System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting login information by a user; server means for authenticating the login information; AI means for analyzing the collected business data to execute a designated business; means for automatically outputting a processing result by the AI means to various tools; means for notifying the user of the result and receiving a feedback; and means for storing the feedback in a database and using the feedback as training data of a AI model.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] Currently, generative AI is used as a standalone tool in many business processes, and the information output by AI must be manually remade for other tools, resulting in a time-consuming process. It is necessary to provide a means to improve the time and inefficiency of such work and promote business efficiency by centrally managing AI as a platform.

[0005] Furthermore, the current manual process of linking various tools increases the complexity of the process and the risk of errors, which requires a great deal of time and effort. [Means for solving the problem]

[0006] This invention provides a means for a user to input login information and for the server to authenticate the login information, and after authentication, provides a means for the user to collect business data and for the server to store the data.

[0007] Furthermore, the collected business data is analyzed, and AI methods are incorporated to carry out designated tasks, and the AI ​​generates processing results. This also includes methods for automatically outputting the generated processing results to various tools.

[0008] It also provides a means for users to be notified of the results, receive feedback, and have the server store that feedback in a database for use as learning data for the AI ​​model, thereby reducing the workload and improving efficiency.

[0009] Furthermore, it also provides an API calling method to automate integration with various tools, multiplexing processing results, and a method to quickly provide past business information, eliminating the complexity of business operations and reducing the risk of errors.

[0010] A "user" is an entity that operates the system, inputs login information, and provides business data.

[0011] "Login Information" refers to authentication information such as ID and password required for a User to access the System.

[0012] "Authentication" is the process by which a server verifies a user's login information and verifies that the user is legitimate.

[0013] "Business Data" refers to information such as text, files, and images related to business that a user inputs or uploads to the system.

[0014] "Server" refers to a central computer device that manages the entire system, stores and processes data, and performs authentication tasks.

[0015] "AI means" refers to a function within a system that uses artificial intelligence to analyze input business data and carry out specified tasks.

[0016] "Processing results" refers to the deliverables (e.g., presentation materials, minutes, etc.) generated by AI means after analyzing business data.

[0017] "Various tools" refers to software and applications (e.g., presentation software, project management tools, etc.) used by users when performing their work.

[0018] "API calling means" refers to the application programming interface used by the server within the system to automate collaboration with various tools.

[0019] "Feedback" refers to information such as evaluations and requests for improvement provided by users after checking the processing results.

[0020] "Database" refers to the information management system for storing data and feedback collected within the System.

[0021] "Training data" refers to past business data and feedback information used to improve the performance of AI models. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] This invention relates to a system that allows a user to input login information and a server to provide a means for authenticating the login information. Below, we will create a program for this system and explain its processing in natural language.

[0044] Overall system overview

[0045] This system allows users to access and input business data, and the AI ​​processes the data to carry out the specified tasks, automatically outputting the results to various tools. The system also has the ability to receive user feedback and use it as learning data for the AI.

[0046] Program processing flow

[0047] 1. Enter and authenticate your login information

[0048] The user accesses the platform's login screen and enters their account information (ID and password).

[0049] The terminal transmits the login information entered by the user to the server.

[0050] The server checks the received login information against a database to verify whether the user is a valid user. If authentication is successful, the server starts a user session and sends the dashboard screen to the terminal.

[0051] 2. Collection of business data

[0052] The user selects a new task (e.g., creating a presentation) from the dashboard and enters or uploads the required data.

[0053] The terminal sends business data to the server, and the server stores the data in temporary storage.

[0054] 3. AI-powered data processing

[0055] The server provides the collected business data to the AI ​​tool.

[0056] AI analyzes the data it receives and performs the specified tasks. For example, when creating presentation materials, it generates slides based on the data and arranges the content.

[0057] 4. Automatic output of processing results

[0058] The server receives the processing results generated by the AI ​​means and automatically outputs them to various tools specified by the user (e.g., Google Slides or Microsoft PowerPoint).

[0059] After the output is successful, the server notifies the user that the processing results are ready.

[0060] 5. Review and feedback of results

[0061] The user checks the processing results (e.g., presentation materials) on the platform and makes corrections as necessary.

[0062] The terminal transmits the feedback provided by the user to the server.

[0063] The server stores the feedback in a database and uses it as learning data for the AI.

[0064] Specific examples

[0065] Creating presentation materials

[0066] 1. Login Process

[0067] A user logs in to the system and accesses the dashboard. The login information (ID and password) is authenticated by the server.

[0068] 2. Provision of business data

[0069] A user uploads data (e.g., a spreadsheet file) needed to create presentation materials for an upcoming meeting. The data is temporarily stored in the server's storage.

[0070] 3. AI-powered data processing

[0071] The server passes the collected data to the AI, which then begins analyzing it. The AI ​​then automatically generates presentation slides based on the data, inserting explanatory text and graphs.

[0072] 4. Automatic output of processing results

[0073] The server calls the API to upload the generated presentation to Google Slides. After the upload is successfully completed, the server notifies the user.

[0074] 5. Review and Feedback

[0075] The user checks the presentation materials on the platform and makes any necessary corrections. The corrections and feedback are sent to the server and stored in the database.

[0076] This system enables efficient use of generative AI, significantly reducing the effort and time required for work.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] The user accesses the platform's login screen and enters their account information (ID and password).

[0080] Step 2:

[0081] The terminal sends the user's login information to the server.

[0082] Step 3:

[0083] The server checks the received login information against the database to verify whether the user is a valid user, and if authentication is successful, starts a user session.

[0084] Step 4:

[0085] The server sends the authenticated dashboard screen to the terminal, allowing the user to access this dashboard.

[0086] Step 5:

[0087] The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required task data.

[0088] Step 6:

[0089] The terminal transmits the business data entered by the user to the server.

[0090] Step 7:

[0091] The server stores the received business data in temporary storage and registers the metadata in the database.

[0092] Step 8:

[0093] The server collects the necessary business data from the database and provides it to the appropriate AI model.

[0094] Step 9:

[0095] AI analyzes business data and performs specified tasks, such as generating slides based on the data to create presentation materials and adding relevant content.

[0096] Step 10:

[0097] The AI ​​returns the analysis results and generated deliverables (e.g., presentation materials) to the server.

[0098] Step 11:

[0099] The server receives the processing results from the AI ​​tool and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[0100] Step 12:

[0101] The server uploads the processing results to various tools and notifies the user when the results are ready.

[0102] Step 13:

[0103] Users can check the processing results on the platform and make any necessary corrections.

[0104] Step 14:

[0105] The terminal sends the user's modifications and feedback to the server.

[0106] Step 15:

[0107] The server stores the feedback and correction information in a database and uses it as training data for the AI ​​model.

[0108] Example 1

[0109] 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."

[0110] In conventional business systems, the process of collecting business data after a user logs in, processing it using AI, and outputting the results to various tools is often done manually, which poses the issue of taking time and effort. In addition, there is no mechanism in place to use feedback as learning data for the AI, making it difficult to improve the performance of the AI. Furthermore, since integration with various tools is not automated, business efficiency can sometimes decrease.

[0111] 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.

[0112] In this invention, the server includes a terminal to which a user uploads business data, a means for the server to temporarily store the business data, and a means for the server to provide processing results to the AI ​​means and output the generated processing results to a specified tool. This makes it possible to automate the process from inputting business data to analyzing it with AI, outputting it to various tools, and collecting feedback.

[0113] "User" refers to any individual or corporation that uses this system.

[0114] "Login information" refers to authentication information for a user to access a system, and is usually composed of an ID and password.

[0115] "Server" refers to a computer system that receives and processes data sent by users.

[0116] "Business Data" refers to data that a user inputs or uploads to the system in order to perform a specific business.

[0117] "AI means" refers to artificial intelligence technology that analyzes collected business data and carries out specified tasks.

[0118] "Processing results" refers to the deliverables generated by AI means by analyzing business data.

[0119] "Various tools" refers to external applications and services used to output processing results.

[0120] "Notification" refers to the action of the server informing the user that the processing results are ready.

[0121] "Feedback" refers to evaluations and information on improvements provided by users regarding processing results.

[0122] "Database" means the data management system for storing Feedback and other data.

[0123] "Terminal" refers to a device through which a User accesses the System and inputs or uploads data.

[0124] "Temporary storage" refers to a storage area where a server temporarily stores business data.

[0125] "Automation" refers to a system in which a series of processes are carried out without manual operation.

[0126] "API" refers to a program interface that enables integration with external applications and services.

[0127] This invention relates to a system in which a user inputs login information and a server authenticates the login information. Furthermore, the system has a mechanism in which the user inputs business data, the AI ​​analyzes the data, performs the specified business, and automatically outputs the processing results to various tools. The system also has a function to receive user feedback and use it as learning data for the AI ​​model.

[0128] Hardware and Software Configuration

[0129] 1. User end devices (terminals)

[0130] The terminals used by users are computing devices such as PCs and mobile devices.

[0131] A web browser (e.g., Google Chrome) is installed on the terminal, and is used by the user to access the system.

[0132] 2. Server

[0133] A server is a powerful computing system that receives and processes data from users.

[0134] Software used includes database management systems (e.g., MySQL) and temporary storage systems (e.g., Amazon S3).

[0135] 3. AI means

[0136] As an AI tool, a generative AI model (e.g., BERT model) is used to analyze the business data provided by the user and carry out the specified tasks.

[0137] AI models use data analysis, natural language processing, and machine learning algorithms to generate optimal results.

[0138] Example of a system

[0139] Automatic generation of presentation materials

[0140] 1. Login Process

[0141] The user launches a browser and accesses the system's login screen, enters login information (ID and password), and sends it to the server.

[0142] The server compares the information stored in the database and, if authentication is successful, displays the dashboard screen on the user's device.

[0143] 2. Provision of business data

[0144] Users select "New Work" from the dashboard and upload the data (e.g., spreadsheet files) needed to create presentation materials.

[0145] The terminal sends the uploaded file to the server, and the server stores it in temporary storage (e.g., Amazon S3).

[0146] 3. AI-powered data processing

[0147] The server retrieves the data from the temporary storage and provides it to the AI ​​means, which analyzes the uploaded data and automatically generates presentation slides.

[0148] For example, you can generate a presentation using the following prompt: "Create a presentation based on the following data: {data content}"

[0149] 4. Output of processing results

[0150] The server retrieves the presentation materials generated by the AI ​​means and automatically uploads them to a designated tool (e.g., Google Slides).

[0151] After a successful upload, the server will notify the user via email or in-app notification that the results are ready.

[0152] 5. Review and feedback of results

[0153] After receiving the notification, the user can check the presentation materials on the platform and make any necessary corrections.

[0154] User feedback is sent via the device to a server, which stores it in a database and uses it as training data for the AI ​​model.

[0155] This system allows users to streamline their work through generative AI models, significantly reducing the time and effort required for their work. In addition, by incorporating feedback into the AI's learning data, it is possible to continue improving the AI's performance.

[0156] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0157] Step 1: The user launches a browser and accesses the system's login screen. The user enters their login information (ID and password).

[0158] Input: ID, password

[0159] Output: Sends login information to the server

[0160] Specific operation: The user opens the system's login page in a browser, enters the ID and password in the designated input fields, and clicks the login button.

[0161] Step 2: The terminal sends the login information entered by the user to the server, which checks the received login information against its database and returns the authentication result.

[0162] Input: Login information (ID, password)

[0163] Output: Authentication result (success / failure)

[0164] Specific operation: The terminal sends login information to the server via the HTTPS protocol. The server verifies the login information against a database (e.g., MySQL), and if authentication is successful, it creates a user session and returns the session ID.

[0165] Step 3: If the authentication is successful, the server creates a user session and returns a session ID to the terminal. The terminal receives a notification of successful authentication and transitions to the dashboard screen.

[0166] Input: Authentication result (success)

[0167] Output: Dashboard screen

[0168] Specific operation: The server saves the session ID in session storage (e.g., Redis) and returns the session ID to the terminal in the HTTP response header. The terminal changes the URL to the dashboard screen.

[0169] Step 4: The user selects a new task (e.g., creating presentation materials) from the operation panel on the dashboard.

[0170] Input: Business selection information

[0171] Output: Business data input screen

[0172] Specific operation: The user selects "New Task" from the dashboard menu and moves to the business data input screen.

[0173] Step 5: The user enters or uploads the required data (e.g., a spreadsheet file or text input). The device sends the business data to the server, which stores it in temporary storage.

[0174] Input: Business data (spreadsheet files, etc.)

[0175] Output: Data saved in temporary storage

[0176] Specific operation: The user selects a spreadsheet file using the file selection dialog and clicks the upload button. The terminal sends the file to the server in the Multipart Form data format, and the server saves it in temporary storage (e.g., Amazon S3).

[0177] Step 6: The server provides the collected business data to the AI ​​means, which analyzes the data and performs the specified tasks.

[0178] Input: Business data stored in temporary storage

[0179] Output: AI analysis results

[0180] Specific operation: The server sends an API request to the AI ​​means to retrieve business data. The AI ​​means analyzes the data and generates presentation materials using prompts.

[0181] Example prompt: "Please create a presentation based on the following data: {data content}"

[0182] Step 7: The server receives the processing results generated by the AI ​​means and automatically outputs them to various tools specified by the user (e.g., Google Slides).

[0183] Input: AI analysis results

[0184] Output: Processing results output to various tools

[0185] Specific operation: The server calls the API, uploads the generated presentation materials to Google Slides, and returns the URL when the upload is complete.

[0186] Step 8: The server notifies the user that the processing results are ready. The user can check the processing results (e.g., presentation materials) on the platform and make any necessary corrections.

[0187] Input: Notification that processing results are ready

[0188] Output: Confirmation screen on the platform

[0189] Specific operation: The server sends an email or in-app notification to the user, providing a link to check the processing results. The user clicks the link to view and edit the presentation materials.

[0190] Step 9: The device sends the user-provided feedback to the server, which stores it in a database and uses it as training data for the AI ​​model.

[0191] Input: Feedback

[0192] Output: Feedback stored in a database

[0193] Specific operation: The user fills in the feedback form and clicks the submit button. The device sends the feedback data to the server, which stores it in a database. The feedback stored in the database will be used the next time the AI ​​model is trained.

[0194] (Application example 1)

[0195] 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."

[0196] Conventional security systems perform primary authentication using only login information, which increases the risk of unauthorized access. While introducing multi-factor authentication can strengthen security, it also increases the operational burden on users. Furthermore, a system is needed to centrally manage user access history and authentication failure history and detect fraudulent activity.

[0197] 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.

[0198] In this invention, the server includes a means for a user to input login information, a server means for authenticating the login information, a means for collecting business data after authentication, an AI means for analyzing the collected business data and performing a specified task, a means for automatically outputting the processing results of the AI ​​means to various tools, a means for notifying the user of the results and receiving feedback, a means for saving the feedback in a database and using it as learning data for an AI model, a means for performing multi-factor authentication, and a means for recording the user's access history and authentication failure history. This makes it possible to prevent unauthorized access through multi-factor authentication, reduce the operational burden on the user, and detect and manage fraudulent activity.

[0199] "Means for users to input login information" refers to means for providing an interface for users to input account information (ID and password).

[0200] "Server means for authenticating login information" refers to the function of the server to check the transmitted login information against a database and confirm whether the user is a legitimate user.

[0201] The "means for collecting business data" refers to the means by which a user inputs or uploads data necessary for performing a business operation after authentication.

[0202] "AI means for analyzing collected business data and carrying out designated tasks" refers to the function of artificial intelligence to automatically analyze and carry out tasks based on collected business data.

[0203] "Means for automatically outputting processing results by AI means to various tools" refers to means for automatically outputting processing results generated by AI means to external tools designated by the user.

[0204] The "means for notifying the user of the results and receiving feedback" refers to a means for informing the user of the processing results and receiving the user's opinions and corrections regarding the results.

[0205] "Means for storing feedback in a database and using it as training data for an AI model" refers to a means for storing received feedback in a database and later using it as training data for an AI model.

[0206] A "means of implementing multi-factor authentication" is a means of strengthening authentication after primary authentication by using additional authentication factors (such as tokens).

[0207] "Means for recording user access history and authentication failure history" refers to a means for centrally recording and managing the history of user access to the system and the history of authentication failures.

[0208] This invention relates to a security system using multi-factor authentication, and provides a server means for authenticating login information entered by a user. After authentication, the system collects business data, analyzes it using AI means, and performs the specified business. It also has a function for automatically outputting the processing results of the AI ​​means to various tools, notifying the user and receiving feedback. Furthermore, this feedback is stored in a database and used as training data for the AI ​​model.

[0209] As an example of the present system, a system used to provide security services using multi-factor authentication for businesses will be described.

[0210] Program Overview

[0211] The server includes the following functions:

[0212] 1. User authentication function: The login information entered by the user is checked against a database to confirm that the user is a legitimate user.

[0213] 2. Multi-factor authentication function: After successful primary authentication, multi-factor authentication is performed using additional authentication factors (such as tokens).

[0214] 3. Business data collection function: Collects business data entered or uploaded by the user after authentication.

[0215] 4. AI data analysis function: Analyzes collected business data and automatically carries out specified tasks.

[0216] 5. Automatic result output function: The generated processing results are automatically output to various tools specified by the user.

[0217] 6. Feedback processing function: Receives user feedback on the processing results, saves the data, and uses it as learning data for the AI ​​model.

[0218] 7. Log management function: Records and manages user access history and authentication failure history.

[0219] Hardware and software used

[0220] The server hardware used is a cloud server such as Amazon EC2.

[0221] Amazon RDS (MySQL, etc.) is used as the database.

[0222] AI models are built using TensorFlow or PyTorch.

[0223] A common multi-factor authentication library is used to generate the authentication token.

[0224] Amazon CloudWatch is used for log management.

[0225] Specific examples of implementation

[0226] Example 1: Multi-factor authentication system for businesses

[0227] This system will be implemented to enhance the security of your company by:

[0228] A user accesses the company's security platform and enters their user ID and password. The information is sent to the server and checked against a database. If the initial authentication is successful, the server sends a multi-factor authentication token to the user's device.

[0229] The user checks the token and enters it in the specified application. The server checks the validity of the token, and if authentication is successful, the dashboard screen is displayed to the user. The user uploads business data, which is analyzed and processed by the AI. The generated processing results are automatically output to various tools and notified to the user. User feedback is stored in a database and used as learning data for the AI ​​model. Access history and failed authentication attempts are recorded as logs and made available for administrators to review.

[0230] Prompt Sentence Examples

[0231] Create a program to authenticate user login information and implement the multi-factor authentication flow. Receive the user ID and password and check them against a database. If the initial authentication is successful, generate a multi-factor authentication token and send it to the user. Once the user enters the token and confirms its validity, final authentication is completed and the user session begins. Also, record log information throughout the authentication flow. Use the Flask framework and MySQL database.

[0232] By implementing this invention, it is possible to realize robust security measures and reduce the burden on users through multi-factor authentication, thereby significantly improving the security of corporate systems.

[0233] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0234] Step 1:

[0235] User enters login information

[0236] A user accesses the system's login screen and enters their user ID and password. The input information is sent to the server by the terminal. The input is in the form of keystrokes, and the output is a string of login information.

[0237] Step 2:

[0238] Server authentication of login information

[0239] The server checks the received user ID and password against the database to verify that the user is a valid user. It searches for matching records using a database query and retrieves the results. The input is login information and the output is the authentication result (success or failure). If authentication is successful, it proceeds to the next step.

[0240] Step 3:

[0241] Generate and send multi-factor authentication tokens

[0242] If authentication is successful, the server generates a multi-factor authentication token and sends it to the user's registered device (e.g., smartphone). A token generation algorithm is used to create a temporary authentication code and transmit it to the user device. The input is the user ID, and the output is the generated token.

[0243] Step 4:

[0244] User confirms and enters token

[0245] The user receives the token on their smartphone or authentication application, then returns to the system to enter the token. The user manually enters the token and sends it back to the server. The input is the token, and the output is the token string.

[0246] Step 5:

[0247] Token validation by the server

[0248] The server checks the token received from the user in its database to verify that it is a valid authentication token. It checks whether it matches the token record in the database and obtains the result. The input is the token and the output is the verification result (success or failure). If the token is correct, it proceeds to the next step.

[0249] Step 6:

[0250] Entering and uploading business data

[0251] After authentication is complete, the user inputs or uploads business data (e.g., spreadsheets or document files) into the system. The data uploaded by the user is sent to the server and stored in temporary storage. The input is the business data, and the output is the reference ID of the stored data.

[0252] Step 7:

[0253] Analysis of business data by the server

[0254] The server provides the stored business data to the AI ​​means to analyze the data. The AI ​​model (e.g., TensorFlow or PyTorch) is used to analyze the data and perform the specified task (e.g., report generation or data analysis). The input is the reference ID of the business data, and the output is the processing result.

[0255] Step 8:

[0256] Automatic output of processing results to tools

[0257] The processing results generated by the AI ​​tool are automatically output to an external tool specified by the user (e.g., Google Slides or Microsoft PowerPoint). The API is called to send the result data to the tool. The input is the processing result, and the output is a confirmation message from the tool.

[0258] Step 9:

[0259] Notification of processing results and receiving feedback

[0260] The user receives a notification of the processing result on the system and checks the result. If necessary, the user can input feedback and send it to the system. The feedback is saved on the server. The input is the feedback, and the output is the saved feedback data.

[0261] Step 10:

[0262] Storing feedback and using data to train AI models

[0263] The server stores the feedback in a database and uses it as training data for the AI ​​model. The stored feedback is provided to the AI ​​model and added as new training data. The input is the feedback data, and the output is an updated AI model.

[0264] Step 11:

[0265] Recording and management of access history and authentication failure history

[0266] The server centrally records user access history and authentication failure history, allowing administrators to check them. The log data is sent to a cloud log management system (e.g., Amazon CloudWatch). The input is access history data, and the output is the recorded log data.

[0267] 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.

[0268] Overall system overview

[0269] The system allows users to access and input business data, and the AI ​​processes the data to carry out the specified tasks, automatically outputting the results to various tools. The system also has an emotion engine that recognizes the user's emotions, and changes the content presented on the interface based on the emotional data, and has the ability to use this as feedback for learning data.

[0270] Program processing flow

[0271] 1. Enter and authenticate your login information

[0272] The user accesses the platform's login screen and enters their account information (ID and password).

[0273] The terminal sends the user's login information to the server.

[0274] The server authenticates the login information against a database, and if authentication is successful, the user session begins.

[0275] 2. Collection of business data

[0276] Users select a new task (e.g., creating presentation materials) from the dashboard and enter or upload the required data.

[0277] The terminal sends business data to the server, and the server stores the data in storage.

[0278] 3. Emotion Recognition by Emotion Engine

[0279] The server analyzes the user's operation data and input text data on the dashboard in real time and uses an emotion engine to recognize the user's emotions.

[0280] The interface content is automatically adjusted based on the recognized emotion.

[0281] 4. AI-powered data processing

[0282] The server provides the collected business data to the AI ​​means and carries out the specified tasks.

[0283] AI analyzes the data and generates the final processed results (e.g., presentation materials).

[0284] 5. Automatic output of processing results

[0285] The server receives the processing results returned by the AI ​​and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[0286] The generated processing results are automatically uploaded to the following tools.

[0287] 6. Notification of results and feedback

[0288] The server notifies the user when the results are ready.

[0289] Users can check the results on the platform, make any necessary corrections, and provide feedback.

[0290] The terminal sends the feedback to the server.

[0291] The server stores the feedback and emotional data in a database and uses it as learning data for the AI ​​and emotion engine.

[0292] Specific examples

[0293] Creating presentation materials and emotional feedback

[0294] 1. Login Process

[0295] A user logs into the system and accesses the dashboard. The server authenticates the login information.

[0296] 2. Provision of business data

[0297] A user uploads data (e.g., a spreadsheet file) needed to create a presentation for the next meeting. The data is temporarily stored in the server's storage.

[0298] 3. Emotion recognition

[0299] The server analyzes the user's input data and operation data in real time, recognizes the user's emotions using an emotion engine, and adjusts the user's interface experience according to the emotion data.

[0300] 4. AI Processing

[0301] The server passes the collected data to the AI, which then begins analyzing it. The AI ​​then automatically generates presentation slides based on the data, inserting explanatory text and graphs.

[0302] 5. Output

[0303] The server calls the API to upload the generated presentation to Google Slides. After the upload is successfully completed, the server notifies the user.

[0304] 6. Review and Feedback

[0305] The user checks the presentation materials on the platform and makes any necessary corrections. The corrections and feedback are then sent to the server along with the emotion data.

[0306] The server stores the feedback and emotion data in a database and uses it as learning data for the AI ​​and emotion engine.

[0307] This system enables efficient use of generative AI and emotion engines, significantly reducing the amount of work required and time required while improving the user experience.

[0308] The processing flow will be explained below.

[0309] Step 1:

[0310] The user accesses the platform's login screen and enters their account information (ID and password).

[0311] Step 2:

[0312] The terminal sends the user's login information to the server.

[0313] Step 3:

[0314] The server checks the received login information against the database to verify whether the user is a valid user, and if authentication is successful, starts a user session.

[0315] Step 4:

[0316] The server sends the authenticated dashboard screen to the terminal, allowing the user to access this dashboard.

[0317] Step 5:

[0318] The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required task data.

[0319] Step 6:

[0320] The terminal transmits the business data entered by the user to the server.

[0321] Step 7:

[0322] The server stores the received business data in temporary storage and registers the metadata in the database.

[0323] Step 8:

[0324] The server monitors the user's operation data and input text data on the dashboard in real time and inputs it into the emotion engine.

[0325] Step 9:

[0326] The emotion engine analyzes user operation data and text data to recognize the user's emotions.

[0327] Step 10:

[0328] The server receives the emotion data from the emotion engine and automatically adjusts the interface presentation according to the user's mood. For example, if the user is irritated, the system provides a simple and easy-to-understand interface.

[0329] Step 11:

[0330] The business data collected by the server is provided to AI means.

[0331] Step 12:

[0332] AI analyzes business data and performs specified tasks, such as generating slides based on the data to create presentation materials and adding relevant content.

[0333] Step 13:

[0334] The AI ​​returns the analysis results and generated deliverables (e.g., presentation materials) to the server.

[0335] Step 14:

[0336] The server receives the processing results from the AI ​​tool and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[0337] Step 15:

[0338] The server uploads the processing results to various tools, and once the upload is completed successfully, it notifies the user that the results are ready.

[0339] Step 16:

[0340] The user checks the processing results on the platform, and if the user wants to make any corrections, they input the details.

[0341] Step 17:

[0342] The terminal sends the user's modifications and feedback to the server.

[0343] Step 18:

[0344] The server stores the feedback and emotional data in a database and uses it as learning data for the AI ​​and emotion engine.

[0345] Example 2

[0346] 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."

[0347] Conventional business systems require users to manually collect and analyze business data, which is time-consuming, and outputting results and processing feedback takes time. Furthermore, because they do not take into account user emotions or stress, there are still issues with the user experience. Furthermore, updates to the AI ​​model's learning data are delayed, often failing to reflect the latest data.

[0348] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0349] In this invention, the server includes emotion engine means for recognizing user emotions, means for dynamically adjusting the interface based on the emotion data, and means for automatically outputting the processing results of the AI ​​means to various tools. This not only automates the collection and analysis of user business data, but also enables dynamic interface adjustment that takes user emotions into consideration, thereby improving the user experience.

[0350] A "user" is an individual who accesses the system, enters login information, provides business data, and checks and provides feedback on the results.

[0351] "Login Information" refers to the account ID and password used by a User to access the System.

[0352] A "server" is a computer system that performs a series of processes, including authenticating login information sent by a user, collecting and analyzing business data, and outputting the final processing results to various tools.

[0353] "Business data" refers to data related to a business provided by a user, and examples include spreadsheet files, text data, image files, and the like.

[0354] "AI means" refers to artificial intelligence technology that analyzes collected business data and carries out specified tasks.

[0355] "Processing Results" means the final output generated by the AI ​​Means, including presentation materials and other business-related materials submitted to a User.

[0356] "Various tools" refers to external systems or software for outputting processing results, such as Google Slides and Microsoft PowerPoint.

[0357] The "emotion engine means" is a technology for analyzing the user's operation data and input data and recognizing the user's emotions.

[0358] "Emotion data" is data that indicates the emotional state of the user analyzed by the emotion engine means.

[0359] "Feedback" refers to opinions and correction requests provided by users regarding the processing results generated by the system, and is used as learning data for the system.

[0360] This invention is a system in which users access the system, input business data, and AI processes the data to perform the specified tasks, automatically outputting the processing results to various tools. This system operates in cooperation with three entities: a server, a terminal, and a user.

[0361] server

[0362] The server authenticates the login information entered by the user and collects and stores business data. It also includes an emotion engine that analyzes user emotion data in real time. It also provides the collected business data to an AI model, which then automatically outputs the processed results to various tools (e.g., Google Slides, Microsoft PowerPoint).

[0363] Terminal

[0364] The terminal provides an interface that supports the user's data input and operation. The terminal also transmits the login information and business data entered by the user to the server. Furthermore, feedback information is also transmitted from the terminal to the server.

[0365] User

[0366] Users log in to the system, input or upload business data, and check and correct the generated processing results. Users also provide feedback to the system on the generated results, which is used as learning data for the AI ​​model.

[0367] Specific hardware and software

[0368] The servers are equipped with high-performance CPUs and GPUs to rapidly process and analyze data. Relational databases such as MySQL and PostgreSQL are used as databases. The emotion engine uses natural language processing technology, and generative AI models such as GPT-4 are used as AI models. A browser and dedicated applications are installed on the terminals, which function as the user interface.

[0369] Specific examples

[0370] For example, consider a user creating a presentation for an upcoming meeting. First, the user logs in to the system and accesses the dashboard. Next, they upload the necessary business data (e.g., a spreadsheet file). This data is sent to the server, which uses an emotion engine to analyze the user's emotions and adjust the interface. The server then passes the collected data to the AI, which generates presentation slides in the specified format. The generated presentation slides are automatically uploaded to Google Slides or Microsoft PowerPoint. Finally, the user reviews the slides on the platform and provides feedback. This feedback is stored on the server and used as future learning data.

[0371] Prompt Sentence Examples

[0372] The following prompts can be used:

[0373] "Create a presentation for my next meeting. Generate slides based on this data."

[0374] In this way, the present invention provides a system that significantly reduces the workload while improving the user experience. The collaboration between the generative AI model and the emotion engine enables tasks to be performed with extremely high efficiency and accuracy.

[0375] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0376] Step 1:

[0377] Enter and authenticate your login information

[0378] Input: The user uses a browser or a dedicated application to enter their account ID and password on the login screen.

[0379] Specific behavior:

[0380] The user accesses the system's login screen and enters their account ID and password.

[0381] The device encrypts the login information entered by the user and sends it to the server via a secure communication protocol such as HTTPS.

[0382] Data processing:

[0383] The server receives the submitted login information and checks it against the user information in its database.

[0384] Output: If authentication is successful, start a user session and redirect the user to the dashboard.

[0385] Step 2:

[0386] Business data collection

[0387] Input: The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required data.

[0388] Specific behavior:

[0389] The user selects the "Create Presentation" option.

[0390] The user uploads the necessary business data, such as a spreadsheet file or text file.

[0391] Data processing:

[0392] The terminal sends the uploaded file to the server.

[0393] Output: Business data is sent to the server and stored in the server's storage.

[0394] Step 3:

[0395] Emotion recognition by emotion engine

[0396] Input: User operation data and input data

[0397] Specific behavior:

[0398] The server analyzes the user's operation data and input text data in real time.

[0399] The server uses an emotion engine to recognize the user's emotions (e.g., joy, anger, sadness).

[0400] Data Calculation:

[0401] The emotion engine uses natural language processing techniques to analyze the emotional tone of the input data.

[0402] Output: Dynamically adjust the content displayed in the user interface based on the recognized emotion data.

[0403] Step 4:

[0404] AI-powered data processing

[0405] Input: User-provided business data

[0406] Specific behavior:

[0407] The server generates prompts to input the collected business data into a generative AI model (e.g., GPT-4). For example, it generates a prompt like, "Please create a presentation for the next meeting. Please generate slides based on this data."

[0408] The AI ​​analyzes business data based on the generated prompt text and automatically generates presentation slides.

[0409] Data Calculation:

[0410] The AI ​​analyzes the input business data and generates presentation materials in the specified format.

[0411] Output: Generated presentation materials (slides, explanatory text, graphs, etc.)

[0412] Step 5:

[0413] Automatic output of processing results

[0414] Input: AI-generated processing results (presentation materials)

[0415] Specific behavior:

[0416] The server receives the processing results returned by the AI ​​and calls an API to upload them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[0417] Data processing:

[0418] The server makes an API call to upload the generated file to an external service.

[0419] Output: A confirmation message and a URL indicating a successful upload.

[0420] Step 6:

[0421] Notification of results and feedback

[0422] Input: Server-generated confirmation message and URL

[0423] Specific behavior:

[0424] The server notifies the user when the processing results are ready (e.g. email notification, dashboard notification).

[0425] The user can check the processing results on the platform.

[0426] The user makes corrections as needed and provides feedback.

[0427] Data processing:

[0428] The terminal transmits the feedback information to the server.

[0429] Output: Feedback and emotion data are stored on the server.

[0430] Through these processing steps, a system is realized that can efficiently collect business data, analyze it using AI, output the generated results, and collect feedback.

[0431] (Application example 2)

[0432] 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."

[0433] Conventional systems require users to spend a great deal of time and effort when creating content. Furthermore, they perform tasks without considering the user's feelings, making it difficult to improve the user experience. Furthermore, there is a need to output the generated content to various tools quickly and efficiently.

[0434] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0435] In this invention, the server includes means for analyzing user operation data and input data and recognizing the user's emotions using an emotion engine, means for automatically adjusting the interface based on the recognized emotions, and AI means for analyzing collected business data and performing specified tasks, thereby making it possible to provide an interface that reflects the user's emotions and automate tasks.

[0436] "Login Information" means the authentication information a user enters to access a system.

[0437] "Server means" refers to a central processing unit that authenticates login information and collects, stores, and analyzes business data.

[0438] "Business data" refers to data that a user inputs or uploads in order to perform a specific business.

[0439] "AI means" is an artificial intelligence engine that analyzes collected business data and carries out specified tasks.

[0440] "Various tools" are external applications or platforms for outputting or using the generated processing results.

[0441] "Feedback" refers to the evaluation and correction instructions provided by the user regarding the processing results of the system.

[0442] A "database" is a digital storage device for storing information such as business data, processing results, and feedback.

[0443] An "emotion engine" is a software engine that analyzes user operation data and input data and recognizes the user's emotions.

[0444] An "interface" is a display screen or operating means that allows a user to interact with a system.

[0445] An "API calling means" is a function realized by a program for exchanging data and services between different software components.

[0446] The system for implementing the present invention includes a server that authenticates login information entered by the user, collects business data, recognizes the user's emotions using an emotion engine, adjusts the interface based on the recognition results, and performs business operations using AI means.

[0447] The system's hardware uses terminal devices such as smartphones and head-mounted displays. The software includes an emotion engine, an AI content generation module, and a data processing module. The emotion engine is software that analyzes user operation data and input data in real time and recognizes emotions. The AI ​​content generation module is software that performs specified tasks (such as automatically generating content) based on collected business data.

[0448] The main steps of this system are for the user to log in to the system and upload the necessary business data. The server collects this data and analyzes the user's emotions using an emotion engine. The interface is automatically adjusted based on the analysis results. The collected business data is then passed to the AI ​​means to carry out the task. The generated processing results are automatically output to a specified tool and notified to the user. The user checks the processing results and provides feedback. The feedback is stored in a database and used as learning data for future AI and emotion engines.

[0449] As a concrete example, suppose a user uploads a video clip and a text script to create their next video content. This data is sent to the server, where the emotion engine recognizes the user's emotions and adjusts the software accordingly. The AI ​​content generation module then uses this data to automatically edit the video and upload it to the specified platform.

[0450] An example of a prompt is the following text:

[0451] "User ID: example_user"

[0452] Password: example_password

[0453] "Content data: example_video_clip.mp4"

[0454] "Platform Name: YouTube"

[0455] This enables the provision of an interface that takes user emotions into consideration and the automation of tasks, improving the user experience and work efficiency.

[0456] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0457] Step 1:

[0458] The user enters login information. The user accesses the system's login screen and enters their ID and password. The terminal sends the entered login information to the server.

[0459] Step 2:

[0460] The server authenticates the login information. It checks the login information against a database and starts the user session if authentication is successful. If authentication is unsuccessful, the server prompts the user for login information again.

[0461] Step 3:

[0462] The user collects task data. The user then selects the task to be performed (e.g., video content creation) and uploads the required data (e.g., video clips, text scripts). The device then transmits this data to the server.

[0463] Step 4:

[0464] The server uses an emotion engine to recognize emotions. The server analyzes user operation data and input data in real time and uses the emotion engine to recognize the user's emotions. The interface is automatically adjusted based on the recognition results.

[0465] Step 5:

[0466] The server provides business data to the AI ​​means. The collected business data is passed to the AI ​​content generation module. The AI ​​analyzes this data and generates optimal content (e.g., automatically edited videos).

[0467] Step 6:

[0468] The server automatically outputs the processing results to various tools. The generated processing results are uploaded to the specified platform (e.g. YouTube, Spotify) by calling an API to output them. This automatically publishes the generated content to the platform.

[0469] Step 7:

[0470] The server notifies the user of the results. When the upload of the processing results is complete, the server sends a notification to the user. The user receives this notification and checks the processing results.

[0471] Step 8:

[0472] The user provides feedback. The user checks the processing results on the platform, makes necessary corrections, and provides feedback. The terminal sends the feedback to the server.

[0473] Step 9:

[0474] The server stores the feedback in a database, which will be used as learning data for future AI and emotion engines.

[0475] 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.

[0476] 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.

[0477] 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.

[0478] [Second embodiment]

[0479] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0480] 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.

[0481] 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).

[0482] 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.

[0483] 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.

[0484] 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).

[0485] 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.

[0486] 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.

[0487] 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.

[0488] 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.

[0489] 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.

[0490] 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."

[0491] This invention relates to a system that allows a user to input login information and a server to provide a means for authenticating the login information. Below, we will create a program for this system and explain its processing in natural language.

[0492] Overall system overview

[0493] This system allows users to access and input business data, and the AI ​​processes the data to carry out the specified tasks, automatically outputting the results to various tools. The system also has the ability to receive user feedback and use it as learning data for the AI.

[0494] Program processing flow

[0495] 1. Enter and authenticate your login information

[0496] The user accesses the platform's login screen and enters their account information (ID and password).

[0497] The terminal transmits the login information entered by the user to the server.

[0498] The server checks the received login information against a database to verify whether the user is a valid user. If authentication is successful, the server starts a user session and sends the dashboard screen to the terminal.

[0499] 2. Collection of business data

[0500] The user selects a new task (e.g., creating a presentation) from the dashboard and enters or uploads the required data.

[0501] The terminal sends business data to the server, and the server stores the data in temporary storage.

[0502] 3. AI-powered data processing

[0503] The server provides the collected business data to the AI ​​tool.

[0504] AI analyzes the data it receives and performs the specified tasks. For example, when creating presentation materials, it generates slides based on the data and arranges the content.

[0505] 4. Automatic output of processing results

[0506] The server receives the processing results generated by the AI ​​means and automatically outputs them to various tools specified by the user (e.g., Google Slides or Microsoft PowerPoint).

[0507] After the output is successful, the server notifies the user that the processing results are ready.

[0508] 5. Review and feedback of results

[0509] The user checks the processing results (e.g., presentation materials) on the platform and makes corrections as necessary.

[0510] The terminal transmits the feedback provided by the user to the server.

[0511] The server stores the feedback in a database and uses it as learning data for the AI.

[0512] Specific examples

[0513] Creating presentation materials

[0514] 1. Login Process

[0515] A user logs in to the system and accesses the dashboard. The login information (ID and password) is authenticated by the server.

[0516] 2. Provision of business data

[0517] A user uploads data (e.g., a spreadsheet file) needed to create presentation materials for an upcoming meeting. The data is temporarily stored in the server's storage.

[0518] 3. AI-powered data processing

[0519] The server passes the collected data to the AI, which then begins analyzing it. The AI ​​then automatically generates presentation slides based on the data, inserting explanatory text and graphs.

[0520] 4. Automatic output of processing results

[0521] The server calls the API to upload the generated presentation to Google Slides. After the upload is successfully completed, the server notifies the user.

[0522] 5. Review and Feedback

[0523] The user checks the presentation materials on the platform and makes any necessary corrections. The corrections and feedback are sent to the server and stored in the database.

[0524] This system enables efficient use of generative AI, significantly reducing the effort and time required for work.

[0525] The processing flow will be explained below.

[0526] Step 1:

[0527] The user accesses the platform's login screen and enters their account information (ID and password).

[0528] Step 2:

[0529] The terminal sends the user's login information to the server.

[0530] Step 3:

[0531] The server checks the received login information against the database to verify whether the user is a valid user, and if authentication is successful, starts a user session.

[0532] Step 4:

[0533] The server sends the authenticated dashboard screen to the terminal, allowing the user to access this dashboard.

[0534] Step 5:

[0535] The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required task data.

[0536] Step 6:

[0537] The terminal transmits the business data entered by the user to the server.

[0538] Step 7:

[0539] The server stores the received business data in temporary storage and registers the metadata in the database.

[0540] Step 8:

[0541] The server collects the necessary business data from the database and provides it to the appropriate AI model.

[0542] Step 9:

[0543] AI analyzes business data and performs specified tasks, such as generating slides based on the data to create presentation materials and adding relevant content.

[0544] Step 10:

[0545] The AI ​​returns the analysis results and generated deliverables (e.g., presentation materials) to the server.

[0546] Step 11:

[0547] The server receives the processing results from the AI ​​tool and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[0548] Step 12:

[0549] The server uploads the processing results to various tools and notifies the user when the results are ready.

[0550] Step 13:

[0551] Users can check the processing results on the platform and make any necessary corrections.

[0552] Step 14:

[0553] The terminal sends the user's modifications and feedback to the server.

[0554] Step 15:

[0555] The server stores the feedback and correction information in a database and uses it as training data for the AI ​​model.

[0556] Example 1

[0557] 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."

[0558] In conventional business systems, the process of collecting business data after a user logs in, processing it using AI, and outputting the results to various tools is often done manually, which poses the issue of taking time and effort. In addition, there is no mechanism in place to use feedback as learning data for the AI, making it difficult to improve the performance of the AI. Furthermore, since integration with various tools is not automated, business efficiency can sometimes decrease.

[0559] 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.

[0560] In this invention, the server includes a terminal to which a user uploads business data, a means for the server to temporarily store the business data, and a means for the server to provide processing results to the AI ​​means and output the generated processing results to a specified tool. This makes it possible to automate the process from inputting business data to analyzing it with AI, outputting it to various tools, and collecting feedback.

[0561] "User" refers to any individual or corporation that uses this system.

[0562] "Login information" refers to authentication information for a user to access a system, and is usually composed of an ID and password.

[0563] "Server" refers to a computer system that receives and processes data sent by users.

[0564] "Business Data" refers to data that a user inputs or uploads to the system in order to perform a specific business.

[0565] "AI means" refers to artificial intelligence technology that analyzes collected business data and carries out specified tasks.

[0566] "Processing results" refers to the deliverables generated by AI means by analyzing business data.

[0567] "Various tools" refers to external applications and services used to output processing results.

[0568] "Notification" refers to the action of the server informing the user that the processing results are ready.

[0569] "Feedback" refers to evaluations and information on improvements provided by users regarding processing results.

[0570] "Database" means the data management system for storing Feedback and other data.

[0571] "Terminal" refers to a device through which a User accesses the System and inputs or uploads data.

[0572] "Temporary storage" refers to a storage area where a server temporarily stores business data.

[0573] "Automation" refers to a system in which a series of processes are carried out without manual operation.

[0574] "API" refers to a program interface that enables integration with external applications and services.

[0575] This invention relates to a system in which a user inputs login information and a server authenticates the login information. Furthermore, the system has a mechanism in which the user inputs business data, the AI ​​analyzes the data, performs the specified business, and automatically outputs the processing results to various tools. The system also has a function to receive user feedback and use it as learning data for the AI ​​model.

[0576] Hardware and Software Configuration

[0577] 1. User end devices (terminals)

[0578] The terminals used by users are computing devices such as PCs and mobile devices.

[0579] A web browser (e.g., Google Chrome) is installed on the terminal, and is used by the user to access the system.

[0580] 2. Server

[0581] A server is a powerful computing system that receives and processes data from users.

[0582] Software used includes database management systems (e.g., MySQL) and temporary storage systems (e.g., Amazon S3).

[0583] 3. AI means

[0584] As an AI tool, a generative AI model (e.g., BERT model) is used to analyze the business data provided by the user and carry out the specified tasks.

[0585] AI models use data analysis, natural language processing, and machine learning algorithms to generate optimal results.

[0586] Example of a system

[0587] Automatic generation of presentation materials

[0588] 1. Login Process

[0589] The user launches a browser and accesses the system's login screen, enters login information (ID and password), and sends it to the server.

[0590] The server compares the information stored in the database and, if authentication is successful, displays the dashboard screen on the user's device.

[0591] 2. Provision of business data

[0592] Users select "New Work" from the dashboard and upload the data (e.g., spreadsheet files) needed to create presentation materials.

[0593] The terminal sends the uploaded file to the server, and the server stores it in temporary storage (e.g., Amazon S3).

[0594] 3. AI-powered data processing

[0595] The server retrieves the data from the temporary storage and provides it to the AI ​​means, which analyzes the uploaded data and automatically generates presentation slides.

[0596] For example, you can generate a presentation using the following prompt: "Create a presentation based on the following data: {data content}"

[0597] 4. Output of processing results

[0598] The server retrieves the presentation materials generated by the AI ​​means and automatically uploads them to a designated tool (e.g., Google Slides).

[0599] After a successful upload, the server will notify the user via email or in-app notification that the results are ready.

[0600] 5. Review and feedback of results

[0601] After receiving the notification, the user can check the presentation materials on the platform and make any necessary corrections.

[0602] User feedback is sent via the device to a server, which stores it in a database and uses it as training data for the AI ​​model.

[0603] This system allows users to streamline their work through generative AI models, significantly reducing the time and effort required for their work. In addition, by incorporating feedback into the AI's learning data, it is possible to continue improving the AI's performance.

[0604] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0605] Step 1: The user launches a browser and accesses the system's login screen. The user enters their login information (ID and password).

[0606] Input: ID, password

[0607] Output: Sends login information to the server

[0608] Specific operation: The user opens the system's login page in a browser, enters the ID and password in the designated input fields, and clicks the login button.

[0609] Step 2: The terminal sends the login information entered by the user to the server, which checks the received login information against its database and returns the authentication result.

[0610] Input: Login information (ID, password)

[0611] Output: Authentication result (success / failure)

[0612] Specific operation: The terminal sends login information to the server via the HTTPS protocol. The server verifies the login information against a database (e.g., MySQL), and if authentication is successful, it creates a user session and returns the session ID.

[0613] Step 3: If the authentication is successful, the server creates a user session and returns a session ID to the terminal. The terminal receives a notification of successful authentication and transitions to the dashboard screen.

[0614] Input: Authentication result (success)

[0615] Output: Dashboard screen

[0616] Specific operation: The server saves the session ID in session storage (e.g., Redis) and returns the session ID to the terminal in the HTTP response header. The terminal changes the URL to the dashboard screen.

[0617] Step 4: The user selects a new task (e.g., creating presentation materials) from the operation panel on the dashboard.

[0618] Input: Business selection information

[0619] Output: Business data input screen

[0620] Specific operation: The user selects "New Task" from the dashboard menu and moves to the business data input screen.

[0621] Step 5: The user enters or uploads the required data (e.g., a spreadsheet file or text input). The device sends the business data to the server, which stores it in temporary storage.

[0622] Input: Business data (spreadsheet files, etc.)

[0623] Output: Data saved in temporary storage

[0624] Specific operation: The user selects a spreadsheet file using the file selection dialog and clicks the upload button. The terminal sends the file to the server in the Multipart Form data format, and the server saves it in temporary storage (e.g., Amazon S3).

[0625] Step 6: The server provides the collected business data to the AI ​​means, which analyzes the data and performs the specified tasks.

[0626] Input: Business data stored in temporary storage

[0627] Output: AI analysis results

[0628] Specific operation: The server sends an API request to the AI ​​means to retrieve business data. The AI ​​means analyzes the data and generates presentation materials using prompts.

[0629] Example prompt: "Please create a presentation based on the following data: {data content}"

[0630] Step 7: The server receives the processing results generated by the AI ​​means and automatically outputs them to various tools specified by the user (e.g., Google Slides).

[0631] Input: AI analysis results

[0632] Output: Processing results output to various tools

[0633] Specific operation: The server calls the API, uploads the generated presentation materials to Google Slides, and returns the URL when the upload is complete.

[0634] Step 8: The server notifies the user that the processing results are ready. The user can check the processing results (e.g., presentation materials) on the platform and make any necessary corrections.

[0635] Input: Notification that processing results are ready

[0636] Output: Confirmation screen on the platform

[0637] Specific operation: The server sends an email or in-app notification to the user, providing a link to check the processing results. The user clicks the link to view and edit the presentation materials.

[0638] Step 9: The device sends the user-provided feedback to the server, which stores it in a database and uses it as training data for the AI ​​model.

[0639] Input: Feedback

[0640] Output: Feedback stored in a database

[0641] Specific operation: The user fills in the feedback form and clicks the submit button. The device sends the feedback data to the server, which stores it in a database. The feedback stored in the database will be used the next time the AI ​​model is trained.

[0642] (Application example 1)

[0643] 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."

[0644] Conventional security systems perform primary authentication using only login information, which increases the risk of unauthorized access. While introducing multi-factor authentication can strengthen security, it also increases the operational burden on users. Furthermore, a system is needed to centrally manage user access history and authentication failure history and detect fraudulent activity.

[0645] 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.

[0646] In this invention, the server includes a means for a user to input login information, a server means for authenticating the login information, a means for collecting business data after authentication, an AI means for analyzing the collected business data and performing a specified task, a means for automatically outputting the processing results of the AI ​​means to various tools, a means for notifying the user of the results and receiving feedback, a means for saving the feedback in a database and using it as learning data for an AI model, a means for performing multi-factor authentication, and a means for recording the user's access history and authentication failure history. This makes it possible to prevent unauthorized access through multi-factor authentication, reduce the operational burden on the user, and detect and manage fraudulent activity.

[0647] "Means for users to input login information" refers to means for providing an interface for users to input account information (ID and password).

[0648] "Server means for authenticating login information" refers to the function of the server to check the transmitted login information against a database and confirm whether the user is a legitimate user.

[0649] The "means for collecting business data" refers to the means by which a user inputs or uploads data necessary for performing a business operation after authentication.

[0650] "AI means for analyzing collected business data and carrying out designated tasks" refers to the function of artificial intelligence to automatically analyze and carry out tasks based on collected business data.

[0651] "Means for automatically outputting processing results by AI means to various tools" refers to means for automatically outputting processing results generated by AI means to external tools designated by the user.

[0652] The "means for notifying the user of the results and receiving feedback" refers to a means for informing the user of the processing results and receiving the user's opinions and corrections regarding the results.

[0653] "Means for storing feedback in a database and using it as training data for an AI model" refers to a means for storing received feedback in a database and later using it as training data for an AI model.

[0654] A "means of implementing multi-factor authentication" is a means of strengthening authentication after primary authentication by using additional authentication factors (such as tokens).

[0655] "Means for recording user access history and authentication failure history" refers to a means for centrally recording and managing the history of user access to the system and the history of authentication failures.

[0656] This invention relates to a security system using multi-factor authentication, and provides a server means for authenticating login information entered by a user. After authentication, the system collects business data, analyzes it using AI means, and performs the specified business. It also has a function for automatically outputting the processing results of the AI ​​means to various tools, notifying the user and receiving feedback. Furthermore, this feedback is stored in a database and used as training data for the AI ​​model.

[0657] As an example of the present system, a system used to provide security services using multi-factor authentication for businesses will be described.

[0658] Program Overview

[0659] The server includes the following functions:

[0660] 1. User authentication function: The login information entered by the user is checked against a database to confirm that the user is a legitimate user.

[0661] 2. Multi-factor authentication function: After successful primary authentication, multi-factor authentication is performed using additional authentication factors (such as tokens).

[0662] 3. Business data collection function: Collects business data entered or uploaded by the user after authentication.

[0663] 4. AI data analysis function: Analyzes collected business data and automatically carries out specified tasks.

[0664] 5. Automatic result output function: The generated processing results are automatically output to various tools specified by the user.

[0665] 6. Feedback processing function: Receives user feedback on the processing results, saves the data, and uses it as learning data for the AI ​​model.

[0666] 7. Log management function: Records and manages user access history and authentication failure history.

[0667] Hardware and software used

[0668] The server hardware used is a cloud server such as Amazon EC2.

[0669] Amazon RDS (MySQL, etc.) is used as the database.

[0670] AI models are built using TensorFlow or PyTorch.

[0671] A common multi-factor authentication library is used to generate the authentication token.

[0672] Amazon CloudWatch is used for log management.

[0673] Specific examples of implementation

[0674] Example 1: Multi-factor authentication system for businesses

[0675] This system will be implemented to enhance the security of your company by:

[0676] A user accesses the company's security platform and enters their user ID and password. The information is sent to the server and checked against a database. If the initial authentication is successful, the server sends a multi-factor authentication token to the user's device.

[0677] The user checks the token and enters it in the specified application. The server checks the validity of the token, and if authentication is successful, the dashboard screen is displayed to the user. The user uploads business data, which is analyzed and processed by the AI. The generated processing results are automatically output to various tools and notified to the user. User feedback is stored in a database and used as learning data for the AI ​​model. Access history and failed authentication attempts are recorded as logs and made available for administrators to review.

[0678] Prompt Sentence Examples

[0679] Create a program to authenticate user login information and implement the multi-factor authentication flow. Receive the user ID and password and check them against a database. If the initial authentication is successful, generate a multi-factor authentication token and send it to the user. Once the user enters the token and confirms its validity, final authentication is completed and the user session begins. Also, record log information throughout the authentication flow. Use the Flask framework and MySQL database.

[0680] By implementing this invention, it is possible to realize robust security measures and reduce the burden on users through multi-factor authentication, thereby significantly improving the security of corporate systems.

[0681] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0682] Step 1:

[0683] User enters login information

[0684] A user accesses the system's login screen and enters their user ID and password. The input information is sent to the server by the terminal. The input is in the form of keystrokes, and the output is a string of login information.

[0685] Step 2:

[0686] Server authentication of login information

[0687] The server checks the received user ID and password against the database to verify that the user is a valid user. It searches for matching records using a database query and retrieves the results. The input is login information and the output is the authentication result (success or failure). If authentication is successful, it proceeds to the next step.

[0688] Step 3:

[0689] Generate and send multi-factor authentication tokens

[0690] If authentication is successful, the server generates a multi-factor authentication token and sends it to the user's registered device (e.g., smartphone). A token generation algorithm is used to create a temporary authentication code and transmit it to the user device. The input is the user ID, and the output is the generated token.

[0691] Step 4:

[0692] User confirms and enters token

[0693] The user receives the token on their smartphone or authentication application, then returns to the system to enter the token. The user manually enters the token and sends it back to the server. The input is the token, and the output is the token string.

[0694] Step 5:

[0695] Token validation by the server

[0696] The server checks the token received from the user in its database to verify that it is a valid authentication token. It checks whether it matches the token record in the database and obtains the result. The input is the token and the output is the verification result (success or failure). If the token is correct, it proceeds to the next step.

[0697] Step 6:

[0698] Entering and uploading business data

[0699] After authentication is complete, the user inputs or uploads business data (e.g., spreadsheets or document files) into the system. The data uploaded by the user is sent to the server and stored in temporary storage. The input is the business data, and the output is the reference ID of the stored data.

[0700] Step 7:

[0701] Analysis of business data by the server

[0702] The server provides the stored business data to the AI ​​means to analyze the data. The AI ​​model (e.g., TensorFlow or PyTorch) is used to analyze the data and perform the specified task (e.g., report generation or data analysis). The input is the reference ID of the business data, and the output is the processing result.

[0703] Step 8:

[0704] Automatic output of processing results to tools

[0705] The processing results generated by the AI ​​tool are automatically output to an external tool specified by the user (e.g., Google Slides or Microsoft PowerPoint). The API is called to send the result data to the tool. The input is the processing result, and the output is a confirmation message from the tool.

[0706] Step 9:

[0707] Notification of processing results and receiving feedback

[0708] The user receives a notification of the processing result on the system and checks the result. If necessary, the user can input feedback and send it to the system. The feedback is saved on the server. The input is the feedback, and the output is the saved feedback data.

[0709] Step 10:

[0710] Storing feedback and using data to train AI models

[0711] The server stores the feedback in a database and uses it as training data for the AI ​​model. The stored feedback is provided to the AI ​​model and added as new training data. The input is the feedback data, and the output is an updated AI model.

[0712] Step 11:

[0713] Recording and management of access history and authentication failure history

[0714] The server centrally records user access history and authentication failure history, allowing administrators to check them. The log data is sent to a cloud log management system (e.g., Amazon CloudWatch). The input is access history data, and the output is the recorded log data.

[0715] 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.

[0716] Overall system overview

[0717] The system allows users to access and input business data, and the AI ​​processes the data to carry out the specified tasks, automatically outputting the results to various tools. The system also has an emotion engine that recognizes the user's emotions, and changes the content presented on the interface based on the emotional data, and has the ability to use this as feedback for learning data.

[0718] Program processing flow

[0719] 1. Enter and authenticate your login information

[0720] The user accesses the platform's login screen and enters their account information (ID and password).

[0721] The terminal sends the user's login information to the server.

[0722] The server authenticates the login information against a database, and if authentication is successful, the user session begins.

[0723] 2. Collection of business data

[0724] Users select a new task (e.g., creating presentation materials) from the dashboard and enter or upload the required data.

[0725] The terminal sends business data to the server, and the server stores the data in storage.

[0726] 3. Emotion Recognition by Emotion Engine

[0727] The server analyzes the user's operation data and input text data on the dashboard in real time and uses an emotion engine to recognize the user's emotions.

[0728] The interface content is automatically adjusted based on the recognized emotion.

[0729] 4. AI-powered data processing

[0730] The server provides the collected business data to the AI ​​means and carries out the specified tasks.

[0731] AI analyzes the data and generates the final processed results (e.g., presentation materials).

[0732] 5. Automatic output of processing results

[0733] The server receives the processing results returned by the AI ​​and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[0734] The generated processing results are automatically uploaded to the following tools.

[0735] 6. Notification of results and feedback

[0736] The server notifies the user when the results are ready.

[0737] Users can check the results on the platform, make any necessary corrections, and provide feedback.

[0738] The terminal sends the feedback to the server.

[0739] The server stores the feedback and emotional data in a database and uses it as learning data for the AI ​​and emotion engine.

[0740] Specific examples

[0741] Creating presentation materials and emotional feedback

[0742] 1. Login Process

[0743] A user logs into the system and accesses the dashboard. The server authenticates the login information.

[0744] 2. Provision of business data

[0745] A user uploads data (e.g., a spreadsheet file) needed to create a presentation for the next meeting. The data is temporarily stored in the server's storage.

[0746] 3. Emotion recognition

[0747] The server analyzes the user's input data and operation data in real time, recognizes the user's emotions using an emotion engine, and adjusts the user's interface experience according to the emotion data.

[0748] 4. AI Processing

[0749] The server passes the collected data to the AI, which then begins analyzing it. The AI ​​then automatically generates presentation slides based on the data, inserting explanatory text and graphs.

[0750] 5. Output

[0751] The server calls the API to upload the generated presentation to Google Slides. After the upload is successfully completed, the server notifies the user.

[0752] 6. Review and Feedback

[0753] The user checks the presentation materials on the platform and makes any necessary corrections. The corrections and feedback are then sent to the server along with the emotion data.

[0754] The server stores the feedback and emotion data in a database and uses it as learning data for the AI ​​and emotion engine.

[0755] This system enables efficient use of generative AI and emotion engines, significantly reducing the amount of work required and time required while improving the user experience.

[0756] The processing flow will be explained below.

[0757] Step 1:

[0758] The user accesses the platform's login screen and enters their account information (ID and password).

[0759] Step 2:

[0760] The terminal sends the user's login information to the server.

[0761] Step 3:

[0762] The server checks the received login information against the database to verify whether the user is a valid user, and if authentication is successful, starts a user session.

[0763] Step 4:

[0764] The server sends the authenticated dashboard screen to the terminal, allowing the user to access this dashboard.

[0765] Step 5:

[0766] The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required task data.

[0767] Step 6:

[0768] The terminal transmits the business data entered by the user to the server.

[0769] Step 7:

[0770] The server stores the received business data in temporary storage and registers the metadata in the database.

[0771] Step 8:

[0772] The server monitors the user's operation data and input text data on the dashboard in real time and inputs it into the emotion engine.

[0773] Step 9:

[0774] The emotion engine analyzes user operation data and text data to recognize the user's emotions.

[0775] Step 10:

[0776] The server receives the emotion data from the emotion engine and automatically adjusts the interface presentation according to the user's mood. For example, if the user is irritated, the system provides a simple and easy-to-understand interface.

[0777] Step 11:

[0778] The business data collected by the server is provided to AI means.

[0779] Step 12:

[0780] AI analyzes business data and performs specified tasks, such as generating slides based on the data to create presentation materials and adding relevant content.

[0781] Step 13:

[0782] The AI ​​returns the analysis results and generated deliverables (e.g., presentation materials) to the server.

[0783] Step 14:

[0784] The server receives the processing results from the AI ​​tool and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[0785] Step 15:

[0786] The server uploads the processing results to various tools, and once the upload is completed successfully, it notifies the user that the results are ready.

[0787] Step 16:

[0788] The user checks the processing results on the platform, and if the user wants to make any corrections, they input the details.

[0789] Step 17:

[0790] The terminal sends the user's modifications and feedback to the server.

[0791] Step 18:

[0792] The server stores the feedback and emotional data in a database and uses it as learning data for the AI ​​and emotion engine.

[0793] Example 2

[0794] 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."

[0795] Conventional business systems require users to manually collect and analyze business data, which is time-consuming, and outputting results and processing feedback takes time. Furthermore, because they do not take into account user emotions or stress, there are still issues with the user experience. Furthermore, updates to the AI ​​model's learning data are delayed, often failing to reflect the latest data.

[0796] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0797] In this invention, the server includes emotion engine means for recognizing user emotions, means for dynamically adjusting the interface based on the emotion data, and means for automatically outputting the processing results of the AI ​​means to various tools. This not only automates the collection and analysis of user business data, but also enables dynamic interface adjustment that takes user emotions into consideration, thereby improving the user experience.

[0798] A "user" is an individual who accesses the system, enters login information, provides business data, and checks and provides feedback on the results.

[0799] "Login Information" refers to the account ID and password used by a User to access the System.

[0800] A "server" is a computer system that performs a series of processes, including authenticating login information sent by a user, collecting and analyzing business data, and outputting the final processing results to various tools.

[0801] "Business data" refers to data related to a business provided by a user, and examples include spreadsheet files, text data, image files, and the like.

[0802] "AI means" refers to artificial intelligence technology that analyzes collected business data and carries out specified tasks.

[0803] "Processing Results" means the final output generated by the AI ​​Means, including presentation materials and other business-related materials submitted to a User.

[0804] "Various tools" refers to external systems or software for outputting processing results, such as Google Slides and Microsoft PowerPoint.

[0805] The "emotion engine means" is a technology for analyzing the user's operation data and input data and recognizing the user's emotions.

[0806] "Emotion data" is data that indicates the emotional state of the user analyzed by the emotion engine means.

[0807] "Feedback" refers to opinions and correction requests provided by users regarding the processing results generated by the system, and is used as learning data for the system.

[0808] This invention is a system in which users access the system, input business data, and AI processes the data to perform the specified tasks, automatically outputting the processing results to various tools. This system operates in cooperation with three entities: a server, a terminal, and a user.

[0809] server

[0810] The server authenticates the login information entered by the user and collects and stores business data. It also includes an emotion engine that analyzes user emotion data in real time. It also provides the collected business data to an AI model, which then automatically outputs the processed results to various tools (e.g., Google Slides, Microsoft PowerPoint).

[0811] Terminal

[0812] The terminal provides an interface that supports the user's data input and operation. The terminal also transmits the login information and business data entered by the user to the server. Furthermore, feedback information is also transmitted from the terminal to the server.

[0813] User

[0814] Users log in to the system, input or upload business data, and check and correct the generated processing results. Users also provide feedback to the system on the generated results, which is used as learning data for the AI ​​model.

[0815] Specific hardware and software

[0816] The servers are equipped with high-performance CPUs and GPUs to rapidly process and analyze data. Relational databases such as MySQL and PostgreSQL are used as databases. The emotion engine uses natural language processing technology, and generative AI models such as GPT-4 are used as AI models. A browser and dedicated applications are installed on the terminals, which function as the user interface.

[0817] Specific examples

[0818] For example, consider a user creating a presentation for an upcoming meeting. First, the user logs in to the system and accesses the dashboard. Next, they upload the necessary business data (e.g., a spreadsheet file). This data is sent to the server, which uses an emotion engine to analyze the user's emotions and adjust the interface. The server then passes the collected data to the AI, which generates presentation slides in the specified format. The generated presentation slides are automatically uploaded to Google Slides or Microsoft PowerPoint. Finally, the user reviews the slides on the platform and provides feedback. This feedback is stored on the server and used as future learning data.

[0819] Prompt Sentence Examples

[0820] The following prompts can be used:

[0821] "Create a presentation for my next meeting. Generate slides based on this data."

[0822] In this way, the present invention provides a system that significantly reduces the workload while improving the user experience. The collaboration between the generative AI model and the emotion engine enables tasks to be performed with extremely high efficiency and accuracy.

[0823] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0824] Step 1:

[0825] Enter and authenticate your login information

[0826] Input: The user uses a browser or a dedicated application to enter their account ID and password on the login screen.

[0827] Specific behavior:

[0828] The user accesses the system's login screen and enters their account ID and password.

[0829] The device encrypts the login information entered by the user and sends it to the server via a secure communication protocol such as HTTPS.

[0830] Data processing:

[0831] The server receives the submitted login information and checks it against the user information in its database.

[0832] Output: If authentication is successful, start a user session and redirect the user to the dashboard.

[0833] Step 2:

[0834] Business data collection

[0835] Input: The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required data.

[0836] Specific behavior:

[0837] The user selects the "Create Presentation" option.

[0838] The user uploads the necessary business data, such as a spreadsheet file or text file.

[0839] Data processing:

[0840] The terminal sends the uploaded file to the server.

[0841] Output: Business data is sent to the server and stored in the server's storage.

[0842] Step 3:

[0843] Emotion recognition by emotion engine

[0844] Input: User operation data and input data

[0845] Specific behavior:

[0846] The server analyzes the user's operation data and input text data in real time.

[0847] The server uses an emotion engine to recognize the user's emotions (e.g., joy, anger, sadness).

[0848] Data Calculation:

[0849] The emotion engine uses natural language processing techniques to analyze the emotional tone of the input data.

[0850] Output: Dynamically adjust the content displayed in the user interface based on the recognized emotion data.

[0851] Step 4:

[0852] AI-powered data processing

[0853] Input: User-provided business data

[0854] Specific behavior:

[0855] The server generates prompts to input the collected business data into a generative AI model (e.g., GPT-4). For example, it generates a prompt like, "Please create a presentation for the next meeting. Please generate slides based on this data."

[0856] The AI ​​analyzes business data based on the generated prompt text and automatically generates presentation slides.

[0857] Data Calculation:

[0858] The AI ​​analyzes the input business data and generates presentation materials in the specified format.

[0859] Output: Generated presentation materials (slides, explanatory text, graphs, etc.)

[0860] Step 5:

[0861] Automatic output of processing results

[0862] Input: AI-generated processing results (presentation materials)

[0863] Specific behavior:

[0864] The server receives the processing results returned by the AI ​​and calls an API to upload them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[0865] Data processing:

[0866] The server makes an API call to upload the generated file to an external service.

[0867] Output: A confirmation message and a URL indicating a successful upload.

[0868] Step 6:

[0869] Notification of results and feedback

[0870] Input: Server-generated confirmation message and URL

[0871] Specific behavior:

[0872] The server notifies the user when the processing results are ready (e.g. email notification, dashboard notification).

[0873] The user can check the processing results on the platform.

[0874] The user makes corrections as needed and provides feedback.

[0875] Data processing:

[0876] The terminal transmits the feedback information to the server.

[0877] Output: Feedback and emotion data are stored on the server.

[0878] Through these processing steps, a system is realized that can efficiently collect business data, analyze it using AI, output the generated results, and collect feedback.

[0879] (Application example 2)

[0880] 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."

[0881] Conventional systems require users to spend a great deal of time and effort when creating content. Furthermore, they perform tasks without considering the user's feelings, making it difficult to improve the user experience. Furthermore, there is a need to output the generated content to various tools quickly and efficiently.

[0882] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0883] In this invention, the server includes means for analyzing user operation data and input data and recognizing the user's emotions using an emotion engine, means for automatically adjusting the interface based on the recognized emotions, and AI means for analyzing collected business data and performing specified tasks, thereby making it possible to provide an interface that reflects the user's emotions and automate tasks.

[0884] "Login Information" means the authentication information a user enters to access a system.

[0885] "Server means" refers to a central processing unit that authenticates login information and collects, stores, and analyzes business data.

[0886] "Business data" refers to data that a user inputs or uploads in order to perform a specific business.

[0887] "AI means" is an artificial intelligence engine that analyzes collected business data and carries out specified tasks.

[0888] "Various tools" are external applications or platforms for outputting or using the generated processing results.

[0889] "Feedback" refers to the evaluation and correction instructions provided by the user regarding the processing results of the system.

[0890] A "database" is a digital storage device for storing information such as business data, processing results, and feedback.

[0891] An "emotion engine" is a software engine that analyzes user operation data and input data and recognizes the user's emotions.

[0892] An "interface" is a display screen or operating means that allows a user to interact with a system.

[0893] An "API calling means" is a function realized by a program for exchanging data and services between different software components.

[0894] The system for implementing the present invention includes a server that authenticates login information entered by the user, collects business data, recognizes the user's emotions using an emotion engine, adjusts the interface based on the recognition results, and performs business operations using AI means.

[0895] The system's hardware uses terminal devices such as smartphones and head-mounted displays. The software includes an emotion engine, an AI content generation module, and a data processing module. The emotion engine is software that analyzes user operation data and input data in real time and recognizes emotions. The AI ​​content generation module is software that performs specified tasks (such as automatically generating content) based on collected business data.

[0896] The main steps of this system are for the user to log in to the system and upload the necessary business data. The server collects this data and analyzes the user's emotions using an emotion engine. The interface is automatically adjusted based on the analysis results. The collected business data is then passed to the AI ​​means to carry out the task. The generated processing results are automatically output to a specified tool and notified to the user. The user checks the processing results and provides feedback. The feedback is stored in a database and used as learning data for future AI and emotion engines.

[0897] As a concrete example, suppose a user uploads a video clip and a text script to create their next video content. This data is sent to the server, where the emotion engine recognizes the user's emotions and adjusts the software accordingly. The AI ​​content generation module then uses this data to automatically edit the video and upload it to the specified platform.

[0898] An example of a prompt is the following text:

[0899] "User ID: example_user"

[0900] Password: example_password

[0901] "Content data: example_video_clip.mp4"

[0902] "Platform Name: YouTube"

[0903] This enables the provision of an interface that takes user emotions into consideration and the automation of tasks, improving the user experience and work efficiency.

[0904] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0905] Step 1:

[0906] The user enters login information. The user accesses the system's login screen and enters their ID and password. The terminal sends the entered login information to the server.

[0907] Step 2:

[0908] The server authenticates the login information. It checks the login information against a database and starts the user session if authentication is successful. If authentication is unsuccessful, the server prompts the user for login information again.

[0909] Step 3:

[0910] The user collects task data. The user then selects the task to be performed (e.g., video content creation) and uploads the required data (e.g., video clips, text scripts). The device then transmits this data to the server.

[0911] Step 4:

[0912] The server uses an emotion engine to recognize emotions. The server analyzes user operation data and input data in real time and uses the emotion engine to recognize the user's emotions. The interface is automatically adjusted based on the recognition results.

[0913] Step 5:

[0914] The server provides business data to the AI ​​means. The collected business data is passed to the AI ​​content generation module. The AI ​​analyzes this data and generates optimal content (e.g., automatically edited videos).

[0915] Step 6:

[0916] The server automatically outputs the processing results to various tools. The generated processing results are uploaded to the specified platform (e.g. YouTube, Spotify) by calling an API to output them. This automatically publishes the generated content to the platform.

[0917] Step 7:

[0918] The server notifies the user of the results. When the upload of the processing results is complete, the server sends a notification to the user. The user receives this notification and checks the processing results.

[0919] Step 8:

[0920] The user provides feedback. The user checks the processing results on the platform, makes necessary corrections, and provides feedback. The terminal sends the feedback to the server.

[0921] Step 9:

[0922] The server stores the feedback in a database, which will be used as learning data for future AI and emotion engines.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] [Third embodiment]

[0927] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0928] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0929] 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).

[0930] 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.

[0931] 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.

[0932] 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).

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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."

[0939] This invention relates to a system that allows a user to input login information and a server to provide a means for authenticating the login information. Below, we will create a program for this system and explain its processing in natural language.

[0940] Overall system overview

[0941] This system allows users to access and input business data, and the AI ​​processes the data to carry out the specified tasks, automatically outputting the results to various tools. The system also has the ability to receive user feedback and use it as learning data for the AI.

[0942] Program processing flow

[0943] 1. Enter and authenticate your login information

[0944] The user accesses the platform's login screen and enters their account information (ID and password).

[0945] The terminal transmits the login information entered by the user to the server.

[0946] The server checks the received login information against a database to verify whether the user is a valid user. If authentication is successful, the server starts a user session and sends the dashboard screen to the terminal.

[0947] 2. Collection of business data

[0948] The user selects a new task (e.g., creating a presentation) from the dashboard and enters or uploads the required data.

[0949] The terminal sends business data to the server, and the server stores the data in temporary storage.

[0950] 3. AI-powered data processing

[0951] The server provides the collected business data to the AI ​​tool.

[0952] AI analyzes the data it receives and performs the specified tasks. For example, when creating presentation materials, it generates slides based on the data and arranges the content.

[0953] 4. Automatic output of processing results

[0954] The server receives the processing results generated by the AI ​​means and automatically outputs them to various tools specified by the user (e.g., Google Slides or Microsoft PowerPoint).

[0955] After the output is successful, the server notifies the user that the processing results are ready.

[0956] 5. Review and feedback of results

[0957] The user checks the processing results (e.g., presentation materials) on the platform and makes corrections as necessary.

[0958] The terminal transmits the feedback provided by the user to the server.

[0959] The server stores the feedback in a database and uses it as learning data for the AI.

[0960] Specific examples

[0961] Creating presentation materials

[0962] 1. Login Process

[0963] A user logs in to the system and accesses the dashboard. The login information (ID and password) is authenticated by the server.

[0964] 2. Provision of business data

[0965] A user uploads data (e.g., a spreadsheet file) needed to create presentation materials for an upcoming meeting. The data is temporarily stored in the server's storage.

[0966] 3. AI-powered data processing

[0967] The server passes the collected data to the AI, which then begins analyzing it. The AI ​​then automatically generates presentation slides based on the data, inserting explanatory text and graphs.

[0968] 4. Automatic output of processing results

[0969] The server calls the API to upload the generated presentation to Google Slides. After the upload is successfully completed, the server notifies the user.

[0970] 5. Review and Feedback

[0971] The user checks the presentation materials on the platform and makes any necessary corrections. The corrections and feedback are sent to the server and stored in the database.

[0972] This system enables efficient use of generative AI, significantly reducing the effort and time required for work.

[0973] The processing flow will be explained below.

[0974] Step 1:

[0975] The user accesses the platform's login screen and enters their account information (ID and password).

[0976] Step 2:

[0977] The terminal sends the user's login information to the server.

[0978] Step 3:

[0979] The server checks the received login information against the database to verify whether the user is a valid user, and if authentication is successful, starts a user session.

[0980] Step 4:

[0981] The server sends the authenticated dashboard screen to the terminal, allowing the user to access this dashboard.

[0982] Step 5:

[0983] The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required task data.

[0984] Step 6:

[0985] The terminal transmits the business data entered by the user to the server.

[0986] Step 7:

[0987] The server stores the received business data in temporary storage and registers the metadata in the database.

[0988] Step 8:

[0989] The server collects the necessary business data from the database and provides it to the appropriate AI model.

[0990] Step 9:

[0991] AI analyzes business data and performs specified tasks, such as generating slides based on the data to create presentation materials and adding relevant content.

[0992] Step 10:

[0993] The AI ​​returns the analysis results and generated deliverables (e.g., presentation materials) to the server.

[0994] Step 11:

[0995] The server receives the processing results from the AI ​​tool and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[0996] Step 12:

[0997] The server uploads the processing results to various tools and notifies the user when the results are ready.

[0998] Step 13:

[0999] Users can check the processing results on the platform and make any necessary corrections.

[1000] Step 14:

[1001] The terminal sends the user's modifications and feedback to the server.

[1002] Step 15:

[1003] The server stores the feedback and correction information in a database and uses it as training data for the AI ​​model.

[1004] Example 1

[1005] 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."

[1006] In conventional business systems, the process of collecting business data after a user logs in, processing it using AI, and outputting the results to various tools is often done manually, which poses the issue of taking time and effort. In addition, there is no mechanism in place to use feedback as learning data for the AI, making it difficult to improve the performance of the AI. Furthermore, since integration with various tools is not automated, business efficiency can sometimes decrease.

[1007] 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.

[1008] In this invention, the server includes a terminal to which a user uploads business data, a means for the server to temporarily store the business data, and a means for the server to provide processing results to the AI ​​means and output the generated processing results to a specified tool. This makes it possible to automate the process from inputting business data to analyzing it with AI, outputting it to various tools, and collecting feedback.

[1009] "User" refers to any individual or corporation that uses this system.

[1010] "Login information" refers to authentication information for a user to access a system, and is usually composed of an ID and password.

[1011] "Server" refers to a computer system that receives and processes data sent by users.

[1012] "Business Data" refers to data that a user inputs or uploads to the system in order to perform a specific business.

[1013] "AI means" refers to artificial intelligence technology that analyzes collected business data and carries out specified tasks.

[1014] "Processing results" refers to the deliverables generated by AI means by analyzing business data.

[1015] "Various tools" refers to external applications and services used to output processing results.

[1016] "Notification" refers to the action of the server informing the user that the processing results are ready.

[1017] "Feedback" refers to evaluations and information on improvements provided by users regarding processing results.

[1018] "Database" means the data management system for storing Feedback and other data.

[1019] "Terminal" refers to a device through which a User accesses the System and inputs or uploads data.

[1020] "Temporary storage" refers to a storage area where a server temporarily stores business data.

[1021] "Automation" refers to a system in which a series of processes are carried out without manual operation.

[1022] "API" refers to a program interface that enables integration with external applications and services.

[1023] This invention relates to a system in which a user inputs login information and a server authenticates the login information. Furthermore, the system has a mechanism in which the user inputs business data, the AI ​​analyzes the data, performs the specified business, and automatically outputs the processing results to various tools. The system also has a function to receive user feedback and use it as learning data for the AI ​​model.

[1024] Hardware and Software Configuration

[1025] 1. User end devices (terminals)

[1026] The terminals used by users are computing devices such as PCs and mobile devices.

[1027] A web browser (e.g., Google Chrome) is installed on the terminal, and is used by the user to access the system.

[1028] 2. Server

[1029] A server is a powerful computing system that receives and processes data from users.

[1030] Software used includes database management systems (e.g., MySQL) and temporary storage systems (e.g., Amazon S3).

[1031] 3. AI means

[1032] As an AI tool, a generative AI model (e.g., BERT model) is used to analyze the business data provided by the user and carry out the specified tasks.

[1033] AI models use data analysis, natural language processing, and machine learning algorithms to generate optimal results.

[1034] Example of a system

[1035] Automatic generation of presentation materials

[1036] 1. Login Process

[1037] The user launches a browser and accesses the system's login screen, enters login information (ID and password), and sends it to the server.

[1038] The server compares the information stored in the database and, if authentication is successful, displays the dashboard screen on the user's device.

[1039] 2. Provision of business data

[1040] Users select "New Work" from the dashboard and upload the data (e.g., spreadsheet files) needed to create presentation materials.

[1041] The terminal sends the uploaded file to the server, and the server stores it in temporary storage (e.g., Amazon S3).

[1042] 3. AI-powered data processing

[1043] The server retrieves the data from the temporary storage and provides it to the AI ​​means, which analyzes the uploaded data and automatically generates presentation slides.

[1044] For example, you can generate a presentation using the following prompt: "Create a presentation based on the following data: {data content}"

[1045] 4. Output of processing results

[1046] The server retrieves the presentation materials generated by the AI ​​means and automatically uploads them to a designated tool (e.g., Google Slides).

[1047] After a successful upload, the server will notify the user via email or in-app notification that the results are ready.

[1048] 5. Review and feedback of results

[1049] After receiving the notification, the user can check the presentation materials on the platform and make any necessary corrections.

[1050] User feedback is sent via the device to a server, which stores it in a database and uses it as training data for the AI ​​model.

[1051] This system allows users to streamline their work through generative AI models, significantly reducing the time and effort required for their work. In addition, by incorporating feedback into the AI's learning data, it is possible to continue improving the AI's performance.

[1052] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1053] Step 1: The user launches a browser and accesses the system's login screen. The user enters their login information (ID and password).

[1054] Input: ID, password

[1055] Output: Sends login information to the server

[1056] Specific operation: The user opens the system's login page in a browser, enters the ID and password in the designated input fields, and clicks the login button.

[1057] Step 2: The terminal sends the login information entered by the user to the server, which checks the received login information against its database and returns the authentication result.

[1058] Input: Login information (ID, password)

[1059] Output: Authentication result (success / failure)

[1060] Specific operation: The terminal sends login information to the server via the HTTPS protocol. The server verifies the login information against a database (e.g., MySQL), and if authentication is successful, it creates a user session and returns the session ID.

[1061] Step 3: If the authentication is successful, the server creates a user session and returns a session ID to the terminal. The terminal receives a notification of successful authentication and transitions to the dashboard screen.

[1062] Input: Authentication result (success)

[1063] Output: Dashboard screen

[1064] Specific operation: The server saves the session ID in session storage (e.g., Redis) and returns the session ID to the terminal in the HTTP response header. The terminal changes the URL to the dashboard screen.

[1065] Step 4: The user selects a new task (e.g., creating presentation materials) from the operation panel on the dashboard.

[1066] Input: Business selection information

[1067] Output: Business data input screen

[1068] Specific operation: The user selects "New Task" from the dashboard menu and moves to the business data input screen.

[1069] Step 5: The user enters or uploads the required data (e.g., a spreadsheet file or text input). The device sends the business data to the server, which stores it in temporary storage.

[1070] Input: Business data (spreadsheet files, etc.)

[1071] Output: Data saved in temporary storage

[1072] Specific operation: The user selects a spreadsheet file using the file selection dialog and clicks the upload button. The terminal sends the file to the server in the Multipart Form data format, and the server saves it in temporary storage (e.g., Amazon S3).

[1073] Step 6: The server provides the collected business data to the AI ​​means, which analyzes the data and performs the specified tasks.

[1074] Input: Business data stored in temporary storage

[1075] Output: AI analysis results

[1076] Specific operation: The server sends an API request to the AI ​​means to retrieve business data. The AI ​​means analyzes the data and generates presentation materials using prompts.

[1077] Example prompt: "Please create a presentation based on the following data: {data content}"

[1078] Step 7: The server receives the processing results generated by the AI ​​means and automatically outputs them to various tools specified by the user (e.g., Google Slides).

[1079] Input: AI analysis results

[1080] Output: Processing results output to various tools

[1081] Specific operation: The server calls the API, uploads the generated presentation materials to Google Slides, and returns the URL when the upload is complete.

[1082] Step 8: The server notifies the user that the processing results are ready. The user can check the processing results (e.g., presentation materials) on the platform and make any necessary corrections.

[1083] Input: Notification that processing results are ready

[1084] Output: Confirmation screen on the platform

[1085] Specific operation: The server sends an email or in-app notification to the user, providing a link to check the processing results. The user clicks the link to view and edit the presentation materials.

[1086] Step 9: The device sends the user-provided feedback to the server, which stores it in a database and uses it as training data for the AI ​​model.

[1087] Input: Feedback

[1088] Output: Feedback stored in a database

[1089] Specific operation: The user fills in the feedback form and clicks the submit button. The device sends the feedback data to the server, which stores it in a database. The feedback stored in the database will be used the next time the AI ​​model is trained.

[1090] (Application example 1)

[1091] 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."

[1092] Conventional security systems perform primary authentication using only login information, which increases the risk of unauthorized access. While introducing multi-factor authentication can strengthen security, it also increases the operational burden on users. Furthermore, a system is needed to centrally manage user access history and authentication failure history and detect fraudulent activity.

[1093] 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.

[1094] In this invention, the server includes a means for a user to input login information, a server means for authenticating the login information, a means for collecting business data after authentication, an AI means for analyzing the collected business data and performing a specified task, a means for automatically outputting the processing results of the AI ​​means to various tools, a means for notifying the user of the results and receiving feedback, a means for saving the feedback in a database and using it as learning data for an AI model, a means for performing multi-factor authentication, and a means for recording the user's access history and authentication failure history. This makes it possible to prevent unauthorized access through multi-factor authentication, reduce the operational burden on the user, and detect and manage fraudulent activity.

[1095] "Means for users to input login information" refers to means for providing an interface for users to input account information (ID and password).

[1096] "Server means for authenticating login information" refers to the function of the server to check the transmitted login information against a database and confirm whether the user is a legitimate user.

[1097] The "means for collecting business data" refers to the means by which a user inputs or uploads data necessary for performing a business operation after authentication.

[1098] "AI means for analyzing collected business data and carrying out designated tasks" refers to the function of artificial intelligence to automatically analyze and carry out tasks based on collected business data.

[1099] "Means for automatically outputting processing results by AI means to various tools" refers to means for automatically outputting processing results generated by AI means to external tools designated by the user.

[1100] The "means for notifying the user of the results and receiving feedback" refers to a means for informing the user of the processing results and receiving the user's opinions and corrections regarding the results.

[1101] "Means for storing feedback in a database and using it as training data for an AI model" refers to a means for storing received feedback in a database and later using it as training data for an AI model.

[1102] A "means of implementing multi-factor authentication" is a means of strengthening authentication after primary authentication by using additional authentication factors (such as tokens).

[1103] "Means for recording user access history and authentication failure history" refers to a means for centrally recording and managing the history of user access to the system and the history of authentication failures.

[1104] This invention relates to a security system using multi-factor authentication, and provides a server means for authenticating login information entered by a user. After authentication, the system collects business data, analyzes it using AI means, and performs the specified business. It also has a function for automatically outputting the processing results of the AI ​​means to various tools, notifying the user and receiving feedback. Furthermore, this feedback is stored in a database and used as training data for the AI ​​model.

[1105] As an example of the present system, a system used to provide security services using multi-factor authentication for businesses will be described.

[1106] Program Overview

[1107] The server includes the following functions:

[1108] 1. User authentication function: The login information entered by the user is checked against a database to confirm that the user is a legitimate user.

[1109] 2. Multi-factor authentication function: After successful primary authentication, multi-factor authentication is performed using additional authentication factors (such as tokens).

[1110] 3. Business data collection function: Collects business data entered or uploaded by the user after authentication.

[1111] 4. AI data analysis function: Analyzes collected business data and automatically carries out specified tasks.

[1112] 5. Automatic result output function: The generated processing results are automatically output to various tools specified by the user.

[1113] 6. Feedback processing function: Receives user feedback on the processing results, saves the data, and uses it as learning data for the AI ​​model.

[1114] 7. Log management function: Records and manages user access history and authentication failure history.

[1115] Hardware and software used

[1116] The server hardware used is a cloud server such as Amazon EC2.

[1117] Amazon RDS (MySQL, etc.) is used as the database.

[1118] AI models are built using TensorFlow or PyTorch.

[1119] A common multi-factor authentication library is used to generate the authentication token.

[1120] Amazon CloudWatch is used for log management.

[1121] Specific examples of implementation

[1122] Example 1: Multi-factor authentication system for businesses

[1123] This system will be implemented to enhance the security of your company by:

[1124] A user accesses the company's security platform and enters their user ID and password. The information is sent to the server and checked against a database. If the initial authentication is successful, the server sends a multi-factor authentication token to the user's device.

[1125] The user checks the token and enters it in the specified application. The server checks the validity of the token, and if authentication is successful, the dashboard screen is displayed to the user. The user uploads business data, which is analyzed and processed by the AI. The generated processing results are automatically output to various tools and notified to the user. User feedback is stored in a database and used as learning data for the AI ​​model. Access history and failed authentication attempts are recorded as logs and made available for administrators to review.

[1126] Prompt Sentence Examples

[1127] Create a program to authenticate user login information and implement the multi-factor authentication flow. Receive the user ID and password and check them against a database. If the initial authentication is successful, generate a multi-factor authentication token and send it to the user. Once the user enters the token and confirms its validity, final authentication is completed and the user session begins. Also, record log information throughout the authentication flow. Use the Flask framework and MySQL database.

[1128] By implementing this invention, it is possible to realize robust security measures and reduce the burden on users through multi-factor authentication, thereby significantly improving the security of corporate systems.

[1129] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1130] Step 1:

[1131] User enters login information

[1132] A user accesses the system's login screen and enters their user ID and password. The input information is sent to the server by the terminal. The input is in the form of keystrokes, and the output is a string of login information.

[1133] Step 2:

[1134] Server authentication of login information

[1135] The server checks the received user ID and password against the database to verify that the user is a valid user. It searches for matching records using a database query and retrieves the results. The input is login information and the output is the authentication result (success or failure). If authentication is successful, it proceeds to the next step.

[1136] Step 3:

[1137] Generate and send multi-factor authentication tokens

[1138] If authentication is successful, the server generates a multi-factor authentication token and sends it to the user's registered device (e.g., smartphone). A token generation algorithm is used to create a temporary authentication code and transmit it to the user device. The input is the user ID, and the output is the generated token.

[1139] Step 4:

[1140] User confirms and enters token

[1141] The user receives the token on their smartphone or authentication application, then returns to the system to enter the token. The user manually enters the token and sends it back to the server. The input is the token, and the output is the token string.

[1142] Step 5:

[1143] Token validation by the server

[1144] The server checks the token received from the user in its database to verify that it is a valid authentication token. It checks whether it matches the token record in the database and obtains the result. The input is the token and the output is the verification result (success or failure). If the token is correct, it proceeds to the next step.

[1145] Step 6:

[1146] Entering and uploading business data

[1147] After authentication is complete, the user inputs or uploads business data (e.g., spreadsheets or document files) into the system. The data uploaded by the user is sent to the server and stored in temporary storage. The input is the business data, and the output is the reference ID of the stored data.

[1148] Step 7:

[1149] Analysis of business data by the server

[1150] The server provides the stored business data to the AI ​​means to analyze the data. The AI ​​model (e.g., TensorFlow or PyTorch) is used to analyze the data and perform the specified task (e.g., report generation or data analysis). The input is the reference ID of the business data, and the output is the processing result.

[1151] Step 8:

[1152] Automatic output of processing results to tools

[1153] The processing results generated by the AI ​​tool are automatically output to an external tool specified by the user (e.g., Google Slides or Microsoft PowerPoint). The API is called to send the result data to the tool. The input is the processing result, and the output is a confirmation message from the tool.

[1154] Step 9:

[1155] Notification of processing results and receiving feedback

[1156] The user receives a notification of the processing result on the system and checks the result. If necessary, the user can input feedback and send it to the system. The feedback is saved on the server. The input is the feedback, and the output is the saved feedback data.

[1157] Step 10:

[1158] Storing feedback and using data to train AI models

[1159] The server stores the feedback in a database and uses it as training data for the AI ​​model. The stored feedback is provided to the AI ​​model and added as new training data. The input is the feedback data, and the output is an updated AI model.

[1160] Step 11:

[1161] Recording and management of access history and authentication failure history

[1162] The server centrally records user access history and authentication failure history, allowing administrators to check them. The log data is sent to a cloud log management system (e.g., Amazon CloudWatch). The input is access history data, and the output is the recorded log data.

[1163] 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.

[1164] Overall system overview

[1165] The system allows users to access and input business data, and the AI ​​processes the data to carry out the specified tasks, automatically outputting the results to various tools. The system also has an emotion engine that recognizes the user's emotions, and changes the content presented on the interface based on the emotional data, and has the ability to use this as feedback for learning data.

[1166] Program processing flow

[1167] 1. Enter and authenticate your login information

[1168] The user accesses the platform's login screen and enters their account information (ID and password).

[1169] The terminal sends the user's login information to the server.

[1170] The server authenticates the login information against a database, and if authentication is successful, the user session begins.

[1171] 2. Collection of business data

[1172] Users select a new task (e.g., creating presentation materials) from the dashboard and enter or upload the required data.

[1173] The terminal sends business data to the server, and the server stores the data in storage.

[1174] 3. Emotion Recognition by Emotion Engine

[1175] The server analyzes the user's operation data and input text data on the dashboard in real time and uses an emotion engine to recognize the user's emotions.

[1176] The interface content is automatically adjusted based on the recognized emotion.

[1177] 4. AI-powered data processing

[1178] The server provides the collected business data to the AI ​​means and carries out the specified tasks.

[1179] AI analyzes the data and generates the final processed results (e.g., presentation materials).

[1180] 5. Automatic output of processing results

[1181] The server receives the processing results returned by the AI ​​and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[1182] The generated processing results are automatically uploaded to the following tools.

[1183] 6. Notification of results and feedback

[1184] The server notifies the user when the results are ready.

[1185] Users can check the results on the platform, make any necessary corrections, and provide feedback.

[1186] The terminal sends the feedback to the server.

[1187] The server stores the feedback and emotional data in a database and uses it as learning data for the AI ​​and emotion engine.

[1188] Specific examples

[1189] Creating presentation materials and emotional feedback

[1190] 1. Login Process

[1191] A user logs into the system and accesses the dashboard. The server authenticates the login information.

[1192] 2. Provision of business data

[1193] A user uploads data (e.g., a spreadsheet file) needed to create a presentation for the next meeting. The data is temporarily stored in the server's storage.

[1194] 3. Emotion recognition

[1195] The server analyzes the user's input data and operation data in real time, recognizes the user's emotions using an emotion engine, and adjusts the user's interface experience according to the emotion data.

[1196] 4. AI Processing

[1197] The server passes the collected data to the AI, which then begins analyzing it. The AI ​​then automatically generates presentation slides based on the data, inserting explanatory text and graphs.

[1198] 5. Output

[1199] The server calls the API to upload the generated presentation to Google Slides. After the upload is successfully completed, the server notifies the user.

[1200] 6. Review and Feedback

[1201] The user checks the presentation materials on the platform and makes any necessary corrections. The corrections and feedback are then sent to the server along with the emotion data.

[1202] The server stores the feedback and emotion data in a database and uses it as learning data for the AI ​​and emotion engine.

[1203] This system enables efficient use of generative AI and emotion engines, significantly reducing the amount of work required and time required while improving the user experience.

[1204] The processing flow will be explained below.

[1205] Step 1:

[1206] The user accesses the platform's login screen and enters their account information (ID and password).

[1207] Step 2:

[1208] The terminal sends the user's login information to the server.

[1209] Step 3:

[1210] The server checks the received login information against the database to verify whether the user is a valid user, and if authentication is successful, starts a user session.

[1211] Step 4:

[1212] The server sends the authenticated dashboard screen to the terminal, allowing the user to access this dashboard.

[1213] Step 5:

[1214] The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required task data.

[1215] Step 6:

[1216] The terminal transmits the business data entered by the user to the server.

[1217] Step 7:

[1218] The server stores the received business data in temporary storage and registers the metadata in the database.

[1219] Step 8:

[1220] The server monitors the user's operation data and input text data on the dashboard in real time and inputs it into the emotion engine.

[1221] Step 9:

[1222] The emotion engine analyzes user operation data and text data to recognize the user's emotions.

[1223] Step 10:

[1224] The server receives the emotion data from the emotion engine and automatically adjusts the interface presentation according to the user's mood. For example, if the user is irritated, the system provides a simple and easy-to-understand interface.

[1225] Step 11:

[1226] The business data collected by the server is provided to AI means.

[1227] Step 12:

[1228] AI analyzes business data and performs specified tasks, such as generating slides based on the data to create presentation materials and adding relevant content.

[1229] Step 13:

[1230] The AI ​​returns the analysis results and generated deliverables (e.g., presentation materials) to the server.

[1231] Step 14:

[1232] The server receives the processing results from the AI ​​tool and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[1233] Step 15:

[1234] The server uploads the processing results to various tools, and once the upload is completed successfully, it notifies the user that the results are ready.

[1235] Step 16:

[1236] The user checks the processing results on the platform, and if the user wants to make any corrections, they input the details.

[1237] Step 17:

[1238] The terminal sends the user's modifications and feedback to the server.

[1239] Step 18:

[1240] The server stores the feedback and emotional data in a database and uses it as learning data for the AI ​​and emotion engine.

[1241] Example 2

[1242] 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."

[1243] Conventional business systems require users to manually collect and analyze business data, which is time-consuming, and outputting results and processing feedback takes time. Furthermore, because they do not take into account user emotions or stress, there are still issues with the user experience. Furthermore, updates to the AI ​​model's learning data are delayed, often failing to reflect the latest data.

[1244] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1245] In this invention, the server includes emotion engine means for recognizing user emotions, means for dynamically adjusting the interface based on the emotion data, and means for automatically outputting the processing results of the AI ​​means to various tools. This not only automates the collection and analysis of user business data, but also enables dynamic interface adjustment that takes user emotions into consideration, thereby improving the user experience.

[1246] A "user" is an individual who accesses the system, enters login information, provides business data, and checks and provides feedback on the results.

[1247] "Login Information" refers to the account ID and password used by a User to access the System.

[1248] A "server" is a computer system that performs a series of processes, including authenticating login information sent by a user, collecting and analyzing business data, and outputting the final processing results to various tools.

[1249] "Business data" refers to data related to a business provided by a user, and examples include spreadsheet files, text data, image files, and the like.

[1250] "AI means" refers to artificial intelligence technology that analyzes collected business data and carries out specified tasks.

[1251] "Processing Results" means the final output generated by the AI ​​Means, including presentation materials and other business-related materials submitted to a User.

[1252] "Various tools" refers to external systems or software for outputting processing results, such as Google Slides and Microsoft PowerPoint.

[1253] The "emotion engine means" is a technology for analyzing the user's operation data and input data and recognizing the user's emotions.

[1254] "Emotion data" is data that indicates the emotional state of the user analyzed by the emotion engine means.

[1255] "Feedback" refers to opinions and correction requests provided by users regarding the processing results generated by the system, and is used as learning data for the system.

[1256] This invention is a system in which users access the system, input business data, and AI processes the data to perform the specified tasks, automatically outputting the processing results to various tools. This system operates in cooperation with three entities: a server, a terminal, and a user.

[1257] server

[1258] The server authenticates the login information entered by the user and collects and stores business data. It also includes an emotion engine that analyzes user emotion data in real time. It also provides the collected business data to an AI model, which then automatically outputs the processed results to various tools (e.g., Google Slides, Microsoft PowerPoint).

[1259] Terminal

[1260] The terminal provides an interface that supports the user's data input and operation. The terminal also transmits the login information and business data entered by the user to the server. Furthermore, feedback information is also transmitted from the terminal to the server.

[1261] User

[1262] Users log in to the system, input or upload business data, and check and correct the generated processing results. Users also provide feedback to the system on the generated results, which is used as learning data for the AI ​​model.

[1263] Specific hardware and software

[1264] The servers are equipped with high-performance CPUs and GPUs to rapidly process and analyze data. Relational databases such as MySQL and PostgreSQL are used as databases. The emotion engine uses natural language processing technology, and generative AI models such as GPT-4 are used as AI models. A browser and dedicated applications are installed on the terminals, which function as the user interface.

[1265] Specific examples

[1266] For example, consider a user creating a presentation for an upcoming meeting. First, the user logs in to the system and accesses the dashboard. Next, they upload the necessary business data (e.g., a spreadsheet file). This data is sent to the server, which uses an emotion engine to analyze the user's emotions and adjust the interface. The server then passes the collected data to the AI, which generates presentation slides in the specified format. The generated presentation slides are automatically uploaded to Google Slides or Microsoft PowerPoint. Finally, the user reviews the slides on the platform and provides feedback. This feedback is stored on the server and used as future learning data.

[1267] Prompt Sentence Examples

[1268] The following prompts can be used:

[1269] "Create a presentation for my next meeting. Generate slides based on this data."

[1270] In this way, the present invention provides a system that significantly reduces the workload while improving the user experience. The collaboration between the generative AI model and the emotion engine enables tasks to be performed with extremely high efficiency and accuracy.

[1271] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1272] Step 1:

[1273] Enter and authenticate your login information

[1274] Input: The user uses a browser or a dedicated application to enter their account ID and password on the login screen.

[1275] Specific behavior:

[1276] The user accesses the system's login screen and enters their account ID and password.

[1277] The device encrypts the login information entered by the user and sends it to the server via a secure communication protocol such as HTTPS.

[1278] Data processing:

[1279] The server receives the submitted login information and checks it against the user information in its database.

[1280] Output: If authentication is successful, start a user session and redirect the user to the dashboard.

[1281] Step 2:

[1282] Business data collection

[1283] Input: The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required data.

[1284] Specific behavior:

[1285] The user selects the "Create Presentation" option.

[1286] The user uploads the necessary business data, such as a spreadsheet file or text file.

[1287] Data processing:

[1288] The terminal sends the uploaded file to the server.

[1289] Output: Business data is sent to the server and stored in the server's storage.

[1290] Step 3:

[1291] Emotion recognition by emotion engine

[1292] Input: User operation data and input data

[1293] Specific behavior:

[1294] The server analyzes the user's operation data and input text data in real time.

[1295] The server uses an emotion engine to recognize the user's emotions (e.g., joy, anger, sadness).

[1296] Data Calculation:

[1297] The emotion engine uses natural language processing techniques to analyze the emotional tone of the input data.

[1298] Output: Dynamically adjust the content displayed in the user interface based on the recognized emotion data.

[1299] Step 4:

[1300] AI-powered data processing

[1301] Input: User-provided business data

[1302] Specific behavior:

[1303] The server generates prompts to input the collected business data into a generative AI model (e.g., GPT-4). For example, it generates a prompt like, "Please create a presentation for the next meeting. Please generate slides based on this data."

[1304] The AI ​​analyzes business data based on the generated prompt text and automatically generates presentation slides.

[1305] Data Calculation:

[1306] The AI ​​analyzes the input business data and generates presentation materials in the specified format.

[1307] Output: Generated presentation materials (slides, explanatory text, graphs, etc.)

[1308] Step 5:

[1309] Automatic output of processing results

[1310] Input: AI-generated processing results (presentation materials)

[1311] Specific behavior:

[1312] The server receives the processing results returned by the AI ​​and calls an API to upload them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[1313] Data processing:

[1314] The server makes an API call to upload the generated file to an external service.

[1315] Output: A confirmation message and a URL indicating a successful upload.

[1316] Step 6:

[1317] Notification of results and feedback

[1318] Input: Server-generated confirmation message and URL

[1319] Specific behavior:

[1320] The server notifies the user when the processing results are ready (e.g. email notification, dashboard notification).

[1321] The user can check the processing results on the platform.

[1322] The user makes corrections as needed and provides feedback.

[1323] Data processing:

[1324] The terminal transmits the feedback information to the server.

[1325] Output: Feedback and emotion data are stored on the server.

[1326] Through these processing steps, a system is realized that can efficiently collect business data, analyze it using AI, output the generated results, and collect feedback.

[1327] (Application example 2)

[1328] 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."

[1329] Conventional systems require users to spend a great deal of time and effort when creating content. Furthermore, they perform tasks without considering the user's feelings, making it difficult to improve the user experience. Furthermore, there is a need to output the generated content to various tools quickly and efficiently.

[1330] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1331] In this invention, the server includes means for analyzing user operation data and input data and recognizing the user's emotions using an emotion engine, means for automatically adjusting the interface based on the recognized emotions, and AI means for analyzing collected business data and performing specified tasks, thereby making it possible to provide an interface that reflects the user's emotions and automate tasks.

[1332] "Login Information" means the authentication information a user enters to access a system.

[1333] "Server means" refers to a central processing unit that authenticates login information and collects, stores, and analyzes business data.

[1334] "Business data" refers to data that a user inputs or uploads in order to perform a specific business.

[1335] "AI means" is an artificial intelligence engine that analyzes collected business data and carries out specified tasks.

[1336] "Various tools" are external applications or platforms for outputting or using the generated processing results.

[1337] "Feedback" refers to the evaluation and correction instructions provided by the user regarding the processing results of the system.

[1338] A "database" is a digital storage device for storing information such as business data, processing results, and feedback.

[1339] An "emotion engine" is a software engine that analyzes user operation data and input data and recognizes the user's emotions.

[1340] An "interface" is a display screen or operating means that allows a user to interact with a system.

[1341] An "API calling means" is a function realized by a program for exchanging data and services between different software components.

[1342] The system for implementing the present invention includes a server that authenticates login information entered by the user, collects business data, recognizes the user's emotions using an emotion engine, adjusts the interface based on the recognition results, and performs business operations using AI means.

[1343] The system's hardware uses terminal devices such as smartphones and head-mounted displays. The software includes an emotion engine, an AI content generation module, and a data processing module. The emotion engine is software that analyzes user operation data and input data in real time and recognizes emotions. The AI ​​content generation module is software that performs specified tasks (such as automatically generating content) based on collected business data.

[1344] The main steps of this system are for the user to log in to the system and upload the necessary business data. The server collects this data and analyzes the user's emotions using an emotion engine. The interface is automatically adjusted based on the analysis results. The collected business data is then passed to the AI ​​means to carry out the task. The generated processing results are automatically output to a specified tool and notified to the user. The user checks the processing results and provides feedback. The feedback is stored in a database and used as learning data for future AI and emotion engines.

[1345] As a concrete example, suppose a user uploads a video clip and a text script to create their next video content. This data is sent to the server, where the emotion engine recognizes the user's emotions and adjusts the software accordingly. The AI ​​content generation module then uses this data to automatically edit the video and upload it to the specified platform.

[1346] An example of a prompt is the following text:

[1347] "User ID: example_user"

[1348] Password: example_password

[1349] "Content data: example_video_clip.mp4"

[1350] "Platform Name: YouTube"

[1351] This enables the provision of an interface that takes user emotions into consideration and the automation of tasks, improving the user experience and work efficiency.

[1352] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1353] Step 1:

[1354] The user enters login information. The user accesses the system's login screen and enters their ID and password. The terminal sends the entered login information to the server.

[1355] Step 2:

[1356] The server authenticates the login information. It checks the login information against a database and starts the user session if authentication is successful. If authentication is unsuccessful, the server prompts the user for login information again.

[1357] Step 3:

[1358] The user collects task data. The user then selects the task to be performed (e.g., video content creation) and uploads the required data (e.g., video clips, text scripts). The device then transmits this data to the server.

[1359] Step 4:

[1360] The server uses an emotion engine to recognize emotions. The server analyzes user operation data and input data in real time and uses the emotion engine to recognize the user's emotions. The interface is automatically adjusted based on the recognition results.

[1361] Step 5:

[1362] The server provides business data to the AI ​​means. The collected business data is passed to the AI ​​content generation module. The AI ​​analyzes this data and generates optimal content (e.g., automatically edited videos).

[1363] Step 6:

[1364] The server automatically outputs the processing results to various tools. The generated processing results are uploaded to the specified platform (e.g. YouTube, Spotify) by calling an API to output them. This automatically publishes the generated content to the platform.

[1365] Step 7:

[1366] The server notifies the user of the results. When the upload of the processing results is complete, the server sends a notification to the user. The user receives this notification and checks the processing results.

[1367] Step 8:

[1368] The user provides feedback. The user checks the processing results on the platform, makes necessary corrections, and provides feedback. The terminal sends the feedback to the server.

[1369] Step 9:

[1370] The server stores the feedback in a database, which will be used as learning data for future AI and emotion engines.

[1371] 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.

[1372] 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.

[1373] 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.

[1374] [Fourth embodiment]

[1375] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1376] 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.

[1377] 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).

[1378] 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.

[1379] 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.

[1380] 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).

[1381] 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.

[1382] 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.

[1383] 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.

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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."

[1388] This invention relates to a system that allows a user to input login information and a server to provide a means for authenticating the login information. Below, we will create a program for this system and explain its processing in natural language.

[1389] Overall system overview

[1390] This system allows users to access and input business data, and the AI ​​processes the data to carry out the specified tasks, automatically outputting the results to various tools. The system also has the ability to receive user feedback and use it as learning data for the AI.

[1391] Program processing flow

[1392] 1. Enter and authenticate your login information

[1393] The user accesses the platform's login screen and enters their account information (ID and password).

[1394] The terminal transmits the login information entered by the user to the server.

[1395] The server checks the received login information against a database to verify whether the user is a valid user. If authentication is successful, the server starts a user session and sends the dashboard screen to the terminal.

[1396] 2. Collection of business data

[1397] The user selects a new task (e.g., creating a presentation) from the dashboard and enters or uploads the required data.

[1398] The terminal sends business data to the server, and the server stores the data in temporary storage.

[1399] 3. AI-powered data processing

[1400] The server provides the collected business data to the AI ​​tool.

[1401] AI analyzes the data it receives and performs the specified tasks. For example, when creating presentation materials, it generates slides based on the data and arranges the content.

[1402] 4. Automatic output of processing results

[1403] The server receives the processing results generated by the AI ​​means and automatically outputs them to various tools specified by the user (e.g., Google Slides or Microsoft PowerPoint).

[1404] After the output is successful, the server notifies the user that the processing results are ready.

[1405] 5. Review and feedback of results

[1406] The user checks the processing results (e.g., presentation materials) on the platform and makes corrections as necessary.

[1407] The terminal transmits the feedback provided by the user to the server.

[1408] The server stores the feedback in a database and uses it as learning data for the AI.

[1409] Specific examples

[1410] Creating presentation materials

[1411] 1. Login Process

[1412] A user logs in to the system and accesses the dashboard. The login information (ID and password) is authenticated by the server.

[1413] 2. Provision of business data

[1414] A user uploads data (e.g., a spreadsheet file) needed to create presentation materials for an upcoming meeting. The data is temporarily stored in the server's storage.

[1415] 3. AI-powered data processing

[1416] The server passes the collected data to the AI, which then begins analyzing it. The AI ​​then automatically generates presentation slides based on the data, inserting explanatory text and graphs.

[1417] 4. Automatic output of processing results

[1418] The server calls the API to upload the generated presentation to Google Slides. After the upload is successfully completed, the server notifies the user.

[1419] 5. Review and Feedback

[1420] The user checks the presentation materials on the platform and makes any necessary corrections. The corrections and feedback are sent to the server and stored in the database.

[1421] This system enables efficient use of generative AI, significantly reducing the effort and time required for work.

[1422] The processing flow will be explained below.

[1423] Step 1:

[1424] The user accesses the platform's login screen and enters their account information (ID and password).

[1425] Step 2:

[1426] The terminal sends the user's login information to the server.

[1427] Step 3:

[1428] The server checks the received login information against the database to verify whether the user is a valid user, and if authentication is successful, starts a user session.

[1429] Step 4:

[1430] The server sends the authenticated dashboard screen to the terminal, allowing the user to access this dashboard.

[1431] Step 5:

[1432] The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required task data.

[1433] Step 6:

[1434] The terminal transmits the business data entered by the user to the server.

[1435] Step 7:

[1436] The server stores the received business data in temporary storage and registers the metadata in the database.

[1437] Step 8:

[1438] The server collects the necessary business data from the database and provides it to the appropriate AI model.

[1439] Step 9:

[1440] AI analyzes business data and performs specified tasks, such as generating slides based on the data to create presentation materials and adding relevant content.

[1441] Step 10:

[1442] The AI ​​returns the analysis results and generated deliverables (e.g., presentation materials) to the server.

[1443] Step 11:

[1444] The server receives the processing results from the AI ​​tool and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[1445] Step 12:

[1446] The server uploads the processing results to various tools and notifies the user when the results are ready.

[1447] Step 13:

[1448] Users can check the processing results on the platform and make any necessary corrections.

[1449] Step 14:

[1450] The terminal sends the user's modifications and feedback to the server.

[1451] Step 15:

[1452] The server stores the feedback and correction information in a database and uses it as training data for the AI ​​model.

[1453] Example 1

[1454] 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."

[1455] In conventional business systems, the process of collecting business data after a user logs in, processing it using AI, and outputting the results to various tools is often done manually, which poses the issue of taking time and effort. In addition, there is no mechanism in place to use feedback as learning data for the AI, making it difficult to improve the performance of the AI. Furthermore, since integration with various tools is not automated, business efficiency can sometimes decrease.

[1456] 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.

[1457] In this invention, the server includes a terminal to which a user uploads business data, a means for the server to temporarily store the business data, and a means for the server to provide processing results to the AI ​​means and output the generated processing results to a specified tool. This makes it possible to automate the process from inputting business data to analyzing it with AI, outputting it to various tools, and collecting feedback.

[1458] "User" refers to any individual or corporation that uses this system.

[1459] "Login information" refers to authentication information for a user to access a system, and is usually composed of an ID and password.

[1460] "Server" refers to a computer system that receives and processes data sent by users.

[1461] "Business Data" refers to data that a user inputs or uploads to the system in order to perform a specific business.

[1462] "AI means" refers to artificial intelligence technology that analyzes collected business data and carries out specified tasks.

[1463] "Processing results" refers to the deliverables generated by AI means by analyzing business data.

[1464] "Various tools" refers to external applications and services used to output processing results.

[1465] "Notification" refers to the action of the server informing the user that the processing results are ready.

[1466] "Feedback" refers to evaluations and information on improvements provided by users regarding processing results.

[1467] "Database" means the data management system for storing Feedback and other data.

[1468] "Terminal" refers to a device through which a User accesses the System and inputs or uploads data.

[1469] "Temporary storage" refers to a storage area where a server temporarily stores business data.

[1470] "Automation" refers to a system in which a series of processes are carried out without manual operation.

[1471] "API" refers to a program interface that enables integration with external applications and services.

[1472] This invention relates to a system in which a user inputs login information and a server authenticates the login information. Furthermore, the system has a mechanism in which the user inputs business data, the AI ​​analyzes the data, performs the specified business, and automatically outputs the processing results to various tools. The system also has a function to receive user feedback and use it as learning data for the AI ​​model.

[1473] Hardware and Software Configuration

[1474] 1. User end devices (terminals)

[1475] The terminals used by users are computing devices such as PCs and mobile devices.

[1476] A web browser (e.g., Google Chrome) is installed on the terminal, and is used by the user to access the system.

[1477] 2. Server

[1478] A server is a powerful computing system that receives and processes data from users.

[1479] Software used includes database management systems (e.g., MySQL) and temporary storage systems (e.g., Amazon S3).

[1480] 3. AI means

[1481] As an AI tool, a generative AI model (e.g., BERT model) is used to analyze the business data provided by the user and carry out the specified tasks.

[1482] AI models use data analysis, natural language processing, and machine learning algorithms to generate optimal results.

[1483] Example of a system

[1484] Automatic generation of presentation materials

[1485] 1. Login Process

[1486] The user launches a browser and accesses the system's login screen, enters login information (ID and password), and sends it to the server.

[1487] The server compares the information stored in the database and, if authentication is successful, displays the dashboard screen on the user's device.

[1488] 2. Provision of business data

[1489] Users select "New Work" from the dashboard and upload the data (e.g., spreadsheet files) needed to create presentation materials.

[1490] The terminal sends the uploaded file to the server, and the server stores it in temporary storage (e.g., Amazon S3).

[1491] 3. AI-powered data processing

[1492] The server retrieves the data from the temporary storage and provides it to the AI ​​means, which analyzes the uploaded data and automatically generates presentation slides.

[1493] For example, you can generate a presentation using the following prompt: "Create a presentation based on the following data: {data content}"

[1494] 4. Output of processing results

[1495] The server retrieves the presentation materials generated by the AI ​​means and automatically uploads them to a designated tool (e.g., Google Slides).

[1496] After a successful upload, the server will notify the user via email or in-app notification that the results are ready.

[1497] 5. Review and feedback of results

[1498] After receiving the notification, the user can check the presentation materials on the platform and make any necessary corrections.

[1499] User feedback is sent via the device to a server, which stores it in a database and uses it as training data for the AI ​​model.

[1500] This system allows users to streamline their work through generative AI models, significantly reducing the time and effort required for their work. In addition, by incorporating feedback into the AI's learning data, it is possible to continue improving the AI's performance.

[1501] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1502] Step 1: The user launches a browser and accesses the system's login screen. The user enters their login information (ID and password).

[1503] Input: ID, password

[1504] Output: Sends login information to the server

[1505] Specific operation: The user opens the system's login page in a browser, enters the ID and password in the designated input fields, and clicks the login button.

[1506] Step 2: The terminal sends the login information entered by the user to the server, which checks the received login information against its database and returns the authentication result.

[1507] Input: Login information (ID, password)

[1508] Output: Authentication result (success / failure)

[1509] Specific operation: The terminal sends login information to the server via the HTTPS protocol. The server verifies the login information against a database (e.g., MySQL), and if authentication is successful, it creates a user session and returns the session ID.

[1510] Step 3: If the authentication is successful, the server creates a user session and returns a session ID to the terminal. The terminal receives a notification of successful authentication and transitions to the dashboard screen.

[1511] Input: Authentication result (success)

[1512] Output: Dashboard screen

[1513] Specific operation: The server saves the session ID in session storage (e.g., Redis) and returns the session ID to the terminal in the HTTP response header. The terminal changes the URL to the dashboard screen.

[1514] Step 4: The user selects a new task (e.g., creating presentation materials) from the operation panel on the dashboard.

[1515] Input: Business selection information

[1516] Output: Business data input screen

[1517] Specific operation: The user selects "New Task" from the dashboard menu and moves to the business data input screen.

[1518] Step 5: The user enters or uploads the required data (e.g., a spreadsheet file or text input). The device sends the business data to the server, which stores it in temporary storage.

[1519] Input: Business data (spreadsheet files, etc.)

[1520] Output: Data saved in temporary storage

[1521] Specific operation: The user selects a spreadsheet file using the file selection dialog and clicks the upload button. The terminal sends the file to the server in the Multipart Form data format, and the server saves it in temporary storage (e.g., Amazon S3).

[1522] Step 6: The server provides the collected business data to the AI ​​means, which analyzes the data and performs the specified tasks.

[1523] Input: Business data stored in temporary storage

[1524] Output: AI analysis results

[1525] Specific operation: The server sends an API request to the AI ​​means to retrieve business data. The AI ​​means analyzes the data and generates presentation materials using prompts.

[1526] Example prompt: "Please create a presentation based on the following data: {data content}"

[1527] Step 7: The server receives the processing results generated by the AI ​​means and automatically outputs them to various tools specified by the user (e.g., Google Slides).

[1528] Input: AI analysis results

[1529] Output: Processing results output to various tools

[1530] Specific operation: The server calls the API, uploads the generated presentation materials to Google Slides, and returns the URL when the upload is complete.

[1531] Step 8: The server notifies the user that the processing results are ready. The user can check the processing results (e.g., presentation materials) on the platform and make any necessary corrections.

[1532] Input: Notification that processing results are ready

[1533] Output: Confirmation screen on the platform

[1534] Specific operation: The server sends an email or in-app notification to the user, providing a link to check the processing results. The user clicks the link to view and edit the presentation materials.

[1535] Step 9: The device sends the user-provided feedback to the server, which stores it in a database and uses it as training data for the AI ​​model.

[1536] Input: Feedback

[1537] Output: Feedback stored in a database

[1538] Specific operation: The user fills in the feedback form and clicks the submit button. The device sends the feedback data to the server, which stores it in a database. The feedback stored in the database will be used the next time the AI ​​model is trained.

[1539] (Application example 1)

[1540] 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."

[1541] Conventional security systems perform primary authentication using only login information, which increases the risk of unauthorized access. While introducing multi-factor authentication can strengthen security, it also increases the operational burden on users. Furthermore, a system is needed to centrally manage user access history and authentication failure history and detect fraudulent activity.

[1542] 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.

[1543] In this invention, the server includes a means for a user to input login information, a server means for authenticating the login information, a means for collecting business data after authentication, an AI means for analyzing the collected business data and performing a specified task, a means for automatically outputting the processing results of the AI ​​means to various tools, a means for notifying the user of the results and receiving feedback, a means for saving the feedback in a database and using it as learning data for an AI model, a means for performing multi-factor authentication, and a means for recording the user's access history and authentication failure history. This makes it possible to prevent unauthorized access through multi-factor authentication, reduce the operational burden on the user, and detect and manage fraudulent activity.

[1544] "Means for users to input login information" refers to means for providing an interface for users to input account information (ID and password).

[1545] "Server means for authenticating login information" refers to the function of the server to check the transmitted login information against a database and confirm whether the user is a legitimate user.

[1546] The "means for collecting business data" refers to the means by which a user inputs or uploads data necessary for performing a business operation after authentication.

[1547] "AI means for analyzing collected business data and carrying out designated tasks" refers to the function of artificial intelligence to automatically analyze and carry out tasks based on collected business data.

[1548] "Means for automatically outputting processing results by AI means to various tools" refers to means for automatically outputting processing results generated by AI means to external tools designated by the user.

[1549] The "means for notifying the user of the results and receiving feedback" refers to a means for informing the user of the processing results and receiving the user's opinions and corrections regarding the results.

[1550] "Means for storing feedback in a database and using it as training data for an AI model" refers to a means for storing received feedback in a database and later using it as training data for an AI model.

[1551] A "means of implementing multi-factor authentication" is a means of strengthening authentication after primary authentication by using additional authentication factors (such as tokens).

[1552] "Means for recording user access history and authentication failure history" refers to a means for centrally recording and managing the history of user access to the system and the history of authentication failures.

[1553] This invention relates to a security system using multi-factor authentication, and provides a server means for authenticating login information entered by a user. After authentication, the system collects business data, analyzes it using AI means, and performs the specified business. It also has a function for automatically outputting the processing results of the AI ​​means to various tools, notifying the user and receiving feedback. Furthermore, this feedback is stored in a database and used as training data for the AI ​​model.

[1554] As an example of the present system, a system used to provide security services using multi-factor authentication for businesses will be described.

[1555] Program Overview

[1556] The server includes the following functions:

[1557] 1. User authentication function: The login information entered by the user is checked against a database to confirm that the user is a legitimate user.

[1558] 2. Multi-factor authentication function: After successful primary authentication, multi-factor authentication is performed using additional authentication factors (such as tokens).

[1559] 3. Business data collection function: Collects business data entered or uploaded by the user after authentication.

[1560] 4. AI data analysis function: Analyzes collected business data and automatically carries out specified tasks.

[1561] 5. Automatic result output function: The generated processing results are automatically output to various tools specified by the user.

[1562] 6. Feedback processing function: Receives user feedback on the processing results, saves the data, and uses it as learning data for the AI ​​model.

[1563] 7. Log management function: Records and manages user access history and authentication failure history.

[1564] Hardware and software used

[1565] The server hardware used is a cloud server such as Amazon EC2.

[1566] Amazon RDS (MySQL, etc.) is used as the database.

[1567] AI models are built using TensorFlow or PyTorch.

[1568] A common multi-factor authentication library is used to generate the authentication token.

[1569] Amazon CloudWatch is used for log management.

[1570] Specific examples of implementation

[1571] Example 1: Multi-factor authentication system for businesses

[1572] This system will be implemented to enhance the security of your company by:

[1573] A user accesses the company's security platform and enters their user ID and password. The information is sent to the server and checked against a database. If the initial authentication is successful, the server sends a multi-factor authentication token to the user's device.

[1574] The user checks the token and enters it in the specified application. The server checks the validity of the token, and if authentication is successful, the dashboard screen is displayed to the user. The user uploads business data, which is analyzed and processed by the AI. The generated processing results are automatically output to various tools and notified to the user. User feedback is stored in a database and used as learning data for the AI ​​model. Access history and failed authentication attempts are recorded as logs and made available for administrators to review.

[1575] Prompt Sentence Examples

[1576] Create a program to authenticate user login information and implement the multi-factor authentication flow. Receive the user ID and password and check them against a database. If the initial authentication is successful, generate a multi-factor authentication token and send it to the user. Once the user enters the token and confirms its validity, final authentication is completed and the user session begins. Also, record log information throughout the authentication flow. Use the Flask framework and MySQL database.

[1577] By implementing this invention, it is possible to realize robust security measures and reduce the burden on users through multi-factor authentication, thereby significantly improving the security of corporate systems.

[1578] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1579] Step 1:

[1580] User enters login information

[1581] A user accesses the system's login screen and enters their user ID and password. The input information is sent to the server by the terminal. The input is in the form of keystrokes, and the output is a string of login information.

[1582] Step 2:

[1583] Server authentication of login information

[1584] The server checks the received user ID and password against the database to verify that the user is a valid user. It searches for matching records using a database query and retrieves the results. The input is login information and the output is the authentication result (success or failure). If authentication is successful, it proceeds to the next step.

[1585] Step 3:

[1586] Generate and send multi-factor authentication tokens

[1587] If authentication is successful, the server generates a multi-factor authentication token and sends it to the user's registered device (e.g., smartphone). A token generation algorithm is used to create a temporary authentication code and transmit it to the user device. The input is the user ID, and the output is the generated token.

[1588] Step 4:

[1589] User confirms and enters token

[1590] The user receives the token on their smartphone or authentication application, then returns to the system to enter the token. The user manually enters the token and sends it back to the server. The input is the token, and the output is the token string.

[1591] Step 5:

[1592] Token validation by the server

[1593] The server checks the token received from the user in its database to verify that it is a valid authentication token. It checks whether it matches the token record in the database and obtains the result. The input is the token and the output is the verification result (success or failure). If the token is correct, it proceeds to the next step.

[1594] Step 6:

[1595] Entering and uploading business data

[1596] After authentication is complete, the user inputs or uploads business data (e.g., spreadsheets or document files) into the system. The data uploaded by the user is sent to the server and stored in temporary storage. The input is the business data, and the output is the reference ID of the stored data.

[1597] Step 7:

[1598] Analysis of business data by the server

[1599] The server provides the stored business data to the AI ​​means to analyze the data. The AI ​​model (e.g., TensorFlow or PyTorch) is used to analyze the data and perform the specified task (e.g., report generation or data analysis). The input is the reference ID of the business data, and the output is the processing result.

[1600] Step 8:

[1601] Automatic output of processing results to tools

[1602] The processing results generated by the AI ​​tool are automatically output to an external tool specified by the user (e.g., Google Slides or Microsoft PowerPoint). The API is called to send the result data to the tool. The input is the processing result, and the output is a confirmation message from the tool.

[1603] Step 9:

[1604] Notification of processing results and receiving feedback

[1605] The user receives a notification of the processing result on the system and checks the result. If necessary, the user can input feedback and send it to the system. The feedback is saved on the server. The input is the feedback, and the output is the saved feedback data.

[1606] Step 10:

[1607] Storing feedback and using data to train AI models

[1608] The server stores the feedback in a database and uses it as training data for the AI ​​model. The stored feedback is provided to the AI ​​model and added as new training data. The input is the feedback data, and the output is an updated AI model.

[1609] Step 11:

[1610] Recording and management of access history and authentication failure history

[1611] The server centrally records user access history and authentication failure history, allowing administrators to check them. The log data is sent to a cloud log management system (e.g., Amazon CloudWatch). The input is access history data, and the output is the recorded log data.

[1612] 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.

[1613] Overall system overview

[1614] The system allows users to access and input business data, and the AI ​​processes the data to carry out the specified tasks, automatically outputting the results to various tools. The system also has an emotion engine that recognizes the user's emotions, and changes the content presented on the interface based on the emotional data, and has the ability to use this as feedback for learning data.

[1615] Program processing flow

[1616] 1. Enter and authenticate your login information

[1617] The user accesses the platform's login screen and enters their account information (ID and password).

[1618] The terminal sends the user's login information to the server.

[1619] The server authenticates the login information against a database, and if authentication is successful, the user session begins.

[1620] 2. Collection of business data

[1621] Users select a new task (e.g., creating presentation materials) from the dashboard and enter or upload the required data.

[1622] The terminal sends business data to the server, and the server stores the data in storage.

[1623] 3. Emotion Recognition by Emotion Engine

[1624] The server analyzes the user's operation data and input text data on the dashboard in real time and uses an emotion engine to recognize the user's emotions.

[1625] The interface content is automatically adjusted based on the recognized emotion.

[1626] 4. AI-powered data processing

[1627] The server provides the collected business data to the AI ​​means and carries out the specified tasks.

[1628] AI analyzes the data and generates the final processed results (e.g., presentation materials).

[1629] 5. Automatic output of processing results

[1630] The server receives the processing results returned by the AI ​​and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[1631] The generated processing results are automatically uploaded to the following tools.

[1632] 6. Notification of results and feedback

[1633] The server notifies the user when the results are ready.

[1634] Users can check the results on the platform, make any necessary corrections, and provide feedback.

[1635] The terminal sends the feedback to the server.

[1636] The server stores the feedback and emotional data in a database and uses it as learning data for the AI ​​and emotion engine.

[1637] Specific examples

[1638] Creating presentation materials and emotional feedback

[1639] 1. Login Process

[1640] A user logs into the system and accesses the dashboard. The server authenticates the login information.

[1641] 2. Provision of business data

[1642] A user uploads data (e.g., a spreadsheet file) needed to create a presentation for the next meeting. The data is temporarily stored in the server's storage.

[1643] 3. Emotion recognition

[1644] The server analyzes the user's input data and operation data in real time, recognizes the user's emotions using an emotion engine, and adjusts the user's interface experience according to the emotion data.

[1645] 4. AI Processing

[1646] The server passes the collected data to the AI, which then begins analyzing it. The AI ​​then automatically generates presentation slides based on the data, inserting explanatory text and graphs.

[1647] 5. Output

[1648] The server calls the API to upload the generated presentation to Google Slides. After the upload is successfully completed, the server notifies the user.

[1649] 6. Review and Feedback

[1650] The user checks the presentation materials on the platform and makes any necessary corrections. The corrections and feedback are then sent to the server along with the emotion data.

[1651] The server stores the feedback and emotion data in a database and uses it as learning data for the AI ​​and emotion engine.

[1652] This system enables efficient use of generative AI and emotion engines, significantly reducing the amount of work required and time required while improving the user experience.

[1653] The processing flow will be explained below.

[1654] Step 1:

[1655] The user accesses the platform's login screen and enters their account information (ID and password).

[1656] Step 2:

[1657] The terminal sends the user's login information to the server.

[1658] Step 3:

[1659] The server checks the received login information against the database to verify whether the user is a valid user, and if authentication is successful, starts a user session.

[1660] Step 4:

[1661] The server sends the authenticated dashboard screen to the terminal, allowing the user to access this dashboard.

[1662] Step 5:

[1663] The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required task data.

[1664] Step 6:

[1665] The terminal transmits the business data entered by the user to the server.

[1666] Step 7:

[1667] The server stores the received business data in temporary storage and registers the metadata in the database.

[1668] Step 8:

[1669] The server monitors the user's operation data and input text data on the dashboard in real time and inputs it into the emotion engine.

[1670] Step 9:

[1671] The emotion engine analyzes user operation data and text data to recognize the user's emotions.

[1672] Step 10:

[1673] The server receives the emotion data from the emotion engine and automatically adjusts the interface presentation according to the user's mood. For example, if the user is irritated, the system provides a simple and easy-to-understand interface.

[1674] Step 11:

[1675] The business data collected by the server is provided to AI means.

[1676] Step 12:

[1677] AI analyzes business data and performs specified tasks, such as generating slides based on the data to create presentation materials and adding relevant content.

[1678] Step 13:

[1679] The AI ​​returns the analysis results and generated deliverables (e.g., presentation materials) to the server.

[1680] Step 14:

[1681] The server receives the processing results from the AI ​​tool and calls an API to output them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[1682] Step 15:

[1683] The server uploads the processing results to various tools, and once the upload is completed successfully, it notifies the user that the results are ready.

[1684] Step 16:

[1685] The user checks the processing results on the platform, and if the user wants to make any corrections, they input the details.

[1686] Step 17:

[1687] The terminal sends the user's modifications and feedback to the server.

[1688] Step 18:

[1689] The server stores the feedback and emotional data in a database and uses it as learning data for the AI ​​and emotion engine.

[1690] Example 2

[1691] 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."

[1692] Conventional business systems require users to manually collect and analyze business data, which is time-consuming, and outputting results and processing feedback takes time. Furthermore, because they do not take into account user emotions or stress, there are still issues with the user experience. Furthermore, updates to the AI ​​model's learning data are delayed, often failing to reflect the latest data.

[1693] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1694] In this invention, the server includes emotion engine means for recognizing user emotions, means for dynamically adjusting the interface based on the emotion data, and means for automatically outputting the processing results of the AI ​​means to various tools. This not only automates the collection and analysis of user business data, but also enables dynamic interface adjustment that takes user emotions into consideration, thereby improving the user experience.

[1695] A "user" is an individual who accesses the system, enters login information, provides business data, and checks and provides feedback on the results.

[1696] "Login Information" refers to the account ID and password used by a User to access the System.

[1697] A "server" is a computer system that performs a series of processes, including authenticating login information sent by a user, collecting and analyzing business data, and outputting the final processing results to various tools.

[1698] "Business data" refers to data related to a business provided by a user, and examples include spreadsheet files, text data, image files, and the like.

[1699] "AI means" refers to artificial intelligence technology that analyzes collected business data and carries out specified tasks.

[1700] "Processing Results" means the final output generated by the AI ​​Means, including presentation materials and other business-related materials submitted to a User.

[1701] "Various tools" refers to external systems or software for outputting processing results, such as Google Slides and Microsoft PowerPoint.

[1702] The "emotion engine means" is a technology for analyzing the user's operation data and input data and recognizing the user's emotions.

[1703] "Emotion data" is data that indicates the emotional state of the user analyzed by the emotion engine means.

[1704] "Feedback" refers to opinions and correction requests provided by users regarding the processing results generated by the system, and is used as learning data for the system.

[1705] This invention is a system in which users access the system, input business data, and AI processes the data to perform the specified tasks, automatically outputting the processing results to various tools. This system operates in cooperation with three entities: a server, a terminal, and a user.

[1706] server

[1707] The server authenticates the login information entered by the user and collects and stores business data. It also includes an emotion engine that analyzes user emotion data in real time. It also provides the collected business data to an AI model, which then automatically outputs the processed results to various tools (e.g., Google Slides, Microsoft PowerPoint).

[1708] Terminal

[1709] The terminal provides an interface that supports the user's data input and operation. The terminal also transmits the login information and business data entered by the user to the server. Furthermore, feedback information is also transmitted from the terminal to the server.

[1710] User

[1711] Users log in to the system, input or upload business data, and check and correct the generated processing results. Users also provide feedback to the system on the generated results, which is used as learning data for the AI ​​model.

[1712] Specific hardware and software

[1713] The servers are equipped with high-performance CPUs and GPUs to rapidly process and analyze data. Relational databases such as MySQL and PostgreSQL are used as databases. The emotion engine uses natural language processing technology, and generative AI models such as GPT-4 are used as AI models. A browser and dedicated applications are installed on the terminals, which function as the user interface.

[1714] Specific examples

[1715] For example, consider a user creating a presentation for an upcoming meeting. First, the user logs in to the system and accesses the dashboard. Next, they upload the necessary business data (e.g., a spreadsheet file). This data is sent to the server, which uses an emotion engine to analyze the user's emotions and adjust the interface. The server then passes the collected data to the AI, which generates presentation slides in the specified format. The generated presentation slides are automatically uploaded to Google Slides or Microsoft PowerPoint. Finally, the user reviews the slides on the platform and provides feedback. This feedback is stored on the server and used as future learning data.

[1716] Prompt Sentence Examples

[1717] The following prompts can be used:

[1718] "Create a presentation for my next meeting. Generate slides based on this data."

[1719] In this way, the present invention provides a system that significantly reduces the workload while improving the user experience. The collaboration between the generative AI model and the emotion engine enables tasks to be performed with extremely high efficiency and accuracy.

[1720] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1721] Step 1:

[1722] Enter and authenticate your login information

[1723] Input: The user uses a browser or a dedicated application to enter their account ID and password on the login screen.

[1724] Specific behavior:

[1725] The user accesses the system's login screen and enters their account ID and password.

[1726] The device encrypts the login information entered by the user and sends it to the server via a secure communication protocol such as HTTPS.

[1727] Data processing:

[1728] The server receives the submitted login information and checks it against the user information in its database.

[1729] Output: If authentication is successful, start a user session and redirect the user to the dashboard.

[1730] Step 2:

[1731] Business data collection

[1732] Input: The user selects a new task (e.g., creating presentation materials) from the dashboard and enters or uploads the required data.

[1733] Specific behavior:

[1734] The user selects the "Create Presentation" option.

[1735] The user uploads the necessary business data, such as a spreadsheet file or text file.

[1736] Data processing:

[1737] The terminal sends the uploaded file to the server.

[1738] Output: Business data is sent to the server and stored in the server's storage.

[1739] Step 3:

[1740] Emotion recognition by emotion engine

[1741] Input: User operation data and input data

[1742] Specific behavior:

[1743] The server analyzes the user's operation data and input text data in real time.

[1744] The server uses an emotion engine to recognize the user's emotions (e.g., joy, anger, sadness).

[1745] Data Calculation:

[1746] The emotion engine uses natural language processing techniques to analyze the emotional tone of the input data.

[1747] Output: Dynamically adjust the content displayed in the user interface based on the recognized emotion data.

[1748] Step 4:

[1749] AI-powered data processing

[1750] Input: User-provided business data

[1751] Specific behavior:

[1752] The server generates prompts to input the collected business data into a generative AI model (e.g., GPT-4). For example, it generates a prompt like, "Please create a presentation for the next meeting. Please generate slides based on this data."

[1753] The AI ​​analyzes business data based on the generated prompt text and automatically generates presentation slides.

[1754] Data Calculation:

[1755] The AI ​​analyzes the input business data and generates presentation materials in the specified format.

[1756] Output: Generated presentation materials (slides, explanatory text, graphs, etc.)

[1757] Step 5:

[1758] Automatic output of processing results

[1759] Input: AI-generated processing results (presentation materials)

[1760] Specific behavior:

[1761] The server receives the processing results returned by the AI ​​and calls an API to upload them to the specified tool (e.g., Google Slides, Microsoft PowerPoint).

[1762] Data processing:

[1763] The server makes an API call to upload the generated file to an external service.

[1764] Output: A confirmation message and a URL indicating a successful upload.

[1765] Step 6:

[1766] Notification of results and feedback

[1767] Input: Server-generated confirmation message and URL

[1768] Specific behavior:

[1769] The server notifies the user when the processing results are ready (e.g. email notification, dashboard notification).

[1770] The user can check the processing results on the platform.

[1771] The user makes corrections as needed and provides feedback.

[1772] Data processing:

[1773] The terminal transmits the feedback information to the server.

[1774] Output: Feedback and emotion data are stored on the server.

[1775] Through these processing steps, a system is realized that can efficiently collect business data, analyze it using AI, output the generated results, and collect feedback.

[1776] (Application example 2)

[1777] 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."

[1778] Conventional systems require users to spend a great deal of time and effort when creating content. Furthermore, they perform tasks without considering the user's feelings, making it difficult to improve the user experience. Furthermore, there is a need to output the generated content to various tools quickly and efficiently.

[1779] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1780] In this invention, the server includes means for analyzing user operation data and input data and recognizing the user's emotions using an emotion engine, means for automatically adjusting the interface based on the recognized emotions, and AI means for analyzing collected business data and performing specified tasks, thereby making it possible to provide an interface that reflects the user's emotions and automate tasks.

[1781] "Login Information" means the authentication information a user enters to access a system.

[1782] "Server means" refers to a central processing unit that authenticates login information and collects, stores, and analyzes business data.

[1783] "Business data" refers to data that a user inputs or uploads in order to perform a specific business.

[1784] "AI means" is an artificial intelligence engine that analyzes collected business data and carries out specified tasks.

[1785] "Various tools" are external applications or platforms for outputting or using the generated processing results.

[1786] "Feedback" refers to the evaluation and correction instructions provided by the user regarding the processing results of the system.

[1787] A "database" is a digital storage device for storing information such as business data, processing results, and feedback.

[1788] An "emotion engine" is a software engine that analyzes user operation data and input data and recognizes the user's emotions.

[1789] An "interface" is a display screen or operating means that allows a user to interact with a system.

[1790] An "API calling means" is a function realized by a program for exchanging data and services between different software components.

[1791] The system for implementing the present invention includes a server that authenticates login information entered by the user, collects business data, recognizes the user's emotions using an emotion engine, adjusts the interface based on the recognition results, and performs business operations using AI means.

[1792] The system's hardware uses terminal devices such as smartphones and head-mounted displays. The software includes an emotion engine, an AI content generation module, and a data processing module. The emotion engine is software that analyzes user operation data and input data in real time and recognizes emotions. The AI ​​content generation module is software that performs specified tasks (such as automatically generating content) based on collected business data.

[1793] The main steps of this system are for the user to log in to the system and upload the necessary business data. The server collects this data and analyzes the user's emotions using an emotion engine. The interface is automatically adjusted based on the analysis results. The collected business data is then passed to the AI ​​means to carry out the task. The generated processing results are automatically output to a specified tool and notified to the user. The user checks the processing results and provides feedback. The feedback is stored in a database and used as learning data for future AI and emotion engines.

[1794] As a concrete example, suppose a user uploads a video clip and a text script to create their next video content. This data is sent to the server, where the emotion engine recognizes the user's emotions and adjusts the software accordingly. The AI ​​content generation module then uses this data to automatically edit the video and upload it to the specified platform.

[1795] An example of a prompt is the following text:

[1796] "User ID: example_user"

[1797] Password: example_password

[1798] "Content data: example_video_clip.mp4"

[1799] "Platform Name: YouTube"

[1800] This enables the provision of an interface that takes user emotions into consideration and the automation of tasks, improving the user experience and work efficiency.

[1801] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1802] Step 1:

[1803] The user enters login information. The user accesses the system's login screen and enters their ID and password. The terminal sends the entered login information to the server.

[1804] Step 2:

[1805] The server authenticates the login information. It checks the login information against a database and starts the user session if authentication is successful. If authentication is unsuccessful, the server prompts the user for login information again.

[1806] Step 3:

[1807] The user collects task data. The user then selects the task to be performed (e.g., video content creation) and uploads the required data (e.g., video clips, text scripts). The device then transmits this data to the server.

[1808] Step 4:

[1809] The server uses an emotion engine to recognize emotions. The server analyzes user operation data and input data in real time and uses the emotion engine to recognize the user's emotions. The interface is automatically adjusted based on the recognition results.

[1810] Step 5:

[1811] The server provides business data to the AI ​​means. The collected business data is passed to the AI ​​content generation module. The AI ​​analyzes this data and generates optimal content (e.g., automatically edited videos).

[1812] Step 6:

[1813] The server automatically outputs the processing results to various tools. The generated processing results are uploaded to the specified platform (e.g. YouTube, Spotify) by calling an API to output them. This automatically publishes the generated content to the platform.

[1814] Step 7:

[1815] The server notifies the user of the results. When the upload of the processing results is complete, the server sends a notification to the user. The user receives this notification and checks the processing results.

[1816] Step 8:

[1817] The user provides feedback. The user checks the processing results on the platform, makes necessary corrections, and provides feedback. The terminal sends the feedback to the server.

[1818] Step 9:

[1819] The server stores the feedback in a database, which will be used as learning data for future AI and emotion engines.

[1820] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1821] 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.

[1822] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1823] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1824] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1825] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1826] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1827] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1828] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1829] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1830] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1831] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1832] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1833] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1834] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1835] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1836] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1837] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1838] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1839] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1840] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1841] The following is further disclosed regarding the above embodiment.

[1842] (Claim 1)

[1843] a means for a user to enter login information;

[1844] a server means for authenticating the login information;

[1845] a means for collecting business data after authentication;

[1846] AI means to analyze collected business data and carry out designated tasks;

[1847] A means to automatically output the processing results by AI means to various tools,

[1848] a means for notifying the user of the results and receiving feedback;

[1849] A means to store the feedback in a database and use it as training data for AI models,

[1850] A system including:

[1851] (Claim 2)

[1852] 2. The system according to claim 1, further comprising an API calling means for automating cooperation with various tools.

[1853] (Claim 3)

[1854] 2. The system according to claim 1, further comprising means for storing processing results in multiple locations and for quickly providing past business information.

[1855] "Example 1"

[1856] (Claim 1)

[1857] a means for a user to enter login information;

[1858] a server means for authenticating the login information;

[1859] a means for collecting business data after authentication;

[1860] AI means to analyze collected business data and carry out designated tasks;

[1861] A means to automatically output the processing results by AI means to various tools,

[1862] a means for notifying the user of the results and receiving feedback;

[1863] A means to store the feedback in a database and use it as training data for AI models,

[1864] a terminal to which a user uploads business data;

[1865] A means for the server to store business data in temporary storage;

[1866] A server provides the processing result to the AI ​​means and outputs the generated processing result to a designated tool;

[1867] A system including:

[1868] (Claim 2)

[1869] 2. The system according to claim 1, further comprising an API calling means for automating cooperation with various tools.

[1870] (Claim 3)

[1871] 2. The system according to claim 1, further comprising means for storing processing results in multiple locations and for quickly providing past business information.

[1872] "Application Example 1"

[1873] (Claim 1)

[1874] a means for a user to enter login information;

[1875] a server means for authenticating the login information;

[1876] a means for collecting business data after authentication;

[1877] AI means to analyze collected business data and carry out designated tasks;

[1878] A means to automatically output the processing results by AI means to various tools,

[1879] a means for notifying the user of the results and receiving feedback;

[1880] A means to store the feedback in a database and use it as training data for AI models,

[1881] a means for performing multi-factor authentication;

[1882] a means for recording a user's access history and authentication failure history;

[1883] A system including:

[1884] (Claim 2)

[1885] 2. The system according to claim 1, further comprising an API calling means for automating cooperation with various tools.

[1886] (Claim 3)

[1887] 2. The system according to claim 1, further comprising means for storing processing results in multiple locations and for quickly providing past business information.

[1888] "Example 2: Combining Emotion Engines"

[1889] (Claim 1)

[1890] a means for a user to enter login information;

[1891] a server means for authenticating the login information;

[1892] a means for collecting business data after authentication;

[1893] AI means to analyze collected business data and carry out designated tasks;

[1894] A means to automatically output the processing results by AI means to various tools,

[1895] a means for notifying the user of the results and receiving feedback;

[1896] A means to store the feedback in a database and use it as training data for AI models,

[1897] emotion engine means for recognizing an emotion of a user;

[1898] means for dynamically adjusting the interface based on the emotion data;

[1899] A system including:

[1900] (Claim 2)

[1901] 2. The system according to claim 1, further comprising an API calling means for automating cooperation with various tools.

[1902] (Claim 3)

[1903] 2. The system according to claim 1, further comprising means for storing processing results in multiple locations and for quickly providing past business information.

[1904] "Application example 2 when combining emotion engines"

[1905] (Claim 1)

[1906] a means for a user to enter login information;

[1907] a server means for authenticating the login information;

[1908] a means for collecting business data after authentication;

[1909] AI means to analyze collected business data and carry out designated tasks;

[1910] A means to automatically output the processing results by AI means to various tools,

[1911] a means for notifying the user of the results and receiving feedback;

[1912] A means to store the feedback in a database and use it as training data for AI models,

[1913] A means for analyzing user operation data and input data and recognizing user emotions using an emotion engine;

[1914] means for automatically adjusting the interface based on the recognized emotion;

[1915] A system including:

[1916] (Claim 2)

[1917] 2. The system according to claim 1, further comprising an API calling means for automating cooperation with various tools.

[1918] (Claim 3)

[1919] 2. The system according to claim 1, further comprising means for storing processing results in multiple locations and for quickly providing past business information. [Explanation of symbols]

[1920] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to enter login information; a server means for authenticating the login information; a means for collecting business data after authentication; AI means to analyze collected business data and carry out designated tasks; A means to automatically output the processing results by AI means to various tools, a means for notifying the user of the results and receiving feedback; A means to store the feedback in a database and use it as training data for AI models, A system including:

2. 2. The system according to claim 1, further comprising an API calling means for automating cooperation with various tools.

3. 2. The system according to claim 1, further comprising means for storing the processing results in multiple locations and for quickly providing past business information.

Images