inputting new project information" is a function for inputting detailed information about a project that the user is currently working on.

[0782] "Means by which the generative AI generates a draft grant application document" refers to the function by which the generative AI creates the initial stage of the document based on user input and extracted key information.

[0783] The "means for displaying to the user and inputting corrections" is a function for showing the generated draft to the user and reflecting necessary changes.

[0784] The "means for saving the final version of the grant application document" is a function for storing the final version of the document that reflects the user's modifications.

[0785] The "means for sending to the submission destination" is a function for sending the generated final version of the grant application documents to the specified destination.

[0786] The "means for analyzing the user's facial expression" is a function that uses a camera to identify the user's facial expression and generate emotion data.

[0787] The "means for analyzing voice tone" is a function that uses a microphone to analyze the tone of the user's voice and generate emotion data.

[0788] The "means for analyzing input speed" is a function that measures the input speed of the user and generates emotion data from it.

[0789] "Means of providing feedback to the generation AI" is a function that instructs the generation AI to make adjustments based on the extracted emotional data.

[0790] "Means to adjust the tone and style of a document" refers to the ability of generative AI to optimize the expression of a document based on emotional data.

[0791] The present invention, when applied to a smartphone application, is described in detail below. This invention is intended to analyze user emotions in real time in a food delivery service and provide appropriate responses using generative AI.

[0792] System Configuration

[0793] The system includes the following main components:

[0794] 1. User device: A smartphone (compatible with iOS or Android) uses the camera and microphone to collect user emotion data.

[0795] 2. Server: Hosts data processing and generative AI.

[0796] 3. Generative AI: Generates documents and responses based on user emotional data and input information.

[0797] 4. Emotion engine: Generates emotion data by analyzing facial expressions, voice tone, and typing speed.

[0798] Hardware and software used

[0799] Face recognition library: OpenCV (open source)

[0800] Speech recognition: Google Cloud Speech-to-Text

[0801] Generative AI: OpenAI GPT-3 (using API)

[0802] Data Processing Overview

[0803] 1. User Registration and Login

[0804] A user downloads a smartphone application and creates a new account. They enter the required information (name, email address, password, etc.) and send it to the server. The server stores this information in a database, generates authentication information, and returns it to the user.

[0805] 2. Collecting Emotional Data

[0806] When a user uses the application, the smartphone's camera and microphone are used to collect the user's facial expressions and voice. The emotion engine analyzes this data and generates emotion data.

[0807] 3. Response generation using generative AI

[0808] The server then feeds the emotion data obtained from the emotion engine back to the AI ​​generator to generate an appropriate response. For example, if the user is dissatisfied, the AI ​​generator will provide an apology message or a discount coupon for the next order.

[0809] 4. View and edit the draft

[0810] The generated drafts and responses are displayed to the user, who can make corrections as needed, and the final corrections are sent to the server and saved as the final version.

[0811] Specific examples

[0812] For example, if a user is complaining about a late delivery, use a prompt like this:

[0813] "Generate an appropriate apology message if a customer is upset about a delayed delivery."

[0814] Based on this prompt, the Generative AI would generate a response like this:

[0815] "We sincerely apologize for the delay in delivery. As a token of our apology, we will provide you with a discount coupon for your next purchase."

[0816] This makes it possible to analyze user emotions in real time and provide appropriate responses. The system of the present invention improves the user experience and significantly improves the efficiency of customer support.

[0817] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0818] Step 1:

[0819] User Registration and Login

[0820] The user launches the smartphone application and creates a new account. The required information (name, email address, password, etc.) is entered and sent from the device to the server. The server stores this information in a database, generates authentication information, and returns it to the user. When logging in, the authentication information entered by the user is sent to the server for authentication.

[0821] Input: User information (name, email address, password)

[0822] Output: Credentials

[0823] Step 2:

[0824] Collecting Emotional Data

[0825] When a user uses the application, facial expressions and voice are collected using the smartphone's camera and microphone. The device then transmits this data in real time to the emotion engine, which analyzes the collected data and identifies the user's emotions.

[0826] Input: User's facial expression video and voice data

[0827] Output: Emotion data (e.g., joy, anger, sadness)

[0828] Step 3:

[0829] Response generation by generative AI

[0830] The device sends emotional data and user input information to the server. The server then feeds the emotional data back to the AI ​​generator, which then generates an appropriate response or suggestion. For example, if the user is dissatisfied, the AI ​​generator generates an apology message.

[0831] Input: Emotion data, user input information

[0832] Output: Generated response (e.g., apology message)

[0833] Step 4:

[0834] View and modify drafts

[0835] The terminal displays the generated responses and suggestions to the user, who can then make any necessary corrections and send them back to the server, which then generates a final version incorporating the final corrections and stores it in a database.

[0836] Input: Generated response, user modifications

[0837] Output: Final version after corrections are reflected

[0838] Step 5:

[0839] Final communication and storage

[0840] The server saves the final document and, if necessary, sends it to the submission destination, and notifies the user that the submission is complete.

[0841] Input: Final document

[0842] Output: Submission confirmation notification

[0843] These steps enable real-time analysis of user sentiment and use of generative AI to provide appropriate responses, thereby improving the quality of customer support for food delivery services.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] [Third embodiment]

[0848] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0849] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0850] 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).

[0851] 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.

[0852] 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.

[0853] 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).

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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."

[0860] Specific embodiments for carrying out the present invention are described below.

[0861] System Overview

[0862] The system of the present invention automates the creation of grant application documents using generative AI. The system extracts necessary information from past proposal documents and generates high-quality draft grant application documents based on new project information entered by the user. The system includes a server, a terminal, and generative AI.

[0863] Program processing overview

[0864] User Registration and Login

[0865] A user accesses the system's website and creates a new account. The device prompts the user to enter the required information (such as name, email address, and password) and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information to access the system.

[0866] Initial Setup and Upload Information

[0867] Users upload past grant proposals and related materials. The device sends these materials to the server, which analyzes them, extracts key information (project name, summary, budget, etc.), and stores it in a database.

[0868] Preparation of grant application documents

[0869] A user inputs project information to create a new grant proposal. The device sends this input information to a server. The server uses generative AI to generate a draft grant proposal based on the input information and past materials. The generative AI generates answers to questions and automatically generates high-quality sections of the proposal. The server returns the generated draft to the user.

[0870] Review and revise the draft

[0871] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final proposal document and stores it in a database.

[0872] Submitting a grant application

[0873] The user reviews the final proposal and submits the grant. The server verifies the information of the grant recipients and provides a mechanism to automatically submit the proposal (if necessary).

[0874] Specific examples

[0875] Project Manager at a Nonprofit Organization

[0876] A project manager at a nonprofit organization applying for a grant for a new environmental project follows these steps:

[0877] 1. User registration and login: The project manager creates a new account and logs in.

[0878] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[0879] 3. Creating grant proposals: Enter details about a new project (such as the name, purpose, and budget) and have the Generative AI generate a draft proposal. For example, in response to a question like, "What is the social significance of this project?", the Generative AI might respond with, "This project aims to raise environmental awareness in the local community and contribute to achieving the Sustainable Development Goals."

[0880] 4. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[0881] 5. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[0882] Through this system, project managers can create high-quality grant proposal documents with minimal effort and efficiently obtain grant funding.

[0883] The processing flow will be explained below.

[0884] Step 1:

[0885] The user accesses the system's website, enters the required information (such as name, email address, and password) on the new registration screen, and clicks the "Register" button.

[0886] Step 2:

[0887] The device sends the entered information to the server, which receives it, stores it in a database, generates an authentication token, and returns it to the device.

[0888] Step 3:

[0889] The user uses the authentication token returned from the server to log in. The terminal sends the login information to the server and receives the authentication result.

[0890] Step 4:

[0891] If the user authentication is successful, the server returns information for displaying a dashboard screen to the terminal, which then displays the dashboard screen to the user.

[0892] Step 5:

[0893] Users can click the "Upload Documents" button on the dashboard, select and upload past grant proposal documents.

[0894] Step 6:

[0895] The device sends the selected file to the server, which parses it, extracts key information such as the project name, summary, and budget information, and stores it in a database.

[0896] Step 7:

[0897] Users click the "Create a new proposal" button from the dashboard and enter information about the new project (such as name, purpose, budget, and schedule).

[0898] Step 8:

[0899] The device sends the input information to a server, which then compares it with data from past proposal documents and queries the AI ​​to generate a draft of the grant application.

[0900] Step 9:

[0901] The generative AI generates optimal answers to requested questions (e.g., "What is the social significance of this project?") and fills in each section of the proposal.

[0902] Step 10:

[0903] The server stores the generated draft in a database and returns the proposal draft to the terminal, which displays the draft to the user.

[0904] Step 11:

[0905] The user checks the displayed draft, enters any corrections, and submits it. The terminal then transmits the corrections to the server.

[0906] Step 12:

[0907] The server reflects the received corrections in the draft and stores the updated draft in the database again. The terminal displays the updated draft again to the user.

[0908] Step 13:

[0909] The user checks the final version of the proposal document and clicks the "Submit" button. The terminal displays information on the recipient and any additional documents required.

[0910] Step 14:

[0911] The user uploads any additional documents required and clicks the "Final Submission" button. The terminal sends all information to the server.

[0912] Step 15:

[0913] The server packages the proposal documents and additional documents based on the format of the submission destination and executes the submission process. If the submission is successful, the server sends a confirmation notice to the user via an in-system notification or email.

[0914] Example 1

[0915] 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."

[0916] The traditional grant application document preparation process is time-consuming, labor-intensive, and largely manual, making it inefficient. Furthermore, creating high-quality applications requires experience and skill, which presents a barrier to many applicants. Furthermore, analyzing proposal documents and extracting key information is time-consuming, creating a need for rapid information organization.

[0917] 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.

[0918] In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft grant application document based on the new project information and the extracted key information, means for displaying the generated draft to a user and for inputting corrections, means for saving the final grant application document and transmitting it to a submission destination, means for generating an authentication token and performing user authentication, means for analyzing the uploaded materials using an OCR tool or natural language processing technology and identifying key information, and means for the generation AI to generate prompt sentences and generate high-quality document sections based on the prompt sentences, thereby enabling users to efficiently create high-quality grant application documents with little effort and submit them quickly.

[0919] "Generative AI" is a technology that uses artificial intelligence to generate text and data.

[0920] "Grant application" means an official document containing the information required for the purpose of receiving a grant.

[0921] A "proposal document" is a document that was previously created for the purpose of applying for a grant or other purpose, and contains project details and plans.

[0922] "Upload" refers to the operation of sending data from a terminal to a server.

[0923] "Analysis" is the process of examining data in detail and extracting useful information from it.

[0924] "Key Information" refers to the most important and required information items in a grant application.

[0925] "New project information" is detailed information about a new project that the user is about to start.

[0926] A "draft" is a rough draft or rough draft created before the final version.

[0927] "User" refers to any individual or organization that uses this system.

[0928] An "authentication token" is a unique credential used to authenticate a user.

[0929] An "OCR tool" is a tool that uses optical character recognition technology to read text in an image and convert it into digital data.

[0930] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language.

[0931] A "prompt sentence" is an input sentence that causes a generation AI to generate text for a specific purpose.

[0932] "Submission Destination" refers to the destination or institution to which grant applications are submitted.

[0933] The following describes an embodiment of the present invention.

[0934] System Configuration

[0935] The system of the present invention utilizes generative AI to automate the creation of grant application documents. The system includes a server, a terminal, and generative AI. The server can be a cloud server, and the terminal can be a PC or smartphone. The generative AI model uses the latest AI technology based on natural language processing (NLP).

[0936] User Registration and Login

[0937] A user accesses the system's website and creates a new account. The device prompts the user for necessary information, such as name, email address, and password, and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information.

[0938] Initial Setup and Upload Information

[0939] Users upload past grant proposals and related materials. The terminal provides a drag-and-drop upload interface. Users select and upload files. The terminal then sends the uploaded files to the server, which analyzes the materials using OCR tools and natural language processing technology, extracting key information (project name, summary, budget, etc.) and storing it in a database.

[0940] Preparation of grant application documents

[0941] The user enters new project information. The device displays a form for entering details such as the project name, purpose, and budget. After the user enters and submits the information, the device sends this data to the server. The server then uses the received data to generate a prompt using the generation AI.

[0942] As a specific example, it generates a prompt such as, "What is the social significance of this project?" Based on this prompt, the generation AI generates an answer such as, "This project aims to raise environmental awareness in the local community and contributes to achieving the Sustainable Development Goals." The generated draft is sent back to the user's device via the server.

[0943] Review and revise the draft

[0944] The user can check the generated draft and enter any necessary corrections. The terminal provides a display interface for the draft and allows the user to edit any section. When the user enters the corrections and clicks Done, the terminal sends the correction information to the server. The server generates a final version reflecting the received corrections and stores it in the database.

[0945] Submitting a grant application

[0946] The user reviews the final version of the grant application documents and clicks the submit button. The terminal displays the final version and sends a submission instruction to the server. The server verifies the information of the designated recipients and electronically transmits the proposal documents. After the application is completed, the server generates a confirmation notice and sends it to the user's terminal.

[0947] Specific examples

[0948] When a project manager at a nonprofit organization applies for a grant for a new environmental protection project, they follow these steps: First, they register and log in, then upload a previous successful grant proposal. After entering the details of the new project, they let the generative AI generate a draft proposal. They review the generated draft and make any necessary revisions to finalize it. Finally, they submit it to the grant recipient and receive a confirmation notification.

[0949] This allows users to efficiently create high-quality grant application documents with minimal effort and submit them quickly.

[0950] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0951] Step 1: User Registration and Login

[0952] input

[0953] The user enters their name, email address, and password.

[0954] Processing and Data Manipulation

[0955] The device sends the entered information to the server, which stores the information in a database and generates an authentication token.

[0956] output

[0957] The server returns an authentication token to the terminal, and the user logs in using the authentication token.

[0958] Specific actions

[0959] When a user visits a website and creates a new account, the device displays a form for them to fill out. After completing the form, the device sends the information to the server, which stores the information and generates an authentication token that is returned to the device. The user then logs in using this token.

[0960] Step 2: Initial setup and upload information

[0961] input

[0962] Users upload past grant proposals.

[0963] Processing and Data Manipulation

[0964] The device sends the uploaded documents to a server, which analyzes them using OCR tools and natural language processing technology to extract key information.

[0965] output

[0966] The server stores the extracted information in a database.

[0967] Specific actions

[0968] Users upload documents using a drag-and-drop method, and the device sends the files to the server, which then uses OCR tools and natural language processing technology to extract information such as project name, summary, and budget from the documents and stores it in a database.

[0969] Step 3: Compile your grant application

[0970] input

[0971] The user enters the new project information.

[0972] Processing and Data Manipulation

[0973] The device sends the input data to the server, which generates a prompt based on the received data and passes it to the generation AI, which then generates a draft based on the prompt.

[0974] output

[0975] The server returns the generated draft to the terminal.

[0976] Specific actions

[0977] When a user inputs and submits new project information, the device sends this data to the server, which generates a prompt (e.g., "What is the social significance of this project?") and passes it to the generation AI. The generation AI creates a draft based on this prompt, and the server returns the draft to the device.

[0978] Step 4: Review and revise the draft

[0979] input

[0980] The user checks the generated draft and enters any corrections.

[0981] Processing and Data Manipulation

[0982] The device sends the modifications to the server, which then generates the final version incorporating the modifications.

[0983] output

[0984] The server generates the final version and stores it in a database.

[0985] Specific actions

[0986] The user reviews the draft and corrects any sections that need to be corrected. The device sends the corrections to the server, which then reflects them and generates the final version, which is then stored in the database.

[0987] Step 5: Submit your grant application

[0988] input

[0989] The user checks the final version of the proposal document and submits it.

[0990] Processing and Data Manipulation

[0991] The terminal sends the final version as a submission instruction to the server, which then checks the information of the designated application destination and electronically transmits the proposal documents.

[0992] output

[0993] The server generates an acknowledgement and sends it to the user's terminal.

[0994] Specific actions

[0995] The user checks the final version and clicks the submit button. The terminal sends a submission instruction to the server, which then electronically transmits the proposal documents to the submission destination. After the application is completed, the server generates a confirmation notice and sends it to the user's terminal.

[0996] (Application example 1)

[0997] 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."

[0998] In the past, creating grant application documents and managing inventory in brick-and-mortar stores was often done manually, hindering efficiency and requiring time and effort. Furthermore, determining the appropriate inventory levels and product placement during inventory management relied on experience and intuition, making it prone to errors and difficult to achieve optimal results. It was necessary to solve these problems and achieve more efficient and accurate creation of grant application documents and inventory management.

[0999] 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.

[1000] In this invention, the server includes a means for uploading past proposal documents, a means for analyzing the uploaded proposal documents to extract key information, and a means for inputting new project information, which enables the generation AI to generate draft grant application documents and optimal inventory quantities and product placement proposals for inventory management based on past data.

[1001] The "means for uploading past proposal documents" is an interface for a user to import electronic data of proposal documents previously created into the system.

[1002] "Means for analyzing uploaded proposal documents and extracting key information" refers to an algorithm that automatically analyzes imported proposal documents and populates a database with key information (e.g., project name, summary, budget, schedule, etc.).

[1003] The "means for inputting new project information" is a UI (user interface) that allows a user to input information about a new project that is being planned into the system.

[1004] "Means for generative AI to generate draft grant application documents" refers to a function in which AI automatically creates an initial draft of a grant application document based on input new project information and information extracted from past proposal documents.

[1005] "Means for displaying the generated draft to the user and allowing the user to input corrections" refers to a function that displays the draft created by the generation AI in a user interface and allows the user to make corrections to it.

[1006] The "means for saving the final version of the grant application documents and sending them to the recipient" is a function that saves the final version of the grant application documents that the user has revised in a database and sends them electronically to the recipient as needed.

[1007] "A means of using generative AI to analyze past inventory data and sales data and propose optimal inventory quantities and product placement" is a function that analyzes past inventory data and sales data, and uses AI to calculate the ideal inventory quantity and product placement method, and proposes this to the user.

[1008] Overall system configuration

[1009] This invention is a system that utilizes generative AI to streamline the creation of grant application documents and inventory management for physical stores. The system includes a server, a terminal, generative AI, and a database. The server manages user registration, information upload, draft generation by generative AI, and the storage and submission of the final version.

[1010] User Registration and Login

[1011] A user accesses the system using a device (e.g., smart glasses) and creates a new account. The user enters their name, email address, and password, which are sent to the server. The server stores the information in a database and generates an authentication token, which is sent back to the device. The user then logs into the system using the authentication information.

[1012] Initial Setup and Upload Information

[1013] Users upload past proposal documents, inventory data, and sales data from their devices to the server. The server analyzes the uploaded data, extracts key information, and stores it in a database. This makes it easier for users to receive data-based proposals in the future.

[1014] Preparation of grant application documents

[1015] The user inputs new project information. The device sends this information to the server, which then uses a generative AI to generate a draft application document. The generative AI generates optimal answers to questions based on past proposal documents and the new project information, resulting in a high-quality draft.

[1016] Review and revise the draft

[1017] The user reviews the draft sent from the server on their device and makes any necessary corrections. The corrected information is sent back to the server, and the final version is generated. The final version is saved in the database and can be viewed by the user.

[1018] Submitting a grant application

[1019] Once the user has reviewed the final version of the grant application, it is automatically sent to the submission destination via the server. Once the application has been submitted successfully, the user is notified.

[1020] Optimizing inventory management

[1021] A user wearing smart glasses scans inventory in a physical store and sends the information to a server. The server analyzes past inventory and sales data and uses a generative AI to suggest optimal inventory levels and product placement. Specifically, a barcode reader is used to obtain product information, and the generative AI makes suggestions for inventory replenishment and placement based on past data.

[1022] Hardware and software used

[1023] Hardware: Smart glasses (e.g. Google Glass, Microsoft HoloLens), built-in barcode reader (or external device Bluetooth connection)

[1024] Software: Server-side uses Python, Django, Flask, etc. Databases use PostgreSQL and MySQL. Generative AI models use GPT-4, TensorFlow, and PyTorch. Communication uses WebSocket and REST API.

[1025] Examples of concrete examples and prompts

[1026] When a user scans inventory information using smart glasses and a barcode reader, the following prompt is sent to the generation AI:

[1027] Example prompt sentence:

[1028] Product name: Product A

[1029] Current stock: 10

[1030] Average sales: 30 / week

[1031] Please suggest a refill.

[1032] Based on this prompt, the AI ​​generator will suggest an appropriate replenishment amount, such as, "The recommended replenishment amount for your inventory is 20 units. This will ensure you have enough stock for next week's sales."

[1033] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1034] Step 1:

[1035] The user puts on the smart glasses and starts the system. They scan the QR code to register as a user. The user enters their name, email address, and password using voice input or the touchpad, and this information is sent from the device to the server. The server stores the received information in a database, generates an authentication token, and sends it back to the device. The input data is user information, and the output is an authentication token.

[1036] Step 2:

[1037] As an initial setting, the user uploads past proposal documents, inventory data, and sales data to the server via their terminal. The server analyzes the uploaded data, extracts key information, and stores it in a database. Specifically, it extracts project names, summaries, budgets, schedules, etc. from proposal documents. The input data are proposal documents and past data, and the output is the extracted key information stored in a database.

[1038] Step 3:

[1039] The user inputs new project information. The user inputs the project name, objectives, budget, etc. through the smart glasses, and this information is sent from the device to the server. The server combines this input information with past information, stores it in a database, and prepares the data to be input to the generative AI. The input data is the new project information, and the output is the input data for the generative AI.

[1040] Step 4:

[1041] The server uses generative AI to generate a draft grant application. The generative AI model (e.g., GPT-4) generates optimal answers to questions based on data from past proposal documents and new project information, and automatically generates each section of the grant application. Specifically, in response to the question, "What is the social significance of this project?", the server generates the answer, "This project aims to raise environmental awareness in the local community and contribute to achieving the Sustainable Development Goals." The input data is the new project information and past data, and the output is the draft grant application.

[1042] Step 5:

[1043] The generated draft is sent to the terminal, where the user reviews the draft through the smart glasses and makes any necessary corrections. The user inputs the corrections using voice input or the touchpad, and the terminal sends the corrections to the server. The server receives the corrections, generates the final grant application document, and stores it in a database. The input data are the user's corrections, and the output is the final grant application document.

[1044] Step 6:

[1045] The user reviews the final grant application and automatically sends it to the recipient through the server. The server verifies the recipient's information and sends the application in the appropriate format. Once the submission is complete, the server notifies the user. The input data is the final grant application and the output is a notification that the submission is complete.

[1046] Step 7:

[1047] A user uses smart glasses to browse the shelves in a store and scans inventory information using a barcode reader. The device then sends the acquired product information to a server. The input data is barcode information, and the output is updated inventory information.

[1048] Step 8:

[1049] The server combines and analyzes the acquired inventory information with past inventory and sales data, and uses generation AI to generate optimal inventory quantity and product placement proposals. For example, the generation AI might suggest, "Product A is running low on stock. Replenishment is required." The input data is inventory scan information and past data, and the output is a stock replenishment proposal.

[1050] Step 9:

[1051] The generated inventory management proposal is sent to the terminal, and the user can check the proposal through the smart glasses. If the user accepts the proposal, inventory replenishment and product allocation are carried out based on it. The input data is the inventory proposal content, and the output is the execution of inventory management.

[1052] 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.

[1053] Specific embodiments for carrying out the present invention are described below.

[1054] System Overview

[1055] The system of the present invention automates the creation of grant application documents using generative AI and incorporates an emotion engine that recognizes user emotions. The system extracts necessary information from past proposal documents and generates high-quality draft grant application documents based on new project information entered by the user. The system also uses the emotion engine to analyze user emotions and incorporates that feedback into the generated draft. The system includes a server, a terminal, generative AI, and the emotion engine.

[1056] Program processing overview

[1057] User Registration and Login

[1058] A user accesses the system's website and creates a new account. The device prompts the user to enter the required information (such as name, email address, and password) and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information to access the system.

[1059] Initial Setup and Upload Information

[1060] Users upload past grant proposals and related materials. The device sends these materials to the server, which analyzes them, extracts key information (project name, summary, budget, etc.), and stores it in a database.

[1061] Emotion Engine Operation

[1062] The emotion engine recognizes the user's emotions by detecting micro-expressions, tone of voice, and typing speed. The device sends this emotion data to the server, which then feeds it back to the AI ​​and takes it into account when generating the draft.

[1063] Preparation of grant application documents

[1064] A user inputs project information to create a new grant proposal. The device sends this input information to a server. The server uses generative AI to generate a draft grant proposal based on the input information and past materials. The generative AI takes emotional data into account and adjusts the tone and style of the text to match the user's intentions and emotions. The server returns the generated draft to the user.

[1065] Review and revise the draft

[1066] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final proposal document and stores it in a database.

[1067] Submitting a grant application

[1068] The user reviews the final proposal and submits the grant. The server verifies the information of the grant recipients and provides a mechanism to automatically submit the proposal (if necessary).

[1069] Specific examples

[1070] Project Manager at a Nonprofit Organization

[1071] A project manager at a nonprofit organization applying for a grant for a new environmental project follows these steps:

[1072] 1. User registration and login: The project manager creates a new account and logs in.

[1073] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[1074] 3. Emotion engine operation: While the project manager is entering information about a new project, the emotion engine recognizes the user's emotions by detecting their micro-expressions and typing speed. For example, if the user is nervous, it will detect this and provide feedback to the generation AI.

[1075] 4. Grant proposal creation: Enter details about a new project and let the generative AI generate a draft proposal. The generative AI will adjust the tone and style of the text appropriately based on emotional data. For example, if a user is passionate about the question, "What is the social significance of this project?", the AI ​​will generate an answer such as, "This project aims to dramatically increase environmental awareness in the local community and promote significant change."

[1076] 5. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[1077] 6. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[1078] This system allows users to create high-quality grant proposal documents with minimal effort and efficiently obtain grants. In addition, by combining it with an emotion engine, proposal documents can be generated that are more suited to the user.

[1079] The processing flow will be explained below.

[1080] Step 1:

[1081] The user accesses the system's website, enters the required information (such as name, email address, and password) on the new registration screen, and clicks the "Register" button.

[1082] Step 2:

[1083] The device sends the entered information to the server, which receives it, stores it in a database, generates an authentication token, and returns it to the device.

[1084] Step 3:

[1085] The user uses the authentication token returned from the server to log in. The terminal sends the login information to the server and receives the authentication result.

[1086] Step 4:

[1087] If the user authentication is successful, the server returns information for displaying a dashboard screen to the terminal, which then displays the dashboard screen to the user.

[1088] Step 5:

[1089] Users can click the "Upload Documents" button on the dashboard, select and upload past grant proposal documents.

[1090] Step 6:

[1091] The device sends the selected file to the server, which parses it, extracts key information such as the project name, summary, and budget information, and stores it in a database.

[1092] Step 7:

[1093] Users click the "Create a new proposal" button from the dashboard and enter information about the new project (such as name, purpose, budget, and schedule).

[1094] Step 8:

[1095] The device sends the input information to a server, which then compares it with data from past proposal documents and queries the AI ​​to generate a draft of the grant application.

[1096] Step 9:

[1097] The emotion engine detects micro-expressions and typing speed as the user enters new project information, and the device transmits the emotion data to the server.

[1098] Step 10:

[1099] The server feeds emotional data back to the AI ​​generator, which takes this into account when generating drafts, and the AI ​​then generates text in a tone and style that matches the user's emotions.

[1100] Step 11:

[1101] The generative AI generates answers to the requested questions and fills in each section of the grant application. The server stores the generated draft in a database and returns the draft proposal to the device.

[1102] Step 12:

[1103] The user checks the displayed draft, enters any corrections, and submits it. The terminal then transmits the corrections to the server.

[1104] Step 13:

[1105] The server reflects the received corrections in the draft and stores the updated draft in the database again. The terminal displays the updated draft again to the user.

[1106] Step 14:

[1107] The user checks the final version of the proposal document and clicks the "Submit" button. The terminal displays information on the recipient and any additional documents required.

[1108] Step 15:

[1109] The user uploads any additional documents required and clicks the "Final Submission" button. The terminal sends all information to the server.

[1110] Step 16:

[1111] The server packages the proposal documents and additional documents based on the format of the submission destination and executes the submission process. If the submission is successful, the server sends a confirmation notice to the user via an in-system notification or email.

[1112] Example 2

[1113] 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."

[1114] Creating grant proposals is a complex and time-consuming task, especially when extracting information from past proposals and combining it with new project information. Furthermore, if the tone and style of a grant proposal doesn't reflect the user's emotions, its quality and persuasiveness will be affected. There is a need for a system that automates this process and generates high-quality proposals that are both efficient and emotionally sensitive.

[1115] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft of grant application documents based on the new project information and the extracted key information, means for detecting a user's emotions and analyzing the data, means for feeding back the detected emotion data to the generation AI and reflecting it in the tone and style of the generated draft, means for displaying the generated draft to the user and allowing the user to input corrections, and means for saving the final grant application documents and transmitting them to the submission destination. This improves the efficiency of grant application document creation and enables the generation of high-quality application documents that are adapted to the user's emotions.

[1116] 1. "Generative AI" is a system that uses artificial intelligence to generate natural language and create new documents and texts.

[1117] 2. "Grant application" means an official document submitted to obtain a grant for a specific project or activity.

[1118] 3. "Server" means a computer system that provides services to other devices on a network.

[1119] 4. "Terminal" means an electronic device with a user interface for inputting, displaying, and transmitting information.

[1120] 5. "User" means a person or individual who operates the system.

[1121] 6. "Emotion Engine" means software or a system for detecting and analyzing a user's emotions.

[1122] 7. "Draft" means an early manuscript or draft of a grant application.

[1123] 8. "Project Information" means detailed data and details relating to a particular project.

[1124] 9. "Key Information" means the essential data required to prepare a grant application.

[1125] 10. "Feedback" is the process of re-inputting results or reactions into a system or process.

[1126] System Overview

[1127] This invention is a system that automatically generates grant application documents using generative AI. This system extracts key information from past proposal documents and combines it with new project information to generate high-quality grant application documents. In addition, by incorporating an emotion engine that recognizes the user's emotions, the system can reflect the user's emotions in the generated draft. This system includes a server, a terminal, generative AI, and an emotion engine.

[1128] User Registration and Login

[1129] A user accesses the system's website and creates a new account. The device receives necessary information from the user, such as name, email address, and password, and sends it to the server. The server stores the received information in a database, generates an authentication token, and sends it to the device. The user then logs in to the system using this authentication information.

[1130] Initial Setup and Upload Information

[1131] Users upload previously created grant proposals and related materials. The device then sends these materials to the server, which analyzes the received materials, extracts key information such as the project name, summary, and budget, and stores it in a database.

[1132] Emotion Engine Operation

[1133] When a user inputs new information, the emotion engine recognizes the user's emotions by detecting micro-expressions, tone of voice, and input speed through the device. The device then sends this emotion data to the server, which then feeds it back to the generation AI, which takes this into account when generating the draft.

[1134] Preparation of grant application documents

[1135] A user inputs project information to create a new grant proposal. The device sends this information to a server. The server uses a generative AI to generate a draft of the grant proposal based on the input information and previous documents. The generative AI takes emotional data into account and adjusts the tone and style of the text to match the user's intentions and emotions. The server returns the generated draft to the user.

[1136] Review and revise the draft

[1137] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final grant application document and stores it in a database.

[1138] Submitting a grant application

[1139] The user checks the final grant application documents and submits them to the grant recipient. The server checks the recipient information and automatically sends the proposal documents to the recipient. The server then sends the user a confirmation that submission is complete.

[1140] Specific examples

[1141] Project Manager at a Nonprofit Organization

[1142] A project manager at a nonprofit organization might apply for a grant for a new environmental protection project by following these steps:

[1143] 1. User registration and login: The project manager creates a new account and logs into the system.

[1144] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[1145] 3. Emotion engine operation: While the project manager is entering new project information, the emotion engine recognizes emotions by detecting micro-expressions, typing speed, etc. For example, if the user is nervous, it will detect this and provide feedback to the generation AI.

[1146] 4. Grant proposal creation: Enter details about a new project and let the generative AI generate a draft proposal. The generative AI will adjust the tone and style of the writing based on emotional data. For example, if a user writes passionately in response to the question, "What is the social significance of this project?", the AI ​​will generate an answer such as, "This project aims to dramatically increase environmental awareness in the local community and promote significant change."

[1147] 5. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[1148] 6. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[1149] Through these steps, users can create high-quality grant proposals with minimal effort and efficiently obtain grants. In addition, by combining the emotion engine, proposals can be generated that are more tailored to the user.

[1150] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1151] Step 1:

[1152] A user visits the system's website and creates a new account.

[1153] Input: Name, email address, password, etc.

[1154] Output: Authentication token

[1155] Specific operation: The terminal receives information entered by the user and sends it to the server. The server stores the received information in a database, generates an authentication token, and sends it to the terminal. The user uses this token to log in to the system.

[1156] Step 2:

[1157] Users upload past grant proposals and related materials.

[1158] Input: Past proposal documents, related materials

[1159] Output: Main information (project name, summary, budget, etc.)

[1160] Specific operation: The terminal sends the documents uploaded by the user to the server. The server analyzes the received documents, extracts key information such as the project name, summary, and budget, and stores it in a database.

[1161] Step 3:

[1162] The emotion engine works when a user enters new project information.

[1163] Input: New project information, user emotional data (micro-expressions, tone of voice, typing speed, etc.)

[1164] Output: Parsed emotion data

[1165] Specific operation: The device detects the user's facial expressions, tone of voice, input speed, etc. while inputting, and collects them as emotional data. The device then sends the collected emotional data to the server, which then feeds it back to the generation AI.

[1166] Step 4:

[1167] The server uses the generative AI to generate a draft of the grant application.

[1168] Input: New project information, information extracted from historical documents, analyzed sentiment data

[1169] Output: Draft grant application

[1170] Specific operation: The server uses generative AI to generate a draft of a grant application document based on new project information, information extracted from past documents, and emotional data. The generative AI adjusts the tone and style of the text, taking the emotional data into account. The server then sends the generated draft to the device and displays it to the user.

[1171] Step 5:

[1172] The user reviews the generated draft and enters any corrections.

[1173] Input: Correction

[1174] Output: Final grant application

[1175] Specific operation: The terminal sends the corrections entered by the user to the server, which then generates a final version of the grant application document that reflects the corrections and stores it in the database.

[1176] Step 6:

[1177] The server sends the final grant application to the submission destination.

[1178] Input: Final grant application documents and submission information

[1179] Output: Notification of submission completion

[1180] Specific operation: The server automatically sends the proposal documents to the recipient based on the final grant application documents and the recipient information. Once the submission is complete, a confirmation notification of the submission is sent to the user's device.

[1181] (Application example 2)

[1182] 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."

[1183] In food delivery services, the challenge is to recognize in real time the dissatisfaction and anxiety that many users feel regarding their orders and delivery status, and to respond quickly and accurately. Conventional systems lack a means of accurately grasping users' emotions, making it difficult to provide appropriate customer support. Furthermore, the inability to accurately analyze and reflect user feedback makes it difficult to improve customer satisfaction.

[1184] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1185] In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft of a grant application document based on the new project information and the extracted key information, means for displaying the generated draft to a user and for inputting corrections, means for saving the final grant application document and transmitting it to the submission destination, means for analyzing a user's facial expression, tone of voice, and input speed to extract emotional data, and means for feeding the extracted emotional data back to the generation AI and adjusting the tone and style of the document. This enables the user's emotions to be analyzed in real time and the generation AI to provide an appropriate response.

[1186] "Generative AI" is artificial intelligence that automatically generates optimal answers and suggestions from a variety of data.

[1187] A "grant proposal" is a formal document requesting funding for a specific project.

[1188] "Emotion data" is information that indicates the emotional state of the user, analyzed from facial expressions, tone of voice, input speed, and the like.

[1189] "Key information" refers to important data extracted from the proposal documents, such as the project name, summary, budget, and schedule.

[1190] A "draft" is an early stage grant application document created by the generative AI, which will become the final version after being revised by the user.

[1191] "Means for uploading" is a function that allows users to send past proposal documents to the system.

[1192] "Means of analyzing and extracting key information" is a function that automatically identifies important data from uploaded proposal documents.

[1193] The "means for inputting new project information" is a function for inputting detailed information about a project that the user is currently working on.

[1194] "Means by which the generative AI generates a draft grant application document" refers to the function by which the generative AI creates the initial stage of the document based on user input and extracted key information.

[1195] The "means for displaying to the user and inputting corrections" is a function for showing the generated draft to the user and reflecting necessary changes.

[1196] The "means for saving the final version of the grant application document" is a function for storing the final version of the document that reflects the user's modifications.

[1197] The "means for sending to the submission destination" is a function for sending the generated final version of the grant application documents to the specified destination.

[1198] The "means for analyzing the user's facial expression" is a function that uses a camera to identify the user's facial expression and generate emotion data.

[1199] The "means for analyzing voice tone" is a function that uses a microphone to analyze the tone of the user's voice and generate emotion data.

[1200] The "means for analyzing input speed" is a function that measures the input speed of the user and generates emotion data from it.

[1201] "Means of providing feedback to the generation AI" is a function that instructs the generation AI to make adjustments based on the extracted emotional data.

[1202] "Means to adjust the tone and style of a document" refers to the ability of generative AI to optimize the expression of a document based on emotional data.

[1203] The present invention, when applied to a smartphone application, is described in detail below. This invention is intended to analyze user emotions in real time in a food delivery service and provide appropriate responses using generative AI.

[1204] System Configuration

[1205] The system includes the following main components:

[1206] 1. User device: A smartphone (compatible with iOS or Android) uses the camera and microphone to collect user emotion data.

[1207] 2. Server: Hosts data processing and generative AI.

[1208] 3. Generative AI: Generates documents and responses based on user emotional data and input information.

[1209] 4. Emotion engine: Generates emotion data by analyzing facial expressions, voice tone, and typing speed.

[1210] Hardware and software used

[1211] Face recognition library: OpenCV (open source)

[1212] Speech recognition: Google Cloud Speech-to-Text

[1213] Generative AI: OpenAI GPT-3 (using API)

[1214] Data Processing Overview

[1215] 1. User Registration and Login

[1216] A user downloads a smartphone application and creates a new account. They enter the required information (name, email address, password, etc.) and send it to the server. The server stores this information in a database, generates authentication information, and returns it to the user.

[1217] 2. Collecting Emotional Data

[1218] When a user uses the application, the smartphone's camera and microphone are used to collect the user's facial expressions and voice. The emotion engine analyzes this data and generates emotion data.

[1219] 3. Response generation using generative AI

[1220] The server then feeds the emotion data obtained from the emotion engine back to the AI ​​generator to generate an appropriate response. For example, if the user is dissatisfied, the AI ​​generator will provide an apology message or a discount coupon for the next order.

[1221] 4. View and edit the draft

[1222] The generated drafts and responses are displayed to the user, who can make corrections as needed, and the final corrections are sent to the server and saved as the final version.

[1223] Specific examples

[1224] For example, if a user is complaining about a late delivery, use a prompt like this:

[1225] "Generate an appropriate apology message if a customer is upset about a delayed delivery."

[1226] Based on this prompt, the Generative AI would generate a response like this:

[1227] "We sincerely apologize for the delay in delivery. As a token of our apology, we will provide you with a discount coupon for your next purchase."

[1228] This makes it possible to analyze user emotions in real time and provide appropriate responses. The system of the present invention improves the user experience and significantly improves the efficiency of customer support.

[1229] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1230] Step 1:

[1231] User Registration and Login

[1232] The user launches the smartphone application and creates a new account. The required information (name, email address, password, etc.) is entered and sent from the device to the server. The server stores this information in a database, generates authentication information, and returns it to the user. When logging in, the authentication information entered by the user is sent to the server for authentication.

[1233] Input: User information (name, email address, password)

[1234] Output: Credentials

[1235] Step 2:

[1236] Collecting Emotional Data

[1237] When a user uses the application, facial expressions and voice are collected using the smartphone's camera and microphone. The device then transmits this data in real time to the emotion engine, which analyzes the collected data and identifies the user's emotions.

[1238] Input: User's facial expression video and voice data

[1239] Output: Emotion data (e.g., joy, anger, sadness)

[1240] Step 3:

[1241] Response generation by generative AI

[1242] The device sends emotional data and user input information to the server. The server then feeds the emotional data back to the AI ​​generator, which then generates an appropriate response or suggestion. For example, if the user is dissatisfied, the AI ​​generator generates an apology message.

[1243] Input: Emotion data, user input information

[1244] Output: Generated response (e.g., apology message)

[1245] Step 4:

[1246] View and modify drafts

[1247] The terminal displays the generated responses and suggestions to the user, who can then make any necessary corrections and send them back to the server, which then generates a final version incorporating the final corrections and stores it in a database.

[1248] Input: Generated response, user modifications

[1249] Output: Final version after corrections are reflected

[1250] Step 5:

[1251] Final communication and storage

[1252] The server saves the final document and, if necessary, sends it to the submission destination, and notifies the user that the submission is complete.

[1253] Input: Final document

[1254] Output: Submission confirmation notification

[1255] These steps enable real-time analysis of user sentiment and use of generative AI to provide appropriate responses, thereby improving the quality of customer support for food delivery services.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] [Fourth embodiment]

[1260] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1261] 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.

[1262] 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).

[1263] 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.

[1264] 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.

[1265] 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).

[1266] 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.

[1267] 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.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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."

[1273] Specific embodiments for carrying out the present invention are described below.

[1274] System Overview

[1275] The system of the present invention automates the creation of grant application documents using generative AI. The system extracts necessary information from past proposal documents and generates high-quality draft grant application documents based on new project information entered by the user. The system includes a server, a terminal, and generative AI.

[1276] Program processing overview

[1277] User Registration and Login

[1278] A user accesses the system's website and creates a new account. The device prompts the user to enter the required information (such as name, email address, and password) and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information to access the system.

[1279] Initial Setup and Upload Information

[1280] Users upload past grant proposals and related materials. The device sends these materials to the server, which analyzes them, extracts key information (project name, summary, budget, etc.), and stores it in a database.

[1281] Preparation of grant application documents

[1282] A user inputs project information to create a new grant proposal. The device sends this input information to a server. The server uses generative AI to generate a draft grant proposal based on the input information and past materials. The generative AI generates answers to questions and automatically generates high-quality sections of the proposal. The server returns the generated draft to the user.

[1283] Review and revise the draft

[1284] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final proposal document and stores it in a database.

[1285] Submitting a grant application

[1286] The user reviews the final proposal and submits the grant. The server verifies the information of the grant recipients and provides a mechanism to automatically submit the proposal (if necessary).

[1287] Specific examples

[1288] Project Manager at a Nonprofit Organization

[1289] A project manager at a nonprofit organization applying for a grant for a new environmental project follows these steps:

[1290] 1. User registration and login: The project manager creates a new account and logs in.

[1291] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[1292] 3. Creating grant proposals: Enter details about a new project (such as the name, purpose, and budget) and have the Generative AI generate a draft proposal. For example, in response to a question like, "What is the social significance of this project?", the Generative AI might respond with, "This project aims to raise environmental awareness in the local community and contribute to achieving the Sustainable Development Goals."

[1293] 4. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[1294] 5. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[1295] Through this system, project managers can create high-quality grant proposal documents with minimal effort and efficiently obtain grant funding.

[1296] The processing flow will be explained below.

[1297] Step 1:

[1298] The user accesses the system's website, enters the required information (such as name, email address, and password) on the new registration screen, and clicks the "Register" button.

[1299] Step 2:

[1300] The device sends the entered information to the server, which receives it, stores it in a database, generates an authentication token, and returns it to the device.

[1301] Step 3:

[1302] The user uses the authentication token returned from the server to log in. The terminal sends the login information to the server and receives the authentication result.

[1303] Step 4:

[1304] If the user authentication is successful, the server returns information for displaying a dashboard screen to the terminal, which then displays the dashboard screen to the user.

[1305] Step 5:

[1306] Users can click the "Upload Documents" button on the dashboard, select and upload past grant proposal documents.

[1307] Step 6:

[1308] The device sends the selected file to the server, which parses it, extracts key information such as the project name, summary, and budget information, and stores it in a database.

[1309] Step 7:

[1310] Users click the "Create a new proposal" button from the dashboard and enter information about the new project (such as name, purpose, budget, and schedule).

[1311] Step 8:

[1312] The device sends the input information to a server, which then compares it with data from past proposal documents and queries the AI ​​to generate a draft of the grant application.

[1313] Step 9:

[1314] The generative AI generates optimal answers to requested questions (e.g., "What is the social significance of this project?") and fills in each section of the proposal.

[1315] Step 10:

[1316] The server stores the generated draft in a database and returns the proposal draft to the terminal, which displays the draft to the user.

[1317] Step 11:

[1318] The user checks the displayed draft, enters any corrections, and submits it. The terminal then transmits the corrections to the server.

[1319] Step 12:

[1320] The server reflects the received corrections in the draft and stores the updated draft in the database again. The terminal displays the updated draft again to the user.

[1321] Step 13:

[1322] The user checks the final version of the proposal document and clicks the "Submit" button. The terminal displays information on the recipient and any additional documents required.

[1323] Step 14:

[1324] The user uploads any additional documents required and clicks the "Final Submission" button. The terminal sends all information to the server.

[1325] Step 15:

[1326] The server packages the proposal documents and additional documents based on the format of the submission destination and executes the submission process. If the submission is successful, the server sends a confirmation notice to the user via an in-system notification or email.

[1327] Example 1

[1328] 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."

[1329] The traditional grant application document preparation process is time-consuming, labor-intensive, and largely manual, making it inefficient. Furthermore, creating high-quality applications requires experience and skill, which presents a barrier to many applicants. Furthermore, analyzing proposal documents and extracting key information is time-consuming, creating a need for rapid information organization.

[1330] 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.

[1331] In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft grant application document based on the new project information and the extracted key information, means for displaying the generated draft to a user and for inputting corrections, means for saving the final grant application document and transmitting it to a submission destination, means for generating an authentication token and performing user authentication, means for analyzing the uploaded materials using an OCR tool or natural language processing technology and identifying key information, and means for the generation AI to generate prompt sentences and generate high-quality document sections based on the prompt sentences, thereby enabling users to efficiently create high-quality grant application documents with little effort and submit them quickly.

[1332] "Generative AI" is a technology that uses artificial intelligence to generate text and data.

[1333] "Grant application" means an official document containing the information required for the purpose of receiving a grant.

[1334] A "proposal document" is a document that was previously created for the purpose of applying for a grant or other purpose, and contains project details and plans.

[1335] "Upload" refers to the operation of sending data from a terminal to a server.

[1336] "Analysis" is the process of examining data in detail and extracting useful information from it.

[1337] "Key Information" refers to the most important and required information items in a grant application.

[1338] "New project information" is detailed information about a new project that the user is about to start.

[1339] A "draft" is a rough draft or rough draft created before the final version.

[1340] "User" refers to any individual or organization that uses this system.

[1341] An "authentication token" is a unique credential used to authenticate a user.

[1342] An "OCR tool" is a tool that uses optical character recognition technology to read text in an image and convert it into digital data.

[1343] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language.

[1344] A "prompt sentence" is an input sentence that causes a generation AI to generate text for a specific purpose.

[1345] "Submission Destination" refers to the destination or institution to which grant applications are submitted.

[1346] The following describes an embodiment of the present invention.

[1347] System Configuration

[1348] The system of the present invention utilizes generative AI to automate the creation of grant application documents. The system includes a server, a terminal, and generative AI. The server can be a cloud server, and the terminal can be a PC or smartphone. The generative AI model uses the latest AI technology based on natural language processing (NLP).

[1349] User Registration and Login

[1350] A user accesses the system's website and creates a new account. The device prompts the user for necessary information, such as name, email address, and password, and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information.

[1351] Initial Setup and Upload Information

[1352] Users upload past grant proposals and related materials. The terminal provides a drag-and-drop upload interface. Users select and upload files. The terminal then sends the uploaded files to the server, which analyzes the materials using OCR tools and natural language processing technology, extracting key information (project name, summary, budget, etc.) and storing it in a database.

[1353] Preparation of grant application documents

[1354] The user enters new project information. The device displays a form for entering details such as the project name, purpose, and budget. After the user enters and submits the information, the device sends this data to the server. The server then uses the received data to generate a prompt using the generation AI.

[1355] As a specific example, it generates a prompt such as, "What is the social significance of this project?" Based on this prompt, the generation AI generates an answer such as, "This project aims to raise environmental awareness in the local community and contributes to achieving the Sustainable Development Goals." The generated draft is sent back to the user's device via the server.

[1356] Review and revise the draft

[1357] The user can check the generated draft and enter any necessary corrections. The terminal provides a display interface for the draft and allows the user to edit any section. When the user enters the corrections and clicks Done, the terminal sends the correction information to the server. The server generates a final version reflecting the received corrections and stores it in the database.

[1358] Submitting a grant application

[1359] The user reviews the final version of the grant application documents and clicks the submit button. The terminal displays the final version and sends a submission instruction to the server. The server verifies the information of the designated recipients and electronically transmits the proposal documents. After the application is completed, the server generates a confirmation notice and sends it to the user's terminal.

[1360] Specific examples

[1361] When a project manager at a nonprofit organization applies for a grant for a new environmental protection project, they follow these steps: First, they register and log in, then upload a previous successful grant proposal. After entering the details of the new project, they let the generative AI generate a draft proposal. They review the generated draft and make any necessary revisions to finalize it. Finally, they submit it to the grant recipient and receive a confirmation notification.

[1362] This allows users to efficiently create high-quality grant application documents with minimal effort and submit them quickly.

[1363] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1364] Step 1: User Registration and Login

[1365] input

[1366] The user enters their name, email address, and password.

[1367] Processing and Data Manipulation

[1368] The device sends the entered information to the server, which stores the information in a database and generates an authentication token.

[1369] output

[1370] The server returns an authentication token to the terminal, and the user logs in using the authentication token.

[1371] Specific actions

[1372] When a user visits a website and creates a new account, the device displays a form for them to fill out. After completing the form, the device sends the information to the server, which stores the information and generates an authentication token that is returned to the device. The user then logs in using this token.

[1373] Step 2: Initial setup and upload information

[1374] input

[1375] Users upload past grant proposals.

[1376] Processing and Data Manipulation

[1377] The device sends the uploaded documents to a server, which analyzes them using OCR tools and natural language processing technology to extract key information.

[1378] output

[1379] The server stores the extracted information in a database.

[1380] Specific actions

[1381] Users upload documents using a drag-and-drop method, and the device sends the files to the server, which then uses OCR tools and natural language processing technology to extract information such as project name, summary, and budget from the documents and stores it in a database.

[1382] Step 3: Compile your grant application

[1383] input

[1384] The user enters the new project information.

[1385] Processing and Data Manipulation

[1386] The device sends the input data to the server, which generates a prompt based on the received data and passes it to the generation AI, which then generates a draft based on the prompt.

[1387] output

[1388] The server returns the generated draft to the terminal.

[1389] Specific actions

[1390] When a user inputs and submits new project information, the device sends this data to the server, which generates a prompt (e.g., "What is the social significance of this project?") and passes it to the generation AI. The generation AI creates a draft based on this prompt, and the server returns the draft to the device.

[1391] Step 4: Review and revise the draft

[1392] input

[1393] The user checks the generated draft and enters any corrections.

[1394] Processing and Data Manipulation

[1395] The device sends the modifications to the server, which then generates the final version incorporating the modifications.

[1396] output

[1397] The server generates the final version and stores it in a database.

[1398] Specific actions

[1399] The user reviews the draft and corrects any sections that need to be corrected. The device sends the corrections to the server, which then reflects them and generates the final version, which is then stored in the database.

[1400] Step 5: Submit your grant application

[1401] input

[1402] The user checks the final version of the proposal document and submits it.

[1403] Processing and Data Manipulation

[1404] The terminal sends the final version as a submission instruction to the server, which then checks the information of the designated application destination and electronically transmits the proposal documents.

[1405] output

[1406] The server generates an acknowledgement and sends it to the user's terminal.

[1407] Specific actions

[1408] The user checks the final version and clicks the submit button. The terminal sends a submission instruction to the server, which then electronically transmits the proposal documents to the submission destination. After the application is completed, the server generates a confirmation notice and sends it to the user's terminal.

[1409] (Application example 1)

[1410] 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."

[1411] In the past, creating grant application documents and managing inventory in brick-and-mortar stores was often done manually, hindering efficiency and requiring time and effort. Furthermore, determining the appropriate inventory levels and product placement during inventory management relied on experience and intuition, making it prone to errors and difficult to achieve optimal results. It was necessary to solve these problems and achieve more efficient and accurate creation of grant application documents and inventory management.

[1412] 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.

[1413] In this invention, the server includes a means for uploading past proposal documents, a means for analyzing the uploaded proposal documents to extract key information, and a means for inputting new project information, which enables the generation AI to generate draft grant application documents and optimal inventory quantities and product placement proposals for inventory management based on past data.

[1414] The "means for uploading past proposal documents" is an interface for a user to import electronic data of proposal documents previously created into the system.

[1415] "Means for analyzing uploaded proposal documents and extracting key information" refers to an algorithm that automatically analyzes imported proposal documents and populates a database with key information (e.g., project name, summary, budget, schedule, etc.).

[1416] The "means for inputting new project information" is a UI (user interface) that allows a user to input information about a new project that is being planned into the system.

[1417] "Means for generative AI to generate draft grant application documents" refers to a function in which AI automatically creates an initial draft of a grant application document based on input new project information and information extracted from past proposal documents.

[1418] "Means for displaying the generated draft to the user and allowing the user to input corrections" refers to a function that displays the draft created by the generation AI in a user interface and allows the user to make corrections to it.

[1419] The "means for saving the final version of the grant application documents and sending them to the recipient" is a function that saves the final version of the grant application documents that the user has revised in a database and sends them electronically to the recipient as needed.

[1420] "A means of using generative AI to analyze past inventory data and sales data and propose optimal inventory quantities and product placement" is a function that analyzes past inventory data and sales data, and uses AI to calculate the ideal inventory quantity and product placement method, and proposes this to the user.

[1421] Overall system configuration

[1422] This invention is a system that utilizes generative AI to streamline the creation of grant application documents and inventory management for physical stores. The system includes a server, a terminal, generative AI, and a database. The server manages user registration, information upload, draft generation by generative AI, and the storage and submission of the final version.

[1423] User Registration and Login

[1424] A user accesses the system using a device (e.g., smart glasses) and creates a new account. The user enters their name, email address, and password, which are sent to the server. The server stores the information in a database and generates an authentication token, which is sent back to the device. The user then logs into the system using the authentication information.

[1425] Initial Setup and Upload Information

[1426] Users upload past proposal documents, inventory data, and sales data from their devices to the server. The server analyzes the uploaded data, extracts key information, and stores it in a database. This makes it easier for users to receive data-based proposals in the future.

[1427] Preparation of grant application documents

[1428] The user inputs new project information. The device sends this information to the server, which then uses a generative AI to generate a draft application document. The generative AI generates optimal answers to questions based on past proposal documents and the new project information, resulting in a high-quality draft.

[1429] Review and revise the draft

[1430] The user reviews the draft sent from the server on their device and makes any necessary corrections. The corrected information is sent back to the server, and the final version is generated. The final version is saved in the database and can be viewed by the user.

[1431] Submitting a grant application

[1432] Once the user has reviewed the final version of the grant application, it is automatically sent to the submission destination via the server. Once the application has been submitted successfully, the user is notified.

[1433] Optimizing inventory management

[1434] A user wearing smart glasses scans inventory in a physical store and sends the information to a server. The server analyzes past inventory and sales data and uses a generative AI to suggest optimal inventory levels and product placement. Specifically, a barcode reader is used to obtain product information, and the generative AI makes suggestions for inventory replenishment and placement based on past data.

[1435] Hardware and software used

[1436] Hardware: Smart glasses (e.g. Google Glass, Microsoft HoloLens), built-in barcode reader (or external device Bluetooth connection)

[1437] Software: Server-side uses Python, Django, Flask, etc. Databases use PostgreSQL and MySQL. Generative AI models use GPT-4, TensorFlow, and PyTorch. Communication uses WebSocket and REST API.

[1438] Examples of concrete examples and prompts

[1439] When a user scans inventory information using smart glasses and a barcode reader, the following prompt is sent to the generation AI:

[1440] Example prompt sentence:

[1441] Product name: Product A

[1442] Current stock: 10

[1443] Average sales: 30 / week

[1444] Please suggest a refill.

[1445] Based on this prompt, the AI ​​generator will suggest an appropriate replenishment amount, such as, "The recommended replenishment amount for your inventory is 20 units. This will ensure you have enough stock for next week's sales."

[1446] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1447] Step 1:

[1448] The user puts on the smart glasses and starts the system. They scan the QR code to register as a user. The user enters their name, email address, and password using voice input or the touchpad, and this information is sent from the device to the server. The server stores the received information in a database, generates an authentication token, and sends it back to the device. The input data is user information, and the output is an authentication token.

[1449] Step 2:

[1450] As an initial setting, the user uploads past proposal documents, inventory data, and sales data to the server via their terminal. The server analyzes the uploaded data, extracts key information, and stores it in a database. Specifically, it extracts project names, summaries, budgets, schedules, etc. from proposal documents. The input data are proposal documents and past data, and the output is the extracted key information stored in a database.

[1451] Step 3:

[1452] The user inputs new project information. The user inputs the project name, objectives, budget, etc. through the smart glasses, and this information is sent from the device to the server. The server combines this input information with past information, stores it in a database, and prepares the data to be input to the generative AI. The input data is the new project information, and the output is the input data for the generative AI.

[1453] Step 4:

[1454] The server uses generative AI to generate a draft grant application. The generative AI model (e.g., GPT-4) generates optimal answers to questions based on data from past proposal documents and new project information, and automatically generates each section of the grant application. Specifically, in response to the question, "What is the social significance of this project?", the server generates the answer, "This project aims to raise environmental awareness in the local community and contribute to achieving the Sustainable Development Goals." The input data is the new project information and past data, and the output is the draft grant application.

[1455] Step 5:

[1456] The generated draft is sent to the terminal, where the user reviews the draft through the smart glasses and makes any necessary corrections. The user inputs the corrections using voice input or the touchpad, and the terminal sends the corrections to the server. The server receives the corrections, generates the final grant application document, and stores it in a database. The input data are the user's corrections, and the output is the final grant application document.

[1457] Step 6:

[1458] The user reviews the final grant application and automatically sends it to the recipient through the server. The server verifies the recipient's information and sends the application in the appropriate format. Once the submission is complete, the server notifies the user. The input data is the final grant application and the output is a notification that the submission is complete.

[1459] Step 7:

[1460] A user uses smart glasses to browse the shelves in a store and scans inventory information using a barcode reader. The device then sends the acquired product information to a server. The input data is barcode information, and the output is updated inventory information.

[1461] Step 8:

[1462] The server combines and analyzes the acquired inventory information with past inventory and sales data, and uses generation AI to generate optimal inventory quantity and product placement proposals. For example, the generation AI might suggest, "Product A is running low on stock. Replenishment is required." The input data is inventory scan information and past data, and the output is a stock replenishment proposal.

[1463] Step 9:

[1464] The generated inventory management proposal is sent to the terminal, and the user can check the proposal through the smart glasses. If the user accepts the proposal, inventory replenishment and product allocation are carried out based on it. The input data is the inventory proposal content, and the output is the execution of inventory management.

[1465] 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.

[1466] Specific embodiments for carrying out the present invention are described below.

[1467] System Overview

[1468] The system of the present invention automates the creation of grant application documents using generative AI and incorporates an emotion engine that recognizes user emotions. The system extracts necessary information from past proposal documents and generates high-quality draft grant application documents based on new project information entered by the user. The system also uses the emotion engine to analyze user emotions and incorporates that feedback into the generated draft. The system includes a server, a terminal, generative AI, and the emotion engine.

[1469] Program processing overview

[1470] User Registration and Login

[1471] A user accesses the system's website and creates a new account. The device prompts the user to enter the required information (such as name, email address, and password) and sends it to the server. The server stores the received information in a database, generates an authentication token, and returns it to the device. The user then logs in using the authentication information to access the system.

[1472] Initial Setup and Upload Information

[1473] Users upload past grant proposals and related materials. The device sends these materials to the server, which analyzes them, extracts key information (project name, summary, budget, etc.), and stores it in a database.

[1474] Emotion Engine Operation

[1475] The emotion engine recognizes the user's emotions by detecting micro-expressions, tone of voice, and typing speed. The device sends this emotion data to the server, which then feeds it back to the AI ​​and takes it into account when generating the draft.

[1476] Preparation of grant application documents

[1477] A user inputs project information to create a new grant proposal. The device sends this input information to a server. The server uses generative AI to generate a draft grant proposal based on the input information and past materials. The generative AI takes emotional data into account and adjusts the tone and style of the text to match the user's intentions and emotions. The server returns the generated draft to the user.

[1478] Review and revise the draft

[1479] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final proposal document and stores it in a database.

[1480] Submitting a grant application

[1481] The user reviews the final proposal and submits the grant. The server verifies the information of the grant recipients and provides a mechanism to automatically submit the proposal (if necessary).

[1482] Specific examples

[1483] Project Manager at a Nonprofit Organization

[1484] A project manager at a nonprofit organization applying for a grant for a new environmental project follows these steps:

[1485] 1. User registration and login: The project manager creates a new account and logs in.

[1486] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[1487] 3. Emotion engine operation: While the project manager is entering information about a new project, the emotion engine recognizes the user's emotions by detecting their micro-expressions and typing speed. For example, if the user is nervous, it will detect this and provide feedback to the generation AI.

[1488] 4. Grant proposal creation: Enter details about a new project and let the generative AI generate a draft proposal. The generative AI will adjust the tone and style of the text appropriately based on emotional data. For example, if a user is passionate about the question, "What is the social significance of this project?", the AI ​​will generate an answer such as, "This project aims to dramatically increase environmental awareness in the local community and promote significant change."

[1489] 5. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[1490] 6. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[1491] This system allows users to create high-quality grant proposal documents with minimal effort and efficiently obtain grants. In addition, by combining it with an emotion engine, proposal documents can be generated that are more suited to the user.

[1492] The processing flow will be explained below.

[1493] Step 1:

[1494] The user accesses the system's website, enters the required information (such as name, email address, and password) on the new registration screen, and clicks the "Register" button.

[1495] Step 2:

[1496] The device sends the entered information to the server, which receives it, stores it in a database, generates an authentication token, and returns it to the device.

[1497] Step 3:

[1498] The user uses the authentication token returned from the server to log in. The terminal sends the login information to the server and receives the authentication result.

[1499] Step 4:

[1500] If the user authentication is successful, the server returns information for displaying a dashboard screen to the terminal, which then displays the dashboard screen to the user.

[1501] Step 5:

[1502] Users can click the "Upload Documents" button on the dashboard, select and upload past grant proposal documents.

[1503] Step 6:

[1504] The device sends the selected file to the server, which parses it, extracts key information such as the project name, summary, and budget information, and stores it in a database.

[1505] Step 7:

[1506] Users click the "Create a new proposal" button from the dashboard and enter information about the new project (such as name, purpose, budget, and schedule).

[1507] Step 8:

[1508] The device sends the input information to a server, which then compares it with data from past proposal documents and queries the AI ​​to generate a draft of the grant application.

[1509] Step 9:

[1510] The emotion engine detects micro-expressions and typing speed as the user enters new project information, and the device transmits the emotion data to the server.

[1511] Step 10:

[1512] The server feeds emotional data back to the AI ​​generator, which takes this into account when generating drafts, and the AI ​​then generates text in a tone and style that matches the user's emotions.

[1513] Step 11:

[1514] The generative AI generates answers to the requested questions and fills in each section of the grant application. The server stores the generated draft in a database and returns the draft proposal to the device.

[1515] Step 12:

[1516] The user checks the displayed draft, enters any corrections, and submits it. The terminal then transmits the corrections to the server.

[1517] Step 13:

[1518] The server reflects the received corrections in the draft and stores the updated draft in the database again. The terminal displays the updated draft again to the user.

[1519] Step 14:

[1520] The user checks the final version of the proposal document and clicks the "Submit" button. The terminal displays information on the recipient and any additional documents required.

[1521] Step 15:

[1522] The user uploads any additional documents required and clicks the "Final Submission" button. The terminal sends all information to the server.

[1523] Step 16:

[1524] The server packages the proposal documents and additional documents based on the format of the submission destination and executes the submission process. If the submission is successful, the server sends a confirmation notice to the user via an in-system notification or email.

[1525] Example 2

[1526] 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."

[1527] Creating grant proposals is a complex and time-consuming task, especially when extracting information from past proposals and combining it with new project information. Furthermore, if the tone and style of a grant proposal doesn't reflect the user's emotions, its quality and persuasiveness will be affected. There is a need for a system that automates this process and generates high-quality proposals that are both efficient and emotionally sensitive.

[1528] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft of grant application documents based on the new project information and the extracted key information, means for detecting a user's emotions and analyzing the data, means for feeding back the detected emotion data to the generation AI and reflecting it in the tone and style of the generated draft, means for displaying the generated draft to the user and allowing the user to input corrections, and means for saving the final grant application documents and transmitting them to the submission destination. This improves the efficiency of grant application document creation and enables the generation of high-quality application documents that are adapted to the user's emotions.

[1529] 1. "Generative AI" is a system that uses artificial intelligence to generate natural language and create new documents and texts.

[1530] 2. "Grant application" means an official document submitted to obtain a grant for a specific project or activity.

[1531] 3. "Server" means a computer system that provides services to other devices on a network.

[1532] 4. "Terminal" means an electronic device with a user interface for inputting, displaying, and transmitting information.

[1533] 5. "User" means a person or individual who operates the system.

[1534] 6. "Emotion Engine" means software or a system for detecting and analyzing a user's emotions.

[1535] 7. "Draft" means an early manuscript or draft of a grant application.

[1536] 8. "Project Information" means detailed data and details relating to a particular project.

[1537] 9. "Key Information" means the essential data required to prepare a grant application.

[1538] 10. "Feedback" is the process of re-inputting results or reactions into a system or process.

[1539] System Overview

[1540] This invention is a system that automatically generates grant application documents using generative AI. This system extracts key information from past proposal documents and combines it with new project information to generate high-quality grant application documents. In addition, by incorporating an emotion engine that recognizes the user's emotions, the system can reflect the user's emotions in the generated draft. This system includes a server, a terminal, generative AI, and an emotion engine.

[1541] User Registration and Login

[1542] A user accesses the system's website and creates a new account. The device receives necessary information from the user, such as name, email address, and password, and sends it to the server. The server stores the received information in a database, generates an authentication token, and sends it to the device. The user then logs in to the system using this authentication information.

[1543] Initial Setup and Upload Information

[1544] Users upload previously created grant proposals and related materials. The device then sends these materials to the server, which analyzes the received materials, extracts key information such as the project name, summary, and budget, and stores it in a database.

[1545] Emotion Engine Operation

[1546] When a user inputs new information, the emotion engine recognizes the user's emotions by detecting micro-expressions, tone of voice, and input speed through the device. The device then sends this emotion data to the server, which then feeds it back to the generation AI, which takes this into account when generating the draft.

[1547] Preparation of grant application documents

[1548] A user inputs project information to create a new grant proposal. The device sends this information to a server. The server uses a generative AI to generate a draft of the grant proposal based on the input information and previous documents. The generative AI takes emotional data into account and adjusts the tone and style of the text to match the user's intentions and emotions. The server returns the generated draft to the user.

[1549] Review and revise the draft

[1550] The user reviews the draft received from the server and inputs any necessary corrections. The terminal sends the user's corrections to the server, which generates the final grant application document and stores it in a database.

[1551] Submitting a grant application

[1552] The user checks the final grant application documents and submits them to the grant recipient. The server checks the recipient information and automatically sends the proposal documents to the recipient. The server then sends the user a confirmation that submission is complete.

[1553] Specific examples

[1554] Project Manager at a Nonprofit Organization

[1555] A project manager at a nonprofit organization might apply for a grant for a new environmental protection project by following these steps:

[1556] 1. User registration and login: The project manager creates a new account and logs into the system.

[1557] 2. Initial setup and information upload: Upload a successful grant proposal. The server parses it and extracts key information.

[1558] 3. Emotion engine operation: While the project manager is entering new project information, the emotion engine recognizes emotions by detecting micro-expressions, typing speed, etc. For example, if the user is nervous, it will detect this and provide feedback to the generation AI.

[1559] 4. Grant proposal creation: Enter details about a new project and let the generative AI generate a draft proposal. The generative AI will adjust the tone and style of the writing based on emotional data. For example, if a user writes passionately in response to the question, "What is the social significance of this project?", the AI ​​will generate an answer such as, "This project aims to dramatically increase environmental awareness in the local community and promote significant change."

[1560] 5. Review and revise the draft: The project manager reviews the draft and makes any necessary revisions. The server then generates the final version incorporating the revisions.

[1561] 6. Submit the grant proposal: Review the final version and submit to the grant recipient. The server sends the proposal to the recipient and sends a confirmation to the project manager.

[1562] Through these steps, users can create high-quality grant proposals with minimal effort and efficiently obtain grants. In addition, by combining the emotion engine, proposals can be generated that are more tailored to the user.

[1563] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1564] Step 1:

[1565] A user visits the system's website and creates a new account.

[1566] Input: Name, email address, password, etc.

[1567] Output: Authentication token

[1568] Specific operation: The terminal receives information entered by the user and sends it to the server. The server stores the received information in a database, generates an authentication token, and sends it to the terminal. The user uses this token to log in to the system.

[1569] Step 2:

[1570] Users upload past grant proposals and related materials.

[1571] Input: Past proposal documents, related materials

[1572] Output: Main information (project name, summary, budget, etc.)

[1573] Specific operation: The terminal sends the documents uploaded by the user to the server. The server analyzes the received documents, extracts key information such as the project name, summary, and budget, and stores it in a database.

[1574] Step 3:

[1575] The emotion engine works when a user enters new project information.

[1576] Input: New project information, user emotional data (micro-expressions, tone of voice, typing speed, etc.)

[1577] Output: Parsed emotion data

[1578] Specific operation: The device detects the user's facial expressions, tone of voice, input speed, etc. while inputting, and collects them as emotional data. The device then sends the collected emotional data to the server, which then feeds it back to the generation AI.

[1579] Step 4:

[1580] The server uses the generative AI to generate a draft of the grant application.

[1581] Input: New project information, information extracted from historical documents, analyzed sentiment data

[1582] Output: Draft grant application

[1583] Specific operation: The server uses generative AI to generate a draft of a grant application document based on new project information, information extracted from past documents, and emotional data. The generative AI adjusts the tone and style of the text, taking the emotional data into account. The server then sends the generated draft to the device and displays it to the user.

[1584] Step 5:

[1585] The user reviews the generated draft and enters any corrections.

[1586] Input: Correction

[1587] Output: Final grant application

[1588] Specific operation: The terminal sends the corrections entered by the user to the server, which then generates a final version of the grant application document that reflects the corrections and stores it in the database.

[1589] Step 6:

[1590] The server sends the final grant application to the submission destination.

[1591] Input: Final grant application documents and submission information

[1592] Output: Notification of submission completion

[1593] Specific operation: The server automatically sends the proposal documents to the recipient based on the final grant application documents and the recipient information. Once the submission is complete, a confirmation notification of the submission is sent to the user's device.

[1594] (Application example 2)

[1595] 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 robot 414 will be referred to as a "terminal."

[1596] In food delivery services, the challenge is to recognize in real time the dissatisfaction and anxiety that many users feel regarding their orders and delivery status, and to respond quickly and accurately. Conventional systems lack a means of accurately grasping users' emotions, making it difficult to provide appropriate customer support. Furthermore, the inability to accurately analyze and reflect user feedback makes it difficult to improve customer satisfaction.

[1597] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1598] In this invention, the server includes means for uploading past proposal documents, means for analyzing the uploaded proposal documents and extracting key information, means for inputting new project information, means for a generation AI to generate a draft of a grant application document based on the new project information and the extracted key information, means for displaying the generated draft to a user and for inputting corrections, means for saving the final grant application document and transmitting it to the submission destination, means for analyzing a user's facial expression, tone of voice, and input speed to extract emotional data, and means for feeding the extracted emotional data back to the generation AI and adjusting the tone and style of the document. This enables the user's emotions to be analyzed in real time and the generation AI to provide an appropriate response.

[1599] "Generative AI" is artificial intelligence that automatically generates optimal answers and suggestions from a variety of data.

[1600] A "grant proposal" is a formal document requesting funding for a specific project.

[1601] "Emotion data" is information that indicates the emotional state of the user, analyzed from facial expressions, tone of voice, input speed, and the like.

[1602] "Key information" refers to important data extracted from the proposal documents, such as the project name, summary, budget, and schedule.

[1603] A "draft" is an early stage grant application document created by the generative AI, which will become the final version after being revised by the user.

[1604] "Means for uploading" is a function that allows users to send past proposal documents to the system.

[1605] "Means of analyzing and extracting key information" is a function that automatically identifies important data from uploaded proposal documents.

[1606] The "means for inputting new project information" is a function for inputting detailed information about a project that the user is currently working on.

[1607] "Means by which the generative AI generates a draft grant application document" refers to the function by which the generative AI creates the initial stage of the document based on user input and extracted key information.

[1608] The "means for displaying to the user and inputting corrections" is a function for showing the generated draft to the user and reflecting necessary changes.

[1609] The "means for saving the final version of the grant application document" is a function for storing the final version of the document that reflects the user's modifications.

[1610] The "means for sending to the submission destination" is a function for sending the generated final version of the grant application documents to the specified destination.

[1611] The "means for analyzing the user's facial expression" is a function that uses a camera to identify the user's facial expression and generate emotion data.

[1612] The "means for analyzing voice tone" is a function that uses a microphone to analyze the tone of the user's voice and generate emotion data.

[1613] The "means for analyzing input speed" is a function that measures the input speed of the user and generates emotion data from it.

[1614] "Means of providing feedback to the generation AI" is a function that instructs the generation AI to make adjustments based on the extracted emotional data.

[1615] "Means to adjust the tone and style of a document" refers to the ability of generative AI to optimize the expression of a document based on emotional data.

[1616] The present invention, when applied to a smartphone application, is described in detail below. This invention is intended to analyze user emotions in real time in a food delivery service and provide appropriate responses using generative AI.

[1617] System Configuration

[1618] The system includes the following main components:

[1619] 1. User device: A smartphone (compatible with iOS or Android) uses the camera and microphone to collect user emotion data.

[1620] 2. Server: Hosts data processing and generative AI.

[1621] 3. Generative AI: Generates documents and responses based on user emotional data and input information.

[1622] 4. Emotion engine: Generates emotion data by analyzing facial expressions, voice tone, and typing speed.

[1623] Hardware and software used

[1624] Face recognition library: OpenCV (open source)

[1625] Speech recognition: Google Cloud Speech-to-Text

[1626] Generative AI: OpenAI GPT-3 (using API)

[1627] Data Processing Overview

[1628] 1. User Registration and Login

[1629] A user downloads a smartphone application and creates a new account. They enter the required information (name, email address, password, etc.) and send it to the server. The server stores this information in a database, generates authentication information, and returns it to the user.

[1630] 2. Collecting Emotional Data

[1631] When a user uses the application, the smartphone's camera and microphone are used to collect the user's facial expressions and voice. The emotion engine analyzes this data and generates emotion data.

[1632] 3. Response generation using generative AI

[1633] The server then feeds the emotion data obtained from the emotion engine back to the AI ​​generator to generate an appropriate response. For example, if the user is dissatisfied, the AI ​​generator will provide an apology message or a discount coupon for the next order.

[1634] 4. View and edit the draft

[1635] The generated drafts and responses are displayed to the user, who can make corrections as needed, and the final corrections are sent to the server and saved as the final version.

[1636] Specific examples

[1637] For example, if a user is complaining about a late delivery, use a prompt like this:

[1638] "Generate an appropriate apology message if a customer is upset about a delayed delivery."

[1639] Based on this prompt, the Generative AI would generate a response like this:

[1640] "We sincerely apologize for the delay in delivery. As a token of our apology, we will provide you with a discount coupon for your next purchase."

[1641] This makes it possible to analyze user emotions in real time and provide appropriate responses. The system of the present invention improves the user experience and significantly improves the efficiency of customer support.

[1642] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1643] Step 1:

[1644] User Registration and Login

[1645] The user launches the smartphone application and creates a new account. The required information (name, email address, password, etc.) is entered and sent from the device to the server. The server stores this information in a database, generates authentication information, and returns it to the user. When logging in, the authentication information entered by the user is sent to the server for authentication.

[1646] Input: User information (name, email address, password)

[1647] Output: Credentials

[1648] Step 2:

[1649] Collecting Emotional Data

[1650] When a user uses the application, facial expressions and voice are collected using the smartphone's camera and microphone. The device then transmits this data in real time to the emotion engine, which analyzes the collected data and identifies the user's emotions.

[1651] Input: User's facial expression video and voice data

[1652] Output: Emotion data (e.g., joy, anger, sadness)

[1653] Step 3:

[1654] Response generation by generative AI

[1655] The device sends emotional data and user input information to the server. The server then feeds the emotional data back to the AI ​​generator, which then generates an appropriate response or suggestion. For example, if the user is dissatisfied, the AI ​​generator generates an apology message.

[1656] Input: Emotion data, user input information

[1657] Output: Generated response (e.g., apology message)

[1658] Step 4:

[1659] View and modify drafts

[1660] The terminal displays the generated responses and suggestions to the user, who can then make any necessary corrections and send them back to the server, which then generates a final version incorporating the final corrections and stores it in a database.

[1661] Input: Generated response, user modifications

[1662] Output: Final version after corrections are reflected

[1663] Step 5:

[1664] Final communication and storage

[1665] The server saves the final document and, if necessary, sends it to the submission destination, and notifies the user that the submission is complete.

[1666] Input: Final document

[1667] Output: Submission confirmation notification

[1668] These steps enable real-time analysis of user sentiment and use of generative AI to provide appropriate responses, thereby improving the quality of customer support for food delivery services.

[1669] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1670] 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.

[1671] 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 robot 414.

[1672] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1673] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1674] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1675] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1676] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1677] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1678] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1679] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1680] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1681] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1682] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1683] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1684] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1685] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1686] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1687] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1688] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1689] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1690] The following is further disclosed regarding the above embodiment.

[1691] (Claim 1)

[1692] A system for automatically generating grant application documents using generation AI,

[1693] A means to upload past proposal documents;

[1694] A means of analyzing uploaded proposal documents to extract key information;

[1695] a means for entering new project information;

[1696] A means for generating a draft grant application document based on the new project information and the extracted key information by the generation AI;

[1697] means for displaying the generated draft to a user and for inputting modifications;

[1698] A means for saving and transmitting the final grant application;

[1699] A system including:

[1700] (Claim 2)

[1701] 2. The system of claim 1, wherein the extracted key information includes the project name, summary, budget, and schedule.

[1702] (Claim 3)

[1703] 2. The system of claim 1, wherein the generating AI generates optimal answers to inquiries corresponding to each section of a grant application document.

[1704] "Example 1"

[1705] (Claim 1)

[1706] A system for automatically generating grant application documents using generation AI,

[1707] A means to upload past proposal documents;

[1708] A means of analyzing uploaded proposal documents to extract key information;

[1709] a means for entering new project information;

[1710] A means for generating a draft grant application document based on the new project information and the extracted key information by the generation AI;

[1711] means for displaying the generated draft to a user and for inputting modifications;

[1712] A means for saving and transmitting the final grant application;

[1713] means for generating an authentication token to perform user authentication;

[1714] A means of analyzing uploaded documents using OCR tools and natural language processing technology to identify important information,

[1715] A means for the generative AI to generate prompt sentences and generate high-quality document sections based on the prompt sentences;

[1716] A system including:

[1717] (Claim 2)

[1718] 2. The system of claim 1, wherein the extracted key information includes the project name, summary, budget, and schedule.

[1719] (Claim 3)

[1720] The generative AI generates optimal answers to queries corresponding to each section of the grant application document,

[1721] 10. The system of claim 1, wherein the system generates prompt sentences to automatically generate high-quality document sections.

[1722] "Application Example 1"

[1723] (Claim 1)

[1724] A system for automatically generating grant application documents using generation AI,

[1725] A means to upload past proposal documents;

[1726] A means of analyzing uploaded proposal documents to extract key information;

[1727] a means for entering new project information;

[1728] A means for generating a draft grant application document based on the new project information and the extracted key information by the generation AI;

[1729] means for displaying the generated draft to a user and for inputting modifications;

[1730] A means for saving and transmitting the final grant application;

[1731] A method to use generative AI to analyze past inventory data and sales data and propose optimal inventory levels and product placement.

[1732] A system including:

[1733] (Claim 2)

[1734] 2. The system of claim 1, wherein the extracted key information includes the project name, summary, budget, and schedule.

[1735] (Claim 3)

[1736] The system of claim 1, wherein the generation AI generates optimal answers to inquiries corresponding to each section of the grant application document, and further generates optimization proposals for inventory quantities and product placement in inventory management.

[1737] "Example 2: Combining Emotion Engines"

[1738] (Claim 1)

[1739] A system for automatically generating grant application documents using generation AI,

[1740] A means to upload past proposal documents;

[1741] A means of analyzing uploaded proposal documents to extract key information;

[1742] a means for entering new project information;

[1743] A means for generating a draft grant application document based on the new project information and the extracted key information by the generation AI;

[1744] means for detecting user emotions and analyzing the data;

[1745] The detected emotional data is fed back to the AI ​​generator to influence the tone and style of the draft.

[1746] means for displaying the generated draft to a user and for inputting modifications;

[1747] A means for saving and transmitting the final grant application;

[1748] A system including:

[1749] (Claim 2)

[1750] 2. The system of claim 1, wherein the extracted key information includes the project name, summary, budget, and schedule.

[1751] (Claim 3)

[1752] 2. The system of claim 1, wherein the generating AI generates optimal answers to inquiries corresponding to each section of a grant application document.

[1753] "Application example 2 when combining emotion engines"

[1754] (Claim 1)

[1755] A system for automatically generating grant application documents using generation AI,

[1756] A means to upload past proposal documents;

[1757] A means of analyzing uploaded proposal documents to extract key information;

[1758] a means for entering new project information;

[1759] A means for generating a draft grant application document based on the new project information and the extracted key information by the generation AI;

[1760] means for displaying the generated draft to a user and for inputting modifications;

[1761] A means for saving and transmitting the final grant application;

[1762] A means for analyzing a user's facial expression, voice tone, and input speed to extract emotion data;

[1763] The extracted emotion data is fed back to the generative AI to adjust the tone and style of the document.

[1764] A system including:

[1765] (Claim 2)

[1766] 2. The system of claim 1, wherein the extracted key information includes the project name, summary, budget, and schedule.

[1767] (Claim 3)

[1768] 2. The system of claim 1, wherein the generating AI generates optimal answers to inquiries corresponding to each section of a grant application document. [Explanation of symbols]

[1769] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A system for automatically generating grant application documents using generation AI, A means to upload past proposal documents; A means of analyzing uploaded proposal documents to extract key information; a means for entering new project information; A means for generating a draft grant application document based on the new project information and the extracted key information by the generation AI; means for displaying the generated draft to a user and for inputting modifications; A means for saving and transmitting the final grant application; A system including:

2. 2. The system of claim 1, wherein the extracted primary information includes a project name, an outline, a budget, and a schedule.

3. 2. The system of claim 1, wherein the generating AI generates optimal answers to queries corresponding to each section of the grant application document.

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