System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input a question about a carrier; means for storing the user's question and a past question history in a database; means for acquiring a past question and an advice history of the user from the database; means for generating an advice based on the acquired past question and the acquired past advice history by using a generative AI; and means for providing the generated advice to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, there are many systems that provide career consultation and advice, but these systems have difficulty providing personalized advice based on the user's individual history and characteristics. Furthermore, companies lack support tools for efficiently recruiting the right talent, making it difficult to properly match the needs of users and companies. Furthermore, there is no established monetization model, making sustainable operation difficult. The present invention aims to solve these problems. [Means for solving the problem]

[0005] The present invention is a system including: a means for a user to input career-related questions; a means for saving the user's questions and past question history in a database; a means for retrieving the user's past questions and advice history from the database; a means for generating advice based on the retrieved past questions and advice history using generative AI; and a means for providing the generated advice to the user. The system also includes a means for companies to input positions they are looking to hire for and the profile of the person they are looking for, a means for saving the input from the company in a database, a means for matching the user's skill set with the profile of the person the company is looking for from the database, and a means for providing the user with suitable job information based on the matching results. The system also includes a means for displaying advertisements relevant to the user, a means for periodically collecting fees from companies for feedback and data usage, and a means for charging users for access to specific advanced features, thereby achieving monetization.

[0006] "User" refers to an individual who uses the system to enter career-related questions and receive advice and job information.

[0007] "Server" refers to a computer system that receives, stores, and analyzes input data from users and companies, and generates advice and matches job information using generative AI.

[0008] "Terminal" refers to the device used by company personnel to enter recruitment information.

[0009] "Database" refers to a system that stores and manages data such as user question history, advice history, and company job information.

[0010] "Generative AI" refers to artificial intelligence technology that analyzes a user's past questions and advice history to generate appropriate advice.

[0011] "Question input means" refers to an interface and its implementation that allows a user to input a question about a career.

[0012] "Advice providing means" refers to the interface and its implementation for displaying or sending advice generated by the generative AI to the user.

[0013] "Recruitment information input means" refers to the interface and its implementation that allows company personnel to input recruitment information.

[0014] "Matching method" refers to the algorithm and its implementation that compares the user's skill set with the profile of the person the company is looking for and identifies appropriate job information.

[0015] "Advertisement display means" refers to an interface and its implementation for displaying advertisements relevant to the user.

[0016] "Cost collection method" refers to the task and its implementation for charging service fees or subscription fees from companies or users.

[0017] "Question history" refers to data including career-related questions that users have previously entered into the system and the advice given in response to those questions.

[0018] A "position" refers to a specific job title or role that a company is recruiting for. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] This invention relates to a system that matches users' career questions with companies' job information and provides personalized advice and job information. Here, we will generate a program for the system and explain its processing in natural language. We will also use concrete examples and explain the server, terminal, and user as subjects.

[0041] Parts Overview

[0042] User: An individual who uses the system to enter career-related questions and receive advice and job information.

[0043] Server: A computer system that processes data, generates advice using generative AI, and matches jobs with companies' job listings.

[0044] Terminal: The device used by the company representative to enter recruitment information.

[0045] Database: Storage for question history, advice history, and company job information.

[0046] Program processing explanation

[0047] User Registration and Login

[0048] 1. When a user registers, they enter information such as their name, email address, and password into a dedicated input form.

[0049] 2. The server saves the input information in a database and sends a confirmation email to the user for authentication.

[0050] 3. The user clicks the link in the confirmation email to complete account verification.

[0051] 4. The user enters their email address and password on the login screen to log in to the system.

[0052] 5. The server checks the user's credentials against the database and allows them to log in.

[0053] Career Question and Advice Generation

[0054] 1. The user enters career-related questions into a dedicated input form.

[0055] 2. The server receives this question and records it in a database.

[0056] 3. The server retrieves the user's past questions and their answer history from the database.

[0057] 4. The server inputs the question history and user profile data into the generative AI to generate personalized advice.

[0058] 5. The server displays the generated advice in the user's interface.

[0059] Entering and matching company job information

[0060] 1. The terminal (company representative) logs in and enters recruitment information.

[0061] 2. The server saves the job information entered in the database.

[0062] 3. The server matches company job information with the user's skill set, past work experience, etc.

[0063] 4. The server analyzes the matching results and identifies suitable job listings for the user.

[0064] 5. The server displays the identified job listings on the user's interface.

[0065] Advertisement and monetization

[0066] 1. The server selects advertisements related to the user's question history and advice content.

[0067] 2. The server displays the selected advertisement on the user's screen.

[0068] 3. The server generates periodic reports and trend reports from the company and bills them for usage.

[0069] 4. The server collects premium feature fees and subscription fees from the user.

[0070] Specific examples

[0071] Example 1: User A's career questions and advice

[0072] 1. User A enters a question: "What skills do I need to learn in the future?"

[0073] 2. The server references user A's past question history and passes it as input data to the generative AI.

[0074] 3. The server uses generative AI to generate advice recommending "acquiring data analysis and Python skills" and provides it to User A.

[0075] Example 2: Matching job information from Company B with User C

[0076] 1. Company B enters job information for a "Data Scientist."

[0077] 2. The server saves the job information in a database and matches it with User C's skill set.

[0078] 3. The server identifies that User C's skill set matches the requirements of Company B and recommends it to User C.

[0079] This clearly shows that the present invention is a system that can respond to individual users' career consultations while also matching with the recruitment needs of companies, thereby realizing efficient job-hunting support and profit generation.

[0080] The processing flow will be explained below.

[0081] Career Question and Advice Generation

[0082] Step 1:

[0083] The user enters career-related questions into a dedicated input form.

[0084] Step 2:

[0085] The server receives the question from the user, checks that the input format is correct, and if there are no problems with the format, stores the question in the database.

[0086] Step 3:

[0087] The server retrieves the user's past question and answer history from the database.

[0088] Step 4:

[0089] The server inputs past question history and user profile data into the generative AI.

[0090] Step 5:

[0091] Generative AI analyzes past data and generates appropriate advice.

[0092] Step 6:

[0093] The server sends the generated advice content to the user and displays it on the interface.

[0094] Entering and matching company job information

[0095] Step 1:

[0096] The terminal (company representative) logs in to the system and accesses the recruitment information input screen.

[0097] Step 2:

[0098] The terminal allows users to input the available positions and the desired skill sets.

[0099] Step 3:

[0100] The server receives the job information sent by the company, checks that the format is correct, and if there are no problems with the format, stores the information in a database.

[0101] Step 4:

[0102] The server uses a matching algorithm to compare the user's skill set with the company's desired skill set from the database.

[0103] Step 5:

[0104] The server analyzes the matching results and identifies suitable users.

[0105] Step 6:

[0106] The server notifies the identified user of the company's job information and displays it on the interface.

[0107] Advertisement and monetization

[0108] Step 1:

[0109] The server selects relevant advertisements based on the user's question history and advice content.

[0110] Step 2:

[0111] The server retrieves the selected advertisement data and displays it on the user's interface.

[0112] Step 3:

[0113] The server periodically creates recruitment data and trend reports for companies and collects a fee for the feedback.

[0114] Step 4:

[0115] The server handles the process of collecting premium feature fees and subscription fees from users.

[0116] Example: User A's career questions and advice

[0117] Step 1:

[0118] User A enters the question "What skills do you want to learn in the future?" into an input form.

[0119] Step 2:

[0120] The server stores User A's question in a database.

[0121] Step 3:

[0122] The server retrieves past question history from the database and passes it to the generative AI.

[0123] Step 4:

[0124] Generative AI analyzes the data and generates advice recommending "acquiring data analysis and Python skills."

[0125] Step 5:

[0126] The server sends the advice to User A and displays it on the interface.

[0127] Example: Matching job information from Company B with User C

[0128] Step 1:

[0129] A representative from Company B logs in and enters job information for a "Data Scientist."

[0130] Step 2:

[0131] The server stores the job listings in a database.

[0132] Step 3:

[0133] The server matches User C's skill set with job listings.

[0134] Step 4:

[0135] The server analyzes the matching results and identifies job information suitable for User C.

[0136] Step 5:

[0137] The server notifies User C of the job information and displays it on the interface.

[0138] Example 1

[0139] 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."

[0140] In today's job market, it is difficult for individual users to obtain appropriate advice and information about their careers. It is also difficult for companies to quickly and accurately find job seekers who fit their desired profile. Furthermore, there is a lack of mechanisms for effectively displaying relevant advertisements to users and monetizing the service. A system that solves these issues is needed.

[0141] 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.

[0142] In this invention, the server includes means for a user to input a career-related question, means for saving the user's question and past question history in a database, means for acquiring the user's past question and advice history from the database, means for generating advice based on the acquired past question and advice history using a generative AI, means for providing the generated advice to the user, means for passing the user's past history and profile data as prompts to the generative AI model, and means for displaying the generated advice on the user's interface, thereby enabling the user to quickly obtain personalized advice and information.

[0143] "User" refers to an individual who uses the system to enter career-related questions and receive advice and job information.

[0144] "Server" refers to a computer system that processes data, generates advice using generative AI, and matches it with company job information.

[0145] "Database" refers to storage that stores question history, advice history, and company job information.

[0146] "Generative AI" refers to an artificial intelligence model that generates personalized advice based on a user's question history and profile data.

[0147] "Profile Data" refers to personal information about a user, such as age, occupation, and work experience.

[0148] "Interface" refers to a user interface that includes screens and input forms that allow a user to interact with a system.

[0149] "Company" refers to the entity that provides job information and inputs the desired profile and recruitment information.

[0150] "Job information" refers to information such as the types of jobs a company is looking to hire for and the skill sets they are looking for.

[0151] "Matching" refers to the process of comparing a user's skill set and work experience with a company's job listings to determine suitability.

[0152] "Advertisement" refers to commercial information related to the user's question history and advice content.

[0153] The present invention relates to a system that matches users' career questions with companies' job information and provides personalized advice and job information. The program processing of this system will be described in detail below.

[0154] Hardware and software used

[0155] The following hardware and software are used to implement this system.

[0156] Server: A computer system that processes data, generates advice using generative AI, and matches with company job information. Specifically, it uses a cloud server.

[0157] Terminal: The device on which company personnel enter recruitment information. Specifically, a computer or tablet with a web browser is used.

[0158] Database: Storage for storing user question history, advice history, and company job information. Specifically, a relational database management system (RDBMS) is used.

[0159] Generative AI: An AI model for generating personalized advice, specifically using natural language processing models such as GPT-3 (registered trademark).

[0160] Specific processing explanation of the program

[0161] User Registration and Login

[0162] When a user registers, they enter information such as their name, email address, and password into a dedicated input form. The server receives this information, stores it in a database, and sends the user a confirmation email for authentication. When the user clicks the link in the confirmation email, account authentication is complete. The user then enters their authentication information on the login screen, and the server compares the information with the database and allows them to log in.

[0163] Career Question and Advice Generation

[0164] When a user inputs a question about their career, the server receives the question and records it in a database. The server then retrieves the user's past questions and advice history from the database and inputs it into the generative AI. At this time, the user's past history and profile data are used as prompts. For example, the following prompts are generated:

[0165] "User's previous question: What skills should I learn in the future? User profile: Age 25, Occupation Engineer"

[0166] The generative AI generates advice based on this, and the server displays that advice on the user's interface.

[0167] Entering and matching company job information

[0168] The terminal (company representative) logs in and enters recruitment information. For example, "Job position: Data scientist, Required skills: Python, data analysis." The server receives this information and stores it in a database. The server then matches the company's recruitment information with the user's skill set, past work experience, etc., to identify users who are highly suitable. The server analyzes the identified matching results and displays the recruitment information on the user's interface to provide the user with appropriate recruitment information.

[0169] Advertisement and monetization

[0170] The server selects advertisements related to the user's question history and advice content and displays them on the user's screen. This allows for highly relevant advertisements to be displayed, enabling effective marketing. The server periodically creates reports and trend reports from companies and bills them for usage. It also includes a mechanism for collecting premium feature fees and subscription fees from users.

[0171] The above is a detailed embodiment of the system according to the present invention. This allows users to obtain appropriate career advice and companies to quickly and accurately find the talent they are looking for. In addition, by effectively displaying relevant advertisements, the service can be monetized.

[0172] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0173] User Registration and Login

[0174] Processing Steps:

[0175] Step 1:

[0176] A user registers by entering information such as name, email address, and password into a dedicated input form and clicking the "Register" button.

[0177] Specific actions: Enter "Yamada Taro", "yamada@example.com", and "password123" and click the button.

[0178] Input: User information (name, email address, password)

[0179] Output: The entered user information is sent to the server.

[0180] Step 2:

[0181] The server receives this information, stores it in a database, and simultaneously sends a confirmation email to the user's email address.

[0182] What it does: Creates a new record in the database and sends a confirmation email via the SMTP server.

[0183] Input: User information (name, email address, password)

[0184] Output: A confirmation email is sent to the user.

[0185] Step 3:

[0186] The user clicks the confirmation link in the email to complete account authentication.

[0187] What happens: Open the confirmation email and click the link.

[0188] Input: Link from confirmation email

[0189] Output: The account is authenticated.

[0190] Step 4:

[0191] The user enters their email address and password on the login screen and clicks the "Login" button.

[0192] Specific action: Enter "yamada@example.com" and "password123" into the form and click the button.

[0193] Input: User's email address and password

[0194] Output: An authentication request is sent to the server.

[0195] Step 5:

[0196] The server checks the user's input information against the registered information in the database, and if it matches, allows the user to log in.

[0197] Specific operation: A matching process is performed, and if there is a match, a session is started.

[0198] Input: User's email address and password

[0199] Output: Successful login and session start

[0200] Career Question and Advice Generation

[0201] Processing Steps:

[0202] Step 1:

[0203] The user enters career-related questions into a dedicated input form and clicks the "Submit" button.

[0204] Specific action: Fill out the form with "What skills would you like to learn next?" and click the button.

[0205] Input: User's career question

[0206] Output: The carrier question is sent to the server.

[0207] Step 2:

[0208] The server receives this question and records it in a database.

[0209] Specific behavior: The question is stored in the database as a new record.

[0210] Input: Career Question

[0211] Output: Question data stored in a database

[0212] Step 3:

[0213] The server retrieves the user's past questions and advice history from a database.

[0214] What it does: Retrieves past question history using a SQL query.

[0215] Input: User ID

[0216] Output: Past questions and advice history

[0217] Step 4:

[0218] The server passes the user's past history and profile data as prompts to the generative AI model to generate advice.

[0219] Specific operation: Input the prompt sentence: 'User's previous question: What skills should I learn in the future? User profile: Age 25, Occupation Engineer' into the generative AI model.

[0220] Input: Previous questions, advice history, profile data

[0221] Output: The generated advice

[0222] Step 5:

[0223] The server displays the generated advice on the user's interface.

[0224] Specific behavior: Display advice on the user's screen.

[0225] Input: Generated advice

[0226] Output: Advice displayed on the user's interface

[0227] Entering and matching company job information

[0228] Processing Steps:

[0229] Step 1:

[0230] The terminal (company representative) logs in and enters recruitment information. The user enters "Job position: Data scientist, Required skills: Python, data analysis."

[0231] Specific action: Enter "Data Scientist, Python, Data Analysis" into the form and submit it.

[0232] Input: Company recruitment information

[0233] Output: The job information is sent to the server.

[0234] Step 2:

[0235] The server saves the job information entered in the database.

[0236] Specific behavior: The entered job information is stored in the database as a new record.

[0237] Input: Company recruitment information

[0238] Output: Job listings stored in a database

[0239] Step 3:

[0240] The server matches companies' job listings with the user's skill set, past work experience, etc.

[0241] What it does: Search for skillsets and work experience using SQL queries.

[0242] Input: User's skill set, past work experience

[0243] Output: Matching results

[0244] Step 4:

[0245] The server analyzes the matching results and identifies suitable users.

[0246] What it does: Runs a matching algorithm to identify highly suitable users.

[0247] Input: Matching results

[0248] Output: A list of highly relevant users

[0249] Step 5:

[0250] The server displays the identified job listings on the user's interface.

[0251] Specific operation: Display "Company B is hiring a data scientist" on User C's screen.

[0252] Input: Matched Jobs

[0253] Output: The job displayed in the user's interface

[0254] Advertisement and monetization

[0255] Processing Steps:

[0256] Step 1:

[0257] The server selects advertisements related to the user's question history and advice content.

[0258] What it does: Runs a relevance algorithm to select "data analytics tool ads."

[0259] Input: User's question history, advice history

[0260] Output: Selected ads

[0261] Step 2:

[0262] The server displays the selected advertisement on the user's screen.

[0263] Specific behavior: Displayed as a banner ad on the user's screen.

[0264] Input: Selected Ad

[0265] Output: The ad displayed in the user's interface.

[0266] Step 3:

[0267] The server generates periodic reports and trend reports from the company and bills the company for usage.

[0268] Specific behavior: Generate a report in PDF format and send it to the company's registered email address.

[0269] Input: Company usage information

[0270] Output: Generated reports and invoices

[0271] Step 4:

[0272] The server collects premium feature fees and subscription fees from users.

[0273] Specific operation: Collect fees periodically via credit card payments.

[0274] Input: User's payment information

[0275] Output: Usage fees collected

[0276] (Application example 1)

[0277] 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."

[0278] Conventional career counseling systems lack the means to instantly receive appropriate advice in real time when users ask career-related questions. They also have limited means to efficiently match companies' job information with users' skill sets. Furthermore, they lack sufficient means to provide users with relevant advertising and billing information in real time. To solve these problems, it is necessary to provide an effective system and means.

[0279] 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.

[0280] In this invention, the server includes means for a user to input a career-related question, means for saving the user's question and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for generating advice based on the acquired past questions and advice history using a generative AI, means for providing the generated advice to the user, and means for using a display device in the store to allow the user to receive career-related advice in real time, thereby enabling the user to receive personalized career advice in real time.

[0281] A "user" is an individual who enters career-related questions into the system and receives advice and job information.

[0282] A "company" is an organization that inputs the position it is looking to hire for and the profile of the person it is looking for into the system, and then uses that information to search for suitable candidates.

[0283] The "database" is a storage device within the system that stores users' question history, advice history, and company job information, and retrieves and uses them as needed.

[0284] "Generative AI" refers to artificial intelligence that generates new advice based on a user's past questions and advice history.

[0285] A "display device" is a device used by a user to receive real-time career advice and job information, such as smart glasses or a terminal.

[0286] "Advice" is a suggestion for specific courses of action or skill acquisition that is provided in response to a user's career-related questions.

[0287] "Matching" is the process of comparing a user's skill set with the profile of the person a company is looking for and finding suitable job information.

[0288] "Advertisement" is promotional information that provides information related to the user's question history and advice content through the display device used by the user.

[0289] To implement this invention, a system using users, companies, servers, terminals, a database, generative AI, and a display device is required. Specific embodiments of this system are described below.

[0290] User Registration and Login

[0291] A user first registers with the system. They enter information such as their name, email address, and password into a dedicated input form and send it to the server. The server saves the entered information in a database and sends the user a confirmation email for authentication. When the user clicks the link in the confirmation email, account authentication is completed, and they then enter their email address and password on the login screen to log in to the system. The server compares the user's authentication information with the database and allows them to log in.

[0292] Career Question and Advice Generation

[0293] Users enter career-related questions into a dedicated input form. The server receives these questions and records them in a database. The server then retrieves the user's past questions and their answer history from the database and passes them as input data to the generative AI. The generative AI generates personalized advice based on the input data. The generated advice is displayed on the user's interface, allowing the user to receive the advice in real time. Specifically, the advice is provided through a display device such as smart glasses.

[0294] Entering and matching company job information

[0295] Company personnel also log in to the system and enter recruitment information. The server stores the entered recruitment information in a database. The server matches the company's recruitment information with the user's skill set, past work experience, etc., and recommends suitable jobs to the user. The user can receive this recommendation in real time on a display device.

[0296] Ad Display and Monetization

[0297] The server selects advertisements relevant to the user's question history and advice, and displays the selected advertisements on the user's screen. The server periodically collects fees from companies for feedback and data usage. It also charges users for using specific advanced features. Advertisement and billing information are also provided in real time through the smart glasses.

[0298] Specific examples

[0299] For example, User A inputs a question such as, "What skills should I learn in the future?" The server references User A's past question history and passes it as input data to the generative AI. The generative AI generates advice recommending "acquiring data analysis and Python skills" and provides it through the smart glasses. At the same time, if Company B inputs a job posting for a "data scientist," the server compares it with User A's skill set and presents suitable job postings in real time.

[0300] Prompt Sentence Examples

[0301] For example, provide the generative AI with a prompt like the following:

[0302] Prompt: "User A has been recommended to learn 'Data Analysis' and 'Python' in the past. Based on recent trends and User A's current skill set, please provide advice on which skills they should learn next."

[0303] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0304] Step 1:

[0305] The user enters a question about a career. The question is entered through an input form and the data is sent to the server.

[0306] Enter: Career Questions

[0307] Output: Sends the query data to the server

[0308] Step 2:

[0309] The server receives the user's question and stores it in a database, allowing for further processing.

[0310] Input: User question data

[0311] Output: Save the question data to the database

[0312] Step 3:

[0313] The server retrieves the user's past questions and advice history from the database, allowing the user to refer to their past question patterns and the advice they received.

[0314] Input: User ID

[0315] Output: User's past questions and advice history data

[0316] Step 4:

[0317] The server inputs the user's past questions and advice history into the generative AI to generate personalized advice. The generative AI generates advice based on the prompt sentence.

[0318] Input: Past questions and advice history, prompt text

[0319] Output: Personalized advice

[0320] Step 5:

[0321] The server displays the generated advice on the user's interface, for example, by using smart glasses to provide advice in real time.

[0322] Input: Generated advice

[0323] Output: Advice is displayed on the user's display device

[0324] Step 6:

[0325] A company representative logs in to the system and enters recruitment information, which is then sent to the server.

[0326] Input: Company recruitment information

[0327] Output: Send job information to the server

[0328] Step 7:

[0329] The server stores the recruitment information received from companies in a database, where job information from companies is accumulated.

[0330] Input: Company recruitment information

[0331] Output: Job saved to database

[0332] Step 8:

[0333] The server matches the user's skill set with company job information by retrieving the user's history and skill information from a database and using an algorithm to perform the matching.

[0334] Input: User skill set, company job postings

[0335] Output: Matching results

[0336] Step 9:

[0337] The server displays suitable job information to the user based on the matching results, allowing the user to receive suitable job information in real time.

[0338] Input: Matching results

[0339] Output: The job listing is displayed on the user's display device.

[0340] Step 10:

[0341] The server selects advertisements related to the user's question history and advice content, and displays them on the user's display device. The selection of advertisement data is based on the user's history information.

[0342] Input: User's question history, advice content

[0343] Output: Display the ad

[0344] Step 11:

[0345] The server collects fees from the company for feedback and data usage, and also charges users for certain advanced features, as a means of monetization.

[0346] Input: Company feedback, user usage history

[0347] Output: Toll collection

[0348] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0349] This invention relates to a system that recognizes a user's emotional state and provides career advice based on that. This system matches the user's career-related questions with company job information, analyzes the user's emotional state using an emotion engine, and provides personalized advice, advertisements, and job information. Here, we will generate a system program and explain its processing in natural language. We will explain the server, terminal, and user as subjects, using concrete examples.

[0350] Parts Overview

[0351] User: An individual who uses the system to enter career-related questions and receive advice and job information.

[0352] Server: A computer system that processes data, generates advice using generative AI, analyzes emotional states using an emotion engine, and matches users with company job listings.

[0353] Terminal: The device used by the company representative to enter recruitment information.

[0354] Database: Storage for question history, advice history, company job information, and sentiment data.

[0355] Program processing explanation

[0356] User Registration and Login

[0357] 1. The user registers and enters the required information.

[0358] 2. The server saves the information entered in a database and sends a confirmation email to the user.

[0359] 3. The user clicks the link in the confirmation email to authenticate their account and complete the login.

[0360] 4. The server checks the user's credentials against a database and allows them to log in.

[0361] Career Question and Advice Generation

[0362] 1. The user enters a career question.

[0363] 2. The server stores the question in a database and uses an emotion engine to analyze the question and recognize the user's emotional state.

[0364] 3. The server retrieves past question and answer history from the database and passes the data to the generative AI.

[0365] 4. Generative AI generates advice based on past data and current emotional state.

[0366] 5. The server sends the generated advice to the user and displays it in the interface.

[0367] Entering and matching company job information

[0368] 1. The terminal (company representative) logs in and enters recruitment information.

[0369] 2. The server saves the input information in a database.

[0370] 3. The server compares the user's skill set with company job listings and runs a matching algorithm.

[0371] 4. The server analyzes the matching results and adjusts the order in which job listings are presented, taking into account the user's emotional state using an emotion engine.

[0372] 5. The server notifies the user of suitable job information and displays it on the interface.

[0373] Advertisement and monetization

[0374] 1. The server selects relevant advertisements based on the user's question history and emotional state.

[0375] 2. The server displays the selected advertisement on the user's interface.

[0376] 3. The server provides companies with regular recruitment data and trend reports and collects a fee for the feedback.

[0377] 4. The server collects premium feature fees and subscription fees from the user.

[0378] Specific examples

[0379] Example 1: User A's career questions and advice

[0380] 1. User A enters the question, "What skills do I need to learn next?"

[0381] 2. The server stores User A's question in a database and analyzes it with an emotion engine to recognize User A's emotional state.

[0382] 3. The server passes the past question history and current emotional state to the generative AI.

[0383] 4. Generative AI generates advice recommending "acquire data analysis and Python skills."

[0384] 5. The server sends the AI ​​advice to User A and displays it on the interface.

[0385] Example 2: Matching job information from Company B with User C

[0386] 1. A person in charge at Company B logs in and enters the job information for a "Data Scientist."

[0387] 2. The server saves the job information in a database.

[0388] 3. The server matches User C's skill set with Company B's job listings.

[0389] 4. The server analyzes the matching results and adjusts the order in which job information is presented using an emotion engine, taking into account User C's emotional state.

[0390] 5. The server notifies User C of the job information and displays it on the interface.

[0391] This demonstrates that the present invention is a system that realizes personalized career counseling and job matching for companies that takes into account the user's emotional state, thereby providing more effective advice to users and recommending more suitable candidates to companies.

[0392] The processing flow will be explained below.

[0393] Career Question and Advice Generation

[0394] Step 1:

[0395] The user enters career-related questions into a dedicated input form.

[0396] Step 2:

[0397] The server receives the question from the user, checks that the input format is correct, and if there are no problems with the format, stores the question in the database.

[0398] Step 3:

[0399] The server passes the question content to the emotion engine, which analyzes and recognizes the user's emotional state.

[0400] Step 4:

[0401] The server retrieves the user's past question and answer history from the database.

[0402] Step 5:

[0403] The server inputs question history, user profile data, and current emotional state information into the generative AI.

[0404] Step 6:

[0405] Generative AI generates personalized advice for users based on input data.

[0406] Step 7:

[0407] The server displays the generated advice on the user's interface.

[0408] Entering and matching company job information

[0409] Step 1:

[0410] The terminal (company representative) logs in to the system and accesses the recruitment information input screen.

[0411] Step 2:

[0412] The terminal allows users to input the available positions and the desired skill sets.

[0413] Step 3:

[0414] The server receives the recruitment information sent by the company, checks whether the input format is correct, and if there are no problems with the format, stores the information in the database.

[0415] Step 4:

[0416] The server compares the user's skill set with the skill set required by the company from the database and runs a matching algorithm.

[0417] Step 5:

[0418] The server analyzes the matching results and utilizes an emotion engine to adjust the order in which job listings are presented, taking into account the user's emotional state.

[0419] Step 6:

[0420] The server notifies the user of suitable job information and displays it on the interface.

[0421] Advertisement and monetization

[0422] Step 1:

[0423] The server selects relevant advertisements based on the user's question history and emotional state.

[0424] Step 2:

[0425] The server retrieves the selected advertisement data and displays it on the user's interface.

[0426] Step 3:

[0427] The server periodically creates recruitment data and trend reports for companies and collects a fee for the feedback.

[0428] Step 4:

[0429] The server handles the process of collecting premium feature fees and subscription fees from users.

[0430] Example: User A's career questions and advice

[0431] Step 1:

[0432] User A enters the question "What skills do you want to learn in the future?" into an input form.

[0433] Step 2:

[0434] The server stores User A's question in a database.

[0435] Step 3:

[0436] The server passes the question to the emotion engine, which analyzes and recognizes the emotional state of User A. For example, it may determine that User A is feeling anxious.

[0437] Step 4:

[0438] The server retrieves past question history from the database and passes it to the generative AI.

[0439] Step 5:

[0440] Based on past data and current emotional state, generative AI generates advice such as "learn data analysis and Python skills."

[0441] Step 6:

[0442] The server sends the advice to User A and displays it on the interface.

[0443] Example: Matching job information from Company B with User C

[0444] Step 1:

[0445] A representative from Company B logs in and enters job information for a "Data Scientist."

[0446] Step 2:

[0447] The server stores the job listings in a database.

[0448] Step 3:

[0449] The server uses a matching algorithm to compare User C's skill set with Company B's job listings.

[0450] Step 4:

[0451] The server analyzes the matching results and uses an emotion engine to consider the emotional state of user C. For example, if user C shows enthusiasm, job information reflecting that emotion will be presented preferentially.

[0452] Step 5:

[0453] The server notifies User C of the job information and displays it on the interface.

[0454] Example 2

[0455] 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."

[0456] Conventional career advice systems do not always provide optimal advice for users because they do not take into account the user's emotional state. Furthermore, matching between company job information and the user's skill set is limited to a simple comparison, and does not take into account the user's emotions or motivations, making it difficult to provide job suggestions that satisfy the user. To solve these problems, there is a need for the development of a system that analyzes the user's emotional state and provides personalized advice and matching based on that analysis.

[0457] 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.

[0458] In this invention, the server includes means for a user to input a career-related question, means for saving the user's questions and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for analyzing the user's emotional state using an emotion engine, means for generating advice based on the acquired past questions and emotional state using a generative AI, and means for providing the generated advice to the user, thereby making it possible to provide personalized career advice that reflects the user's emotional state.

[0459] "Career questions" refer to doubts or questions users have about their occupation, work style, or skill development.

[0460] "Database" refers to a storage system for storing a user's question history, advice history, emotional state information, and company job information.

[0461] An "emotion engine" refers to software or algorithms that analyze text data entered by a user and recognize the user's emotional state from its content.

[0462] "Generative AI" refers to an artificial intelligence model that generates optimal advice and information based on past data and the user's current emotional state.

[0463] "Job information" refers to information that includes details about the positions a company is hiring for and the type of person they are looking for.

[0464] "Skill set" refers to the collection of technical or professional skills a user possesses.

[0465] "Matching" refers to the process of comparing a user's skill set with a company's job listings, calculating the degree of compatibility, and finding the best match.

[0466] "Personalized advice" refers to advice that is optimized for a specific user based on the user's individual characteristics and emotional state.

[0467] "Advertisement" refers to promotional information for products or services related to the user's question history or emotional state.

[0468] "Feedback" refers to evaluations and opinions about the system from companies and users, and refers to information used to improve the system and services.

[0469] The present invention relates to a system for recognizing a user's emotional state and providing career advice based on the recognition. The following describes how the program processing of this system is implemented.

[0470] This system is composed of the following main components: users, servers, terminals, and databases. Each component cooperates to provide personalized career advice to users.

[0471] User Registration and Login

[0472] The user registers by entering their name, email address, and password on the new registration screen. The server stores this information in a database and sends a confirmation email. The user clicks the link in the confirmation email to authenticate their account, and then authenticates again on the login screen. The server compares the user's authentication information with the database and allows them to log in if they match.

[0473] Career Question and Advice Generation

[0474] A user inputs a question about their career, such as "What should I pay attention to when choosing a career?" The server stores the user's question in a database and uses an emotion engine to analyze the question and recognize the user's emotional state. A natural language processing toolkit is used for this analysis.

[0475] Next, the server retrieves the past question history and advice history from the database and passes that data to the generative AI. Based on the retrieved data and the current emotional state, the generative AI generates advice. This AI could use, for example, OpenAI's (registered trademark) GPT-3 model.

[0476] The generated advice is displayed on the user's interface via the server. For example, advice such as "I recommend you improve your project management skills" is presented.

[0477] Entering and matching company job information

[0478] The terminal (company representative) logs into the system and inputs the company's job information. For example, they register information such as "Recruiting front-end engineers." The server stores that information in a database. The server then compares the user's skill set with the company's job information and runs a matching algorithm.

[0479] The matching results are analyzed by an emotion engine, which adjusts the presentation order based on the user's emotional state. The most suitable job listings are then notified to the user and displayed on the interface.

[0480] Advertisement and monetization

[0481] The server selects relevant advertisements based on the user's question history and emotional state and displays them in the user's interface. The server also periodically collects fees from companies for using the feedback and recruitment data. The server also handles the process of collecting fees for certain advanced features and subscription fees from users.

[0482] Specific examples

[0483] For example:

[0484] Example prompt sentence:

[0485] User A enters the question "What skills are important when choosing a job?" into the system, the server saves the question, and the emotion engine analyzes it to determine that "User A is feeling anxious." The generative AI generates advice such as "We recommend you improve your data analysis skills," and the server displays this advice on User A's interface.

[0486] The present invention aims to provide more effective and personalized career advice by taking into account the user's emotional state, which will greatly contribute to helping users improve their practical skills and make appropriate career choices.

[0487] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0488] User Registration and Login

[0489] Step 1: Enter your new registration information

[0490] Operation:

[0491] The user enters their name, email address, and password on the new registration screen.

[0492] input:

[0493] Name, email address, password

[0494] output:

[0495] The registration information is sent to the server.

[0496] Step 2: Save your registration information and send a confirmation email

[0497] Operation:

[0498] The server stores the entered information in a database and sends a confirmation email.

[0499] input:

[0500] Registration information (name, email address, password)

[0501] Data processing:

[0502] Saves the new user information in the database and generates and sends a confirmation email via the SMTP protocol.

[0503] output:

[0504] A confirmation email will be sent to the user's email address.

[0505] Step 3: Verify your account

[0506] Operation:

[0507] The user clicks on the link in the confirmation email.

[0508] input:

[0509] Link in the confirmation email

[0510] output:

[0511] After authentication, a response is returned from the server.

[0512] Step 4: Complete the login

[0513] Operation:

[0514] The server checks the user's credentials against a database and allows them to log in.

[0515] input:

[0516] Authentication information (email address, password)

[0517] Data processing:

[0518] The authentication information is checked against the database, and if it matches a session is created.

[0519] output:

[0520] The dashboard screen is displayed to the user.

[0521] Career Question and Advice Generation

[0522] Step 1: Fill in the career questions

[0523] Operation:

[0524] The user enters a career question and clicks the "Submit" button.

[0525] input:

[0526] Career-related questions (e.g., "What should you pay attention to when choosing a career?")

[0527] output:

[0528] The question is sent to the server.

[0529] Step 2: Analyze emotional state

[0530] Operation:

[0531] The server stores the question content in a database, and an emotion engine analyzes the question content to recognize the user's emotional state.

[0532] input:

[0533] Career Questions

[0534] Data processing:

[0535] The question data is stored in a database and sentiment analysis is performed using a natural language processing (NLP) toolkit.

[0536] output:

[0537] The user's emotional state (e.g., anxiety) is recognized.

[0538] Step 3: Obtaining historical data

[0539] Operation:

[0540] The server retrieves the past question history and advice history from the database.

[0541] input:

[0542] User ID

[0543] Data processing:

[0544] Execute a database query to retrieve past question and advice history.

[0545] output:

[0546] A history of past questions and advice is obtained.

[0547] Step 4: Generating Advice

[0548] Operation:

[0549] Generative AI generates advice based on past data and current emotional state.

[0550] input:

[0551] Past question history, advice history, current emotional state

[0552] Data processing:

[0553] Provide data to a generative AI model (e.g., GPT-3) to generate advice based on a prompt.

[0554] output:

[0555] The advice generated (e.g., "I recommend you improve your project management skills")

[0556] Step 5: Providing advice

[0557] Operation:

[0558] The server sends the generated advice to the user and displays it in the interface.

[0559] input:

[0560] Generated Advice

[0561] output:

[0562] Advice is displayed on the user's interface.

[0563] Entering and matching company job information

[0564] Step 1: Enter your job information

[0565] Operation:

[0566] The terminal (company representative) logs into the system and enters job information.

[0567] input:

[0568] Job postings (e.g., "Front-end engineer wanted")

[0569] output:

[0570] The job information is sent to the server.

[0571] Step 2: Saving to the database

[0572] Operation:

[0573] The server stores the job listings in a database.

[0574] input:

[0575] Job information

[0576] Data processing:

[0577] Store job information in a database.

[0578] output:

[0579] Saved Jobs

[0580] Step 3: Performing the Match

[0581] Operation:

[0582] The server compares the user's skill set with company job listings and runs a matching algorithm.

[0583] input:

[0584] User skill sets, job information

[0585] Data processing:

[0586] Run a matching algorithm that compares your skill set with the job posting and calculates a suitability score.

[0587] output:

[0588] Matching results (relevance score)

[0589] Step 4: Presentation taking into account emotional state

[0590] Operation:

[0591] The server analyzes the matching results and the user's emotional state to adjust the presentation order.

[0592] input:

[0593] Matching results, user emotional state

[0594] Data processing:

[0595] Prioritize job postings based on sentiment analysis data.

[0596] output:

[0597] Adjusted job posting order

[0598] Step 5: Post a job

[0599] Operation:

[0600] The server notifies the user of suitable job information and displays it on the interface.

[0601] input:

[0602] Adjusted job posting order

[0603] output:

[0604] The job listing is displayed in the user interface.

[0605] Advertisement and monetization

[0606] Step 1: Ad selection

[0607] Operation:

[0608] The server selects relevant advertisements based on the user's question history and emotional state.

[0609] input:

[0610] Question history, emotional state

[0611] Data processing:

[0612] It runs an ad selection algorithm based on question history and emotional state.

[0613] output:

[0614] Selected Advertisements

[0615] Step 2: Displaying the ad

[0616] Operation:

[0617] The server displays the selected advertisement on the user's interface.

[0618] input:

[0619] Selected Advertisements

[0620] output:

[0621] Advertisements are displayed in the user's interface.

[0622] Step 3: Collect fees

[0623] Operation:

[0624] The server periodically collects fees from companies for feedback and data usage, and charges users for certain advanced features or subscription fees.

[0625] input:

[0626] Company usage information, user usage information

[0627] Data processing:

[0628] Manage billing information through a fee collection system and collect fees from businesses and users.

[0629] output:

[0630] Fees collected

[0631] Through these steps, the system can provide personalized career advice and job information that takes into account the user's emotional state, providing more effective advice to users and recommending more suitable candidates to companies.

[0632] (Application example 2)

[0633] 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."

[0634] Conventional food delivery services have systems that suggest meals based on a user's order history and preferences, but they are unable to make personalized suggestions that take into account the user's emotional state. As a result, meals and services that are appropriate for the user's emotional state may not be provided, hindering the improvement of the user experience.

[0635] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0636] In this invention, the server includes means for a user to input a question about a service, means for saving the user's question and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for generating advice based on the acquired past questions and advice history using a generative AI, means for providing the generated advice to the user, means for analyzing the user's emotional state using an emotion engine, and means for providing personalized information or services based on the emotional state. This makes it possible to provide personalized meal suggestions and services that take the user's emotional state into consideration.

[0637] A "user" is an individual who utilizes the system to enter service-related questions and receive suggestions and information.

[0638] A "database" is a storage device that stores data such as user questions, past question history, advice history, skill sets, and the type of person a company is looking for.

[0639] "Generative AI" is an artificial intelligence model that generates new advice and suggestions based on past question and advice history.

[0640] The "emotion engine" is a system component that analyzes the user's emotional state from their input and expressions.

[0641] "Personalized information or services" are suggestions or information that are individually tailored based on a user's emotional state and past behavioral history.

[0642] A "company" is an organization that inputs information and the profile of the person it is looking for in order to provide a service.

[0643] A "skill set" is a collection of skills and knowledge possessed by a user.

[0644] A "matching method" is a system component that compares the user's skill set with the profile of the person the company is looking for and finds the optimal combination.

[0645] "Emotional state" refers to the state of emotion at that time that is estimated from the user's input and actions.

[0646] This invention relates to a food delivery system that analyzes emotional states and provides personalized meal recommendations and services based on those results. The entire system can be accessed by users through a smartphone application. The main hardware components include a smartphone, a server, and a database. The main software components used are a generative AI model and an emotion engine (EmotionAPI).

[0647] Hardware and software used

[0648] 1. Smartphone: Provides the user interface and receives user input.

[0649] 2. Server: Processes data, runs generative AI models, analyzes emotional states using the emotion engine, and performs data matching.

[0650] 3. Database: Stores data such as user questions, past question history, advice history, skill sets, and the type of person the company is looking for.

[0651] 4. Generative AI model (GPT model): Generates new suggestions and advice based on user input and past data.

[0652] 5. Emotion Engine (Emotion API): Analyzes the user's emotional state from their input and expressions.

[0653] System configuration description

[0654] First, a user registers an account using a smartphone application and enters the necessary information. After completing the registration, the user inputs their emotional state through the application. For example, they can express their emotions by uploading photos or text. Based on this, the server uses an emotion engine to analyze the user's emotional state.

[0655] Based on the analysis results, the server retrieves past question and advice history from the database and passes the data to a generative AI model. This generative AI model generates personalized meal suggestions based on the analyzed emotional state and past data. The generated suggestions are displayed in the user's smartphone application.

[0656] When a user places an order from the suggested meal menu, the server provides relevant coupons and offers and stores this order information in a database. The server can also display personalized advertisements based on the user's emotional state and order history. The server also includes a means to collect feedback from advertisers, data usage fees, and premium feature fees from users.

[0657] Specific examples

[0658] For example, consider the case where a user enters the text "I'm feeling stressed today." The server's emotion engine analyzes and detects "stress." The server then passes the data to a generative AI model based on past order history and the user's current emotional state. The generative AI model generates a suggestion for a "healthy salad bowl that's good for relieving stress," and the server displays this suggestion on the smartphone application.

[0659] Example prompt sentence:

[0660] Emotional state: Stress

[0661] Past orders: Japanese food, healthy food, smoothies

[0662] Suggestion: Generate appropriate meal suggestions for the user, taking into account their emotional state today.

[0663] This allows the user to receive suggestions for meals that best suit their mood at the time, allowing them to receive a more satisfying service.

[0664] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0665] Program processing steps

[0666] Step 1: User Registration and Login

[0667] The user opens the smartphone application and enters the required information into the new registration form.

[0668] The server receives the entered information and stores it in a database.

[0669] The server will send a confirmation email to the user, prompting them to click on a link to confirm their account.

[0670] The user clicks the link in the verification email to confirm their account.

[0671] The server checks the user's verification information against the database, confirms that authentication has been completed, and permits login.

[0672] Input: User registration information (name, email address, password, etc.)

[0673] Output: User account authentication and login permission

[0674] Step 2: Input and analysis of the user's emotional state

[0675] Users upload photos and text that describe their emotional state within a smartphone application.

[0676] The server receives the uploaded data, sends it to the emotion engine (EmotionAPI), and analyzes the emotional state.

[0677] The emotion engine analyzes the user's emotional state from the input and returns the results to the server.

[0678] Input: User-uploaded photos and text

[0679] Output: Sentiment analysis results (e.g., stress, joy, sadness, etc.)

[0680] Step 3: Obtaining past data and generating proposals using generative AI

[0681] The server retrieves the user's past question and advice history from a database, along with the analyzed emotional state.

[0682] The server passes this data to a generative AI model (GPT model) to generate personalized suggestions and advice.

[0683] The generative AI generates optimal menu suggestions based on the provided data and the user's emotional state and returns them to the server.

[0684] Input: User's emotional state, past question history, advice history

[0685] Output: Personalized menu suggestions

[0686] Step 4: View proposals and process orders

[0687] The server displays the suggestions returned by the generative AI on the user's smartphone application.

[0688] The user reviews the proposed menu and selects an order.

[0689] The server stores the order information selected by the user in a database and arranges for delivery service.

[0690] Input: Generative AI suggestions, user menu selections

[0691] Output: Store order information and arrange delivery

[0692] Step 5: Offer coupons and display ads

[0693] The server generates relevant coupons and rewards based on the user's orders and past behavioral history.

[0694] The server displays the coupons and offers on the user's smartphone application.

[0695] Additionally, the server displays personalized advertisements based on the user's emotional state and order history.

[0696] Input: User order information, past behavior history

[0697] Output: Coupons, offers, personalized ads

[0698] In this way, users can receive meal suggestions that suit their emotional state and can order and receive rewards based on them.

[0699] 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.

[0700] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0701] 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.

[0702] [Second embodiment]

[0703] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0704] 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.

[0705] 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).

[0706] 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.

[0707] 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.

[0708] 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).

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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."

[0715] This invention relates to a system that matches users' career questions with companies' job information and provides personalized advice and job information. Here, we will generate a program for the system and explain its processing in natural language. We will also use concrete examples and explain the server, terminal, and user as subjects.

[0716] Parts Overview

[0717] User: An individual who uses the system to enter career-related questions and receive advice and job information.

[0718] Server: A computer system that processes data, generates advice using generative AI, and matches jobs with companies' job listings.

[0719] Terminal: The device used by the company representative to enter recruitment information.

[0720] Database: Storage for question history, advice history, and company job information.

[0721] Program processing explanation

[0722] User Registration and Login

[0723] 1. When a user registers, they enter information such as their name, email address, and password into a dedicated input form.

[0724] 2. The server saves the input information in a database and sends a confirmation email to the user for authentication.

[0725] 3. The user clicks the link in the confirmation email to complete account verification.

[0726] 4. The user enters their email address and password on the login screen to log in to the system.

[0727] 5. The server checks the user's credentials against the database and allows them to log in.

[0728] Career Question and Advice Generation

[0729] 1. The user enters career-related questions into a dedicated input form.

[0730] 2. The server receives this question and records it in a database.

[0731] 3. The server retrieves the user's past questions and their answer history from the database.

[0732] 4. The server inputs the question history and user profile data into the generative AI to generate personalized advice.

[0733] 5. The server displays the generated advice in the user's interface.

[0734] Entering and matching company job information

[0735] 1. The terminal (company representative) logs in and enters recruitment information.

[0736] 2. The server saves the job information entered in the database.

[0737] 3. The server matches company job information with the user's skill set, past work experience, etc.

[0738] 4. The server analyzes the matching results and identifies suitable job listings for the user.

[0739] 5. The server displays the identified job listings on the user's interface.

[0740] Advertisement and monetization

[0741] 1. The server selects advertisements related to the user's question history and advice content.

[0742] 2. The server displays the selected advertisement on the user's screen.

[0743] 3. The server generates periodic reports and trend reports from the company and bills them for usage.

[0744] 4. The server collects premium feature fees and subscription fees from the user.

[0745] Specific examples

[0746] Example 1: User A's career questions and advice

[0747] 1. User A enters a question: "What skills do I need to learn in the future?"

[0748] 2. The server references user A's past question history and passes it as input data to the generative AI.

[0749] 3. The server uses generative AI to generate advice recommending "acquiring data analysis and Python skills" and provides it to User A.

[0750] Example 2: Matching job information from Company B with User C

[0751] 1. Company B enters job information for a "Data Scientist."

[0752] 2. The server saves the job information in a database and matches it with User C's skill set.

[0753] 3. The server identifies that User C's skill set matches the requirements of Company B and recommends it to User C.

[0754] This clearly shows that the present invention is a system that can respond to individual users' career consultations while also matching with the recruitment needs of companies, thereby realizing efficient job-hunting support and profit generation.

[0755] The processing flow will be explained below.

[0756] Career Question and Advice Generation

[0757] Step 1:

[0758] The user enters career-related questions into a dedicated input form.

[0759] Step 2:

[0760] The server receives the question from the user, checks that the input format is correct, and if there are no problems with the format, stores the question in the database.

[0761] Step 3:

[0762] The server retrieves the user's past question and answer history from the database.

[0763] Step 4:

[0764] The server inputs past question history and user profile data into the generative AI.

[0765] Step 5:

[0766] Generative AI analyzes past data and generates appropriate advice.

[0767] Step 6:

[0768] The server sends the generated advice content to the user and displays it on the interface.

[0769] Entering and matching company job information

[0770] Step 1:

[0771] The terminal (company representative) logs in to the system and accesses the recruitment information input screen.

[0772] Step 2:

[0773] The terminal allows users to input the available positions and the desired skill sets.

[0774] Step 3:

[0775] The server receives the job information sent by the company, checks that the format is correct, and if there are no problems with the format, stores the information in a database.

[0776] Step 4:

[0777] The server uses a matching algorithm to compare the user's skill set with the company's desired skill set from the database.

[0778] Step 5:

[0779] The server analyzes the matching results and identifies suitable users.

[0780] Step 6:

[0781] The server notifies the identified user of the company's job information and displays it on the interface.

[0782] Advertisement and monetization

[0783] Step 1:

[0784] The server selects relevant advertisements based on the user's question history and advice content.

[0785] Step 2:

[0786] The server retrieves the selected advertisement data and displays it on the user's interface.

[0787] Step 3:

[0788] The server periodically creates recruitment data and trend reports for companies and collects a fee for the feedback.

[0789] Step 4:

[0790] The server handles the process of collecting premium feature fees and subscription fees from users.

[0791] Example: User A's career questions and advice

[0792] Step 1:

[0793] User A enters the question "What skills do you want to learn in the future?" into an input form.

[0794] Step 2:

[0795] The server stores User A's question in a database.

[0796] Step 3:

[0797] The server retrieves past question history from the database and passes it to the generative AI.

[0798] Step 4:

[0799] Generative AI analyzes the data and generates advice recommending "acquiring data analysis and Python skills."

[0800] Step 5:

[0801] The server sends the advice to User A and displays it on the interface.

[0802] Example: Matching job information from Company B with User C

[0803] Step 1:

[0804] A representative from Company B logs in and enters job information for a "Data Scientist."

[0805] Step 2:

[0806] The server stores the job listings in a database.

[0807] Step 3:

[0808] The server matches User C's skill set with job listings.

[0809] Step 4:

[0810] The server analyzes the matching results and identifies job information suitable for User C.

[0811] Step 5:

[0812] The server notifies User C of the job information and displays it on the interface.

[0813] Example 1

[0814] 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."

[0815] In today's job market, it is difficult for individual users to obtain appropriate advice and information about their careers. It is also difficult for companies to quickly and accurately find job seekers who fit their desired profile. Furthermore, there is a lack of mechanisms for effectively displaying relevant advertisements to users and monetizing the service. A system that solves these issues is needed.

[0816] 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.

[0817] In this invention, the server includes means for a user to input a career-related question, means for saving the user's question and past question history in a database, means for acquiring the user's past question and advice history from the database, means for generating advice based on the acquired past question and advice history using a generative AI, means for providing the generated advice to the user, means for passing the user's past history and profile data as prompts to the generative AI model, and means for displaying the generated advice on the user's interface, thereby enabling the user to quickly obtain personalized advice and information.

[0818] "User" refers to an individual who uses the system to enter career-related questions and receive advice and job information.

[0819] "Server" refers to a computer system that processes data, generates advice using generative AI, and matches it with company job information.

[0820] "Database" refers to storage that stores question history, advice history, and company job information.

[0821] "Generative AI" refers to an artificial intelligence model that generates personalized advice based on a user's question history and profile data.

[0822] "Profile Data" refers to personal information about a user, such as age, occupation, and work experience.

[0823] "Interface" refers to a user interface that includes screens and input forms that allow a user to interact with a system.

[0824] "Company" refers to the entity that provides job information and inputs the desired profile and recruitment information.

[0825] "Job information" refers to information such as the types of jobs a company is looking to hire for and the skill sets they are looking for.

[0826] "Matching" refers to the process of comparing a user's skill set and work experience with a company's job listings to determine suitability.

[0827] "Advertisement" refers to commercial information related to the user's question history and advice content.

[0828] The present invention relates to a system that matches users' career questions with companies' job information and provides personalized advice and job information. The program processing of this system will be described in detail below.

[0829] Hardware and software used

[0830] The following hardware and software are used to implement this system.

[0831] Server: A computer system that processes data, generates advice using generative AI, and matches with company job information. Specifically, it uses a cloud server.

[0832] Terminal: The device on which company personnel enter recruitment information. Specifically, a computer or tablet with a web browser is used.

[0833] Database: Storage for storing user question history, advice history, and company job information. Specifically, a relational database management system (RDBMS) is used.

[0834] Generative AI: AI models for generating personalized advice, specifically using natural language processing models such as GPT-3.

[0835] Specific processing explanation of the program

[0836] User Registration and Login

[0837] When a user registers, they enter information such as their name, email address, and password into a dedicated input form. The server receives this information, stores it in a database, and sends the user a confirmation email for authentication. When the user clicks the link in the confirmation email, account authentication is complete. The user then enters their authentication information on the login screen, and the server compares the information with the database and allows them to log in.

[0838] Career Question and Advice Generation

[0839] When a user inputs a question about their career, the server receives the question and records it in a database. The server then retrieves the user's past questions and advice history from the database and inputs it into the generative AI. At this time, the user's past history and profile data are used as prompts. For example, the following prompts are generated:

[0840] "User's previous question: What skills should I learn in the future? User profile: Age 25, Occupation Engineer"

[0841] The generative AI generates advice based on this, and the server displays that advice on the user's interface.

[0842] Entering and matching company job information

[0843] The terminal (company representative) logs in and enters recruitment information. For example, "Job position: Data scientist, Required skills: Python, data analysis." The server receives this information and stores it in a database. The server then matches the company's recruitment information with the user's skill set, past work experience, etc., to identify users who are highly suitable. The server analyzes the identified matching results and displays the recruitment information on the user's interface to provide the user with appropriate recruitment information.

[0844] Advertisement and monetization

[0845] The server selects advertisements related to the user's question history and advice content and displays them on the user's screen. This allows for highly relevant advertisements to be displayed, enabling effective marketing. The server periodically creates reports and trend reports from companies and bills them for usage. It also includes a mechanism for collecting premium feature fees and subscription fees from users.

[0846] The above is a detailed embodiment of the system according to the present invention. This allows users to obtain appropriate career advice and companies to quickly and accurately find the talent they are looking for. In addition, by effectively displaying relevant advertisements, the service can be monetized.

[0847] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0848] User Registration and Login

[0849] Processing Steps:

[0850] Step 1:

[0851] A user registers by entering information such as name, email address, and password into a dedicated input form and clicking the "Register" button.

[0852] Specific actions: Enter "Yamada Taro", "yamada@example.com", and "password123" and click the button.

[0853] Input: User information (name, email address, password)

[0854] Output: The entered user information is sent to the server.

[0855] Step 2:

[0856] The server receives this information, stores it in a database, and simultaneously sends a confirmation email to the user's email address.

[0857] What it does: Creates a new record in the database and sends a confirmation email via the SMTP server.

[0858] Input: User information (name, email address, password)

[0859] Output: A confirmation email is sent to the user.

[0860] Step 3:

[0861] The user clicks the confirmation link in the email to complete account authentication.

[0862] What happens: Open the confirmation email and click the link.

[0863] Input: Link from confirmation email

[0864] Output: The account is authenticated.

[0865] Step 4:

[0866] The user enters their email address and password on the login screen and clicks the "Login" button.

[0867] Specific action: Enter "yamada@example.com" and "password123" into the form and click the button.

[0868] Input: User's email address and password

[0869] Output: An authentication request is sent to the server.

[0870] Step 5:

[0871] The server checks the user's input information against the registered information in the database, and if it matches, allows the user to log in.

[0872] Specific operation: A matching process is performed, and if there is a match, a session is started.

[0873] Input: User's email address and password

[0874] Output: Successful login and session start

[0875] Career Question and Advice Generation

[0876] Processing Steps:

[0877] Step 1:

[0878] The user enters career-related questions into a dedicated input form and clicks the "Submit" button.

[0879] Specific action: Fill out the form with "What skills would you like to learn next?" and click the button.

[0880] Input: User's career question

[0881] Output: The carrier question is sent to the server.

[0882] Step 2:

[0883] The server receives this question and records it in a database.

[0884] Specific behavior: The question is stored in the database as a new record.

[0885] Input: Career Question

[0886] Output: Question data stored in a database

[0887] Step 3:

[0888] The server retrieves the user's past questions and advice history from a database.

[0889] What it does: Retrieves past question history using a SQL query.

[0890] Input: User ID

[0891] Output: Past questions and advice history

[0892] Step 4:

[0893] The server passes the user's past history and profile data as prompts to the generative AI model to generate advice.

[0894] Specific operation: Input the prompt sentence: 'User's previous question: What skills should I learn in the future? User profile: Age 25, Occupation Engineer' into the generative AI model.

[0895] Input: Previous questions, advice history, profile data

[0896] Output: The generated advice

[0897] Step 5:

[0898] The server displays the generated advice on the user's interface.

[0899] Specific behavior: Display advice on the user's screen.

[0900] Input: Generated advice

[0901] Output: Advice displayed on the user's interface

[0902] Entering and matching company job information

[0903] Processing Steps:

[0904] Step 1:

[0905] The terminal (company representative) logs in and enters recruitment information. The user enters "Job position: Data scientist, Required skills: Python, data analysis."

[0906] Specific action: Enter "Data Scientist, Python, Data Analysis" into the form and submit it.

[0907] Input: Company recruitment information

[0908] Output: The job information is sent to the server.

[0909] Step 2:

[0910] The server saves the job information entered in the database.

[0911] Specific behavior: The entered job information is stored in the database as a new record.

[0912] Input: Company recruitment information

[0913] Output: Job listings stored in a database

[0914] Step 3:

[0915] The server matches companies' job listings with the user's skill set, past work experience, etc.

[0916] What it does: Search for skillsets and work experience using SQL queries.

[0917] Input: User's skill set, past work experience

[0918] Output: Matching results

[0919] Step 4:

[0920] The server analyzes the matching results and identifies suitable users.

[0921] What it does: Runs a matching algorithm to identify highly suitable users.

[0922] Input: Matching results

[0923] Output: A list of highly relevant users

[0924] Step 5:

[0925] The server displays the identified job listings on the user's interface.

[0926] Specific operation: Display "Company B is hiring a data scientist" on User C's screen.

[0927] Input: Matched Jobs

[0928] Output: The job displayed in the user's interface

[0929] Advertisement and monetization

[0930] Processing Steps:

[0931] Step 1:

[0932] The server selects advertisements related to the user's question history and advice content.

[0933] What it does: Runs a relevance algorithm to select "data analytics tool ads."

[0934] Input: User's question history, advice history

[0935] Output: Selected ads

[0936] Step 2:

[0937] The server displays the selected advertisement on the user's screen.

[0938] Specific behavior: Displayed as a banner ad on the user's screen.

[0939] Input: Selected Ad

[0940] Output: The ad displayed in the user's interface.

[0941] Step 3:

[0942] The server generates periodic reports and trend reports from the company and bills the company for usage.

[0943] Specific behavior: Generate a report in PDF format and send it to the company's registered email address.

[0944] Input: Company usage information

[0945] Output: Generated reports and invoices

[0946] Step 4:

[0947] The server collects premium feature fees and subscription fees from users.

[0948] Specific operation: Collect fees periodically via credit card payments.

[0949] Input: User's payment information

[0950] Output: Usage fees collected

[0951] (Application example 1)

[0952] 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."

[0953] Conventional career counseling systems lack the means to instantly receive appropriate advice in real time when users ask career-related questions. They also have limited means to efficiently match companies' job information with users' skill sets. Furthermore, they lack sufficient means to provide users with relevant advertising and billing information in real time. To solve these problems, it is necessary to provide an effective system and means.

[0954] 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.

[0955] In this invention, the server includes means for a user to input a career-related question, means for saving the user's question and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for generating advice based on the acquired past questions and advice history using a generative AI, means for providing the generated advice to the user, and means for using a display device in the store to allow the user to receive career-related advice in real time, thereby enabling the user to receive personalized career advice in real time.

[0956] A "user" is an individual who enters career-related questions into the system and receives advice and job information.

[0957] A "company" is an organization that inputs the position it is looking to hire for and the profile of the person it is looking for into the system, and then uses that information to search for suitable candidates.

[0958] The "database" is a storage device within the system that stores users' question history, advice history, and company job information, and retrieves and uses them as needed.

[0959] "Generative AI" refers to artificial intelligence that generates new advice based on a user's past questions and advice history.

[0960] A "display device" is a device used by a user to receive real-time career advice and job information, such as smart glasses or a terminal.

[0961] "Advice" is a suggestion for specific courses of action or skill acquisition that is provided in response to a user's career-related questions.

[0962] "Matching" is the process of comparing a user's skill set with the profile of the person a company is looking for and finding suitable job information.

[0963] "Advertisement" is promotional information that provides information related to the user's question history and advice content through the display device used by the user.

[0964] To implement this invention, a system using users, companies, servers, terminals, a database, generative AI, and a display device is required. Specific embodiments of this system are described below.

[0965] User Registration and Login

[0966] A user first registers with the system. They enter information such as their name, email address, and password into a dedicated input form and send it to the server. The server saves the entered information in a database and sends the user a confirmation email for authentication. When the user clicks the link in the confirmation email, account authentication is completed, and they then enter their email address and password on the login screen to log in to the system. The server compares the user's authentication information with the database and allows them to log in.

[0967] Career Question and Advice Generation

[0968] Users enter career-related questions into a dedicated input form. The server receives these questions and records them in a database. The server then retrieves the user's past questions and their answer history from the database and passes them as input data to the generative AI. The generative AI generates personalized advice based on the input data. The generated advice is displayed on the user's interface, allowing the user to receive the advice in real time. Specifically, the advice is provided through a display device such as smart glasses.

[0969] Entering and matching company job information

[0970] Company personnel also log in to the system and enter recruitment information. The server stores the entered recruitment information in a database. The server matches the company's recruitment information with the user's skill set, past work experience, etc., and recommends suitable jobs to the user. The user can receive this recommendation in real time on a display device.

[0971] Ad Display and Monetization

[0972] The server selects advertisements relevant to the user's question history and advice, and displays the selected advertisements on the user's screen. The server periodically collects fees from companies for feedback and data usage. It also charges users for using specific advanced features. Advertisement and billing information are also provided in real time through the smart glasses.

[0973] Specific examples

[0974] For example, User A inputs a question such as, "What skills should I learn in the future?" The server references User A's past question history and passes it as input data to the generative AI. The generative AI generates advice recommending "acquiring data analysis and Python skills" and provides it through the smart glasses. At the same time, if Company B inputs a job posting for a "data scientist," the server compares it with User A's skill set and presents suitable job postings in real time.

[0975] Prompt Sentence Examples

[0976] For example, provide the generative AI with a prompt like the following:

[0977] Prompt: "User A has been recommended to learn 'Data Analysis' and 'Python' in the past. Based on recent trends and User A's current skill set, please provide advice on which skills they should learn next."

[0978] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0979] Step 1:

[0980] The user enters a question about a career. The question is entered through an input form and the data is sent to the server.

[0981] Enter: Career Questions

[0982] Output: Sends the query data to the server

[0983] Step 2:

[0984] The server receives the user's question and stores it in a database, allowing for further processing.

[0985] Input: User question data

[0986] Output: Save the question data to the database

[0987] Step 3:

[0988] The server retrieves the user's past questions and advice history from the database, allowing the user to refer to their past question patterns and the advice they received.

[0989] Input: User ID

[0990] Output: User's past questions and advice history data

[0991] Step 4:

[0992] The server inputs the user's past questions and advice history into the generative AI to generate personalized advice. The generative AI generates advice based on the prompt sentence.

[0993] Input: Past questions and advice history, prompt text

[0994] Output: Personalized advice

[0995] Step 5:

[0996] The server displays the generated advice on the user's interface, for example, by using smart glasses to provide advice in real time.

[0997] Input: Generated advice

[0998] Output: Advice is displayed on the user's display device

[0999] Step 6:

[1000] A company representative logs in to the system and enters recruitment information, which is then sent to the server.

[1001] Input: Company recruitment information

[1002] Output: Send job information to the server

[1003] Step 7:

[1004] The server stores the recruitment information received from companies in a database, where job information from companies is accumulated.

[1005] Input: Company recruitment information

[1006] Output: Job saved to database

[1007] Step 8:

[1008] The server matches the user's skill set with company job information by retrieving the user's history and skill information from a database and using an algorithm to perform the matching.

[1009] Input: User skill set, company job postings

[1010] Output: Matching results

[1011] Step 9:

[1012] The server displays suitable job information to the user based on the matching results, allowing the user to receive suitable job information in real time.

[1013] Input: Matching results

[1014] Output: The job listing is displayed on the user's display device.

[1015] Step 10:

[1016] The server selects advertisements related to the user's question history and advice content, and displays them on the user's display device. The selection of advertisement data is based on the user's history information.

[1017] Input: User's question history, advice content

[1018] Output: Display the ad

[1019] Step 11:

[1020] The server collects fees from the company for feedback and data usage, and also charges users for certain advanced features, as a means of monetization.

[1021] Input: Company feedback, user usage history

[1022] Output: Toll collection

[1023] 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.

[1024] This invention relates to a system that recognizes a user's emotional state and provides career advice based on that. This system matches the user's career-related questions with company job information, analyzes the user's emotional state using an emotion engine, and provides personalized advice, advertisements, and job information. Here, we will generate a system program and explain its processing in natural language. We will explain the server, terminal, and user as subjects, using concrete examples.

[1025] Parts Overview

[1026] User: An individual who uses the system to enter career-related questions and receive advice and job information.

[1027] Server: A computer system that processes data, generates advice using generative AI, analyzes emotional states using an emotion engine, and matches users with company job listings.

[1028] Terminal: The device used by the company representative to enter recruitment information.

[1029] Database: Storage for question history, advice history, company job information, and sentiment data.

[1030] Program processing explanation

[1031] User Registration and Login

[1032] 1. The user registers and enters the required information.

[1033] 2. The server saves the information entered in a database and sends a confirmation email to the user.

[1034] 3. The user clicks the link in the confirmation email to authenticate their account and complete the login.

[1035] 4. The server checks the user's credentials against a database and allows them to log in.

[1036] Career Question and Advice Generation

[1037] 1. The user enters a career question.

[1038] 2. The server stores the question in a database and uses an emotion engine to analyze the question and recognize the user's emotional state.

[1039] 3. The server retrieves past question and answer history from the database and passes the data to the generative AI.

[1040] 4. Generative AI generates advice based on past data and current emotional state.

[1041] 5. The server sends the generated advice to the user and displays it in the interface.

[1042] Entering and matching company job information

[1043] 1. The terminal (company representative) logs in and enters recruitment information.

[1044] 2. The server saves the input information in a database.

[1045] 3. The server compares the user's skill set with company job listings and runs a matching algorithm.

[1046] 4. The server analyzes the matching results and adjusts the order in which job listings are presented, taking into account the user's emotional state using an emotion engine.

[1047] 5. The server notifies the user of suitable job information and displays it on the interface.

[1048] Advertisement and monetization

[1049] 1. The server selects relevant advertisements based on the user's question history and emotional state.

[1050] 2. The server displays the selected advertisement on the user's interface.

[1051] 3. The server provides companies with regular recruitment data and trend reports and collects a fee for the feedback.

[1052] 4. The server collects premium feature fees and subscription fees from the user.

[1053] Specific examples

[1054] Example 1: User A's career questions and advice

[1055] 1. User A enters the question, "What skills do I need to learn next?"

[1056] 2. The server stores User A's question in a database and analyzes it with an emotion engine to recognize User A's emotional state.

[1057] 3. The server passes the past question history and current emotional state to the generative AI.

[1058] 4. Generative AI generates advice recommending "acquire data analysis and Python skills."

[1059] 5. The server sends the AI ​​advice to User A and displays it on the interface.

[1060] Example 2: Matching job information from Company B with User C

[1061] 1. A person in charge at Company B logs in and enters the job information for a "Data Scientist."

[1062] 2. The server saves the job information in a database.

[1063] 3. The server matches User C's skill set with Company B's job listings.

[1064] 4. The server analyzes the matching results and adjusts the order in which job information is presented using an emotion engine, taking into account User C's emotional state.

[1065] 5. The server notifies User C of the job information and displays it on the interface.

[1066] This demonstrates that the present invention is a system that realizes personalized career counseling and job matching for companies that takes into account the user's emotional state, thereby providing more effective advice to users and recommending more suitable candidates to companies.

[1067] The processing flow will be explained below.

[1068] Career Question and Advice Generation

[1069] Step 1:

[1070] The user enters career-related questions into a dedicated input form.

[1071] Step 2:

[1072] The server receives the question from the user, checks that the input format is correct, and if there are no problems with the format, stores the question in the database.

[1073] Step 3:

[1074] The server passes the question content to the emotion engine, which analyzes and recognizes the user's emotional state.

[1075] Step 4:

[1076] The server retrieves the user's past question and answer history from the database.

[1077] Step 5:

[1078] The server inputs question history, user profile data, and current emotional state information into the generative AI.

[1079] Step 6:

[1080] Generative AI generates personalized advice for users based on input data.

[1081] Step 7:

[1082] The server displays the generated advice on the user's interface.

[1083] Entering and matching company job information

[1084] Step 1:

[1085] The terminal (company representative) logs in to the system and accesses the recruitment information input screen.

[1086] Step 2:

[1087] The terminal allows users to input the available positions and the desired skill sets.

[1088] Step 3:

[1089] The server receives the recruitment information sent by the company, checks whether the input format is correct, and if there are no problems with the format, stores the information in the database.

[1090] Step 4:

[1091] The server compares the user's skill set with the skill set required by the company from the database and runs a matching algorithm.

[1092] Step 5:

[1093] The server analyzes the matching results and utilizes an emotion engine to adjust the order in which job listings are presented, taking into account the user's emotional state.

[1094] Step 6:

[1095] The server notifies the user of suitable job information and displays it on the interface.

[1096] Advertisement and monetization

[1097] Step 1:

[1098] The server selects relevant advertisements based on the user's question history and emotional state.

[1099] Step 2:

[1100] The server retrieves the selected advertisement data and displays it on the user's interface.

[1101] Step 3:

[1102] The server periodically creates recruitment data and trend reports for companies and collects a fee for the feedback.

[1103] Step 4:

[1104] The server handles the process of collecting premium feature fees and subscription fees from users.

[1105] Example: User A's career questions and advice

[1106] Step 1:

[1107] User A enters the question "What skills do you want to learn in the future?" into an input form.

[1108] Step 2:

[1109] The server stores User A's question in a database.

[1110] Step 3:

[1111] The server passes the question to the emotion engine, which analyzes and recognizes the emotional state of User A. For example, it may determine that User A is feeling anxious.

[1112] Step 4:

[1113] The server retrieves past question history from the database and passes it to the generative AI.

[1114] Step 5:

[1115] Based on past data and current emotional state, generative AI generates advice such as "learn data analysis and Python skills."

[1116] Step 6:

[1117] The server sends the advice to User A and displays it on the interface.

[1118] Example: Matching job information from Company B with User C

[1119] Step 1:

[1120] A representative from Company B logs in and enters job information for a "Data Scientist."

[1121] Step 2:

[1122] The server stores the job listings in a database.

[1123] Step 3:

[1124] The server uses a matching algorithm to compare User C's skill set with Company B's job listings.

[1125] Step 4:

[1126] The server analyzes the matching results and uses an emotion engine to consider the emotional state of user C. For example, if user C shows enthusiasm, job information reflecting that emotion will be presented preferentially.

[1127] Step 5:

[1128] The server notifies User C of the job information and displays it on the interface.

[1129] Example 2

[1130] 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."

[1131] Conventional career advice systems do not always provide optimal advice for users because they do not take into account the user's emotional state. Furthermore, matching between company job information and the user's skill set is limited to a simple comparison, and does not take into account the user's emotions or motivations, making it difficult to provide job suggestions that satisfy the user. To solve these problems, there is a need for the development of a system that analyzes the user's emotional state and provides personalized advice and matching based on that analysis.

[1132] 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.

[1133] In this invention, the server includes means for a user to input a career-related question, means for saving the user's questions and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for analyzing the user's emotional state using an emotion engine, means for generating advice based on the acquired past questions and emotional state using a generative AI, and means for providing the generated advice to the user, thereby making it possible to provide personalized career advice that reflects the user's emotional state.

[1134] "Career questions" refer to doubts or questions users have about their occupation, work style, or skill development.

[1135] "Database" refers to a storage system for storing a user's question history, advice history, emotional state information, and company job information.

[1136] An "emotion engine" refers to software or algorithms that analyze text data entered by a user and recognize the user's emotional state from its content.

[1137] "Generative AI" refers to an artificial intelligence model that generates optimal advice and information based on past data and the user's current emotional state.

[1138] "Job information" refers to information that includes details about the positions a company is hiring for and the type of person they are looking for.

[1139] "Skill set" refers to the collection of technical or professional skills a user possesses.

[1140] "Matching" refers to the process of comparing a user's skill set with a company's job listings, calculating the degree of compatibility, and finding the best match.

[1141] "Personalized advice" refers to advice that is optimized for a specific user based on the user's individual characteristics and emotional state.

[1142] "Advertisement" refers to promotional information for products or services related to the user's question history or emotional state.

[1143] "Feedback" refers to evaluations and opinions about the system from companies and users, and refers to information used to improve the system and services.

[1144] The present invention relates to a system for recognizing a user's emotional state and providing career advice based on the recognition. The following describes how the program processing of this system is implemented.

[1145] This system is composed of the following main components: users, servers, terminals, and databases. Each component cooperates to provide personalized career advice to users.

[1146] User Registration and Login

[1147] The user registers by entering their name, email address, and password on the new registration screen. The server stores this information in a database and sends a confirmation email. The user clicks the link in the confirmation email to authenticate their account, and then authenticates again on the login screen. The server compares the user's authentication information with the database and allows them to log in if they match.

[1148] Career Question and Advice Generation

[1149] A user inputs a question about their career, such as "What should I pay attention to when choosing a career?" The server stores the user's question in a database and uses an emotion engine to analyze the question and recognize the user's emotional state. A natural language processing toolkit is used for this analysis.

[1150] Next, the server retrieves the past question history and advice history from the database and passes that data to the generative AI. Based on the retrieved data and the current emotional state, the generative AI generates advice. This AI could use, for example, OpenAI's GPT-3 model.

[1151] The generated advice is displayed on the user's interface via the server. For example, advice such as "I recommend you improve your project management skills" is presented.

[1152] Entering and matching company job information

[1153] The terminal (company representative) logs into the system and inputs the company's job information. For example, they register information such as "Recruiting front-end engineers." The server stores that information in a database. The server then compares the user's skill set with the company's job information and runs a matching algorithm.

[1154] The matching results are analyzed by an emotion engine, which adjusts the presentation order based on the user's emotional state. The most suitable job listings are then notified to the user and displayed on the interface.

[1155] Advertisement and monetization

[1156] The server selects relevant advertisements based on the user's question history and emotional state and displays them in the user's interface. The server also periodically collects fees from companies for using the feedback and recruitment data. The server also handles the process of collecting fees for certain advanced features and subscription fees from users.

[1157] Specific examples

[1158] For example:

[1159] Example prompt sentence:

[1160] User A enters the question "What skills are important when choosing a job?" into the system, the server saves the question, and the emotion engine analyzes it to determine that "User A is feeling anxious." The generative AI generates advice such as "We recommend you improve your data analysis skills," and the server displays this advice on User A's interface.

[1161] The present invention aims to provide more effective and personalized career advice by taking into account the user's emotional state, which will greatly contribute to helping users improve their practical skills and make appropriate career choices.

[1162] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1163] User Registration and Login

[1164] Step 1: Enter your new registration information

[1165] Operation:

[1166] The user enters their name, email address, and password on the new registration screen.

[1167] input:

[1168] Name, email address, password

[1169] output:

[1170] The registration information is sent to the server.

[1171] Step 2: Save your registration information and send a confirmation email

[1172] Operation:

[1173] The server stores the entered information in a database and sends a confirmation email.

[1174] input:

[1175] Registration information (name, email address, password)

[1176] Data processing:

[1177] Saves the new user information in the database and generates and sends a confirmation email via the SMTP protocol.

[1178] output:

[1179] A confirmation email will be sent to the user's email address.

[1180] Step 3: Verify your account

[1181] Operation:

[1182] The user clicks on the link in the confirmation email.

[1183] input:

[1184] Link in the confirmation email

[1185] output:

[1186] After authentication, a response is returned from the server.

[1187] Step 4: Complete the login

[1188] Operation:

[1189] The server checks the user's credentials against a database and allows them to log in.

[1190] input:

[1191] Authentication information (email address, password)

[1192] Data processing:

[1193] The authentication information is checked against the database, and if it matches a session is created.

[1194] output:

[1195] The dashboard screen is displayed to the user.

[1196] Career Question and Advice Generation

[1197] Step 1: Fill in the career questions

[1198] Operation:

[1199] The user enters a career question and clicks the "Submit" button.

[1200] input:

[1201] Career-related questions (e.g., "What should you pay attention to when choosing a career?")

[1202] output:

[1203] The question is sent to the server.

[1204] Step 2: Analyze emotional state

[1205] Operation:

[1206] The server stores the question content in a database, and an emotion engine analyzes the question content to recognize the user's emotional state.

[1207] input:

[1208] Career Questions

[1209] Data processing:

[1210] The question data is stored in a database and sentiment analysis is performed using a natural language processing (NLP) toolkit.

[1211] output:

[1212] The user's emotional state (e.g., anxiety) is recognized.

[1213] Step 3: Obtaining historical data

[1214] Operation:

[1215] The server retrieves the past question history and advice history from the database.

[1216] input:

[1217] User ID

[1218] Data processing:

[1219] Execute a database query to retrieve past question and advice history.

[1220] output:

[1221] A history of past questions and advice is obtained.

[1222] Step 4: Generating Advice

[1223] Operation:

[1224] Generative AI generates advice based on past data and current emotional state.

[1225] input:

[1226] Past question history, advice history, current emotional state

[1227] Data processing:

[1228] Provide data to a generative AI model (e.g., GPT-3) to generate advice based on a prompt.

[1229] output:

[1230] The advice generated (e.g., "I recommend you improve your project management skills")

[1231] Step 5: Providing advice

[1232] Operation:

[1233] The server sends the generated advice to the user and displays it in the interface.

[1234] input:

[1235] Generated Advice

[1236] output:

[1237] Advice is displayed on the user's interface.

[1238] Entering and matching company job information

[1239] Step 1: Enter your job information

[1240] Operation:

[1241] The terminal (company representative) logs into the system and enters job information.

[1242] input:

[1243] Job postings (e.g., "Front-end engineer wanted")

[1244] output:

[1245] The job information is sent to the server.

[1246] Step 2: Saving to the database

[1247] Operation:

[1248] The server stores the job listings in a database.

[1249] input:

[1250] Job information

[1251] Data processing:

[1252] Store job information in a database.

[1253] output:

[1254] Saved Jobs

[1255] Step 3: Performing the Match

[1256] Operation:

[1257] The server compares the user's skill set with company job listings and runs a matching algorithm.

[1258] input:

[1259] User skill sets, job information

[1260] Data processing:

[1261] Run a matching algorithm that compares your skill set with the job posting and calculates a suitability score.

[1262] output:

[1263] Matching results (relevance score)

[1264] Step 4: Presentation taking into account emotional state

[1265] Operation:

[1266] The server analyzes the matching results and the user's emotional state to adjust the presentation order.

[1267] input:

[1268] Matching results, user emotional state

[1269] Data processing:

[1270] Prioritize job postings based on sentiment analysis data.

[1271] output:

[1272] Adjusted job posting order

[1273] Step 5: Post a job

[1274] Operation:

[1275] The server notifies the user of suitable job information and displays it on the interface.

[1276] input:

[1277] Adjusted job posting order

[1278] output:

[1279] The job listing is displayed in the user interface.

[1280] Advertisement and monetization

[1281] Step 1: Ad selection

[1282] Operation:

[1283] The server selects relevant advertisements based on the user's question history and emotional state.

[1284] input:

[1285] Question history, emotional state

[1286] Data processing:

[1287] It runs an ad selection algorithm based on question history and emotional state.

[1288] output:

[1289] Selected Advertisements

[1290] Step 2: Displaying the ad

[1291] Operation:

[1292] The server displays the selected advertisement on the user's interface.

[1293] input:

[1294] Selected Advertisements

[1295] output:

[1296] Advertisements are displayed in the user's interface.

[1297] Step 3: Collect fees

[1298] Operation:

[1299] The server periodically collects fees from companies for feedback and data usage, and charges users for certain advanced features or subscription fees.

[1300] input:

[1301] Company usage information, user usage information

[1302] Data processing:

[1303] Manage billing information through a fee collection system and collect fees from businesses and users.

[1304] output:

[1305] Fees collected

[1306] Through these steps, the system can provide personalized career advice and job information that takes into account the user's emotional state, providing more effective advice to users and recommending more suitable candidates to companies.

[1307] (Application example 2)

[1308] 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."

[1309] Conventional food delivery services have systems that suggest meals based on a user's order history and preferences, but they are unable to make personalized suggestions that take into account the user's emotional state. As a result, meals and services that are appropriate for the user's emotional state may not be provided, hindering the improvement of the user experience.

[1310] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1311] In this invention, the server includes means for a user to input a question about a service, means for saving the user's question and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for generating advice based on the acquired past questions and advice history using a generative AI, means for providing the generated advice to the user, means for analyzing the user's emotional state using an emotion engine, and means for providing personalized information or services based on the emotional state. This makes it possible to provide personalized meal suggestions and services that take the user's emotional state into consideration.

[1312] A "user" is an individual who utilizes the system to enter service-related questions and receive suggestions and information.

[1313] A "database" is a storage device that stores data such as user questions, past question history, advice history, skill sets, and the type of person a company is looking for.

[1314] "Generative AI" is an artificial intelligence model that generates new advice and suggestions based on past question and advice history.

[1315] The "emotion engine" is a system component that analyzes the user's emotional state from their input and expressions.

[1316] "Personalized information or services" are suggestions or information that are individually tailored based on a user's emotional state and past behavioral history.

[1317] A "company" is an organization that inputs information and the profile of the person it is looking for in order to provide a service.

[1318] A "skill set" is a collection of skills and knowledge possessed by a user.

[1319] A "matching method" is a system component that compares the user's skill set with the profile of the person the company is looking for and finds the optimal combination.

[1320] "Emotional state" refers to the state of emotion at that time that is estimated from the user's input and actions.

[1321] This invention relates to a food delivery system that analyzes emotional states and provides personalized meal recommendations and services based on those results. The entire system can be accessed by users through a smartphone application. The main hardware components include a smartphone, a server, and a database. The main software components used are a generative AI model and an emotion engine (EmotionAPI).

[1322] Hardware and software used

[1323] 1. Smartphone: Provides the user interface and receives user input.

[1324] 2. Server: Processes data, runs generative AI models, analyzes emotional states using the emotion engine, and performs data matching.

[1325] 3. Database: Stores data such as user questions, past question history, advice history, skill sets, and the type of person the company is looking for.

[1326] 4. Generative AI model (GPT model): Generates new suggestions and advice based on user input and past data.

[1327] 5. Emotion Engine (Emotion API): Analyzes the user's emotional state from their input and expressions.

[1328] System configuration description

[1329] First, a user registers an account using a smartphone application and enters the necessary information. After completing the registration, the user inputs their emotional state through the application. For example, they can express their emotions by uploading photos or text. Based on this, the server uses an emotion engine to analyze the user's emotional state.

[1330] Based on the analysis results, the server retrieves past question and advice history from the database and passes the data to a generative AI model. This generative AI model generates personalized meal suggestions based on the analyzed emotional state and past data. The generated suggestions are displayed in the user's smartphone application.

[1331] When a user places an order from the suggested meal menu, the server provides relevant coupons and offers and stores this order information in a database. The server can also display personalized advertisements based on the user's emotional state and order history. The server also includes a means to collect feedback from advertisers, data usage fees, and premium feature fees from users.

[1332] Specific examples

[1333] For example, consider the case where a user enters the text "I'm feeling stressed today." The server's emotion engine analyzes and detects "stress." The server then passes the data to a generative AI model based on past order history and the user's current emotional state. The generative AI model generates a suggestion for a "healthy salad bowl that's good for relieving stress," and the server displays this suggestion on the smartphone application.

[1334] Example prompt sentence:

[1335] Emotional state: Stress

[1336] Past orders: Japanese food, healthy food, smoothies

[1337] Suggestion: Generate appropriate meal suggestions for the user, taking into account their emotional state today.

[1338] This allows the user to receive suggestions for meals that best suit their mood at the time, allowing them to receive a more satisfying service.

[1339] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1340] Program processing steps

[1341] Step 1: User Registration and Login

[1342] The user opens the smartphone application and enters the required information into the new registration form.

[1343] The server receives the entered information and stores it in a database.

[1344] The server will send a confirmation email to the user, prompting them to click on a link to confirm their account.

[1345] The user clicks the link in the verification email to confirm their account.

[1346] The server checks the user's verification information against the database, confirms that authentication has been completed, and permits login.

[1347] Input: User registration information (name, email address, password, etc.)

[1348] Output: User account authentication and login permission

[1349] Step 2: Input and analysis of the user's emotional state

[1350] Users upload photos and text that describe their emotional state within a smartphone application.

[1351] The server receives the uploaded data, sends it to the emotion engine (EmotionAPI), and analyzes the emotional state.

[1352] The emotion engine analyzes the user's emotional state from the input and returns the results to the server.

[1353] Input: User-uploaded photos and text

[1354] Output: Sentiment analysis results (e.g., stress, joy, sadness, etc.)

[1355] Step 3: Obtaining past data and generating proposals using generative AI

[1356] The server retrieves the user's past question and advice history from a database, along with the analyzed emotional state.

[1357] The server passes this data to a generative AI model (GPT model) to generate personalized suggestions and advice.

[1358] The generative AI generates optimal menu suggestions based on the provided data and the user's emotional state and returns them to the server.

[1359] Input: User's emotional state, past question history, advice history

[1360] Output: Personalized menu suggestions

[1361] Step 4: View proposals and process orders

[1362] The server displays the suggestions returned by the generative AI on the user's smartphone application.

[1363] The user reviews the proposed menu and selects an order.

[1364] The server stores the order information selected by the user in a database and arranges for delivery service.

[1365] Input: Generative AI suggestions, user menu selections

[1366] Output: Store order information and arrange delivery

[1367] Step 5: Offer coupons and display ads

[1368] The server generates relevant coupons and rewards based on the user's orders and past behavioral history.

[1369] The server displays the coupons and offers on the user's smartphone application.

[1370] Additionally, the server displays personalized advertisements based on the user's emotional state and order history.

[1371] Input: User order information, past behavior history

[1372] Output: Coupons, offers, personalized ads

[1373] In this way, users can receive meal suggestions that suit their emotional state and can order and receive rewards based on them.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] [Third embodiment]

[1378] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1379] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1380] 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).

[1381] 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.

[1382] 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.

[1383] 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).

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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."

[1390] This invention relates to a system that matches users' career questions with companies' job information and provides personalized advice and job information. Here, we will generate a program for the system and explain its processing in natural language. We will also use concrete examples and explain the server, terminal, and user as subjects.

[1391] Parts Overview

[1392] User: An individual who uses the system to enter career-related questions and receive advice and job information.

[1393] Server: A computer system that processes data, generates advice using generative AI, and matches jobs with companies' job listings.

[1394] Terminal: The device used by the company representative to enter recruitment information.

[1395] Database: Storage for question history, advice history, and company job information.

[1396] Program processing explanation

[1397] User Registration and Login

[1398] 1. When a user registers, they enter information such as their name, email address, and password into a dedicated input form.

[1399] 2. The server saves the input information in a database and sends a confirmation email to the user for authentication.

[1400] 3. The user clicks the link in the confirmation email to complete account verification.

[1401] 4. The user enters their email address and password on the login screen to log in to the system.

[1402] 5. The server checks the user's credentials against the database and allows them to log in.

[1403] Career Question and Advice Generation

[1404] 1. The user enters career-related questions into a dedicated input form.

[1405] 2. The server receives this question and records it in a database.

[1406] 3. The server retrieves the user's past questions and their answer history from the database.

[1407] 4. The server inputs the question history and user profile data into the generative AI to generate personalized advice.

[1408] 5. The server displays the generated advice in the user's interface.

[1409] Entering and matching company job information

[1410] 1. The terminal (company representative) logs in and enters recruitment information.

[1411] 2. The server saves the job information entered in the database.

[1412] 3. The server matches company job information with the user's skill set, past work experience, etc.

[1413] 4. The server analyzes the matching results and identifies suitable job listings for the user.

[1414] 5. The server displays the identified job listings on the user's interface.

[1415] Advertisement and monetization

[1416] 1. The server selects advertisements related to the user's question history and advice content.

[1417] 2. The server displays the selected advertisement on the user's screen.

[1418] 3. The server generates periodic reports and trend reports from the company and bills them for usage.

[1419] 4. The server collects premium feature fees and subscription fees from the user.

[1420] Specific examples

[1421] Example 1: User A's career questions and advice

[1422] 1. User A enters a question: "What skills do I need to learn in the future?"

[1423] 2. The server references user A's past question history and passes it as input data to the generative AI.

[1424] 3. The server uses generative AI to generate advice recommending "acquiring data analysis and Python skills" and provides it to User A.

[1425] Example 2: Matching job information from Company B with User C

[1426] 1. Company B enters job information for a "Data Scientist."

[1427] 2. The server saves the job information in a database and matches it with User C's skill set.

[1428] 3. The server identifies that User C's skill set matches the requirements of Company B and recommends it to User C.

[1429] This clearly shows that the present invention is a system that can respond to individual users' career consultations while also matching with the recruitment needs of companies, thereby realizing efficient job-hunting support and profit generation.

[1430] The processing flow will be explained below.

[1431] Career Question and Advice Generation

[1432] Step 1:

[1433] The user enters career-related questions into a dedicated input form.

[1434] Step 2:

[1435] The server receives the question from the user, checks that the input format is correct, and if there are no problems with the format, stores the question in the database.

[1436] Step 3:

[1437] The server retrieves the user's past question and answer history from the database.

[1438] Step 4:

[1439] The server inputs past question history and user profile data into the generative AI.

[1440] Step 5:

[1441] Generative AI analyzes past data and generates appropriate advice.

[1442] Step 6:

[1443] The server sends the generated advice content to the user and displays it on the interface.

[1444] Entering and matching company job information

[1445] Step 1:

[1446] The terminal (company representative) logs in to the system and accesses the recruitment information input screen.

[1447] Step 2:

[1448] The terminal allows users to input the available positions and the desired skill sets.

[1449] Step 3:

[1450] The server receives the job information sent by the company, checks that the format is correct, and if there are no problems with the format, stores the information in a database.

[1451] Step 4:

[1452] The server uses a matching algorithm to compare the user's skill set with the company's desired skill set from the database.

[1453] Step 5:

[1454] The server analyzes the matching results and identifies suitable users.

[1455] Step 6:

[1456] The server notifies the identified user of the company's job information and displays it on the interface.

[1457] Advertisement and monetization

[1458] Step 1:

[1459] The server selects relevant advertisements based on the user's question history and advice content.

[1460] Step 2:

[1461] The server retrieves the selected advertisement data and displays it on the user's interface.

[1462] Step 3:

[1463] The server periodically creates recruitment data and trend reports for companies and collects a fee for the feedback.

[1464] Step 4:

[1465] The server handles the process of collecting premium feature fees and subscription fees from users.

[1466] Example: User A's career questions and advice

[1467] Step 1:

[1468] User A enters the question "What skills do you want to learn in the future?" into an input form.

[1469] Step 2:

[1470] The server stores User A's question in a database.

[1471] Step 3:

[1472] The server retrieves past question history from the database and passes it to the generative AI.

[1473] Step 4:

[1474] Generative AI analyzes the data and generates advice recommending "acquiring data analysis and Python skills."

[1475] Step 5:

[1476] The server sends the advice to User A and displays it on the interface.

[1477] Example: Matching job information from Company B with User C

[1478] Step 1:

[1479] A representative from Company B logs in and enters job information for a "Data Scientist."

[1480] Step 2:

[1481] The server stores the job listings in a database.

[1482] Step 3:

[1483] The server matches User C's skill set with job listings.

[1484] Step 4:

[1485] The server analyzes the matching results and identifies job information suitable for User C.

[1486] Step 5:

[1487] The server notifies User C of the job information and displays it on the interface.

[1488] Example 1

[1489] 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."

[1490] In today's job market, it is difficult for individual users to obtain appropriate advice and information about their careers. It is also difficult for companies to quickly and accurately find job seekers who fit their desired profile. Furthermore, there is a lack of mechanisms for effectively displaying relevant advertisements to users and monetizing the service. A system that solves these issues is needed.

[1491] 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.

[1492] In this invention, the server includes means for a user to input a career-related question, means for saving the user's question and past question history in a database, means for acquiring the user's past question and advice history from the database, means for generating advice based on the acquired past question and advice history using a generative AI, means for providing the generated advice to the user, means for passing the user's past history and profile data as prompts to the generative AI model, and means for displaying the generated advice on the user's interface, thereby enabling the user to quickly obtain personalized advice and information.

[1493] "User" refers to an individual who uses the system to enter career-related questions and receive advice and job information.

[1494] "Server" refers to a computer system that processes data, generates advice using generative AI, and matches it with company job information.

[1495] "Database" refers to storage that stores question history, advice history, and company job information.

[1496] "Generative AI" refers to an artificial intelligence model that generates personalized advice based on a user's question history and profile data.

[1497] "Profile Data" refers to personal information about a user, such as age, occupation, and work experience.

[1498] "Interface" refers to a user interface that includes screens and input forms that allow a user to interact with a system.

[1499] "Company" refers to the entity that provides job information and inputs the desired profile and recruitment information.

[1500] "Job information" refers to information such as the types of jobs a company is looking to hire for and the skill sets they are looking for.

[1501] "Matching" refers to the process of comparing a user's skill set and work experience with a company's job listings to determine suitability.

[1502] "Advertisement" refers to commercial information related to the user's question history and advice content.

[1503] The present invention relates to a system that matches users' career questions with companies' job information and provides personalized advice and job information. The program processing of this system will be described in detail below.

[1504] Hardware and software used

[1505] The following hardware and software are used to implement this system.

[1506] Server: A computer system that processes data, generates advice using generative AI, and matches with company job information. Specifically, it uses a cloud server.

[1507] Terminal: The device on which company personnel enter recruitment information. Specifically, a computer or tablet with a web browser is used.

[1508] Database: Storage for storing user question history, advice history, and company job information. Specifically, a relational database management system (RDBMS) is used.

[1509] Generative AI: AI models for generating personalized advice, specifically using natural language processing models such as GPT-3.

[1510] Specific processing explanation of the program

[1511] User Registration and Login

[1512] When a user registers, they enter information such as their name, email address, and password into a dedicated input form. The server receives this information, stores it in a database, and sends the user a confirmation email for authentication. When the user clicks the link in the confirmation email, account authentication is complete. The user then enters their authentication information on the login screen, and the server compares the information with the database and allows them to log in.

[1513] Career Question and Advice Generation

[1514] When a user inputs a question about their career, the server receives the question and records it in a database. The server then retrieves the user's past questions and advice history from the database and inputs it into the generative AI. At this time, the user's past history and profile data are used as prompts. For example, the following prompts are generated:

[1515] "User's previous question: What skills should I learn in the future? User profile: Age 25, Occupation Engineer"

[1516] The generative AI generates advice based on this, and the server displays that advice on the user's interface.

[1517] Entering and matching company job information

[1518] The terminal (company representative) logs in and enters recruitment information. For example, "Job position: Data scientist, Required skills: Python, data analysis." The server receives this information and stores it in a database. The server then matches the company's recruitment information with the user's skill set, past work experience, etc., to identify users who are highly suitable. The server analyzes the identified matching results and displays the recruitment information on the user's interface to provide the user with appropriate recruitment information.

[1519] Advertisement and monetization

[1520] The server selects advertisements related to the user's question history and advice content and displays them on the user's screen. This allows for highly relevant advertisements to be displayed, enabling effective marketing. The server periodically creates reports and trend reports from companies and bills them for usage. It also includes a mechanism for collecting premium feature fees and subscription fees from users.

[1521] The above is a detailed embodiment of the system according to the present invention. This allows users to obtain appropriate career advice and companies to quickly and accurately find the talent they are looking for. In addition, by effectively displaying relevant advertisements, the service can be monetized.

[1522] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1523] User Registration and Login

[1524] Processing Steps:

[1525] Step 1:

[1526] A user registers by entering information such as name, email address, and password into a dedicated input form and clicking the "Register" button.

[1527] Specific actions: Enter "Yamada Taro", "yamada@example.com", and "password123" and click the button.

[1528] Input: User information (name, email address, password)

[1529] Output: The entered user information is sent to the server.

[1530] Step 2:

[1531] The server receives this information, stores it in a database, and simultaneously sends a confirmation email to the user's email address.

[1532] What it does: Creates a new record in the database and sends a confirmation email via the SMTP server.

[1533] Input: User information (name, email address, password)

[1534] Output: A confirmation email is sent to the user.

[1535] Step 3:

[1536] The user clicks the confirmation link in the email to complete account authentication.

[1537] What happens: Open the confirmation email and click the link.

[1538] Input: Link from confirmation email

[1539] Output: The account is authenticated.

[1540] Step 4:

[1541] The user enters their email address and password on the login screen and clicks the "Login" button.

[1542] Specific action: Enter "yamada@example.com" and "password123" into the form and click the button.

[1543] Input: User's email address and password

[1544] Output: An authentication request is sent to the server.

[1545] Step 5:

[1546] The server checks the user's input information against the registered information in the database, and if it matches, allows the user to log in.

[1547] Specific operation: A matching process is performed, and if there is a match, a session is started.

[1548] Input: User's email address and password

[1549] Output: Successful login and session start

[1550] Career Question and Advice Generation

[1551] Processing Steps:

[1552] Step 1:

[1553] The user enters career-related questions into a dedicated input form and clicks the "Submit" button.

[1554] Specific action: Fill out the form with "What skills would you like to learn next?" and click the button.

[1555] Input: User's career question

[1556] Output: The carrier question is sent to the server.

[1557] Step 2:

[1558] The server receives this question and records it in a database.

[1559] Specific behavior: The question is stored in the database as a new record.

[1560] Input: Career Question

[1561] Output: Question data stored in a database

[1562] Step 3:

[1563] The server retrieves the user's past questions and advice history from a database.

[1564] What it does: Retrieves past question history using a SQL query.

[1565] Input: User ID

[1566] Output: Past questions and advice history

[1567] Step 4:

[1568] The server passes the user's past history and profile data as prompts to the generative AI model to generate advice.

[1569] Specific operation: Input the prompt sentence: 'User's previous question: What skills should I learn in the future? User profile: Age 25, Occupation Engineer' into the generative AI model.

[1570] Input: Previous questions, advice history, profile data

[1571] Output: The generated advice

[1572] Step 5:

[1573] The server displays the generated advice on the user's interface.

[1574] Specific behavior: Display advice on the user's screen.

[1575] Input: Generated advice

[1576] Output: Advice displayed on the user's interface

[1577] Entering and matching company job information

[1578] Processing Steps:

[1579] Step 1:

[1580] The terminal (company representative) logs in and enters recruitment information. The user enters "Job position: Data scientist, Required skills: Python, data analysis."

[1581] Specific action: Enter "Data Scientist, Python, Data Analysis" into the form and submit it.

[1582] Input: Company recruitment information

[1583] Output: The job information is sent to the server.

[1584] Step 2:

[1585] The server saves the job information entered in the database.

[1586] Specific behavior: The entered job information is stored in the database as a new record.

[1587] Input: Company recruitment information

[1588] Output: Job listings stored in a database

[1589] Step 3:

[1590] The server matches companies' job listings with the user's skill set, past work experience, etc.

[1591] What it does: Search for skillsets and work experience using SQL queries.

[1592] Input: User's skill set, past work experience

[1593] Output: Matching results

[1594] Step 4:

[1595] The server analyzes the matching results and identifies suitable users.

[1596] What it does: Runs a matching algorithm to identify highly suitable users.

[1597] Input: Matching results

[1598] Output: A list of highly relevant users

[1599] Step 5:

[1600] The server displays the identified job listings on the user's interface.

[1601] Specific operation: Display "Company B is hiring a data scientist" on User C's screen.

[1602] Input: Matched Jobs

[1603] Output: The job displayed in the user's interface

[1604] Advertisement and monetization

[1605] Processing Steps:

[1606] Step 1:

[1607] The server selects advertisements related to the user's question history and advice content.

[1608] What it does: Runs a relevance algorithm to select "data analytics tool ads."

[1609] Input: User's question history, advice history

[1610] Output: Selected ads

[1611] Step 2:

[1612] The server displays the selected advertisement on the user's screen.

[1613] Specific behavior: Displayed as a banner ad on the user's screen.

[1614] Input: Selected Ad

[1615] Output: The ad displayed in the user's interface.

[1616] Step 3:

[1617] The server generates periodic reports and trend reports from the company and bills the company for usage.

[1618] Specific behavior: Generate a report in PDF format and send it to the company's registered email address.

[1619] Input: Company usage information

[1620] Output: Generated reports and invoices

[1621] Step 4:

[1622] The server collects premium feature fees and subscription fees from users.

[1623] Specific operation: Collect fees periodically via credit card payments.

[1624] Input: User's payment information

[1625] Output: Usage fees collected

[1626] (Application example 1)

[1627] 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."

[1628] Conventional career counseling systems lack the means to instantly receive appropriate advice in real time when users ask career-related questions. They also have limited means to efficiently match companies' job information with users' skill sets. Furthermore, they lack sufficient means to provide users with relevant advertising and billing information in real time. To solve these problems, it is necessary to provide an effective system and means.

[1629] 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.

[1630] In this invention, the server includes means for a user to input a career-related question, means for saving the user's question and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for generating advice based on the acquired past questions and advice history using a generative AI, means for providing the generated advice to the user, and means for using a display device in the store to allow the user to receive career-related advice in real time, thereby enabling the user to receive personalized career advice in real time.

[1631] A "user" is an individual who enters career-related questions into the system and receives advice and job information.

[1632] A "company" is an organization that inputs the position it is looking to hire for and the profile of the person it is looking for into the system, and then uses that information to search for suitable candidates.

[1633] The "database" is a storage device within the system that stores users' question history, advice history, and company job information, and retrieves and uses them as needed.

[1634] "Generative AI" refers to artificial intelligence that generates new advice based on a user's past questions and advice history.

[1635] A "display device" is a device used by a user to receive real-time career advice and job information, such as smart glasses or a terminal.

[1636] "Advice" is a suggestion for specific courses of action or skill acquisition that is provided in response to a user's career-related questions.

[1637] "Matching" is the process of comparing a user's skill set with the profile of the person a company is looking for and finding suitable job information.

[1638] "Advertisement" is promotional information that provides information related to the user's question history and advice content through the display device used by the user.

[1639] To implement this invention, a system using users, companies, servers, terminals, a database, generative AI, and a display device is required. Specific embodiments of this system are described below.

[1640] User Registration and Login

[1641] A user first registers with the system. They enter information such as their name, email address, and password into a dedicated input form and send it to the server. The server saves the entered information in a database and sends the user a confirmation email for authentication. When the user clicks the link in the confirmation email, account authentication is completed, and they then enter their email address and password on the login screen to log in to the system. The server compares the user's authentication information with the database and allows them to log in.

[1642] Career Question and Advice Generation

[1643] Users enter career-related questions into a dedicated input form. The server receives these questions and records them in a database. The server then retrieves the user's past questions and their answer history from the database and passes them as input data to the generative AI. The generative AI generates personalized advice based on the input data. The generated advice is displayed on the user's interface, allowing the user to receive the advice in real time. Specifically, the advice is provided through a display device such as smart glasses.

[1644] Entering and matching company job information

[1645] Company personnel also log in to the system and enter recruitment information. The server stores the entered recruitment information in a database. The server matches the company's recruitment information with the user's skill set, past work experience, etc., and recommends suitable jobs to the user. The user can receive this recommendation in real time on a display device.

[1646] Ad Display and Monetization

[1647] The server selects advertisements relevant to the user's question history and advice, and displays the selected advertisements on the user's screen. The server periodically collects fees from companies for feedback and data usage. It also charges users for using specific advanced features. Advertisement and billing information are also provided in real time through the smart glasses.

[1648] Specific examples

[1649] For example, User A inputs a question such as, "What skills should I learn in the future?" The server references User A's past question history and passes it as input data to the generative AI. The generative AI generates advice recommending "acquiring data analysis and Python skills" and provides it through the smart glasses. At the same time, if Company B inputs a job posting for a "data scientist," the server compares it with User A's skill set and presents suitable job postings in real time.

[1650] Prompt Sentence Examples

[1651] For example, provide the generative AI with a prompt like the following:

[1652] Prompt: "User A has been recommended to learn 'Data Analysis' and 'Python' in the past. Based on recent trends and User A's current skill set, please provide advice on which skills they should learn next."

[1653] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1654] Step 1:

[1655] The user enters a question about a career. The question is entered through an input form and the data is sent to the server.

[1656] Enter: Career Questions

[1657] Output: Sends the query data to the server

[1658] Step 2:

[1659] The server receives the user's question and stores it in a database, allowing for further processing.

[1660] Input: User question data

[1661] Output: Save the question data to the database

[1662] Step 3:

[1663] The server retrieves the user's past questions and advice history from the database, allowing the user to refer to their past question patterns and the advice they received.

[1664] Input: User ID

[1665] Output: User's past questions and advice history data

[1666] Step 4:

[1667] The server inputs the user's past questions and advice history into the generative AI to generate personalized advice. The generative AI generates advice based on the prompt sentence.

[1668] Input: Past questions and advice history, prompt text

[1669] Output: Personalized advice

[1670] Step 5:

[1671] The server displays the generated advice on the user's interface, for example, by using smart glasses to provide advice in real time.

[1672] Input: Generated advice

[1673] Output: Advice is displayed on the user's display device

[1674] Step 6:

[1675] A company representative logs in to the system and enters recruitment information, which is then sent to the server.

[1676] Input: Company recruitment information

[1677] Output: Send job information to the server

[1678] Step 7:

[1679] The server stores the recruitment information received from companies in a database, where job information from companies is accumulated.

[1680] Input: Company recruitment information

[1681] Output: Job saved to database

[1682] Step 8:

[1683] The server matches the user's skill set with company job information by retrieving the user's history and skill information from a database and using an algorithm to perform the matching.

[1684] Input: User skill set, company job postings

[1685] Output: Matching results

[1686] Step 9:

[1687] The server displays suitable job information to the user based on the matching results, allowing the user to receive suitable job information in real time.

[1688] Input: Matching results

[1689] Output: The job listing is displayed on the user's display device.

[1690] Step 10:

[1691] The server selects advertisements related to the user's question history and advice content, and displays them on the user's display device. The selection of advertisement data is based on the user's history information.

[1692] Input: User's question history, advice content

[1693] Output: Display the ad

[1694] Step 11:

[1695] The server collects fees from the company for feedback and data usage, and also charges users for certain advanced features, as a means of monetization.

[1696] Input: Company feedback, user usage history

[1697] Output: Toll collection

[1698] 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.

[1699] This invention relates to a system that recognizes a user's emotional state and provides career advice based on that. This system matches the user's career-related questions with company job information, analyzes the user's emotional state using an emotion engine, and provides personalized advice, advertisements, and job information. Here, we will generate a system program and explain its processing in natural language. We will explain the server, terminal, and user as subjects, using concrete examples.

[1700] Parts Overview

[1701] User: An individual who uses the system to enter career-related questions and receive advice and job information.

[1702] Server: A computer system that processes data, generates advice using generative AI, analyzes emotional states using an emotion engine, and matches users with company job listings.

[1703] Terminal: The device used by the company representative to enter recruitment information.

[1704] Database: Storage for question history, advice history, company job information, and sentiment data.

[1705] Program processing explanation

[1706] User Registration and Login

[1707] 1. The user registers and enters the required information.

[1708] 2. The server saves the information entered in a database and sends a confirmation email to the user.

[1709] 3. The user clicks the link in the confirmation email to authenticate their account and complete the login.

[1710] 4. The server checks the user's credentials against a database and allows them to log in.

[1711] Career Question and Advice Generation

[1712] 1. The user enters a career question.

[1713] 2. The server stores the question in a database and uses an emotion engine to analyze the question and recognize the user's emotional state.

[1714] 3. The server retrieves past question and answer history from the database and passes the data to the generative AI.

[1715] 4. Generative AI generates advice based on past data and current emotional state.

[1716] 5. The server sends the generated advice to the user and displays it in the interface.

[1717] Entering and matching company job information

[1718] 1. The terminal (company representative) logs in and enters recruitment information.

[1719] 2. The server saves the input information in a database.

[1720] 3. The server compares the user's skill set with company job listings and runs a matching algorithm.

[1721] 4. The server analyzes the matching results and adjusts the order in which job listings are presented, taking into account the user's emotional state using an emotion engine.

[1722] 5. The server notifies the user of suitable job information and displays it on the interface.

[1723] Advertisement and monetization

[1724] 1. The server selects relevant advertisements based on the user's question history and emotional state.

[1725] 2. The server displays the selected advertisement on the user's interface.

[1726] 3. The server provides companies with regular recruitment data and trend reports and collects a fee for the feedback.

[1727] 4. The server collects premium feature fees and subscription fees from the user.

[1728] Specific examples

[1729] Example 1: User A's career questions and advice

[1730] 1. User A enters the question, "What skills do I need to learn next?"

[1731] 2. The server stores User A's question in a database and analyzes it with an emotion engine to recognize User A's emotional state.

[1732] 3. The server passes the past question history and current emotional state to the generative AI.

[1733] 4. Generative AI generates advice recommending "acquire data analysis and Python skills."

[1734] 5. The server sends the AI ​​advice to User A and displays it on the interface.

[1735] Example 2: Matching job information from Company B with User C

[1736] 1. A person in charge at Company B logs in and enters the job information for a "Data Scientist."

[1737] 2. The server saves the job information in a database.

[1738] 3. The server matches User C's skill set with Company B's job listings.

[1739] 4. The server analyzes the matching results and adjusts the order in which job information is presented using an emotion engine, taking into account User C's emotional state.

[1740] 5. The server notifies User C of the job information and displays it on the interface.

[1741] This demonstrates that the present invention is a system that realizes personalized career counseling and job matching for companies that takes into account the user's emotional state, thereby providing more effective advice to users and recommending more suitable candidates to companies.

[1742] The processing flow will be explained below.

[1743] Career Question and Advice Generation

[1744] Step 1:

[1745] The user enters career-related questions into a dedicated input form.

[1746] Step 2:

[1747] The server receives the question from the user, checks that the input format is correct, and if there are no problems with the format, stores the question in the database.

[1748] Step 3:

[1749] The server passes the question content to the emotion engine, which analyzes and recognizes the user's emotional state.

[1750] Step 4:

[1751] The server retrieves the user's past question and answer history from the database.

[1752] Step 5:

[1753] The server inputs question history, user profile data, and current emotional state information into the generative AI.

[1754] Step 6:

[1755] Generative AI generates personalized advice for users based on input data.

[1756] Step 7:

[1757] The server displays the generated advice on the user's interface.

[1758] Entering and matching company job information

[1759] Step 1:

[1760] The terminal (company representative) logs in to the system and accesses the recruitment information input screen.

[1761] Step 2:

[1762] The terminal allows users to input the available positions and the desired skill sets.

[1763] Step 3:

[1764] The server receives the recruitment information sent by the company, checks whether the input format is correct, and if there are no problems with the format, stores the information in the database.

[1765] Step 4:

[1766] The server compares the user's skill set with the skill set required by the company from the database and runs a matching algorithm.

[1767] Step 5:

[1768] The server analyzes the matching results and utilizes an emotion engine to adjust the order in which job listings are presented, taking into account the user's emotional state.

[1769] Step 6:

[1770] The server notifies the user of suitable job information and displays it on the interface.

[1771] Advertisement and monetization

[1772] Step 1:

[1773] The server selects relevant advertisements based on the user's question history and emotional state.

[1774] Step 2:

[1775] The server retrieves the selected advertisement data and displays it on the user's interface.

[1776] Step 3:

[1777] The server periodically creates recruitment data and trend reports for companies and collects a fee for the feedback.

[1778] Step 4:

[1779] The server handles the process of collecting premium feature fees and subscription fees from users.

[1780] Example: User A's career questions and advice

[1781] Step 1:

[1782] User A enters the question "What skills do you want to learn in the future?" into an input form.

[1783] Step 2:

[1784] The server stores User A's question in a database.

[1785] Step 3:

[1786] The server passes the question to the emotion engine, which analyzes and recognizes the emotional state of User A. For example, it may determine that User A is feeling anxious.

[1787] Step 4:

[1788] The server retrieves past question history from the database and passes it to the generative AI.

[1789] Step 5:

[1790] Based on past data and current emotional state, generative AI generates advice such as "learn data analysis and Python skills."

[1791] Step 6:

[1792] The server sends the advice to User A and displays it on the interface.

[1793] Example: Matching job information from Company B with User C

[1794] Step 1:

[1795] A representative from Company B logs in and enters job information for a "Data Scientist."

[1796] Step 2:

[1797] The server stores the job listings in a database.

[1798] Step 3:

[1799] The server uses a matching algorithm to compare User C's skill set with Company B's job listings.

[1800] Step 4:

[1801] The server analyzes the matching results and uses an emotion engine to consider the emotional state of user C. For example, if user C shows enthusiasm, job information reflecting that emotion will be presented preferentially.

[1802] Step 5:

[1803] The server notifies User C of the job information and displays it on the interface.

[1804] Example 2

[1805] 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."

[1806] Conventional career advice systems do not always provide optimal advice for users because they do not take into account the user's emotional state. Furthermore, matching between company job information and the user's skill set is limited to a simple comparison, and does not take into account the user's emotions or motivations, making it difficult to provide job suggestions that satisfy the user. To solve these problems, there is a need for the development of a system that analyzes the user's emotional state and provides personalized advice and matching based on that analysis.

[1807] 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.

[1808] In this invention, the server includes means for a user to input a career-related question, means for saving the user's questions and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for analyzing the user's emotional state using an emotion engine, means for generating advice based on the acquired past questions and emotional state using a generative AI, and means for providing the generated advice to the user, thereby making it possible to provide personalized career advice that reflects the user's emotional state.

[1809] "Career questions" refer to doubts or questions users have about their occupation, work style, or skill development.

[1810] "Database" refers to a storage system for storing a user's question history, advice history, emotional state information, and company job information.

[1811] An "emotion engine" refers to software or algorithms that analyze text data entered by a user and recognize the user's emotional state from its content.

[1812] "Generative AI" refers to an artificial intelligence model that generates optimal advice and information based on past data and the user's current emotional state.

[1813] "Job information" refers to information that includes details about the positions a company is hiring for and the type of person they are looking for.

[1814] "Skill set" refers to the collection of technical or professional skills a user possesses.

[1815] "Matching" refers to the process of comparing a user's skill set with a company's job listings, calculating the degree of compatibility, and finding the best match.

[1816] "Personalized advice" refers to advice that is optimized for a specific user based on the user's individual characteristics and emotional state.

[1817] "Advertisement" refers to promotional information for products or services related to the user's question history or emotional state.

[1818] "Feedback" refers to evaluations and opinions about the system from companies and users, and refers to information used to improve the system and services.

[1819] The present invention relates to a system for recognizing a user's emotional state and providing career advice based on the recognition. The following describes how the program processing of this system is implemented.

[1820] This system is composed of the following main components: users, servers, terminals, and databases. Each component cooperates to provide personalized career advice to users.

[1821] User Registration and Login

[1822] The user registers by entering their name, email address, and password on the new registration screen. The server stores this information in a database and sends a confirmation email. The user clicks the link in the confirmation email to authenticate their account, and then authenticates again on the login screen. The server compares the user's authentication information with the database and allows them to log in if they match.

[1823] Career Question and Advice Generation

[1824] A user inputs a question about their career, such as "What should I pay attention to when choosing a career?" The server stores the user's question in a database and uses an emotion engine to analyze the question and recognize the user's emotional state. A natural language processing toolkit is used for this analysis.

[1825] Next, the server retrieves the past question history and advice history from the database and passes that data to the generative AI. Based on the retrieved data and the current emotional state, the generative AI generates advice. This AI could use, for example, OpenAI's GPT-3 model.

[1826] The generated advice is displayed on the user's interface via the server. For example, advice such as "I recommend you improve your project management skills" is presented.

[1827] Entering and matching company job information

[1828] The terminal (company representative) logs into the system and inputs the company's job information. For example, they register information such as "Recruiting front-end engineers." The server stores that information in a database. The server then compares the user's skill set with the company's job information and runs a matching algorithm.

[1829] The matching results are analyzed by an emotion engine, which adjusts the presentation order based on the user's emotional state. The most suitable job listings are then notified to the user and displayed on the interface.

[1830] Advertisement and monetization

[1831] The server selects relevant advertisements based on the user's question history and emotional state and displays them in the user's interface. The server also periodically collects fees from companies for using the feedback and recruitment data. The server also handles the process of collecting fees for certain advanced features and subscription fees from users.

[1832] Specific examples

[1833] For example:

[1834] Example prompt sentence:

[1835] User A enters the question "What skills are important when choosing a job?" into the system, the server saves the question, and the emotion engine analyzes it to determine that "User A is feeling anxious." The generative AI generates advice such as "We recommend you improve your data analysis skills," and the server displays this advice on User A's interface.

[1836] The present invention aims to provide more effective and personalized career advice by taking into account the user's emotional state, which will greatly contribute to helping users improve their practical skills and make appropriate career choices.

[1837] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1838] User Registration and Login

[1839] Step 1: Enter your new registration information

[1840] Operation:

[1841] The user enters their name, email address, and password on the new registration screen.

[1842] input:

[1843] Name, email address, password

[1844] output:

[1845] The registration information is sent to the server.

[1846] Step 2: Save your registration information and send a confirmation email

[1847] Operation:

[1848] The server stores the entered information in a database and sends a confirmation email.

[1849] input:

[1850] Registration information (name, email address, password)

[1851] Data processing:

[1852] Saves the new user information in the database and generates and sends a confirmation email via the SMTP protocol.

[1853] output:

[1854] A confirmation email will be sent to the user's email address.

[1855] Step 3: Verify your account

[1856] Operation:

[1857] The user clicks on the link in the confirmation email.

[1858] input:

[1859] Link in the confirmation email

[1860] output:

[1861] After authentication, a response is returned from the server.

[1862] Step 4: Complete the login

[1863] Operation:

[1864] The server checks the user's credentials against a database and allows them to log in.

[1865] input:

[1866] Authentication information (email address, password)

[1867] Data processing:

[1868] The authentication information is checked against the database, and if it matches a session is created.

[1869] output:

[1870] The dashboard screen is displayed to the user.

[1871] Career Question and Advice Generation

[1872] Step 1: Fill in the career questions

[1873] Operation:

[1874] The user enters a career question and clicks the "Submit" button.

[1875] input:

[1876] Career-related questions (e.g., "What should you pay attention to when choosing a career?")

[1877] output:

[1878] The question is sent to the server.

[1879] Step 2: Analyze emotional state

[1880] Operation:

[1881] The server stores the question content in a database, and an emotion engine analyzes the question content to recognize the user's emotional state.

[1882] input:

[1883] Career Questions

[1884] Data processing:

[1885] The question data is stored in a database and sentiment analysis is performed using a natural language processing (NLP) toolkit.

[1886] output:

[1887] The user's emotional state (e.g., anxiety) is recognized.

[1888] Step 3: Obtaining historical data

[1889] Operation:

[1890] The server retrieves the past question history and advice history from the database.

[1891] input:

[1892] User ID

[1893] Data processing:

[1894] Execute a database query to retrieve past question and advice history.

[1895] output:

[1896] A history of past questions and advice is obtained.

[1897] Step 4: Generating Advice

[1898] Operation:

[1899] Generative AI generates advice based on past data and current emotional state.

[1900] input:

[1901] Past question history, advice history, current emotional state

[1902] Data processing:

[1903] Provide data to a generative AI model (e.g., GPT-3) to generate advice based on a prompt.

[1904] output:

[1905] The advice generated (e.g., "I recommend you improve your project management skills")

[1906] Step 5: Providing advice

[1907] Operation:

[1908] The server sends the generated advice to the user and displays it in the interface.

[1909] input:

[1910] Generated Advice

[1911] output:

[1912] Advice is displayed on the user's interface.

[1913] Entering and matching company job information

[1914] Step 1: Enter your job information

[1915] Operation:

[1916] The terminal (company representative) logs into the system and enters job information.

[1917] input:

[1918] Job postings (e.g., "Front-end engineer wanted")

[1919] output:

[1920] The job information is sent to the server.

[1921] Step 2: Saving to the database

[1922] Operation:

[1923] The server stores the job listings in a database.

[1924] input:

[1925] Job information

[1926] Data processing:

[1927] Store job information in a database.

[1928] output:

[1929] Saved Jobs

[1930] Step 3: Performing the Match

[1931] Operation:

[1932] The server compares the user's skill set with company job listings and runs a matching algorithm.

[1933] input:

[1934] User skill sets, job information

[1935] Data processing:

[1936] Run a matching algorithm that compares your skill set with the job posting and calculates a suitability score.

[1937] output:

[1938] Matching results (relevance score)

[1939] Step 4: Presentation taking into account emotional state

[1940] Operation:

[1941] The server analyzes the matching results and the user's emotional state to adjust the presentation order.

[1942] input:

[1943] Matching results, user emotional state

[1944] Data processing:

[1945] Prioritize job postings based on sentiment analysis data.

[1946] output:

[1947] Adjusted job posting order

[1948] Step 5: Post a job

[1949] Operation:

[1950] The server notifies the user of suitable job information and displays it on the interface.

[1951] input:

[1952] Adjusted job posting order

[1953] output:

[1954] The job listing is displayed in the user interface.

[1955] Advertisement and monetization

[1956] Step 1: Ad selection

[1957] Operation:

[1958] The server selects relevant advertisements based on the user's question history and emotional state.

[1959] input:

[1960] Question history, emotional state

[1961] Data processing:

[1962] It runs an ad selection algorithm based on question history and emotional state.

[1963] output:

[1964] Selected Advertisements

[1965] Step 2: Displaying the ad

[1966] Operation:

[1967] The server displays the selected advertisement on the user's interface.

[1968] input:

[1969] Selected Advertisements

[1970] output:

[1971] Advertisements are displayed in the user's interface.

[1972] Step 3: Collect fees

[1973] Operation:

[1974] The server periodically collects fees from companies for feedback and data usage, and charges users for certain advanced features or subscription fees.

[1975] input:

[1976] Company usage information, user usage information

[1977] Data processing:

[1978] Manage billing information through a fee collection system and collect fees from businesses and users.

[1979] output:

[1980] Fees collected

[1981] Through these steps, the system can provide personalized career advice and job information that takes into account the user's emotional state, providing more effective advice to users and recommending more suitable candidates to companies.

[1982] (Application example 2)

[1983] 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."

[1984] Conventional food delivery services have systems that suggest meals based on a user's order history and preferences, but they are unable to make personalized suggestions that take into account the user's emotional state. As a result, meals and services that are appropriate for the user's emotional state may not be provided, hindering the improvement of the user experience.

[1985] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1986] In this invention, the server includes means for a user to input a question about a service, means for saving the user's question and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for generating advice based on the acquired past questions and advice history using a generative AI, means for providing the generated advice to the user, means for analyzing the user's emotional state using an emotion engine, and means for providing personalized information or services based on the emotional state. This makes it possible to provide personalized meal suggestions and services that take the user's emotional state into consideration.

[1987] A "user" is an individual who utilizes the system to enter service-related questions and receive suggestions and information.

[1988] A "database" is a storage device that stores data such as user questions, past question history, advice history, skill sets, and the type of person a company is looking for.

[1989] "Generative AI" is an artificial intelligence model that generates new advice and suggestions based on past question and advice history.

[1990] The "emotion engine" is a system component that analyzes the user's emotional state from their input and expressions.

[1991] "Personalized information or services" are suggestions or information that are individually tailored based on a user's emotional state and past behavioral history.

[1992] A "company" is an organization that inputs information and the profile of the person it is looking for in order to provide a service.

[1993] A "skill set" is a collection of skills and knowledge possessed by a user.

[1994] A "matching method" is a system component that compares the user's skill set with the profile of the person the company is looking for and finds the optimal combination.

[1995] "Emotional state" refers to the state of emotion at that time that is estimated from the user's input and actions.

[1996] This invention relates to a food delivery system that analyzes emotional states and provides personalized meal recommendations and services based on those results. The entire system can be accessed by users through a smartphone application. The main hardware components include a smartphone, a server, and a database. The main software components used are a generative AI model and an emotion engine (EmotionAPI).

[1997] Hardware and software used

[1998] 1. Smartphone: Provides the user interface and receives user input.

[1999] 2. Server: Processes data, runs generative AI models, analyzes emotional states using the emotion engine, and performs data matching.

[2000] 3. Database: Stores data such as user questions, past question history, advice history, skill sets, and the type of person the company is looking for.

[2001] 4. Generative AI model (GPT model): Generates new suggestions and advice based on user input and past data.

[2002] 5. Emotion Engine (Emotion API): Analyzes the user's emotional state from their input and expressions.

[2003] System configuration description

[2004] First, a user registers an account using a smartphone application and enters the necessary information. After completing the registration, the user inputs their emotional state through the application. For example, they can express their emotions by uploading photos or text. Based on this, the server uses an emotion engine to analyze the user's emotional state.

[2005] Based on the analysis results, the server retrieves past question and advice history from the database and passes the data to a generative AI model. This generative AI model generates personalized meal suggestions based on the analyzed emotional state and past data. The generated suggestions are displayed in the user's smartphone application.

[2006] When a user places an order from the suggested meal menu, the server provides relevant coupons and offers and stores this order information in a database. The server can also display personalized advertisements based on the user's emotional state and order history. The server also includes a means to collect feedback from advertisers, data usage fees, and premium feature fees from users.

[2007] Specific examples

[2008] For example, consider the case where a user enters the text "I'm feeling stressed today." The server's emotion engine analyzes and detects "stress." The server then passes the data to a generative AI model based on past order history and the user's current emotional state. The generative AI model generates a suggestion for a "healthy salad bowl that's good for relieving stress," and the server displays this suggestion on the smartphone application.

[2009] Example prompt sentence:

[2010] Emotional state: Stress

[2011] Past orders: Japanese food, healthy food, smoothies

[2012] Suggestion: Generate appropriate meal suggestions for the user, taking into account their emotional state today.

[2013] This allows the user to receive suggestions for meals that best suit their mood at the time, allowing them to receive a more satisfying service.

[2014] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2015] Program processing steps

[2016] Step 1: User Registration and Login

[2017] The user opens the smartphone application and enters the required information into the new registration form.

[2018] The server receives the entered information and stores it in a database.

[2019] The server will send a confirmation email to the user, prompting them to click on a link to confirm their account.

[2020] The user clicks the link in the verification email to confirm their account.

[2021] The server checks the user's verification information against the database, confirms that authentication has been completed, and permits login.

[2022] Input: User registration information (name, email address, password, etc.)

[2023] Output: User account authentication and login permission

[2024] Step 2: Input and analysis of the user's emotional state

[2025] Users upload photos and text that describe their emotional state within a smartphone application.

[2026] The server receives the uploaded data, sends it to the emotion engine (EmotionAPI), and analyzes the emotional state.

[2027] The emotion engine analyzes the user's emotional state from the input and returns the results to the server.

[2028] Input: User-uploaded photos and text

[2029] Output: Sentiment analysis results (e.g., stress, joy, sadness, etc.)

[2030] Step 3: Obtaining past data and generating proposals using generative AI

[2031] The server retrieves the user's past question and advice history from a database, along with the analyzed emotional state.

[2032] The server passes this data to a generative AI model (GPT model) to generate personalized suggestions and advice.

[2033] The generative AI generates optimal menu suggestions based on the provided data and the user's emotional state and returns them to the server.

[2034] Input: User's emotional state, past question history, advice history

[2035] Output: Personalized menu suggestions

[2036] Step 4: View proposals and process orders

[2037] The server displays the suggestions returned by the generative AI on the user's smartphone application.

[2038] The user reviews the proposed menu and selects an order.

[2039] The server stores the order information selected by the user in a database and arranges for delivery service.

[2040] Input: Generative AI suggestions, user menu selections

[2041] Output: Store order information and arrange delivery

[2042] Step 5: Offer coupons and display ads

[2043] The server generates relevant coupons and rewards based on the user's orders and past behavioral history.

[2044] The server displays the coupons and offers on the user's smartphone application.

[2045] Additionally, the server displays personalized advertisements based on the user's emotional state and order history.

[2046] Input: User order information, past behavior history

[2047] Output: Coupons, offers, personalized ads

[2048] In this way, users can receive meal suggestions that suit their emotional state and can order and receive rewards based on them.

[2049] 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.

[2050] 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.

[2051] 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.

[2052] [Fourth embodiment]

[2053] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2054] 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.

[2055] 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).

[2056] 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.

[2057] 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.

[2058] 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).

[2059] 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.

[2060] 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.

[2061] 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.

[2062] 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.

[2063] 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.

[2064] 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.

[2065] 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."

[2066] This invention relates to a system that matches users' career questions with companies' job information and provides personalized advice and job information. Here, we will generate a program for the system and explain its processing in natural language. We will also use concrete examples and explain the server, terminal, and user as subjects.

[2067] Parts Overview

[2068] User: An individual who uses the system to enter career-related questions and receive advice and job information.

[2069] Server: A computer system that processes data, generates advice using generative AI, and matches jobs with companies' job listings.

[2070] Terminal: The device used by the company representative to enter recruitment information.

[2071] Database: Storage for question history, advice history, and company job information.

[2072] Program processing explanation

[2073] User Registration and Login

[2074] 1. When a user registers, they enter information such as their name, email address, and password into a dedicated input form.

[2075] 2. The server saves the input information in a database and sends a confirmation email to the user for authentication.

[2076] 3. The user clicks the link in the confirmation email to complete account verification.

[2077] 4. The user enters their email address and password on the login screen to log in to the system.

[2078] 5. The server checks the user's credentials against the database and allows them to log in.

[2079] Career Question and Advice Generation

[2080] 1. The user enters career-related questions into a dedicated input form.

[2081] 2. The server receives this question and records it in a database.

[2082] 3. The server retrieves the user's past questions and their answer history from the database.

[2083] 4. The server inputs the question history and user profile data into the generative AI to generate personalized advice.

[2084] 5. The server displays the generated advice in the user's interface.

[2085] Entering and matching company job information

[2086] 1. The terminal (company representative) logs in and enters recruitment information.

[2087] 2. The server saves the job information entered in the database.

[2088] 3. The server matches company job information with the user's skill set, past work experience, etc.

[2089] 4. The server analyzes the matching results and identifies suitable job listings for the user.

[2090] 5. The server displays the identified job listings on the user's interface.

[2091] Advertisement and monetization

[2092] 1. The server selects advertisements related to the user's question history and advice content.

[2093] 2. The server displays the selected advertisement on the user's screen.

[2094] 3. The server generates periodic reports and trend reports from the company and bills them for usage.

[2095] 4. The server collects premium feature fees and subscription fees from the user.

[2096] Specific examples

[2097] Example 1: User A's career questions and advice

[2098] 1. User A enters a question: "What skills do I need to learn in the future?"

[2099] 2. The server references user A's past question history and passes it as input data to the generative AI.

[2100] 3. The server uses generative AI to generate advice recommending "acquiring data analysis and Python skills" and provides it to User A.

[2101] Example 2: Matching job information from Company B with User C

[2102] 1. Company B enters job information for a "Data Scientist."

[2103] 2. The server saves the job information in a database and matches it with User C's skill set.

[2104] 3. The server identifies that User C's skill set matches the requirements of Company B and recommends it to User C.

[2105] This clearly shows that the present invention is a system that can respond to individual users' career consultations while also matching with the recruitment needs of companies, thereby realizing efficient job-hunting support and profit generation.

[2106] The processing flow will be explained below.

[2107] Career Question and Advice Generation

[2108] Step 1:

[2109] The user enters career-related questions into a dedicated input form.

[2110] Step 2:

[2111] The server receives the question from the user, checks that the input format is correct, and if there are no problems with the format, stores the question in the database.

[2112] Step 3:

[2113] The server retrieves the user's past question and answer history from the database.

[2114] Step 4:

[2115] The server inputs past question history and user profile data into the generative AI.

[2116] Step 5:

[2117] Generative AI analyzes past data and generates appropriate advice.

[2118] Step 6:

[2119] The server sends the generated advice content to the user and displays it on the interface.

[2120] Entering and matching company job information

[2121] Step 1:

[2122] The terminal (company representative) logs in to the system and accesses the recruitment information input screen.

[2123] Step 2:

[2124] The terminal allows users to input the available positions and the desired skill sets.

[2125] Step 3:

[2126] The server receives the job information sent by the company, checks that the format is correct, and if there are no problems with the format, stores the information in a database.

[2127] Step 4:

[2128] The server uses a matching algorithm to compare the user's skill set with the company's desired skill set from the database.

[2129] Step 5:

[2130] The server analyzes the matching results and identifies suitable users.

[2131] Step 6:

[2132] The server notifies the identified user of the company's job information and displays it on the interface.

[2133] Advertisement and monetization

[2134] Step 1:

[2135] The server selects relevant advertisements based on the user's question history and advice content.

[2136] Step 2:

[2137] The server retrieves the selected advertisement data and displays it on the user's interface.

[2138] Step 3:

[2139] The server periodically creates recruitment data and trend reports for companies and collects a fee for the feedback.

[2140] Step 4:

[2141] The server handles the process of collecting premium feature fees and subscription fees from users.

[2142] Example: User A's career questions and advice

[2143] Step 1:

[2144] User A enters the question "What skills do you want to learn in the future?" into an input form.

[2145] Step 2:

[2146] The server stores User A's question in a database.

[2147] Step 3:

[2148] The server retrieves past question history from the database and passes it to the generative AI.

[2149] Step 4:

[2150] Generative AI analyzes the data and generates advice recommending "acquiring data analysis and Python skills."

[2151] Step 5:

[2152] The server sends the advice to User A and displays it on the interface.

[2153] Example: Matching job information from Company B with User C

[2154] Step 1:

[2155] A representative from Company B logs in and enters job information for a "Data Scientist."

[2156] Step 2:

[2157] The server stores the job listings in a database.

[2158] Step 3:

[2159] The server matches User C's skill set with job listings.

[2160] Step 4:

[2161] The server analyzes the matching results and identifies job information suitable for User C.

[2162] Step 5:

[2163] The server notifies User C of the job information and displays it on the interface.

[2164] Example 1

[2165] 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."

[2166] In today's job market, it is difficult for individual users to obtain appropriate advice and information about their careers. It is also difficult for companies to quickly and accurately find job seekers who fit their desired profile. Furthermore, there is a lack of mechanisms for effectively displaying relevant advertisements to users and monetizing the service. A system that solves these issues is needed.

[2167] 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.

[2168] In this invention, the server includes means for a user to input a career-related question, means for saving the user's question and past question history in a database, means for acquiring the user's past question and advice history from the database, means for generating advice based on the acquired past question and advice history using a generative AI, means for providing the generated advice to the user, means for passing the user's past history and profile data as prompts to the generative AI model, and means for displaying the generated advice on the user's interface, thereby enabling the user to quickly obtain personalized advice and information.

[2169] "User" refers to an individual who uses the system to enter career-related questions and receive advice and job information.

[2170] "Server" refers to a computer system that processes data, generates advice using generative AI, and matches it with company job information.

[2171] "Database" refers to storage that stores question history, advice history, and company job information.

[2172] "Generative AI" refers to an artificial intelligence model that generates personalized advice based on a user's question history and profile data.

[2173] "Profile Data" refers to personal information about a user, such as age, occupation, and work experience.

[2174] "Interface" refers to a user interface that includes screens and input forms that allow a user to interact with a system.

[2175] "Company" refers to the entity that provides job information and inputs the desired profile and recruitment information.

[2176] "Job information" refers to information such as the types of jobs a company is looking to hire for and the skill sets they are looking for.

[2177] "Matching" refers to the process of comparing a user's skill set and work experience with a company's job listings to determine suitability.

[2178] "Advertisement" refers to commercial information related to the user's question history and advice content.

[2179] The present invention relates to a system that matches users' career questions with companies' job information and provides personalized advice and job information. The program processing of this system will be described in detail below.

[2180] Hardware and software used

[2181] The following hardware and software are used to implement this system.

[2182] Server: A computer system that processes data, generates advice using generative AI, and matches with company job information. Specifically, it uses a cloud server.

[2183] Terminal: The device on which company personnel enter recruitment information. Specifically, a computer or tablet with a web browser is used.

[2184] Database: Storage for storing user question history, advice history, and company job information. Specifically, a relational database management system (RDBMS) is used.

[2185] Generative AI: AI models for generating personalized advice, specifically using natural language processing models such as GPT-3.

[2186] Specific processing explanation of the program

[2187] User Registration and Login

[2188] When a user registers, they enter information such as their name, email address, and password into a dedicated input form. The server receives this information, stores it in a database, and sends the user a confirmation email for authentication. When the user clicks the link in the confirmation email, account authentication is complete. The user then enters their authentication information on the login screen, and the server compares the information with the database and allows them to log in.

[2189] Career Question and Advice Generation

[2190] When a user inputs a question about their career, the server receives the question and records it in a database. The server then retrieves the user's past questions and advice history from the database and inputs it into the generative AI. At this time, the user's past history and profile data are used as prompts. For example, the following prompts are generated:

[2191] "User's previous question: What skills should I learn in the future? User profile: Age 25, Occupation Engineer"

[2192] The generative AI generates advice based on this, and the server displays that advice on the user's interface.

[2193] Entering and matching company job information

[2194] The terminal (company representative) logs in and enters recruitment information. For example, "Job position: Data scientist, Required skills: Python, data analysis." The server receives this information and stores it in a database. The server then matches the company's recruitment information with the user's skill set, past work experience, etc., to identify users who are highly suitable. The server analyzes the identified matching results and displays the recruitment information on the user's interface to provide the user with appropriate recruitment information.

[2195] Advertisement and monetization

[2196] The server selects advertisements related to the user's question history and advice content and displays them on the user's screen. This allows for highly relevant advertisements to be displayed, enabling effective marketing. The server periodically creates reports and trend reports from companies and bills them for usage. It also includes a mechanism for collecting premium feature fees and subscription fees from users.

[2197] The above is a detailed embodiment of the system according to the present invention. This allows users to obtain appropriate career advice and companies to quickly and accurately find the talent they are looking for. In addition, by effectively displaying relevant advertisements, the service can be monetized.

[2198] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2199] User Registration and Login

[2200] Processing Steps:

[2201] Step 1:

[2202] A user registers by entering information such as name, email address, and password into a dedicated input form and clicking the "Register" button.

[2203] Specific actions: Enter "Yamada Taro", "yamada@example.com", and "password123" and click the button.

[2204] Input: User information (name, email address, password)

[2205] Output: The entered user information is sent to the server.

[2206] Step 2:

[2207] The server receives this information, stores it in a database, and simultaneously sends a confirmation email to the user's email address.

[2208] What it does: Creates a new record in the database and sends a confirmation email via the SMTP server.

[2209] Input: User information (name, email address, password)

[2210] Output: A confirmation email is sent to the user.

[2211] Step 3:

[2212] The user clicks the confirmation link in the email to complete account authentication.

[2213] What happens: Open the confirmation email and click the link.

[2214] Input: Link from confirmation email

[2215] Output: The account is authenticated.

[2216] Step 4:

[2217] The user enters their email address and password on the login screen and clicks the "Login" button.

[2218] Specific action: Enter "yamada@example.com" and "password123" into the form and click the button.

[2219] Input: User's email address and password

[2220] Output: An authentication request is sent to the server.

[2221] Step 5:

[2222] The server checks the user's input information against the registered information in the database, and if it matches, allows the user to log in.

[2223] Specific operation: A matching process is performed, and if there is a match, a session is started.

[2224] Input: User's email address and password

[2225] Output: Successful login and session start

[2226] Career Question and Advice Generation

[2227] Processing Steps:

[2228] Step 1:

[2229] The user enters career-related questions into a dedicated input form and clicks the "Submit" button.

[2230] Specific action: Fill out the form with "What skills would you like to learn next?" and click the button.

[2231] Input: User's career question

[2232] Output: The carrier question is sent to the server.

[2233] Step 2:

[2234] The server receives this question and records it in a database.

[2235] Specific behavior: The question is stored in the database as a new record.

[2236] Input: Career Question

[2237] Output: Question data stored in a database

[2238] Step 3:

[2239] The server retrieves the user's past questions and advice history from a database.

[2240] What it does: Retrieves past question history using a SQL query.

[2241] Input: User ID

[2242] Output: Past questions and advice history

[2243] Step 4:

[2244] The server passes the user's past history and profile data as prompts to the generative AI model to generate advice.

[2245] Specific operation: Input the prompt sentence: 'User's previous question: What skills should I learn in the future? User profile: Age 25, Occupation Engineer' into the generative AI model.

[2246] Input: Previous questions, advice history, profile data

[2247] Output: The generated advice

[2248] Step 5:

[2249] The server displays the generated advice on the user's interface.

[2250] Specific behavior: Display advice on the user's screen.

[2251] Input: Generated advice

[2252] Output: Advice displayed on the user's interface

[2253] Entering and matching company job information

[2254] Processing Steps:

[2255] Step 1:

[2256] The terminal (company representative) logs in and enters recruitment information. The user enters "Job position: Data scientist, Required skills: Python, data analysis."

[2257] Specific action: Enter "Data Scientist, Python, Data Analysis" into the form and submit it.

[2258] Input: Company recruitment information

[2259] Output: The job information is sent to the server.

[2260] Step 2:

[2261] The server saves the job information entered in the database.

[2262] Specific behavior: The entered job information is stored in the database as a new record.

[2263] Input: Company recruitment information

[2264] Output: Job listings stored in a database

[2265] Step 3:

[2266] The server matches companies' job listings with the user's skill set, past work experience, etc.

[2267] What it does: Search for skillsets and work experience using SQL queries.

[2268] Input: User's skill set, past work experience

[2269] Output: Matching results

[2270] Step 4:

[2271] The server analyzes the matching results and identifies suitable users.

[2272] What it does: Runs a matching algorithm to identify highly suitable users.

[2273] Input: Matching results

[2274] Output: A list of highly relevant users

[2275] Step 5:

[2276] The server displays the identified job listings on the user's interface.

[2277] Specific operation: Display "Company B is hiring a data scientist" on User C's screen.

[2278] Input: Matched Jobs

[2279] Output: The job displayed in the user's interface

[2280] Advertisement and monetization

[2281] Processing Steps:

[2282] Step 1:

[2283] The server selects advertisements related to the user's question history and advice content.

[2284] What it does: Runs a relevance algorithm to select "data analytics tool ads."

[2285] Input: User's question history, advice history

[2286] Output: Selected ads

[2287] Step 2:

[2288] The server displays the selected advertisement on the user's screen.

[2289] Specific behavior: Displayed as a banner ad on the user's screen.

[2290] Input: Selected Ad

[2291] Output: The ad displayed in the user's interface.

[2292] Step 3:

[2293] The server generates periodic reports and trend reports from the company and bills the company for usage.

[2294] Specific behavior: Generate a report in PDF format and send it to the company's registered email address.

[2295] Input: Company usage information

[2296] Output: Generated reports and invoices

[2297] Step 4:

[2298] The server collects premium feature fees and subscription fees from users.

[2299] Specific operation: Collect fees periodically via credit card payments.

[2300] Input: User's payment information

[2301] Output: Usage fees collected

[2302] (Application example 1)

[2303] 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."

[2304] Conventional career counseling systems lack the means to instantly receive appropriate advice in real time when users ask career-related questions. They also have limited means to efficiently match companies' job information with users' skill sets. Furthermore, they lack sufficient means to provide users with relevant advertising and billing information in real time. To solve these problems, it is necessary to provide an effective system and means.

[2305] 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.

[2306] In this invention, the server includes means for a user to input a career-related question, means for saving the user's question and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for generating advice based on the acquired past questions and advice history using a generative AI, means for providing the generated advice to the user, and means for using a display device in the store to allow the user to receive career-related advice in real time, thereby enabling the user to receive personalized career advice in real time.

[2307] A "user" is an individual who enters career-related questions into the system and receives advice and job information.

[2308] A "company" is an organization that inputs the position it is looking to hire for and the profile of the person it is looking for into the system, and then uses that information to search for suitable candidates.

[2309] The "database" is a storage device within the system that stores users' question history, advice history, and company job information, and retrieves and uses them as needed.

[2310] "Generative AI" refers to artificial intelligence that generates new advice based on a user's past questions and advice history.

[2311] A "display device" is a device used by a user to receive real-time career advice and job information, such as smart glasses or a terminal.

[2312] "Advice" is a suggestion for specific courses of action or skill acquisition that is provided in response to a user's career-related questions.

[2313] "Matching" is the process of comparing a user's skill set with the profile of the person a company is looking for and finding suitable job information.

[2314] "Advertisement" is promotional information that provides information related to the user's question history and advice content through the display device used by the user.

[2315] To implement this invention, a system using users, companies, servers, terminals, a database, generative AI, and a display device is required. Specific embodiments of this system are described below.

[2316] User Registration and Login

[2317] A user first registers with the system. They enter information such as their name, email address, and password into a dedicated input form and send it to the server. The server saves the entered information in a database and sends the user a confirmation email for authentication. When the user clicks the link in the confirmation email, account authentication is completed, and they then enter their email address and password on the login screen to log in to the system. The server compares the user's authentication information with the database and allows them to log in.

[2318] Career Question and Advice Generation

[2319] Users enter career-related questions into a dedicated input form. The server receives these questions and records them in a database. The server then retrieves the user's past questions and their answer history from the database and passes them as input data to the generative AI. The generative AI generates personalized advice based on the input data. The generated advice is displayed on the user's interface, allowing the user to receive the advice in real time. Specifically, the advice is provided through a display device such as smart glasses.

[2320] Entering and matching company job information

[2321] Company personnel also log in to the system and enter recruitment information. The server stores the entered recruitment information in a database. The server matches the company's recruitment information with the user's skill set, past work experience, etc., and recommends suitable jobs to the user. The user can receive this recommendation in real time on a display device.

[2322] Ad Display and Monetization

[2323] The server selects advertisements relevant to the user's question history and advice, and displays the selected advertisements on the user's screen. The server periodically collects fees from companies for feedback and data usage. It also charges users for using specific advanced features. Advertisement and billing information are also provided in real time through the smart glasses.

[2324] Specific examples

[2325] For example, User A inputs a question such as, "What skills should I learn in the future?" The server references User A's past question history and passes it as input data to the generative AI. The generative AI generates advice recommending "acquiring data analysis and Python skills" and provides it through the smart glasses. At the same time, if Company B inputs a job posting for a "data scientist," the server compares it with User A's skill set and presents suitable job postings in real time.

[2326] Prompt Sentence Examples

[2327] For example, provide the generative AI with a prompt like the following:

[2328] Prompt: "User A has been recommended to learn 'Data Analysis' and 'Python' in the past. Based on recent trends and User A's current skill set, please provide advice on which skills they should learn next."

[2329] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2330] Step 1:

[2331] The user enters a question about a career. The question is entered through an input form and the data is sent to the server.

[2332] Enter: Career Questions

[2333] Output: Sends the query data to the server

[2334] Step 2:

[2335] The server receives the user's question and stores it in a database, allowing for further processing.

[2336] Input: User question data

[2337] Output: Save the question data to the database

[2338] Step 3:

[2339] The server retrieves the user's past questions and advice history from the database, allowing the user to refer to their past question patterns and the advice they received.

[2340] Input: User ID

[2341] Output: User's past questions and advice history data

[2342] Step 4:

[2343] The server inputs the user's past questions and advice history into the generative AI to generate personalized advice. The generative AI generates advice based on the prompt sentence.

[2344] Input: Past questions and advice history, prompt text

[2345] Output: Personalized advice

[2346] Step 5:

[2347] The server displays the generated advice on the user's interface, for example, by using smart glasses to provide advice in real time.

[2348] Input: Generated advice

[2349] Output: Advice is displayed on the user's display device

[2350] Step 6:

[2351] A company representative logs in to the system and enters recruitment information, which is then sent to the server.

[2352] Input: Company recruitment information

[2353] Output: Send job information to the server

[2354] Step 7:

[2355] The server stores the recruitment information received from companies in a database, where job information from companies is accumulated.

[2356] Input: Company recruitment information

[2357] Output: Job saved to database

[2358] Step 8:

[2359] The server matches the user's skill set with company job information by retrieving the user's history and skill information from a database and using an algorithm to perform the matching.

[2360] Input: User skill set, company job postings

[2361] Output: Matching results

[2362] Step 9:

[2363] The server displays suitable job information to the user based on the matching results, allowing the user to receive suitable job information in real time.

[2364] Input: Matching results

[2365] Output: The job listing is displayed on the user's display device.

[2366] Step 10:

[2367] The server selects advertisements related to the user's question history and advice content, and displays them on the user's display device. The selection of advertisement data is based on the user's history information.

[2368] Input: User's question history, advice content

[2369] Output: Display the ad

[2370] Step 11:

[2371] The server collects fees from the company for feedback and data usage, and also charges users for certain advanced features, as a means of monetization.

[2372] Input: Company feedback, user usage history

[2373] Output: Toll collection

[2374] 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.

[2375] This invention relates to a system that recognizes a user's emotional state and provides career advice based on that. This system matches the user's career-related questions with company job information, analyzes the user's emotional state using an emotion engine, and provides personalized advice, advertisements, and job information. Here, we will generate a system program and explain its processing in natural language. We will explain the server, terminal, and user as subjects, using concrete examples.

[2376] Parts Overview

[2377] User: An individual who uses the system to enter career-related questions and receive advice and job information.

[2378] Server: A computer system that processes data, generates advice using generative AI, analyzes emotional states using an emotion engine, and matches users with company job listings.

[2379] Terminal: The device used by the company representative to enter recruitment information.

[2380] Database: Storage for question history, advice history, company job information, and sentiment data.

[2381] Program processing explanation

[2382] User Registration and Login

[2383] 1. The user registers and enters the required information.

[2384] 2. The server saves the information entered in a database and sends a confirmation email to the user.

[2385] 3. The user clicks the link in the confirmation email to authenticate their account and complete the login.

[2386] 4. The server checks the user's credentials against a database and allows them to log in.

[2387] Career Question and Advice Generation

[2388] 1. The user enters a career question.

[2389] 2. The server stores the question in a database and uses an emotion engine to analyze the question and recognize the user's emotional state.

[2390] 3. The server retrieves past question and answer history from the database and passes the data to the generative AI.

[2391] 4. Generative AI generates advice based on past data and current emotional state.

[2392] 5. The server sends the generated advice to the user and displays it in the interface.

[2393] Entering and matching company job information

[2394] 1. The terminal (company representative) logs in and enters recruitment information.

[2395] 2. The server saves the input information in a database.

[2396] 3. The server compares the user's skill set with company job listings and runs a matching algorithm.

[2397] 4. The server analyzes the matching results and adjusts the order in which job listings are presented, taking into account the user's emotional state using an emotion engine.

[2398] 5. The server notifies the user of suitable job information and displays it on the interface.

[2399] Advertisement and monetization

[2400] 1. The server selects relevant advertisements based on the user's question history and emotional state.

[2401] 2. The server displays the selected advertisement on the user's interface.

[2402] 3. The server provides companies with regular recruitment data and trend reports and collects a fee for the feedback.

[2403] 4. The server collects premium feature fees and subscription fees from the user.

[2404] Specific examples

[2405] Example 1: User A's career questions and advice

[2406] 1. User A enters the question, "What skills do I need to learn next?"

[2407] 2. The server stores User A's question in a database and analyzes it with an emotion engine to recognize User A's emotional state.

[2408] 3. The server passes the past question history and current emotional state to the generative AI.

[2409] 4. Generative AI generates advice recommending "acquire data analysis and Python skills."

[2410] 5. The server sends the AI ​​advice to User A and displays it on the interface.

[2411] Example 2: Matching job information from Company B with User C

[2412] 1. A person in charge at Company B logs in and enters the job information for a "Data Scientist."

[2413] 2. The server saves the job information in a database.

[2414] 3. The server matches User C's skill set with Company B's job listings.

[2415] 4. The server analyzes the matching results and adjusts the order in which job information is presented using an emotion engine, taking into account User C's emotional state.

[2416] 5. The server notifies User C of the job information and displays it on the interface.

[2417] This demonstrates that the present invention is a system that realizes personalized career counseling and job matching for companies that takes into account the user's emotional state, thereby providing more effective advice to users and recommending more suitable candidates to companies.

[2418] The processing flow will be explained below.

[2419] Career Question and Advice Generation

[2420] Step 1:

[2421] The user enters career-related questions into a dedicated input form.

[2422] Step 2:

[2423] The server receives the question from the user, checks that the input format is correct, and if there are no problems with the format, stores the question in the database.

[2424] Step 3:

[2425] The server passes the question content to the emotion engine, which analyzes and recognizes the user's emotional state.

[2426] Step 4:

[2427] The server retrieves the user's past question and answer history from the database.

[2428] Step 5:

[2429] The server inputs question history, user profile data, and current emotional state information into the generative AI.

[2430] Step 6:

[2431] Generative AI generates personalized advice for users based on input data.

[2432] Step 7:

[2433] The server displays the generated advice on the user's interface.

[2434] Entering and matching company job information

[2435] Step 1:

[2436] The terminal (company representative) logs in to the system and accesses the recruitment information input screen.

[2437] Step 2:

[2438] The terminal allows users to input the available positions and the desired skill sets.

[2439] Step 3:

[2440] The server receives the recruitment information sent by the company, checks whether the input format is correct, and if there are no problems with the format, stores the information in the database.

[2441] Step 4:

[2442] The server compares the user's skill set with the skill set required by the company from the database and runs a matching algorithm.

[2443] Step 5:

[2444] The server analyzes the matching results and utilizes an emotion engine to adjust the order in which job listings are presented, taking into account the user's emotional state.

[2445] Step 6:

[2446] The server notifies the user of suitable job information and displays it on the interface.

[2447] Advertisement and monetization

[2448] Step 1:

[2449] The server selects relevant advertisements based on the user's question history and emotional state.

[2450] Step 2:

[2451] The server retrieves the selected advertisement data and displays it on the user's interface.

[2452] Step 3:

[2453] The server periodically creates recruitment data and trend reports for companies and collects a fee for the feedback.

[2454] Step 4:

[2455] The server handles the process of collecting premium feature fees and subscription fees from users.

[2456] Example: User A's career questions and advice

[2457] Step 1:

[2458] User A enters the question "What skills do you want to learn in the future?" into an input form.

[2459] Step 2:

[2460] The server stores User A's question in a database.

[2461] Step 3:

[2462] The server passes the question to the emotion engine, which analyzes and recognizes the emotional state of User A. For example, it may determine that User A is feeling anxious.

[2463] Step 4:

[2464] The server retrieves past question history from the database and passes it to the generative AI.

[2465] Step 5:

[2466] Based on past data and current emotional state, generative AI generates advice such as "learn data analysis and Python skills."

[2467] Step 6:

[2468] The server sends the advice to User A and displays it on the interface.

[2469] Example: Matching job information from Company B with User C

[2470] Step 1:

[2471] A representative from Company B logs in and enters job information for a "Data Scientist."

[2472] Step 2:

[2473] The server stores the job listings in a database.

[2474] Step 3:

[2475] The server uses a matching algorithm to compare User C's skill set with Company B's job listings.

[2476] Step 4:

[2477] The server analyzes the matching results and uses an emotion engine to consider the emotional state of user C. For example, if user C shows enthusiasm, job information reflecting that emotion will be presented preferentially.

[2478] Step 5:

[2479] The server notifies User C of the job information and displays it on the interface.

[2480] Example 2

[2481] 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."

[2482] Conventional career advice systems do not always provide optimal advice for users because they do not take into account the user's emotional state. Furthermore, matching between company job information and the user's skill set is limited to a simple comparison, and does not take into account the user's emotions or motivations, making it difficult to provide job suggestions that satisfy the user. To solve these problems, there is a need for the development of a system that analyzes the user's emotional state and provides personalized advice and matching based on that analysis.

[2483] 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.

[2484] In this invention, the server includes means for a user to input a career-related question, means for saving the user's questions and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for analyzing the user's emotional state using an emotion engine, means for generating advice based on the acquired past questions and emotional state using a generative AI, and means for providing the generated advice to the user, thereby making it possible to provide personalized career advice that reflects the user's emotional state.

[2485] "Career questions" refer to doubts or questions users have about their occupation, work style, or skill development.

[2486] "Database" refers to a storage system for storing a user's question history, advice history, emotional state information, and company job information.

[2487] An "emotion engine" refers to software or algorithms that analyze text data entered by a user and recognize the user's emotional state from its content.

[2488] "Generative AI" refers to an artificial intelligence model that generates optimal advice and information based on past data and the user's current emotional state.

[2489] "Job information" refers to information that includes details about the positions a company is hiring for and the type of person they are looking for.

[2490] "Skill set" refers to the collection of technical or professional skills a user possesses.

[2491] "Matching" refers to the process of comparing a user's skill set with a company's job listings, calculating the degree of compatibility, and finding the best match.

[2492] "Personalized advice" refers to advice that is optimized for a specific user based on the user's individual characteristics and emotional state.

[2493] "Advertisement" refers to promotional information for products or services related to the user's question history or emotional state.

[2494] "Feedback" refers to evaluations and opinions about the system from companies and users, and refers to information used to improve the system and services.

[2495] The present invention relates to a system for recognizing a user's emotional state and providing career advice based on the recognition. The following describes how the program processing of this system is implemented.

[2496] This system is composed of the following main components: users, servers, terminals, and databases. Each component cooperates to provide personalized career advice to users.

[2497] User Registration and Login

[2498] The user registers by entering their name, email address, and password on the new registration screen. The server stores this information in a database and sends a confirmation email. The user clicks the link in the confirmation email to authenticate their account, and then authenticates again on the login screen. The server compares the user's authentication information with the database and allows them to log in if they match.

[2499] Career Question and Advice Generation

[2500] A user inputs a question about their career, such as "What should I pay attention to when choosing a career?" The server stores the user's question in a database and uses an emotion engine to analyze the question and recognize the user's emotional state. A natural language processing toolkit is used for this analysis.

[2501] Next, the server retrieves the past question history and advice history from the database and passes that data to the generative AI. Based on the retrieved data and the current emotional state, the generative AI generates advice. This AI could use, for example, OpenAI's GPT-3 model.

[2502] The generated advice is displayed on the user's interface via the server. For example, advice such as "I recommend you improve your project management skills" is presented.

[2503] Entering and matching company job information

[2504] The terminal (company representative) logs into the system and inputs the company's job information. For example, they register information such as "Recruiting front-end engineers." The server stores that information in a database. The server then compares the user's skill set with the company's job information and runs a matching algorithm.

[2505] The matching results are analyzed by an emotion engine, which adjusts the presentation order based on the user's emotional state. The most suitable job listings are then notified to the user and displayed on the interface.

[2506] Advertisement and monetization

[2507] The server selects relevant advertisements based on the user's question history and emotional state and displays them in the user's interface. The server also periodically collects fees from companies for using the feedback and recruitment data. The server also handles the process of collecting fees for certain advanced features and subscription fees from users.

[2508] Specific examples

[2509] For example:

[2510] Example prompt sentence:

[2511] User A enters the question "What skills are important when choosing a job?" into the system, the server saves the question, and the emotion engine analyzes it to determine that "User A is feeling anxious." The generative AI generates advice such as "We recommend you improve your data analysis skills," and the server displays this advice on User A's interface.

[2512] The present invention aims to provide more effective and personalized career advice by taking into account the user's emotional state, which will greatly contribute to helping users improve their practical skills and make appropriate career choices.

[2513] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2514] User Registration and Login

[2515] Step 1: Enter your new registration information

[2516] Operation:

[2517] The user enters their name, email address, and password on the new registration screen.

[2518] input:

[2519] Name, email address, password

[2520] output:

[2521] The registration information is sent to the server.

[2522] Step 2: Save your registration information and send a confirmation email

[2523] Operation:

[2524] The server stores the entered information in a database and sends a confirmation email.

[2525] input:

[2526] Registration information (name, email address, password)

[2527] Data processing:

[2528] Saves the new user information in the database and generates and sends a confirmation email via the SMTP protocol.

[2529] output:

[2530] A confirmation email will be sent to the user's email address.

[2531] Step 3: Verify your account

[2532] Operation:

[2533] The user clicks on the link in the confirmation email.

[2534] input:

[2535] Link in the confirmation email

[2536] output:

[2537] After authentication, a response is returned from the server.

[2538] Step 4: Complete the login

[2539] Operation:

[2540] The server checks the user's credentials against a database and allows them to log in.

[2541] input:

[2542] Authentication information (email address, password)

[2543] Data processing:

[2544] The authentication information is checked against the database, and if it matches a session is created.

[2545] output:

[2546] The dashboard screen is displayed to the user.

[2547] Career Question and Advice Generation

[2548] Step 1: Fill in the career questions

[2549] Operation:

[2550] The user enters a career question and clicks the "Submit" button.

[2551] input:

[2552] Career-related questions (e.g., "What should you pay attention to when choosing a career?")

[2553] output:

[2554] The question is sent to the server.

[2555] Step 2: Analyze emotional state

[2556] Operation:

[2557] The server stores the question content in a database, and an emotion engine analyzes the question content to recognize the user's emotional state.

[2558] input:

[2559] Career Questions

[2560] Data processing:

[2561] The question data is stored in a database and sentiment analysis is performed using a natural language processing (NLP) toolkit.

[2562] output:

[2563] The user's emotional state (e.g., anxiety) is recognized.

[2564] Step 3: Obtaining historical data

[2565] Operation:

[2566] The server retrieves the past question history and advice history from the database.

[2567] input:

[2568] User ID

[2569] Data processing:

[2570] Execute a database query to retrieve past question and advice history.

[2571] output:

[2572] A history of past questions and advice is obtained.

[2573] Step 4: Generating Advice

[2574] Operation:

[2575] Generative AI generates advice based on past data and current emotional state.

[2576] input:

[2577] Past question history, advice history, current emotional state

[2578] Data processing:

[2579] Provide data to a generative AI model (e.g., GPT-3) to generate advice based on a prompt.

[2580] output:

[2581] The advice generated (e.g., "I recommend you improve your project management skills")

[2582] Step 5: Providing advice

[2583] Operation:

[2584] The server sends the generated advice to the user and displays it in the interface.

[2585] input:

[2586] Generated Advice

[2587] output:

[2588] Advice is displayed on the user's interface.

[2589] Entering and matching company job information

[2590] Step 1: Enter your job information

[2591] Operation:

[2592] The terminal (company representative) logs into the system and enters job information.

[2593] input:

[2594] Job postings (e.g., "Front-end engineer wanted")

[2595] output:

[2596] The job information is sent to the server.

[2597] Step 2: Saving to the database

[2598] Operation:

[2599] The server stores the job listings in a database.

[2600] input:

[2601] Job information

[2602] Data processing:

[2603] Store job information in a database.

[2604] output:

[2605] Saved Jobs

[2606] Step 3: Performing the Match

[2607] Operation:

[2608] The server compares the user's skill set with company job listings and runs a matching algorithm.

[2609] input:

[2610] User skill sets, job information

[2611] Data processing:

[2612] Run a matching algorithm that compares your skill set with the job posting and calculates a suitability score.

[2613] output:

[2614] Matching results (relevance score)

[2615] Step 4: Presentation taking into account emotional state

[2616] Operation:

[2617] The server analyzes the matching results and the user's emotional state to adjust the presentation order.

[2618] input:

[2619] Matching results, user emotional state

[2620] Data processing:

[2621] Prioritize job postings based on sentiment analysis data.

[2622] output:

[2623] Adjusted job posting order

[2624] Step 5: Post a job

[2625] Operation:

[2626] The server notifies the user of suitable job information and displays it on the interface.

[2627] input:

[2628] Adjusted job posting order

[2629] output:

[2630] The job listing is displayed in the user interface.

[2631] Advertisement and monetization

[2632] Step 1: Ad selection

[2633] Operation:

[2634] The server selects relevant advertisements based on the user's question history and emotional state.

[2635] input:

[2636] Question history, emotional state

[2637] Data processing:

[2638] It runs an ad selection algorithm based on question history and emotional state.

[2639] output:

[2640] Selected Advertisements

[2641] Step 2: Displaying the ad

[2642] Operation:

[2643] The server displays the selected advertisement on the user's interface.

[2644] input:

[2645] Selected Advertisements

[2646] output:

[2647] Advertisements are displayed in the user's interface.

[2648] Step 3: Collect fees

[2649] Operation:

[2650] The server periodically collects fees from companies for feedback and data usage, and charges users for certain advanced features or subscription fees.

[2651] input:

[2652] Company usage information, user usage information

[2653] Data processing:

[2654] Manage billing information through a fee collection system and collect fees from businesses and users.

[2655] output:

[2656] Fees collected

[2657] Through these steps, the system can provide personalized career advice and job information that takes into account the user's emotional state, providing more effective advice to users and recommending more suitable candidates to companies.

[2658] (Application example 2)

[2659] 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."

[2660] Conventional food delivery services have systems that suggest meals based on a user's order history and preferences, but they are unable to make personalized suggestions that take into account the user's emotional state. As a result, meals and services that are appropriate for the user's emotional state may not be provided, hindering the improvement of the user experience.

[2661] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2662] In this invention, the server includes means for a user to input a question about a service, means for saving the user's question and past question history in a database, means for acquiring the user's past questions and advice history from the database, means for generating advice based on the acquired past questions and advice history using a generative AI, means for providing the generated advice to the user, means for analyzing the user's emotional state using an emotion engine, and means for providing personalized information or services based on the emotional state. This makes it possible to provide personalized meal suggestions and services that take the user's emotional state into consideration.

[2663] A "user" is an individual who utilizes the system to enter service-related questions and receive suggestions and information.

[2664] A "database" is a storage device that stores data such as user questions, past question history, advice history, skill sets, and the type of person a company is looking for.

[2665] "Generative AI" is an artificial intelligence model that generates new advice and suggestions based on past question and advice history.

[2666] The "emotion engine" is a system component that analyzes the user's emotional state from their input and expressions.

[2667] "Personalized information or services" are suggestions or information that are individually tailored based on a user's emotional state and past behavioral history.

[2668] A "company" is an organization that inputs information and the profile of the person it is looking for in order to provide a service.

[2669] A "skill set" is a collection of skills and knowledge possessed by a user.

[2670] A "matching method" is a system component that compares the user's skill set with the profile of the person the company is looking for and finds the optimal combination.

[2671] "Emotional state" refers to the state of emotion at that time that is estimated from the user's input and actions.

[2672] This invention relates to a food delivery system that analyzes emotional states and provides personalized meal recommendations and services based on those results. The entire system can be accessed by users through a smartphone application. The main hardware components include a smartphone, a server, and a database. The main software components used are a generative AI model and an emotion engine (EmotionAPI).

[2673] Hardware and software used

[2674] 1. Smartphone: Provides the user interface and receives user input.

[2675] 2. Server: Processes data, runs generative AI models, analyzes emotional states using the emotion engine, and performs data matching.

[2676] 3. Database: Stores data such as user questions, past question history, advice history, skill sets, and the type of person the company is looking for.

[2677] 4. Generative AI model (GPT model): Generates new suggestions and advice based on user input and past data.

[2678] 5. Emotion Engine (Emotion API): Analyzes the user's emotional state from their input and expressions.

[2679] System configuration description

[2680] First, a user registers an account using a smartphone application and enters the necessary information. After completing the registration, the user inputs their emotional state through the application. For example, they can express their emotions by uploading photos or text. Based on this, the server uses an emotion engine to analyze the user's emotional state.

[2681] Based on the analysis results, the server retrieves past question and advice history from the database and passes the data to a generative AI model. This generative AI model generates personalized meal suggestions based on the analyzed emotional state and past data. The generated suggestions are displayed in the user's smartphone application.

[2682] When a user places an order from the suggested meal menu, the server provides relevant coupons and offers and stores this order information in a database. The server can also display personalized advertisements based on the user's emotional state and order history. The server also includes a means to collect feedback from advertisers, data usage fees, and premium feature fees from users.

[2683] Specific examples

[2684] For example, consider the case where a user enters the text "I'm feeling stressed today." The server's emotion engine analyzes and detects "stress." The server then passes the data to a generative AI model based on past order history and the user's current emotional state. The generative AI model generates a suggestion for a "healthy salad bowl that's good for relieving stress," and the server displays this suggestion on the smartphone application.

[2685] Example prompt sentence:

[2686] Emotional state: Stress

[2687] Past orders: Japanese food, healthy food, smoothies

[2688] Suggestion: Generate appropriate meal suggestions for the user, taking into account their emotional state today.

[2689] This allows the user to receive suggestions for meals that best suit their mood at the time, allowing them to receive a more satisfying service.

[2690] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2691] Program processing steps

[2692] Step 1: User Registration and Login

[2693] The user opens the smartphone application and enters the required information into the new registration form.

[2694] The server receives the entered information and stores it in a database.

[2695] The server will send a confirmation email to the user, prompting them to click on a link to confirm their account.

[2696] The user clicks the link in the verification email to confirm their account.

[2697] The server checks the user's verification information against the database, confirms that authentication has been completed, and permits login.

[2698] Input: User registration information (name, email address, password, etc.)

[2699] Output: User account authentication and login permission

[2700] Step 2: Input and analysis of the user's emotional state

[2701] Users upload photos and text that describe their emotional state within a smartphone application.

[2702] The server receives the uploaded data, sends it to the emotion engine (EmotionAPI), and analyzes the emotional state.

[2703] The emotion engine analyzes the user's emotional state from the input and returns the results to the server.

[2704] Input: User-uploaded photos and text

[2705] Output: Sentiment analysis results (e.g., stress, joy, sadness, etc.)

[2706] Step 3: Obtaining past data and generating proposals using generative AI

[2707] The server retrieves the user's past question and advice history from a database, along with the analyzed emotional state.

[2708] The server passes this data to a generative AI model (GPT model) to generate personalized suggestions and advice.

[2709] The generative AI generates optimal menu suggestions based on the provided data and the user's emotional state and returns them to the server.

[2710] Input: User's emotional state, past question history, advice history

[2711] Output: Personalized menu suggestions

[2712] Step 4: View proposals and process orders

[2713] The server displays the suggestions returned by the generative AI on the user's smartphone application.

[2714] The user reviews the proposed menu and selects an order.

[2715] The server stores the order information selected by the user in a database and arranges for delivery service.

[2716] Input: Generative AI suggestions, user menu selections

[2717] Output: Store order information and arrange delivery

[2718] Step 5: Offer coupons and display ads

[2719] The server generates relevant coupons and rewards based on the user's orders and past behavioral history.

[2720] The server displays the coupons and offers on the user's smartphone application.

[2721] Additionally, the server displays personalized advertisements based on the user's emotional state and order history.

[2722] Input: User order information, past behavior history

[2723] Output: Coupons, offers, personalized ads

[2724] In this way, users can receive meal suggestions that suit their emotional state and can order and receive rewards based on them.

[2725] 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.

[2726] 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.

[2727] 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.

[2728] 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.

[2729] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2730] 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.

[2731] 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).

[2732] 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.

[2733] 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."

[2734] 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.

[2735] 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).

[2736] 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.

[2737] 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.

[2738] 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.

[2739] 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.

[2740] 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.

[2741] The hardware resource that executes the specific processing may be configured w...

Claims

1. a means for a user to input a career question; means for storing the user's questions and past question history in a database; means for acquiring the user's past question and advice history from the database; A means for generating advice based on the acquired past questions and advice history using generative AI; means for providing the generated advice to the user; A system including:

2. A way for companies to input the positions they are looking to hire and the type of person they are looking for, means for storing input from said business in a database; A means for matching a user's skill set with the profile of a person desired by a company from the database; A means for providing job information suitable for the user based on the matching result; The system of claim 1 further comprising:

3. means for displaying advertisements relevant to the user; A means of collecting fees from companies periodically for feedback and data usage; means for charging users for certain advanced features; The system of claim 1 further comprising:

Images