System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to enter and transmit personal information, values, and interests in a registration form; means for a server to verify and store received user data in a database; means for the server to analyze the stored user data and generate optimal path candidates for the user; means for the user to select from the candidates and request detailed information; and means for the server to obtain and transmit detailed information of the selected path to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The problem with the traditional career choice process is that users must spend a great deal of time and effort searching through a vast amount of information to find the best option for them. Furthermore, because many choices depend on personal sensibilities and subjectivity, the best career path is not always chosen. As a result, it is difficult to maximize the abilities and value of individuals, and it is inefficient for society as a whole. [Means for solving the problem]

[0005] The present invention provides a system that utilizes AI to assist in career selection based on data such as personal information, values, and interests entered by the user. The server receives the data entered by the user into a registration form and stores it in a database after verification. The server then analyzes the stored user data using a machine learning algorithm to generate career options that are optimal for the user. The user can select from the generated options and request detailed information about them. The server obtains the detailed information and sends it to the user. Furthermore, the server can provide next action steps based on the user's selection and receive feedback from the user. By analyzing this feedback and improving the accuracy of the algorithm, more personalized career support can be achieved.

[0006] "User Data" refers to personal information, values, interests, and other data that users enter into registration forms.

[0007] A "registration form" is an interface that allows a user to enter their own information and is displayed on a web page or application screen.

[0008] The "server" is a computer system that receives, verifies, and stores user data, and then uses AI to analyze and suggest future paths based on that data.

[0009] A "database" is a repository where user data is stored and a system that allows for fast search and information retrieval.

[0010] An "AI engine" is software that uses machine learning algorithms to analyze user data and generate the most suitable career options for each user.

[0011] A "machine learning algorithm" is a mathematical model that learns from user data and recommends appropriate career options based on that data.

[0012] "Career options" are schools, cram schools, teachers, friends, future partners, etc. recommended by AI based on the user's personal information, values, and interests.

[0013] "Detailed information" is more specific information about potential career paths, such as the cram school's curriculum, track record, and school reviews.

[0014] An "action step" is a specific action or procedure that should be taken next based on the career path selected by the user.

[0015] "Feedback" is information that the user sends back to the server based on the results of their choices of possible career paths and actions.

[0016] "Analysis" is the process of finding correlations and patterns in information based on collected data and making optimal suggestions to users. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] A specific system configuration and its operation will be described below as an embodiment of the present invention.

[0039] System Configuration

[0040] The system of the present invention includes a user terminal, a server, a database, and an AI engine. The user terminal communicates with the server via the Internet and provides an interface for data input and result display.

[0041] User devices: personal computers, tablets, smartphones, etc.

[0042] Server: Receives, stores, analyzes user data, and generates results

[0043] Database: Stores user data and analysis results

[0044] AI engine: Runs machine learning algorithms to generate optimal career paths based on user data

[0045] Program processing

[0046] 1. User Registration

[0047] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[0048] The terminal transmits the input data to the server.

[0049] The server receives the data, validates the necessary fields, and saves the data to the database if it passes validation.

[0050] 2. Data analysis and career suggestions

[0051] The server sends the saved user data to the AI ​​engine.

[0052] The AI ​​engine uses machine learning algorithms to analyze the data and generate the most suitable career options for the user (schools, cram schools, teachers, friends, etc.).

[0053] The server transmits the generated course candidates to the user.

[0054] 3. User selection and confirmation of details

[0055] The user checks the career options sent from the server and selects the option that interests them.

[0056] The user requests more information.

[0057] The server retrieves the details of the selected candidate from the database and sends them to the user.

[0058] The terminal displays the detailed information to the user.

[0059] 4. Next steps and feedback

[0060] The user performs a specific action according to the next action step presented to them, for example, enrolling in a cram school.

[0061] The server receives the feedback from the user and stores it in a database.

[0062] The server analyzes the feedback and improves the algorithms of the AI ​​engine, and this feedback loop improves the accuracy of the system.

[0063] Specific examples

[0064] As a specific example, a case where user A is searching for a school to attend will be described.

[0065] 1. User A enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[0066] 2. The device sends this information to the server.

[0067] 3. The server validates the data and saves it to the database.

[0068] 4. The server passes the data to the AI ​​engine for analysis.

[0069] 5. The AI ​​engine generates the best school candidates based on User A's information. For example, it will list schools with tuition fees that match User A's expectations and a wide range of departments that interest User A.

[0070] 6. The server sends the generated list of schools to User A.

[0071] 7. User A selects a school from the list and requests more information.

[0072] 8. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User A.

[0073] 9. The terminal displays the detailed information to User A.

[0074] 10. User A begins the admission process for the school that interests him most and feeds the results back to the server.

[0075] 11. The server receives the feedback and uses it to improve the AI ​​engine algorithm.

[0076] In this way, the system supports users in choosing a career path that is optimized to their individual needs, and streamlines user decision-making.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[0080] Step 2:

[0081] The terminal transmits the input data to the server.

[0082] Step 3:

[0083] The server validates the data it receives, ensuring that all required fields are filled in. If there are any omissions, it generates an error message and sends it back to the terminal.

[0084] Step 4:

[0085] The server saves the data that passes the validation in the database.

[0086] Step 5:

[0087] The server sends the saved user data to the AI ​​engine and instructs it to analyze it.

[0088] Step 6:

[0089] The AI ​​engine uses machine learning algorithms to analyze user data and generate the best possible career paths for the user.

[0090] Step 7:

[0091] The server transmits the generated course candidates to the user.

[0092] Step 8:

[0093] The user checks the career options sent from the server and selects the one that interests them from among the multiple options.

[0094] Step 9:

[0095] Request more information about the candidate selected by the user.

[0096] Step 10:

[0097] The server retrieves the details from the database based on the user's request.

[0098] Step 11:

[0099] The server sends the retrieved details to the user.

[0100] Step 12:

[0101] The device visually displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[0102] Step 13:

[0103] Users take specific actions regarding the career options they are most interested in. For example, they can apply online to enroll in a cram school.

[0104] Step 14:

[0105] The server designs the next action steps based on the user's selection and actions and guides the user.

[0106] Step 15:

[0107] The user completes the presented procedure and provides feedback on the results to the server.

[0108] Step 16:

[0109] The server analyzes the feedback it receives, stores it in a database, and uses it as data to improve the AI ​​engine's algorithms.

[0110] This series of steps allows users to make efficient and optimal career choices, and the system is continuously improved by incorporating user feedback.

[0111] Example 1

[0112] 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."

[0113] In today's educational environment, it is extremely important for users to choose the most appropriate career path based on their own values ​​and interests. However, it is not easy to find a career path that matches individual needs from a vast amount of information, and it is difficult to make an appropriate decision in a short amount of time. For this reason, there is a need for a system that can effectively suggest the most suitable career path for users, provide detailed information, and guide them on next steps.

[0114] 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.

[0115] In this invention, the server includes means for a user to enter and send personal information, values, and interests into a registration form, means for a terminal to send the entered data to the server, means for the server to verify the received user data and store it in a database, means for the server to analyze the stored user data and send it to an AI engine, means for the AI ​​engine to analyze the data using a machine learning algorithm and generate career path candidates optimal for the user, means for the server to send the generated career path candidates to the user, means for the user to select from the candidates and request detailed information, means for the server to obtain detailed information about the selected career path and send it to the user, means for the terminal to display the detailed information to the user, and means for the server to provide next action steps based on the user's selection and receive feedback. This makes it possible to support career selection optimized to the user's individual needs and streamline decision-making.

[0116] "User" refers to a person who inputs information into the system and receives the results of processing.

[0117] "Terminal" refers to a device such as a personal computer, tablet, or smartphone that a user uses to input information and check the displayed results.

[0118] "Server" refers to a central processing unit that receives, stores, analyzes user data, and generates results.

[0119] "Database" refers to an information management system for storing user data and analysis results.

[0120] "AI engine" refers to a component that runs machine learning algorithms to generate optimal career options based on user data.

[0121] "Registration Form" refers to the interface used by a user to enter information such as personal details, values, and interests.

[0122] A "machine learning algorithm" refers to a mathematical model used to analyze data and find patterns and trends.

[0123] "Career options" refer to options such as schools, cram schools, teachers, and friends that are proposed to the user.

[0124] "Detailed information" refers to detailed explanations and information about the career options selected by the user.

[0125] "Action steps" refer to specific actions or procedures that a user should take next.

[0126] "Feedback" refers to the evaluations, opinions, and result information that users provide to the system.

[0127] This invention is a support system for users to choose the most suitable career path. This system is composed of a user terminal, a server, a database, and an AI engine. Each component and its operation are explained in detail below.

[0128] System Configuration

[0129] User device: A personal computer, tablet, smartphone, etc. used by users to input information and view results. Examples include a common web browser or mobile application.

[0130] Server: Receives, stores, and analyzes user data, generates results, and sends them to users. The server is a computer system equipped with a high-performance processor and sufficient memory, and also includes a database server and an AI engine.

[0131] Database: An information management system for storing user data and analysis results, such as an SQL database.

[0132] AI engine: Analyzes user data using machine learning algorithms to generate optimal career paths. For example, TensorFlow and PyTorch, which use Python, are examples of this type of engine.

[0133] Program processing

[0134] 1. User Registration

[0135] The user uses a terminal to enter personal information (name, age, current grade), values, interests, desired tuition fees, commuting distance, and future career goals into a registration form. The entered data is sent from the terminal to the server, which verifies the received data and stores it in a database.

[0136] 2. Data analysis and career suggestions

[0137] The server sends the saved user data to the AI ​​engine, which uses a machine learning algorithm to analyze the input data and generate optimal career path candidates. The generated career path candidates are then sent to the user's device via the server.

[0138] 3. User selection and confirmation of details

[0139] The user selects an option that interests them from the list of available options and requests detailed information. The server retrieves detailed information about the selected option from the database and sends it to the user. The terminal displays the detailed information to the user.

[0140] 4. Next steps and feedback

[0141] The user follows the suggested next action steps and takes specific actions, such as enrolling in a cram school. This feedback is received by the server and stored in a database. The server analyzes the received feedback and improves the algorithm of the AI ​​engine.

[0142] Specific examples

[0143] For example, if user A is looking for a school to attend, he or she will take the following steps:

[0144] 1. User A enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[0145] Example prompt: "My name is Taro Yamada. I'm 17 years old and a second-year high school student. I'm interested in computer science and my desired tuition is 500,000 yen per year. The school is 30 kilometers from my home. My future career goal is to become a software engineer."

[0146] 2. The device sends this information to the server.

[0147] 3. The server validates the data and saves it to the database.

[0148] 4. The server passes the data to the AI ​​engine for analysis.

[0149] 5. The AI ​​engine generates the best school candidates based on User A's information. For example, it will list schools with tuition fees that match User A's expectations and a wide range of departments that interest User A.

[0150] Example prompt: "Please tell me the best school for me. Here is my information: My name is Taro Yamada, I'm 17 years old, I'm a second-year high school student, I'm interested in computer science, I would like to pay 500,000 yen per year in tuition, I live within a 30km commute, and my future career goal is to become a software engineer."

[0151] 6. The server sends the generated list of schools to User A.

[0152] 7. User A selects a school from the list and requests more information.

[0153] 8. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User A.

[0154] 9. The terminal displays the detailed information to User A.

[0155] 10. User A begins the admission process for the school that interests him most and feeds the results back to the server.

[0156] 11. The server receives the feedback and uses it to improve the AI ​​engine algorithm.

[0157] Through the detailed processing steps described above, the present system supports users in choosing a career path that is optimized to their individual needs, and streamlines decision-making.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1:

[0160] The user enters information into an input form on the device, such as name, age, current year at school, department of interest, desired tuition fee, distance to school, and future career goals.

[0161] Input: Data that a user enters into a form field.

[0162] Output: User input data that is temporarily stored in the device's memory.

[0163] What happens: A user fills in a form in a browser or application and clicks the "Submit" button.

[0164] Step 2:

[0165] The terminal formats the entered data and transmits it over the Internet to a server.

[0166] Input: User input data.

[0167] Output: The HTTP POST request sent to the server.

[0168] Specific operation: The device converts the data obtained from the user into a format such as JSON or XML and sends it to the server as an HTTP request.

[0169] Step 3:

[0170] The server validates the user data received, checking that all required fields are present and that the data is in the correct format.

[0171] Input: User data sent from the device.

[0172] Output: The validation results and, if appropriate, the data stored in a database.

[0173] What happens: The server runs validation scripts to check for missing fields or improper formatting. Data that passes validation is saved to the database using a SQL INSERT statement.

[0174] Step 4:

[0175] The server sends the saved user data to the AI ​​engine.

[0176] Input: User data stored in the database.

[0177] Output: The data sent to the AI ​​engine.

[0178] Specific operation: The server retrieves user data from the database and sends a request to the AI ​​engine using REST API or gRPC.

[0179] Step 5:

[0180] The AI ​​engine uses machine learning algorithms to analyze the data and generate optimal career options.

[0181] Input: User data received from the server.

[0182] Output: A list of optimal career paths.

[0183] How it works: The AI ​​engine inputs data into a predictive model, runs algorithms, and generates a ranking of the best career options.

[0184] Step 6:

[0185] The server transmits the generated course candidates to the user.

[0186] Input: A list of possible career paths received from the AI ​​engine.

[0187] Output: The HTTP response sent to the user's device.

[0188] Specific operation: The server sends the data obtained from the AI ​​engine to the user device as an HTTP response.

[0189] Step 7:

[0190] The user checks the list of career options sent to them and selects the option that interests them.

[0191] Input: A list of possible career paths sent from the server.

[0192] Output: The career options selected by the user.

[0193] Specific actions: The user checks the list on the device and clicks a button to view details of the selected career option.

[0194] Step 8:

[0195] The user requests more information.

[0196] Input: The career path selected by the user.

[0197] Output: A more information request sent to the server.

[0198] What happens: The user clicks the "View Details" button, sending a request to the server.

[0199] Step 9:

[0200] The server retrieves details of the selected route from the database and sends them to the user.

[0201] Input: Request more information about the selected career path.

[0202] Output: Detailed information sent to the user's device.

[0203] Specific operation: The server retrieves detailed information from the database and sends it to the device in an HTTP response.

[0204] Step 10:

[0205] The terminal displays the detailed information to the user.

[0206] Input: The details received from the server.

[0207] Output: Detailed information displayed on the device screen.

[0208] Specific behavior: The device UI processes the received data and displays it to the user in an appropriate format.

[0209] Step 11:

[0210] The user starts the admission procedure for the school in which he or she is most interested, and feeds back the results to the server.

[0211] Input: User's admissions results and feedback.

[0212] Output: Feedback information sent to the server.

[0213] Specific behavior: The user completes the procedure, enters the required information in the feedback form, and submits it to the server.

[0214] Step 12:

[0215] The server receives the feedback and analyzes it to improve the AI ​​engine's algorithms.

[0216] Input: Feedback information from the user.

[0217] Output: Improved AI algorithm.

[0218] What it does: The server stores the feedback information in a database, then runs a data analysis script to generate data for retraining the AI ​​model.

[0219] (Application example 1)

[0220] 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."

[0221] Conventional online shopping sites lacked personalized product suggestions based on users' interests and purchase history. This meant that users had to spend time finding the product that best suited them, reducing their motivation to purchase. Furthermore, there was a lack of a convenient way to obtain detailed information about suggested products, which led to lower user satisfaction.

[0222] 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.

[0223] In this invention, the server includes a means for a user to input and submit personal information, values, and interests into a registration form, a means for the server to verify the received user data and store it in a database, and a means for the server to analyze the stored user data and generate product candidates that are optimal for the user, thereby enabling the user to efficiently find optimal product candidates based on their interests and values.

[0224] A "user" is a person who uses the system to input personal information, values, and interests and receives product proposals.

[0225] A "registration form" is an interface through which a user enters personal information, values, and interests.

[0226] "Personal information" refers to personal data such as a user's name, age, address, and contact details.

[0227] "Values" is information about a user's beliefs and priorities.

[0228] "Interests" is information about fields or product categories in which the user is particularly interested.

[0229] The "means for sending" is a function for sending the data entered by the user in the registration form to the server.

[0230] "Server" means a central device for receiving, storing and analyzing User Data.

[0231] "Means for verifying" refers to a function that allows the server to verify the accuracy and completeness of the user data received.

[0232] A "database" is a data storage device for saving user data and analysis results.

[0233] The "means for storing" is a function for storing the user data received by the server in a database.

[0234] The "means for analyzing" is a function for analyzing stored user data using a machine learning algorithm.

[0235] "Product candidates" are products that are proposed to the user as optimal options based on the analysis results.

[0236] The "means for generating" is a function for generating a product candidate list based on the analysis results.

[0237] "Detailed information" includes specific product specifications, reviews, pricing information, etc.

[0238] The "request means" is a function that allows a user to request detailed information about a product candidate.

[0239] The "means for obtaining" is a function that allows the server to retrieve detailed information from the database and send it to the user.

[0240] The "transmitting means" is a function that allows the server to transmit detailed information to the user's terminal.

[0241] The "next action step" is specific guidance for the user to purchase the product.

[0242] "Feedback" is information that users send back to the system regarding their reactions and evaluations of the proposed products.

[0243] A "machine learning algorithm" is a computer program that analyzes user data and calculates optimal product candidates.

[0244] To implement the present invention, the following system configuration and operation should be considered.

[0245] System Configuration

[0246] The system includes a user terminal, a server, a database, and an AI engine. The user terminal communicates with the server via the Internet and provides an interface for data input and results display.

[0247] User devices: personal computers, tablets, smartphones, etc.

[0248] Server: Receives, stores, analyzes user data, and generates results

[0249] Database: Stores user data and analysis results

[0250] AI engine: Runs machine learning algorithms to generate optimal product candidates based on user data

[0251] Program processing and explanation

[0252] 1. User Registration

[0253] The user enters personal information, values, interests, etc. into the registration form and submits it. The user's device sends the entered data to the server. The server receives the data and verifies it. After confirming that the required information has been entered correctly, the data is saved in the database.

[0254] 2. Data analysis and product proposals

[0255] The server sends the saved user data to the AI ​​engine, which analyzes the data using a machine learning algorithm (e.g., TensorFlow) and generates the best product candidates for the user. The server then sends the generated product candidate list to the user.

[0256] 3. Request more information

[0257] The user selects an item of interest from the sent product candidate list and requests detailed information. The server retrieves detailed information about the selected item from the database and sends it to the user.

[0258] 4. Next steps and feedback

[0259] The user adds products to their cart based on the details they provide, checks out, and sends their post-purchase feedback to the server, which stores this feedback in a database and uses it to improve the AI ​​engine's algorithms.

[0260] Hardware and software used

[0261] Hardware:

[0262] User devices: smartphones, tablets, personal computers

[0263] Server: Cloud server (e.g. AWS, Google Cloud)

[0264] software:

[0265] Server: Flask (Python framework)

[0266] Database: SQLite

[0267] AI engine: TensorFlow (machine learning algorithm)

[0268] Specific examples

[0269] As a specific example, a case where a user is searching for a new electronic device that interests him will be described.

[0270] 1. What the user enters in the registration form:

[0271] Name, age, product categories of interest (e.g. electronics), past purchase history

[0272] 2. Submit and validate input data:

[0273] The server receives the data, validates the personal information, values, and interests, and stores them in a database.

[0274] 3. Analysis by AI engine:

[0275] It uses machine learning algorithms to analyze the data and generate a list of the best electronic devices for the user.

[0276] 4. User requests more information:

[0277] The user retrieves detailed information (e.g., specifications, reviews, and pricing information) about the suggested products, which the system displays to the user.

[0278] 5. Next Action Steps and Feedback:

[0279] Users purchase products and the server receives their feedback, which is used to improve the accuracy of the AI ​​engine.

[0280] Prompt Sentence Examples

[0281] Specific examples of prompts are as follows:

[0282] "Please suggest products that the user is likely to purchase next based on their purchase history. The user's name is Taro Yamada, he is 35 years old, and his recent purchases have been home appliances, with a particular interest in televisions."

[0283] Using this prompt, the generative AI model analyzes data to suggest the best products for the user.

[0284] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0285] Step 1:

[0286] The user enters personal information, values, and interests into a registration form and submits it. The data entered by the user is sent from the device to the server. The input here is name, age, product categories of interest, and details, and the output is unverified data that is stored on the server.

[0287] Step 2:

[0288] The server validates the received user data. Specifically, the server checks that all required fields in the input data are filled in correctly and verifies that there are no incomplete inputs. The input is the data submitted in step 1, and the output is the validated data.

[0289] Step 3:

[0290] The server saves the validated data to the database using a SQL query to insert the user data into the appropriate tables. The input is validated data that can be saved, and the output is data stored in the database.

[0291] Step 4:

[0292] The server sends the saved user data to the AI ​​engine. Specifically, it uses a Python library to send the data to the AI ​​engine and start analysis. The input is the user data retrieved from the database, and the output is the analysis result passed to the AI ​​engine.

[0293] Step 5:

[0294] The AI ​​engine analyzes user data and generates optimal product candidates. Here, a machine learning algorithm (e.g., TensorFlow) is used to recommend products based on the given data. The input is user data received from the server, and the output is a list of product candidates.

[0295] Step 6:

[0296] The server sends the analysis results from the AI ​​engine to the user. The server receives the generated product candidate list and sends it in a format to be displayed on the user's device. The input is the product candidate list, and the output is the data displayed on the user's device.

[0297] Step 7:

[0298] The user selects a product of interest from the suggested product candidates and requests detailed information. A request for detailed information about the product selected by the user is sent from the terminal to the server. The input is the product candidate list and the selected data, and the output is a detailed information request.

[0299] Step 8:

[0300] The server retrieves the details of the selected product from the database and sends them to the user. It uses a database query to pull the details of the relevant product and returns them to the user. The input is the details request and the output is the product details.

[0301] Step 9:

[0302] The user checks the details, adds the product to the cart, and completes the purchase. The purchase data is sent from the terminal to the server. The input is the product details and purchase data, and the output is purchase procedure confirmation data.

[0303] Step 10:

[0304] The server receives post-purchase feedback from users. The feedback data is sent from the device to the server, where it is stored in a database and used to improve the AI ​​engine's algorithm. The input is user feedback, and the output is an improved algorithm.

[0305] 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.

[0306] A specific system configuration and its operation will be described below as an embodiment of the present invention.

[0307] System Configuration

[0308] The system of the present invention includes a user terminal, a server, a database, an AI engine, and an emotion engine. The user terminal communicates with the server via the Internet and provides an interface for data input and result display.

[0309] User devices: personal computers, tablets, smartphones, etc.

[0310] Server: Receives, stores, analyzes user data, and generates results

[0311] Database: Stores user data and analysis results

[0312] AI engine: Runs machine learning algorithms to generate optimal career paths based on user data

[0313] Emotion Engine: Software for analyzing emotions from user input data and feedback.

[0314] Program processing

[0315] 1. User Registration

[0316] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[0317] The terminal transmits the input data and the user's emotional state to the server.

[0318] The server receives the data, validates the necessary fields, and saves the data to the database if it passes validation.

[0319] 2. Emotion analysis

[0320] The server sends the data received from the user to the emotion engine, which analyzes the user's emotional state.

[0321] The emotion engine analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) and feeds the results back to the AI ​​engine.

[0322] 3. Data analysis and career suggestions

[0323] The server sends the stored user data and emotion analysis results to the AI ​​engine.

[0324] The AI ​​engine uses machine learning algorithms to analyze the data and generate the most suitable career options for the user (schools, cram schools, teachers, friends, etc.).

[0325] The server transmits the generated course candidates to the user.

[0326] 4. User selection and confirmation of details

[0327] The user checks the career options sent from the server and selects the option that interests them.

[0328] The user requests more information.

[0329] The server retrieves the details of the selected candidate from the database.

[0330] The server sends the retrieved details to the user.

[0331] The device displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[0332] 5. Next steps and feedback

[0333] The user performs a specific action according to the next action step presented to them, for example, completing the online procedure to enroll in a cram school.

[0334] The server designs the next action steps based on the user's selection and actions and guides the user.

[0335] The user completes the procedure and provides feedback on the results to the server.

[0336] The server analyzes the received feedback and improves the algorithms of the emotion engine and AI engine.

[0337] Specific examples

[0338] As a specific example, a case where user B is searching for a school to attend will be described.

[0339] 1. User B enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[0340] 2. The device sends this information and the emotional state the user indicated during input (e.g., facial expression recognition and speed changes) to the server.

[0341] 3. The server validates the data and saves it to the database.

[0342] 4. The server sends the received data to the emotion engine for emotion analysis.

[0343] 5. The emotion engine analyzes User B's emotional state and feeds the results back to the AI ​​engine.

[0344] 6. The server sends the saved user data and emotion analysis results to the AI ​​engine for analysis.

[0345] 7. The AI ​​engine generates the best school candidates based on User B's information. For example, it will list schools with tuition fees that match User B's expectations and a wide range of departments that interest him or her.

[0346] 8. The server sends the generated list of schools to User B.

[0347] 9. User B selects a school from the list and requests more information.

[0348] 10. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User B.

[0349] 11. The device displays the detailed information to User B.

[0350] 12. User B begins the admission process for the school he is most interested in and feeds the results back to the server.

[0351] 13. The server receives feedback and uses it to improve the emotion engine and AI engine algorithms.

[0352] In this way, the system supports career choices that take into account the user's emotional state, making user decision-making more personalized and efficient.

[0353] The processing flow will be explained below.

[0354] Step 1:

[0355] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[0356] Step 2:

[0357] The device sends the input data and information for measuring the user's emotions (for example, facial expression data and input speed) to the server.

[0358] Step 3:

[0359] The server validates the data received and ensures that all required fields are filled in. If there are any omissions, it generates an error message and sends it back to the terminal.

[0360] Step 4:

[0361] The server stores the verified data and emotion data in a database.

[0362] Step 5:

[0363] The server transmits the stored user data and emotion data to the emotion engine, which analyzes the user's emotional state.

[0364] Step 6:

[0365] The emotion engine analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) and feeds the results back to the AI ​​engine.

[0366] Step 7:

[0367] The server sends the saved user data and emotion analysis results to the AI ​​engine.

[0368] Step 8:

[0369] The AI ​​engine uses machine learning algorithms to analyze the data and generate the best possible career paths for the user.

[0370] Step 9:

[0371] The server transmits the generated course candidates to the user.

[0372] Step 10:

[0373] The user checks the career options sent from the server and selects the one that interests them from among the multiple options.

[0374] Step 11:

[0375] Request more information about the candidate selected by the user.

[0376] Step 12:

[0377] The server retrieves the details from the database based on the user's request.

[0378] Step 13:

[0379] The server sends the retrieved details to the user.

[0380] Step 14:

[0381] The device visually displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[0382] Step 15:

[0383] Users take specific actions regarding the career options they are most interested in. For example, they can apply online to enroll in a cram school.

[0384] Step 16:

[0385] The server designs the next action steps based on the user's selection and actions and guides the user.

[0386] Step 17:

[0387] The user completes the presented procedure and provides feedback on the results to the server.

[0388] Step 18:

[0389] The server analyzes the feedback it receives and uses it as data to improve the algorithms of the emotion engine and AI engine.

[0390] This series of steps allows users to make efficient and optimal career choices. The system is continuously improved by incorporating user feedback. The use of an emotion engine enables advanced career suggestions that take into account the user's emotional state.

[0391] Example 2

[0392] 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."

[0393] Conventional career suggestion systems have difficulty in proposing a career path that takes into account the user's emotional state and feedback, and are therefore unable to provide the optimal career path for the user. Furthermore, because they suggest a career path based solely on the information provided by the user, they lack the flexibility to respond to individual situations. This can lead to inefficient user decision-making and unsatisfactory results.

[0394] 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.

[0395] In this invention, the server includes means for a user to enter and transmit personal information, values, and interests into a registration form, means for a terminal to transmit the input data and the user's emotional state to the server, means for the server to verify the received user data and store it in a database, means for the server to send the received data to an emotion analysis engine and analyze the user's emotional state, means for an AI engine to apply a machine learning algorithm using the user data and emotion analysis results stored by the server to generate optimal career path candidates for the user, means for the user to review the generated career path candidates, select one that interests them, and request detailed information, means for the server to retrieve detailed information about the selected career path from the database and send it to the user, means for the terminal to display the detailed information to the user, means for the user to take specific actions according to the next action step and feed the results back to the server, and means for the server to receive the user's feedback and improve the algorithms of the emotion engine and the AI ​​engine. This enables optimal career path suggestions that take the user's emotional state and feedback into consideration.

[0396] "User" refers to an individual who utilizes the system to input information and receive career suggestions.

[0397] "Terminal" refers to a device that allows a user to input information and communicate with a server to send and receive data, including personal computers, tablets, and smartphones.

[0398] "Server" refers to the central processing unit that receives, stores, analyzes data from users, and generates and transmits results.

[0399] "Database" refers to a management system for the server to store user data and analysis results.

[0400] An "emotion analysis engine" refers to software that analyzes a user's emotional state from input data and feedback.

[0401] An "AI engine" refers to software that uses machine learning algorithms to analyze user data and generate optimal career path candidates.

[0402] "Machine learning algorithm" refers to an algorithm that analyzes user data and learns patterns and characteristics to generate optimal career path candidates.

[0403] "Career options" refer to options such as the most suitable school, cram school, teacher, or friend that are provided to the user.

[0404] "Emotional state" refers to a user's psychological state such as excitement, joy, or anxiety.

[0405] "Feedback" refers to information sent back to the server by the user regarding the results of performing behavioral steps.

[0406] MODE FOR CARRYING OUT THE INVENTION

[0407] A specific system configuration and its operation will be described below for an embodiment of the present invention. The system described below includes a user terminal, a server, a database, an AI engine, and an emotion engine.

[0408] Hardware and software used

[0409] User devices: Personal computers, tablets, smartphones, etc. Users use these devices to interact with the system.

[0410] Server: A central processing unit that receives, stores, analyzes user data, and generates results.

[0411] Database: A management system that stores user data and analysis results.

[0412] AI Engine: Software that uses machine learning algorithms to analyze user data and generate optimal career path suggestions.

[0413] Emotion engine: Software that analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) from their input data and feedback.

[0414] System Operation

[0415] User Registration

[0416] When a user enters personal information, values, and interests into the input form on the device and presses the send button, the device sends the entered data and the user's emotional state to the server. The server receives the data, verifies the necessary fields, and saves it in a database.

[0417] Emotion analysis

[0418] The server sends the received data to the emotion engine, which analyzes the user's emotional state and feeds the analysis results back to the AI ​​engine.

[0419] Data analysis and career suggestions

[0420] The server sends the saved user data and the results of the emotion analysis to the AI ​​engine, which then uses a machine learning algorithm to analyze the data and generate the most suitable career path candidates for the user. The generated career path candidates are then sent to the user via the server.

[0421] User selection and confirmation of details

[0422] The user checks the career options sent from the server and selects the option they are interested in. When they request detailed information, the server retrieves the details of the selected option from the database and sends it to the user. The terminal displays the details to the user.

[0423] Next steps and feedback

[0424] The user performs specific actions according to the next action steps presented and provides feedback on the results to the server, which then receives the feedback and improves the algorithms of the emotion engine and AI engine.

[0425] Specific examples

[0426] For example, consider a case where a user is looking for a school to attend. The user enters their name, age, current year of study, department of interest, desired tuition fees, commuting distance, and future career goals into an input form on their device and presses the submit button. At this time, the device also sends their emotional state, including facial recognition data, to the server. The server verifies the data and stores it in a database. The emotion engine then analyzes the emotional state, and the AI ​​engine generates the most suitable school candidates. For example, it may list schools with tuition fees that match the user's expectations and a wide range of departments that interest the user.

[0427] Example prompts for generative AI models

[0428] "Create a program that lists schools that the user is likely to be interested in and displays detailed information. Implement it to analyze the user's emotional state and recommend schools that are preferred when the user is feeling particularly safe or excited."

[0429] By inputting this prompt into a generative AI model, program code can be generated based on specific system behavior and algorithms.

[0430] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0431] Step 1: User Input and Data Submission

[0432] The user enters personal information, values, and interests into a form on the device and presses the submit button. The information entered includes name, age, current year of school, department of interest, desired tuition fee, commuting distance, and future career goals.

[0433] Input: Personal information and interest data entered by you.

[0434] Output: User data sent to the server via the device.

[0435] The device sends input data and the user's emotional state (e.g., input speed, facial expression recognition data) to the server. The emotional state is measured using the device's sensors and camera.

[0436] Step 2: Data reception and verification

[0437] The server validates the user data received, ensuring that all required fields (name, age, subject, etc.) are filled in and that it is in the correct format. If the data passes validation, it is stored in the database.

[0438] Input: User data and emotional state data sent from the device.

[0439] Output: Validated data is stored in a database.

[0440] Specific behavior: The server validates the data format, presence of required fields, etc., and generates an error message if there is an inconsistency.

[0441] Step 3: Sentiment Analysis

[0442] The server sends the received data to the emotion engine, which analyzes the user's emotional state, including emotions such as excitement, joy, and anxiety.

[0443] Input: User data and emotional state data sent by the server.

[0444] Output: Sentiment analysis results are generated and fed back to the AI ​​engine.

[0445] Specific behavior: The emotion engine analyzes data such as the user's facial expressions and typing speed to identify their emotional state.

[0446] Step 4: Data analysis and career suggestions

[0447] The server sends the saved user data and the results of the emotion analysis to the AI ​​engine, which then analyzes the data using machine learning algorithms. Specifically, it recognizes patterns in the user data and generates optimal career options (schools, cram schools, teachers, friends, etc.).

[0448] Input: User data and sentiment analysis results.

[0449] Output: A list of the best career paths for the user.

[0450] Specific operation: The AI ​​engine compares with past data and generates career options that best match the user's interests and emotional state.

[0451] Step 5: Review your shortlist and request more information

[0452] The user checks the list of career options sent by the server, selects the option they are interested in, and then requests detailed information.

[0453] Input: A list of possible career paths sent from the server.

[0454] Output: The specific candidate selected by the user and the details requested.

[0455] Specific behavior: The user scrolls through a list of career options on the screen and clicks on an option that interests them to request more information.

[0456] Step 6: Get and send details

[0457] The server retrieves detailed information about the selected career path from the database and sends it to the user, including the school's curriculum, tuition fees, and the career paths of past graduates.

[0458] Input: Detailed information about the career path requested by the user.

[0459] Output: The retrieved details are sent to the user and displayed on their terminal.

[0460] What happens: The server pulls the details from the database and sends them to the user as content.

[0461] Step 7: Guide next steps and receive feedback

[0462] The user performs specific actions according to the next action steps presented to them, such as completing the procedures to enroll in a cram school online.

[0463] The server designs the next action step based on the user's selection and actions, and guides the user through it. The user completes the procedure and returns the results to the server.

[0464] Input: The result of the user's action following the next behavioral step.

[0465] Output: Feedback data based on the action.

[0466] Specific operation: The user completes the online process and sends a completion notification to the server, which receives the feedback and uses it to improve the emotion engine and AI engine algorithms.

[0467] This allows for optimal course suggestions that take into account the user's emotional state and feedback.

[0468] (Application example 2)

[0469] 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."

[0470] While conventional systems suggest career paths based on a user's personal information and interests, they lack the functionality to analyze the user's emotional state and environmental changes in real time and propose alert levels and countermeasures. This makes it difficult to respond quickly and appropriately in specific situations, resulting in the problem of insufficient security effectiveness.

[0471] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state based on user data and proposing an alert level and countermeasures, means for the security system engine to guide the next action based on the recommended alert level and countermeasures, and means for receiving feedback from security personnel and improving the system algorithm. This enables security responses that reflect the user's real-time emotional state.

[0472] "User Data" means the personal information, values, interests, and real-time emotional state and environmental data that a user enters into a registration form.

[0473] "Emotion analysis" is the process by which the emotion engine analyzes the user's emotional state based on user data.

[0474] The "alert level" is an indicator of the degree of alertness calculated by the security system based on the results of the user's emotion analysis and other data.

[0475] "Countermeasures" are specific actions or measures recommended by the security system, and are provided according to the alert level.

[0476] A "security engine" is a system element that operates within a security system and generates alert levels and countermeasures based on emotion analysis results and user data.

[0477] "Feedback" is information provided to the server about the actions taken by the user and their results, which is used to improve the system's algorithms.

[0478] A "registration form" is an interface through which a user can enter personal information, values, interests, etc.

[0479] "Real-time data" refers to dynamic information such as heart rate, body temperature, and facial expressions acquired while the user is using the system.

[0480] An embodiment of the present invention will be described.

[0481] System configuration

[0482] The present invention is a security system that analyzes the emotional state of a user based on user data and proposes alert levels and countermeasures. The main components of the system are as follows:

[0483] User terminal: A device that allows users to input personal information and real-time data (heart rate, body temperature, facial expressions, etc.), such as smart glasses.

[0484] Server: Validates and analyzes the received user data, and generates and sends alert levels and countermeasures.

[0485] Database: Stores archived user data and analysis results.

[0486] AI Engine: Runs machine learning algorithms based on user data to generate optimal alert levels.

[0487] Emotion engine: Analyzes emotions from user input data and real-time data.

[0488] Processing flow

[0489] 1. User registration: The user enters personal information, values, interests, etc. into a registration form through the smart glasses and sends the information to the server, which verifies the data and stores it in a database.

[0490] 2. Emotion analysis: While the user is wearing the smart glasses, real-time data (heart rate, body temperature, facial expressions, etc.) is collected and analyzed by the emotion engine, and the analysis results are sent to the server.

[0491] 3. Alert Level Generation: The server sends the analysis results received from the emotion engine to the AI ​​engine, which generates the optimal alert level and countermeasures. For example, it may suggest, "If the heart rate rises sharply, set the alert level to high and increase patrols."

[0492] 4. Guidance on response actions: Security personnel check the alert level and response measures sent from the server and take specific actions (patrols, reporting, etc.).

[0493] 5. Feedback collection: Security personnel provide feedback on the results of the actions taken to the server, which analyzes this feedback and improves the system's algorithms.

[0494] Hardware and software used

[0495] Smart glasses: devices that collect real-time data about the user.

[0496] Internet: Used for data communication between user terminals and servers.

[0497] Server: A system (using a framework such as Django) for receiving, validating, parsing, and sending data.

[0498] Database: A system (such as MySQL or PostgreSQL) that stores user data and analysis results.

[0499] AI Engine: A system that runs machine learning algorithms (such as PyTorch or TensorFlow).

[0500] Emotion engine: Software for analyzing user emotions (Face API, Emotion API, etc.).

[0501] Specific examples

[0502] For example, if a security officer's heart rate suddenly rises during a nighttime patrol, the smart glasses will detect this fluctuation and send it to the server as real-time data. The server will then use the AI ​​engine to set an appropriate alert level based on the results of the emotion engine's analysis, and suggest a countermeasure called "increased patrols." The security officer will then intensify their patrols in accordance with this countermeasure, and the results will be sent as feedback to the server. This feedback will help improve the system's algorithms.

[0503] Prompt Sentence Examples

[0504] "A security officer's heart rate spiked during nighttime patrol. Please suggest the optimal alert level and response in this situation."

[0505] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0506] Step 1:

[0507] The user enters personal information, values, interests, etc. into a registration form through the smart glasses, and the device sends the information to the server. The input data includes name, age, position, etc. The server receives this data, validates it, and stores it in a database. During the validation phase, it checks whether required fields have been filled in and prompts the user to re-enter any uncertain data.

[0508] Step 2:

[0509] The server retrieves stored user data from the database and sends it to the emotion engine. The emotion engine analyzes the user's emotional state based on the data provided by the user and real-time data (heart rate, body temperature, facial expressions, etc.). During this analysis process, an AI algorithm is used to analyze the data and assign an emotion label (e.g., excitement, relief, anxiety, etc.). The analysis results are fed back to the server.

[0510] Step 3:

[0511] The server receives emotion analysis results from the emotion engine and sends them to the AI ​​engine, which then generates the optimal alert level and countermeasures. For example, if the heart rate rises sharply and the facial expression is analyzed as "surprise," the AI ​​engine will set the alert level to "high" and suggest "increased patrols" as a countermeasure. The AI ​​engine uses machine learning algorithms to learn patterns from large amounts of data and propose optimal countermeasures.

[0512] Step 4:

[0513] The server sends the generated alert level and countermeasures to the user's device, and the user (security officer) receives them through the smart glasses. The user checks the displayed alert level and countermeasures and takes necessary crime prevention actions. For example, if "increased patrols" is suggested, the security officer will focus on patrolling the designated area.

[0514] Step 5:

[0515] The results of the crime prevention actions taken by the user are input into the device as feedback and sent to the server. This feedback includes the action taken and its results (for example, whether or not the user was safe). The server receives this feedback and uses it to improve the algorithms of the emotion engine and AI engine. Feedback analysis will enable more accurate alert levels and countermeasures to be proposed in the future.

[0516] 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.

[0517] 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.

[0518] 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.

[0519] [Second embodiment]

[0520] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0521] 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.

[0522] 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).

[0523] 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.

[0524] 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.

[0525] 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).

[0526] 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.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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."

[0532] A specific system configuration and its operation will be described below as an embodiment of the present invention.

[0533] System Configuration

[0534] The system of the present invention includes a user terminal, a server, a database, and an AI engine. The user terminal communicates with the server via the Internet and provides an interface for data input and result display.

[0535] User devices: personal computers, tablets, smartphones, etc.

[0536] Server: Receives, stores, analyzes user data, and generates results

[0537] Database: Stores user data and analysis results

[0538] AI engine: Runs machine learning algorithms to generate optimal career paths based on user data

[0539] Program processing

[0540] 1. User Registration

[0541] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[0542] The terminal transmits the input data to the server.

[0543] The server receives the data, validates the necessary fields, and saves the data to the database if it passes validation.

[0544] 2. Data analysis and career suggestions

[0545] The server sends the saved user data to the AI ​​engine.

[0546] The AI ​​engine uses machine learning algorithms to analyze the data and generate the most suitable career options for the user (schools, cram schools, teachers, friends, etc.).

[0547] The server transmits the generated course candidates to the user.

[0548] 3. User selection and confirmation of details

[0549] The user checks the career options sent from the server and selects the option that interests them.

[0550] The user requests more information.

[0551] The server retrieves the details of the selected candidate from the database and sends them to the user.

[0552] The terminal displays the detailed information to the user.

[0553] 4. Next steps and feedback

[0554] The user performs a specific action according to the next action step presented to them, for example, enrolling in a cram school.

[0555] The server receives the feedback from the user and stores it in a database.

[0556] The server analyzes the feedback and improves the algorithms of the AI ​​engine, and this feedback loop improves the accuracy of the system.

[0557] Specific examples

[0558] As a specific example, a case where user A is searching for a school to attend will be described.

[0559] 1. User A enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[0560] 2. The device sends this information to the server.

[0561] 3. The server validates the data and saves it to the database.

[0562] 4. The server passes the data to the AI ​​engine for analysis.

[0563] 5. The AI ​​engine generates the best school candidates based on User A's information. For example, it will list schools with tuition fees that match User A's expectations and a wide range of departments that interest User A.

[0564] 6. The server sends the generated list of schools to User A.

[0565] 7. User A selects a school from the list and requests more information.

[0566] 8. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User A.

[0567] 9. The terminal displays the detailed information to User A.

[0568] 10. User A begins the admission process for the school that interests him most and feeds the results back to the server.

[0569] 11. The server receives the feedback and uses it to improve the AI ​​engine algorithm.

[0570] In this way, the system supports users in choosing a career path that is optimized to their individual needs, and streamlines user decision-making.

[0571] The processing flow will be explained below.

[0572] Step 1:

[0573] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[0574] Step 2:

[0575] The terminal transmits the input data to the server.

[0576] Step 3:

[0577] The server validates the data it receives, ensuring that all required fields are filled in. If there are any omissions, it generates an error message and sends it back to the terminal.

[0578] Step 4:

[0579] The server saves the data that passes the validation in the database.

[0580] Step 5:

[0581] The server sends the saved user data to the AI ​​engine and instructs it to analyze it.

[0582] Step 6:

[0583] The AI ​​engine uses machine learning algorithms to analyze user data and generate the best possible career paths for the user.

[0584] Step 7:

[0585] The server transmits the generated course candidates to the user.

[0586] Step 8:

[0587] The user checks the career options sent from the server and selects the one that interests them from among the multiple options.

[0588] Step 9:

[0589] Request more information about the candidate selected by the user.

[0590] Step 10:

[0591] The server retrieves the details from the database based on the user's request.

[0592] Step 11:

[0593] The server sends the retrieved details to the user.

[0594] Step 12:

[0595] The device visually displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[0596] Step 13:

[0597] Users take specific actions regarding the career options they are most interested in. For example, they can apply online to enroll in a cram school.

[0598] Step 14:

[0599] The server designs the next action steps based on the user's selection and actions and guides the user.

[0600] Step 15:

[0601] The user completes the presented procedure and provides feedback on the results to the server.

[0602] Step 16:

[0603] The server analyzes the feedback it receives, stores it in a database, and uses it as data to improve the AI ​​engine's algorithms.

[0604] This series of steps allows users to make efficient and optimal career choices, and the system is continuously improved by incorporating user feedback.

[0605] Example 1

[0606] 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."

[0607] In today's educational environment, it is extremely important for users to choose the most appropriate career path based on their own values ​​and interests. However, it is not easy to find a career path that matches individual needs from a vast amount of information, and it is difficult to make an appropriate decision in a short amount of time. For this reason, there is a need for a system that can effectively suggest the most suitable career path for users, provide detailed information, and guide them on next steps.

[0608] 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.

[0609] In this invention, the server includes means for a user to enter and send personal information, values, and interests into a registration form, means for a terminal to send the entered data to the server, means for the server to verify the received user data and store it in a database, means for the server to analyze the stored user data and send it to an AI engine, means for the AI ​​engine to analyze the data using a machine learning algorithm and generate career path candidates optimal for the user, means for the server to send the generated career path candidates to the user, means for the user to select from the candidates and request detailed information, means for the server to obtain detailed information about the selected career path and send it to the user, means for the terminal to display the detailed information to the user, and means for the server to provide next action steps based on the user's selection and receive feedback. This makes it possible to support career selection optimized to the user's individual needs and streamline decision-making.

[0610] "User" refers to a person who inputs information into the system and receives the results of processing.

[0611] "Terminal" refers to a device such as a personal computer, tablet, or smartphone that a user uses to input information and check the displayed results.

[0612] "Server" refers to a central processing unit that receives, stores, analyzes user data, and generates results.

[0613] "Database" refers to an information management system for storing user data and analysis results.

[0614] "AI engine" refers to a component that runs machine learning algorithms to generate optimal career options based on user data.

[0615] "Registration Form" refers to the interface used by a user to enter information such as personal details, values, and interests.

[0616] A "machine learning algorithm" refers to a mathematical model used to analyze data and find patterns and trends.

[0617] "Career options" refer to options such as schools, cram schools, teachers, and friends that are proposed to the user.

[0618] "Detailed information" refers to detailed explanations and information about the career options selected by the user.

[0619] "Action steps" refer to specific actions or procedures that a user should take next.

[0620] "Feedback" refers to the evaluations, opinions, and result information that users provide to the system.

[0621] This invention is a support system for users to choose the most suitable career path. This system is composed of a user terminal, a server, a database, and an AI engine. Each component and its operation are explained in detail below.

[0622] System Configuration

[0623] User device: A personal computer, tablet, smartphone, etc. used by users to input information and view results. Examples include a common web browser or mobile application.

[0624] Server: Receives, stores, and analyzes user data, generates results, and sends them to users. The server is a computer system equipped with a high-performance processor and sufficient memory, and also includes a database server and an AI engine.

[0625] Database: An information management system for storing user data and analysis results, such as an SQL database.

[0626] AI engine: Analyzes user data using machine learning algorithms to generate optimal career paths. For example, TensorFlow and PyTorch, which use Python, are examples of this type of engine.

[0627] Program processing

[0628] 1. User Registration

[0629] The user uses a terminal to enter personal information (name, age, current grade), values, interests, desired tuition fees, commuting distance, and future career goals into a registration form. The entered data is sent from the terminal to the server, which verifies the received data and stores it in a database.

[0630] 2. Data analysis and career suggestions

[0631] The server sends the saved user data to the AI ​​engine, which uses a machine learning algorithm to analyze the input data and generate optimal career path candidates. The generated career path candidates are then sent to the user's device via the server.

[0632] 3. User selection and confirmation of details

[0633] The user selects an option that interests them from the list of available options and requests detailed information. The server retrieves detailed information about the selected option from the database and sends it to the user. The terminal displays the detailed information to the user.

[0634] 4. Next steps and feedback

[0635] The user follows the suggested next action steps and takes specific actions, such as enrolling in a cram school. This feedback is received by the server and stored in a database. The server analyzes the received feedback and improves the algorithm of the AI ​​engine.

[0636] Specific examples

[0637] For example, if user A is looking for a school to attend, he or she will take the following steps:

[0638] 1. User A enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[0639] Example prompt: "My name is Taro Yamada. I'm 17 years old and a second-year high school student. I'm interested in computer science and my desired tuition is 500,000 yen per year. The school is 30 kilometers from my home. My future career goal is to become a software engineer."

[0640] 2. The device sends this information to the server.

[0641] 3. The server validates the data and saves it to the database.

[0642] 4. The server passes the data to the AI ​​engine for analysis.

[0643] 5. The AI ​​engine generates the best school candidates based on User A's information. For example, it will list schools with tuition fees that match User A's expectations and a wide range of departments that interest User A.

[0644] Example prompt: "Please tell me the best school for me. Here is my information: My name is Taro Yamada, I'm 17 years old, I'm a second-year high school student, I'm interested in computer science, I would like to pay 500,000 yen per year in tuition, I live within a 30km commute, and my future career goal is to become a software engineer."

[0645] 6. The server sends the generated list of schools to User A.

[0646] 7. User A selects a school from the list and requests more information.

[0647] 8. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User A.

[0648] 9. The terminal displays the detailed information to User A.

[0649] 10. User A begins the admission process for the school that interests him most and feeds the results back to the server.

[0650] 11. The server receives the feedback and uses it to improve the AI ​​engine algorithm.

[0651] Through the detailed processing steps described above, the present system supports users in choosing a career path that is optimized to their individual needs, and streamlines decision-making.

[0652] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0653] Step 1:

[0654] The user enters information into an input form on the device, such as name, age, current year at school, department of interest, desired tuition fee, distance to school, and future career goals.

[0655] Input: Data that a user enters into a form field.

[0656] Output: User input data that is temporarily stored in the device's memory.

[0657] What happens: A user fills in a form in a browser or application and clicks the "Submit" button.

[0658] Step 2:

[0659] The terminal formats the entered data and transmits it over the Internet to a server.

[0660] Input: User input data.

[0661] Output: The HTTP POST request sent to the server.

[0662] Specific operation: The device converts the data obtained from the user into a format such as JSON or XML and sends it to the server as an HTTP request.

[0663] Step 3:

[0664] The server validates the user data received, checking that all required fields are present and that the data is in the correct format.

[0665] Input: User data sent from the device.

[0666] Output: The validation results and, if appropriate, the data stored in a database.

[0667] What happens: The server runs validation scripts to check for missing fields or improper formatting. Data that passes validation is saved to the database using a SQL INSERT statement.

[0668] Step 4:

[0669] The server sends the saved user data to the AI ​​engine.

[0670] Input: User data stored in the database.

[0671] Output: The data sent to the AI ​​engine.

[0672] Specific operation: The server retrieves user data from the database and sends a request to the AI ​​engine using REST API or gRPC.

[0673] Step 5:

[0674] The AI ​​engine uses machine learning algorithms to analyze the data and generate optimal career options.

[0675] Input: User data received from the server.

[0676] Output: A list of optimal career paths.

[0677] How it works: The AI ​​engine inputs data into a predictive model, runs algorithms, and generates a ranking of the best career options.

[0678] Step 6:

[0679] The server transmits the generated course candidates to the user.

[0680] Input: A list of possible career paths received from the AI ​​engine.

[0681] Output: The HTTP response sent to the user's device.

[0682] Specific operation: The server sends the data obtained from the AI ​​engine to the user device as an HTTP response.

[0683] Step 7:

[0684] The user checks the list of career options sent to them and selects the option that interests them.

[0685] Input: A list of possible career paths sent from the server.

[0686] Output: The career options selected by the user.

[0687] Specific actions: The user checks the list on the device and clicks a button to view details of the selected career option.

[0688] Step 8:

[0689] The user requests more information.

[0690] Input: The career path selected by the user.

[0691] Output: A more information request sent to the server.

[0692] What happens: The user clicks the "View Details" button, sending a request to the server.

[0693] Step 9:

[0694] The server retrieves details of the selected route from the database and sends them to the user.

[0695] Input: Request more information about the selected career path.

[0696] Output: Detailed information sent to the user's device.

[0697] Specific operation: The server retrieves detailed information from the database and sends it to the device in an HTTP response.

[0698] Step 10:

[0699] The terminal displays the detailed information to the user.

[0700] Input: The details received from the server.

[0701] Output: Detailed information displayed on the device screen.

[0702] Specific behavior: The device UI processes the received data and displays it to the user in an appropriate format.

[0703] Step 11:

[0704] The user starts the admission procedure for the school in which he or she is most interested, and feeds back the results to the server.

[0705] Input: User's admissions results and feedback.

[0706] Output: Feedback information sent to the server.

[0707] Specific behavior: The user completes the procedure, enters the required information in the feedback form, and submits it to the server.

[0708] Step 12:

[0709] The server receives the feedback and analyzes it to improve the AI ​​engine's algorithms.

[0710] Input: Feedback information from the user.

[0711] Output: Improved AI algorithm.

[0712] What it does: The server stores the feedback information in a database, then runs a data analysis script to generate data for retraining the AI ​​model.

[0713] (Application example 1)

[0714] 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."

[0715] Conventional online shopping sites lacked personalized product suggestions based on users' interests and purchase history. This meant that users had to spend time finding the product that best suited them, reducing their motivation to purchase. Furthermore, there was a lack of a convenient way to obtain detailed information about suggested products, which led to lower user satisfaction.

[0716] 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.

[0717] In this invention, the server includes a means for a user to input and submit personal information, values, and interests into a registration form, a means for the server to verify the received user data and store it in a database, and a means for the server to analyze the stored user data and generate product candidates that are optimal for the user, thereby enabling the user to efficiently find optimal product candidates based on their interests and values.

[0718] A "user" is a person who uses the system to input personal information, values, and interests and receives product proposals.

[0719] A "registration form" is an interface through which a user enters personal information, values, and interests.

[0720] "Personal information" refers to personal data such as a user's name, age, address, and contact details.

[0721] "Values" is information about a user's beliefs and priorities.

[0722] "Interests" is information about fields or product categories in which the user is particularly interested.

[0723] The "means for sending" is a function for sending the data entered by the user in the registration form to the server.

[0724] "Server" means a central device for receiving, storing and analyzing User Data.

[0725] "Means for verifying" refers to a function that allows the server to verify the accuracy and completeness of the user data received.

[0726] A "database" is a data storage device for saving user data and analysis results.

[0727] The "means for storing" is a function for storing the user data received by the server in a database.

[0728] The "means for analyzing" is a function for analyzing stored user data using a machine learning algorithm.

[0729] "Product candidates" are products that are proposed to the user as optimal options based on the analysis results.

[0730] The "means for generating" is a function for generating a product candidate list based on the analysis results.

[0731] "Detailed information" includes specific product specifications, reviews, pricing information, etc.

[0732] The "request means" is a function that allows a user to request detailed information about a product candidate.

[0733] The "means for obtaining" is a function that allows the server to retrieve detailed information from the database and send it to the user.

[0734] The "transmitting means" is a function that allows the server to transmit detailed information to the user's terminal.

[0735] The "next action step" is specific guidance for the user to purchase the product.

[0736] "Feedback" is information that users send back to the system regarding their reactions and evaluations of the proposed products.

[0737] A "machine learning algorithm" is a computer program that analyzes user data and calculates optimal product candidates.

[0738] To implement the present invention, the following system configuration and operation should be considered.

[0739] System Configuration

[0740] The system includes a user terminal, a server, a database, and an AI engine. The user terminal communicates with the server via the Internet and provides an interface for data input and results display.

[0741] User devices: personal computers, tablets, smartphones, etc.

[0742] Server: Receives, stores, analyzes user data, and generates results

[0743] Database: Stores user data and analysis results

[0744] AI engine: Runs machine learning algorithms to generate optimal product candidates based on user data

[0745] Program processing and explanation

[0746] 1. User Registration

[0747] The user enters personal information, values, interests, etc. into the registration form and submits it. The user's device sends the entered data to the server. The server receives the data and verifies it. After confirming that the required information has been entered correctly, the data is saved in the database.

[0748] 2. Data analysis and product proposals

[0749] The server sends the saved user data to the AI ​​engine, which analyzes the data using a machine learning algorithm (e.g., TensorFlow) and generates the best product candidates for the user. The server then sends the generated product candidate list to the user.

[0750] 3. Request more information

[0751] The user selects an item of interest from the sent product candidate list and requests detailed information. The server retrieves detailed information about the selected item from the database and sends it to the user.

[0752] 4. Next steps and feedback

[0753] The user adds products to their cart based on the details they provide, checks out, and sends their post-purchase feedback to the server, which stores this feedback in a database and uses it to improve the AI ​​engine's algorithms.

[0754] Hardware and software used

[0755] Hardware:

[0756] User devices: smartphones, tablets, personal computers

[0757] Server: Cloud server (e.g. AWS, Google Cloud)

[0758] software:

[0759] Server: Flask (Python framework)

[0760] Database: SQLite

[0761] AI engine: TensorFlow (machine learning algorithm)

[0762] Specific examples

[0763] As a specific example, a case where a user is searching for a new electronic device that interests him will be described.

[0764] 1. What the user enters in the registration form:

[0765] Name, age, product categories of interest (e.g. electronics), past purchase history

[0766] 2. Submit and validate input data:

[0767] The server receives the data, validates the personal information, values, and interests, and stores them in a database.

[0768] 3. Analysis by AI engine:

[0769] It uses machine learning algorithms to analyze the data and generate a list of the best electronic devices for the user.

[0770] 4. User requests more information:

[0771] The user retrieves detailed information (e.g., specifications, reviews, and pricing information) about the suggested products, which the system displays to the user.

[0772] 5. Next Action Steps and Feedback:

[0773] Users purchase products and the server receives their feedback, which is used to improve the accuracy of the AI ​​engine.

[0774] Prompt Sentence Examples

[0775] Specific examples of prompts are as follows:

[0776] "Please suggest products that the user is likely to purchase next based on their purchase history. The user's name is Taro Yamada, he is 35 years old, and his recent purchases have been home appliances, with a particular interest in televisions."

[0777] Using this prompt, the generative AI model analyzes data to suggest the best products for the user.

[0778] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0779] Step 1:

[0780] The user enters personal information, values, and interests into a registration form and submits it. The data entered by the user is sent from the device to the server. The input here is name, age, product categories of interest, and details, and the output is unverified data that is stored on the server.

[0781] Step 2:

[0782] The server validates the received user data. Specifically, the server checks that all required fields in the input data are filled in correctly and verifies that there are no incomplete inputs. The input is the data submitted in step 1, and the output is the validated data.

[0783] Step 3:

[0784] The server saves the validated data to the database using a SQL query to insert the user data into the appropriate tables. The input is validated data that can be saved, and the output is data stored in the database.

[0785] Step 4:

[0786] The server sends the saved user data to the AI ​​engine. Specifically, it uses a Python library to send the data to the AI ​​engine and start analysis. The input is the user data retrieved from the database, and the output is the analysis result passed to the AI ​​engine.

[0787] Step 5:

[0788] The AI ​​engine analyzes user data and generates optimal product candidates. Here, a machine learning algorithm (e.g., TensorFlow) is used to recommend products based on the given data. The input is user data received from the server, and the output is a list of product candidates.

[0789] Step 6:

[0790] The server sends the analysis results from the AI ​​engine to the user. The server receives the generated product candidate list and sends it in a format to be displayed on the user's device. The input is the product candidate list, and the output is the data displayed on the user's device.

[0791] Step 7:

[0792] The user selects a product of interest from the suggested product candidates and requests detailed information. A request for detailed information about the product selected by the user is sent from the terminal to the server. The input is the product candidate list and the selected data, and the output is a detailed information request.

[0793] Step 8:

[0794] The server retrieves the details of the selected product from the database and sends them to the user. It uses a database query to pull the details of the relevant product and returns them to the user. The input is the details request and the output is the product details.

[0795] Step 9:

[0796] The user checks the details, adds the product to the cart, and completes the purchase. The purchase data is sent from the terminal to the server. The input is the product details and purchase data, and the output is purchase procedure confirmation data.

[0797] Step 10:

[0798] The server receives post-purchase feedback from users. The feedback data is sent from the device to the server, where it is stored in a database and used to improve the AI ​​engine's algorithm. The input is user feedback, and the output is an improved algorithm.

[0799] 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.

[0800] A specific system configuration and its operation will be described below as an embodiment of the present invention.

[0801] System Configuration

[0802] The system of the present invention includes a user terminal, a server, a database, an AI engine, and an emotion engine. The user terminal communicates with the server via the Internet and provides an interface for data input and result display.

[0803] User devices: personal computers, tablets, smartphones, etc.

[0804] Server: Receives, stores, analyzes user data, and generates results

[0805] Database: Stores user data and analysis results

[0806] AI engine: Runs machine learning algorithms to generate optimal career paths based on user data

[0807] Emotion Engine: Software for analyzing emotions from user input data and feedback.

[0808] Program processing

[0809] 1. User Registration

[0810] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[0811] The terminal transmits the input data and the user's emotional state to the server.

[0812] The server receives the data, validates the necessary fields, and saves the data to the database if it passes validation.

[0813] 2. Emotion analysis

[0814] The server sends the data received from the user to the emotion engine, which analyzes the user's emotional state.

[0815] The emotion engine analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) and feeds the results back to the AI ​​engine.

[0816] 3. Data analysis and career suggestions

[0817] The server sends the stored user data and emotion analysis results to the AI ​​engine.

[0818] The AI ​​engine uses machine learning algorithms to analyze the data and generate the most suitable career options for the user (schools, cram schools, teachers, friends, etc.).

[0819] The server transmits the generated course candidates to the user.

[0820] 4. User selection and confirmation of details

[0821] The user checks the career options sent from the server and selects the option that interests them.

[0822] The user requests more information.

[0823] The server retrieves the details of the selected candidate from the database.

[0824] The server sends the retrieved details to the user.

[0825] The device displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[0826] 5. Next steps and feedback

[0827] The user performs a specific action according to the next action step presented to them, for example, completing the online procedure to enroll in a cram school.

[0828] The server designs the next action steps based on the user's selection and actions and guides the user.

[0829] The user completes the procedure and provides feedback on the results to the server.

[0830] The server analyzes the received feedback and improves the algorithms of the emotion engine and AI engine.

[0831] Specific examples

[0832] As a specific example, a case where user B is searching for a school to attend will be described.

[0833] 1. User B enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[0834] 2. The device sends this information and the emotional state the user indicated during input (e.g., facial expression recognition and speed changes) to the server.

[0835] 3. The server validates the data and saves it to the database.

[0836] 4. The server sends the received data to the emotion engine for emotion analysis.

[0837] 5. The emotion engine analyzes User B's emotional state and feeds the results back to the AI ​​engine.

[0838] 6. The server sends the saved user data and emotion analysis results to the AI ​​engine for analysis.

[0839] 7. The AI ​​engine generates the best school candidates based on User B's information. For example, it will list schools with tuition fees that match User B's expectations and a wide range of departments that interest him or her.

[0840] 8. The server sends the generated list of schools to User B.

[0841] 9. User B selects a school from the list and requests more information.

[0842] 10. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User B.

[0843] 11. The device displays the detailed information to User B.

[0844] 12. User B begins the admission process for the school he is most interested in and feeds the results back to the server.

[0845] 13. The server receives feedback and uses it to improve the emotion engine and AI engine algorithms.

[0846] In this way, the system supports career choices that take into account the user's emotional state, making user decision-making more personalized and efficient.

[0847] The processing flow will be explained below.

[0848] Step 1:

[0849] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[0850] Step 2:

[0851] The device sends the input data and information for measuring the user's emotions (for example, facial expression data and input speed) to the server.

[0852] Step 3:

[0853] The server validates the data received and ensures that all required fields are filled in. If there are any omissions, it generates an error message and sends it back to the terminal.

[0854] Step 4:

[0855] The server stores the verified data and emotion data in a database.

[0856] Step 5:

[0857] The server transmits the stored user data and emotion data to the emotion engine, which analyzes the user's emotional state.

[0858] Step 6:

[0859] The emotion engine analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) and feeds the results back to the AI ​​engine.

[0860] Step 7:

[0861] The server sends the saved user data and emotion analysis results to the AI ​​engine.

[0862] Step 8:

[0863] The AI ​​engine uses machine learning algorithms to analyze the data and generate the best possible career paths for the user.

[0864] Step 9:

[0865] The server transmits the generated course candidates to the user.

[0866] Step 10:

[0867] The user checks the career options sent from the server and selects the one that interests them from among the multiple options.

[0868] Step 11:

[0869] Request more information about the candidate selected by the user.

[0870] Step 12:

[0871] The server retrieves the details from the database based on the user's request.

[0872] Step 13:

[0873] The server sends the retrieved details to the user.

[0874] Step 14:

[0875] The device visually displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[0876] Step 15:

[0877] Users take specific actions regarding the career options they are most interested in. For example, they can apply online to enroll in a cram school.

[0878] Step 16:

[0879] The server designs the next action steps based on the user's selection and actions and guides the user.

[0880] Step 17:

[0881] The user completes the presented procedure and provides feedback on the results to the server.

[0882] Step 18:

[0883] The server analyzes the feedback it receives and uses it as data to improve the algorithms of the emotion engine and AI engine.

[0884] This series of steps allows users to make efficient and optimal career choices. The system is continuously improved by incorporating user feedback. The use of an emotion engine enables advanced career suggestions that take into account the user's emotional state.

[0885] Example 2

[0886] 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."

[0887] Conventional career suggestion systems have difficulty in proposing a career path that takes into account the user's emotional state and feedback, and are therefore unable to provide the optimal career path for the user. Furthermore, because they suggest a career path based solely on the information provided by the user, they lack the flexibility to respond to individual situations. This can lead to inefficient user decision-making and unsatisfactory results.

[0888] 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.

[0889] In this invention, the server includes means for a user to enter and transmit personal information, values, and interests into a registration form, means for a terminal to transmit the input data and the user's emotional state to the server, means for the server to verify the received user data and store it in a database, means for the server to send the received data to an emotion analysis engine and analyze the user's emotional state, means for an AI engine to apply a machine learning algorithm using the user data and emotion analysis results stored by the server to generate optimal career path candidates for the user, means for the user to review the generated career path candidates, select one that interests them, and request detailed information, means for the server to retrieve detailed information about the selected career path from the database and send it to the user, means for the terminal to display the detailed information to the user, means for the user to take specific actions according to the next action step and feed the results back to the server, and means for the server to receive the user's feedback and improve the algorithms of the emotion engine and the AI ​​engine. This enables optimal career path suggestions that take the user's emotional state and feedback into consideration.

[0890] "User" refers to an individual who utilizes the system to input information and receive career suggestions.

[0891] "Terminal" refers to a device that allows a user to input information and communicate with a server to send and receive data, including personal computers, tablets, and smartphones.

[0892] "Server" refers to the central processing unit that receives, stores, analyzes data from users, and generates and transmits results.

[0893] "Database" refers to a management system for the server to store user data and analysis results.

[0894] An "emotion analysis engine" refers to software that analyzes a user's emotional state from input data and feedback.

[0895] An "AI engine" refers to software that uses machine learning algorithms to analyze user data and generate optimal career path candidates.

[0896] "Machine learning algorithm" refers to an algorithm that analyzes user data and learns patterns and characteristics to generate optimal career path candidates.

[0897] "Career options" refer to options such as the most suitable school, cram school, teacher, or friend that are provided to the user.

[0898] "Emotional state" refers to a user's psychological state such as excitement, joy, or anxiety.

[0899] "Feedback" refers to information sent back to the server by the user regarding the results of performing behavioral steps.

[0900] MODE FOR CARRYING OUT THE INVENTION

[0901] A specific system configuration and its operation will be described below for an embodiment of the present invention. The system described below includes a user terminal, a server, a database, an AI engine, and an emotion engine.

[0902] Hardware and software used

[0903] User devices: Personal computers, tablets, smartphones, etc. Users use these devices to interact with the system.

[0904] Server: A central processing unit that receives, stores, analyzes user data, and generates results.

[0905] Database: A management system that stores user data and analysis results.

[0906] AI Engine: Software that uses machine learning algorithms to analyze user data and generate optimal career path suggestions.

[0907] Emotion engine: Software that analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) from their input data and feedback.

[0908] System Operation

[0909] User Registration

[0910] When a user enters personal information, values, and interests into the input form on the device and presses the send button, the device sends the entered data and the user's emotional state to the server. The server receives the data, verifies the necessary fields, and saves it in a database.

[0911] Emotion analysis

[0912] The server sends the received data to the emotion engine, which analyzes the user's emotional state and feeds the analysis results back to the AI ​​engine.

[0913] Data analysis and career suggestions

[0914] The server sends the saved user data and the results of the emotion analysis to the AI ​​engine, which then uses a machine learning algorithm to analyze the data and generate the most suitable career path candidates for the user. The generated career path candidates are then sent to the user via the server.

[0915] User selection and confirmation of details

[0916] The user checks the career options sent from the server and selects the option they are interested in. When they request detailed information, the server retrieves the details of the selected option from the database and sends it to the user. The terminal displays the details to the user.

[0917] Next steps and feedback

[0918] The user performs specific actions according to the next action steps presented and provides feedback on the results to the server, which then receives the feedback and improves the algorithms of the emotion engine and AI engine.

[0919] Specific examples

[0920] For example, consider a case where a user is looking for a school to attend. The user enters their name, age, current year of study, department of interest, desired tuition fees, commuting distance, and future career goals into an input form on their device and presses the submit button. At this time, the device also sends their emotional state, including facial recognition data, to the server. The server verifies the data and stores it in a database. The emotion engine then analyzes the emotional state, and the AI ​​engine generates the most suitable school candidates. For example, it may list schools with tuition fees that match the user's expectations and a wide range of departments that interest the user.

[0921] Example prompts for generative AI models

[0922] "Create a program that lists schools that the user is likely to be interested in and displays detailed information. Implement it to analyze the user's emotional state and recommend schools that are preferred when the user is feeling particularly safe or excited."

[0923] By inputting this prompt into a generative AI model, program code can be generated based on specific system behavior and algorithms.

[0924] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0925] Step 1: User Input and Data Submission

[0926] The user enters personal information, values, and interests into a form on the device and presses the submit button. The information entered includes name, age, current year of school, department of interest, desired tuition fee, commuting distance, and future career goals.

[0927] Input: Personal information and interest data entered by you.

[0928] Output: User data sent to the server via the device.

[0929] The device sends input data and the user's emotional state (e.g., input speed, facial expression recognition data) to the server. The emotional state is measured using the device's sensors and camera.

[0930] Step 2: Data reception and verification

[0931] The server validates the user data received, ensuring that all required fields (name, age, subject, etc.) are filled in and that it is in the correct format. If the data passes validation, it is stored in the database.

[0932] Input: User data and emotional state data sent from the device.

[0933] Output: Validated data is stored in a database.

[0934] Specific behavior: The server validates the data format, presence of required fields, etc., and generates an error message if there is an inconsistency.

[0935] Step 3: Sentiment Analysis

[0936] The server sends the received data to the emotion engine, which analyzes the user's emotional state, including emotions such as excitement, joy, and anxiety.

[0937] Input: User data and emotional state data sent by the server.

[0938] Output: Sentiment analysis results are generated and fed back to the AI ​​engine.

[0939] Specific behavior: The emotion engine analyzes data such as the user's facial expressions and typing speed to identify their emotional state.

[0940] Step 4: Data analysis and career suggestions

[0941] The server sends the saved user data and the results of the emotion analysis to the AI ​​engine, which then analyzes the data using machine learning algorithms. Specifically, it recognizes patterns in the user data and generates optimal career options (schools, cram schools, teachers, friends, etc.).

[0942] Input: User data and sentiment analysis results.

[0943] Output: A list of the best career paths for the user.

[0944] Specific operation: The AI ​​engine compares with past data and generates career options that best match the user's interests and emotional state.

[0945] Step 5: Review your shortlist and request more information

[0946] The user checks the list of career options sent by the server, selects the option they are interested in, and then requests detailed information.

[0947] Input: A list of possible career paths sent from the server.

[0948] Output: The specific candidate selected by the user and the details requested.

[0949] Specific behavior: The user scrolls through a list of career options on the screen and clicks on an option that interests them to request more information.

[0950] Step 6: Get and send details

[0951] The server retrieves detailed information about the selected career path from the database and sends it to the user, including the school's curriculum, tuition fees, and the career paths of past graduates.

[0952] Input: Detailed information about the career path requested by the user.

[0953] Output: The retrieved details are sent to the user and displayed on their terminal.

[0954] What happens: The server pulls the details from the database and sends them to the user as content.

[0955] Step 7: Guide next steps and receive feedback

[0956] The user performs specific actions according to the next action steps presented to them, such as completing the procedures to enroll in a cram school online.

[0957] The server designs the next action step based on the user's selection and actions, and guides the user through it. The user completes the procedure and returns the results to the server.

[0958] Input: The result of the user's action following the next behavioral step.

[0959] Output: Feedback data based on the action.

[0960] Specific operation: The user completes the online process and sends a completion notification to the server, which receives the feedback and uses it to improve the emotion engine and AI engine algorithms.

[0961] This allows for optimal course suggestions that take into account the user's emotional state and feedback.

[0962] (Application example 2)

[0963] 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."

[0964] While conventional systems suggest career paths based on a user's personal information and interests, they lack the functionality to analyze the user's emotional state and environmental changes in real time and propose alert levels and countermeasures. This makes it difficult to respond quickly and appropriately in specific situations, resulting in the problem of insufficient security effectiveness.

[0965] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state based on user data and proposing an alert level and countermeasures, means for the security system engine to guide the next action based on the recommended alert level and countermeasures, and means for receiving feedback from security personnel and improving the system algorithm. This enables security responses that reflect the user's real-time emotional state.

[0966] "User Data" means the personal information, values, interests, and real-time emotional state and environmental data that a user enters into a registration form.

[0967] "Emotion analysis" is the process by which the emotion engine analyzes the user's emotional state based on user data.

[0968] The "alert level" is an indicator of the degree of alertness calculated by the security system based on the results of the user's emotion analysis and other data.

[0969] "Countermeasures" are specific actions or measures recommended by the security system, and are provided according to the alert level.

[0970] A "security engine" is a system element that operates within a security system and generates alert levels and countermeasures based on emotion analysis results and user data.

[0971] "Feedback" is information provided to the server about the actions taken by the user and their results, which is used to improve the system's algorithms.

[0972] A "registration form" is an interface through which a user can enter personal information, values, interests, etc.

[0973] "Real-time data" refers to dynamic information such as heart rate, body temperature, and facial expressions acquired while the user is using the system.

[0974] An embodiment of the present invention will be described.

[0975] System configuration

[0976] The present invention is a security system that analyzes the emotional state of a user based on user data and proposes alert levels and countermeasures. The main components of the system are as follows:

[0977] User terminal: A device that allows users to input personal information and real-time data (heart rate, body temperature, facial expressions, etc.), such as smart glasses.

[0978] Server: Validates and analyzes the received user data, and generates and sends alert levels and countermeasures.

[0979] Database: Stores archived user data and analysis results.

[0980] AI Engine: Runs machine learning algorithms based on user data to generate optimal alert levels.

[0981] Emotion engine: Analyzes emotions from user input data and real-time data.

[0982] Processing flow

[0983] 1. User registration: The user enters personal information, values, interests, etc. into a registration form through the smart glasses and sends the information to the server, which verifies the data and stores it in a database.

[0984] 2. Emotion analysis: While the user is wearing the smart glasses, real-time data (heart rate, body temperature, facial expressions, etc.) is collected and analyzed by the emotion engine, and the analysis results are sent to the server.

[0985] 3. Alert Level Generation: The server sends the analysis results received from the emotion engine to the AI ​​engine, which generates the optimal alert level and countermeasures. For example, it may suggest, "If the heart rate rises sharply, set the alert level to high and increase patrols."

[0986] 4. Guidance on response actions: Security personnel check the alert level and response measures sent from the server and take specific actions (patrols, reporting, etc.).

[0987] 5. Feedback collection: Security personnel provide feedback on the results of the actions taken to the server, which analyzes this feedback and improves the system's algorithms.

[0988] Hardware and software used

[0989] Smart glasses: devices that collect real-time data about the user.

[0990] Internet: Used for data communication between user terminals and servers.

[0991] Server: A system (using a framework such as Django) for receiving, validating, parsing, and sending data.

[0992] Database: A system (such as MySQL or PostgreSQL) that stores user data and analysis results.

[0993] AI Engine: A system that runs machine learning algorithms (such as PyTorch or TensorFlow).

[0994] Emotion engine: Software for analyzing user emotions (Face API, Emotion API, etc.).

[0995] Specific examples

[0996] For example, if a security officer's heart rate suddenly rises during a nighttime patrol, the smart glasses will detect this fluctuation and send it to the server as real-time data. The server will then use the AI ​​engine to set an appropriate alert level based on the results of the emotion engine's analysis, and suggest a countermeasure called "increased patrols." The security officer will then intensify their patrols in accordance with this countermeasure, and the results will be sent as feedback to the server. This feedback will help improve the system's algorithms.

[0997] Prompt Sentence Examples

[0998] "A security officer's heart rate spiked during nighttime patrol. Please suggest the optimal alert level and response in this situation."

[0999] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1000] Step 1:

[1001] The user enters personal information, values, interests, etc. into a registration form through the smart glasses, and the device sends the information to the server. The input data includes name, age, position, etc. The server receives this data, validates it, and stores it in a database. During the validation phase, it checks whether required fields have been filled in and prompts the user to re-enter any uncertain data.

[1002] Step 2:

[1003] The server retrieves stored user data from the database and sends it to the emotion engine. The emotion engine analyzes the user's emotional state based on the data provided by the user and real-time data (heart rate, body temperature, facial expressions, etc.). During this analysis process, an AI algorithm is used to analyze the data and assign an emotion label (e.g., excitement, relief, anxiety, etc.). The analysis results are fed back to the server.

[1004] Step 3:

[1005] The server receives emotion analysis results from the emotion engine and sends them to the AI ​​engine, which then generates the optimal alert level and countermeasures. For example, if the heart rate rises sharply and the facial expression is analyzed as "surprise," the AI ​​engine will set the alert level to "high" and suggest "increased patrols" as a countermeasure. The AI ​​engine uses machine learning algorithms to learn patterns from large amounts of data and propose optimal countermeasures.

[1006] Step 4:

[1007] The server sends the generated alert level and countermeasures to the user's device, and the user (security officer) receives them through the smart glasses. The user checks the displayed alert level and countermeasures and takes necessary crime prevention actions. For example, if "increased patrols" is suggested, the security officer will focus on patrolling the designated area.

[1008] Step 5:

[1009] The results of the crime prevention actions taken by the user are input into the device as feedback and sent to the server. This feedback includes the action taken and its results (for example, whether or not the user was safe). The server receives this feedback and uses it to improve the algorithms of the emotion engine and AI engine. Feedback analysis will enable more accurate alert levels and countermeasures to be proposed in the future.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] [Third embodiment]

[1014] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1015] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1016] 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).

[1017] 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.

[1018] 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.

[1019] 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).

[1020] 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.

[1021] 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.

[1022] 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.

[1023] 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.

[1024] 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.

[1025] 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."

[1026] A specific system configuration and its operation will be described below as an embodiment of the present invention.

[1027] System Configuration

[1028] The system of the present invention includes a user terminal, a server, a database, and an AI engine. The user terminal communicates with the server via the Internet and provides an interface for data input and result display.

[1029] User devices: personal computers, tablets, smartphones, etc.

[1030] Server: Receives, stores, analyzes user data, and generates results

[1031] Database: Stores user data and analysis results

[1032] AI engine: Runs machine learning algorithms to generate optimal career paths based on user data

[1033] Program processing

[1034] 1. User Registration

[1035] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[1036] The terminal transmits the input data to the server.

[1037] The server receives the data, validates the necessary fields, and saves the data to the database if it passes validation.

[1038] 2. Data analysis and career suggestions

[1039] The server sends the saved user data to the AI ​​engine.

[1040] The AI ​​engine uses machine learning algorithms to analyze the data and generate the most suitable career options for the user (schools, cram schools, teachers, friends, etc.).

[1041] The server transmits the generated course candidates to the user.

[1042] 3. User selection and confirmation of details

[1043] The user checks the career options sent from the server and selects the option that interests them.

[1044] The user requests more information.

[1045] The server retrieves the details of the selected candidate from the database and sends them to the user.

[1046] The terminal displays the detailed information to the user.

[1047] 4. Next steps and feedback

[1048] The user performs a specific action according to the next action step presented to them, for example, enrolling in a cram school.

[1049] The server receives the feedback from the user and stores it in a database.

[1050] The server analyzes the feedback and improves the algorithms of the AI ​​engine, and this feedback loop improves the accuracy of the system.

[1051] Specific examples

[1052] As a specific example, a case where user A is searching for a school to attend will be described.

[1053] 1. User A enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[1054] 2. The device sends this information to the server.

[1055] 3. The server validates the data and saves it to the database.

[1056] 4. The server passes the data to the AI ​​engine for analysis.

[1057] 5. The AI ​​engine generates the best school candidates based on User A's information. For example, it will list schools with tuition fees that match User A's expectations and a wide range of departments that interest User A.

[1058] 6. The server sends the generated list of schools to User A.

[1059] 7. User A selects a school from the list and requests more information.

[1060] 8. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User A.

[1061] 9. The terminal displays the detailed information to User A.

[1062] 10. User A begins the admission process for the school that interests him most and feeds the results back to the server.

[1063] 11. The server receives the feedback and uses it to improve the AI ​​engine algorithm.

[1064] In this way, the system supports users in choosing a career path that is optimized to their individual needs, and streamlines user decision-making.

[1065] The processing flow will be explained below.

[1066] Step 1:

[1067] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[1068] Step 2:

[1069] The terminal transmits the input data to the server.

[1070] Step 3:

[1071] The server validates the data it receives, ensuring that all required fields are filled in. If there are any omissions, it generates an error message and sends it back to the terminal.

[1072] Step 4:

[1073] The server saves the data that passes the validation in the database.

[1074] Step 5:

[1075] The server sends the saved user data to the AI ​​engine and instructs it to analyze it.

[1076] Step 6:

[1077] The AI ​​engine uses machine learning algorithms to analyze user data and generate the best possible career paths for the user.

[1078] Step 7:

[1079] The server transmits the generated course candidates to the user.

[1080] Step 8:

[1081] The user checks the career options sent from the server and selects the one that interests them from among the multiple options.

[1082] Step 9:

[1083] Request more information about the candidate selected by the user.

[1084] Step 10:

[1085] The server retrieves the details from the database based on the user's request.

[1086] Step 11:

[1087] The server sends the retrieved details to the user.

[1088] Step 12:

[1089] The device visually displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[1090] Step 13:

[1091] Users take specific actions regarding the career options they are most interested in. For example, they can apply online to enroll in a cram school.

[1092] Step 14:

[1093] The server designs the next action steps based on the user's selection and actions and guides the user.

[1094] Step 15:

[1095] The user completes the presented procedure and provides feedback on the results to the server.

[1096] Step 16:

[1097] The server analyzes the feedback it receives, stores it in a database, and uses it as data to improve the AI ​​engine's algorithms.

[1098] This series of steps allows users to make efficient and optimal career choices, and the system is continuously improved by incorporating user feedback.

[1099] Example 1

[1100] 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."

[1101] In today's educational environment, it is extremely important for users to choose the most appropriate career path based on their own values ​​and interests. However, it is not easy to find a career path that matches individual needs from a vast amount of information, and it is difficult to make an appropriate decision in a short amount of time. For this reason, there is a need for a system that can effectively suggest the most suitable career path for users, provide detailed information, and guide them on next steps.

[1102] 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.

[1103] In this invention, the server includes means for a user to enter and send personal information, values, and interests into a registration form, means for a terminal to send the entered data to the server, means for the server to verify the received user data and store it in a database, means for the server to analyze the stored user data and send it to an AI engine, means for the AI ​​engine to analyze the data using a machine learning algorithm and generate career path candidates optimal for the user, means for the server to send the generated career path candidates to the user, means for the user to select from the candidates and request detailed information, means for the server to obtain detailed information about the selected career path and send it to the user, means for the terminal to display the detailed information to the user, and means for the server to provide next action steps based on the user's selection and receive feedback. This makes it possible to support career selection optimized to the user's individual needs and streamline decision-making.

[1104] "User" refers to a person who inputs information into the system and receives the results of processing.

[1105] "Terminal" refers to a device such as a personal computer, tablet, or smartphone that a user uses to input information and check the displayed results.

[1106] "Server" refers to a central processing unit that receives, stores, analyzes user data, and generates results.

[1107] "Database" refers to an information management system for storing user data and analysis results.

[1108] "AI engine" refers to a component that runs machine learning algorithms to generate optimal career options based on user data.

[1109] "Registration Form" refers to the interface used by a user to enter information such as personal details, values, and interests.

[1110] A "machine learning algorithm" refers to a mathematical model used to analyze data and find patterns and trends.

[1111] "Career options" refer to options such as schools, cram schools, teachers, and friends that are proposed to the user.

[1112] "Detailed information" refers to detailed explanations and information about the career options selected by the user.

[1113] "Action steps" refer to specific actions or procedures that a user should take next.

[1114] "Feedback" refers to the evaluations, opinions, and result information that users provide to the system.

[1115] This invention is a support system for users to choose the most suitable career path. This system is composed of a user terminal, a server, a database, and an AI engine. Each component and its operation are explained in detail below.

[1116] System Configuration

[1117] User device: A personal computer, tablet, smartphone, etc. used by users to input information and view results. Examples include a common web browser or mobile application.

[1118] Server: Receives, stores, and analyzes user data, generates results, and sends them to users. The server is a computer system equipped with a high-performance processor and sufficient memory, and also includes a database server and an AI engine.

[1119] Database: An information management system for storing user data and analysis results, such as an SQL database.

[1120] AI engine: Analyzes user data using machine learning algorithms to generate optimal career paths. For example, TensorFlow and PyTorch, which use Python, are examples of this type of engine.

[1121] Program processing

[1122] 1. User Registration

[1123] The user uses a terminal to enter personal information (name, age, current grade), values, interests, desired tuition fees, commuting distance, and future career goals into a registration form. The entered data is sent from the terminal to the server, which verifies the received data and stores it in a database.

[1124] 2. Data analysis and career suggestions

[1125] The server sends the saved user data to the AI ​​engine, which uses a machine learning algorithm to analyze the input data and generate optimal career path candidates. The generated career path candidates are then sent to the user's device via the server.

[1126] 3. User selection and confirmation of details

[1127] The user selects an option that interests them from the list of available options and requests detailed information. The server retrieves detailed information about the selected option from the database and sends it to the user. The terminal displays the detailed information to the user.

[1128] 4. Next steps and feedback

[1129] The user follows the suggested next action steps and takes specific actions, such as enrolling in a cram school. This feedback is received by the server and stored in a database. The server analyzes the received feedback and improves the algorithm of the AI ​​engine.

[1130] Specific examples

[1131] For example, if user A is looking for a school to attend, he or she will take the following steps:

[1132] 1. User A enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[1133] Example prompt: "My name is Taro Yamada. I'm 17 years old and a second-year high school student. I'm interested in computer science and my desired tuition is 500,000 yen per year. The school is 30 kilometers from my home. My future career goal is to become a software engineer."

[1134] 2. The device sends this information to the server.

[1135] 3. The server validates the data and saves it to the database.

[1136] 4. The server passes the data to the AI ​​engine for analysis.

[1137] 5. The AI ​​engine generates the best school candidates based on User A's information. For example, it will list schools with tuition fees that match User A's expectations and a wide range of departments that interest User A.

[1138] Example prompt: "Please tell me the best school for me. Here is my information: My name is Taro Yamada, I'm 17 years old, I'm a second-year high school student, I'm interested in computer science, I would like to pay 500,000 yen per year in tuition, I live within a 30km commute, and my future career goal is to become a software engineer."

[1139] 6. The server sends the generated list of schools to User A.

[1140] 7. User A selects a school from the list and requests more information.

[1141] 8. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User A.

[1142] 9. The terminal displays the detailed information to User A.

[1143] 10. User A begins the admission process for the school that interests him most and feeds the results back to the server.

[1144] 11. The server receives the feedback and uses it to improve the AI ​​engine algorithm.

[1145] Through the detailed processing steps described above, the present system supports users in choosing a career path that is optimized to their individual needs, and streamlines decision-making.

[1146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1147] Step 1:

[1148] The user enters information into an input form on the device, such as name, age, current year at school, department of interest, desired tuition fee, distance to school, and future career goals.

[1149] Input: Data that a user enters into a form field.

[1150] Output: User input data that is temporarily stored in the device's memory.

[1151] What happens: A user fills in a form in a browser or application and clicks the "Submit" button.

[1152] Step 2:

[1153] The terminal formats the entered data and transmits it over the Internet to a server.

[1154] Input: User input data.

[1155] Output: The HTTP POST request sent to the server.

[1156] Specific operation: The device converts the data obtained from the user into a format such as JSON or XML and sends it to the server as an HTTP request.

[1157] Step 3:

[1158] The server validates the user data received, checking that all required fields are present and that the data is in the correct format.

[1159] Input: User data sent from the device.

[1160] Output: The validation results and, if appropriate, the data stored in a database.

[1161] What happens: The server runs validation scripts to check for missing fields or improper formatting. Data that passes validation is saved to the database using a SQL INSERT statement.

[1162] Step 4:

[1163] The server sends the saved user data to the AI ​​engine.

[1164] Input: User data stored in the database.

[1165] Output: The data sent to the AI ​​engine.

[1166] Specific operation: The server retrieves user data from the database and sends a request to the AI ​​engine using REST API or gRPC.

[1167] Step 5:

[1168] The AI ​​engine uses machine learning algorithms to analyze the data and generate optimal career options.

[1169] Input: User data received from the server.

[1170] Output: A list of optimal career paths.

[1171] How it works: The AI ​​engine inputs data into a predictive model, runs algorithms, and generates a ranking of the best career options.

[1172] Step 6:

[1173] The server transmits the generated course candidates to the user.

[1174] Input: A list of possible career paths received from the AI ​​engine.

[1175] Output: The HTTP response sent to the user's device.

[1176] Specific operation: The server sends the data obtained from the AI ​​engine to the user device as an HTTP response.

[1177] Step 7:

[1178] The user checks the list of career options sent to them and selects the option that interests them.

[1179] Input: A list of possible career paths sent from the server.

[1180] Output: The career options selected by the user.

[1181] Specific actions: The user checks the list on the device and clicks a button to view details of the selected career option.

[1182] Step 8:

[1183] The user requests more information.

[1184] Input: The career path selected by the user.

[1185] Output: A more information request sent to the server.

[1186] What happens: The user clicks the "View Details" button, sending a request to the server.

[1187] Step 9:

[1188] The server retrieves details of the selected route from the database and sends them to the user.

[1189] Input: Request more information about the selected career path.

[1190] Output: Detailed information sent to the user's device.

[1191] Specific operation: The server retrieves detailed information from the database and sends it to the device in an HTTP response.

[1192] Step 10:

[1193] The terminal displays the detailed information to the user.

[1194] Input: The details received from the server.

[1195] Output: Detailed information displayed on the device screen.

[1196] Specific behavior: The device UI processes the received data and displays it to the user in an appropriate format.

[1197] Step 11:

[1198] The user starts the admission procedure for the school in which he or she is most interested, and feeds back the results to the server.

[1199] Input: User's admissions results and feedback.

[1200] Output: Feedback information sent to the server.

[1201] Specific behavior: The user completes the procedure, enters the required information in the feedback form, and submits it to the server.

[1202] Step 12:

[1203] The server receives the feedback and analyzes it to improve the AI ​​engine's algorithms.

[1204] Input: Feedback information from the user.

[1205] Output: Improved AI algorithm.

[1206] What it does: The server stores the feedback information in a database, then runs a data analysis script to generate data for retraining the AI ​​model.

[1207] (Application example 1)

[1208] 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."

[1209] Conventional online shopping sites lacked personalized product suggestions based on users' interests and purchase history. This meant that users had to spend time finding the product that best suited them, reducing their motivation to purchase. Furthermore, there was a lack of a convenient way to obtain detailed information about suggested products, which led to lower user satisfaction.

[1210] 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.

[1211] In this invention, the server includes a means for a user to input and submit personal information, values, and interests into a registration form, a means for the server to verify the received user data and store it in a database, and a means for the server to analyze the stored user data and generate product candidates that are optimal for the user, thereby enabling the user to efficiently find optimal product candidates based on their interests and values.

[1212] A "user" is a person who uses the system to input personal information, values, and interests and receives product proposals.

[1213] A "registration form" is an interface through which a user enters personal information, values, and interests.

[1214] "Personal information" refers to personal data such as a user's name, age, address, and contact details.

[1215] "Values" is information about a user's beliefs and priorities.

[1216] "Interests" is information about fields or product categories in which the user is particularly interested.

[1217] The "means for sending" is a function for sending the data entered by the user in the registration form to the server.

[1218] "Server" means a central device for receiving, storing and analyzing User Data.

[1219] "Means for verifying" refers to a function that allows the server to verify the accuracy and completeness of the user data received.

[1220] A "database" is a data storage device for saving user data and analysis results.

[1221] The "means for storing" is a function for storing the user data received by the server in a database.

[1222] The "means for analyzing" is a function for analyzing stored user data using a machine learning algorithm.

[1223] "Product candidates" are products that are proposed to the user as optimal options based on the analysis results.

[1224] The "means for generating" is a function for generating a product candidate list based on the analysis results.

[1225] "Detailed information" includes specific product specifications, reviews, pricing information, etc.

[1226] The "request means" is a function that allows a user to request detailed information about a product candidate.

[1227] The "means for obtaining" is a function that allows the server to retrieve detailed information from the database and send it to the user.

[1228] The "transmitting means" is a function that allows the server to transmit detailed information to the user's terminal.

[1229] The "next action step" is specific guidance for the user to purchase the product.

[1230] "Feedback" is information that users send back to the system regarding their reactions and evaluations of the proposed products.

[1231] A "machine learning algorithm" is a computer program that analyzes user data and calculates optimal product candidates.

[1232] To implement the present invention, the following system configuration and operation should be considered.

[1233] System Configuration

[1234] The system includes a user terminal, a server, a database, and an AI engine. The user terminal communicates with the server via the Internet and provides an interface for data input and results display.

[1235] User devices: personal computers, tablets, smartphones, etc.

[1236] Server: Receives, stores, analyzes user data, and generates results

[1237] Database: Stores user data and analysis results

[1238] AI engine: Runs machine learning algorithms to generate optimal product candidates based on user data

[1239] Program processing and explanation

[1240] 1. User Registration

[1241] The user enters personal information, values, interests, etc. into the registration form and submits it. The user's device sends the entered data to the server. The server receives the data and verifies it. After confirming that the required information has been entered correctly, the data is saved in the database.

[1242] 2. Data analysis and product proposals

[1243] The server sends the saved user data to the AI ​​engine, which analyzes the data using a machine learning algorithm (e.g., TensorFlow) and generates the best product candidates for the user. The server then sends the generated product candidate list to the user.

[1244] 3. Request more information

[1245] The user selects an item of interest from the sent product candidate list and requests detailed information. The server retrieves detailed information about the selected item from the database and sends it to the user.

[1246] 4. Next steps and feedback

[1247] The user adds products to their cart based on the details they provide, checks out, and sends their post-purchase feedback to the server, which stores this feedback in a database and uses it to improve the AI ​​engine's algorithms.

[1248] Hardware and software used

[1249] Hardware:

[1250] User devices: smartphones, tablets, personal computers

[1251] Server: Cloud server (e.g. AWS, Google Cloud)

[1252] software:

[1253] Server: Flask (Python framework)

[1254] Database: SQLite

[1255] AI engine: TensorFlow (machine learning algorithm)

[1256] Specific examples

[1257] As a specific example, a case where a user is searching for a new electronic device that interests him will be described.

[1258] 1. What the user enters in the registration form:

[1259] Name, age, product categories of interest (e.g. electronics), past purchase history

[1260] 2. Submit and validate input data:

[1261] The server receives the data, validates the personal information, values, and interests, and stores them in a database.

[1262] 3. Analysis by AI engine:

[1263] It uses machine learning algorithms to analyze the data and generate a list of the best electronic devices for the user.

[1264] 4. User requests more information:

[1265] The user retrieves detailed information (e.g., specifications, reviews, and pricing information) about the suggested products, which the system displays to the user.

[1266] 5. Next Action Steps and Feedback:

[1267] Users purchase products and the server receives their feedback, which is used to improve the accuracy of the AI ​​engine.

[1268] Prompt Sentence Examples

[1269] Specific examples of prompts are as follows:

[1270] "Please suggest products that the user is likely to purchase next based on their purchase history. The user's name is Taro Yamada, he is 35 years old, and his recent purchases have been home appliances, with a particular interest in televisions."

[1271] Using this prompt, the generative AI model analyzes data to suggest the best products for the user.

[1272] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1273] Step 1:

[1274] The user enters personal information, values, and interests into a registration form and submits it. The data entered by the user is sent from the device to the server. The input here is name, age, product categories of interest, and details, and the output is unverified data that is stored on the server.

[1275] Step 2:

[1276] The server validates the received user data. Specifically, the server checks that all required fields in the input data are filled in correctly and verifies that there are no incomplete inputs. The input is the data submitted in step 1, and the output is the validated data.

[1277] Step 3:

[1278] The server saves the validated data to the database using a SQL query to insert the user data into the appropriate tables. The input is validated data that can be saved, and the output is data stored in the database.

[1279] Step 4:

[1280] The server sends the saved user data to the AI ​​engine. Specifically, it uses a Python library to send the data to the AI ​​engine and start analysis. The input is the user data retrieved from the database, and the output is the analysis result passed to the AI ​​engine.

[1281] Step 5:

[1282] The AI ​​engine analyzes user data and generates optimal product candidates. Here, a machine learning algorithm (e.g., TensorFlow) is used to recommend products based on the given data. The input is user data received from the server, and the output is a list of product candidates.

[1283] Step 6:

[1284] The server sends the analysis results from the AI ​​engine to the user. The server receives the generated product candidate list and sends it in a format to be displayed on the user's device. The input is the product candidate list, and the output is the data displayed on the user's device.

[1285] Step 7:

[1286] The user selects a product of interest from the suggested product candidates and requests detailed information. A request for detailed information about the product selected by the user is sent from the terminal to the server. The input is the product candidate list and the selected data, and the output is a detailed information request.

[1287] Step 8:

[1288] The server retrieves the details of the selected product from the database and sends them to the user. It uses a database query to pull the details of the relevant product and returns them to the user. The input is the details request and the output is the product details.

[1289] Step 9:

[1290] The user checks the details, adds the product to the cart, and completes the purchase. The purchase data is sent from the terminal to the server. The input is the product details and purchase data, and the output is purchase procedure confirmation data.

[1291] Step 10:

[1292] The server receives post-purchase feedback from users. The feedback data is sent from the device to the server, where it is stored in a database and used to improve the AI ​​engine's algorithm. The input is user feedback, and the output is an improved algorithm.

[1293] 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.

[1294] A specific system configuration and its operation will be described below as an embodiment of the present invention.

[1295] System Configuration

[1296] The system of the present invention includes a user terminal, a server, a database, an AI engine, and an emotion engine. The user terminal communicates with the server via the Internet and provides an interface for data input and result display.

[1297] User devices: personal computers, tablets, smartphones, etc.

[1298] Server: Receives, stores, analyzes user data, and generates results

[1299] Database: Stores user data and analysis results

[1300] AI engine: Runs machine learning algorithms to generate optimal career paths based on user data

[1301] Emotion Engine: Software for analyzing emotions from user input data and feedback.

[1302] Program processing

[1303] 1. User Registration

[1304] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[1305] The terminal transmits the input data and the user's emotional state to the server.

[1306] The server receives the data, validates the necessary fields, and saves the data to the database if it passes validation.

[1307] 2. Emotion analysis

[1308] The server sends the data received from the user to the emotion engine, which analyzes the user's emotional state.

[1309] The emotion engine analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) and feeds the results back to the AI ​​engine.

[1310] 3. Data analysis and career suggestions

[1311] The server sends the stored user data and emotion analysis results to the AI ​​engine.

[1312] The AI ​​engine uses machine learning algorithms to analyze the data and generate the most suitable career options for the user (schools, cram schools, teachers, friends, etc.).

[1313] The server transmits the generated course candidates to the user.

[1314] 4. User selection and confirmation of details

[1315] The user checks the career options sent from the server and selects the option that interests them.

[1316] The user requests more information.

[1317] The server retrieves the details of the selected candidate from the database.

[1318] The server sends the retrieved details to the user.

[1319] The device displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[1320] 5. Next steps and feedback

[1321] The user performs a specific action according to the next action step presented to them, for example, completing the online procedure to enroll in a cram school.

[1322] The server designs the next action steps based on the user's selection and actions and guides the user.

[1323] The user completes the procedure and provides feedback on the results to the server.

[1324] The server analyzes the received feedback and improves the algorithms of the emotion engine and AI engine.

[1325] Specific examples

[1326] As a specific example, a case where user B is searching for a school to attend will be described.

[1327] 1. User B enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[1328] 2. The device sends this information and the emotional state the user indicated during input (e.g., facial expression recognition and speed changes) to the server.

[1329] 3. The server validates the data and saves it to the database.

[1330] 4. The server sends the received data to the emotion engine for emotion analysis.

[1331] 5. The emotion engine analyzes User B's emotional state and feeds the results back to the AI ​​engine.

[1332] 6. The server sends the saved user data and emotion analysis results to the AI ​​engine for analysis.

[1333] 7. The AI ​​engine generates the best school candidates based on User B's information. For example, it will list schools with tuition fees that match User B's expectations and a wide range of departments that interest him or her.

[1334] 8. The server sends the generated list of schools to User B.

[1335] 9. User B selects a school from the list and requests more information.

[1336] 10. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User B.

[1337] 11. The device displays the detailed information to User B.

[1338] 12. User B begins the admission process for the school he is most interested in and feeds the results back to the server.

[1339] 13. The server receives feedback and uses it to improve the emotion engine and AI engine algorithms.

[1340] In this way, the system supports career choices that take into account the user's emotional state, making user decision-making more personalized and efficient.

[1341] The processing flow will be explained below.

[1342] Step 1:

[1343] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[1344] Step 2:

[1345] The device sends the input data and information for measuring the user's emotions (for example, facial expression data and input speed) to the server.

[1346] Step 3:

[1347] The server validates the data received and ensures that all required fields are filled in. If there are any omissions, it generates an error message and sends it back to the terminal.

[1348] Step 4:

[1349] The server stores the verified data and emotion data in a database.

[1350] Step 5:

[1351] The server transmits the stored user data and emotion data to the emotion engine, which analyzes the user's emotional state.

[1352] Step 6:

[1353] The emotion engine analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) and feeds the results back to the AI ​​engine.

[1354] Step 7:

[1355] The server sends the saved user data and emotion analysis results to the AI ​​engine.

[1356] Step 8:

[1357] The AI ​​engine uses machine learning algorithms to analyze the data and generate the best possible career paths for the user.

[1358] Step 9:

[1359] The server transmits the generated course candidates to the user.

[1360] Step 10:

[1361] The user checks the career options sent from the server and selects the one that interests them from among the multiple options.

[1362] Step 11:

[1363] Request more information about the candidate selected by the user.

[1364] Step 12:

[1365] The server retrieves the details from the database based on the user's request.

[1366] Step 13:

[1367] The server sends the retrieved details to the user.

[1368] Step 14:

[1369] The device visually displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[1370] Step 15:

[1371] Users take specific actions regarding the career options they are most interested in. For example, they can apply online to enroll in a cram school.

[1372] Step 16:

[1373] The server designs the next action steps based on the user's selection and actions and guides the user.

[1374] Step 17:

[1375] The user completes the presented procedure and provides feedback on the results to the server.

[1376] Step 18:

[1377] The server analyzes the feedback it receives and uses it as data to improve the algorithms of the emotion engine and AI engine.

[1378] This series of steps allows users to make efficient and optimal career choices. The system is continuously improved by incorporating user feedback. The use of an emotion engine enables advanced career suggestions that take into account the user's emotional state.

[1379] Example 2

[1380] 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."

[1381] Conventional career suggestion systems have difficulty in proposing a career path that takes into account the user's emotional state and feedback, and are therefore unable to provide the optimal career path for the user. Furthermore, because they suggest a career path based solely on the information provided by the user, they lack the flexibility to respond to individual situations. This can lead to inefficient user decision-making and unsatisfactory results.

[1382] 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.

[1383] In this invention, the server includes means for a user to enter and transmit personal information, values, and interests into a registration form, means for a terminal to transmit the input data and the user's emotional state to the server, means for the server to verify the received user data and store it in a database, means for the server to send the received data to an emotion analysis engine and analyze the user's emotional state, means for an AI engine to apply a machine learning algorithm using the user data and emotion analysis results stored by the server to generate optimal career path candidates for the user, means for the user to review the generated career path candidates, select one that interests them, and request detailed information, means for the server to retrieve detailed information about the selected career path from the database and send it to the user, means for the terminal to display the detailed information to the user, means for the user to take specific actions according to the next action step and feed the results back to the server, and means for the server to receive the user's feedback and improve the algorithms of the emotion engine and the AI ​​engine. This enables optimal career path suggestions that take the user's emotional state and feedback into consideration.

[1384] "User" refers to an individual who utilizes the system to input information and receive career suggestions.

[1385] "Terminal" refers to a device that allows a user to input information and communicate with a server to send and receive data, including personal computers, tablets, and smartphones.

[1386] "Server" refers to the central processing unit that receives, stores, analyzes data from users, and generates and transmits results.

[1387] "Database" refers to a management system for the server to store user data and analysis results.

[1388] An "emotion analysis engine" refers to software that analyzes a user's emotional state from input data and feedback.

[1389] An "AI engine" refers to software that uses machine learning algorithms to analyze user data and generate optimal career path candidates.

[1390] "Machine learning algorithm" refers to an algorithm that analyzes user data and learns patterns and characteristics to generate optimal career path candidates.

[1391] "Career options" refer to options such as the most suitable school, cram school, teacher, or friend that are provided to the user.

[1392] "Emotional state" refers to a user's psychological state such as excitement, joy, or anxiety.

[1393] "Feedback" refers to information sent back to the server by the user regarding the results of performing behavioral steps.

[1394] MODE FOR CARRYING OUT THE INVENTION

[1395] A specific system configuration and its operation will be described below for an embodiment of the present invention. The system described below includes a user terminal, a server, a database, an AI engine, and an emotion engine.

[1396] Hardware and software used

[1397] User devices: Personal computers, tablets, smartphones, etc. Users use these devices to interact with the system.

[1398] Server: A central processing unit that receives, stores, analyzes user data, and generates results.

[1399] Database: A management system that stores user data and analysis results.

[1400] AI Engine: Software that uses machine learning algorithms to analyze user data and generate optimal career path suggestions.

[1401] Emotion engine: Software that analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) from their input data and feedback.

[1402] System Operation

[1403] User Registration

[1404] When a user enters personal information, values, and interests into the input form on the device and presses the send button, the device sends the entered data and the user's emotional state to the server. The server receives the data, verifies the necessary fields, and saves it in a database.

[1405] Emotion analysis

[1406] The server sends the received data to the emotion engine, which analyzes the user's emotional state and feeds the analysis results back to the AI ​​engine.

[1407] Data analysis and career suggestions

[1408] The server sends the saved user data and the results of the emotion analysis to the AI ​​engine, which then uses a machine learning algorithm to analyze the data and generate the most suitable career path candidates for the user. The generated career path candidates are then sent to the user via the server.

[1409] User selection and confirmation of details

[1410] The user checks the career options sent from the server and selects the option they are interested in. When they request detailed information, the server retrieves the details of the selected option from the database and sends it to the user. The terminal displays the details to the user.

[1411] Next steps and feedback

[1412] The user performs specific actions according to the next action steps presented and provides feedback on the results to the server, which then receives the feedback and improves the algorithms of the emotion engine and AI engine.

[1413] Specific examples

[1414] For example, consider a case where a user is looking for a school to attend. The user enters their name, age, current year of study, department of interest, desired tuition fees, commuting distance, and future career goals into an input form on their device and presses the submit button. At this time, the device also sends their emotional state, including facial recognition data, to the server. The server verifies the data and stores it in a database. The emotion engine then analyzes the emotional state, and the AI ​​engine generates the most suitable school candidates. For example, it may list schools with tuition fees that match the user's expectations and a wide range of departments that interest the user.

[1415] Example prompts for generative AI models

[1416] "Create a program that lists schools that the user is likely to be interested in and displays detailed information. Implement it to analyze the user's emotional state and recommend schools that are preferred when the user is feeling particularly safe or excited."

[1417] By inputting this prompt into a generative AI model, program code can be generated based on specific system behavior and algorithms.

[1418] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1419] Step 1: User Input and Data Submission

[1420] The user enters personal information, values, and interests into a form on the device and presses the submit button. The information entered includes name, age, current year of school, department of interest, desired tuition fee, commuting distance, and future career goals.

[1421] Input: Personal information and interest data entered by you.

[1422] Output: User data sent to the server via the device.

[1423] The device sends input data and the user's emotional state (e.g., input speed, facial expression recognition data) to the server. The emotional state is measured using the device's sensors and camera.

[1424] Step 2: Data reception and verification

[1425] The server validates the user data received, ensuring that all required fields (name, age, subject, etc.) are filled in and that it is in the correct format. If the data passes validation, it is stored in the database.

[1426] Input: User data and emotional state data sent from the device.

[1427] Output: Validated data is stored in a database.

[1428] Specific behavior: The server validates the data format, presence of required fields, etc., and generates an error message if there is an inconsistency.

[1429] Step 3: Sentiment Analysis

[1430] The server sends the received data to the emotion engine, which analyzes the user's emotional state, including emotions such as excitement, joy, and anxiety.

[1431] Input: User data and emotional state data sent by the server.

[1432] Output: Sentiment analysis results are generated and fed back to the AI ​​engine.

[1433] Specific behavior: The emotion engine analyzes data such as the user's facial expressions and typing speed to identify their emotional state.

[1434] Step 4: Data analysis and career suggestions

[1435] The server sends the saved user data and the results of the emotion analysis to the AI ​​engine, which then analyzes the data using machine learning algorithms. Specifically, it recognizes patterns in the user data and generates optimal career options (schools, cram schools, teachers, friends, etc.).

[1436] Input: User data and sentiment analysis results.

[1437] Output: A list of the best career paths for the user.

[1438] Specific operation: The AI ​​engine compares with past data and generates career options that best match the user's interests and emotional state.

[1439] Step 5: Review your shortlist and request more information

[1440] The user checks the list of career options sent by the server, selects the option they are interested in, and then requests detailed information.

[1441] Input: A list of possible career paths sent from the server.

[1442] Output: The specific candidate selected by the user and the details requested.

[1443] Specific behavior: The user scrolls through a list of career options on the screen and clicks on an option that interests them to request more information.

[1444] Step 6: Get and send details

[1445] The server retrieves detailed information about the selected career path from the database and sends it to the user, including the school's curriculum, tuition fees, and the career paths of past graduates.

[1446] Input: Detailed information about the career path requested by the user.

[1447] Output: The retrieved details are sent to the user and displayed on their terminal.

[1448] What happens: The server pulls the details from the database and sends them to the user as content.

[1449] Step 7: Guide next steps and receive feedback

[1450] The user performs specific actions according to the next action steps presented to them, such as completing the procedures to enroll in a cram school online.

[1451] The server designs the next action step based on the user's selection and actions, and guides the user through it. The user completes the procedure and returns the results to the server.

[1452] Input: The result of the user's action following the next behavioral step.

[1453] Output: Feedback data based on the action.

[1454] Specific operation: The user completes the online process and sends a completion notification to the server, which receives the feedback and uses it to improve the emotion engine and AI engine algorithms.

[1455] This allows for optimal course suggestions that take into account the user's emotional state and feedback.

[1456] (Application example 2)

[1457] 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."

[1458] While conventional systems suggest career paths based on a user's personal information and interests, they lack the functionality to analyze the user's emotional state and environmental changes in real time and propose alert levels and countermeasures. This makes it difficult to respond quickly and appropriately in specific situations, resulting in the problem of insufficient security effectiveness.

[1459] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state based on user data and proposing an alert level and countermeasures, means for the security system engine to guide the next action based on the recommended alert level and countermeasures, and means for receiving feedback from security personnel and improving the system algorithm. This enables security responses that reflect the user's real-time emotional state.

[1460] "User Data" means the personal information, values, interests, and real-time emotional state and environmental data that a user enters into a registration form.

[1461] "Emotion analysis" is the process by which the emotion engine analyzes the user's emotional state based on user data.

[1462] The "alert level" is an indicator of the degree of alertness calculated by the security system based on the results of the user's emotion analysis and other data.

[1463] "Countermeasures" are specific actions or measures recommended by the security system, and are provided according to the alert level.

[1464] A "security engine" is a system element that operates within a security system and generates alert levels and countermeasures based on emotion analysis results and user data.

[1465] "Feedback" is information provided to the server about the actions taken by the user and their results, which is used to improve the system's algorithms.

[1466] A "registration form" is an interface through which a user can enter personal information, values, interests, etc.

[1467] "Real-time data" refers to dynamic information such as heart rate, body temperature, and facial expressions acquired while the user is using the system.

[1468] An embodiment of the present invention will be described.

[1469] System configuration

[1470] The present invention is a security system that analyzes the emotional state of a user based on user data and proposes alert levels and countermeasures. The main components of the system are as follows:

[1471] User terminal: A device that allows users to input personal information and real-time data (heart rate, body temperature, facial expressions, etc.), such as smart glasses.

[1472] Server: Validates and analyzes the received user data, and generates and sends alert levels and countermeasures.

[1473] Database: Stores archived user data and analysis results.

[1474] AI Engine: Runs machine learning algorithms based on user data to generate optimal alert levels.

[1475] Emotion engine: Analyzes emotions from user input data and real-time data.

[1476] Processing flow

[1477] 1. User registration: The user enters personal information, values, interests, etc. into a registration form through the smart glasses and sends the information to the server, which verifies the data and stores it in a database.

[1478] 2. Emotion analysis: While the user is wearing the smart glasses, real-time data (heart rate, body temperature, facial expressions, etc.) is collected and analyzed by the emotion engine, and the analysis results are sent to the server.

[1479] 3. Alert Level Generation: The server sends the analysis results received from the emotion engine to the AI ​​engine, which generates the optimal alert level and countermeasures. For example, it may suggest, "If the heart rate rises sharply, set the alert level to high and increase patrols."

[1480] 4. Guidance on response actions: Security personnel check the alert level and response measures sent from the server and take specific actions (patrols, reporting, etc.).

[1481] 5. Feedback collection: Security personnel provide feedback on the results of the actions taken to the server, which analyzes this feedback and improves the system's algorithms.

[1482] Hardware and software used

[1483] Smart glasses: devices that collect real-time data about the user.

[1484] Internet: Used for data communication between user terminals and servers.

[1485] Server: A system (using a framework such as Django) for receiving, validating, parsing, and sending data.

[1486] Database: A system (such as MySQL or PostgreSQL) that stores user data and analysis results.

[1487] AI Engine: A system that runs machine learning algorithms (such as PyTorch or TensorFlow).

[1488] Emotion engine: Software for analyzing user emotions (Face API, Emotion API, etc.).

[1489] Specific examples

[1490] For example, if a security officer's heart rate suddenly rises during a nighttime patrol, the smart glasses will detect this fluctuation and send it to the server as real-time data. The server will then use the AI ​​engine to set an appropriate alert level based on the results of the emotion engine's analysis, and suggest a countermeasure called "increased patrols." The security officer will then intensify their patrols in accordance with this countermeasure, and the results will be sent as feedback to the server. This feedback will help improve the system's algorithms.

[1491] Prompt Sentence Examples

[1492] "A security officer's heart rate spiked during nighttime patrol. Please suggest the optimal alert level and response in this situation."

[1493] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1494] Step 1:

[1495] The user enters personal information, values, interests, etc. into a registration form through the smart glasses, and the device sends the information to the server. The input data includes name, age, position, etc. The server receives this data, validates it, and stores it in a database. During the validation phase, it checks whether required fields have been filled in and prompts the user to re-enter any uncertain data.

[1496] Step 2:

[1497] The server retrieves stored user data from the database and sends it to the emotion engine. The emotion engine analyzes the user's emotional state based on the data provided by the user and real-time data (heart rate, body temperature, facial expressions, etc.). During this analysis process, an AI algorithm is used to analyze the data and assign an emotion label (e.g., excitement, relief, anxiety, etc.). The analysis results are fed back to the server.

[1498] Step 3:

[1499] The server receives emotion analysis results from the emotion engine and sends them to the AI ​​engine, which then generates the optimal alert level and countermeasures. For example, if the heart rate rises sharply and the facial expression is analyzed as "surprise," the AI ​​engine will set the alert level to "high" and suggest "increased patrols" as a countermeasure. The AI ​​engine uses machine learning algorithms to learn patterns from large amounts of data and propose optimal countermeasures.

[1500] Step 4:

[1501] The server sends the generated alert level and countermeasures to the user's device, and the user (security officer) receives them through the smart glasses. The user checks the displayed alert level and countermeasures and takes necessary crime prevention actions. For example, if "increased patrols" is suggested, the security officer will focus on patrolling the designated area.

[1502] Step 5:

[1503] The results of the crime prevention actions taken by the user are input into the device as feedback and sent to the server. This feedback includes the action taken and its results (for example, whether or not the user was safe). The server receives this feedback and uses it to improve the algorithms of the emotion engine and AI engine. Feedback analysis will enable more accurate alert levels and countermeasures to be proposed in the future.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] [Fourth embodiment]

[1508] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1509] 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.

[1510] 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).

[1511] 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.

[1512] 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.

[1513] 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).

[1514] 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.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] 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.

[1520] 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."

[1521] A specific system configuration and its operation will be described below as an embodiment of the present invention.

[1522] System Configuration

[1523] The system of the present invention includes a user terminal, a server, a database, and an AI engine. The user terminal communicates with the server via the Internet and provides an interface for data input and result display.

[1524] User devices: personal computers, tablets, smartphones, etc.

[1525] Server: Receives, stores, analyzes user data, and generates results

[1526] Database: Stores user data and analysis results

[1527] AI engine: Runs machine learning algorithms to generate optimal career paths based on user data

[1528] Program processing

[1529] 1. User Registration

[1530] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[1531] The terminal transmits the input data to the server.

[1532] The server receives the data, validates the necessary fields, and saves the data to the database if it passes validation.

[1533] 2. Data analysis and career suggestions

[1534] The server sends the saved user data to the AI ​​engine.

[1535] The AI ​​engine uses machine learning algorithms to analyze the data and generate the most suitable career options for the user (schools, cram schools, teachers, friends, etc.).

[1536] The server transmits the generated course candidates to the user.

[1537] 3. User selection and confirmation of details

[1538] The user checks the career options sent from the server and selects the option that interests them.

[1539] The user requests more information.

[1540] The server retrieves the details of the selected candidate from the database and sends them to the user.

[1541] The terminal displays the detailed information to the user.

[1542] 4. Next steps and feedback

[1543] The user performs a specific action according to the next action step presented to them, for example, enrolling in a cram school.

[1544] The server receives the feedback from the user and stores it in a database.

[1545] The server analyzes the feedback and improves the algorithms of the AI ​​engine, and this feedback loop improves the accuracy of the system.

[1546] Specific examples

[1547] As a specific example, a case where user A is searching for a school to attend will be described.

[1548] 1. User A enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[1549] 2. The device sends this information to the server.

[1550] 3. The server validates the data and saves it to the database.

[1551] 4. The server passes the data to the AI ​​engine for analysis.

[1552] 5. The AI ​​engine generates the best school candidates based on User A's information. For example, it will list schools with tuition fees that match User A's expectations and a wide range of departments that interest User A.

[1553] 6. The server sends the generated list of schools to User A.

[1554] 7. User A selects a school from the list and requests more information.

[1555] 8. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User A.

[1556] 9. The terminal displays the detailed information to User A.

[1557] 10. User A begins the admission process for the school that interests him most and feeds the results back to the server.

[1558] 11. The server receives the feedback and uses it to improve the AI ​​engine algorithm.

[1559] In this way, the system supports users in choosing a career path that is optimized to their individual needs, and streamlines user decision-making.

[1560] The processing flow will be explained below.

[1561] Step 1:

[1562] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[1563] Step 2:

[1564] The terminal transmits the input data to the server.

[1565] Step 3:

[1566] The server validates the data it receives, ensuring that all required fields are filled in. If there are any omissions, it generates an error message and sends it back to the terminal.

[1567] Step 4:

[1568] The server saves the data that passes the validation in the database.

[1569] Step 5:

[1570] The server sends the saved user data to the AI ​​engine and instructs it to analyze it.

[1571] Step 6:

[1572] The AI ​​engine uses machine learning algorithms to analyze user data and generate the best possible career paths for the user.

[1573] Step 7:

[1574] The server transmits the generated course candidates to the user.

[1575] Step 8:

[1576] The user checks the career options sent from the server and selects the one that interests them from among the multiple options.

[1577] Step 9:

[1578] Request more information about the candidate selected by the user.

[1579] Step 10:

[1580] The server retrieves the details from the database based on the user's request.

[1581] Step 11:

[1582] The server sends the retrieved details to the user.

[1583] Step 12:

[1584] The device visually displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[1585] Step 13:

[1586] Users take specific actions regarding the career options they are most interested in. For example, they can apply online to enroll in a cram school.

[1587] Step 14:

[1588] The server designs the next action steps based on the user's selection and actions and guides the user.

[1589] Step 15:

[1590] The user completes the presented procedure and provides feedback on the results to the server.

[1591] Step 16:

[1592] The server analyzes the feedback it receives, stores it in a database, and uses it as data to improve the AI ​​engine's algorithms.

[1593] This series of steps allows users to make efficient and optimal career choices, and the system is continuously improved by incorporating user feedback.

[1594] Example 1

[1595] 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."

[1596] In today's educational environment, it is extremely important for users to choose the most appropriate career path based on their own values ​​and interests. However, it is not easy to find a career path that matches individual needs from a vast amount of information, and it is difficult to make an appropriate decision in a short amount of time. For this reason, there is a need for a system that can effectively suggest the most suitable career path for users, provide detailed information, and guide them on next steps.

[1597] 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.

[1598] In this invention, the server includes means for a user to enter and send personal information, values, and interests into a registration form, means for a terminal to send the entered data to the server, means for the server to verify the received user data and store it in a database, means for the server to analyze the stored user data and send it to an AI engine, means for the AI ​​engine to analyze the data using a machine learning algorithm and generate career path candidates optimal for the user, means for the server to send the generated career path candidates to the user, means for the user to select from the candidates and request detailed information, means for the server to obtain detailed information about the selected career path and send it to the user, means for the terminal to display the detailed information to the user, and means for the server to provide next action steps based on the user's selection and receive feedback. This makes it possible to support career selection optimized to the user's individual needs and streamline decision-making.

[1599] "User" refers to a person who inputs information into the system and receives the results of processing.

[1600] "Terminal" refers to a device such as a personal computer, tablet, or smartphone that a user uses to input information and check the displayed results.

[1601] "Server" refers to a central processing unit that receives, stores, analyzes user data, and generates results.

[1602] "Database" refers to an information management system for storing user data and analysis results.

[1603] "AI engine" refers to a component that runs machine learning algorithms to generate optimal career options based on user data.

[1604] "Registration Form" refers to the interface used by a user to enter information such as personal details, values, and interests.

[1605] A "machine learning algorithm" refers to a mathematical model used to analyze data and find patterns and trends.

[1606] "Career options" refer to options such as schools, cram schools, teachers, and friends that are proposed to the user.

[1607] "Detailed information" refers to detailed explanations and information about the career options selected by the user.

[1608] "Action steps" refer to specific actions or procedures that a user should take next.

[1609] "Feedback" refers to the evaluations, opinions, and result information that users provide to the system.

[1610] This invention is a support system for users to choose the most suitable career path. This system is composed of a user terminal, a server, a database, and an AI engine. Each component and its operation are explained in detail below.

[1611] System Configuration

[1612] User device: A personal computer, tablet, smartphone, etc. used by users to input information and view results. Examples include a common web browser or mobile application.

[1613] Server: Receives, stores, and analyzes user data, generates results, and sends them to users. The server is a computer system equipped with a high-performance processor and sufficient memory, and also includes a database server and an AI engine.

[1614] Database: An information management system for storing user data and analysis results, such as an SQL database.

[1615] AI engine: Analyzes user data using machine learning algorithms to generate optimal career paths. For example, TensorFlow and PyTorch, which use Python, are examples of this type of engine.

[1616] Program processing

[1617] 1. User Registration

[1618] The user uses a terminal to enter personal information (name, age, current grade), values, interests, desired tuition fees, commuting distance, and future career goals into a registration form. The entered data is sent from the terminal to the server, which verifies the received data and stores it in a database.

[1619] 2. Data analysis and career suggestions

[1620] The server sends the saved user data to the AI ​​engine, which uses a machine learning algorithm to analyze the input data and generate optimal career path candidates. The generated career path candidates are then sent to the user's device via the server.

[1621] 3. User selection and confirmation of details

[1622] The user selects an option that interests them from the list of available options and requests detailed information. The server retrieves detailed information about the selected option from the database and sends it to the user. The terminal displays the detailed information to the user.

[1623] 4. Next steps and feedback

[1624] The user follows the suggested next action steps and takes specific actions, such as enrolling in a cram school. This feedback is received by the server and stored in a database. The server analyzes the received feedback and improves the algorithm of the AI ​​engine.

[1625] Specific examples

[1626] For example, if user A is looking for a school to attend, he or she will take the following steps:

[1627] 1. User A enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[1628] Example prompt: "My name is Taro Yamada. I'm 17 years old and a second-year high school student. I'm interested in computer science and my desired tuition is 500,000 yen per year. The school is 30 kilometers from my home. My future career goal is to become a software engineer."

[1629] 2. The device sends this information to the server.

[1630] 3. The server validates the data and saves it to the database.

[1631] 4. The server passes the data to the AI ​​engine for analysis.

[1632] 5. The AI ​​engine generates the best school candidates based on User A's information. For example, it will list schools with tuition fees that match User A's expectations and a wide range of departments that interest User A.

[1633] Example prompt: "Please tell me the best school for me. Here is my information: My name is Taro Yamada, I'm 17 years old, I'm a second-year high school student, I'm interested in computer science, I would like to pay 500,000 yen per year in tuition, I live within a 30km commute, and my future career goal is to become a software engineer."

[1634] 6. The server sends the generated list of schools to User A.

[1635] 7. User A selects a school from the list and requests more information.

[1636] 8. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User A.

[1637] 9. The terminal displays the detailed information to User A.

[1638] 10. User A begins the admission process for the school that interests him most and feeds the results back to the server.

[1639] 11. The server receives the feedback and uses it to improve the AI ​​engine algorithm.

[1640] Through the detailed processing steps described above, the present system supports users in choosing a career path that is optimized to their individual needs, and streamlines decision-making.

[1641] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1642] Step 1:

[1643] The user enters information into an input form on the device, such as name, age, current year at school, department of interest, desired tuition fee, distance to school, and future career goals.

[1644] Input: Data that a user enters into a form field.

[1645] Output: User input data that is temporarily stored in the device's memory.

[1646] What happens: A user fills in a form in a browser or application and clicks the "Submit" button.

[1647] Step 2:

[1648] The terminal formats the entered data and transmits it over the Internet to a server.

[1649] Input: User input data.

[1650] Output: The HTTP POST request sent to the server.

[1651] Specific operation: The device converts the data obtained from the user into a format such as JSON or XML and sends it to the server as an HTTP request.

[1652] Step 3:

[1653] The server validates the user data received, checking that all required fields are present and that the data is in the correct format.

[1654] Input: User data sent from the device.

[1655] Output: The validation results and, if appropriate, the data stored in a database.

[1656] What happens: The server runs validation scripts to check for missing fields or improper formatting. Data that passes validation is saved to the database using a SQL INSERT statement.

[1657] Step 4:

[1658] The server sends the saved user data to the AI ​​engine.

[1659] Input: User data stored in the database.

[1660] Output: The data sent to the AI ​​engine.

[1661] Specific operation: The server retrieves user data from the database and sends a request to the AI ​​engine using REST API or gRPC.

[1662] Step 5:

[1663] The AI ​​engine uses machine learning algorithms to analyze the data and generate optimal career options.

[1664] Input: User data received from the server.

[1665] Output: A list of optimal career paths.

[1666] How it works: The AI ​​engine inputs data into a predictive model, runs algorithms, and generates a ranking of the best career options.

[1667] Step 6:

[1668] The server transmits the generated course candidates to the user.

[1669] Input: A list of possible career paths received from the AI ​​engine.

[1670] Output: The HTTP response sent to the user's device.

[1671] Specific operation: The server sends the data obtained from the AI ​​engine to the user device as an HTTP response.

[1672] Step 7:

[1673] The user checks the list of career options sent to them and selects the option that interests them.

[1674] Input: A list of possible career paths sent from the server.

[1675] Output: The career options selected by the user.

[1676] Specific actions: The user checks the list on the device and clicks a button to view details of the selected career option.

[1677] Step 8:

[1678] The user requests more information.

[1679] Input: The career path selected by the user.

[1680] Output: A more information request sent to the server.

[1681] What happens: The user clicks the "View Details" button, sending a request to the server.

[1682] Step 9:

[1683] The server retrieves details of the selected route from the database and sends them to the user.

[1684] Input: Request more information about the selected career path.

[1685] Output: Detailed information sent to the user's device.

[1686] Specific operation: The server retrieves detailed information from the database and sends it to the device in an HTTP response.

[1687] Step 10:

[1688] The terminal displays the detailed information to the user.

[1689] Input: The details received from the server.

[1690] Output: Detailed information displayed on the device screen.

[1691] Specific behavior: The device UI processes the received data and displays it to the user in an appropriate format.

[1692] Step 11:

[1693] The user starts the admission procedure for the school in which he or she is most interested, and feeds back the results to the server.

[1694] Input: User's admissions results and feedback.

[1695] Output: Feedback information sent to the server.

[1696] Specific behavior: The user completes the procedure, enters the required information in the feedback form, and submits it to the server.

[1697] Step 12:

[1698] The server receives the feedback and analyzes it to improve the AI ​​engine's algorithms.

[1699] Input: Feedback information from the user.

[1700] Output: Improved AI algorithm.

[1701] What it does: The server stores the feedback information in a database, then runs a data analysis script to generate data for retraining the AI ​​model.

[1702] (Application example 1)

[1703] 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."

[1704] Conventional online shopping sites lacked personalized product suggestions based on users' interests and purchase history. This meant that users had to spend time finding the product that best suited them, reducing their motivation to purchase. Furthermore, there was a lack of a convenient way to obtain detailed information about suggested products, which led to lower user satisfaction.

[1705] 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.

[1706] In this invention, the server includes a means for a user to input and submit personal information, values, and interests into a registration form, a means for the server to verify the received user data and store it in a database, and a means for the server to analyze the stored user data and generate product candidates that are optimal for the user, thereby enabling the user to efficiently find optimal product candidates based on their interests and values.

[1707] A "user" is a person who uses the system to input personal information, values, and interests and receives product proposals.

[1708] A "registration form" is an interface through which a user enters personal information, values, and interests.

[1709] "Personal information" refers to personal data such as a user's name, age, address, and contact details.

[1710] "Values" is information about a user's beliefs and priorities.

[1711] "Interests" is information about fields or product categories in which the user is particularly interested.

[1712] The "means for sending" is a function for sending the data entered by the user in the registration form to the server.

[1713] "Server" means a central device for receiving, storing and analyzing User Data.

[1714] "Means for verifying" refers to a function that allows the server to verify the accuracy and completeness of the user data received.

[1715] A "database" is a data storage device for saving user data and analysis results.

[1716] The "means for storing" is a function for storing the user data received by the server in a database.

[1717] The "means for analyzing" is a function for analyzing stored user data using a machine learning algorithm.

[1718] "Product candidates" are products that are proposed to the user as optimal options based on the analysis results.

[1719] The "means for generating" is a function for generating a product candidate list based on the analysis results.

[1720] "Detailed information" includes specific product specifications, reviews, pricing information, etc.

[1721] The "request means" is a function that allows a user to request detailed information about a product candidate.

[1722] The "means for obtaining" is a function that allows the server to retrieve detailed information from the database and send it to the user.

[1723] The "transmitting means" is a function that allows the server to transmit detailed information to the user's terminal.

[1724] The "next action step" is specific guidance for the user to purchase the product.

[1725] "Feedback" is information that users send back to the system regarding their reactions and evaluations of the proposed products.

[1726] A "machine learning algorithm" is a computer program that analyzes user data and calculates optimal product candidates.

[1727] To implement the present invention, the following system configuration and operation should be considered.

[1728] System Configuration

[1729] The system includes a user terminal, a server, a database, and an AI engine. The user terminal communicates with the server via the Internet and provides an interface for data input and results display.

[1730] User devices: personal computers, tablets, smartphones, etc.

[1731] Server: Receives, stores, analyzes user data, and generates results

[1732] Database: Stores user data and analysis results

[1733] AI engine: Runs machine learning algorithms to generate optimal product candidates based on user data

[1734] Program processing and explanation

[1735] 1. User Registration

[1736] The user enters personal information, values, interests, etc. into the registration form and submits it. The user's device sends the entered data to the server. The server receives the data and verifies it. After confirming that the required information has been entered correctly, the data is saved in the database.

[1737] 2. Data analysis and product proposals

[1738] The server sends the saved user data to the AI ​​engine, which analyzes the data using a machine learning algorithm (e.g., TensorFlow) and generates the best product candidates for the user. The server then sends the generated product candidate list to the user.

[1739] 3. Request more information

[1740] The user selects an item of interest from the sent product candidate list and requests detailed information. The server retrieves detailed information about the selected item from the database and sends it to the user.

[1741] 4. Next steps and feedback

[1742] The user adds products to their cart based on the details they provide, checks out, and sends their post-purchase feedback to the server, which stores this feedback in a database and uses it to improve the AI ​​engine's algorithms.

[1743] Hardware and software used

[1744] Hardware:

[1745] User devices: smartphones, tablets, personal computers

[1746] Server: Cloud server (e.g. AWS, Google Cloud)

[1747] software:

[1748] Server: Flask (Python framework)

[1749] Database: SQLite

[1750] AI engine: TensorFlow (machine learning algorithm)

[1751] Specific examples

[1752] As a specific example, a case where a user is searching for a new electronic device that interests him will be described.

[1753] 1. What the user enters in the registration form:

[1754] Name, age, product categories of interest (e.g. electronics), past purchase history

[1755] 2. Submit and validate input data:

[1756] The server receives the data, validates the personal information, values, and interests, and stores them in a database.

[1757] 3. Analysis by AI engine:

[1758] It uses machine learning algorithms to analyze the data and generate a list of the best electronic devices for the user.

[1759] 4. User requests more information:

[1760] The user retrieves detailed information (e.g., specifications, reviews, and pricing information) about the suggested products, which the system displays to the user.

[1761] 5. Next Action Steps and Feedback:

[1762] Users purchase products and the server receives their feedback, which is used to improve the accuracy of the AI ​​engine.

[1763] Prompt Sentence Examples

[1764] Specific examples of prompts are as follows:

[1765] "Please suggest products that the user is likely to purchase next based on their purchase history. The user's name is Taro Yamada, he is 35 years old, and his recent purchases have been home appliances, with a particular interest in televisions."

[1766] Using this prompt, the generative AI model analyzes data to suggest the best products for the user.

[1767] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1768] Step 1:

[1769] The user enters personal information, values, and interests into a registration form and submits it. The data entered by the user is sent from the device to the server. The input here is name, age, product categories of interest, and details, and the output is unverified data that is stored on the server.

[1770] Step 2:

[1771] The server validates the received user data. Specifically, the server checks that all required fields in the input data are filled in correctly and verifies that there are no incomplete inputs. The input is the data submitted in step 1, and the output is the validated data.

[1772] Step 3:

[1773] The server saves the validated data to the database using a SQL query to insert the user data into the appropriate tables. The input is validated data that can be saved, and the output is data stored in the database.

[1774] Step 4:

[1775] The server sends the saved user data to the AI ​​engine. Specifically, it uses a Python library to send the data to the AI ​​engine and start analysis. The input is the user data retrieved from the database, and the output is the analysis result passed to the AI ​​engine.

[1776] Step 5:

[1777] The AI ​​engine analyzes user data and generates optimal product candidates. Here, a machine learning algorithm (e.g., TensorFlow) is used to recommend products based on the given data. The input is user data received from the server, and the output is a list of product candidates.

[1778] Step 6:

[1779] The server sends the analysis results from the AI ​​engine to the user. The server receives the generated product candidate list and sends it in a format to be displayed on the user's device. The input is the product candidate list, and the output is the data displayed on the user's device.

[1780] Step 7:

[1781] The user selects a product of interest from the suggested product candidates and requests detailed information. A request for detailed information about the product selected by the user is sent from the terminal to the server. The input is the product candidate list and the selected data, and the output is a detailed information request.

[1782] Step 8:

[1783] The server retrieves the details of the selected product from the database and sends them to the user. It uses a database query to pull the details of the relevant product and returns them to the user. The input is the details request and the output is the product details.

[1784] Step 9:

[1785] The user checks the details, adds the product to the cart, and completes the purchase. The purchase data is sent from the terminal to the server. The input is the product details and purchase data, and the output is purchase procedure confirmation data.

[1786] Step 10:

[1787] The server receives post-purchase feedback from users. The feedback data is sent from the device to the server, where it is stored in a database and used to improve the AI ​​engine's algorithm. The input is user feedback, and the output is an improved algorithm.

[1788] 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.

[1789] A specific system configuration and its operation will be described below as an embodiment of the present invention.

[1790] System Configuration

[1791] The system of the present invention includes a user terminal, a server, a database, an AI engine, and an emotion engine. The user terminal communicates with the server via the Internet and provides an interface for data input and result display.

[1792] User devices: personal computers, tablets, smartphones, etc.

[1793] Server: Receives, stores, analyzes user data, and generates results

[1794] Database: Stores user data and analysis results

[1795] AI engine: Runs machine learning algorithms to generate optimal career paths based on user data

[1796] Emotion Engine: Software for analyzing emotions from user input data and feedback.

[1797] Program processing

[1798] 1. User Registration

[1799] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[1800] The terminal transmits the input data and the user's emotional state to the server.

[1801] The server receives the data, validates the necessary fields, and saves the data to the database if it passes validation.

[1802] 2. Emotion analysis

[1803] The server sends the data received from the user to the emotion engine, which analyzes the user's emotional state.

[1804] The emotion engine analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) and feeds the results back to the AI ​​engine.

[1805] 3. Data analysis and career suggestions

[1806] The server sends the stored user data and emotion analysis results to the AI ​​engine.

[1807] The AI ​​engine uses machine learning algorithms to analyze the data and generate the most suitable career options for the user (schools, cram schools, teachers, friends, etc.).

[1808] The server transmits the generated course candidates to the user.

[1809] 4. User selection and confirmation of details

[1810] The user checks the career options sent from the server and selects the option that interests them.

[1811] The user requests more information.

[1812] The server retrieves the details of the selected candidate from the database.

[1813] The server sends the retrieved details to the user.

[1814] The device displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[1815] 5. Next steps and feedback

[1816] The user performs a specific action according to the next action step presented to them, for example, completing the online procedure to enroll in a cram school.

[1817] The server designs the next action steps based on the user's selection and actions and guides the user.

[1818] The user completes the procedure and provides feedback on the results to the server.

[1819] The server analyzes the received feedback and improves the algorithms of the emotion engine and AI engine.

[1820] Specific examples

[1821] As a specific example, a case where user B is searching for a school to attend will be described.

[1822] 1. User B enters the following information into the input form on the device: name, age, current year of study, department of interest, desired tuition fee, distance available to commute, and future career goals.

[1823] 2. The device sends this information and the emotional state the user indicated during input (e.g., facial expression recognition and speed changes) to the server.

[1824] 3. The server validates the data and saves it to the database.

[1825] 4. The server sends the received data to the emotion engine for emotion analysis.

[1826] 5. The emotion engine analyzes User B's emotional state and feeds the results back to the AI ​​engine.

[1827] 6. The server sends the saved user data and emotion analysis results to the AI ​​engine for analysis.

[1828] 7. The AI ​​engine generates the best school candidates based on User B's information. For example, it will list schools with tuition fees that match User B's expectations and a wide range of departments that interest him or her.

[1829] 8. The server sends the generated list of schools to User B.

[1830] 9. User B selects a school from the list and requests more information.

[1831] 10. The server retrieves detailed information about the selected school (curriculum, career paths of past graduates, tuition details, etc.) and sends it to User B.

[1832] 11. The device displays the detailed information to User B.

[1833] 12. User B begins the admission process for the school he is most interested in and feeds the results back to the server.

[1834] 13. The server receives feedback and uses it to improve the emotion engine and AI engine algorithms.

[1835] In this way, the system supports career choices that take into account the user's emotional state, making user decision-making more personalized and efficient.

[1836] The processing flow will be explained below.

[1837] Step 1:

[1838] The user enters personal information, values, and interests into the input form on the device and presses the send button.

[1839] Step 2:

[1840] The device sends the input data and information for measuring the user's emotions (for example, facial expression data and input speed) to the server.

[1841] Step 3:

[1842] The server validates the data received and ensures that all required fields are filled in. If there are any omissions, it generates an error message and sends it back to the terminal.

[1843] Step 4:

[1844] The server stores the verified data and emotion data in a database.

[1845] Step 5:

[1846] The server transmits the stored user data and emotion data to the emotion engine, which analyzes the user's emotional state.

[1847] Step 6:

[1848] The emotion engine analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) and feeds the results back to the AI ​​engine.

[1849] Step 7:

[1850] The server sends the saved user data and emotion analysis results to the AI ​​engine.

[1851] Step 8:

[1852] The AI ​​engine uses machine learning algorithms to analyze the data and generate the best possible career paths for the user.

[1853] Step 9:

[1854] The server transmits the generated course candidates to the user.

[1855] Step 10:

[1856] The user checks the career options sent from the server and selects the one that interests them from among the multiple options.

[1857] Step 11:

[1858] Request more information about the candidate selected by the user.

[1859] Step 12:

[1860] The server retrieves the details from the database based on the user's request.

[1861] Step 13:

[1862] The server sends the retrieved details to the user.

[1863] Step 14:

[1864] The device visually displays detailed information to the user, such as the school's curriculum, past performance, and reviews.

[1865] Step 15:

[1866] Users take specific actions regarding the career options they are most interested in. For example, they can apply online to enroll in a cram school.

[1867] Step 16:

[1868] The server designs the next action steps based on the user's selection and actions and guides the user.

[1869] Step 17:

[1870] The user completes the presented procedure and provides feedback on the results to the server.

[1871] Step 18:

[1872] The server analyzes the feedback it receives and uses it as data to improve the algorithms of the emotion engine and AI engine.

[1873] This series of steps allows users to make efficient and optimal career choices. The system is continuously improved by incorporating user feedback. The use of an emotion engine enables advanced career suggestions that take into account the user's emotional state.

[1874] Example 2

[1875] 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."

[1876] Conventional career suggestion systems have difficulty in proposing a career path that takes into account the user's emotional state and feedback, and are therefore unable to provide the optimal career path for the user. Furthermore, because they suggest a career path based solely on the information provided by the user, they lack the flexibility to respond to individual situations. This can lead to inefficient user decision-making and unsatisfactory results.

[1877] 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.

[1878] In this invention, the server includes means for a user to enter and transmit personal information, values, and interests into a registration form, means for a terminal to transmit the input data and the user's emotional state to the server, means for the server to verify the received user data and store it in a database, means for the server to send the received data to an emotion analysis engine and analyze the user's emotional state, means for an AI engine to apply a machine learning algorithm using the user data and emotion analysis results stored by the server to generate optimal career path candidates for the user, means for the user to review the generated career path candidates, select one that interests them, and request detailed information, means for the server to retrieve detailed information about the selected career path from the database and send it to the user, means for the terminal to display the detailed information to the user, means for the user to take specific actions according to the next action step and feed the results back to the server, and means for the server to receive the user's feedback and improve the algorithms of the emotion engine and the AI ​​engine. This enables optimal career path suggestions that take the user's emotional state and feedback into consideration.

[1879] "User" refers to an individual who utilizes the system to input information and receive career suggestions.

[1880] "Terminal" refers to a device that allows a user to input information and communicate with a server to send and receive data, including personal computers, tablets, and smartphones.

[1881] "Server" refers to the central processing unit that receives, stores, analyzes data from users, and generates and transmits results.

[1882] "Database" refers to a management system for the server to store user data and analysis results.

[1883] An "emotion analysis engine" refers to software that analyzes a user's emotional state from input data and feedback.

[1884] An "AI engine" refers to software that uses machine learning algorithms to analyze user data and generate optimal career path candidates.

[1885] "Machine learning algorithm" refers to an algorithm that analyzes user data and learns patterns and characteristics to generate optimal career path candidates.

[1886] "Career options" refer to options such as the most suitable school, cram school, teacher, or friend that are provided to the user.

[1887] "Emotional state" refers to a user's psychological state such as excitement, joy, or anxiety.

[1888] "Feedback" refers to information sent back to the server by the user regarding the results of performing behavioral steps.

[1889] MODE FOR CARRYING OUT THE INVENTION

[1890] A specific system configuration and its operation will be described below for an embodiment of the present invention. The system described below includes a user terminal, a server, a database, an AI engine, and an emotion engine.

[1891] Hardware and software used

[1892] User devices: Personal computers, tablets, smartphones, etc. Users use these devices to interact with the system.

[1893] Server: A central processing unit that receives, stores, analyzes user data, and generates results.

[1894] Database: A management system that stores user data and analysis results.

[1895] AI Engine: Software that uses machine learning algorithms to analyze user data and generate optimal career path suggestions.

[1896] Emotion engine: Software that analyzes the user's emotional state (e.g., excitement, joy, anxiety, etc.) from their input data and feedback.

[1897] System Operation

[1898] User Registration

[1899] When a user enters personal information, values, and interests into the input form on the device and presses the send button, the device sends the entered data and the user's emotional state to the server. The server receives the data, verifies the necessary fields, and saves it in a database.

[1900] Emotion analysis

[1901] The server sends the received data to the emotion engine, which analyzes the user's emotional state and feeds the analysis results back to the AI ​​engine.

[1902] Data analysis and career suggestions

[1903] The server sends the saved user data and the results of the emotion analysis to the AI ​​engine, which then uses a machine learning algorithm to analyze the data and generate the most suitable career path candidates for the user. The generated career path candidates are then sent to the user via the server.

[1904] User selection and confirmation of details

[1905] The user checks the career options sent from the server and selects the option they are interested in. When they request detailed information, the server retrieves the details of the selected option from the database and sends it to the user. The terminal displays the details to the user.

[1906] Next steps and feedback

[1907] The user performs specific actions according to the next action steps presented and provides feedback on the results to the server, which then receives the feedback and improves the algorithms of the emotion engine and AI engine.

[1908] Specific examples

[1909] For example, consider a case where a user is looking for a school to attend. The user enters their name, age, current year of study, department of interest, desired tuition fees, commuting distance, and future career goals into an input form on their device and presses the submit button. At this time, the device also sends their emotional state, including facial recognition data, to the server. The server verifies the data and stores it in a database. The emotion engine then analyzes the emotional state, and the AI ​​engine generates the most suitable school candidates. For example, it may list schools with tuition fees that match the user's expectations and a wide range of departments that interest the user.

[1910] Example prompts for generative AI models

[1911] "Create a program that lists schools that the user is likely to be interested in and displays detailed information. Implement it to analyze the user's emotional state and recommend schools that are preferred when the user is feeling particularly safe or excited."

[1912] By inputting this prompt into a generative AI model, program code can be generated based on specific system behavior and algorithms.

[1913] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1914] Step 1: User Input and Data Submission

[1915] The user enters personal information, values, and interests into a form on the device and presses the submit button. The information entered includes name, age, current year of school, department of interest, desired tuition fee, commuting distance, and future career goals.

[1916] Input: Personal information and interest data entered by you.

[1917] Output: User data sent to the server via the device.

[1918] The device sends input data and the user's emotional state (e.g., input speed, facial expression recognition data) to the server. The emotional state is measured using the device's sensors and camera.

[1919] Step 2: Data reception and verification

[1920] The server validates the user data received, ensuring that all required fields (name, age, subject, etc.) are filled in and that it is in the correct format. If the data passes validation, it is stored in the database.

[1921] Input: User data and emotional state data sent from the device.

[1922] Output: Validated data is stored in a database.

[1923] Specific behavior: The server validates the data format, presence of required fields, etc., and generates an error message if there is an inconsistency.

[1924] Step 3: Sentiment Analysis

[1925] The server sends the received data to the emotion engine, which analyzes the user's emotional state, including emotions such as excitement, joy, and anxiety.

[1926] Input: User data and emotional state data sent by the server.

[1927] Output: Sentiment analysis results are generated and fed back to the AI ​​engine.

[1928] Specific behavior: The emotion engine analyzes data such as the user's facial expressions and typing speed to identify their emotional state.

[1929] Step 4: Data analysis and career suggestions

[1930] The server sends the saved user data and the results of the emotion analysis to the AI ​​engine, which then analyzes the data using machine learning algorithms. Specifically, it recognizes patterns in the user data and generates optimal career options (schools, cram schools, teachers, friends, etc.).

[1931] Input: User data and sentiment analysis results.

[1932] Output: A list of the best career paths for the user.

[1933] Specific operation: The AI ​​engine compares with past data and generates career options that best match the user's interests and emotional state.

[1934] Step 5: Review your shortlist and request more information

[1935] The user checks the list of career options sent by the server, selects the option they are interested in, and then requests detailed information.

[1936] Input: A list of possible career paths sent from the server.

[1937] Output: The specific candidate selected by the user and the details requested.

[1938] Specific behavior: The user scrolls through a list of career options on the screen and clicks on an option that interests them to request more information.

[1939] Step 6: Get and send details

[1940] The server retrieves detailed information about the selected career path from the database and sends it to the user, including the school's curriculum, tuition fees, and the career paths of past graduates.

[1941] Input: Detailed information about the career path requested by the user.

[1942] Output: The retrieved details are sent to the user and displayed on their terminal.

[1943] What happens: The server pulls the details from the database and sends them to the user as content.

[1944] Step 7: Guide next steps and receive feedback

[1945] The user performs specific actions according to the next action steps presented to them, such as completing the procedures to enroll in a cram school online.

[1946] The server designs the next action step based on the user's selection and actions, and guides the user through it. The user completes the procedure and returns the results to the server.

[1947] Input: The result of the user's action following the next behavioral step.

[1948] Output: Feedback data based on the action.

[1949] Specific operation: The user completes the online process and sends a completion notification to the server, which receives the feedback and uses it to improve the emotion engine and AI engine algorithms.

[1950] This allows for optimal course suggestions that take into account the user's emotional state and feedback.

[1951] (Application example 2)

[1952] 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."

[1953] While conventional systems suggest career paths based on a user's personal information and interests, they lack the functionality to analyze the user's emotional state and environmental changes in real time and propose alert levels and countermeasures. This makes it difficult to respond quickly and appropriately in specific situations, resulting in the problem of insufficient security effectiveness.

[1954] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the emotional state based on user data and proposing an alert level and countermeasures, means for the security system engine to guide the next action based on the recommended alert level and countermeasures, and means for receiving feedback from security personnel and improving the system algorithm. This enables security responses that reflect the user's real-time emotional state.

[1955] "User Data" means the personal information, values, interests, and real-time emotional state and environmental data that a user enters into a registration form.

[1956] "Emotion analysis" is the process by which the emotion engine analyzes the user's emotional state based on user data.

[1957] The "alert level" is an indicator of the degree of alertness calculated by the security system based on the results of the user's emotion analysis and other data.

[1958] "Countermeasures" are specific actions or measures recommended by the security system, and are provided according to the alert level.

[1959] A "security engine" is a system element that operates within a security system and generates alert levels and countermeasures based on emotion analysis results and user data.

[1960] "Feedback" is information provided to the server about the actions taken by the user and their results, which is used to improve the system's algorithms.

[1961] A "registration form" is an interface through which a user can enter personal information, values, interests, etc.

[1962] "Real-time data" refers to dynamic information such as heart rate, body temperature, and facial expressions acquired while the user is using the system.

[1963] An embodiment of the present invention will be described.

[1964] System configuration

[1965] The present invention is a security system that analyzes the emotional state of a user based on user data and proposes alert levels and countermeasures. The main components of the system are as follows:

[1966] User terminal: A device that allows users to input personal information and real-time data (heart rate, body temperature, facial expressions, etc.), such as smart glasses.

[1967] Server: Validates and analyzes the received user data, and generates and sends alert levels and countermeasures.

[1968] Database: Stores archived user data and analysis results.

[1969] AI Engine: Runs machine learning algorithms based on user data to generate optimal alert levels.

[1970] Emotion engine: Analyzes emotions from user input data and real-time data.

[1971] Processing flow

[1972] 1. User registration: The user enters personal information, values, interests, etc. into a registration form through the smart glasses and sends the information to the server, which verifies the data and stores it in a database.

[1973] 2. Emotion analysis: While the user is wearing the smart glasses, real-time data (heart rate, body temperature, facial expressions, etc.) is collected and analyzed by the emotion engine, and the analysis results are sent to the server.

[1974] 3. Alert Level Generation: The server sends the analysis results received from the emotion engine to the AI ​​engine, which generates the optimal alert level and countermeasures. For example, it may suggest, "If the heart rate rises sharply, set the alert level to high and increase patrols."

[1975] 4. Guidance on response actions: Security personnel check the alert level and response measures sent from the server and take specific actions (patrols, reporting, etc.).

[1976] 5. Feedback collection: Security personnel provide feedback on the results of the actions taken to the server, which analyzes this feedback and improves the system's algorithms.

[1977] Hardware and software used

[1978] Smart glasses: devices that collect real-time data about the user.

[1979] Internet: Used for data communication between user terminals and servers.

[1980] Server: A system (using a framework such as Django) for receiving, validating, parsing, and sending data.

[1981] Database: A system (such as MySQL or PostgreSQL) that stores user data and analysis results.

[1982] AI Engine: A system that runs machine learning algorithms (such as PyTorch or TensorFlow).

[1983] Emotion engine: Software for analyzing user emotions (Face API, Emotion API, etc.).

[1984] Specific examples

[1985] For example, if a security officer's heart rate suddenly rises during a nighttime patrol, the smart glasses will detect this fluctuation and send it to the server as real-time data. The server will then use the AI ​​engine to set an appropriate alert level based on the results of the emotion engine's analysis, and suggest a countermeasure called "increased patrols." The security officer will then intensify their patrols in accordance with this countermeasure, and the results will be sent as feedback to the server. This feedback will help improve the system's algorithms.

[1986] Prompt Sentence Examples

[1987] "A security officer's heart rate spiked during nighttime patrol. Please suggest the optimal alert level and response in this situation."

[1988] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1989] Step 1:

[1990] The user enters personal information, values, interests, etc. into a registration form through the smart glasses, and the device sends the information to the server. The input data includes name, age, position, etc. The server receives this data, validates it, and stores it in a database. During the validation phase, it checks whether required fields have been filled in and prompts the user to re-enter any uncertain data.

[1991] Step 2:

[1992] The server retrieves stored user data from the database and sends it to the emotion engine. The emotion engine analyzes the user's emotional state based on the data provided by the user and real-time data (heart rate, body temperature, facial expressions, etc.). During this analysis process, an AI algorithm is used to analyze the data and assign an emotion label (e.g., excitement, relief, anxiety, etc.). The analysis results are fed back to the server.

[1993] Step 3:

[1994] The server receives emotion analysis results from the emotion engine and sends them to the AI ​​engine, which then generates the optimal alert level and countermeasures. For example, if the heart rate rises sharply and the facial expression is analyzed as "surprise," the AI ​​engine will set the alert level to "high" and suggest "increased patrols" as a countermeasure. The AI ​​engine uses machine learning algorithms to learn patterns from large amounts of data and propose optimal countermeasures.

[1995] Step 4:

[1996] The server sends the generated alert level and countermeasures to the user's device, and the user (security officer) receives them through the smart glasses. The user checks the displayed alert level and countermeasures and takes necessary crime prevention actions. For example, if "increased patrols" is suggested, the security officer will focus on patrolling the designated area.

[1997] Step 5:

[1998] The results of the crime prevention actions taken by the user are input into the device as feedback and sent to the server. This feedback includes the action taken and its results (for example, whether or not the user was safe). The server receives this feedback and uses it to improve the algorithms of the emotion engine and AI engine. Feedback analysis will enable more accurate alert levels and countermeasures to be proposed in the future.

[1999] 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.

[2000] 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.

[2001] 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.

[2002] 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.

[2003] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion 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.

[2004] 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.

[2005] 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).

[2006] 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.

[2007] 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."

[2008] 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.

[2009] 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).

[2010] 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.

[2011] 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.

[2012] 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.

[2013] 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.

[2014] 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.

[2015] 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.

[2016] 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.

[2017] 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.

[2018] 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.

[2019] 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.

[2020] The following is further disclosed regarding the above embodiment.

[2021] (Claim 1)

[2022] A means for users to enter and submit their personal information, values, and interests in a registration form;

[2023] means for the server to validate the received user data and store it in a database;

[2024] A means for the server to analyze the stored user data and generate optimal career path candidates for the user;

[2025] a means for the user to select from the suggestions and request more information;

[2026] means for the server to obtain detailed information about the selected route and transmit it to the user;

[2027] The system includes a means for the server to provide next action steps based on the user's selection and receive feedback.

[2028] (Claim 2)

[2029] 2. The system of claim 1, wherein the server includes means for applying a machine learning algorithm using the stored user data to calculate optimal career paths for the user.

[2030] (Claim 3)

[2031] 10. The system of claim 1, wherein the server includes means for analyzing user feedback and improving the accuracy of the specified algorithm.

[2032] "Example 1"

[2033] (Claim 1)

[2034] A means for users to enter and submit their personal information, values, and interests in a registration form;

[2035] A means for transmitting input data from the terminal to a server;

[2036] means for the server to validate the received user data and store it in a database;

[2037] A means for the server to analyze the stored user data and send it to the AI ​​engine;

[2038] The AI ​​engine uses machine learning algorithms to analyze data and generate optimal career options for users.

[2039] A means for the server to transmit the generated course candidates to a user;

[2040] a means for the user to select from the suggestions and request more information;

[2041] means for the server to obtain detailed information about the selected route and transmit it to the user;

[2042] means for the terminal to display detailed information to the user;

[2043] The system includes a means for the server to provide next action steps based on the user's selection and receive feedback.

[2044] (Claim 2)

[2045] 2. The system of claim 1, wherein the server includes means for applying a machine learning algorithm using the stored user data to calculate optimal career paths for the user.

[2046] (Claim 3)

[2047] 10. The system of claim 1, wherein the server includes means for analyzing user feedback and improving the accuracy of the specified algorithm.

[2048] "Application Example 1"

[2049] (Claim 1)

[2050] A means for users to enter and submit their personal information, values, and interests in a registration form;

[2051] means for the server to validate the received user data and store it in a database;

[2052] A means for the server to analyze the stored user data and generate product candidates that are optimal for the user;

[2053] a means for the user to select from the suggestions and request more information;

[2054] A means for the server to acquire detailed information about the selected product and transmit it to the user;

[2055] The system includes a means for the server to provide next action steps based on the user's selection and receive feedback.

[2056] (Claim 2)

[2057] The system according to claim 1, further comprising means for applying a machine learning algorithm to the stored user data to calculate optimal product candidates for the user.

[2058] (Claim 3)

[2059] 10. The system of claim 1, wherein the server includes means for analyzing user feedback and improving the accuracy of the specified algorithm.

[2060] "Example 2: Combining Emotion Engines"

[2061] (Claim 1)

[2062] A means for users to enter and submit their personal information, values, and interests in a registration form;

[2063] means for the terminal to transmit the input data and the user's emotional state to a server;

[2064] means for the server to validate the received user data and store it in a database;

[2065] a means for transmitting the received data to an emotion analysis engine by the server to analyze the user's emotional state;

[2066] A means for an AI engine to apply a machine learning algorithm to the user data and emotion analysis results stored by the server to generate optimal career options for the user;

[2067] A means for the user to review the generated career options, select options that interest them, and request more information;

[2068] means for the server to retrieve detailed information about the selected course from the database and send it to the user;

[2069] means for the terminal to display detailed information to the user;

[2070] A means for the user to take specific actions according to the next action step and feed back the results to the server;

[2071] The system includes a means for the server to receive user feedback and improve the emotion engine and AI engine algorithms.

[2072] (Claim 2)

[2073] 2. The system of claim 1, wherein the server includes means for applying a machine learning algorithm using the stored user data and sentiment analysis results to generate optimal career candidates for the user.

[2074] (Claim 3)

[2075] 10. The system of claim 1, wherein the server includes means for analyzing user feedback to improve the accuracy of the emotion engine and AI engine algorithms.

[2076] "Application example 2 when combining emotion engines"

[2077] (Claim 1)

[2078] A means for users to enter and submit their personal information, values, and interests in a registration form;

[2079] means for the server to validate the received user data and store it in a database;

[2080] A means for the server to analyze the stored user data and generate optimal career path candidates for the user;

[2081] a means for the user to select from the suggestions and request more information;

[2082] means for the server to obtain detailed information about the selected route and transmit it to the user;

[2083] means for the server to provide next action steps based on the user's selection and receive feedback;

[2084] A method to analyze the emotional state based on user data and suggest alert levels and countermeasures;

[2085] A means for the security system engine to guide next steps based on the recommended alert level and response measures; and

[2086] A means to receive feedback from security personnel and improve the system's algorithms;

[2087] A system including:

[2088] (Claim 2)

[2089] The system of claim 1, wherein the server includes means for applying a machine learning algorithm using the stored user data to calculate optimal course candidates for the user and means for suggesting an alert level based on the results of emotion analysis.

[2090] (Claim 3)

[2091] 2. The system of claim 1, wherein the server includes means for analyzing user feedback to improve the accuracy of the specified algorithm and means for improving the alert level suggestion algorithm of the security system. [Explanation of symbols]

[2092] 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 users to enter and submit their personal information, values, and interests in a registration form; means for the server to validate the received user data and store it in a database; A means for the server to analyze the stored user data and generate optimal career path candidates for the user; a means for the user to select from the suggestions and request more information; means for the server to obtain detailed information about the selected route and transmit it to the user; The system includes a means for the server to provide next action steps based on the user's selection and receive feedback.

2. The system of claim 1 , wherein the server includes means for applying a machine learning algorithm using the stored user data to calculate optimal career paths for the user.

3. 10. The system of claim 1, wherein the server includes means for analyzing user feedback to improve the accuracy of the specified algorithm.

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