System

Abstract

To provide a system capable of accelerating an information revolution by enabling a user to quickly and efficiently develop an idea.SOLUTION: An input means for inputting information by a user, a server means for receiving the input information, a database means for storing the received information, a notification means for notifying other users of the received information, a means for evaluating and commenting on an idea based on the notified information, a means for storing the evaluation and comments in the database means, and a means for analyzing the received information; A system comprising: generation AI means for generating related suggestions; means for communicating the analysis results and suggestions to a user; generative AI means for improving the received information; and means for communicating the improvement results to the user.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] In today's business environment, the rapid creation and evaluation of ideas is required, but existing systems are inefficient in the process of users sharing their ideas and receiving feedback from other users. Furthermore, they lack a means to automatically generate the improvement proposals needed to improve the quality of ideas. This requires users to spend a lot of time and effort creatively refining their ideas, and there is no way to accelerate the information revolution. This reduces the synergy effect of ideas among users, resulting in a lack of improvement in overall productivity. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following configuration: A system is constructed that includes input means for users to input information, server means for receiving the input information, and database means for storing this information. Furthermore, by providing notification means for notifying other users of the received information, rapid information sharing between users is realized. Furthermore, by providing means for rating and commenting on ideas based on the received information and means for storing the ratings and comments in the database means, users can easily obtain feedback.

[0006] Furthermore, user assistance is enhanced by incorporating a generative AI means for analyzing received information and generating relevant suggestions, and adding a means for notifying the user of the analysis results and suggestions. Finally, a system is provided that receives idea improvement requests, uses generative AI means to list improvements based on the requests, and notifies the user of the improvements, thereby efficiently optimizing the user's ideas. This configuration allows users to refine their ideas quickly and efficiently, and is expected to accelerate the information revolution.

[0007] "User" refers to a person who accesses the system, inputs information, posts, and rates and comments on ideas.

[0008] "Input means" refers to an interface for a user to input information, and is a device including, for example, a keyboard, a touch screen, a mouse, etc.

[0009] "Server means" refers to a computer system for receiving and processing information from a user.

[0010] "Database Means" refers to a data storage system for storing and managing received information.

[0011] "Notification means" refers to a function for notifying users of new ideas and feedback, and includes, for example, email notifications and push notifications.

[0012] The "rating and commenting means" refers to an interface that allows users to rate and comment on ideas posted by other users.

[0013] "Generative AI means" refers to an artificial intelligence system that analyzes received information and generates relevant suggestions.

[0014] "Generative AI methods" refers to artificial intelligence systems that list improvements to an idea.

[0015] An "improvement request" refers to a user's request to improve their own idea, and the generative AI performs an analysis based on that request.

[0016] "Analysis Results" refers to the information and proposals analyzed by the Generating AI Means.

[0017] "Improvements" refers to suggestions for improving the quality of an idea, listed by the generative AI method. [Brief explanation of the drawings]

[0018] [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 illustrating 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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] In the system based on this invention, users can input and post information, exchange ratings and comments, and improve and develop ideas with the support of generative AI and generative system AI. This system has multiple means and functions as follows:

[0040] 1. User Registration Process

[0041] Example

[0042] A user accesses the system's new registration form from a terminal and enters information such as their name, email address, and field of expertise. When this information is sent, the server receives it and stores it in a database. The server then sends the user an email informing them of completion of registration.

[0043] 2. Idea submission process

[0044] Example

[0045] A user accesses the idea submission form from their device and enters the title and body of their idea. When this data is sent, the server receives it and stores it in a database. The server then notifies other users that a new idea has been posted. This allows users to quickly share their ideas.

[0046] 3. Idea Evaluation and Comment Process

[0047] Example

[0048] A user accesses a list of ideas posted by other users from their device and displays the details page of an idea they are interested in. The user then presses the rating button and enters comment feedback. This rating and comment are sent to the server and stored in a database. The server then sends a notification of the rating and comment to the poster of the idea.

[0049] 4. AI-based idea analysis and proposal process

[0050] Example

[0051] The server periodically sends the idea data in the database to the generation AI. The generation AI analyzes the received idea data and generates related information and additional suggestions. The generated suggestions are sent to the server, which notifies the user. This allows the user to review and improve their ideas based on the analysis results and suggestions.

[0052] 5. Idea Improvement Process

[0053] Example

[0054] The user accesses the details page of their idea from their device and presses the "Improvement Request" button. This request is sent to the server, which then sends the idea data to the generative AI. The generative AI analyzes the idea and lists improvements. The generated improvements are then sent to the server, which notifies the user. The user can then use this information to modify and improve their idea.

[0055] This system provides an environment where users can easily post ideas, receive feedback, and refine their ideas while referring to automatically generated suggestions and improvements, accelerating idea generation and promoting the information revolution.

[0056] The processing flow will be explained below.

[0057] 1. User Registration Process

[0058] Step 1:

[0059] A user accesses the system from a terminal and displays a new registration form.

[0060] Step 2:

[0061] The user enters profile information such as name, email address, and area of ​​expertise.

[0062] Step 3:

[0063] The user presses the "Register" button and sends the input data to the server.

[0064] Step 4:

[0065] The server receives the transmitted data.

[0066] Step 5:

[0067] The server checks the integrity of the data it receives and, if there are no errors, stores it in the database.

[0068] Step 6:

[0069] The server sends a registration completion email to the user.

[0070] 2. Idea submission process

[0071] Step 1:

[0072] A user accesses the system from a terminal and displays the idea submission form.

[0073] Step 2:

[0074] The user enters the title and body of the idea.

[0075] Step 3:

[0076] The user presses the "Submit" button and sends the input data to the server.

[0077] Step 4:

[0078] The server receives the transmitted idea information.

[0079] Step 5:

[0080] The server stores the idea data in a database.

[0081] Step 6:

[0082] The server notifies other users that a new idea has been posted.

[0083] 3. Idea Evaluation and Comment Process

[0084] Step 1:

[0085] A user accesses the system from a terminal and displays a list of ideas posted by other users.

[0086] Step 2:

[0087] The user selects an idea that interests them and accesses the detail view page.

[0088] Step 3:

[0089] The user presses the rating button and enters feedback in the comment field.

[0090] Step 4:

[0091] The user presses the "Submit" button to send the evaluation data and comments to the server.

[0092] Step 5:

[0093] The server receives the rating data and comments and stores them in a database.

[0094] Step 6:

[0095] The server sends a rating and comment notification to the idea poster.

[0096] 4. AI-based idea analysis and proposal process

[0097] Step 1:

[0098] The server periodically sends the idea data in the database to the generation AI.

[0099] Step 2:

[0100] The generation AI analyzes the received idea data.

[0101] Step 3:

[0102] Generative AI generates relevant information and additional suggestions.

[0103] Step 4:

[0104] The generation AI sends the analysis results and suggestions to the server.

[0105] Step 5:

[0106] The server notifies the user of the analysis results and suggestions.

[0107] 5. Idea Improvement Process

[0108] Step 1:

[0109] The user accesses their idea details page from their device and presses the "Request for Improvement" button.

[0110] Step 2:

[0111] The user's request is sent to the server.

[0112] Step 3:

[0113] The server sends the relevant idea data to the generative AI.

[0114] Step 4:

[0115] Generative AI analyzes ideas and lists areas for improvement.

[0116] Step 5:

[0117] The generative AI sends improvements to the server.

[0118] Step 6:

[0119] The server will notify the user of the improvements.

[0120] Step 7:

[0121] Users receive notifications, review improvements, and revise their ideas.

[0122] Example 1

[0123] 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."

[0124] Conventional idea-sharing platforms have a problem in that the process of users posting ideas and receiving feedback from other users is cumbersome, making it difficult to efficiently improve ideas. Furthermore, there is a lack of a means for users to receive specific suggestions for voluntarily improving their ideas. Furthermore, there is no system for regularly analyzing ideas and generating suggestions, which limits the quality of creative ideas.

[0125] 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.

[0126] In this invention, the server includes an input means for a user to input information, a server means for receiving the input information, a database means for storing the received information, a notification means for notifying other users of the received information, a means for rating and commenting on ideas based on the notified information, a means for storing the rating and comment in the database means, a generative AI means for periodically analyzing the received information and generating related proposals, a means for notifying users of the analysis results and proposals, a means for receiving idea improvement requests from users, a generative AI means for analyzing the received information and listing improvements, and a means for notifying users of the improvements. This enables users to intuitively post ideas, quickly receive feedback from other users, and efficiently improve their ideas based on specific suggestions and improvements from the generative AI and the generative AI.

[0127] "User" refers to an individual who inputs information, posts ideas, ratings and comments.

[0128] "Input means" refers to the interface through which a user enters information or ideas, such as a web form or an input field in a mobile app.

[0129] "Server means" refers to a computer system that receives information sent from a user and performs various processes.

[0130] "Database Means" refers to a data storage device or system for storing and managing received information.

[0131] "Notification mechanism" refers to a method or system for communicating specific information or updates to other users.

[0132] "Means for rating and commenting" refers to a function that allows users to input ratings and comments on posted ideas.

[0133] "Generative AI Means" refers to the artificial intelligence model used to analyze received information and generate relevant recommendations.

[0134] "Generative AI means" refers to an artificial intelligence model that analyzes received information based on user requests and lists areas for improvement.

[0135] The system based on this invention allows users to input and post information, exchange ratings and comments, and improve and develop ideas with the support of generative AI and generative system AI. This system has the following multiple means and functions:

[0136] 1. User Registration Process

[0137] The user accesses the system's new registration form on their own device (PC, smartphone, etc.) and enters information such as their name, email address, and field of expertise. After entering the information, they press the "Submit" button, and the server receives the information and stores it in a database (e.g., MySQL (registered trademark)). The server then sends the user a registration completion email.

[0138] Example: A user fills in a new registration form with information such as "Yamada Taro", "example@example.com", and "Engineering", and submits it.

[0139] 2. Idea submission process

[0140] A user accesses the idea submission form on their device and enters the title and text of their idea. For example, "A new solar panel design" and "This solar panel will improve efficiency by 50%." After entering the information, they press the "Submit" button. The server receives the data and stores it in a database. The server then notifies other users that a new idea has been submitted.

[0141] Example: A user enters the title and body of an idea "New solar panel design" and submits it.

[0142] 3. Idea Evaluation and Comment Process

[0143] A user accesses a list of ideas posted by other users from their device and displays the details page of an idea they are interested in. For example, they can enter a comment such as "I think this is practical" for the idea "New solar panel design" and rate it "5 stars." When the rating and comment are submitted, they are sent to the server and stored in a database. The server then notifies the idea poster that the rating and comment have been received.

[0144] Example: A user submits a 5-star rating for "New Solar Panel Design" and a comment saying "Great idea."

[0145] 4. AI-based idea analysis and proposal process

[0146] The server periodically sends the idea data in the database to a generation AI (e.g., OpenAI's GPT-4). The generation AI analyzes the received ideas and generates related information and additional suggestions. These suggestions are then sent back to the server, which notifies the user.

[0147] Example prompt: An idea has been submitted for "New Solar Panel Design." Check it out if you're interested.

[0148] 5. Idea Improvement Process

[0149] When a user accesses the details page of their idea on their device and presses the "Improvement Request" button, a request is sent to the server. The server sends the idea data to a generative AI (e.g., DeepAI), which analyzes the idea and lists improvements. The generated improvements are sent to the server, which notifies the user. The user can then use this information to revise and improve their idea.

[0150] Example: After the user presses the "improvement request" button, the suggestion for improvement is presented: "The amount of silicon used should be reduced to further improve area efficiency."

[0151] This program is implemented as a web platform to make user operation intuitive and simple, and uses HTML, CSS, JavaScript (registered trademark), etc. as the user interface. The server side is implemented using languages ​​such as Python and Node.js, and a relational database such as MySQL is used as the database. OpenAI's GPT-4 and DeepAI's API are used for the generative AI and generative AI models, which automatically perform periodic analysis processing and respond to user requests.

[0152] This system allows users to intuitively post ideas, quickly receive feedback from other users, and efficiently improve their ideas based on specific suggestions and improvements from the generative AI and generative system AI. As a result, the quality of ideas improves, providing an environment that promotes the creation of new value.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] User Registration Process

[0155] Step 1:

[0156] The user accesses the system's new registration page on their own device (PC, smartphone, etc.).

[0157] Input: Enter the URL of the system's new registration page in the URL bar of your web browser.

[0158] Output: A new registration form is displayed.

[0159] Step 2:

[0160] Users enter information such as their name, email address, and area of ​​expertise into the new registration form.

[0161] Input: Enter your name, email address, area of ​​expertise, etc. in the input fields.

[0162] Output: The input data is displayed in a form.

[0163] Step 3:

[0164] The user clicks the "Submit" button.

[0165] Input: The "Submit" button is clicked.

[0166] Output: The form data is sent to the server.

[0167] Step 4:

[0168] The server receives the user's input information as an HTTP POST request.

[0169] Input: User information included in the HTTP POST request.

[0170] Output: User information temporarily stored in the server's processing memory.

[0171] Step 5:

[0172] The server stores the received data in a database.

[0173] Input: User information temporarily stored in the server's processing memory.

[0174] Output: User information stored in the users table in the MySQL database.

[0175] Step 6:

[0176] The server sends a registration completion email to the user.

[0177] Input: The content of the registration confirmation email and the user's email address.

[0178] Output: A registration confirmation email sent to the user's email account.

[0179] Idea Submission Process

[0180] Step 1:

[0181] The user accesses the idea submission page on the terminal.

[0182] Enter the URL of the idea submission page in the URL bar of your web browser.

[0183] Output: The idea submission form is displayed.

[0184] Step 2:

[0185] The user enters the title and body of the idea.

[0186] Input: Enter the title and text of your idea in the fields provided.

[0187] Output: The input data is displayed in a form.

[0188] Step 3:

[0189] The user clicks the "Submit" button.

[0190] Input: The "Submit" button is clicked.

[0191] Output: The form data is sent to the server.

[0192] Step 4:

[0193] The server receives the idea data as an HTTP POST request.

[0194] Input: Idea information included in the HTTP POST request.

[0195] Output: Idea information temporarily stored in the server's processing memory.

[0196] Step 5:

[0197] The server stores the received data in a database.

[0198] Input: Idea information temporarily stored in the server's processing memory.

[0199] Output: Idea information stored in the Ideas table in a MySQL database.

[0200] Step 6:

[0201] The server notifies other users that a new idea has been posted.

[0202] Input: New idea information and a list of people to notify.

[0203] Output: A notification email or in-app notification is sent to other users.

[0204] Idea evaluation and comment process

[0205] Step 1:

[0206] The user accesses a list page of ideas posted by other users from their terminal.

[0207] Input: Enter the URL of the idea list page into the URL bar of your web browser.

[0208] Output: A list of ideas is displayed.

[0209] Step 2:

[0210] View the detail page of the idea that the user wants to rate.

[0211] Enter: Click on a specific idea from the list of ideas.

[0212] Output: The details page for a particular idea is displayed.

[0213] Step 3:

[0214] The user presses the rating button.

[0215] Input: The rating button is clicked.

[0216] Output: Evaluation data is temporarily stored.

[0217] Step 4:

[0218] The user enters comment feedback.

[0219] Enter your feedback in the comments section.

[0220] Output: The comment data is displayed in a form.

[0221] Step 5:

[0222] The user clicks the "Submit" button.

[0223] Input: The "Submit" button is clicked.

[0224] Output: The form data is sent to the server.

[0225] Step 6:

[0226] The server receives the ratings and comments as HTTP POST requests.

[0227] Input: Rating and comment information included in the HTTP POST request.

[0228] Output: Rating and comment information temporarily stored in the server's processing memory.

[0229] Step 7:

[0230] The server stores the received data in a database.

[0231] Input: Rating and comment information temporarily stored in the server's processing memory.

[0232] Output: Rating and comment information stored in the ratings table in a MySQL database.

[0233] Step 8:

[0234] The server notifies the idea poster.

[0235] Input: Rating and comment information and author information to notify.

[0236] Output: A notification email or in-app notification is sent to the contributor.

[0237] AI-based idea analysis and proposal process

[0238] Step 1:

[0239] The server periodically scans the database.

[0240] Input: A periodic timer trigger.

[0241] Output: Obtaining new idea information.

[0242] Step 2:

[0243] The server sends new idea data to the generation AI.

[0244] Input: New idea information.

[0245] Output: API request to the generating AI.

[0246] Step 3:

[0247] Generative AI analyzes idea data.

[0248] Input: Idea information.

[0249] Output: Analysis results and recommendations.

[0250] Step 4:

[0251] Generative AI generates suggestions.

[0252] Input: Analysis results.

[0253] Output: Proposal data.

[0254] Step 5:

[0255] The proposal is sent to the server.

[0256] Input: Proposal data.

[0257] Output: The API response to the server.

[0258] Step 6:

[0259] The server notifies the user of the offer.

[0260] Input: Proposal data and user information to notify.

[0261] Output: A notification email or in-app notification is sent to the user.

[0262] Idea Improvement Process

[0263] Step 1:

[0264] A user visits their idea detail page.

[0265] Input: Enter the URL of the idea detail page into the URL bar of your web browser.

[0266] Output: The idea details page is displayed.

[0267] Step 2:

[0268] The user presses the "improvement request" button.

[0269] Input: The "Improvement Request" button is clicked.

[0270] Output: An enhancement request is sent to the server.

[0271] Step 3:

[0272] The request is sent to the server.

[0273] Input: Enhancement request data.

[0274] Output: Request data temporarily stored in the server's processing memory.

[0275] Step 4:

[0276] The server sends the idea data to the generative AI.

[0277] Input: Idea information and improvement request data.

[0278] Output: API request to the generative AI.

[0279] Step 5:

[0280] Generative AI analyzes ideas.

[0281] Input: Idea information and improvement requests.

[0282] Output: Analysis results and improvements.

[0283] Step 6:

[0284] Generative AI lists areas for improvement.

[0285] Input: Analysis results.

[0286] Output: A list of improvements.

[0287] Step 7:

[0288] The improvements are sent to the server.

[0289] Input: A list of improvements.

[0290] Output: The API response to the server.

[0291] Step 8:

[0292] The server notifies the user of the improvements.

[0293] Input: A list of improvements and the users to notify.

[0294] Output: A notification email or in-app notification is sent to the user.

[0295] (Application example 1)

[0296] 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."

[0297] Conventional content distribution services have a problem in that even when users post new content or ideas, they do not receive prompt and effective feedback or evaluations, making it difficult to create high-quality content. Furthermore, there is a lack of a mechanism for automatically analyzing posted content and providing suggestions for improvements or additions, limiting the means by which users can effectively improve their own content. The objective of this invention is to solve these problems and provide an environment in which users can create high-quality content.

[0298] 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.

[0299] In this invention, the server includes input means for a user to input information, server means for receiving the input information, database means for saving the received information, notification means for notifying other information processing devices of the received information, means for rating and commenting on ideas based on the notified information, means for saving the rating and comment in the database means, generative AI means for analyzing the received information and generating related proposals, means for notifying the information processing device of the analysis results and proposals, generative AI means for improving the received information, means for notifying the information processing device of the improvement results, means for a user to post new content or ideas in a content distribution service and receive feedback and evaluations thereon from other information processing devices, and means for analyzing the user's posted content using generative AI and providing suggestions for improvements and additions. This allows users to receive feedback quickly and effectively and to improve their own content to a higher quality by utilizing the analysis and suggestions by the generative AI.

[0300] An "input means" is a device or interface through which a user inputs information.

[0301] The "server means" refers to a server device or its function for receiving and processing input information.

[0302] "Database Means" means a database system or function thereof for storing and managing received information.

[0303] The "notification means" is a device or function for notifying other information processing devices of received information.

[0304] The "evaluation and comment means" is a device or function for evaluating and commenting on ideas based on the notified information.

[0305] "Generative AI means" means an artificial intelligence model or function thereof for analyzing received information and generating relevant recommendations.

[0306] "Information processing device" refers to any device that a user uses to input, receive, and process information.

[0307] A "generative AI means" is an artificial intelligence model or function that analyzes received information to improve it and lists areas for improvement.

[0308] A "content distribution service" is a service that provides information and media content to users via the Internet or the like.

[0309] "Feedback" refers to evaluations and comments received from other information processing devices.

[0310] "Analysis results" refers to suggestions and improvements generated by the generative AI means.

[0311] This system supports users in posting new content and ideas on a content distribution service, and receives feedback and evaluations from other information processing devices. Furthermore, it uses generative AI to analyze users' posts and provide suggestions for improvements and additions, helping users to create high-quality content.

[0312] The server uses the following hardware and software to process information such as reception, storage, notification, analysis, and improvement.

[0313] Hardware: High-performance server equipment, database server

[0314] Software: Flask (web framework), SQLAlchemy (database ORM), TENSORFLOW (registered trademark) / PyTorch (generative AI model), Twilio (email sending)

[0315] Input Method

[0316] A web form is provided as an input mechanism for users to enter information, allowing them to enter information such as their name, email address, content categories of interest, and the title and body of the content.

[0317] Server Means

[0318] The server receives the information entered by the user and stores it in a database. The receiving process is performed by a web application using Flask.

[0319] Database Means

[0320] The received information is stored in a database using SQLAlchemy, and the stored data is used for subsequent processing (notification, analysis, improvement, etc.).

[0321] Notification means

[0322] When new content or ideas are posted, the server notifies other users, using Twilio to send emails.

[0323] Rating and commenting tools

[0324] Other users can access the information and provide ratings and comments, which are then sent back to the server and stored in a database.

[0325] Generation AI means

[0326] The server periodically sends the submitted data in the database to a generative AI model (TensorFlow or PyTorch), which analyzes the received data and generates relevant suggestions and improvements. The generated suggestions are returned to the server and notified to the user.

[0327] Information processing device

[0328] Information processing devices are devices that users use to input, receive, and process information, such as smartphones, smart glasses, head-mounted displays, and robots. This allows similar functions to be realized on any device.

[0329] Improvement AI method

[0330] When a user submits an improvement request, the server sends the request to the generative AI means, which analyzes the request and lists the improvements. The server then notifies the user of the improvements.

[0331] Examples and prompts

[0332] For example, if a user posts an idea for a new web browser design, the following prompt is sent: "This is an idea for a new web browser design. This browser is for the visually impaired and comes standard with a voice guide function." Based on this prompt, the generative AI model analyzes the proposal and provides specific suggestions such as "improvement suggestions to make the browser UI more intuitive" and "ways to enhance the voice guide function."

[0333] This allows users to receive feedback quickly and effectively, and leverage generative AI analysis and suggestions to improve their content to a higher quality.

[0334] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0335] Step 1:

[0336] A user accesses the system's new registration form from a terminal and enters their name, email address, content categories of interest, etc. The entered information is received by the server and saved in a database. The entered data is processed by checking for format and duplication. The server then uses Twilio to send the user a registration completion email. The user's personal information is input, and the output is saved in the database and a completion email is sent.

[0337] Step 2:

[0338] A user accesses the idea submission form from their device and enters the title and content body. The entered data is sent to the server and saved in the database. The server uses the notification function to notify other users via email via Twilio that a new idea has been posted. The idea content is input, and the output is saving to the database and sending a notification.

[0339] Step 3:

[0340] Other users check the new ideas notified from their devices and post ratings and comments. The ratings and comments are sent to the server and stored in a database. The server then notifies the original poster that the rating or comment has been posted. The input is the rating and comment, and the output is storage in the database and a notification to the poster.

[0341] Step 4:

[0342] The server periodically sends the posted data in the database to a generative AI model (TensorFlow or PyTorch). The generative AI model analyzes the received data and generates relevant suggestions. This analysis and data calculation uses natural language processing and clustering techniques. The generated suggestions are returned to the server and notified to the user. The user is notified of the posted data as input and the suggestions that are the analysis results as output.

[0343] Step 5:

[0344] The user accesses their idea details page from their device and presses the "Improvement Request" button. The request is sent to the server, which then sends the idea data to the generative AI model. The generative AI model analyzes the idea and lists improvements. The generated improvements are returned to the server and notified to the user. The improvement request and idea data are input, and the improvements are notified to the user as output.

[0345] Step 6:

[0346] Users receive notifications from the server and use the suggestions and improvements to revise and improve their ideas. The revised idea is then resubmitted to the server and stored in the database. At this time, the server resends notifications containing the revisions to other users, requesting their feedback. The revised idea is the input, and the output is saving to the database and re-notifying them.

[0347] This allows users to create and improve high-quality content.

[0348] 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.

[0349] The system based on this invention provides a more personalized experience by combining an emotion engine in the process of users inputting and posting information, receiving ratings and comments from other users, and analyzing and improving that information. A specific embodiment of the system is shown below.

[0350] 1. User Registration Process

[0351] Example

[0352] The user accesses the system's new registration form from their terminal and enters their profile information. When the user submits the information, the server receives it and stores it in the database. Once registration is complete, the server sends the user a registration completion email.

[0353] 2. Idea submission process

[0354] Example

[0355] A user accesses the idea submission form from a terminal and enters the title and text of the idea. Once submission is complete, the server saves the idea information in a database and notifies other users that a new idea has been submitted.

[0356] 3. Idea Evaluation and Comment Process

[0357] Example

[0358] When a user rates or comments on another user's idea from their device, the rating and comment are sent to the server and stored in a database. The server then sends a notification of the rating and comment to the idea poster.

[0359] 4. AI-based idea analysis and proposal process

[0360] Example

[0361] The server periodically sends the idea information in the database to the generation AI, which analyzes it and generates proposals. The generated proposals are notified to the user via the server, allowing the user to review and improve their ideas.

[0362] 5. Idea Improvement Process

[0363] Example

[0364] When a user presses the "Improvement Request" button on their device's idea details page, the request is sent to the server. The server sends the idea to the generative AI, which then lists improvements. The listed improvements are notified to the user via the server, allowing the user to modify the idea.

[0365] 6. Introducing the Emotion Engine

[0366] Example

[0367] The emotion engine analyzes data (text and ratings) entered by users in real time while they use the system from their devices. This emotion analysis is particularly effective when posting ideas or comments, and the server provides the analysis results to the generative AI and generative system AI. This allows suggestions and improvements to be generated that take the user's emotional state into account.

[0368] Sentiment Analysis and Notifications

[0369] Example

[0370] The emotion engine analyzes the user's input data, and if it detects positive or negative emotions, the server uses the emotion analysis results as a notification method. For example, if negative emotions are detected, the server can send the user an encouraging message or provide more specific suggestions for improvement. Conversely, if positive emotions are detected, the server can motivate the user by introducing success stories and other users' positive reactions.

[0371] By integrating an emotion engine, this invention takes into account the user's emotional state to provide more personalized feedback and improvement suggestions, which is expected to increase user satisfaction, improve the quality of ideas, and promote the information revolution.

[0372] The processing flow will be explained below.

[0373] 1. User Registration Process

[0374] Step 1:

[0375] A user accesses the system from a terminal and displays a new registration form.

[0376] Step 2:

[0377] The user enters profile information such as name, email address, and area of ​​expertise.

[0378] Step 3:

[0379] The user presses the "Register" button and sends the input data to the server.

[0380] Step 4:

[0381] The server receives the transmitted data.

[0382] Step 5:

[0383] The server checks the integrity of the data it receives and, if there are no errors, stores it in the database.

[0384] Step 6:

[0385] The server sends a registration completion email to the user.

[0386] 2. Idea submission process

[0387] Step 1:

[0388] A user accesses the system from a terminal and displays the idea submission form.

[0389] Step 2:

[0390] The user enters the title and body of the idea.

[0391] Step 3:

[0392] The user presses the "Submit" button and sends the input data to the server.

[0393] Step 4:

[0394] The server receives the transmitted idea information.

[0395] Step 5:

[0396] The server stores the idea data in a database.

[0397] Step 6:

[0398] The server notifies other users that a new idea has been posted.

[0399] 3. Idea Evaluation and Comment Process

[0400] Step 1:

[0401] A user accesses the system from a terminal and displays a list of ideas posted by other users.

[0402] Step 2:

[0403] The user selects an idea that interests them and accesses the detail view page.

[0404] Step 3:

[0405] The user presses the rating button and enters feedback in the comment field.

[0406] Step 4:

[0407] The user presses the "Submit" button to send the evaluation data and comments to the server.

[0408] Step 5:

[0409] The server receives the rating data and comments and stores them in a database.

[0410] Step 6:

[0411] The server sends a rating and comment notification to the idea poster.

[0412] 4. AI-based idea analysis and proposal process

[0413] Step 1:

[0414] The server periodically sends the idea data in the database to the generation AI.

[0415] Step 2:

[0416] The generation AI analyzes the received idea data.

[0417] Step 3:

[0418] Generative AI generates relevant information and additional suggestions.

[0419] Step 4:

[0420] The generation AI sends the analysis results and suggestions to the server.

[0421] Step 5:

[0422] The server notifies the user of the analysis results and suggestions.

[0423] 5. Idea Improvement Process

[0424] Step 1:

[0425] The user accesses their idea details page from their device and presses the "Request for Improvement" button.

[0426] Step 2:

[0427] The user's request is sent to the server.

[0428] Step 3:

[0429] The server sends the relevant idea data to the generative AI.

[0430] Step 4:

[0431] Generative AI analyzes ideas and lists areas for improvement.

[0432] Step 5:

[0433] The generative AI sends improvements to the server.

[0434] Step 6:

[0435] The server will notify the user of the improvements.

[0436] Step 7:

[0437] Users receive notifications, review improvements, and revise their ideas.

[0438] 6. Introducing the Emotion Engine

[0439] Step 1:

[0440] When a user posts an idea or comment from a terminal, the input data is sent to the server.

[0441] Step 2:

[0442] The server sends the received data to the emotion engine.

[0443] Step 3:

[0444] The emotion engine analyzes the data in real time and evaluates the user's emotional state (positive, negative, etc.).

[0445] Step 4:

[0446] The emotion engine provides the analysis results to the generative AI and generative AI.

[0447] Step 5:

[0448] The generative AI and generative system AI adjust suggestions and improvements based on the emotional state and send them to the server.

[0449] Step 6:

[0450] The server notifies the user of any adjustment suggestions or improvements.

[0451] Step 7:

[0452] Users can check improvements and suggestions from their devices and further refine their ideas. In addition, appropriate feedback and notifications based on sentiment analysis results provide a more personalized user experience.

[0453] Example 2

[0454] 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."

[0455] Conventional information input and posting systems do not adequately provide feedback and suggestions that take into account the user's emotional state. As a result, the quality of the user experience declines and the efficiency of the idea evaluation and improvement process decreases. In particular, there is a lack of appropriate follow-up for users with negative emotions, making it difficult to maintain user motivation.

[0456] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: an input means for a user to input information; a server means for receiving the input information; a data storage means for saving the received information; a notification means for notifying other users of the received information; an evaluation means for rating and commenting on ideas based on the notified information; a memory means for saving the evaluation and comment in the data storage means; a generation AI means for analyzing the received information and generating related proposals; a notification means for notifying the user of the analysis results and proposals; a generative AI means for improving the received information; a notification means for notifying the user of the improvement results; an emotion analysis means for analyzing data input by the user using the system and detecting emotions; a providing means for providing the emotion analysis results to the generation AI and the generative AI means; and a notification means for sending specific proposals or notifications to the user based on the emotion analysis results. This enables the provision of feedback and proposals that take the user's emotional state into consideration, improving the quality of the user experience and increasing the efficiency of the idea evaluation and improvement process.

[0457] The "input means" is an interface for the user to input information.

[0458] "Server means" is a computer system for processing and managing information received from users.

[0459] "Data storage means" means a storage device for storing received information.

[0460] A "notification means" is a system for notifying other users of specific information.

[0461] The "evaluation means" is a function that allows users to evaluate and comment on other users' ideas.

[0462] "Storage means" is a device or system for storing ratings and comments.

[0463] "Generative AI means" means an artificial intelligence system for analyzing received information and generating relevant suggestions.

[0464] The "means of provision" is a system for passing the results of emotion analysis to the generation AI and the generation system AI means.

[0465] "Emotion analysis means" is a technology for analyzing emotions based on data entered by users into the system.

[0466] "Generative AI methods" are artificial intelligence technologies that generate optimal improvement proposals based on user input information.

[0467] The system based on this invention allows users to input and post information, receive ratings and comments from other users, and analyze and improve that information in a process that combines an emotion engine to provide a more personalized experience. A specific embodiment of the system is shown below.

[0468] User Registration Process

[0469] The user accesses the system's new registration form from their terminal and enters their profile information. When the user submits the information, the server receives it and stores it in a database, which is a data storage means. Once registration is complete, the server sends the user an email informing them of completion of registration. During this process, the server uses the SMTP protocol as both an input means and a notification means.

[0470] Examples:

[0471] Device: Smartphone

[0472] Software used: Web browser

[0473] Data Used: User name, email address, password

[0474] Idea Submission Process

[0475] The user accesses the idea submission form from their device and enters the title and text of their idea. Once submission is complete, the server saves the idea information in a database, which serves as a data storage means, and notifies other users that a new idea has been submitted. The server uses a push notification service as a notification method.

[0476] Examples:

[0477] Device: PC

[0478] Software used: Web browser

[0479] Data used: Idea title, content

[0480] Idea evaluation and comment process

[0481] When a user enters a rating and comment on another user's idea, the content is sent to the server and stored in the data storage means, and the server notifies the idea poster of the rating and comment.

[0482] Examples:

[0483] Device: Tablet

[0484] Software used: Web app

[0485] Data used: Comments, ratings

[0486] AI-based idea analysis and proposal process

[0487] The server periodically transmits the idea information stored in the data storage means to the generation AI means, which analyzes the information and generates a proposal. The generated proposal is notified to the user via the server, allowing the user to review and improve the idea.

[0488] Examples:

[0489] Software used: Generative AI models (e.g., OpenAI GPT-4)

[0490] Data used: Content of the submitted idea

[0491] Idea Improvement Process

[0492] When a user presses the "Improvement Request" button on their idea's details page, the request is sent to the server. The server sends the idea to the generative AI means, which then lists improvements. The listed improvements are notified to the user via the server, allowing the user to modify the idea.

[0493] Examples:

[0494] Device: Laptop

[0495] Software used: Web browser

[0496] Data used: Idea content, improvement requests

[0497] Introducing the Emotion Engine

[0498] The emotion analysis means analyzes data (text, ratings, etc.) entered by users using the system in real time. This emotion analysis is particularly effective when posting ideas or comments, and the server provides the analysis results to the generative AI and generative AI means. This allows suggestions and improvements to be generated that take the user's emotional state into account.

[0499] Examples:

[0500] Software used: Sentiment analysis means (e.g., IBM Watson® Sentiment Analysis)

[0501] Data used: Comments, ratings

[0502] Sentiment Analysis and Notifications

[0503] The emotion analysis means analyzes the user's input data, and if it detects positive or negative emotions, the server uses the emotion analysis results as a notification means. If negative emotions are detected, encouraging messages and specific suggestions for improvement are sent. Conversely, if positive emotions are detected, the server can increase the user's motivation by notifying them of success stories and the positive reactions of other users.

[0504] Examples:

[0505] Software used: Sentiment analysis tools, notification tools (e.g., Twilio)

[0506] Data Used: User comments, posts

[0507] Example prompts to input to a generative AI model:

[0508] Entered idea data:

[0509] Title: "New Educational App Ideas"

[0510] Content: "I want to create an app that helps students learn in a fun way. I would like to add various features."

[0511] Emotion analysis results:

[0512] Emotion: Positive

[0513] Prompt to spawn AI:

[0514] "Please list improvements to this idea that reflect your users' positive feelings."

[0515] By showing specific examples of the mode for carrying out this invention, it will help those involved to accurately understand the content of the invention and serve as a reference for putting it into practice. In addition, by showing specific examples of the hardware and software to be used, it will serve as a guideline for implementation.

[0516] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0517] User Registration Process

[0518] Step 1:

[0519] The user accesses the sign-up form from a device.

[0520] Input: Profile information the user enters into the form (name, email address, password).

[0521] Output: The data entered into the form.

[0522] Specific operation: Access the new registration URL using the web browser on the device.

[0523] Step 2:

[0524] The user enters profile information and presses the "Register" button.

[0525] Input: Form data entered by the user.

[0526] Output: The form data is sent to the server.

[0527] What happens: The user fills out the form and clicks the "Register" button.

[0528] Step 3:

[0529] The server receives the entered information and performs verification.

[0530] Input: Form data.

[0531] Output: The validation result (success or failure).

[0532] What happens: The server receives the data and checks the format of the name, email address, and password.

[0533] Step 4:

[0534] The server stores the information in a database, which is a data storage means.

[0535] Input: The validated form data.

[0536] Output: User profile information stored in a database.

[0537] Specific behavior: The server saves the data to the users table in the database.

[0538] Step 5:

[0539] The server sends a registration completion email.

[0540] Input: Saved user profile information.

[0541] Output: A registration completion email is sent to the user.

[0542] Specific operation: The server uses the SMTP protocol to send a registration completion email through the Google (registered trademark) Gmail API.

[0543] Idea Submission Process

[0544] Step 1:

[0545] A user accesses the idea submission form from a terminal.

[0546] Input: Request to access the idea submission page.

[0547] Output: The idea submission form is displayed in the browser.

[0548] Specific actions: Access the idea submission page using a web browser on your device.

[0549] Step 2:

[0550] The user enters the title and text of the idea and presses the "Post" button.

[0551] Input: Idea information (title, body) entered by the user.

[0552] Output: The idea information sent to the server.

[0553] Specific behavior: The user enters a title and body text and clicks the "Post" button.

[0554] Step 3:

[0555] The server receives the submitted data and verifies it.

[0556] Input: Idea information.

[0557] Output: The validation result (success or failure).

[0558] Specific operation: The server receives the sent idea information and checks the format and content of the data.

[0559] Step 4:

[0560] The server stores the idea information in a database, which is a data storage means.

[0561] Input: Verified idea information.

[0562] Output: Idea information stored in a database.

[0563] What happens: The server saves the data to the ideas collection in MongoDB.

[0564] Step 5:

[0565] The server notifies other users that a new idea has been posted.

[0566] Input: Saved idea information.

[0567] Output: Notifications sent to other users.

[0568] Specific behavior: The server uses the push notification service to notify that a new idea has been posted.

[0569] Idea evaluation and comment process

[0570] Step 1:

[0571] A user accesses another user's idea detail page from a terminal.

[0572] Input: A request to access the idea details page.

[0573] Output: The idea detail page is displayed in a browser.

[0574] What happens: Use a web browser on your device to access the idea details page.

[0575] Step 2:

[0576] Users rate ideas and enter comments.

[0577] Input: Rating and Comments.

[0578] Output: Ratings and comments sent to the server.

[0579] Specific behavior: User selects a rating (e.g., 4 out of 5 stars), enters a comment, and clicks the "Submit" button.

[0580] Step 3:

[0581] The server receives and validates the ratings and comments.

[0582] Input: Rating and Comments.

[0583] Output: The validation result (success or failure).

[0584] Specific behavior: The server receives the submitted ratings and comments and checks the format and content of the data.

[0585] Step 4:

[0586] The server stores the ratings and comments in a database, which is a data storage means.

[0587] Input: Verified rating and comments.

[0588] Output: Ratings and comments stored in a database.

[0589] What happens: The server saves the data to the comments collection in Firebase.

[0590] Step 5:

[0591] The server sends a rating and comment notification to the idea poster.

[0592] Input: Saved ratings and comments.

[0593] Output: Notification sent to idea submitter.

[0594] Specific behavior: The server sends a notification to the email address of the idea poster.

[0595] AI-based idea analysis and proposal process

[0596] Step 1:

[0597] The server periodically sends the idea information in the database to the generative AI model.

[0598] Input: Idea information.

[0599] Output: The data sent to the generative AI model.

[0600] What it does: The server retrieves the latest idea information from the ideas collection in MongoDB and sends it to the generative AI model's API.

[0601] Step 2:

[0602] A generative AI model analyzes idea information and generates proposals.

[0603] Input: Idea information.

[0604] Output: The generated proposals.

[0605] How it works: A generative AI model (e.g., OpenAI GPT-4) analyzes idea information and generates relevant suggestions.

[0606] Step 3:

[0607] The server receives the generated proposal and notifies the user.

[0608] Input: The generated proposals.

[0609] Output: The notification sent to the user.

[0610] Specific operation: The server receives suggestions from the generative AI model and sends them to the user via WebSocket notification or email.

[0611] Idea Improvement Process

[0612] Step 1:

[0613] The user accesses their idea details page from a device.

[0614] Input: A request to access the idea details page.

[0615] Output: The idea detail page is displayed in a browser.

[0616] What to do: Use a web browser on your device to access your idea details page.

[0617] Step 2:

[0618] The user presses the "improvement request" button.

[0619] Input: Enhancement request.

[0620] Output: The request sent to the server.

[0621] Specific behavior: The user clicks the "Enhancement Request" button.

[0622] Step 3:

[0623] The server receives the request and sends the idea information to the generative AI.

[0624] Input: Enhancement request and idea information.

[0625] Output: Data sent to the generative AI.

[0626] Specific operation: The server retrieves the relevant idea information from the ideas collection in MongoDB and sends it to the API of the generative AI model.

[0627] Step 4:

[0628] Generative AI lists areas for improvement.

[0629] Input: Idea information.

[0630] Output: Listed improvements.

[0631] How it works: The generative AI model analyzes idea information and lists areas for improvement.

[0632] Step 5:

[0633] The server will notify the user of the listed improvements.

[0634] Input: A list of improvements.

[0635] Output: The notification sent to the user.

[0636] What happens: The server notifies the user of the listed improvements via email and WebSocket notifications.

[0637] Introducing the Emotion Engine

[0638] Step 1:

[0639] The sentiment analysis means receives data (e.g., text and ratings) entered by users using the system.

[0640] Input: User input data.

[0641] Output: Data sent to sentiment analysis means.

[0642] Specific operation: The user enters comments and ratings on the device, and the server sends the data to the sentiment analysis means.

[0643] Step 2:

[0644] Sentiment analysis tools analyze data in real time.

[0645] Input: User input data.

[0646] Output: Emotion analysis results.

[0647] What it does: A sentiment analyzer (e.g., IBM Watson Sentiment Analysis) analyzes the data in real time to detect positive or negative sentiment.

[0648] Step 3:

[0649] The server provides the analysis results to the generation AI and the generation system AI means.

[0650] Input: Sentiment analysis results.

[0651] Output: Data sent to the Generative AI and Generative AI Means.

[0652] Specific operation: The server provides the analysis results to the generation AI and the generation system AI means.

[0653] Step 4:

[0654] The generative AI and generative AI means generate suggestions and improvements that take emotional state into account.

[0655] Input: Sentiment analysis results.

[0656] Output: Suggestions and improvements that take sentiment into account.

[0657] Specific operation: Generative AI and generative AI means generate suggestions and improvements based on the analysis results.

[0658] Sentiment Analysis and Notifications

[0659] Step 1:

[0660] The emotion analysis means analyzes the input data of the user and detects emotions.

[0661] Input: User input data.

[0662] Output: The detected emotion.

[0663] Specific operation: The sentiment analysis means analyzes user comments and ratings to detect emotions.

[0664] Step 2:

[0665] The server uses the emotion analysis results as a notification method.

[0666] Input: Sentiment analysis results.

[0667] Output: Emotion-based notification.

[0668] Specific operation: The server creates notification content based on the results of emotion analysis.

[0669] Step 3:

[0670] If negative sentiment is detected, it will send encouraging messages and specific suggestions for improvement.

[0671] Input: Detected negative sentiment.

[0672] Output: Encouraging messages and suggestions for improvement.

[0673] Specific operation: The server uses the Twilio API to send an encouraging message via SMS or email.

[0674] Step 4:

[0675] If positive sentiment is detected, notifications will be sent out highlighting success stories and positive reactions.

[0676] Input: Detected positive sentiment.

[0677] Output: Success stories and positive responses.

[0678] What happens: The server sends the user an email summarizing success stories and positive feedback.

[0679] (Application example 2)

[0680] 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."

[0681] Conventional feedback systems simply collect user opinions and comments as data and are unable to consider emotional factors such as emotions and attitudes. As a result, they are unable to provide improvement suggestions or responses based on the user's actual emotions, and are unable to fully increase user satisfaction. Furthermore, negative feedback is often not handled appropriately, leaving the problem unresolved. The present invention aims to solve these problems and provide a personalized feedback experience that takes user emotions into consideration.

[0682] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input information; server means for receiving the input information; database means for storing the received information; notification means for notifying other users of the received information; means for rating and commenting on ideas based on the notified information; means for storing the rating and comment in the database means; generative AI means for analyzing the received information and generating related proposals; means for notifying the user of the analysis results and proposals; generative AI means for improving the received information; means for notifying the user of the improvement results; emotion analysis means for analyzing the input information and extracting emotion information; and notification correction means for correcting the notification content based on the emotion information. This enables specific responses and proposals that reflect the user's emotions, thereby increasing user satisfaction.

[0683] An "input means" is a device or software that provides an interface for a user to input information.

[0684] The "server means" is a computer system for receiving and processing input information.

[0685] "Database means" refers to a storage device or system for storing and managing received information.

[0686] "Notification means" is a communication means for informing other users of received information.

[0687] The "rating and commenting means" is a method or device for rating an idea and leaving a comment based on the notified information.

[0688] "Generative AI means" means artificial intelligence technology for analyzing received information and generating relevant suggestions.

[0689] "Generative AI methods" are artificial intelligence techniques for improving received information.

[0690] "Emotion analysis means" is a technology for analyzing and extracting emotions from input information.

[0691] The "notification modification means" is a technique or means for modifying the notification content based on emotion information.

[0692] System Overview

[0693] This invention is a feedback system that collects and analyzes customer feedback in brick-and-mortar stores and provides personalized suggestions and improvements that take emotional information into account. The system is designed by integrating a smartphone application and server-side services.

[0694] Hardware and Software

[0695] Hardware:

[0696] Server: Receives, stores, and analyzes information, and notifies customers and store staff.

[0697] Smartphone: An interface device where customers enter feedback and receive results.

[0698] software:

[0699] Flask: Building an API server.

[0700] SQLite: Database.

[0701] TextBlob: A Python library for sentiment analysis.

[0702] smtplib: The Python standard library for sending email.

[0703] System Operation

[0704] 1. User Registration Process

[0705] When a user registers with the system from a smartphone app, the server receives the user's profile information and stores it in a database. Once registration is complete, the server sends a registration completion notification to the user.

[0706] 2. Feedback submission process

[0707] Users input feedback via a smartphone app. The server receives this feedback and stores it in a database. The server also analyzes this information using emotion analysis to detect positive or negative emotions.

[0708] 3. Sentiment Analysis Process

[0709] The server analyzes the sentiment of the received feedback using TextBlob, and if positive or negative sentiment is detected, it modifies the notification content based on the results and notifies the store staff in real time.

[0710] 4. Notification Process

[0711] The notification correction means introduces success stories and favorable reactions of other users in the case of positive feedback, and generates and sends encouraging messages in the case of negative feedback.

[0712] Specific examples

[0713] For example, if a user posts negative feedback such as "The service in the store was slow," the server analyzes the content using TextBlob to detect negative sentiment. It then generates an encouraging message saying, "We apologize for the inconvenience. We will strive to improve," and notifies the store staff. On the other hand, if a user posts positive feedback such as "The store staff's service was excellent," the server generates and sends a message introducing success stories from other customers.

[0714] Example prompt sentence:

[0715] "The following feedback was identified as negative. Please generate an encouraging message: Slow service in store."

[0716] This system allows responses that reflect the user's feelings, improving user satisfaction.

[0717] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0718] Step 1:

[0719] User Registration Process

[0720] Input: Users enter their profile information using a smartphone app.

[0721] Operation:

[0722] The server receives the user's input.

[0723] The received profile information is stored in an SQLite database.

[0724] Once registration is complete, the server will send a registration completion notification to the user's email address.

[0725] Output: A notification is sent to the user that registration is complete. The new user's information is saved in the database.

[0726] Step 2:

[0727] Feedback submission process

[0728] Input: Users enter feedback using a smartphone app.

[0729] Operation:

[0730] The server receives the user's feedback.

[0731] The received feedback information is stored in an SQLite database.

[0732] Output: The feedback is stored in a database.

[0733] Step 3:

[0734] Sentiment Analysis Process

[0735] Input: Feedback information stored in the database.

[0736] Operation:

[0737] The server uses TextBlob to perform sentiment analysis of the feedback content, which determines the polarity of the feedback (positive or negative).

[0738] Data processing: Analyze the text data (feedback content) using TextBlob to generate sentiment polarity scores.

[0739] Output: Sentiment analysis result (positive or negative).

[0740] Step 4:

[0741] Notification Process

[0742] Input: Sentiment analysis results, feedback content.

[0743] Operation:

[0744] The server generates an appropriate notification based on the sentiment analysis results.

[0745] For positive feedback: Generate messages showcasing success stories and positive reactions from other customers.

[0746] For negative feedback: Generate an encouraging message.

[0747] Use smtplib to email the generated notification to store staff.

[0748] Data processing: Generate notification content based on the analysis results and process it as a text message.

[0749] Output: Notification message sent to store staff.

[0750] Example prompt sentence:

[0751] "The following feedback was identified as negative. Please generate an encouraging message: Slow service in store."

[0752] Through the above processing steps, specific responses and suggestions that reflect the user's feelings are provided, thereby improving customer satisfaction.

[0753] 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.

[0754] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0755] 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.

[0756] [Second embodiment]

[0757] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0758] 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.

[0759] 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).

[0760] 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.

[0761] 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.

[0762] 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).

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0768] 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."

[0769] In the system based on this invention, users can input and post information, exchange ratings and comments, and improve and develop ideas with the support of generative AI and generative system AI. This system has multiple means and functions as follows:

[0770] 1. User Registration Process

[0771] Example

[0772] A user accesses the system's new registration form from a terminal and enters information such as their name, email address, and field of expertise. When this information is sent, the server receives it and stores it in a database. The server then sends the user an email informing them of completion of registration.

[0773] 2. Idea submission process

[0774] Example

[0775] A user accesses the idea submission form from their device and enters the title and body of their idea. When this data is sent, the server receives it and stores it in a database. The server then notifies other users that a new idea has been posted. This allows users to quickly share their ideas.

[0776] 3. Idea Evaluation and Comment Process

[0777] Example

[0778] A user accesses a list of ideas posted by other users from their device and displays the details page of an idea they are interested in. The user then presses the rating button and enters comment feedback. This rating and comment are sent to the server and stored in a database. The server then sends a notification of the rating and comment to the poster of the idea.

[0779] 4. AI-based idea analysis and proposal process

[0780] Example

[0781] The server periodically sends the idea data in the database to the generation AI. The generation AI analyzes the received idea data and generates related information and additional suggestions. The generated suggestions are sent to the server, which notifies the user. This allows the user to review and improve their ideas based on the analysis results and suggestions.

[0782] 5. Idea Improvement Process

[0783] Example

[0784] The user accesses the details page of their idea from their device and presses the "Improvement Request" button. This request is sent to the server, which then sends the idea data to the generative AI. The generative AI analyzes the idea and lists improvements. The generated improvements are then sent to the server, which notifies the user. The user can then use this information to modify and improve their idea.

[0785] This system provides an environment where users can easily post ideas, receive feedback, and refine their ideas while referring to automatically generated suggestions and improvements, accelerating idea generation and promoting the information revolution.

[0786] The processing flow will be explained below.

[0787] 1. User Registration Process

[0788] Step 1:

[0789] A user accesses the system from a terminal and displays a new registration form.

[0790] Step 2:

[0791] The user enters profile information such as name, email address, and area of ​​expertise.

[0792] Step 3:

[0793] The user presses the "Register" button and sends the input data to the server.

[0794] Step 4:

[0795] The server receives the transmitted data.

[0796] Step 5:

[0797] The server checks the integrity of the data it receives and, if there are no errors, stores it in the database.

[0798] Step 6:

[0799] The server sends a registration completion email to the user.

[0800] 2. Idea submission process

[0801] Step 1:

[0802] A user accesses the system from a terminal and displays the idea submission form.

[0803] Step 2:

[0804] The user enters the title and body of the idea.

[0805] Step 3:

[0806] The user presses the "Submit" button and sends the input data to the server.

[0807] Step 4:

[0808] The server receives the transmitted idea information.

[0809] Step 5:

[0810] The server stores the idea data in a database.

[0811] Step 6:

[0812] The server notifies other users that a new idea has been posted.

[0813] 3. Idea Evaluation and Comment Process

[0814] Step 1:

[0815] A user accesses the system from a terminal and displays a list of ideas posted by other users.

[0816] Step 2:

[0817] The user selects an idea that interests them and accesses the detail view page.

[0818] Step 3:

[0819] The user presses the rating button and enters feedback in the comment field.

[0820] Step 4:

[0821] The user presses the "Submit" button to send the evaluation data and comments to the server.

[0822] Step 5:

[0823] The server receives the rating data and comments and stores them in a database.

[0824] Step 6:

[0825] The server sends a rating and comment notification to the idea poster.

[0826] 4. AI-based idea analysis and proposal process

[0827] Step 1:

[0828] The server periodically sends the idea data in the database to the generation AI.

[0829] Step 2:

[0830] The generation AI analyzes the received idea data.

[0831] Step 3:

[0832] Generative AI generates relevant information and additional suggestions.

[0833] Step 4:

[0834] The generation AI sends the analysis results and suggestions to the server.

[0835] Step 5:

[0836] The server notifies the user of the analysis results and suggestions.

[0837] 5. Idea Improvement Process

[0838] Step 1:

[0839] The user accesses their idea details page from their device and presses the "Request for Improvement" button.

[0840] Step 2:

[0841] The user's request is sent to the server.

[0842] Step 3:

[0843] The server sends the relevant idea data to the generative AI.

[0844] Step 4:

[0845] Generative AI analyzes ideas and lists areas for improvement.

[0846] Step 5:

[0847] The generative AI sends improvements to the server.

[0848] Step 6:

[0849] The server will notify the user of the improvements.

[0850] Step 7:

[0851] Users receive notifications, review improvements, and revise their ideas.

[0852] Example 1

[0853] 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."

[0854] Conventional idea-sharing platforms have a problem in that the process of users posting ideas and receiving feedback from other users is cumbersome, making it difficult to efficiently improve ideas. Furthermore, there is a lack of a means for users to receive specific suggestions for voluntarily improving their ideas. Furthermore, there is no system for regularly analyzing ideas and generating suggestions, which limits the quality of creative ideas.

[0855] 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.

[0856] In this invention, the server includes an input means for a user to input information, a server means for receiving the input information, a database means for storing the received information, a notification means for notifying other users of the received information, a means for rating and commenting on ideas based on the notified information, a means for storing the rating and comment in the database means, a generative AI means for periodically analyzing the received information and generating related proposals, a means for notifying users of the analysis results and proposals, a means for receiving idea improvement requests from users, a generative AI means for analyzing the received information and listing improvements, and a means for notifying users of the improvements. This enables users to intuitively post ideas, quickly receive feedback from other users, and efficiently improve their ideas based on specific suggestions and improvements from the generative AI and the generative AI.

[0857] "User" refers to an individual who inputs information, posts ideas, ratings and comments.

[0858] "Input means" refers to the interface through which a user enters information or ideas, such as a web form or an input field in a mobile app.

[0859] "Server means" refers to a computer system that receives information sent from a user and performs various processes.

[0860] "Database Means" refers to a data storage device or system for storing and managing received information.

[0861] "Notification mechanism" refers to a method or system for communicating specific information or updates to other users.

[0862] "Means for rating and commenting" refers to a function that allows users to input ratings and comments on posted ideas.

[0863] "Generative AI Means" refers to the artificial intelligence model used to analyze received information and generate relevant recommendations.

[0864] "Generative AI means" refers to an artificial intelligence model that analyzes received information based on user requests and lists areas for improvement.

[0865] The system based on this invention allows users to input and post information, exchange ratings and comments, and improve and develop ideas with the support of generative AI and generative system AI. This system has the following multiple means and functions:

[0866] 1. User Registration Process

[0867] The user accesses the system's new registration form on their own device (PC, smartphone, etc.) and enters information such as their name, email address, and field of expertise. After entering the information, they press the "Submit" button, and the server receives the information and stores it in a database (e.g., MySQL). The server then sends the user a registration completion email.

[0868] Example: A user fills in a new registration form with information such as "Yamada Taro", "example@example.com", and "Engineering", and submits it.

[0869] 2. Idea submission process

[0870] A user accesses the idea submission form on their device and enters the title and text of their idea. For example, "A new solar panel design" and "This solar panel will improve efficiency by 50%." After entering the information, they press the "Submit" button. The server receives the data and stores it in a database. The server then notifies other users that a new idea has been submitted.

[0871] Example: A user enters the title and body of an idea "New solar panel design" and submits it.

[0872] 3. Idea Evaluation and Comment Process

[0873] A user accesses a list of ideas posted by other users from their device and displays the details page of an idea they are interested in. For example, they can enter a comment such as "I think this is practical" for the idea "New solar panel design" and rate it "5 stars." When the rating and comment are submitted, they are sent to the server and stored in a database. The server then notifies the idea poster that the rating and comment have been received.

[0874] Example: A user submits a 5-star rating for "New Solar Panel Design" and a comment saying "Great idea."

[0875] 4. AI-based idea analysis and proposal process

[0876] The server periodically sends the idea data in the database to a generation AI (e.g., OpenAI's GPT-4). The generation AI analyzes the received ideas and generates related information and additional suggestions. These suggestions are then sent back to the server, which notifies the user.

[0877] Example prompt: An idea has been submitted for "New Solar Panel Design." Check it out if you're interested.

[0878] 5. Idea Improvement Process

[0879] When a user accesses the details page of their idea on their device and presses the "Improvement Request" button, a request is sent to the server. The server sends the idea data to a generative AI (e.g., DeepAI), which analyzes the idea and lists improvements. The generated improvements are sent to the server, which notifies the user. The user can then use this information to revise and improve their idea.

[0880] Example: After the user presses the "improvement request" button, the suggestion for improvement is presented: "The amount of silicon used should be reduced to further improve area efficiency."

[0881] This program is implemented as a web platform to make user operation intuitive and simple, and uses HTML, CSS, JavaScript, etc. as the user interface. The server side is implemented using languages ​​such as Python and Node.js, and a relational database such as MySQL is used as the database. OpenAI's GPT-4 and DeepAI's API are used for the generative AI and generative AI models, which automatically perform periodic analysis processing and respond to user requests.

[0882] This system allows users to intuitively post ideas, quickly receive feedback from other users, and efficiently improve their ideas based on specific suggestions and improvements from the generative AI and generative system AI. As a result, the quality of ideas improves, providing an environment that promotes the creation of new value.

[0883] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0884] User Registration Process

[0885] Step 1:

[0886] The user accesses the system's new registration page on their own device (PC, smartphone, etc.).

[0887] Input: Enter the URL of the system's new registration page in the URL bar of your web browser.

[0888] Output: A new registration form is displayed.

[0889] Step 2:

[0890] Users enter information such as their name, email address, and area of ​​expertise into the new registration form.

[0891] Input: Enter your name, email address, area of ​​expertise, etc. in the input fields.

[0892] Output: The input data is displayed in a form.

[0893] Step 3:

[0894] The user clicks the "Submit" button.

[0895] Input: The "Submit" button is clicked.

[0896] Output: The form data is sent to the server.

[0897] Step 4:

[0898] The server receives the user's input information as an HTTP POST request.

[0899] Input: User information included in the HTTP POST request.

[0900] Output: User information temporarily stored in the server's processing memory.

[0901] Step 5:

[0902] The server stores the received data in a database.

[0903] Input: User information temporarily stored in the server's processing memory.

[0904] Output: User information stored in the users table in the MySQL database.

[0905] Step 6:

[0906] The server sends a registration completion email to the user.

[0907] Input: The content of the registration confirmation email and the user's email address.

[0908] Output: A registration confirmation email sent to the user's email account.

[0909] Idea Submission Process

[0910] Step 1:

[0911] The user accesses the idea submission page on the terminal.

[0912] Enter the URL of the idea submission page in the URL bar of your web browser.

[0913] Output: The idea submission form is displayed.

[0914] Step 2:

[0915] The user enters the title and body of the idea.

[0916] Input: Enter the title and text of your idea in the fields provided.

[0917] Output: The input data is displayed in a form.

[0918] Step 3:

[0919] The user clicks the "Submit" button.

[0920] Input: The "Submit" button is clicked.

[0921] Output: The form data is sent to the server.

[0922] Step 4:

[0923] The server receives the idea data as an HTTP POST request.

[0924] Input: Idea information included in the HTTP POST request.

[0925] Output: Idea information temporarily stored in the server's processing memory.

[0926] Step 5:

[0927] The server stores the received data in a database.

[0928] Input: Idea information temporarily stored in the server's processing memory.

[0929] Output: Idea information stored in the Ideas table in a MySQL database.

[0930] Step 6:

[0931] The server notifies other users that a new idea has been posted.

[0932] Input: New idea information and a list of people to notify.

[0933] Output: A notification email or in-app notification is sent to other users.

[0934] Idea evaluation and comment process

[0935] Step 1:

[0936] The user accesses a list page of ideas posted by other users from their terminal.

[0937] Input: Enter the URL of the idea list page into the URL bar of your web browser.

[0938] Output: A list of ideas is displayed.

[0939] Step 2:

[0940] View the detail page of the idea that the user wants to rate.

[0941] Enter: Click on a specific idea from the list of ideas.

[0942] Output: The details page for a particular idea is displayed.

[0943] Step 3:

[0944] The user presses the rating button.

[0945] Input: The rating button is clicked.

[0946] Output: Evaluation data is temporarily stored.

[0947] Step 4:

[0948] The user enters comment feedback.

[0949] Enter your feedback in the comments section.

[0950] Output: The comment data is displayed in a form.

[0951] Step 5:

[0952] The user clicks the "Submit" button.

[0953] Input: The "Submit" button is clicked.

[0954] Output: The form data is sent to the server.

[0955] Step 6:

[0956] The server receives the ratings and comments as HTTP POST requests.

[0957] Input: Rating and comment information included in the HTTP POST request.

[0958] Output: Rating and comment information temporarily stored in the server's processing memory.

[0959] Step 7:

[0960] The server stores the received data in a database.

[0961] Input: Rating and comment information temporarily stored in the server's processing memory.

[0962] Output: Rating and comment information stored in the ratings table in a MySQL database.

[0963] Step 8:

[0964] The server notifies the idea poster.

[0965] Input: Rating and comment information and author information to notify.

[0966] Output: A notification email or in-app notification is sent to the contributor.

[0967] AI-based idea analysis and proposal process

[0968] Step 1:

[0969] The server periodically scans the database.

[0970] Input: A periodic timer trigger.

[0971] Output: Obtaining new idea information.

[0972] Step 2:

[0973] The server sends new idea data to the generation AI.

[0974] Input: New idea information.

[0975] Output: API request to the generating AI.

[0976] Step 3:

[0977] Generative AI analyzes idea data.

[0978] Input: Idea information.

[0979] Output: Analysis results and recommendations.

[0980] Step 4:

[0981] Generative AI generates suggestions.

[0982] Input: Analysis results.

[0983] Output: Proposal data.

[0984] Step 5:

[0985] The proposal is sent to the server.

[0986] Input: Proposal data.

[0987] Output: The API response to the server.

[0988] Step 6:

[0989] The server notifies the user of the offer.

[0990] Input: Proposal data and user information to notify.

[0991] Output: A notification email or in-app notification is sent to the user.

[0992] Idea Improvement Process

[0993] Step 1:

[0994] A user visits their idea detail page.

[0995] Input: Enter the URL of the idea detail page into the URL bar of your web browser.

[0996] Output: The idea details page is displayed.

[0997] Step 2:

[0998] The user presses the "improvement request" button.

[0999] Input: The "Improvement Request" button is clicked.

[1000] Output: An enhancement request is sent to the server.

[1001] Step 3:

[1002] The request is sent to the server.

[1003] Input: Enhancement request data.

[1004] Output: Request data temporarily stored in the server's processing memory.

[1005] Step 4:

[1006] The server sends the idea data to the generative AI.

[1007] Input: Idea information and improvement request data.

[1008] Output: API request to the generative AI.

[1009] Step 5:

[1010] Generative AI analyzes ideas.

[1011] Input: Idea information and improvement requests.

[1012] Output: Analysis results and improvements.

[1013] Step 6:

[1014] Generative AI lists areas for improvement.

[1015] Input: Analysis results.

[1016] Output: A list of improvements.

[1017] Step 7:

[1018] The improvements are sent to the server.

[1019] Input: A list of improvements.

[1020] Output: The API response to the server.

[1021] Step 8:

[1022] The server notifies the user of the improvements.

[1023] Input: A list of improvements and the users to notify.

[1024] Output: A notification email or in-app notification is sent to the user.

[1025] (Application example 1)

[1026] 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."

[1027] Conventional content distribution services have a problem in that even when users post new content or ideas, they do not receive prompt and effective feedback or evaluations, making it difficult to create high-quality content. Furthermore, there is a lack of a mechanism for automatically analyzing posted content and providing suggestions for improvements or additions, limiting the means by which users can effectively improve their own content. The objective of this invention is to solve these problems and provide an environment in which users can create high-quality content.

[1028] 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.

[1029] In this invention, the server includes input means for a user to input information, server means for receiving the input information, database means for saving the received information, notification means for notifying other information processing devices of the received information, means for rating and commenting on ideas based on the notified information, means for saving the rating and comment in the database means, generative AI means for analyzing the received information and generating related proposals, means for notifying the information processing device of the analysis results and proposals, generative AI means for improving the received information, means for notifying the information processing device of the improvement results, means for a user to post new content or ideas in a content distribution service and receive feedback and evaluations thereon from other information processing devices, and means for analyzing the user's posted content using generative AI and providing suggestions for improvements and additions. This allows users to receive feedback quickly and effectively and to improve their own content to a higher quality by utilizing the analysis and suggestions by the generative AI.

[1030] An "input means" is a device or interface through which a user inputs information.

[1031] The "server means" refers to a server device or its function for receiving and processing input information.

[1032] "Database Means" means a database system or function thereof for storing and managing received information.

[1033] The "notification means" is a device or function for notifying other information processing devices of received information.

[1034] The "evaluation and comment means" is a device or function for evaluating and commenting on ideas based on the notified information.

[1035] "Generative AI means" means an artificial intelligence model or function thereof for analyzing received information and generating relevant recommendations.

[1036] "Information processing device" refers to any device that a user uses to input, receive, and process information.

[1037] A "generative AI means" is an artificial intelligence model or function that analyzes received information to improve it and lists areas for improvement.

[1038] A "content distribution service" is a service that provides information and media content to users via the Internet or the like.

[1039] "Feedback" refers to evaluations and comments received from other information processing devices.

[1040] "Analysis results" refers to suggestions and improvements generated by the generative AI means.

[1041] This system supports users in posting new content and ideas on a content distribution service, and receives feedback and evaluations from other information processing devices. Furthermore, it uses generative AI to analyze users' posts and provide suggestions for improvements and additions, helping users to create high-quality content.

[1042] The server uses the following hardware and software to process information such as reception, storage, notification, analysis, and improvement.

[1043] Hardware: High-performance server equipment, database server

[1044] Software: Flask (web framework), SQLAlchemy (database ORM), TensorFlow / PyTorch (generative AI model), Twilio (email sending)

[1045] Input Method

[1046] A web form is provided as an input mechanism for users to enter information, allowing them to enter information such as their name, email address, content categories of interest, and the title and body of the content.

[1047] Server Means

[1048] The server receives the information entered by the user and stores it in a database. The receiving process is performed by a web application using Flask.

[1049] Database Means

[1050] The received information is stored in a database using SQLAlchemy, and the stored data is used for subsequent processing (notification, analysis, improvement, etc.).

[1051] Notification means

[1052] When new content or ideas are posted, the server notifies other users, using Twilio to send emails.

[1053] Rating and commenting tools

[1054] Other users can access the information and provide ratings and comments, which are then sent back to the server and stored in a database.

[1055] Generation AI means

[1056] The server periodically sends the submitted data in the database to a generative AI model (TensorFlow or PyTorch), which analyzes the received data and generates relevant suggestions and improvements. The generated suggestions are returned to the server and notified to the user.

[1057] Information processing device

[1058] Information processing devices are devices that users use to input, receive, and process information, such as smartphones, smart glasses, head-mounted displays, and robots. This allows similar functions to be realized on any device.

[1059] Improvement AI method

[1060] When a user submits an improvement request, the server sends the request to the generative AI means, which analyzes the request and lists the improvements. The server then notifies the user of the improvements.

[1061] Examples and prompts

[1062] For example, if a user posts an idea for a new web browser design, the following prompt is sent: "This is an idea for a new web browser design. This browser is for the visually impaired and comes standard with a voice guide function." Based on this prompt, the generative AI model analyzes the proposal and provides specific suggestions such as "improvement suggestions to make the browser UI more intuitive" and "ways to enhance the voice guide function."

[1063] This allows users to receive feedback quickly and effectively, and leverage generative AI analysis and suggestions to improve their content to a higher quality.

[1064] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1065] Step 1:

[1066] A user accesses the system's new registration form from a terminal and enters their name, email address, content categories of interest, etc. The entered information is received by the server and saved in a database. The entered data is processed by checking for format and duplication. The server then uses Twilio to send the user a registration completion email. The user's personal information is input, and the output is saved in the database and a completion email is sent.

[1067] Step 2:

[1068] A user accesses the idea submission form from their device and enters the title and content body. The entered data is sent to the server and saved in the database. The server uses the notification function to notify other users via email via Twilio that a new idea has been posted. The idea content is input, and the output is saving to the database and sending a notification.

[1069] Step 3:

[1070] Other users check the new ideas notified from their devices and post ratings and comments. The ratings and comments are sent to the server and stored in a database. The server then notifies the original poster that the rating or comment has been posted. The input is the rating and comment, and the output is storage in the database and a notification to the poster.

[1071] Step 4:

[1072] The server periodically sends the posted data in the database to a generative AI model (TensorFlow or PyTorch). The generative AI model analyzes the received data and generates relevant suggestions. This analysis and data calculation uses natural language processing and clustering techniques. The generated suggestions are returned to the server and notified to the user. The user is notified of the posted data as input and the suggestions that are the analysis results as output.

[1073] Step 5:

[1074] The user accesses their idea details page from their device and presses the "Improvement Request" button. The request is sent to the server, which then sends the idea data to the generative AI model. The generative AI model analyzes the idea and lists improvements. The generated improvements are returned to the server and notified to the user. The improvement request and idea data are input, and the improvements are notified to the user as output.

[1075] Step 6:

[1076] Users receive notifications from the server and use the suggestions and improvements to revise and improve their ideas. The revised idea is then resubmitted to the server and stored in the database. At this time, the server resends notifications containing the revisions to other users, requesting their feedback. The revised idea is the input, and the output is saving to the database and re-notifying them.

[1077] This allows users to create and improve high-quality content.

[1078] 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.

[1079] The system based on this invention provides a more personalized experience by combining an emotion engine in the process of users inputting and posting information, receiving ratings and comments from other users, and analyzing and improving that information. A specific embodiment of the system is shown below.

[1080] 1. User Registration Process

[1081] Example

[1082] The user accesses the system's new registration form from their terminal and enters their profile information. When the user submits the information, the server receives it and stores it in the database. Once registration is complete, the server sends the user a registration completion email.

[1083] 2. Idea submission process

[1084] Example

[1085] A user accesses the idea submission form from a terminal and enters the title and text of the idea. Once submission is complete, the server saves the idea information in a database and notifies other users that a new idea has been submitted.

[1086] 3. Idea Evaluation and Comment Process

[1087] Example

[1088] When a user rates or comments on another user's idea from their device, the rating and comment are sent to the server and stored in a database. The server then sends a notification of the rating and comment to the idea poster.

[1089] 4. AI-based idea analysis and proposal process

[1090] Example

[1091] The server periodically sends the idea information in the database to the generation AI, which analyzes it and generates proposals. The generated proposals are notified to the user via the server, allowing the user to review and improve their ideas.

[1092] 5. Idea Improvement Process

[1093] Example

[1094] When a user presses the "Improvement Request" button on their device's idea details page, the request is sent to the server. The server sends the idea to the generative AI, which then lists improvements. The listed improvements are notified to the user via the server, allowing the user to modify the idea.

[1095] 6. Introducing the Emotion Engine

[1096] Example

[1097] The emotion engine analyzes data (text and ratings) entered by users in real time while they use the system from their devices. This emotion analysis is particularly effective when posting ideas or comments, and the server provides the analysis results to the generative AI and generative system AI. This allows suggestions and improvements to be generated that take the user's emotional state into account.

[1098] Sentiment Analysis and Notifications

[1099] Example

[1100] The emotion engine analyzes the user's input data, and if it detects positive or negative emotions, the server uses the emotion analysis results as a notification method. For example, if negative emotions are detected, the server can send the user an encouraging message or provide more specific suggestions for improvement. Conversely, if positive emotions are detected, the server can motivate the user by introducing success stories and other users' positive reactions.

[1101] By integrating an emotion engine, this invention takes into account the user's emotional state to provide more personalized feedback and improvement suggestions, which is expected to increase user satisfaction, improve the quality of ideas, and promote the information revolution.

[1102] The processing flow will be explained below.

[1103] 1. User Registration Process

[1104] Step 1:

[1105] A user accesses the system from a terminal and displays a new registration form.

[1106] Step 2:

[1107] The user enters profile information such as name, email address, and area of ​​expertise.

[1108] Step 3:

[1109] The user presses the "Register" button and sends the input data to the server.

[1110] Step 4:

[1111] The server receives the transmitted data.

[1112] Step 5:

[1113] The server checks the integrity of the data it receives and, if there are no errors, stores it in the database.

[1114] Step 6:

[1115] The server sends a registration completion email to the user.

[1116] 2. Idea submission process

[1117] Step 1:

[1118] A user accesses the system from a terminal and displays the idea submission form.

[1119] Step 2:

[1120] The user enters the title and body of the idea.

[1121] Step 3:

[1122] The user presses the "Submit" button and sends the input data to the server.

[1123] Step 4:

[1124] The server receives the transmitted idea information.

[1125] Step 5:

[1126] The server stores the idea data in a database.

[1127] Step 6:

[1128] The server notifies other users that a new idea has been posted.

[1129] 3. Idea Evaluation and Comment Process

[1130] Step 1:

[1131] A user accesses the system from a terminal and displays a list of ideas posted by other users.

[1132] Step 2:

[1133] The user selects an idea that interests them and accesses the detail view page.

[1134] Step 3:

[1135] The user presses the rating button and enters feedback in the comment field.

[1136] Step 4:

[1137] The user presses the "Submit" button to send the evaluation data and comments to the server.

[1138] Step 5:

[1139] The server receives the rating data and comments and stores them in a database.

[1140] Step 6:

[1141] The server sends a rating and comment notification to the idea poster.

[1142] 4. AI-based idea analysis and proposal process

[1143] Step 1:

[1144] The server periodically sends the idea data in the database to the generation AI.

[1145] Step 2:

[1146] The generation AI analyzes the received idea data.

[1147] Step 3:

[1148] Generative AI generates relevant information and additional suggestions.

[1149] Step 4:

[1150] The generation AI sends the analysis results and suggestions to the server.

[1151] Step 5:

[1152] The server notifies the user of the analysis results and suggestions.

[1153] 5. Idea Improvement Process

[1154] Step 1:

[1155] The user accesses their idea details page from their device and presses the "Request for Improvement" button.

[1156] Step 2:

[1157] The user's request is sent to the server.

[1158] Step 3:

[1159] The server sends the relevant idea data to the generative AI.

[1160] Step 4:

[1161] Generative AI analyzes ideas and lists areas for improvement.

[1162] Step 5:

[1163] The generative AI sends improvements to the server.

[1164] Step 6:

[1165] The server will notify the user of the improvements.

[1166] Step 7:

[1167] Users receive notifications, review improvements, and revise their ideas.

[1168] 6. Introducing the Emotion Engine

[1169] Step 1:

[1170] When a user posts an idea or comment from a terminal, the input data is sent to the server.

[1171] Step 2:

[1172] The server sends the received data to the emotion engine.

[1173] Step 3:

[1174] The emotion engine analyzes the data in real time and evaluates the user's emotional state (positive, negative, etc.).

[1175] Step 4:

[1176] The emotion engine provides the analysis results to the generative AI and generative AI.

[1177] Step 5:

[1178] The generative AI and generative system AI adjust suggestions and improvements based on the emotional state and send them to the server.

[1179] Step 6:

[1180] The server notifies the user of any adjustment suggestions or improvements.

[1181] Step 7:

[1182] Users can check improvements and suggestions from their devices and further refine their ideas. In addition, appropriate feedback and notifications based on sentiment analysis results provide a more personalized user experience.

[1183] Example 2

[1184] 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."

[1185] Conventional information input and posting systems do not adequately provide feedback and suggestions that take into account the user's emotional state. As a result, the quality of the user experience declines and the efficiency of the idea evaluation and improvement process decreases. In particular, there is a lack of appropriate follow-up for users with negative emotions, making it difficult to maintain user motivation.

[1186] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: an input means for a user to input information; a server means for receiving the input information; a data storage means for saving the received information; a notification means for notifying other users of the received information; an evaluation means for rating and commenting on ideas based on the notified information; a memory means for saving the evaluation and comment in the data storage means; a generation AI means for analyzing the received information and generating related proposals; a notification means for notifying the user of the analysis results and proposals; a generative AI means for improving the received information; a notification means for notifying the user of the improvement results; an emotion analysis means for analyzing data input by the user using the system and detecting emotions; a providing means for providing the emotion analysis results to the generation AI and the generative AI means; and a notification means for sending specific proposals or notifications to the user based on the emotion analysis results. This enables the provision of feedback and proposals that take the user's emotional state into consideration, improving the quality of the user experience and increasing the efficiency of the idea evaluation and improvement process.

[1187] The "input means" is an interface for the user to input information.

[1188] "Server means" is a computer system for processing and managing information received from users.

[1189] "Data storage means" means a storage device for storing received information.

[1190] A "notification means" is a system for notifying other users of specific information.

[1191] The "evaluation means" is a function that allows users to evaluate and comment on other users' ideas.

[1192] "Storage means" is a device or system for storing ratings and comments.

[1193] "Generative AI means" means an artificial intelligence system for analyzing received information and generating relevant suggestions.

[1194] The "means of provision" is a system for passing the results of emotion analysis to the generation AI and the generation system AI means.

[1195] "Emotion analysis means" is a technology for analyzing emotions based on data entered by users into the system.

[1196] "Generative AI methods" are artificial intelligence technologies that generate optimal improvement proposals based on user input information.

[1197] The system based on this invention allows users to input and post information, receive ratings and comments from other users, and analyze and improve that information in a process that combines an emotion engine to provide a more personalized experience. A specific embodiment of the system is shown below.

[1198] User Registration Process

[1199] The user accesses the system's new registration form from their terminal and enters their profile information. When the user submits the information, the server receives it and stores it in a database, which is a data storage means. Once registration is complete, the server sends the user an email informing them of completion of registration. During this process, the server uses the SMTP protocol as both an input means and a notification means.

[1200] Examples:

[1201] Device: Smartphone

[1202] Software used: Web browser

[1203] Data Used: User name, email address, password

[1204] Idea Submission Process

[1205] The user accesses the idea submission form from their device and enters the title and text of their idea. Once submission is complete, the server saves the idea information in a database, which serves as a data storage means, and notifies other users that a new idea has been submitted. The server uses a push notification service as a notification method.

[1206] Examples:

[1207] Device: PC

[1208] Software used: Web browser

[1209] Data used: Idea title, content

[1210] Idea evaluation and comment process

[1211] When a user enters a rating and comment on another user's idea, the content is sent to the server and stored in the data storage means, and the server notifies the idea poster of the rating and comment.

[1212] Examples:

[1213] Device: Tablet

[1214] Software used: Web app

[1215] Data used: Comments, ratings

[1216] AI-based idea analysis and proposal process

[1217] The server periodically transmits the idea information stored in the data storage means to the generation AI means, which analyzes the information and generates a proposal. The generated proposal is notified to the user via the server, allowing the user to review and improve the idea.

[1218] Examples:

[1219] Software used: Generative AI models (e.g., OpenAI GPT-4)

[1220] Data used: Content of the submitted idea

[1221] Idea Improvement Process

[1222] When a user presses the "Improvement Request" button on their idea's details page, the request is sent to the server. The server sends the idea to the generative AI means, which then lists improvements. The listed improvements are notified to the user via the server, allowing the user to modify the idea.

[1223] Examples:

[1224] Device: Laptop

[1225] Software used: Web browser

[1226] Data used: Idea content, improvement requests

[1227] Introducing the Emotion Engine

[1228] The emotion analysis means analyzes data (text, ratings, etc.) entered by users using the system in real time. This emotion analysis is particularly effective when posting ideas or comments, and the server provides the analysis results to the generative AI and generative AI means. This allows suggestions and improvements to be generated that take the user's emotional state into account.

[1229] Examples:

[1230] Software used: Sentiment analysis tools (e.g., IBM Watson Sentiment Analysis)

[1231] Data used: Comments, ratings

[1232] Sentiment Analysis and Notifications

[1233] The emotion analysis means analyzes the user's input data, and if it detects positive or negative emotions, the server uses the emotion analysis results as a notification means. If negative emotions are detected, encouraging messages and specific suggestions for improvement are sent. Conversely, if positive emotions are detected, the server can increase the user's motivation by notifying them of success stories and the positive reactions of other users.

[1234] Examples:

[1235] Software used: Sentiment analysis tools, notification tools (e.g., Twilio)

[1236] Data Used: User comments, posts

[1237] Example prompts to input to a generative AI model:

[1238] Entered idea data:

[1239] Title: "New Educational App Ideas"

[1240] Content: "I want to create an app that helps students learn in a fun way. I would like to add various features."

[1241] Emotion analysis results:

[1242] Emotion: Positive

[1243] Prompt to spawn AI:

[1244] "Please list improvements to this idea that reflect your users' positive feelings."

[1245] By showing specific examples of the mode for carrying out this invention, it will help those involved to accurately understand the content of the invention and serve as a reference for putting it into practice. In addition, by showing specific examples of the hardware and software to be used, it will serve as a guideline for implementation.

[1246] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1247] User Registration Process

[1248] Step 1:

[1249] The user accesses the sign-up form from a device.

[1250] Input: Profile information the user enters into the form (name, email address, password).

[1251] Output: The data entered into the form.

[1252] Specific operation: Access the new registration URL using the web browser on the device.

[1253] Step 2:

[1254] The user enters profile information and presses the "Register" button.

[1255] Input: Form data entered by the user.

[1256] Output: The form data is sent to the server.

[1257] What happens: The user fills out the form and clicks the "Register" button.

[1258] Step 3:

[1259] The server receives the entered information and performs verification.

[1260] Input: Form data.

[1261] Output: The validation result (success or failure).

[1262] What happens: The server receives the data and checks the format of the name, email address, and password.

[1263] Step 4:

[1264] The server stores the information in a database, which is a data storage means.

[1265] Input: The validated form data.

[1266] Output: User profile information stored in a database.

[1267] Specific behavior: The server saves the data to the users table in the database.

[1268] Step 5:

[1269] The server sends a registration completion email.

[1270] Input: Saved user profile information.

[1271] Output: A registration completion email is sent to the user.

[1272] Specific operation: The server uses the SMTP protocol to send a registration completion email through the Google Gmail API.

[1273] Idea Submission Process

[1274] Step 1:

[1275] A user accesses the idea submission form from a terminal.

[1276] Input: Request to access the idea submission page.

[1277] Output: The idea submission form is displayed in the browser.

[1278] Specific actions: Access the idea submission page using a web browser on your device.

[1279] Step 2:

[1280] The user enters the title and text of the idea and presses the "Post" button.

[1281] Input: Idea information (title, body) entered by the user.

[1282] Output: The idea information sent to the server.

[1283] Specific behavior: The user enters a title and body text and clicks the "Post" button.

[1284] Step 3:

[1285] The server receives the submitted data and verifies it.

[1286] Input: Idea information.

[1287] Output: The validation result (success or failure).

[1288] Specific operation: The server receives the sent idea information and checks the format and content of the data.

[1289] Step 4:

[1290] The server stores the idea information in a database, which is a data storage means.

[1291] Input: Verified idea information.

[1292] Output: Idea information stored in a database.

[1293] What happens: The server saves the data to the ideas collection in MongoDB.

[1294] Step 5:

[1295] The server notifies other users that a new idea has been posted.

[1296] Input: Saved idea information.

[1297] Output: Notifications sent to other users.

[1298] Specific behavior: The server uses the push notification service to notify that a new idea has been posted.

[1299] Idea evaluation and comment process

[1300] Step 1:

[1301] A user accesses another user's idea detail page from a terminal.

[1302] Input: A request to access the idea details page.

[1303] Output: The idea detail page is displayed in a browser.

[1304] What happens: Use a web browser on your device to access the idea details page.

[1305] Step 2:

[1306] Users rate ideas and enter comments.

[1307] Input: Rating and Comments.

[1308] Output: Ratings and comments sent to the server.

[1309] Specific behavior: User selects a rating (e.g., 4 out of 5 stars), enters a comment, and clicks the "Submit" button.

[1310] Step 3:

[1311] The server receives and validates the ratings and comments.

[1312] Input: Rating and Comments.

[1313] Output: The validation result (success or failure).

[1314] Specific behavior: The server receives the submitted ratings and comments and checks the format and content of the data.

[1315] Step 4:

[1316] The server stores the ratings and comments in a database, which is a data storage means.

[1317] Input: Verified rating and comments.

[1318] Output: Ratings and comments stored in a database.

[1319] What happens: The server saves the data to the comments collection in Firebase.

[1320] Step 5:

[1321] The server sends a rating and comment notification to the idea poster.

[1322] Input: Saved ratings and comments.

[1323] Output: Notification sent to idea submitter.

[1324] Specific behavior: The server sends a notification to the email address of the idea poster.

[1325] AI-based idea analysis and proposal process

[1326] Step 1:

[1327] The server periodically sends the idea information in the database to the generative AI model.

[1328] Input: Idea information.

[1329] Output: The data sent to the generative AI model.

[1330] What it does: The server retrieves the latest idea information from the ideas collection in MongoDB and sends it to the generative AI model's API.

[1331] Step 2:

[1332] A generative AI model analyzes idea information and generates proposals.

[1333] Input: Idea information.

[1334] Output: The generated proposals.

[1335] How it works: A generative AI model (e.g., OpenAI GPT-4) analyzes idea information and generates relevant suggestions.

[1336] Step 3:

[1337] The server receives the generated proposal and notifies the user.

[1338] Input: The generated proposals.

[1339] Output: The notification sent to the user.

[1340] Specific operation: The server receives suggestions from the generative AI model and sends them to the user via WebSocket notification or email.

[1341] Idea Improvement Process

[1342] Step 1:

[1343] The user accesses their idea details page from a device.

[1344] Input: A request to access the idea details page.

[1345] Output: The idea detail page is displayed in a browser.

[1346] What to do: Use a web browser on your device to access your idea details page.

[1347] Step 2:

[1348] The user presses the "improvement request" button.

[1349] Input: Enhancement request.

[1350] Output: The request sent to the server.

[1351] Specific behavior: The user clicks the "Enhancement Request" button.

[1352] Step 3:

[1353] The server receives the request and sends the idea information to the generative AI.

[1354] Input: Enhancement request and idea information.

[1355] Output: Data sent to the generative AI.

[1356] Specific operation: The server retrieves the relevant idea information from the ideas collection in MongoDB and sends it to the API of the generative AI model.

[1357] Step 4:

[1358] Generative AI lists areas for improvement.

[1359] Input: Idea information.

[1360] Output: Listed improvements.

[1361] How it works: The generative AI model analyzes idea information and lists areas for improvement.

[1362] Step 5:

[1363] The server will notify the user of the listed improvements.

[1364] Input: A list of improvements.

[1365] Output: The notification sent to the user.

[1366] What happens: The server notifies the user of the listed improvements via email and WebSocket notifications.

[1367] Introducing the Emotion Engine

[1368] Step 1:

[1369] The sentiment analysis means receives data (e.g., text and ratings) entered by users using the system.

[1370] Input: User input data.

[1371] Output: Data sent to sentiment analysis means.

[1372] Specific operation: The user enters comments and ratings on the device, and the server sends the data to the sentiment analysis means.

[1373] Step 2:

[1374] Sentiment analysis tools analyze data in real time.

[1375] Input: User input data.

[1376] Output: Emotion analysis results.

[1377] What it does: A sentiment analyzer (e.g., IBM Watson Sentiment Analysis) analyzes the data in real time to detect positive or negative sentiment.

[1378] Step 3:

[1379] The server provides the analysis results to the generation AI and the generation system AI means.

[1380] Input: Sentiment analysis results.

[1381] Output: Data sent to the Generative AI and Generative AI Means.

[1382] Specific operation: The server provides the analysis results to the generation AI and the generation system AI means.

[1383] Step 4:

[1384] The generative AI and generative AI means generate suggestions and improvements that take emotional state into account.

[1385] Input: Sentiment analysis results.

[1386] Output: Suggestions and improvements that take sentiment into account.

[1387] Specific operation: Generative AI and generative AI means generate suggestions and improvements based on the analysis results.

[1388] Sentiment Analysis and Notifications

[1389] Step 1:

[1390] The emotion analysis means analyzes the input data of the user and detects emotions.

[1391] Input: User input data.

[1392] Output: The detected emotion.

[1393] Specific operation: The sentiment analysis means analyzes user comments and ratings to detect emotions.

[1394] Step 2:

[1395] The server uses the emotion analysis results as a notification method.

[1396] Input: Sentiment analysis results.

[1397] Output: Emotion-based notification.

[1398] Specific operation: The server creates notification content based on the results of emotion analysis.

[1399] Step 3:

[1400] If negative sentiment is detected, it will send encouraging messages and specific suggestions for improvement.

[1401] Input: Detected negative sentiment.

[1402] Output: Encouraging messages and suggestions for improvement.

[1403] Specific operation: The server uses the Twilio API to send an encouraging message via SMS or email.

[1404] Step 4:

[1405] If positive sentiment is detected, notifications will be sent out highlighting success stories and positive reactions.

[1406] Input: Detected positive sentiment.

[1407] Output: Success stories and positive responses.

[1408] What happens: The server sends the user an email summarizing success stories and positive feedback.

[1409] (Application example 2)

[1410] 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."

[1411] Conventional feedback systems simply collect user opinions and comments as data and are unable to consider emotional factors such as emotions and attitudes. As a result, they are unable to provide improvement suggestions or responses based on the user's actual emotions, and are unable to fully increase user satisfaction. Furthermore, negative feedback is often not handled appropriately, leaving the problem unresolved. The present invention aims to solve these problems and provide a personalized feedback experience that takes user emotions into consideration.

[1412] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input information; server means for receiving the input information; database means for storing the received information; notification means for notifying other users of the received information; means for rating and commenting on ideas based on the notified information; means for storing the rating and comment in the database means; generative AI means for analyzing the received information and generating related proposals; means for notifying the user of the analysis results and proposals; generative AI means for improving the received information; means for notifying the user of the improvement results; emotion analysis means for analyzing the input information and extracting emotion information; and notification correction means for correcting the notification content based on the emotion information. This enables specific responses and proposals that reflect the user's emotions, thereby increasing user satisfaction.

[1413] An "input means" is a device or software that provides an interface for a user to input information.

[1414] The "server means" is a computer system for receiving and processing input information.

[1415] "Database means" refers to a storage device or system for storing and managing received information.

[1416] "Notification means" is a communication means for informing other users of received information.

[1417] The "rating and commenting means" is a method or device for rating an idea and leaving a comment based on the notified information.

[1418] "Generative AI means" means artificial intelligence technology for analyzing received information and generating relevant suggestions.

[1419] "Generative AI methods" are artificial intelligence techniques for improving received information.

[1420] "Emotion analysis means" is a technology for analyzing and extracting emotions from input information.

[1421] The "notification modification means" is a technique or means for modifying the notification content based on emotion information.

[1422] System Overview

[1423] This invention is a feedback system that collects and analyzes customer feedback in brick-and-mortar stores and provides personalized suggestions and improvements that take emotional information into account. The system is designed by integrating a smartphone application and server-side services.

[1424] Hardware and Software

[1425] Hardware:

[1426] Server: Receives, stores, and analyzes information, and notifies customers and store staff.

[1427] Smartphone: An interface device where customers enter feedback and receive results.

[1428] software:

[1429] Flask: Building an API server.

[1430] SQLite: Database.

[1431] TextBlob: A Python library for sentiment analysis.

[1432] smtplib: The Python standard library for sending email.

[1433] System Operation

[1434] 1. User Registration Process

[1435] When a user registers with the system from a smartphone app, the server receives the user's profile information and stores it in a database. Once registration is complete, the server sends a registration completion notification to the user.

[1436] 2. Feedback submission process

[1437] Users input feedback via a smartphone app. The server receives this feedback and stores it in a database. The server also analyzes this information using emotion analysis to detect positive or negative emotions.

[1438] 3. Sentiment Analysis Process

[1439] The server analyzes the sentiment of the received feedback using TextBlob, and if positive or negative sentiment is detected, it modifies the notification content based on the results and notifies the store staff in real time.

[1440] 4. Notification Process

[1441] The notification correction means introduces success stories and favorable reactions of other users in the case of positive feedback, and generates and sends encouraging messages in the case of negative feedback.

[1442] Specific examples

[1443] For example, if a user posts negative feedback such as "The service in the store was slow," the server analyzes the content using TextBlob to detect negative sentiment. It then generates an encouraging message saying, "We apologize for the inconvenience. We will strive to improve," and notifies the store staff. On the other hand, if a user posts positive feedback such as "The store staff's service was excellent," the server generates and sends a message introducing success stories from other customers.

[1444] Example prompt sentence:

[1445] "The following feedback was identified as negative. Please generate an encouraging message: Slow service in store."

[1446] This system allows responses that reflect the user's feelings, improving user satisfaction.

[1447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1448] Step 1:

[1449] User Registration Process

[1450] Input: Users enter their profile information using a smartphone app.

[1451] Operation:

[1452] The server receives the user's input.

[1453] The received profile information is stored in an SQLite database.

[1454] Once registration is complete, the server will send a registration completion notification to the user's email address.

[1455] Output: A notification is sent to the user that registration is complete. The new user's information is saved in the database.

[1456] Step 2:

[1457] Feedback submission process

[1458] Input: Users enter feedback using a smartphone app.

[1459] Operation:

[1460] The server receives the user's feedback.

[1461] The received feedback information is stored in an SQLite database.

[1462] Output: The feedback is stored in a database.

[1463] Step 3:

[1464] Sentiment Analysis Process

[1465] Input: Feedback information stored in the database.

[1466] Operation:

[1467] The server uses TextBlob to perform sentiment analysis of the feedback content, which determines the polarity of the feedback (positive or negative).

[1468] Data processing: Analyze the text data (feedback content) using TextBlob to generate sentiment polarity scores.

[1469] Output: Sentiment analysis result (positive or negative).

[1470] Step 4:

[1471] Notification Process

[1472] Input: Sentiment analysis results, feedback content.

[1473] Operation:

[1474] The server generates an appropriate notification based on the sentiment analysis results.

[1475] For positive feedback: Generate messages showcasing success stories and positive reactions from other customers.

[1476] For negative feedback: Generate an encouraging message.

[1477] Use smtplib to email the generated notification to store staff.

[1478] Data processing: Generate notification content based on the analysis results and process it as a text message.

[1479] Output: Notification message sent to store staff.

[1480] Example prompt sentence:

[1481] "The following feedback was identified as negative. Please generate an encouraging message: Slow service in store."

[1482] Through the above processing steps, specific responses and suggestions that reflect the user's feelings are provided, thereby improving customer satisfaction.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] [Third embodiment]

[1487] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1488] 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.

[1489] 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).

[1490] 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.

[1491] 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.

[1492] 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).

[1493] 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.

[1494] 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.

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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."

[1499] In the system based on this invention, users can input and post information, exchange ratings and comments, and improve and develop ideas with the support of generative AI and generative system AI. This system has multiple means and functions as follows:

[1500] 1. User Registration Process

[1501] Example

[1502] A user accesses the system's new registration form from a terminal and enters information such as their name, email address, and field of expertise. When this information is sent, the server receives it and stores it in a database. The server then sends the user an email informing them of completion of registration.

[1503] 2. Idea submission process

[1504] Example

[1505] A user accesses the idea submission form from their device and enters the title and body of their idea. When this data is sent, the server receives it and stores it in a database. The server then notifies other users that a new idea has been posted. This allows users to quickly share their ideas.

[1506] 3. Idea Evaluation and Comment Process

[1507] Example

[1508] A user accesses a list of ideas posted by other users from their device and displays the details page of an idea they are interested in. The user then presses the rating button and enters comment feedback. This rating and comment are sent to the server and stored in a database. The server then sends a notification of the rating and comment to the poster of the idea.

[1509] 4. AI-based idea analysis and proposal process

[1510] Example

[1511] The server periodically sends the idea data in the database to the generation AI. The generation AI analyzes the received idea data and generates related information and additional suggestions. The generated suggestions are sent to the server, which notifies the user. This allows the user to review and improve their ideas based on the analysis results and suggestions.

[1512] 5. Idea Improvement Process

[1513] Example

[1514] The user accesses the details page of their idea from their device and presses the "Improvement Request" button. This request is sent to the server, which then sends the idea data to the generative AI. The generative AI analyzes the idea and lists improvements. The generated improvements are then sent to the server, which notifies the user. The user can then use this information to modify and improve their idea.

[1515] This system provides an environment where users can easily post ideas, receive feedback, and refine their ideas while referring to automatically generated suggestions and improvements, accelerating idea generation and promoting the information revolution.

[1516] The processing flow will be explained below.

[1517] 1. User Registration Process

[1518] Step 1:

[1519] A user accesses the system from a terminal and displays a new registration form.

[1520] Step 2:

[1521] The user enters profile information such as name, email address, and area of ​​expertise.

[1522] Step 3:

[1523] The user presses the "Register" button and sends the input data to the server.

[1524] Step 4:

[1525] The server receives the transmitted data.

[1526] Step 5:

[1527] The server checks the integrity of the data it receives and, if there are no errors, stores it in the database.

[1528] Step 6:

[1529] The server sends a registration completion email to the user.

[1530] 2. Idea submission process

[1531] Step 1:

[1532] A user accesses the system from a terminal and displays the idea submission form.

[1533] Step 2:

[1534] The user enters the title and body of the idea.

[1535] Step 3:

[1536] The user presses the "Submit" button and sends the input data to the server.

[1537] Step 4:

[1538] The server receives the transmitted idea information.

[1539] Step 5:

[1540] The server stores the idea data in a database.

[1541] Step 6:

[1542] The server notifies other users that a new idea has been posted.

[1543] 3. Idea Evaluation and Comment Process

[1544] Step 1:

[1545] A user accesses the system from a terminal and displays a list of ideas posted by other users.

[1546] Step 2:

[1547] The user selects an idea that interests them and accesses the detail view page.

[1548] Step 3:

[1549] The user presses the rating button and enters feedback in the comment field.

[1550] Step 4:

[1551] The user presses the "Submit" button to send the evaluation data and comments to the server.

[1552] Step 5:

[1553] The server receives the rating data and comments and stores them in a database.

[1554] Step 6:

[1555] The server sends a rating and comment notification to the idea poster.

[1556] 4. AI-based idea analysis and proposal process

[1557] Step 1:

[1558] The server periodically sends the idea data in the database to the generation AI.

[1559] Step 2:

[1560] The generation AI analyzes the received idea data.

[1561] Step 3:

[1562] Generative AI generates relevant information and additional suggestions.

[1563] Step 4:

[1564] The generation AI sends the analysis results and suggestions to the server.

[1565] Step 5:

[1566] The server notifies the user of the analysis results and suggestions.

[1567] 5. Idea Improvement Process

[1568] Step 1:

[1569] The user accesses their idea details page from their device and presses the "Request for Improvement" button.

[1570] Step 2:

[1571] The user's request is sent to the server.

[1572] Step 3:

[1573] The server sends the relevant idea data to the generative AI.

[1574] Step 4:

[1575] Generative AI analyzes ideas and lists areas for improvement.

[1576] Step 5:

[1577] The generative AI sends improvements to the server.

[1578] Step 6:

[1579] The server will notify the user of the improvements.

[1580] Step 7:

[1581] Users receive notifications, review improvements, and revise their ideas.

[1582] Example 1

[1583] 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."

[1584] Conventional idea-sharing platforms have a problem in that the process of users posting ideas and receiving feedback from other users is cumbersome, making it difficult to efficiently improve ideas. Furthermore, there is a lack of a means for users to receive specific suggestions for voluntarily improving their ideas. Furthermore, there is no system for regularly analyzing ideas and generating suggestions, which limits the quality of creative ideas.

[1585] 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.

[1586] In this invention, the server includes an input means for a user to input information, a server means for receiving the input information, a database means for storing the received information, a notification means for notifying other users of the received information, a means for rating and commenting on ideas based on the notified information, a means for storing the rating and comment in the database means, a generative AI means for periodically analyzing the received information and generating related proposals, a means for notifying users of the analysis results and proposals, a means for receiving idea improvement requests from users, a generative AI means for analyzing the received information and listing improvements, and a means for notifying users of the improvements. This enables users to intuitively post ideas, quickly receive feedback from other users, and efficiently improve their ideas based on specific suggestions and improvements from the generative AI and the generative AI.

[1587] "User" refers to an individual who inputs information, posts ideas, ratings and comments.

[1588] "Input means" refers to the interface through which a user enters information or ideas, such as a web form or an input field in a mobile app.

[1589] "Server means" refers to a computer system that receives information sent from a user and performs various processes.

[1590] "Database Means" refers to a data storage device or system for storing and managing received information.

[1591] "Notification mechanism" refers to a method or system for communicating specific information or updates to other users.

[1592] "Means for rating and commenting" refers to a function that allows users to input ratings and comments on posted ideas.

[1593] "Generative AI Means" refers to the artificial intelligence model used to analyze received information and generate relevant recommendations.

[1594] "Generative AI means" refers to an artificial intelligence model that analyzes received information based on user requests and lists areas for improvement.

[1595] The system based on this invention allows users to input and post information, exchange ratings and comments, and improve and develop ideas with the support of generative AI and generative system AI. This system has the following multiple means and functions:

[1596] 1. User Registration Process

[1597] The user accesses the system's new registration form on their own device (PC, smartphone, etc.) and enters information such as their name, email address, and field of expertise. After entering the information, they press the "Submit" button, and the server receives the information and stores it in a database (e.g., MySQL). The server then sends the user a registration completion email.

[1598] Example: A user fills in a new registration form with information such as "Yamada Taro", "example@example.com", and "Engineering", and submits it.

[1599] 2. Idea submission process

[1600] A user accesses the idea submission form on their device and enters the title and text of their idea. For example, "A new solar panel design" and "This solar panel will improve efficiency by 50%." After entering the information, they press the "Submit" button. The server receives the data and stores it in a database. The server then notifies other users that a new idea has been submitted.

[1601] Example: A user enters the title and body of an idea "New solar panel design" and submits it.

[1602] 3. Idea Evaluation and Comment Process

[1603] A user accesses a list of ideas posted by other users from their device and displays the details page of an idea they are interested in. For example, they can enter a comment such as "I think this is practical" for the idea "New solar panel design" and rate it "5 stars." When the rating and comment are submitted, they are sent to the server and stored in a database. The server then notifies the idea poster that the rating and comment have been received.

[1604] Example: A user submits a 5-star rating for "New Solar Panel Design" and a comment saying "Great idea."

[1605] 4. AI-based idea analysis and proposal process

[1606] The server periodically sends the idea data in the database to a generation AI (e.g., OpenAI's GPT-4). The generation AI analyzes the received ideas and generates related information and additional suggestions. These suggestions are then sent back to the server, which notifies the user.

[1607] Example prompt: An idea has been submitted for "New Solar Panel Design." Check it out if you're interested.

[1608] 5. Idea Improvement Process

[1609] When a user accesses the details page of their idea on their device and presses the "Improvement Request" button, a request is sent to the server. The server sends the idea data to a generative AI (e.g., DeepAI), which analyzes the idea and lists improvements. The generated improvements are sent to the server, which notifies the user. The user can then use this information to revise and improve their idea.

[1610] Example: After the user presses the "improvement request" button, the suggestion for improvement is presented: "The amount of silicon used should be reduced to further improve area efficiency."

[1611] This program is implemented as a web platform to make user operation intuitive and simple, and uses HTML, CSS, JavaScript, etc. as the user interface. The server side is implemented using languages ​​such as Python and Node.js, and a relational database such as MySQL is used as the database. OpenAI's GPT-4 and DeepAI's API are used for the generative AI and generative AI models, which automatically perform periodic analysis processing and respond to user requests.

[1612] This system allows users to intuitively post ideas, quickly receive feedback from other users, and efficiently improve their ideas based on specific suggestions and improvements from the generative AI and generative system AI. As a result, the quality of ideas improves, providing an environment that promotes the creation of new value.

[1613] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1614] User Registration Process

[1615] Step 1:

[1616] The user accesses the system's new registration page on their own device (PC, smartphone, etc.).

[1617] Input: Enter the URL of the system's new registration page in the URL bar of your web browser.

[1618] Output: A new registration form is displayed.

[1619] Step 2:

[1620] Users enter information such as their name, email address, and area of ​​expertise into the new registration form.

[1621] Input: Enter your name, email address, area of ​​expertise, etc. in the input fields.

[1622] Output: The input data is displayed in a form.

[1623] Step 3:

[1624] The user clicks the "Submit" button.

[1625] Input: The "Submit" button is clicked.

[1626] Output: The form data is sent to the server.

[1627] Step 4:

[1628] The server receives the user's input information as an HTTP POST request.

[1629] Input: User information included in the HTTP POST request.

[1630] Output: User information temporarily stored in the server's processing memory.

[1631] Step 5:

[1632] The server stores the received data in a database.

[1633] Input: User information temporarily stored in the server's processing memory.

[1634] Output: User information stored in the users table in the MySQL database.

[1635] Step 6:

[1636] The server sends a registration completion email to the user.

[1637] Input: The content of the registration confirmation email and the user's email address.

[1638] Output: A registration confirmation email sent to the user's email account.

[1639] Idea Submission Process

[1640] Step 1:

[1641] The user accesses the idea submission page on the terminal.

[1642] Enter the URL of the idea submission page in the URL bar of your web browser.

[1643] Output: The idea submission form is displayed.

[1644] Step 2:

[1645] The user enters the title and body of the idea.

[1646] Input: Enter the title and text of your idea in the fields provided.

[1647] Output: The input data is displayed in a form.

[1648] Step 3:

[1649] The user clicks the "Submit" button.

[1650] Input: The "Submit" button is clicked.

[1651] Output: The form data is sent to the server.

[1652] Step 4:

[1653] The server receives the idea data as an HTTP POST request.

[1654] Input: Idea information included in the HTTP POST request.

[1655] Output: Idea information temporarily stored in the server's processing memory.

[1656] Step 5:

[1657] The server stores the received data in a database.

[1658] Input: Idea information temporarily stored in the server's processing memory.

[1659] Output: Idea information stored in the Ideas table in a MySQL database.

[1660] Step 6:

[1661] The server notifies other users that a new idea has been posted.

[1662] Input: New idea information and a list of people to notify.

[1663] Output: A notification email or in-app notification is sent to other users.

[1664] Idea evaluation and comment process

[1665] Step 1:

[1666] The user accesses a list page of ideas posted by other users from their terminal.

[1667] Input: Enter the URL of the idea list page into the URL bar of your web browser.

[1668] Output: A list of ideas is displayed.

[1669] Step 2:

[1670] View the detail page of the idea that the user wants to rate.

[1671] Enter: Click on a specific idea from the list of ideas.

[1672] Output: The details page for a particular idea is displayed.

[1673] Step 3:

[1674] The user presses the rating button.

[1675] Input: The rating button is clicked.

[1676] Output: Evaluation data is temporarily stored.

[1677] Step 4:

[1678] The user enters comment feedback.

[1679] Enter your feedback in the comments section.

[1680] Output: The comment data is displayed in a form.

[1681] Step 5:

[1682] The user clicks the "Submit" button.

[1683] Input: The "Submit" button is clicked.

[1684] Output: The form data is sent to the server.

[1685] Step 6:

[1686] The server receives the ratings and comments as HTTP POST requests.

[1687] Input: Rating and comment information included in the HTTP POST request.

[1688] Output: Rating and comment information temporarily stored in the server's processing memory.

[1689] Step 7:

[1690] The server stores the received data in a database.

[1691] Input: Rating and comment information temporarily stored in the server's processing memory.

[1692] Output: Rating and comment information stored in the ratings table in a MySQL database.

[1693] Step 8:

[1694] The server notifies the idea poster.

[1695] Input: Rating and comment information and author information to notify.

[1696] Output: A notification email or in-app notification is sent to the contributor.

[1697] AI-based idea analysis and proposal process

[1698] Step 1:

[1699] The server periodically scans the database.

[1700] Input: A periodic timer trigger.

[1701] Output: Obtaining new idea information.

[1702] Step 2:

[1703] The server sends new idea data to the generation AI.

[1704] Input: New idea information.

[1705] Output: API request to the generating AI.

[1706] Step 3:

[1707] Generative AI analyzes idea data.

[1708] Input: Idea information.

[1709] Output: Analysis results and recommendations.

[1710] Step 4:

[1711] Generative AI generates suggestions.

[1712] Input: Analysis results.

[1713] Output: Proposal data.

[1714] Step 5:

[1715] The proposal is sent to the server.

[1716] Input: Proposal data.

[1717] Output: The API response to the server.

[1718] Step 6:

[1719] The server notifies the user of the offer.

[1720] Input: Proposal data and user information to notify.

[1721] Output: A notification email or in-app notification is sent to the user.

[1722] Idea Improvement Process

[1723] Step 1:

[1724] A user visits their idea detail page.

[1725] Input: Enter the URL of the idea detail page into the URL bar of your web browser.

[1726] Output: The idea details page is displayed.

[1727] Step 2:

[1728] The user presses the "improvement request" button.

[1729] Input: The "Improvement Request" button is clicked.

[1730] Output: An enhancement request is sent to the server.

[1731] Step 3:

[1732] The request is sent to the server.

[1733] Input: Enhancement request data.

[1734] Output: Request data temporarily stored in the server's processing memory.

[1735] Step 4:

[1736] The server sends the idea data to the generative AI.

[1737] Input: Idea information and improvement request data.

[1738] Output: API request to the generative AI.

[1739] Step 5:

[1740] Generative AI analyzes ideas.

[1741] Input: Idea information and improvement requests.

[1742] Output: Analysis results and improvements.

[1743] Step 6:

[1744] Generative AI lists areas for improvement.

[1745] Input: Analysis results.

[1746] Output: A list of improvements.

[1747] Step 7:

[1748] The improvements are sent to the server.

[1749] Input: A list of improvements.

[1750] Output: The API response to the server.

[1751] Step 8:

[1752] The server notifies the user of the improvements.

[1753] Input: A list of improvements and the users to notify.

[1754] Output: A notification email or in-app notification is sent to the user.

[1755] (Application example 1)

[1756] 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."

[1757] Conventional content distribution services have a problem in that even when users post new content or ideas, they do not receive prompt and effective feedback or evaluations, making it difficult to create high-quality content. Furthermore, there is a lack of a mechanism for automatically analyzing posted content and providing suggestions for improvements or additions, limiting the means by which users can effectively improve their own content. The objective of this invention is to solve these problems and provide an environment in which users can create high-quality content.

[1758] 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.

[1759] In this invention, the server includes input means for a user to input information, server means for receiving the input information, database means for saving the received information, notification means for notifying other information processing devices of the received information, means for rating and commenting on ideas based on the notified information, means for saving the rating and comment in the database means, generative AI means for analyzing the received information and generating related proposals, means for notifying the information processing device of the analysis results and proposals, generative AI means for improving the received information, means for notifying the information processing device of the improvement results, means for a user to post new content or ideas in a content distribution service and receive feedback and evaluations thereon from other information processing devices, and means for analyzing the user's posted content using generative AI and providing suggestions for improvements and additions. This allows users to receive feedback quickly and effectively and to improve their own content to a higher quality by utilizing the analysis and suggestions by the generative AI.

[1760] An "input means" is a device or interface through which a user inputs information.

[1761] The "server means" refers to a server device or its function for receiving and processing input information.

[1762] "Database Means" means a database system or function thereof for storing and managing received information.

[1763] The "notification means" is a device or function for notifying other information processing devices of received information.

[1764] The "evaluation and comment means" is a device or function for evaluating and commenting on ideas based on the notified information.

[1765] "Generative AI means" means an artificial intelligence model or function thereof for analyzing received information and generating relevant recommendations.

[1766] "Information processing device" refers to any device that a user uses to input, receive, and process information.

[1767] A "generative AI means" is an artificial intelligence model or function that analyzes received information to improve it and lists areas for improvement.

[1768] A "content distribution service" is a service that provides information and media content to users via the Internet or the like.

[1769] "Feedback" refers to evaluations and comments received from other information processing devices.

[1770] "Analysis results" refers to suggestions and improvements generated by the generative AI means.

[1771] This system supports users in posting new content and ideas on a content distribution service, and receives feedback and evaluations from other information processing devices. Furthermore, it uses generative AI to analyze users' posts and provide suggestions for improvements and additions, helping users to create high-quality content.

[1772] The server uses the following hardware and software to process information such as reception, storage, notification, analysis, and improvement.

[1773] Hardware: High-performance server equipment, database server

[1774] Software: Flask (web framework), SQLAlchemy (database ORM), TensorFlow / PyTorch (generative AI model), Twilio (email sending)

[1775] Input Method

[1776] A web form is provided as an input mechanism for users to enter information, allowing them to enter information such as their name, email address, content categories of interest, and the title and body of the content.

[1777] Server Means

[1778] The server receives the information entered by the user and stores it in a database. The receiving process is performed by a web application using Flask.

[1779] Database Means

[1780] The received information is stored in a database using SQLAlchemy, and the stored data is used for subsequent processing (notification, analysis, improvement, etc.).

[1781] Notification means

[1782] When new content or ideas are posted, the server notifies other users, using Twilio to send emails.

[1783] Rating and commenting tools

[1784] Other users can access the information and provide ratings and comments, which are then sent back to the server and stored in a database.

[1785] Generation AI means

[1786] The server periodically sends the submitted data in the database to a generative AI model (TensorFlow or PyTorch), which analyzes the received data and generates relevant suggestions and improvements. The generated suggestions are returned to the server and notified to the user.

[1787] Information processing device

[1788] Information processing devices are devices that users use to input, receive, and process information, such as smartphones, smart glasses, head-mounted displays, and robots. This allows similar functions to be realized on any device.

[1789] Improvement AI method

[1790] When a user submits an improvement request, the server sends the request to the generative AI means, which analyzes the request and lists the improvements. The server then notifies the user of the improvements.

[1791] Examples and prompts

[1792] For example, if a user posts an idea for a new web browser design, the following prompt is sent: "This is an idea for a new web browser design. This browser is for the visually impaired and comes standard with a voice guide function." Based on this prompt, the generative AI model analyzes the proposal and provides specific suggestions such as "improvement suggestions to make the browser UI more intuitive" and "ways to enhance the voice guide function."

[1793] This allows users to receive feedback quickly and effectively, and leverage generative AI analysis and suggestions to improve their content to a higher quality.

[1794] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1795] Step 1:

[1796] A user accesses the system's new registration form from a terminal and enters their name, email address, content categories of interest, etc. The entered information is received by the server and saved in a database. The entered data is processed by checking for format and duplication. The server then uses Twilio to send the user a registration completion email. The user's personal information is input, and the output is saved in the database and a completion email is sent.

[1797] Step 2:

[1798] A user accesses the idea submission form from their device and enters the title and content body. The entered data is sent to the server and saved in the database. The server uses the notification function to notify other users via email via Twilio that a new idea has been posted. The idea content is input, and the output is saving to the database and sending a notification.

[1799] Step 3:

[1800] Other users check the new ideas notified from their devices and post ratings and comments. The ratings and comments are sent to the server and stored in a database. The server then notifies the original poster that the rating or comment has been posted. The input is the rating and comment, and the output is storage in the database and a notification to the poster.

[1801] Step 4:

[1802] The server periodically sends the posted data in the database to a generative AI model (TensorFlow or PyTorch). The generative AI model analyzes the received data and generates relevant suggestions. This analysis and data calculation uses natural language processing and clustering techniques. The generated suggestions are returned to the server and notified to the user. The user is notified of the posted data as input and the suggestions that are the analysis results as output.

[1803] Step 5:

[1804] The user accesses their idea details page from their device and presses the "Improvement Request" button. The request is sent to the server, which then sends the idea data to the generative AI model. The generative AI model analyzes the idea and lists improvements. The generated improvements are returned to the server and notified to the user. The improvement request and idea data are input, and the improvements are notified to the user as output.

[1805] Step 6:

[1806] Users receive notifications from the server and use the suggestions and improvements to revise and improve their ideas. The revised idea is then resubmitted to the server and stored in the database. At this time, the server resends notifications containing the revisions to other users, requesting their feedback. The revised idea is the input, and the output is saving to the database and re-notifying them.

[1807] This allows users to create and improve high-quality content.

[1808] 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.

[1809] The system based on this invention provides a more personalized experience by combining an emotion engine in the process of users inputting and posting information, receiving ratings and comments from other users, and analyzing and improving that information. A specific embodiment of the system is shown below.

[1810] 1. User Registration Process

[1811] Example

[1812] The user accesses the system's new registration form from their terminal and enters their profile information. When the user submits the information, the server receives it and stores it in the database. Once registration is complete, the server sends the user a registration completion email.

[1813] 2. Idea submission process

[1814] Example

[1815] A user accesses the idea submission form from a terminal and enters the title and text of the idea. Once submission is complete, the server saves the idea information in a database and notifies other users that a new idea has been submitted.

[1816] 3. Idea Evaluation and Comment Process

[1817] Example

[1818] When a user rates or comments on another user's idea from their device, the rating and comment are sent to the server and stored in a database. The server then sends a notification of the rating and comment to the idea poster.

[1819] 4. AI-based idea analysis and proposal process

[1820] Example

[1821] The server periodically sends the idea information in the database to the generation AI, which analyzes it and generates proposals. The generated proposals are notified to the user via the server, allowing the user to review and improve their ideas.

[1822] 5. Idea Improvement Process

[1823] Example

[1824] When a user presses the "Improvement Request" button on their device's idea details page, the request is sent to the server. The server sends the idea to the generative AI, which then lists improvements. The listed improvements are notified to the user via the server, allowing the user to modify the idea.

[1825] 6. Introducing the Emotion Engine

[1826] Example

[1827] The emotion engine analyzes data (text and ratings) entered by users in real time while they use the system from their devices. This emotion analysis is particularly effective when posting ideas or comments, and the server provides the analysis results to the generative AI and generative system AI. This allows suggestions and improvements to be generated that take the user's emotional state into account.

[1828] Sentiment Analysis and Notifications

[1829] Example

[1830] The emotion engine analyzes the user's input data, and if it detects positive or negative emotions, the server uses the emotion analysis results as a notification method. For example, if negative emotions are detected, the server can send the user an encouraging message or provide more specific suggestions for improvement. Conversely, if positive emotions are detected, the server can motivate the user by introducing success stories and other users' positive reactions.

[1831] By integrating an emotion engine, this invention takes into account the user's emotional state to provide more personalized feedback and improvement suggestions, which is expected to increase user satisfaction, improve the quality of ideas, and promote the information revolution.

[1832] The processing flow will be explained below.

[1833] 1. User Registration Process

[1834] Step 1:

[1835] A user accesses the system from a terminal and displays a new registration form.

[1836] Step 2:

[1837] The user enters profile information such as name, email address, and area of ​​expertise.

[1838] Step 3:

[1839] The user presses the "Register" button and sends the input data to the server.

[1840] Step 4:

[1841] The server receives the transmitted data.

[1842] Step 5:

[1843] The server checks the integrity of the data it receives and, if there are no errors, stores it in the database.

[1844] Step 6:

[1845] The server sends a registration completion email to the user.

[1846] 2. Idea submission process

[1847] Step 1:

[1848] A user accesses the system from a terminal and displays the idea submission form.

[1849] Step 2:

[1850] The user enters the title and body of the idea.

[1851] Step 3:

[1852] The user presses the "Submit" button and sends the input data to the server.

[1853] Step 4:

[1854] The server receives the transmitted idea information.

[1855] Step 5:

[1856] The server stores the idea data in a database.

[1857] Step 6:

[1858] The server notifies other users that a new idea has been posted.

[1859] 3. Idea Evaluation and Comment Process

[1860] Step 1:

[1861] A user accesses the system from a terminal and displays a list of ideas posted by other users.

[1862] Step 2:

[1863] The user selects an idea that interests them and accesses the detail view page.

[1864] Step 3:

[1865] The user presses the rating button and enters feedback in the comment field.

[1866] Step 4:

[1867] The user presses the "Submit" button to send the evaluation data and comments to the server.

[1868] Step 5:

[1869] The server receives the rating data and comments and stores them in a database.

[1870] Step 6:

[1871] The server sends a rating and comment notification to the idea poster.

[1872] 4. AI-based idea analysis and proposal process

[1873] Step 1:

[1874] The server periodically sends the idea data in the database to the generation AI.

[1875] Step 2:

[1876] The generation AI analyzes the received idea data.

[1877] Step 3:

[1878] Generative AI generates relevant information and additional suggestions.

[1879] Step 4:

[1880] The generation AI sends the analysis results and suggestions to the server.

[1881] Step 5:

[1882] The server notifies the user of the analysis results and suggestions.

[1883] 5. Idea Improvement Process

[1884] Step 1:

[1885] The user accesses their idea details page from their device and presses the "Request for Improvement" button.

[1886] Step 2:

[1887] The user's request is sent to the server.

[1888] Step 3:

[1889] The server sends the relevant idea data to the generative AI.

[1890] Step 4:

[1891] Generative AI analyzes ideas and lists areas for improvement.

[1892] Step 5:

[1893] The generative AI sends improvements to the server.

[1894] Step 6:

[1895] The server will notify the user of the improvements.

[1896] Step 7:

[1897] Users receive notifications, review improvements, and revise their ideas.

[1898] 6. Introducing the Emotion Engine

[1899] Step 1:

[1900] When a user posts an idea or comment from a terminal, the input data is sent to the server.

[1901] Step 2:

[1902] The server sends the received data to the emotion engine.

[1903] Step 3:

[1904] The emotion engine analyzes the data in real time and evaluates the user's emotional state (positive, negative, etc.).

[1905] Step 4:

[1906] The emotion engine provides the analysis results to the generative AI and generative AI.

[1907] Step 5:

[1908] The generative AI and generative system AI adjust suggestions and improvements based on the emotional state and send them to the server.

[1909] Step 6:

[1910] The server notifies the user of any adjustment suggestions or improvements.

[1911] Step 7:

[1912] Users can check improvements and suggestions from their devices and further refine their ideas. In addition, appropriate feedback and notifications based on sentiment analysis results provide a more personalized user experience.

[1913] Example 2

[1914] 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."

[1915] Conventional information input and posting systems do not adequately provide feedback and suggestions that take into account the user's emotional state. As a result, the quality of the user experience declines and the efficiency of the idea evaluation and improvement process decreases. In particular, there is a lack of appropriate follow-up for users with negative emotions, making it difficult to maintain user motivation.

[1916] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: an input means for a user to input information; a server means for receiving the input information; a data storage means for saving the received information; a notification means for notifying other users of the received information; an evaluation means for rating and commenting on ideas based on the notified information; a memory means for saving the evaluation and comment in the data storage means; a generation AI means for analyzing the received information and generating related proposals; a notification means for notifying the user of the analysis results and proposals; a generative AI means for improving the received information; a notification means for notifying the user of the improvement results; an emotion analysis means for analyzing data input by the user using the system and detecting emotions; a providing means for providing the emotion analysis results to the generation AI and the generative AI means; and a notification means for sending specific proposals or notifications to the user based on the emotion analysis results. This enables the provision of feedback and proposals that take the user's emotional state into consideration, improving the quality of the user experience and increasing the efficiency of the idea evaluation and improvement process.

[1917] The "input means" is an interface for the user to input information.

[1918] "Server means" is a computer system for processing and managing information received from users.

[1919] "Data storage means" means a storage device for storing received information.

[1920] A "notification means" is a system for notifying other users of specific information.

[1921] The "evaluation means" is a function that allows users to evaluate and comment on other users' ideas.

[1922] "Storage means" is a device or system for storing ratings and comments.

[1923] "Generative AI means" means an artificial intelligence system for analyzing received information and generating relevant suggestions.

[1924] The "means of provision" is a system for passing the results of emotion analysis to the generation AI and the generation system AI means.

[1925] "Emotion analysis means" is a technology for analyzing emotions based on data entered by users into the system.

[1926] "Generative AI methods" are artificial intelligence technologies that generate optimal improvement proposals based on user input information.

[1927] The system based on this invention allows users to input and post information, receive ratings and comments from other users, and analyze and improve that information in a process that combines an emotion engine to provide a more personalized experience. A specific embodiment of the system is shown below.

[1928] User Registration Process

[1929] The user accesses the system's new registration form from their terminal and enters their profile information. When the user submits the information, the server receives it and stores it in a database, which is a data storage means. Once registration is complete, the server sends the user an email informing them of completion of registration. During this process, the server uses the SMTP protocol as both an input means and a notification means.

[1930] Examples:

[1931] Device: Smartphone

[1932] Software used: Web browser

[1933] Data Used: User name, email address, password

[1934] Idea Submission Process

[1935] The user accesses the idea submission form from their device and enters the title and text of their idea. Once submission is complete, the server saves the idea information in a database, which serves as a data storage means, and notifies other users that a new idea has been submitted. The server uses a push notification service as a notification method.

[1936] Examples:

[1937] Device: PC

[1938] Software used: Web browser

[1939] Data used: Idea title, content

[1940] Idea evaluation and comment process

[1941] When a user enters a rating and comment on another user's idea, the content is sent to the server and stored in the data storage means, and the server notifies the idea poster of the rating and comment.

[1942] Examples:

[1943] Device: Tablet

[1944] Software used: Web app

[1945] Data used: Comments, ratings

[1946] AI-based idea analysis and proposal process

[1947] The server periodically transmits the idea information stored in the data storage means to the generation AI means, which analyzes the information and generates a proposal. The generated proposal is notified to the user via the server, allowing the user to review and improve the idea.

[1948] Examples:

[1949] Software used: Generative AI models (e.g., OpenAI GPT-4)

[1950] Data used: Content of the submitted idea

[1951] Idea Improvement Process

[1952] When a user presses the "Improvement Request" button on their idea's details page, the request is sent to the server. The server sends the idea to the generative AI means, which then lists improvements. The listed improvements are notified to the user via the server, allowing the user to modify the idea.

[1953] Examples:

[1954] Device: Laptop

[1955] Software used: Web browser

[1956] Data used: Idea content, improvement requests

[1957] Introducing the Emotion Engine

[1958] The emotion analysis means analyzes data (text, ratings, etc.) entered by users using the system in real time. This emotion analysis is particularly effective when posting ideas or comments, and the server provides the analysis results to the generative AI and generative AI means. This allows suggestions and improvements to be generated that take the user's emotional state into account.

[1959] Examples:

[1960] Software used: Sentiment analysis tools (e.g., IBM Watson Sentiment Analysis)

[1961] Data used: Comments, ratings

[1962] Sentiment Analysis and Notifications

[1963] The emotion analysis means analyzes the user's input data, and if it detects positive or negative emotions, the server uses the emotion analysis results as a notification means. If negative emotions are detected, encouraging messages and specific suggestions for improvement are sent. Conversely, if positive emotions are detected, the server can increase the user's motivation by notifying them of success stories and the positive reactions of other users.

[1964] Examples:

[1965] Software used: Sentiment analysis tools, notification tools (e.g., Twilio)

[1966] Data Used: User comments, posts

[1967] Example prompts to input to a generative AI model:

[1968] Entered idea data:

[1969] Title: "New Educational App Ideas"

[1970] Content: "I want to create an app that helps students learn in a fun way. I would like to add various features."

[1971] Emotion analysis results:

[1972] Emotion: Positive

[1973] Prompt to spawn AI:

[1974] "Please list improvements to this idea that reflect your users' positive feelings."

[1975] By showing specific examples of the mode for carrying out this invention, it will help those involved to accurately understand the content of the invention and serve as a reference for putting it into practice. In addition, by showing specific examples of the hardware and software to be used, it will serve as a guideline for implementation.

[1976] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1977] User Registration Process

[1978] Step 1:

[1979] The user accesses the sign-up form from a device.

[1980] Input: Profile information the user enters into the form (name, email address, password).

[1981] Output: The data entered into the form.

[1982] Specific operation: Access the new registration URL using the web browser on the device.

[1983] Step 2:

[1984] The user enters profile information and presses the "Register" button.

[1985] Input: Form data entered by the user.

[1986] Output: The form data is sent to the server.

[1987] What happens: The user fills out the form and clicks the "Register" button.

[1988] Step 3:

[1989] The server receives the entered information and performs verification.

[1990] Input: Form data.

[1991] Output: The validation result (success or failure).

[1992] What happens: The server receives the data and checks the format of the name, email address, and password.

[1993] Step 4:

[1994] The server stores the information in a database, which is a data storage means.

[1995] Input: The validated form data.

[1996] Output: User profile information stored in a database.

[1997] Specific behavior: The server saves the data to the users table in the database.

[1998] Step 5:

[1999] The server sends a registration completion email.

[2000] Input: Saved user profile information.

[2001] Output: A registration completion email is sent to the user.

[2002] Specific operation: The server uses the SMTP protocol to send a registration completion email through the Google Gmail API.

[2003] Idea Submission Process

[2004] Step 1:

[2005] A user accesses the idea submission form from a terminal.

[2006] Input: Request to access the idea submission page.

[2007] Output: The idea submission form is displayed in the browser.

[2008] Specific actions: Access the idea submission page using a web browser on your device.

[2009] Step 2:

[2010] The user enters the title and text of the idea and presses the "Post" button.

[2011] Input: Idea information (title, body) entered by the user.

[2012] Output: The idea information sent to the server.

[2013] Specific behavior: The user enters a title and body text and clicks the "Post" button.

[2014] Step 3:

[2015] The server receives the submitted data and verifies it.

[2016] Input: Idea information.

[2017] Output: The validation result (success or failure).

[2018] Specific operation: The server receives the sent idea information and checks the format and content of the data.

[2019] Step 4:

[2020] The server stores the idea information in a database, which is a data storage means.

[2021] Input: Verified idea information.

[2022] Output: Idea information stored in a database.

[2023] What happens: The server saves the data to the ideas collection in MongoDB.

[2024] Step 5:

[2025] The server notifies other users that a new idea has been posted.

[2026] Input: Saved idea information.

[2027] Output: Notifications sent to other users.

[2028] Specific behavior: The server uses the push notification service to notify that a new idea has been posted.

[2029] Idea evaluation and comment process

[2030] Step 1:

[2031] A user accesses another user's idea detail page from a terminal.

[2032] Input: A request to access the idea details page.

[2033] Output: The idea detail page is displayed in a browser.

[2034] What happens: Use a web browser on your device to access the idea details page.

[2035] Step 2:

[2036] Users rate ideas and enter comments.

[2037] Input: Rating and Comments.

[2038] Output: Ratings and comments sent to the server.

[2039] Specific behavior: User selects a rating (e.g., 4 out of 5 stars), enters a comment, and clicks the "Submit" button.

[2040] Step 3:

[2041] The server receives and validates the ratings and comments.

[2042] Input: Rating and Comments.

[2043] Output: The validation result (success or failure).

[2044] Specific behavior: The server receives the submitted ratings and comments and checks the format and content of the data.

[2045] Step 4:

[2046] The server stores the ratings and comments in a database, which is a data storage means.

[2047] Input: Verified rating and comments.

[2048] Output: Ratings and comments stored in a database.

[2049] What happens: The server saves the data to the comments collection in Firebase.

[2050] Step 5:

[2051] The server sends a rating and comment notification to the idea poster.

[2052] Input: Saved ratings and comments.

[2053] Output: Notification sent to idea submitter.

[2054] Specific behavior: The server sends a notification to the email address of the idea poster.

[2055] AI-based idea analysis and proposal process

[2056] Step 1:

[2057] The server periodically sends the idea information in the database to the generative AI model.

[2058] Input: Idea information.

[2059] Output: The data sent to the generative AI model.

[2060] What it does: The server retrieves the latest idea information from the ideas collection in MongoDB and sends it to the generative AI model's API.

[2061] Step 2:

[2062] A generative AI model analyzes idea information and generates proposals.

[2063] Input: Idea information.

[2064] Output: The generated proposals.

[2065] How it works: A generative AI model (e.g., OpenAI GPT-4) analyzes idea information and generates relevant suggestions.

[2066] Step 3:

[2067] The server receives the generated proposal and notifies the user.

[2068] Input: The generated proposals.

[2069] Output: The notification sent to the user.

[2070] Specific operation: The server receives suggestions from the generative AI model and sends them to the user via WebSocket notification or email.

[2071] Idea Improvement Process

[2072] Step 1:

[2073] The user accesses their idea details page from a device.

[2074] Input: A request to access the idea details page.

[2075] Output: The idea detail page is displayed in a browser.

[2076] What to do: Use a web browser on your device to access your idea details page.

[2077] Step 2:

[2078] The user presses the "improvement request" button.

[2079] Input: Enhancement request.

[2080] Output: The request sent to the server.

[2081] Specific behavior: The user clicks the "Enhancement Request" button.

[2082] Step 3:

[2083] The server receives the request and sends the idea information to the generative AI.

[2084] Input: Enhancement request and idea information.

[2085] Output: Data sent to the generative AI.

[2086] Specific operation: The server retrieves the relevant idea information from the ideas collection in MongoDB and sends it to the API of the generative AI model.

[2087] Step 4:

[2088] Generative AI lists areas for improvement.

[2089] Input: Idea information.

[2090] Output: Listed improvements.

[2091] How it works: The generative AI model analyzes idea information and lists areas for improvement.

[2092] Step 5:

[2093] The server will notify the user of the listed improvements.

[2094] Input: A list of improvements.

[2095] Output: The notification sent to the user.

[2096] What happens: The server notifies the user of the listed improvements via email and WebSocket notifications.

[2097] Introducing the Emotion Engine

[2098] Step 1:

[2099] The sentiment analysis means receives data (e.g., text and ratings) entered by users using the system.

[2100] Input: User input data.

[2101] Output: Data sent to sentiment analysis means.

[2102] Specific operation: The user enters comments and ratings on the device, and the server sends the data to the sentiment analysis means.

[2103] Step 2:

[2104] Sentiment analysis tools analyze data in real time.

[2105] Input: User input data.

[2106] Output: Emotion analysis results.

[2107] What it does: A sentiment analyzer (e.g., IBM Watson Sentiment Analysis) analyzes the data in real time to detect positive or negative sentiment.

[2108] Step 3:

[2109] The server provides the analysis results to the generation AI and the generation system AI means.

[2110] Input: Sentiment analysis results.

[2111] Output: Data sent to the Generative AI and Generative AI Means.

[2112] Specific operation: The server provides the analysis results to the generation AI and the generation system AI means.

[2113] Step 4:

[2114] The generative AI and generative AI means generate suggestions and improvements that take emotional state into account.

[2115] Input: Sentiment analysis results.

[2116] Output: Suggestions and improvements that take sentiment into account.

[2117] Specific operation: Generative AI and generative AI means generate suggestions and improvements based on the analysis results.

[2118] Sentiment Analysis and Notifications

[2119] Step 1:

[2120] The emotion analysis means analyzes the input data of the user and detects emotions.

[2121] Input: User input data.

[2122] Output: The detected emotion.

[2123] Specific operation: The sentiment analysis means analyzes user comments and ratings to detect emotions.

[2124] Step 2:

[2125] The server uses the emotion analysis results as a notification method.

[2126] Input: Sentiment analysis results.

[2127] Output: Emotion-based notification.

[2128] Specific operation: The server creates notification content based on the results of emotion analysis.

[2129] Step 3:

[2130] If negative sentiment is detected, it will send encouraging messages and specific suggestions for improvement.

[2131] Input: Detected negative sentiment.

[2132] Output: Encouraging messages and suggestions for improvement.

[2133] Specific operation: The server uses the Twilio API to send an encouraging message via SMS or email.

[2134] Step 4:

[2135] If positive sentiment is detected, notifications will be sent out highlighting success stories and positive reactions.

[2136] Input: Detected positive sentiment.

[2137] Output: Success stories and positive responses.

[2138] What happens: The server sends the user an email summarizing success stories and positive feedback.

[2139] (Application example 2)

[2140] 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."

[2141] Conventional feedback systems simply collect user opinions and comments as data and are unable to consider emotional factors such as emotions and attitudes. As a result, they are unable to provide improvement suggestions or responses based on the user's actual emotions, and are unable to fully increase user satisfaction. Furthermore, negative feedback is often not handled appropriately, leaving the problem unresolved. The present invention aims to solve these problems and provide a personalized feedback experience that takes user emotions into consideration.

[2142] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input information; server means for receiving the input information; database means for storing the received information; notification means for notifying other users of the received information; means for rating and commenting on ideas based on the notified information; means for storing the rating and comment in the database means; generative AI means for analyzing the received information and generating related proposals; means for notifying the user of the analysis results and proposals; generative AI means for improving the received information; means for notifying the user of the improvement results; emotion analysis means for analyzing the input information and extracting emotion information; and notification correction means for correcting the notification content based on the emotion information. This enables specific responses and proposals that reflect the user's emotions, thereby increasing user satisfaction.

[2143] An "input means" is a device or software that provides an interface for a user to input information.

[2144] The "server means" is a computer system for receiving and processing input information.

[2145] "Database means" refers to a storage device or system for storing and managing received information.

[2146] "Notification means" is a communication means for informing other users of received information.

[2147] The "rating and commenting means" is a method or device for rating an idea and leaving a comment based on the notified information.

[2148] "Generative AI means" means artificial intelligence technology for analyzing received information and generating relevant suggestions.

[2149] "Generative AI methods" are artificial intelligence techniques for improving received information.

[2150] "Emotion analysis means" is a technology for analyzing and extracting emotions from input information.

[2151] The "notification modification means" is a technique or means for modifying the notification content based on emotion information.

[2152] System Overview

[2153] This invention is a feedback system that collects and analyzes customer feedback in brick-and-mortar stores and provides personalized suggestions and improvements that take emotional information into account. The system is designed by integrating a smartphone application and server-side services.

[2154] Hardware and Software

[2155] Hardware:

[2156] Server: Receives, stores, and analyzes information, and notifies customers and store staff.

[2157] Smartphone: An interface device where customers enter feedback and receive results.

[2158] software:

[2159] Flask: Building an API server.

[2160] SQLite: Database.

[2161] TextBlob: A Python library for sentiment analysis.

[2162] smtplib: The Python standard library for sending email.

[2163] System Operation

[2164] 1. User Registration Process

[2165] When a user registers with the system from a smartphone app, the server receives the user's profile information and stores it in a database. Once registration is complete, the server sends a registration completion notification to the user.

[2166] 2. Feedback submission process

[2167] Users input feedback via a smartphone app. The server receives this feedback and stores it in a database. The server also analyzes this information using emotion analysis to detect positive or negative emotions.

[2168] 3. Sentiment Analysis Process

[2169] The server analyzes the sentiment of the received feedback using TextBlob, and if positive or negative sentiment is detected, it modifies the notification content based on the results and notifies the store staff in real time.

[2170] 4. Notification Process

[2171] The notification correction means introduces success stories and favorable reactions of other users in the case of positive feedback, and generates and sends encouraging messages in the case of negative feedback.

[2172] Specific examples

[2173] For example, if a user posts negative feedback such as "The service in the store was slow," the server analyzes the content using TextBlob to detect negative sentiment. It then generates an encouraging message saying, "We apologize for the inconvenience. We will strive to improve," and notifies the store staff. On the other hand, if a user posts positive feedback such as "The store staff's service was excellent," the server generates and sends a message introducing success stories from other customers.

[2174] Example prompt sentence:

[2175] "The following feedback was identified as negative. Please generate an encouraging message: Slow service in store."

[2176] This system allows responses that reflect the user's feelings, improving user satisfaction.

[2177] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2178] Step 1:

[2179] User Registration Process

[2180] Input: Users enter their profile information using a smartphone app.

[2181] Operation:

[2182] The server receives the user's input.

[2183] The received profile information is stored in an SQLite database.

[2184] Once registration is complete, the server will send a registration completion notification to the user's email address.

[2185] Output: A notification is sent to the user that registration is complete. The new user's information is saved in the database.

[2186] Step 2:

[2187] Feedback submission process

[2188] Input: Users enter feedback using a smartphone app.

[2189] Operation:

[2190] The server receives the user's feedback.

[2191] The received feedback information is stored in an SQLite database.

[2192] Output: The feedback is stored in a database.

[2193] Step 3:

[2194] Sentiment Analysis Process

[2195] Input: Feedback information stored in the database.

[2196] Operation:

[2197] The server uses TextBlob to perform sentiment analysis of the feedback content, which determines the polarity of the feedback (positive or negative).

[2198] Data processing: Analyze the text data (feedback content) using TextBlob to generate sentiment polarity scores.

[2199] Output: Sentiment analysis result (positive or negative).

[2200] Step 4:

[2201] Notification Process

[2202] Input: Sentiment analysis results, feedback content.

[2203] Operation:

[2204] The server generates an appropriate notification based on the sentiment analysis results.

[2205] For positive feedback: Generate messages showcasing success stories and positive reactions from other customers.

[2206] For negative feedback: Generate an encouraging message.

[2207] Use smtplib to email the generated notification to store staff.

[2208] Data processing: Generate notification content based on the analysis results and process it as a text message.

[2209] Output: Notification message sent to store staff.

[2210] Example prompt sentence:

[2211] "The following feedback was identified as negative. Please generate an encouraging message: Slow service in store."

[2212] Through the above processing steps, specific responses and suggestions that reflect the user's feelings are provided, thereby improving customer satisfaction.

[2213] 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.

[2214] 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.

[2215] 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.

[2216] [Fourth embodiment]

[2217] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2218] 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.

[2219] 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).

[2220] 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.

[2221] 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.

[2222] 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).

[2223] 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.

[2224] 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.

[2225] 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.

[2226] 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.

[2227] 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.

[2228] 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.

[2229] 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."

[2230] In the system based on this invention, users can input and post information, exchange ratings and comments, and improve and develop ideas with the support of generative AI and generative system AI. This system has multiple means and functions as follows:

[2231] 1. User Registration Process

[2232] Example

[2233] A user accesses the system's new registration form from a terminal and enters information such as their name, email address, and field of expertise. When this information is sent, the server receives it and stores it in a database. The server then sends the user an email informing them of completion of registration.

[2234] 2. Idea submission process

[2235] Example

[2236] A user accesses the idea submission form from their device and enters the title and body of their idea. When this data is sent, the server receives it and stores it in a database. The server then notifies other users that a new idea has been posted. This allows users to quickly share their ideas.

[2237] 3. Idea Evaluation and Comment Process

[2238] Example

[2239] A user accesses a list of ideas posted by other users from their device and displays the details page of an idea they are interested in. The user then presses the rating button and enters comment feedback. This rating and comment are sent to the server and stored in a database. The server then sends a notification of the rating and comment to the poster of the idea.

[2240] 4. AI-based idea analysis and proposal process

[2241] Example

[2242] The server periodically sends the idea data in the database to the generation AI. The generation AI analyzes the received idea data and generates related information and additional suggestions. The generated suggestions are sent to the server, which notifies the user. This allows the user to review and improve their ideas based on the analysis results and suggestions.

[2243] 5. Idea Improvement Process

[2244] Example

[2245] The user accesses the details page of their idea from their device and presses the "Improvement Request" button. This request is sent to the server, which then sends the idea data to the generative AI. The generative AI analyzes the idea and lists improvements. The generated improvements are then sent to the server, which notifies the user. The user can then use this information to modify and improve their idea.

[2246] This system provides an environment where users can easily post ideas, receive feedback, and refine their ideas while referring to automatically generated suggestions and improvements, accelerating idea generation and promoting the information revolution.

[2247] The processing flow will be explained below.

[2248] 1. User Registration Process

[2249] Step 1:

[2250] A user accesses the system from a terminal and displays a new registration form.

[2251] Step 2:

[2252] The user enters profile information such as name, email address, and area of ​​expertise.

[2253] Step 3:

[2254] The user presses the "Register" button and sends the input data to the server.

[2255] Step 4:

[2256] The server receives the transmitted data.

[2257] Step 5:

[2258] The server checks the integrity of the data it receives and, if there are no errors, stores it in the database.

[2259] Step 6:

[2260] The server sends a registration completion email to the user.

[2261] 2. Idea submission process

[2262] Step 1:

[2263] A user accesses the system from a terminal and displays the idea submission form.

[2264] Step 2:

[2265] The user enters the title and body of the idea.

[2266] Step 3:

[2267] The user presses the "Submit" button and sends the input data to the server.

[2268] Step 4:

[2269] The server receives the transmitted idea information.

[2270] Step 5:

[2271] The server stores the idea data in a database.

[2272] Step 6:

[2273] The server notifies other users that a new idea has been posted.

[2274] 3. Idea Evaluation and Comment Process

[2275] Step 1:

[2276] A user accesses the system from a terminal and displays a list of ideas posted by other users.

[2277] Step 2:

[2278] The user selects an idea that interests them and accesses the detail view page.

[2279] Step 3:

[2280] The user presses the rating button and enters feedback in the comment field.

[2281] Step 4:

[2282] The user presses the "Submit" button to send the evaluation data and comments to the server.

[2283] Step 5:

[2284] The server receives the rating data and comments and stores them in a database.

[2285] Step 6:

[2286] The server sends a rating and comment notification to the idea poster.

[2287] 4. AI-based idea analysis and proposal process

[2288] Step 1:

[2289] The server periodically sends the idea data in the database to the generation AI.

[2290] Step 2:

[2291] The generation AI analyzes the received idea data.

[2292] Step 3:

[2293] Generative AI generates relevant information and additional suggestions.

[2294] Step 4:

[2295] The generation AI sends the analysis results and suggestions to the server.

[2296] Step 5:

[2297] The server notifies the user of the analysis results and suggestions.

[2298] 5. Idea Improvement Process

[2299] Step 1:

[2300] The user accesses their idea details page from their device and presses the "Request for Improvement" button.

[2301] Step 2:

[2302] The user's request is sent to the server.

[2303] Step 3:

[2304] The server sends the relevant idea data to the generative AI.

[2305] Step 4:

[2306] Generative AI analyzes ideas and lists areas for improvement.

[2307] Step 5:

[2308] The generative AI sends improvements to the server.

[2309] Step 6:

[2310] The server will notify the user of the improvements.

[2311] Step 7:

[2312] Users receive notifications, review improvements, and revise their ideas.

[2313] Example 1

[2314] 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."

[2315] Conventional idea-sharing platforms have a problem in that the process of users posting ideas and receiving feedback from other users is cumbersome, making it difficult to efficiently improve ideas. Furthermore, there is a lack of a means for users to receive specific suggestions for voluntarily improving their ideas. Furthermore, there is no system for regularly analyzing ideas and generating suggestions, which limits the quality of creative ideas.

[2316] 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.

[2317] In this invention, the server includes an input means for a user to input information, a server means for receiving the input information, a database means for storing the received information, a notification means for notifying other users of the received information, a means for rating and commenting on ideas based on the notified information, a means for storing the rating and comment in the database means, a generative AI means for periodically analyzing the received information and generating related proposals, a means for notifying users of the analysis results and proposals, a means for receiving idea improvement requests from users, a generative AI means for analyzing the received information and listing improvements, and a means for notifying users of the improvements. This enables users to intuitively post ideas, quickly receive feedback from other users, and efficiently improve their ideas based on specific suggestions and improvements from the generative AI and the generative AI.

[2318] "User" refers to an individual who inputs information, posts ideas, ratings and comments.

[2319] "Input means" refers to the interface through which a user enters information or ideas, such as a web form or an input field in a mobile app.

[2320] "Server means" refers to a computer system that receives information sent from a user and performs various processes.

[2321] "Database Means" refers to a data storage device or system for storing and managing received information.

[2322] "Notification mechanism" refers to a method or system for communicating specific information or updates to other users.

[2323] "Means for rating and commenting" refers to a function that allows users to input ratings and comments on posted ideas.

[2324] "Generative AI Means" refers to the artificial intelligence model used to analyze received information and generate relevant recommendations.

[2325] "Generative AI means" refers to an artificial intelligence model that analyzes received information based on user requests and lists areas for improvement.

[2326] The system based on this invention allows users to input and post information, exchange ratings and comments, and improve and develop ideas with the support of generative AI and generative system AI. This system has the following multiple means and functions:

[2327] 1. User Registration Process

[2328] The user accesses the system's new registration form on their own device (PC, smartphone, etc.) and enters information such as their name, email address, and field of expertise. After entering the information, they press the "Submit" button, and the server receives the information and stores it in a database (e.g., MySQL). The server then sends the user a registration completion email.

[2329] Example: A user fills in a new registration form with information such as "Yamada Taro", "example@example.com", and "Engineering", and submits it.

[2330] 2. Idea submission process

[2331] A user accesses the idea submission form on their device and enters the title and text of their idea. For example, "A new solar panel design" and "This solar panel will improve efficiency by 50%." After entering the information, they press the "Submit" button. The server receives the data and stores it in a database. The server then notifies other users that a new idea has been submitted.

[2332] Example: A user enters the title and body of an idea "New solar panel design" and submits it.

[2333] 3. Idea Evaluation and Comment Process

[2334] A user accesses a list of ideas posted by other users from their device and displays the details page of an idea they are interested in. For example, they can enter a comment such as "I think this is practical" for the idea "New solar panel design" and rate it "5 stars." When the rating and comment are submitted, they are sent to the server and stored in a database. The server then notifies the idea poster that the rating and comment have been received.

[2335] Example: A user submits a 5-star rating for "New Solar Panel Design" and a comment saying "Great idea."

[2336] 4. AI-based idea analysis and proposal process

[2337] The server periodically sends the idea data in the database to a generation AI (e.g., OpenAI's GPT-4). The generation AI analyzes the received ideas and generates related information and additional suggestions. These suggestions are then sent back to the server, which notifies the user.

[2338] Example prompt: An idea has been submitted for "New Solar Panel Design." Check it out if you're interested.

[2339] 5. Idea Improvement Process

[2340] When a user accesses the details page of their idea on their device and presses the "Improvement Request" button, a request is sent to the server. The server sends the idea data to a generative AI (e.g., DeepAI), which analyzes the idea and lists improvements. The generated improvements are sent to the server, which notifies the user. The user can then use this information to revise and improve their idea.

[2341] Example: After the user presses the "improvement request" button, the suggestion for improvement is presented: "The amount of silicon used should be reduced to further improve area efficiency."

[2342] This program is implemented as a web platform to make user operation intuitive and simple, and uses HTML, CSS, JavaScript, etc. as the user interface. The server side is implemented using languages ​​such as Python and Node.js, and a relational database such as MySQL is used as the database. OpenAI's GPT-4 and DeepAI's API are used for the generative AI and generative AI models, which automatically perform periodic analysis processing and respond to user requests.

[2343] This system allows users to intuitively post ideas, quickly receive feedback from other users, and efficiently improve their ideas based on specific suggestions and improvements from the generative AI and generative system AI. As a result, the quality of ideas improves, providing an environment that promotes the creation of new value.

[2344] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2345] User Registration Process

[2346] Step 1:

[2347] The user accesses the system's new registration page on their own device (PC, smartphone, etc.).

[2348] Input: Enter the URL of the system's new registration page in the URL bar of your web browser.

[2349] Output: A new registration form is displayed.

[2350] Step 2:

[2351] Users enter information such as their name, email address, and area of ​​expertise into the new registration form.

[2352] Input: Enter your name, email address, area of ​​expertise, etc. in the input fields.

[2353] Output: The input data is displayed in a form.

[2354] Step 3:

[2355] The user clicks the "Submit" button.

[2356] Input: The "Submit" button is clicked.

[2357] Output: The form data is sent to the server.

[2358] Step 4:

[2359] The server receives the user's input information as an HTTP POST request.

[2360] Input: User information included in the HTTP POST request.

[2361] Output: User information temporarily stored in the server's processing memory.

[2362] Step 5:

[2363] The server stores the received data in a database.

[2364] Input: User information temporarily stored in the server's processing memory.

[2365] Output: User information stored in the users table in the MySQL database.

[2366] Step 6:

[2367] The server sends a registration completion email to the user.

[2368] Input: The content of the registration confirmation email and the user's email address.

[2369] Output: A registration confirmation email sent to the user's email account.

[2370] Idea Submission Process

[2371] Step 1:

[2372] The user accesses the idea submission page on the terminal.

[2373] Enter the URL of the idea submission page in the URL bar of your web browser.

[2374] Output: The idea submission form is displayed.

[2375] Step 2:

[2376] The user enters the title and body of the idea.

[2377] Input: Enter the title and text of your idea in the fields provided.

[2378] Output: The input data is displayed in a form.

[2379] Step 3:

[2380] The user clicks the "Submit" button.

[2381] Input: The "Submit" button is clicked.

[2382] Output: The form data is sent to the server.

[2383] Step 4:

[2384] The server receives the idea data as an HTTP POST request.

[2385] Input: Idea information included in the HTTP POST request.

[2386] Output: Idea information temporarily stored in the server's processing memory.

[2387] Step 5:

[2388] The server stores the received data in a database.

[2389] Input: Idea information temporarily stored in the server's processing memory.

[2390] Output: Idea information stored in the Ideas table in a MySQL database.

[2391] Step 6:

[2392] The server notifies other users that a new idea has been posted.

[2393] Input: New idea information and a list of people to notify.

[2394] Output: A notification email or in-app notification is sent to other users.

[2395] Idea evaluation and comment process

[2396] Step 1:

[2397] The user accesses a list page of ideas posted by other users from their terminal.

[2398] Input: Enter the URL of the idea list page into the URL bar of your web browser.

[2399] Output: A list of ideas is displayed.

[2400] Step 2:

[2401] View the detail page of the idea that the user wants to rate.

[2402] Enter: Click on a specific idea from the list of ideas.

[2403] Output: The details page for a particular idea is displayed.

[2404] Step 3:

[2405] The user presses the rating button.

[2406] Input: The rating button is clicked.

[2407] Output: Evaluation data is temporarily stored.

[2408] Step 4:

[2409] The user enters comment feedback.

[2410] Enter your feedback in the comments section.

[2411] Output: The comment data is displayed in a form.

[2412] Step 5:

[2413] The user clicks the "Submit" button.

[2414] Input: The "Submit" button is clicked.

[2415] Output: The form data is sent to the server.

[2416] Step 6:

[2417] The server receives the ratings and comments as HTTP POST requests.

[2418] Input: Rating and comment information included in the HTTP POST request.

[2419] Output: Rating and comment information temporarily stored in the server's processing memory.

[2420] Step 7:

[2421] The server stores the received data in a database.

[2422] Input: Rating and comment information temporarily stored in the server's processing memory.

[2423] Output: Rating and comment information stored in the ratings table in a MySQL database.

[2424] Step 8:

[2425] The server notifies the idea poster.

[2426] Input: Rating and comment information and author information to notify.

[2427] Output: A notification email or in-app notification is sent to the contributor.

[2428] AI-based idea analysis and proposal process

[2429] Step 1:

[2430] The server periodically scans the database.

[2431] Input: A periodic timer trigger.

[2432] Output: Obtaining new idea information.

[2433] Step 2:

[2434] The server sends new idea data to the generation AI.

[2435] Input: New idea information.

[2436] Output: API request to the generating AI.

[2437] Step 3:

[2438] Generative AI analyzes idea data.

[2439] Input: Idea information.

[2440] Output: Analysis results and recommendations.

[2441] Step 4:

[2442] Generative AI generates suggestions.

[2443] Input: Analysis results.

[2444] Output: Proposal data.

[2445] Step 5:

[2446] The proposal is sent to the server.

[2447] Input: Proposal data.

[2448] Output: The API response to the server.

[2449] Step 6:

[2450] The server notifies the user of the offer.

[2451] Input: Proposal data and user information to notify.

[2452] Output: A notification email or in-app notification is sent to the user.

[2453] Idea Improvement Process

[2454] Step 1:

[2455] A user visits their idea detail page.

[2456] Input: Enter the URL of the idea detail page into the URL bar of your web browser.

[2457] Output: The idea details page is displayed.

[2458] Step 2:

[2459] The user presses the "improvement request" button.

[2460] Input: The "Improvement Request" button is clicked.

[2461] Output: An enhancement request is sent to the server.

[2462] Step 3:

[2463] The request is sent to the server.

[2464] Input: Enhancement request data.

[2465] Output: Request data temporarily stored in the server's processing memory.

[2466] Step 4:

[2467] The server sends the idea data to the generative AI.

[2468] Input: Idea information and improvement request data.

[2469] Output: API request to the generative AI.

[2470] Step 5:

[2471] Generative AI analyzes ideas.

[2472] Input: Idea information and improvement requests.

[2473] Output: Analysis results and improvements.

[2474] Step 6:

[2475] Generative AI lists areas for improvement.

[2476] Input: Analysis results.

[2477] Output: A list of improvements.

[2478] Step 7:

[2479] The improvements are sent to the server.

[2480] Input: A list of improvements.

[2481] Output: The API response to the server.

[2482] Step 8:

[2483] The server notifies the user of the improvements.

[2484] Input: A list of improvements and the users to notify.

[2485] Output: A notification email or in-app notification is sent to the user.

[2486] (Application example 1)

[2487] 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."

[2488] Conventional content distribution services have a problem in that even when users post new content or ideas, they do not receive prompt and effective feedback or evaluations, making it difficult to create high-quality content. Furthermore, there is a lack of a mechanism for automatically analyzing posted content and providing suggestions for improvements or additions, limiting the means by which users can effectively improve their own content. The objective of this invention is to solve these problems and provide an environment in which users can create high-quality content.

[2489] 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.

[2490] In this invention, the server includes input means for a user to input information, server means for receiving the input information, database means for saving the received information, notification means for notifying other information processing devices of the received information, means for rating and commenting on ideas based on the notified information, means for saving the rating and comment in the database means, generative AI means for analyzing the received information and generating related proposals, means for notifying the information processing device of the analysis results and proposals, generative AI means for improving the received information, means for notifying the information processing device of the improvement results, means for a user to post new content or ideas in a content distribution service and receive feedback and evaluations thereon from other information processing devices, and means for analyzing the user's posted content using generative AI and providing suggestions for improvements and additions. This allows users to receive feedback quickly and effectively and to improve their own content to a higher quality by utilizing the analysis and suggestions by the generative AI.

[2491] An "input means" is a device or interface through which a user inputs information.

[2492] The "server means" refers to a server device or its function for receiving and processing input information.

[2493] "Database Means" means a database system or function thereof for storing and managing received information.

[2494] The "notification means" is a device or function for notifying other information processing devices of received information.

[2495] The "evaluation and comment means" is a device or function for evaluating and commenting on ideas based on the notified information.

[2496] "Generative AI means" means an artificial intelligence model or function thereof for analyzing received information and generating relevant recommendations.

[2497] "Information processing device" refers to any device that a user uses to input, receive, and process information.

[2498] A "generative AI means" is an artificial intelligence model or function that analyzes received information to improve it and lists areas for improvement.

[2499] A "content distribution service" is a service that provides information and media content to users via the Internet or the like.

[2500] "Feedback" refers to evaluations and comments received from other information processing devices.

[2501] "Analysis results" refers to suggestions and improvements generated by the generative AI means.

[2502] This system supports users in posting new content and ideas on a content distribution service, and receives feedback and evaluations from other information processing devices. Furthermore, it uses generative AI to analyze users' posts and provide suggestions for improvements and additions, helping users to create high-quality content.

[2503] The server uses the following hardware and software to process information such as reception, storage, notification, analysis, and improvement.

[2504] Hardware: High-performance server equipment, database server

[2505] Software: Flask (web framework), SQLAlchemy (database ORM), TensorFlow / PyTorch (generative AI model), Twilio (email sending)

[2506] Input Method

[2507] A web form is provided as an input mechanism for users to enter information, allowing them to enter information such as their name, email address, content categories of interest, and the title and body of the content.

[2508] Server Means

[2509] The server receives the information entered by the user and stores it in a database. The receiving process is performed by a web application using Flask.

[2510] Database Means

[2511] The received information is stored in a database using SQLAlchemy, and the stored data is used for subsequent processing (notification, analysis, improvement, etc.).

[2512] Notification means

[2513] When new content or ideas are posted, the server notifies other users, using Twilio to send emails.

[2514] Rating and commenting tools

[2515] Other users can access the information and provide ratings and comments, which are then sent back to the server and stored in a database.

[2516] Generation AI means

[2517] The server periodically sends the submitted data in the database to a generative AI model (TensorFlow or PyTorch), which analyzes the received data and generates relevant suggestions and improvements. The generated suggestions are returned to the server and notified to the user.

[2518] Information processing device

[2519] Information processing devices are devices that users use to input, receive, and process information, such as smartphones, smart glasses, head-mounted displays, and robots. This allows similar functions to be realized on any device.

[2520] Improvement AI method

[2521] When a user submits an improvement request, the server sends the request to the generative AI means, which analyzes the request and lists the improvements. The server then notifies the user of the improvements.

[2522] Examples and prompts

[2523] For example, if a user posts an idea for a new web browser design, the following prompt is sent: "This is an idea for a new web browser design. This browser is for the visually impaired and comes standard with a voice guide function." Based on this prompt, the generative AI model analyzes the proposal and provides specific suggestions such as "improvement suggestions to make the browser UI more intuitive" and "ways to enhance the voice guide function."

[2524] This allows users to receive feedback quickly and effectively, and leverage generative AI analysis and suggestions to improve their content to a higher quality.

[2525] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2526] Step 1:

[2527] A user accesses the system's new registration form from a terminal and enters their name, email address, content categories of interest, etc. The entered information is received by the server and saved in a database. The entered data is processed by checking for format and duplication. The server then uses Twilio to send the user a registration completion email. The user's personal information is input, and the output is saved in the database and a completion email is sent.

[2528] Step 2:

[2529] A user accesses the idea submission form from their device and enters the title and content body. The entered data is sent to the server and saved in the database. The server uses the notification function to notify other users via email via Twilio that a new idea has been posted. The idea content is input, and the output is saving to the database and sending a notification.

[2530] Step 3:

[2531] Other users check the new ideas notified from their devices and post ratings and comments. The ratings and comments are sent to the server and stored in a database. The server then notifies the original poster that the rating or comment has been posted. The input is the rating and comment, and the output is storage in the database and a notification to the poster.

[2532] Step 4:

[2533] The server periodically sends the posted data in the database to a generative AI model (TensorFlow or PyTorch). The generative AI model analyzes the received data and generates relevant suggestions. This analysis and data calculation uses natural language processing and clustering techniques. The generated suggestions are returned to the server and notified to the user. The user is notified of the posted data as input and the suggestions that are the analysis results as output.

[2534] Step 5:

[2535] The user accesses their idea details page from their device and presses the "Improvement Request" button. The request is sent to the server, which then sends the idea data to the generative AI model. The generative AI model analyzes the idea and lists improvements. The generated improvements are returned to the server and notified to the user. The improvement request and idea data are input, and the improvements are notified to the user as output.

[2536] Step 6:

[2537] Users receive notifications from the server and use the suggestions and improvements to revise and improve their ideas. The revised idea is then resubmitted to the server and stored in the database. At this time, the server resends notifications containing the revisions to other users, requesting their feedback. The revised idea is the input, and the output is saving to the database and re-notifying them.

[2538] This allows users to create and improve high-quality content.

[2539] 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.

[2540] The system based on this invention provides a more personalized experience by combining an emotion engine in the process of users inputting and posting information, receiving ratings and comments from other users, and analyzing and improving that information. A specific embodiment of the system is shown below.

[2541] 1. User Registration Process

[2542] Example

[2543] The user accesses the system's new registration form from their terminal and enters their profile information. When the user submits the information, the server receives it and stores it in the database. Once registration is complete, the server sends the user a registration completion email.

[2544] 2. Idea submission process

[2545] Example

[2546] A user accesses the idea submission form from a terminal and enters the title and text of the idea. Once submission is complete, the server saves the idea information in a database and notifies other users that a new idea has been submitted.

[2547] 3. Idea Evaluation and Comment Process

[2548] Example

[2549] When a user rates or comments on another user's idea from their device, the rating and comment are sent to the server and stored in a database. The server then sends a notification of the rating and comment to the idea poster.

[2550] 4. AI-based idea analysis and proposal process

[2551] Example

[2552] The server periodically sends the idea information in the database to the generation AI, which analyzes it and generates proposals. The generated proposals are notified to the user via the server, allowing the user to review and improve their ideas.

[2553] 5. Idea Improvement Process

[2554] Example

[2555] When a user presses the "Improvement Request" button on their device's idea details page, the request is sent to the server. The server sends the idea to the generative AI, which then lists improvements. The listed improvements are notified to the user via the server, allowing the user to modify the idea.

[2556] 6. Introducing the Emotion Engine

[2557] Example

[2558] The emotion engine analyzes data (text and ratings) entered by users in real time while they use the system from their devices. This emotion analysis is particularly effective when posting ideas or comments, and the server provides the analysis results to the generative AI and generative system AI. This allows suggestions and improvements to be generated that take the user's emotional state into account.

[2559] Sentiment Analysis and Notifications

[2560] Example

[2561] The emotion engine analyzes the user's input data, and if it detects positive or negative emotions, the server uses the emotion analysis results as a notification method. For example, if negative emotions are detected, the server can send the user an encouraging message or provide more specific suggestions for improvement. Conversely, if positive emotions are detected, the server can motivate the user by introducing success stories and other users' positive reactions.

[2562] By integrating an emotion engine, this invention takes into account the user's emotional state to provide more personalized feedback and improvement suggestions, which is expected to increase user satisfaction, improve the quality of ideas, and promote the information revolution.

[2563] The processing flow will be explained below.

[2564] 1. User Registration Process

[2565] Step 1:

[2566] A user accesses the system from a terminal and displays a new registration form.

[2567] Step 2:

[2568] The user enters profile information such as name, email address, and area of ​​expertise.

[2569] Step 3:

[2570] The user presses the "Register" button and sends the input data to the server.

[2571] Step 4:

[2572] The server receives the transmitted data.

[2573] Step 5:

[2574] The server checks the integrity of the data it receives and, if there are no errors, stores it in the database.

[2575] Step 6:

[2576] The server sends a registration completion email to the user.

[2577] 2. Idea submission process

[2578] Step 1:

[2579] A user accesses the system from a terminal and displays the idea submission form.

[2580] Step 2:

[2581] The user enters the title and body of the idea.

[2582] Step 3:

[2583] The user presses the "Submit" button and sends the input data to the server.

[2584] Step 4:

[2585] The server receives the transmitted idea information.

[2586] Step 5:

[2587] The server stores the idea data in a database.

[2588] Step 6:

[2589] The server notifies other users that a new idea has been posted.

[2590] 3. Idea Evaluation and Comment Process

[2591] Step 1:

[2592] A user accesses the system from a terminal and displays a list of ideas posted by other users.

[2593] Step 2:

[2594] The user selects an idea that interests them and accesses the detail view page.

[2595] Step 3:

[2596] The user presses the rating button and enters feedback in the comment field.

[2597] Step 4:

[2598] The user presses the "Submit" button to send the evaluation data and comments to the server.

[2599] Step 5:

[2600] The server receives the rating data and comments and stores them in a database.

[2601] Step 6:

[2602] The server sends a rating and comment notification to the idea poster.

[2603] 4. AI-based idea analysis and proposal process

[2604] Step 1:

[2605] The server periodically sends the idea data in the database to the generation AI.

[2606] Step 2:

[2607] The generation AI analyzes the received idea data.

[2608] Step 3:

[2609] Generative AI generates relevant information and additional suggestions.

[2610] Step 4:

[2611] The generation AI sends the analysis results and suggestions to the server.

[2612] Step 5:

[2613] The server notifies the user of the analysis results and suggestions.

[2614] 5. Idea Improvement Process

[2615] Step 1:

[2616] The user accesses their idea details page from their device and presses the "Request for Improvement" button.

[2617] Step 2:

[2618] The user's request is sent to the server.

[2619] Step 3:

[2620] The server sends the relevant idea data to the generative AI.

[2621] Step 4:

[2622] Generative AI analyzes ideas and lists areas for improvement.

[2623] Step 5:

[2624] The generative AI sends improvements to the server.

[2625] Step 6:

[2626] The server will notify the user of the improvements.

[2627] Step 7:

[2628] Users receive notifications, review improvements, and revise their ideas.

[2629] 6. Introducing the Emotion Engine

[2630] Step 1:

[2631] When a user posts an idea or comment from a terminal, the input data is sent to the server.

[2632] Step 2:

[2633] The server sends the received data to the emotion engine.

[2634] Step 3:

[2635] The emotion engine analyzes the data in real time and evaluates the user's emotional state (positive, negative, etc.).

[2636] Step 4:

[2637] The emotion engine provides the analysis results to the generative AI and generative AI.

[2638] Step 5:

[2639] The generative AI and generative system AI adjust suggestions and improvements based on the emotional state and send them to the server.

[2640] Step 6:

[2641] The server notifies the user of any adjustment suggestions or improvements.

[2642] Step 7:

[2643] Users can check improvements and suggestions from their devices and further refine their ideas. In addition, appropriate feedback and notifications based on sentiment analysis results provide a more personalized user experience.

[2644] Example 2

[2645] 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."

[2646] Conventional information input and posting systems do not adequately provide feedback and suggestions that take into account the user's emotional state. As a result, the quality of the user experience declines and the efficiency of the idea evaluation and improvement process decreases. In particular, there is a lack of appropriate follow-up for users with negative emotions, making it difficult to maintain user motivation.

[2647] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: an input means for a user to input information; a server means for receiving the input information; a data storage means for saving the received information; a notification means for notifying other users of the received information; an evaluation means for rating and commenting on ideas based on the notified information; a memory means for saving the evaluation and comment in the data storage means; a generation AI means for analyzing the received information and generating related proposals; a notification means for notifying the user of the analysis results and proposals; a generative AI means for improving the received information; a notification means for notifying the user of the improvement results; an emotion analysis means for analyzing data input by the user using the system and detecting emotions; a providing means for providing the emotion analysis results to the generation AI and the generative AI means; and a notification means for sending specific proposals or notifications to the user based on the emotion analysis results. This enables the provision of feedback and proposals that take the user's emotional state into consideration, improving the quality of the user experience and increasing the efficiency of the idea evaluation and improvement process.

[2648] The "input means" is an interface for the user to input information.

[2649] "Server means" is a computer system for processing and managing information received from users.

[2650] "Data storage means" means a storage device for storing received information.

[2651] A "notification means" is a system for notifying other users of specific information.

[2652] The "evaluation means" is a function that allows users to evaluate and comment on other users' ideas.

[2653] "Storage means" is a device or system for storing ratings and comments.

[2654] "Generative AI means" means an artificial intelligence system for analyzing received information and generating relevant suggestions.

[2655] The "means of provision" is a system for passing the results of emotion analysis to the generation AI and the generation system AI means.

[2656] "Emotion analysis means" is a technology for analyzing emotions based on data entered by users into the system.

[2657] "Generative AI methods" are artificial intelligence technologies that generate optimal improvement proposals based on user input information.

[2658] The system based on this invention allows users to input and post information, receive ratings and comments from other users, and analyze and improve that information in a process that combines an emotion engine to provide a more personalized experience. A specific embodiment of the system is shown below.

[2659] User Registration Process

[2660] The user accesses the system's new registration form from their terminal and enters their profile information. When the user submits the information, the server receives it and stores it in a database, which is a data storage means. Once registration is complete, the server sends the user an email informing them of completion of registration. During this process, the server uses the SMTP protocol as both an input means and a notification means.

[2661] Examples:

[2662] Device: Smartphone

[2663] Software used: Web browser

[2664] Data Used: User name, email address, password

[2665] Idea Submission Process

[2666] The user accesses the idea submission form from their device and enters the title and text of their idea. Once submission is complete, the server saves the idea information in a database, which serves as a data storage means, and notifies other users that a new idea has been submitted. The server uses a push notification service as a notification method.

[2667] Examples:

[2668] Device: PC

[2669] Software used: Web browser

[2670] Data used: Idea title, content

[2671] Idea evaluation and comment process

[2672] When a user enters a rating and comment on another user's idea, the content is sent to the server and stored in the data storage means, and the server notifies the idea poster of the rating and comment.

[2673] Examples:

[2674] Device: Tablet

[2675] Software used: Web app

[2676] Data used: Comments, ratings

[2677] AI-based idea analysis and proposal process

[2678] The server periodically transmits the idea information stored in the data storage means to the generation AI means, which analyzes the information and generates a proposal. The generated proposal is notified to the user via the server, allowing the user to review and improve the idea.

[2679] Examples:

[2680] Software used: Generative AI models (e.g., OpenAI GPT-4)

[2681] Data used: Content of the submitted idea

[2682] Idea Improvement Process

[2683] When a user presses the "Improvement Request" button on their idea's details page, the request is sent to the server. The server sends the idea to the generative AI means, which then lists improvements. The listed improvements are notified to the user via the server, allowing the user to modify the idea.

[2684] Examples:

[2685] Device: Laptop

[2686] Software used: Web browser

[2687] Data used: Idea content, improvement requests

[2688] Introducing the Emotion Engine

[2689] The emotion analysis means analyzes data (text, ratings, etc.) entered by users using the system in real time. This emotion analysis is particularly effective when posting ideas or comments, and the server provides the analysis results to the generative AI and generative AI means. This allows suggestions and improvements to be generated that take the user's emotional state into account.

[2690] Examples:

[2691] Software used: Sentiment analysis tools (e.g., IBM Watson Sentiment Analysis)

[2692] Data used: Comments, ratings

[2693] Sentiment Analysis and Notifications

[2694] The emotion analysis means analyzes the user's input data, and if it detects positive or negative emotions, the server uses the emotion analysis results as a notification means. If negative emotions are detected, encouraging messages and specific suggestions for improvement are sent. Conversely, if positive emotions are detected, the server can increase the user's motivation by notifying them of success stories and the positive reactions of other users.

[2695] Examples:

[2696] Software used: Sentiment analysis tools, notification tools (e.g., Twilio)

[2697] Data Used: User comments, posts

[2698] Example prompts to input to a generative AI model:

[2699] Entered idea data:

[2700] Title: "New Educational App Ideas"

[2701] Content: "I want to create an app that helps students learn in a fun way. I would like to add various features."

[2702] Emotion analysis results:

[2703] Emotion: Positive

[2704] Prompt to spawn AI:

[2705] "Please list improvements to this idea that reflect your users' positive feelings."

[2706] By showing specific examples of the mode for carrying out this invention, it will help those involved to accurately understand the content of the invention and serve as a reference for putting it into practice. In addition, by showing specific examples of the hardware and software to be used, it will serve as a guideline for implementation.

[2707] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2708] User Registration Process

[2709] Step 1:

[2710] The user accesses the sign-up form from a device.

[2711] Input: Profile information the user enters into the form (name, email address, password).

[2712] Output: The data entered into the form.

[2713] Specific operation: Access the new registration URL using the web browser on the device.

[2714] Step 2:

[2715] The user enters profile information and presses the "Register" button.

[2716] Input: Form data entered by the user.

[2717] Output: The form data is sent to the server.

[2718] What happens: The user fills out the form and clicks the "Register" button.

[2719] Step 3:

[2720] The server receives the entered information and performs verification.

[2721] Input: Form data.

[2722] Output: The validation result (success or failure).

[2723] What happens: The server receives the data and checks the format of the name, email address, and password.

[2724] Step 4:

[2725] The server stores the information in a database, which is a data storage means.

[2726] Input: The validated form data.

[2727] Output: User profile information stored in a database.

[2728] Specific behavior: The server saves the data to the users table in the database.

[2729] Step 5:

[2730] The server sends a registration completion email.

[2731] Input: Saved user profile information.

[2732] Output: A registration completion email is sent to the user.

[2733] Specific operation: The server uses the SMTP protocol to send a registration completion email through the Google Gmail API.

[2734] Idea Submission Process

[2735] Step 1:

[2736] A user accesses the idea submission form from a terminal.

[2737] Input: Request to access the idea submission page.

[2738] Output: The idea submission form is displayed in the browser.

[2739] Specific actions: Access the idea submission page using a web browser on your device.

[2740] Step 2:

[2741] The user enters the title and text of the idea and presses the "Post" button.

[2742] Input: Idea information (title, body) entered by the user.

[2743] Output: The idea information sent to the server.

[2744] Specific behavior: The user enters a title and body text and clicks the "Post" button.

[2745] Step 3:

[2746] The server receives the submitted data and verifies it.

[2747] Input: Idea information.

[2748] Output: The validation result (success or failure).

[2749] Specific operation: The server receives the sent idea information and checks the format and content of the data.

[2750] Step 4:

[2751] The server stores the idea information in a database, which is a data storage means.

[2752] Input: Verified idea information.

[2753] Output: Idea information stored in a database.

[2754] What happens: The server saves the data to the ideas collection in MongoDB.

[2755] Step 5:

[2756] The server notifies other users that a new idea has been posted.

[2757] Input: Saved idea information.

[2758] Output: Notifications sent to other users.

[2759] Specific behavior: The server uses the push notification service to notify that a new idea has been posted.

[2760] Idea evaluation and comment process

[2761] Step 1:

[2762] A user accesses another user's idea detail page from a terminal.

[2763] Input: A request to access the idea details page.

[2764] Output: The idea detail page is displayed in a browser.

[2765] What happens: Use a web browser on your device to access the idea details page.

[2766] Step 2:

[2767] Users rate ideas and enter comments.

[2768] Input: Rating and Comments.

[2769] Output: Ratings and comments sent to the server.

[2770] Specific behavior: User selects a rating (e.g., 4 out of 5 stars), enters a comment, and clicks the "Submit" button.

[2771] Step 3:

[2772] The server receives and validates the ratings and comments.

[2773] Input: Rating and Comments.

[2774] Output: The validation result (success or failure).

[2775] Specific behavior: The server receives the submitted ratings and comments and checks the format and content of the data.

[2776] Step 4:

[2777] The server stores the ratings and comments in a database, which is a data storage means.

[2778] Input: Verified rating and comments.

[2779] Output: Ratings and comments stored in a database.

[2780] What happens: The server saves the data to the comments collection in Firebase.

[2781] Step 5:

[2782] The server sends a rating and comment notification to the idea poster.

[2783] Input: Saved ratings and comments.

[2784] Output: Notification sent to idea submitter.

[2785] Specific behavior: The server sends a notification to the email address of the idea poster.

[2786] AI-based idea analysis and proposal process

[2787] Step 1:

[2788] The server periodically sends the idea information in the database to the generative AI model.

[2789] Input: Idea information.

[2790] Output: The data sent to the generative AI model.

[2791] What it does: The server retrieves the latest idea information from the ideas collection in MongoDB and sends it to the generative AI model's API.

[2792] Step 2:

[2793] A generative AI model analyzes idea information and generates proposals.

[2794] Input: Idea information.

[2795] Output: The generated proposals.

[2796] How it works: A generative AI model (e.g., OpenAI GPT-4) analyzes idea information and generates relevant suggestions.

[2797] Step 3:

[2798] The server receives the generated proposal and notifies the user.

[2799] Input: The generated proposals.

[2800] Output: The notification sent to the user.

[2801] Specific operation: The server receives suggestions from the generative AI model and sends them to the user via WebSocket notification or email.

[2802] Idea Improvement Process

[2803] Step 1:

[2804] The user accesses their idea details page from a device.

[2805] Input: A request to access the idea details page.

[2806] Output: The idea detail page is displayed in a browser.

[2807] What to do: Use a web browser on your device to access your idea details page.

[2808] Step 2:

[2809] The user presses the "improvement request" button.

[2810] Input: Enhancement request.

[2811] Output: The request sent to the server.

[2812] Specific behavior: The user clicks the "Enhancement Request" button.

[2813] Step 3:

[2814] The server receives the request and sends the idea information to the generative AI.

[2815] Input: Enhancement request and idea information.

[2816] Output: Data sent to the generative AI.

[2817] Specific operation: The server retrieves the relevant idea information from the ideas collection in MongoDB and sends it to the API of the generative AI model.

[2818] Step 4:

[2819] Generative AI lists areas for improvement.

[2820] Input: Idea information.

[2821] Output: Listed improvements.

[2822] How it works: The generative AI model analyzes idea information and lists areas for improvement.

[2823] Step 5:

[2824] The server will notify the user of the listed improvements.

[2825] Input: A list of improvements.

[2826] Output: The notification sent to the user.

[2827] What happens: The server notifies the user of the listed improvements via email and WebSocket notifications.

[2828] Introducing the Emotion Engine

[2829] Step 1:

[2830] The sentiment analysis means receives data (e.g., text and ratings) entered by users using the system.

[2831] Input: User input data.

[2832] Output: Data sent to sentiment analysis means.

[2833] Specific operation: The user enters comments and ratings on the device, and the server sends the data to the sentiment analysis means.

[2834] Step 2:

[2835] Sentiment analysis tools analyze data in real time.

[2836] Input: User input data.

[2837] Output: Emotion analysis results.

[2838] What it does: A sentiment analyzer (e.g., IBM Watson Sentiment Analysis) analyzes the data in real time to detect positive or negative sentiment.

[2839] Step 3:

[2840] The server provides the analysis results to the generation AI and the generation system AI means.

[2841] Input: Sentiment analysis results.

[2842] Output: Data sent to the Generative AI and Generative AI Means.

[2843] Specific operation: The server provides the analysis results to the generation AI and the generation system AI means.

[2844] Step 4:

[2845] The generative AI and generative AI means generate suggestions and improvements that take emotional state into account.

[2846] Input: Sentiment analysis results.

[2847] Output: Suggestions and improvements that take sentiment into account.

[2848] Specific operation: Generative AI and generative AI means generate suggestions and improvements based on the analysis results.

[2849] Sentiment Analysis and Notifications

[2850] Step 1:

[2851] The emotion analysis means analyzes the input data of the user and detects emotions.

[2852] Input: User input data.

[2853] Output: The detected emotion.

[2854] Specific operation: The sentiment analysis means analyzes user comments and ratings to detect emotions.

[2855] Step 2:

[2856] The server uses the emotion analysis results as a notification method.

[2857] Input: Sentiment analysis results.

[2858] Output: Emotion-based notification.

[2859] Specific operation: The server creates notification content based on the results of emotion analysis.

[2860] Step 3:

[2861] If negative sentiment is detected, it will send encouraging messages and specific suggestions for improvement.

[2862] Input: Detected negative sentiment.

[2863] Output: Encouraging messages and suggestions for improvement.

[2864] Specific operation: The server uses the Twilio API to send an encouraging message via SMS or email.

[2865] Step 4:

[2866] If positive sentiment is detected, notifications will be sent out highlighting success stories and positive reactions.

[2867] Input: Detected positive sentiment.

[2868] Output: Success stories and positive responses.

[2869] What happens: The server sends the user an email summarizing success stories and positive feedback.

[2870] (Application example 2)

[2871] 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."

[2872] Conventional feedback systems simply collect user opinions and comments as data and are unable to consider emotional factors such as emotions and attitudes. As a result, they are unable to provide improvement suggestions or responses based on the user's actual emotions, and are unable to fully increase user satisfaction. Furthermore, negative feedback is often not handled appropriately, leaving the problem unresolved. The present invention aims to solve these problems and provide a personalized feedback experience that takes user emotions into consideration.

[2873] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input information; server means for receiving the input information; database means for storing the received information; notification means for notifying other users of the received information; means for rating and commenting on ideas based on the notified information; means for storing the rating and comment in the database means; generative AI means for analyzing the received information and generating related proposals; means for notifying the user of the analysis results and proposals; generative AI means for improving the received information; means for notifying the user of the improvement results; emotion analysis means for analyzing the input information and extracting emotion information; and notification correction means for correcting the notification content based on the emotion information. This enables specific responses and proposals that reflect the user's emotions, thereby increasing user satisfaction.

[2874] An "input means" is a device or software that provides an interface for a user to input information.

[2875] The "server means" is a computer system for receiving and processing input information.

[2876] "Database means" refers to a storage device or system for storing and managing received information.

[2877] "Notification means" is a communication means for informing other users of received information.

[2878] The "rating and commenting means" is a method or device for rating an idea and leaving a comment based on the notified information.

[2879] "Generative AI means" means artificial intelligence technology for analyzing received information and generating relevant suggestions.

[2880] "Generative AI methods" are artificial intelligence techniques for improving received information.

[2881] "Emotion analysis means" is a technology for analyzing and extracting emotions from input information.

[2882] The "notification modification means" is a technique or means for modifying the notification content based on emotion information.

[2883] System Overview

[2884] This invention is a feedback system that collects and analyzes customer feedback in brick-and-mortar stores and provides personalized suggestions and improvements that take emotional information into account. The system is designed by integrating a smartphone application and server-side services.

[2885] Hardware and Software

[2886] Hardware:

[2887] Server: Receives, stores, and analyzes information, and notifies customers and store staff.

[2888] Smartphone: An interface device where customers enter feedback and receive results.

[2889] software:

[2890] Flask: Building an API server.

[2891] SQLite: Database.

[2892] TextBlob: A Python library for sentiment analysis.

[2893] smtplib: The Python standard library for sending email.

[2894] System Operation

[2895] 1. User Registration Process

[2896] When a user registers with the system from a smartphone app, the server receives the user's profile information and stores it in a database. Once registration is complete, the server sends a registration completion notification to the user.

[2897] 2. Feedback submission process

[2898] Users input feedback via a smartphone app. The server receives this feedback and stores it in a database. The server also analyzes this information using emotion analysis to detect positive or negative emotions.

[2899] 3. Sentiment Analysis Process

[2900] The server analyzes the sentiment of the received feedback using TextBlob, and if positive or negative sentiment is detected, it modifies the notification content based on the results and notifies the store staff in real time.

[2901] 4. Notification Process

[2902] The notification correction means introduces success stories and favorable reactions of other users in the case of positive feedback, and generates and sends encouraging messages in the case of negative feedback.

[2903] Specific examples

[2904] For example, if a user posts negative feedback such as "The service in the store was slow," the server analyzes the content using TextBlob to detect negative sentiment. It then generates an encouraging message saying, "We apologize for the inconvenience. We will strive to improve," and notifies the store staff. On the ot...

Claims

1. an input means for a user to input information; a server means for receiving the input information; database means for storing said received information; a notification means for notifying other users of the received information; means for evaluating and commenting on the ideas based on the notified information; means for storing said ratings and comments in a database means; a generative AI means for analyzing the received information and generating relevant suggestions; means for notifying a user of said analysis results and suggestions; generative AI means for improving the received information; The system further includes means for notifying a user of the improvement results.

2. a means for users to input ideas; means for storing said input ideas; means for receiving ratings and comments from other users; 2. The system of claim 1, further comprising: a generating AI means for analyzing the ratings and comments and generating data.

3. means for receiving idea improvement requests from users; a means for using a generative AI means to list improvements based on the improvement requests; The system of claim 1 further comprising means for notifying a user of said improvements.

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