System

Abstract

A system is provided.SOLUTION: This system is provided with a means for inputting the interest, academic ability, special skill and character of a user and a means for transmitting the inputted data to a server, and the server receives the data. A system comprising: means for storing in a database; means for providing the database to a generative AI and generating advice such as a future occupation, a destination to go to a school, a estimated annual salary, and a point of caution which are most suitable for a user; and means for receiving the generated advice and customizing the advice in accordance with characteristics of each school. AI.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] Many high school students are currently struggling with choosing their future career paths. This is primarily due to the fact that self-analysis methods are standardized, making it difficult for students to visualize a future that suits them. Furthermore, it takes a huge amount of time and effort to improve the accuracy of the individual advice and information provided by each school's career guidance office. Furthermore, there is a lack of specific advice regarding further education and employment, which makes it difficult for students to make appropriate career choices. There is a need to overcome this situation and enable students to make more fulfilling career choices. [Means for solving the problem]

[0005] In order to solve the above problems, a system having the following configuration is provided:

[0006] A means for inputting the user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment;

[0007] means for transmitting the input data to a server;

[0008] means for the server to receive the data and store it in a database;

[0009] A means for providing the data to a generation AI and generating advice on the user's optimal future career, future education, expected annual income, points to note, etc.;

[0010] A server receives the generated advice and customizes it to suit the characteristics of each school;

[0011] means for transmitting the customized advice to the terminal and displaying the advice to the user;

[0012] A means for transmitting the user's feedback regarding the advice to the server again and reflecting it in the learning data of the generation AI;

[0013] It is a system including:

[0014] This will enable high school students to receive specific career advice tailored to their individual characteristics, enabling them to make more accurate career choices. It will also reduce the burden on career guidance offices and enable individual responses that reflect each school's characteristics and feedback. Furthermore, the accuracy of the generation AI will be improved based on feedback, allowing for continuous system improvement.

[0015] "User" refers to a high school student, student, or person who uses the system and inputs information to select a career path.

[0016] "Interests" refers to information such as themes, fields, and hobbies that a user is personally interested in.

[0017] "Academic ability" refers to information such as the user's academic performance, knowledge, and ability in a particular subject.

[0018] "Special skills" refers to information about skills, abilities, and special techniques that a user excels at.

[0019] "Character" is information related to the user's nature, behavioral characteristics, and personality.

[0020] "Aspirations and conditions regarding further education and employment" refers to information such as the school the user wishes to attend, the occupation, place of employment, and working conditions in the future.

[0021] "Terminal" refers to a device or application that a user uses to input data.

[0022] A "server" is a computer system that receives, processes, and stores data sent by users.

[0023] The "database" is an information management system for storing and managing user input information and generated advice.

[0024] "Generative AI" is an artificial intelligence system that analyzes user data and generates optimal career advice.

[0025] "Advice" is specific guidance provided by the generative AI based on the user's data regarding future career, further education, expected annual income, points to be aware of, etc.

[0026] "Feedback" refers to the user's reactions, opinions, additional information, etc. to the advice provided. [Brief explanation of the drawings]

[0027] [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

[0028] 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.

[0029] First, the terms used in the following description will be explained.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] [First embodiment]

[0036] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0037] 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.

[0038] 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).

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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."

[0048] This invention relates to a digital career guidance system that helps high school students resolve their concerns about their career choices. This system uses a generation AI to provide optimal career advice based on the user's (high school student's) input of their interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment, and improves the accuracy of the system based on feedback.

[0049] Program processing

[0050] Data entry and submission

[0051] 1. User:

[0052] Users use a device (such as a smartphone or computer) to input their interests, academic ability, special skills, personality, and hopes and requirements regarding further education or employment.

[0053] As a specific example, "User A" inputs his / her interests "biology," academic ability "high," special skill "experiment," personality "intense curiosity," and desired educational goal "medical school."

[0054] 2. Terminal:

[0055] The terminal receives input data from the user, converts it into the required format, and sends it to the server.

[0056] Data reception and analysis

[0057] 3. Server:

[0058] The server receives the data sent from the terminal and stores it in a database.

[0059] The received data is formatted into an analyzable format and provided to the generation AI.

[0060] Advice Generation

[0061] 4. Generation AI:

[0062] The generative AI analyzes the user's interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment.

[0063] As a concrete example, we analyze the data of User A and generate the following advice:

[0064] Future career: Doctor or researcher

[0065] Expected annual income: Over 5 million yen from the first year

[0066] Caution: Be careful of long working hours and excessive stress

[0067] Customize and deliver advice

[0068] 5. Server:

[0069] Advice is received from the generating AI and customized taking into account the characteristics of the school and feedback from the career guidance office.

[0070] The completed advice is optimized for the user and sent to the terminal.

[0071] 6. Terminal:

[0072] The user's terminal receives the advice sent from the server and displays it through a user interface.

[0073] As a specific example, user A checks the career path as a doctor or researcher, related universities, expected annual salary, and points to consider.

[0074] Feedback and Updates

[0075] 7. Users:

[0076] The user provides feedback on the advice provided, for example, by inputting "I would like information on scholarships related to this occupation."

[0077] 8. Terminal:

[0078] The terminal transmits the user's feedback to the server.

[0079] 9. Server:

[0080] The server receives the feedback and stores it in a database.

[0081] Feedback data is provided to the generation AI, and the learning data of the generation AI is updated.

[0082] 10. Generation AI:

[0083] The generating AI will re-learn based on new feedback data, improving the accuracy of advice from the next time onwards.

[0084] In this way, the system of the present invention allows high school students to receive specific and realistic career advice that is optimized for their own characteristics, significantly reducing the worries of career selection. It also reduces the burden on career guidance offices and allows more students to receive highly accurate individual guidance.

[0085] The processing flow will be explained below.

[0086] Step 1:

[0087] User: The user (high school student) uses a web form or application on the device to enter information about their interests, academic ability, special skills, personality, and hopes and requirements for further education or employment.

[0088] Step 2:

[0089] Terminal: The terminal temporarily stores the user's input data locally and then formats it into a data format to send to the server.

[0090] Step 3:

[0091] Terminal: Sends the formatted data to the server as an HTTP request (e.g., POST request).

[0092] Step 4:

[0093] Server: The server receives the data sent from the device, analyzes the data format, and extracts the necessary information.

[0094] Step 5:

[0095] Server: Stores the received data in a database.

[0096] Step 6:

[0097] Server: Converts the data stored in the database into an analytical format and provides it as input data to the generation AI.

[0098] Step 7:

[0099] Generative AI: The generative AI begins analysis based on the provided data. It analyzes the user's interests, academic ability, special skills, and personality data to generate optimal career advice.

[0100] Step 8:

[0101] Generative AI: The generated advice is formatted and sent back to the server. The advice includes future careers, further education, expected annual income, and points to note.

[0102] Step 9:

[0103] Server: The server analyzes the advice data received from the generated AI and customizes it based on feedback from each school's career guidance office and the characteristics of the school.

[0104] Step 10:

[0105] Server: Prepares customized advice data as an HTTP response to send to the device.

[0106] Step 11:

[0107] Device: The device analyzes the advice data received from the server and displays it to the user. For example, the user can check career paths as a doctor or researcher, related universities, expected annual salary, and points to consider.

[0108] Step 12:

[0109] User: The user enters feedback on the advice provided, for example, entering additional information such as "I would also like to know about scholarship information related to this occupation."

[0110] Step 13:

[0111] Device: The device sends the user feedback to the server.

[0112] Step 14:

[0113] Server: The server receives the feedback, stores it in a database, and provides the feedback data to be reflected in the learning data of the generative AI.

[0114] Step 15:

[0115] Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[0116] In this way, the system provides users with specific, personalized career advice and incorporates feedback to continuously improve the service.

[0117] Example 1

[0118] 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."

[0119] There is a need for a method to provide specific, individualized career advice quickly and accurately to high school students struggling with career choices. Traditional career guidance relies on the experience and subjectivity of teachers and counselors, and can lack consistency for all students. Another problem is that it is difficult to customize advice to reflect the characteristics of a specific school or feedback from the career guidance office. Furthermore, there is a lack of a way to incorporate student feedback in real time and improve the accuracy of career advice.

[0120] 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.

[0121] In this invention, the server includes a means for receiving user data and storing it in a database, a means for converting the received data into an analyzable format and providing it to the generative AI model, and a means for receiving advice generated by the generative AI model and customizing it based on the characteristics of the school and feedback from the career guidance office. This allows high school students to receive specific and personalized career advice, significantly reducing the burden of career choice. Customization based on feedback from the career guidance office is also possible, reducing the burden on teachers and counselors. Furthermore, by reflecting student feedback in real time and updating the learning data of the generative AI model, the accuracy of advice can be improved.

[0122] "Users" refers to those who use the system to receive career advice, such as high school students.

[0123] "Interests" refer to the academic subjects or fields of activity in which a user is particularly interested.

[0124] "Academic ability" refers to an indicator that shows a user's academic performance and level of knowledge.

[0125] "Special skills" refer to skills or abilities that a user has that are particularly superior to others.

[0126] "Personality" refers to a user's personal characteristics and behavioral traits.

[0127] "Hope and conditions" refers to the hopes and constraints that the user has regarding future education or employment.

[0128] "Terminal" refers to a device such as a smartphone or computer that a user uses to input data and communicate with a server or generative AI model.

[0129] "Server" refers to the computer system that receives, analyzes, and provides data submitted by users to the generative AI model.

[0130] "Database" refers to a storage device for storing user input data, generated advice, feedback, etc.

[0131] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal career advice based on data such as a user's interests and academic ability.

[0132] "Advice" refers to the advisory information generated by the generative AI model regarding the user's best future career, further education, expected annual income, points to note, etc.

[0133] "Feedback" refers to opinions and requests made by users in response to advice provided.

[0134] "Customization" refers to adjusting the generated advice to take into account the characteristics of the school and feedback from the career guidance office.

[0135] "Display" refers to visually presenting the customized advice on the device screen.

[0136] This invention relates to a digital career guidance system that helps high school students resolve their concerns about their career choices. This system uses a generative AI model to provide optimal career advice based on user (high school student) inputs of their interests, academic ability, special skills, personality, and hopes and requirements for further education and employment, and improves the accuracy of the system based on feedback.

[0137] First, a user uses a device such as a smartphone or PC to input their interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment. For example, User A inputs his / her interests as "biology," academic ability as "high," special skills as "experiment," personality as "curious," and desired education as "medical school."

[0138] Next, the device receives input data from the user, converts it into the required format (e.g., JSON format), and sends it to the server, which receives the data and stores it in a database. The server then formats the data into an analyzable format and provides it to the generative AI model.

[0139] The generative AI model analyzes a user's interests, academic ability, special skills, personality, and hopes and conditions to generate optimal career advice. For example, it analyzes User A's data and generates the following advice: "Future occupation: doctor or researcher," "Expected annual income: 5 million yen or more from the first year," and "Caution: Be careful of long working hours and excessive stress."

[0140] The generated advice is returned to the server and customized based on the characteristics of the school and feedback from the career guidance office. For example, advice optimized for User A is created based on the school's designated recommendation quota and local characteristics.

[0141] The server sends the customized advice to the user's terminal, which displays the advice, allowing the user to review the advice and use it to help them choose their own path.

[0142] The user can also input feedback on the advice provided, for example, "I would like information on scholarships related to this occupation." The terminal then sends this feedback back to the server, which then stores it in a database.

[0143] Finally, the generative AI model retrains based on new feedback data to improve the accuracy of future advice. In this way, the system of the present invention enables high school students to receive specific and realistic career advice optimized for their individual characteristics, significantly reducing the stress of career choice. It also reduces the burden on career guidance offices, enabling more students to receive highly accurate individualized guidance.

[0144] (Example of a prompt)

[0145] As a concrete example, let's use the following prompt: "If User A enters interests: biology, academic ability: high, special skills: experimentation, personality: curious, and educational aspirations: medical school, provide the best career advice."

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] User data entry

[0149] Using a device (such as a smartphone or PC), the user inputs their interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment. Specifically, the user inputs information such as "Interests: Biology," "Academic ability: High," "Special skills: Experiments," "Personality: Very curious," and "Aspirations for further education: Medical school" into the device's input form. The input data is entered in "text format," for example.

[0150] Input: Data such as user interests, academic ability, special skills, personality, and educational aspirations

[0151] Output: Input data formatted on the terminal

[0152] Step 2:

[0153] Data transmission by the terminal

[0154] The device internally converts the collected user data into an appropriate format (for example, JSON format). After conversion, the device sends this data to a server via the Internet. As a specific example, User A's data is converted into a JSON-formatted string and sent as "{"interest": "biology", "academic_level": "high", "skills": "experiment", "personality": "curious", "education_wish": "medical school"}".

[0155] Input: formatted user data

[0156] Output: JSON format data sent to the server

[0157] Step 3:

[0158] Data reception by the server

[0159] The server receives the data sent from the device. Specifically, the server's data reception API is called and the received data is saved in the appropriate database.

[0160] Input: User data in JSON format

[0161] Output: User data stored in the database

[0162] Step 4:

[0163] Data formatting and provision by the server

[0164] The server then formats the received data into an analyzable format, extracting it from the database, splitting it into fields, and cleansing the data as needed.Then, it provides the formatted data to the generative AI model.

[0165] Input: User data in the database

[0166] Output: The formatted data that is fed into the generative AI model.

[0167] Step 5:

[0168] Advice generation using generative AI models

[0169] The generative AI model analyzes the provided user data and generates optimal career advice. Specifically, it analyzes information such as interests, academic ability, special skills, personality, and educational aspirations, and generates specific advice such as "Future occupation: doctor or researcher," "Expected annual income: 5 million yen or more from the first year," and "Points to note: be careful of long working hours and excessive stress."

[0170] Input: formatted user data

[0171] Output: Generated career advice

[0172] Step 6:

[0173] Server-customized advice

[0174] The server receives advice from the generative AI model and customizes it based on the characteristics of the school and feedback from the career guidance office. For example, it creates advice optimized for User A, taking into account the school's specific recommendation quotas and the local job market.

[0175] Input: Advice from a generative AI model

[0176] Output: Customized advice

[0177] Step 7:

[0178] Server-based advice sending

[0179] The server transmits the customized advice to the user's terminal. Specifically, the server converts the customized advice into an appropriate format and transmits it to the user's terminal.

[0180] Input: Customized Advice

[0181] Output: Advice sent to the user's terminal

[0182] Step 8:

[0183] Advice display on the device

[0184] The user's device displays the advice received from the server. Specifically, the advice is displayed in a list or card format, providing an interface that the user can understand visually.

[0185] Input: Customized advice sent by the server

[0186] Output: Advice displayed on the screen

[0187] Step 9:

[0188] User feedback input

[0189] The user inputs feedback on the advice provided, for example, by inputting a specific request such as "I would like information on scholarships related to this occupation."

[0190] Input: User feedback on advice

[0191] Output: Feedback typed into the terminal

[0192] Step 10:

[0193] Sending feedback via device

[0194] The terminal receives the user's feedback and transmits it back to the server.

[0195] Input: Feedback entered into the device

[0196] Output: Feedback sent to the server

[0197] Step 11:

[0198] Server receives and stores feedback

[0199] The server receives the feedback and stores it in the database. Specifically, the server receives the feedback data via the receiving API and stores it in the database.

[0200] Input: Feedback sent to the server

[0201] Output: Feedback stored in a database

[0202] Step 12:

[0203] Retraining generative AI models

[0204] The generative AI model will re-train based on new feedback data to improve the accuracy of advice from the next time onwards. For example, new feedback data will be incorporated and the AI ​​model algorithm will be updated.

[0205] Input: Feedback stored in the database

[0206] Output: Improved advice from an updated generative AI model

[0207] In this way, the system of the present invention enables high school students to receive optimal career advice and reduces the worries of career selection.

[0208] (Application example 1)

[0209] 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."

[0210] With conventional career guidance systems, it is difficult to efficiently provide optimal career advice based on individual user characteristics, and they are also unable to meet the needs of career counseling in a virtual environment.It is also difficult to provide accurate individual guidance to many students while reducing the burden on career guidance offices.

[0211] 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.

[0212] In this invention, the server includes means for inputting a user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment, means for transmitting the input data to the server, means for providing the data to a generating AI and generating advice such as optimal future careers, further education, expected annual income, and points to note for the user, and means for accessing the virtual career counseling counter via a smartphone, tablet, smart glasses, or head-mounted display. This makes it possible to provide optimal career advice based on individual user characteristics even in a virtual environment.

[0213] Definitions of important terms included in the claims

[0214] "Users" refer to individuals who use the system, especially high school students who are struggling to choose their career path.

[0215] "Interests" refers to the academic subjects, fields, or activities in which a user is particularly interested.

[0216] "Academic ability" refers to the user's academic performance and level of knowledge.

[0217] "Special skills" refer to skills or techniques that a user is particularly good at.

[0218] "Personality" refers to the user's characteristics and behavioral patterns.

[0219] "Hope and conditions regarding further education or employment" refers to the specific goals and requirements that the user has regarding further education or employment.

[0220] "Means" refers to the methods or tools used to achieve a particular goal.

[0221] "Server" refers to a computer or system that receives, processes, stores, and distributes data sent by users.

[0222] "Database" refers to a collection of information that receives information and stores it in an organized manner so that it can be easily searched and used.

[0223] "Generative AI" refers to an artificial intelligence model that generates optimal advice based on data provided by the user.

[0224] "Terminal" refers to a device, such as a smartphone or computer, that allows a user to access the system and input and receive data.

[0225] A "virtual career counseling counter" refers to a virtual window set up online or in a virtual space that accepts career-related consultations.

[0226] "Feedback" refers to the user's thoughts, opinions, or requests for improvement regarding the advice provided.

[0227] MODE FOR CARRYING OUT THE INVENTION

[0228] System Program

[0229] The present invention is a digital career guidance system for high school students to resolve their worries about career choices. This system is configured as follows.

[0230] Hardware and Software

[0231] 1. User's device

[0232] It uses a smartphone, tablet, smart glasses, or head-mounted display, and these devices have the ability to receive input data from the user and send it to a server.

[0233] 2. Server

[0234] The server is a computer that has the ability to store data received from users and save it in a database. The server also provides data to the generative AI and generates optimal advice.

[0235] 3. Generation AI

[0236] This is an artificial intelligence model that analyzes a user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment, and generates optimal career advice.

[0237] Data processing and calculation

[0238] 1. Data Receipt and Storage

[0239] The server receives the data sent from the user's device and stores it in a database, which allows the systematic management of the user's interests, academic ability, special skills, personality, and hopes and requirements for further education and employment.

[0240] 2. Data Analysis

[0241] The generative AI analyzes the user's characteristics based on data provided by the server. For example, if a user's interests are "biology," their academic ability is "high," their special skill is "experimentation," and their personality is "curious," it will recommend appropriate schools and careers.

[0242] 3. Generating Advice

[0243] The AI ​​generates career advice based on the results of analyzing the user's data. Specific examples are as follows:

[0244] Future career: Doctor or researcher

[0245] Expected annual income: Over 5 million yen from the first year

[0246] Caution: Be careful of long working hours and excessive stress

[0247] 4. Customize your advice

[0248] The server then customizes the advice received from the AI ​​generator to suit the characteristics of the school and sends it to the user's device, providing optimal career advice based on the individual user's characteristics.

[0249] 5. Incorporating feedback

[0250] The server receives user feedback and reflects it in the learning data of the AI ​​generator. For example, if a user provides feedback such as "I would like information about scholarships related to this occupation," the AI ​​generator's algorithm will take this into consideration to improve the accuracy of future advice.

[0251] Examples and prompts

[0252] Examples:

[0253] "User A has entered the following information: Interests: Biology, Academic ability: High, Special skills: Experiments, Personality: Curious, Aspirations: Medical school. Please generate the most appropriate career advice based on the above information."

[0254] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0255] Processing Steps

[0256] Step 1:

[0257] (Input) Using a smartphone, tablet, smart glasses, or head-mounted display, the user inputs their interests, academic ability, special skills, personality, and hopes and requirements for further education or employment.

[0258] The (operational) terminal allows these data to be entered through a user interface.

[0259] (Output) The entered data is stored on the terminal and is ready to be sent.

[0260] Step 2:

[0261] (Input) Data entered by the user.

[0262] (Operation) The terminal converts the input data into a predetermined format and sends it to the server.

[0263] (Output) Data is sent to the server.

[0264] Step 3:

[0265] (Input) The server receives the data sent from the terminal.

[0266] (Operation) The server stores and organizes the received data in a database.

[0267] (Output) The data is saved in the database.

[0268] Step 4:

[0269] (Input) Data received and stored by the server.

[0270] (Operation) The server formats this data and provides it to the generation AI.

[0271] (Output) The data is prepared in the format provided to the generation AI.

[0272] Step 5:

[0273] (Input) Generative AI is organized user data.

[0274] (Operation) The generation AI analyzes the user's interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment.

[0275] (Output) The optimal career advice is generated.

[0276] Step 6:

[0277] (Input) The generated career advice.

[0278] (Operation) The server receives career advice from the generating AI and customizes it based on the characteristics of each school and feedback from the career guidance office.

[0279] (Output) Customized advice is prepared.

[0280] Step 7:

[0281] (Input) Customized career advice.

[0282] (Operation) The server sends the customized advice to the user's terminal.

[0283] (Output) Advice is displayed on the terminal.

[0284] Step 8:

[0285] (Input) User feedback.

[0286] (Action) The user inputs feedback on the advice provided, for example, asking for information on scholarships.

[0287] (Output) Feedback is input to the terminal.

[0288] Step 9:

[0289] (Input) Feedback entered by the user.

[0290] (Operation) The terminal transmits the input feedback to the server.

[0291] (Output) Feedback is sent to the server.

[0292] Step 10:

[0293] (Input) The server receives the user's feedback.

[0294] (Operation) The server stores the feedback in a database and provides it to the generating AI.

[0295] (Output) Feedback is provided to the generating AI.

[0296] Step 11:

[0297] (Input) Generative AI is new feedback data.

[0298] The (behavior) generative AI re-learns based on feedback data to improve the accuracy of its advice.

[0299] (Output) The advice from next time onwards will be more optimized.

[0300] 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.

[0301] This invention relates to a digital career guidance system that provides optimal career advice that takes emotions into consideration for high school students struggling with career choices. This system uses a generative AI and emotion engine to provide optimal career advice based on the interests, academic ability, special skills, personality, emotions, and hopes and conditions for further education and employment entered by the user (high school student), and improves the accuracy of the system based on feedback.

[0302] Program processing

[0303] Data entry and submission

[0304] 1. User: The user uses a device (smartphone or PC) to input their interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment. As a specific example, "User B" inputs his / her interests "engineering," academic ability "average," special skill "programming," personality "logical," desired education "engineering," and current emotional state "motivated."

[0305] 2. Terminal: The terminal stores the user's input data locally, formats it into a transmission format, and sends it to the server.

[0306] Data reception and analysis

[0307] 3. Server: The server receives the data sent from the device, analyzes the data format, and stores the received data in a database.

[0308] 4. Server: The server converts the data stored in the database into an analytical format and provides it to the emotion engine and generative AI.

[0309] Emotion Recognition and Advice Generation

[0310] 5. Emotion Engine: The emotion engine analyzes the user's emotions from the received data and provides the results to the generative AI. For example, the emotion engine extracts the emotional state of "motivated" as the analysis result.

[0311] 6. Generative AI: The Generative AI begins analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and their hopes and requirements for further education and employment. The Generative AI generates optimal career advice and sends it back to the server. As a concrete example, it analyzes the data of User B and generates the following advice:

[0312] Future job: Software engineer

[0313] Expected annual income: Over 4 million yen from the first year

[0314] Important note: Programming skills need to be continually improved.

[0315] Customize and deliver advice

[0316] 7. Server: The server analyzes the advice data received from the generation AI and customizes it based on feedback from the career guidance office and the characteristics of the school.

[0317] 8. Server: Sends customized advice data to the device.

[0318] 9. Terminal: The terminal analyzes the advice data sent from the server and displays it in the user interface. For example, User B checks the career path as a software engineer, related universities, expected annual salary, and points to consider.

[0319] Feedback and Updates

[0320] 10. User: The user enters feedback on the advice provided. For example, "I would like to know about internship information related to this occupation."

[0321] 11. Terminal: The terminal sends the user feedback to the server.

[0322] 12. Server: The server receives the feedback, stores it in a database, and provides the feedback data to the generative AI to reflect in its training data.

[0323] 13. Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[0324] In this way, the system of the present invention utilizes an emotion engine to provide specific, personalized career advice that takes into account the user's emotions, and incorporates feedback to continuously improve the service.

[0325] The processing flow will be explained below.

[0326] Step 1:

[0327] User: A user (high school student) uses a device (smartphone or PC) to input their interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment. As a specific example, "User B" inputs his / her interests of "engineering," academic ability of "average," special skill of "programming," personality of "logical," desired education of "engineering," and recent emotional state of "motivated."

[0328] Step 2:

[0329] Terminal: The terminal stores the user's input data locally and formats it for transmission. Specifically, it converts the user's input into a data format such as JSON.

[0330] Step 3:

[0331] Terminal: Sends the formatted data to the server as an HTTP request (e.g., POST request). The data sent includes information about the user's interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education or employment.

[0332] Step 4:

[0333] Server: The server receives the data sent from the device, analyzes the data format, extracts the necessary information, and temporarily stores the received data in memory.

[0334] Step 5:

[0335] Server: The analyzed data is stored in a database, including the user's interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education and employment.

[0336] Step 6:

[0337] Server: Converts the data stored in the database into an analytical format and provides it to the emotion engine and generation AI. Specifically, emotion information is sent to the emotion engine, and other data is sent to the generation AI.

[0338] Step 7:

[0339] Emotion engine: The emotion engine analyzes the user's emotions from the received data and provides the results to the generation AI. For example, the emotional state of "motivated" is extracted as the analysis result.

[0340] Step 8:

[0341] Generative AI: The Generative AI begins analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and hopes and conditions regarding further education and employment. The Generative AI generates optimal career advice and sends the results back to the server. As a concrete example, it analyzes User B's data and generates the following advice:

[0342] Future job: Software engineer

[0343] Expected annual income: Over 4 million yen from the first year

[0344] Important note: Programming skills need to be continually improved.

[0345] Step 9:

[0346] Server: The server analyzes the advice data received from the AI ​​generator and customizes it as needed based on feedback from the career guidance office and the characteristics of the school.

[0347] Step 10:

[0348] Server: Prepares customized advice data as an HTTP response to send to the device.

[0349] Step 11:

[0350] Device: The device analyzes the advice data received from the server and displays it through the user interface. For example, User B checks the career path as a software engineer, related universities, expected annual salary, and points to consider.

[0351] Step 12:

[0352] User: The user enters feedback on the advice provided. For example, the user enters additional information such as "I would like to know about scholarships related to this occupation."

[0353] Step 13:

[0354] Device: The device sends the user feedback to the server again.

[0355] Step 14:

[0356] Server: The server receives the feedback, stores it in a database, and provides the feedback data to be reflected in the learning data of the generative AI.

[0357] Step 15:

[0358] Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[0359] In this way, the system of the present invention utilizes an emotion engine to provide specific, personalized career advice that takes into account the user's emotions, and incorporates feedback to continuously improve the service.

[0360] Example 2

[0361] 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."

[0362] While conventional career guidance systems could take into account a user's interests, academic ability, special skills, personality, and hopes and requirements for further education or employment, they were unable to provide career advice that reflected the user's emotional state. Furthermore, they lacked the ability to effectively incorporate user feedback to improve the accuracy of the system. This made it difficult for users to receive advice that they were completely satisfied with when choosing a career path.

[0363] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting a user's interests, academic ability, special skills, personality, emotions, and wishes and conditions regarding further education or employment, means for temporarily saving the input data in the terminal and shaping it in local storage, means for transmitting the shaped data to the server, means for the server to receive the data, analyze the format, and store it in a database, means for converting the data into an analysis format and providing it to an emotion engine and a generation AI, means for the emotion engine to analyze the user's emotions and provide the result to the generation AI, means for the generation AI to generate optimal career advice based on the user's interests, academic ability, special skills, personality, and emotion analysis results and return it to the server, means for the server to receive the generated advice and customize it to suit the characteristics of each school, means for transmitting the customized advice to the terminal and displaying it to the user, and means for transmitting user feedback on the advice back to the server and reflecting it in the learning data of the generation AI. This makes it possible to provide personalized career advice that takes into account the user's emotions and to continuously improve the accuracy of the system by reflecting user feedback.

[0364] A "user" is a person who utilizes the system to input information and provide feedback about their path.

[0365] A "terminal" is a device that a user uses to input and output information, and includes smartphones and personal computers.

[0366] The "server" is a central processing unit that receives and analyzes user input data and provides the data to the generation AI and emotion engine.

[0367] A "database" is a system for storing and managing user input data and analysis results.

[0368] An "emotion engine" is an algorithm or software that analyzes emotions from user input data and provides the results to generative AI.

[0369] "Generative AI" is an artificial intelligence model that generates optimal career advice based on a user's interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education and employment.

[0370] "Advice" refers to recommendations such as future careers, further education, expected annual income, and points to note that are generated by the AI ​​based on the user's input data.

[0371] "Customization" refers to adjusting and changing the advice provided by the generating AI based on the characteristics of each school and feedback from the career guidance office.

[0372] "Feedback" refers to opinions or requests that a user inputs in response to advice provided, and is used as learning data for generating subsequent advice.

[0373] "Local storage" refers to a function and area for temporarily storing data within a terminal.

[0374] The "analysis format" is a format used to format data so that it is easy for the emotion engine and generative AI to understand.

[0375] This invention relates to a digital career guidance system that provides optimal career advice that takes emotions into consideration to individuals struggling with career choices. In this system, a server processes data entered by a user on a terminal, and an emotion engine and generation AI are used to generate optimal career advice and provide it to the user.

[0376] Specific system configuration

[0377] The system includes the following major hardware and software components:

[0378] Terminal: A device such as a smartphone or PC that a user uses to input information. The terminal temporarily stores the input data in local storage and then transmits the data.

[0379] Server: A central processing unit that receives, analyzes, stores, and provides data to the emotion engine and generative AI. Python and Node.js are typical backend technologies used.

[0380] Database: A system that stores and manages received data. Database management systems such as MySQL and PostgreSQL are used.

[0381] Emotion engine: An algorithm or software for analyzing emotions from user input data. An example of this is an emotion analysis model.

[0382] Generative AI: An artificial intelligence model that generates optimal career advice based on a user's interests, academic ability, special skills, personality, emotions, and their hopes and requirements for further education and employment. Specifically, it uses language models such as GPT-3.

[0383] Data entry and submission

[0384] 1. User behavior: The user uses a device to enter information about their interests, academic ability, special skills, personality, feelings, and hopes and requirements for further education or employment using a dedicated app or web form. When entering information, the system is designed to allow users to easily select information using pull-down menus and check boxes.

[0385] Example: User B inputs his / her interests as "Engineering", academic ability as "Average", special skill as "Programming", personality as "Logical", desired education as "Engineering", and emotional state as "Motivated".

[0386] 2. Device operation: The device uses JavaScript to check form input values ​​in real time to prevent input errors, converts the input information into a data format such as JSON, and securely sends it to the server using HTTPS.

[0387] Data reception and analysis

[0388] 3. Server operation: The server receives the data sent from the device, analyzes the format of the received data, and stores it in a database. After analysis, the data is converted into an analytical format that can be understood by the emotion engine and generation AI.

[0389] Emotion Recognition and Advice Generation

[0390] 4. Operation of the emotion engine: The emotion engine analyzes the data provided by the server and recognizes the user's emotional state. For example, it extracts the emotional state of "motivated" and sends it to the generation AI.

[0391] 5. How the Generative AI works: The Generative AI begins its analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and hopes and conditions regarding further education and employment. For example, it creates the following advice based on User B's data:

[0392] Future job: Software engineer

[0393] Expected annual income: Over 4 million yen from the first year

[0394] Important note: Programming skills need to be continually improved.

[0395] The generated advice data is returned to the server.

[0396] Customizing and displaying advice

[0397] 6. Server operation: The server analyzes the advice data received from the generation AI and customizes it based on feedback from the career guidance office and school characteristics, for example, adding information about specific university programs or local employment opportunities.

[0398] 7. Terminal operation: The terminal receives the advice data sent from the server and displays it in a user interface, sometimes using graphics and charts to make it easier for the user to understand intuitively.

[0399] Example: User B reviews software engineer career paths, relevant universities, salary expectations, and caveats to consider.

[0400] Feedback and Updates

[0401] 8. User action: The user enters feedback on the advice provided. For example, the user might enter, "I would like to know about internship information related to this occupation."

[0402] 9. Terminal operation: The terminal formats the feedback data and sends it to the server.

[0403] 10. Server operation: The server receives the feedback, stores it in a database, and provides it to the AI ​​generator to use as learning data for future advice generation.

[0404] 11. How the Generative AI works: The Generative AI retrains based on new feedback data to improve the accuracy of advice generation from the next time onwards. For example, it may include internship information in the advice.

[0405] In this way, the system can provide personalized navigation advice while taking into account the user's emotions and incorporate feedback to continuously improve the system's accuracy.

[0406] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0407] Step 1:

[0408] User Action:

[0409] Using a device, users input their interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education or employment. Specifically, they select information using check boxes and pull-down menus on a dedicated smartphone or PC app or web form, and enter detailed information in text fields. For example, they input information such as "Engineering," "Middle School," "Programming," "Logical," "Faculty of Engineering," and "Motivated."

[0410] Input: Interests, academic ability, special skills, personality, emotions, and desired conditions for further education or employment.

[0411] Output: The information entered by the user is temporarily stored in the device's local storage.

[0412] Step 2:

[0413] Terminal behavior:

[0414] The device temporarily stores the data entered by the user in local storage and converts it to a data format such as JSON. Next, it checks the input data for errors in real time using JavaScript or other methods and displays an error message. If there are no errors, it securely sends the data to the server using HTTPS.

[0415] Input: Data entered by the user.

[0416] Output: The data is converted to JSON format and sent to the server.

[0417] Step 3:

[0418] Server behavior:

[0419] The server receives the data sent from the device and analyzes the data format. Specifically, it uses backend technologies such as Python and Node.js to parse the received data and convert it into a format that can be saved in a database. The analyzed data is then stored in the database.

[0420] Input: JSON format data sent from the terminal.

[0421] Output: The parsed data is stored in a database.

[0422] Step 4:

[0423] Server behavior:

[0424] The server re-analyzes the data stored in the database and formats it to be provided to the emotion engine and generation AI. Specifically, it selects the necessary information and converts it into an appropriate format so that the emotion engine can accurately analyze the user's emotions. The data is then provided to the emotion engine and generation AI.

[0425] Input: User data stored in the database.

[0426] Output: Formatted data to feed into the emotion engine and generative AI.

[0427] Step 5:

[0428] Emotion Engine in action:

[0429] The emotion engine analyzes the data provided by the server and recognizes the user's emotional state. Using an emotion analysis model, it identifies the emotional state, for example, "motivated," and sends the results in JSON format to the generation AI.

[0430] Input: Formatted data provided by the server.

[0431] Output: Parsed emotion data is sent to the generation AI in JSON format.

[0432] Step 6:

[0433] Generative AI behavior:

[0434] The generation AI generates optimal career advice based on the emotion data provided by the emotion engine and other user data provided by the server. For example, it suggests a career path such as "software engineer" to User B and generates advice including expected annual salary and points to note. This generated advice is then sent back to the server.

[0435] Input: Emotion data provided by the emotion engine, other user data provided by the server.

[0436] Output: The generated career advice data.

[0437] Step 7:

[0438] Server behavior:

[0439] The server analyzes and customizes the career advice data received from the generation AI. Specifically, it adjusts the advice content to match feedback from the career guidance office and the characteristics of the university. For example, it adds information about specific university recommendation programs and regional specializations. This customized advice data is then sent to the device.

[0440] Input: Career advice data received from the generation AI.

[0441] Output: Customized career advice data.

[0442] Step 8:

[0443] Terminal behavior:

[0444] The device receives the customized advice data sent from the server and displays it in a user interface. Specifically, it uses a responsive design, displaying advice content according to the screen size of a smartphone or PC. Graphics and charts may be used to make the advice easier for users to understand intuitively.

[0445] Input: Customized advice data sent from the server.

[0446] Output: Career advice displayed in the user interface.

[0447] Step 9:

[0448] User Action:

[0449] The user inputs feedback on the advice provided, for example, a request or opinion such as "I would like to know more about internships related to this occupation."

[0450] Input: Feedback on career advice provided.

[0451] Output: The feedback data is saved to the device.

[0452] Step 10:

[0453] Terminal behavior:

[0454] The terminal formats the user's feedback data and sends it back to the server, where the feedback data is an important factor for subsequent advice generation.

[0455] Input: User feedback data.

[0456] Output: The formatted feedback data is sent to the server.

[0457] Step 11:

[0458] Server behavior:

[0459] The server receives the feedback data, stores it in a database, and provides it to the generation AI to update the advice generation algorithm.

[0460] Input: Formatted feedback data.

[0461] Output: Feedback data stored in a database, providing feedback to the generative AI.

[0462] Step 12:

[0463] Generative AI behavior:

[0464] The AI ​​will retrain based on new feedback data to improve the accuracy of future career advice generation. For example, based on the feedback, it will be able to include internship information in the next advice.

[0465] Input: Feedback data provided by the server.

[0466] Output: The updated advice generation algorithm.

[0467] (Application example 2)

[0468] 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."

[0469] In factories and industrial sites, improving employees' skills and choosing career paths is important, but providing individual advice that takes into account the characteristics and feelings of each employee is difficult. Furthermore, there are currently no effective ways to provide advice on the skills and careers that employees need. To solve these issues, a system is needed that can accurately grasp employees' interests, current skills, desired career paths, and motivation for skill improvement, and provide optimal advice.

[0470] 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.

[0471] In this invention, the server includes: means for inputting a user's interests, academic ability, special skills, personality, emotions, and hopes and requirements regarding further education and employment; means for transmitting the input data to the server; means for the server to receive the data and store it in a database; means for providing the data to a generation AI and generating advice on the user's optimal future career, future education, expected annual income, points to note, etc.; means for the server to receive the generated advice and customize it to suit the characteristics of each organization; means for transmitting the customized advice to a terminal and displaying it to the user; means for transmitting user feedback on the advice back to the server and reflecting it in the learning data of the generation AI; means for the user to input and provide the feedback using a smart terminal; and means for installing the system on a robot to provide career advice to factory employees. This enables personalized advice that takes into account the characteristics and emotions of each employee.

[0472] A "user" is a person or employee who uses the system to input information about interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment.

[0473] "Interests" refers to information about areas or activities in which a user is particularly interested.

[0474] "Academic ability" refers to information indicating the user's current educational level and learning progress.

[0475] "Special skills" refers to information about skills or abilities that a user excels at compared to others.

[0476] "Personality" refers to internal characteristics that indicate a user's tendencies in behavior and reactions.

[0477] "Emotion" refers to information that indicates the user's psychological state or sensation at that time.

[0478] "Aspirations and conditions regarding further education and employment" refers to the desired educational destination or employment destination of the user and related conditions (such as work location and working hours).

[0479] "Server" means a computer system that receives, stores, and analyzes data submitted by users and provides customized generated advice.

[0480] "Database" refers to an information storage system for storing and managing user data and feedback data received by the server.

[0481] "Generative AI" refers to an artificial intelligence model that generates optimal advice based on user data.

[0482] "Customizing" means optimizing the generated advice based on the characteristics of each organization and user feedback.

[0483] A "user interface" is a screen or device through which a user inputs data, receives advice, and provides feedback.

[0484] "Feedback" refers to information that a user sends to the server, such as their thoughts on the advice provided and suggestions for improvement.

[0485] "Robots" are automated machines that install and provide information on career advice systems.

[0486] This invention relates to a career consulting system for factory employees that applies a digital career guidance system. This system uses generative AI and an emotion engine to provide optimal career advice based on the interests, skills, desired career path, and emotional state input by the user (employee).

[0487] First, a user inputs their interests, current skills, special abilities, personality, emotions, and career aspirations and requirements from a smart terminal or interface device. For example, they might enter data such as "I want to improve my welding skills" or "I have a high motivation to work hard." The terminal then stores this data locally, formats it into a transmission format, and sends it to the server.

[0488] The server receives the data sent from the device, analyzes the data format, and stores it in a database. The received data is converted into an analytical format and provided to the emotion engine and generation AI. The emotion engine analyzes the user's emotions from the received data and provides the results to the generation AI. As a specific example, the emotion engine extracts the emotional state of "high motivation to make an effort" as the analysis result.

[0489] The AI ​​begins its analysis based on the user's interests, skills, special abilities, personality, emotional analysis results, and career aspirations and requirements. The AI ​​generates optimal career advice and sends it back to the server. For example, it might suggest a curriculum or course to improve specific skills to become a senior welding engineer.

[0490] The server analyzes the advice data received from the AI ​​generator and customizes the advice based on each organization's characteristics and past feedback. Specifically, it adds appropriate training programs and learning resources. This customized advice is then sent back to the device and displayed in the user interface.

[0491] Users can input feedback on the advice provided, adding information such as "I would like to know about internships related to this occupation." This feedback is sent from the device to the server and stored in a database. The AI ​​then retrains itself based on the new feedback data, improving the accuracy of its advice generation from the next time onwards.

[0492] The hardware used includes smart devices and tablets used by each user, and server systems that perform analysis and calculation processing, while the software used includes databases (e.g., MySQL or PostgreSQL), emotion engines (e.g., Google Cloud Natural Language API), and generative AI (e.g., OpenAI GPT-4).

[0493] Examples of specific prompts include:

[0494] "Interests: Welding, Current Skills: Intermediate, Desired Career Path: Senior Welder, Emotions: Highly motivated to work hard."

[0495] In this way, the system of the present invention provides optimal career advice to factory employees that takes their emotions into consideration, helping users improve their skills. Furthermore, by incorporating user feedback, the system can continuously improve the accuracy and effectiveness of its services.

[0496] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0497] Step 1:

[0498] Users input their interests, skills, special abilities, personality, emotions, and career aspirations and requirements using a smart terminal or interface device. Examples of input data include "Interests: Welding," "Skills: Intermediate," "Desired career path: Senior welding engineer," and "Emotions: Highly motivated to work hard."

[0499] Step 2:

[0500] The terminal locally stores the information entered by the user. After saving, the data is formatted for transmission and sent to the server. Here, the input is the user's information and the output is the formatted data.

[0501] Step 3:

[0502] The server receives data sent from the terminal and analyzes the data format. The analyzed data is stored in a database. The input is the received data, and the output is the data stored in the database.

[0503] Step 4:

[0504] The server converts the data stored in the database into an analytical format and provides it to the emotion engine and generation AI. The input is the data in the database, and the output is the data in the analytical format.

[0505] Step 5:

[0506] The emotion engine analyzes the user's emotions from the data in the analysis format. For example, it extracts the emotional state of "high motivation to make an effort." The input is the data in the analysis format, and the output is the emotion analysis result.

[0507] Step 6:

[0508] The generation AI begins analysis based on the user's interests, skills, special abilities, personality, emotional analysis results, and career-related aspirations and requirements. The generation AI generates optimal career advice and sends it back to the server. For example, it might suggest "curriculum and courses for becoming a senior welding engineer." The input is user data and the results of emotional analysis, and the output is career advice.

[0509] Step 7:

[0510] The server analyzes the advice data received from the generative AI and customizes the advice based on each organization's characteristics and past feedback. For example, it adds specific training programs and learning resources. The input is career advice from the generative AI, and the output is customized advice.

[0511] Step 8:

[0512] The server sends the customized advice to the terminal and displays it to the user. The input is the customized advice, and the output is the advice displayed on the user terminal.

[0513] Step 9:

[0514] The user inputs feedback for the provided advice. For example, the user adds a comment such as, "I would like to know about internship information related to this occupation." The input is the user's feedback, and the output is the transmission of the feedback data by the terminal.

[0515] Step 10:

[0516] The terminal sends the user's feedback to the server and stores it in a database. The input is the feedback data, and the output is the storage in the database.

[0517] Step 11:

[0518] The server provides feedback data to the AI ​​generator, which then retrains based on the new data. This improves the accuracy of advice generation from the next time onwards. The input is the feedback data, and the output is an improved algorithm of the AI ​​generator.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] [Second embodiment]

[0523] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0524] 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.

[0525] 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).

[0526] 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.

[0527] 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.

[0528] 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).

[0529] 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.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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.

[0534] 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."

[0535] This invention relates to a digital career guidance system that helps high school students resolve their concerns about their career choices. This system uses a generation AI to provide optimal career advice based on the user's (high school student's) input of their interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment, and improves the accuracy of the system based on feedback.

[0536] Program processing

[0537] Data entry and submission

[0538] 1. User:

[0539] Users use a device (such as a smartphone or computer) to input their interests, academic ability, special skills, personality, and hopes and requirements regarding further education or employment.

[0540] As a specific example, "User A" inputs his / her interests "biology," academic ability "high," special skill "experiment," personality "intense curiosity," and desired educational goal "medical school."

[0541] 2. Terminal:

[0542] The terminal receives input data from the user, converts it into the required format, and sends it to the server.

[0543] Data reception and analysis

[0544] 3. Server:

[0545] The server receives the data sent from the terminal and stores it in a database.

[0546] The received data is formatted into an analyzable format and provided to the generation AI.

[0547] Advice Generation

[0548] 4. Generation AI:

[0549] The generative AI analyzes the user's interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment.

[0550] As a concrete example, we analyze the data of User A and generate the following advice:

[0551] Future career: Doctor or researcher

[0552] Expected annual income: Over 5 million yen from the first year

[0553] Caution: Be careful of long working hours and excessive stress

[0554] Customize and deliver advice

[0555] 5. Server:

[0556] Advice is received from the generating AI and customized taking into account the characteristics of the school and feedback from the career guidance office.

[0557] The completed advice is optimized for the user and sent to the terminal.

[0558] 6. Terminal:

[0559] The user's terminal receives the advice sent from the server and displays it through a user interface.

[0560] As a specific example, user A checks the career path as a doctor or researcher, related universities, expected annual salary, and points to consider.

[0561] Feedback and Updates

[0562] 7. Users:

[0563] The user provides feedback on the advice provided, for example, by inputting "I would like information on scholarships related to this occupation."

[0564] 8. Terminal:

[0565] The terminal transmits the user's feedback to the server.

[0566] 9. Server:

[0567] The server receives the feedback and stores it in a database.

[0568] Feedback data is provided to the generation AI, and the learning data of the generation AI is updated.

[0569] 10. Generation AI:

[0570] The generating AI will re-learn based on new feedback data, improving the accuracy of advice from the next time onwards.

[0571] In this way, the system of the present invention allows high school students to receive specific and realistic career advice that is optimized for their own characteristics, significantly reducing the worries of career selection. It also reduces the burden on career guidance offices and allows more students to receive highly accurate individual guidance.

[0572] The processing flow will be explained below.

[0573] Step 1:

[0574] User: The user (high school student) uses a web form or application on the device to enter information about their interests, academic ability, special skills, personality, and hopes and requirements for further education or employment.

[0575] Step 2:

[0576] Terminal: The terminal temporarily stores the user's input data locally and then formats it into a data format to send to the server.

[0577] Step 3:

[0578] Terminal: Sends the formatted data to the server as an HTTP request (e.g., POST request).

[0579] Step 4:

[0580] Server: The server receives the data sent from the device, analyzes the data format, and extracts the necessary information.

[0581] Step 5:

[0582] Server: Stores the received data in a database.

[0583] Step 6:

[0584] Server: Converts the data stored in the database into an analytical format and provides it as input data to the generation AI.

[0585] Step 7:

[0586] Generative AI: The generative AI begins analysis based on the provided data. It analyzes the user's interests, academic ability, special skills, and personality data to generate optimal career advice.

[0587] Step 8:

[0588] Generative AI: The generated advice is formatted and sent back to the server. The advice includes future careers, further education, expected annual income, and points to note.

[0589] Step 9:

[0590] Server: The server analyzes the advice data received from the generated AI and customizes it based on feedback from each school's career guidance office and the characteristics of the school.

[0591] Step 10:

[0592] Server: Prepares customized advice data as an HTTP response to send to the device.

[0593] Step 11:

[0594] Device: The device analyzes the advice data received from the server and displays it to the user. For example, the user can check career paths as a doctor or researcher, related universities, expected annual salary, and points to consider.

[0595] Step 12:

[0596] User: The user enters feedback on the advice provided, for example, entering additional information such as "I would also like to know about scholarship information related to this occupation."

[0597] Step 13:

[0598] Device: The device sends the user feedback to the server.

[0599] Step 14:

[0600] Server: The server receives the feedback, stores it in a database, and provides the feedback data to be reflected in the learning data of the generative AI.

[0601] Step 15:

[0602] Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[0603] In this way, the system provides users with specific, personalized career advice and incorporates feedback to continuously improve the service.

[0604] Example 1

[0605] 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."

[0606] There is a need for a method to provide specific, individualized career advice quickly and accurately to high school students struggling with career choices. Traditional career guidance relies on the experience and subjectivity of teachers and counselors, and can lack consistency for all students. Another problem is that it is difficult to customize advice to reflect the characteristics of a specific school or feedback from the career guidance office. Furthermore, there is a lack of a way to incorporate student feedback in real time and improve the accuracy of career advice.

[0607] 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.

[0608] In this invention, the server includes a means for receiving user data and storing it in a database, a means for converting the received data into an analyzable format and providing it to the generative AI model, and a means for receiving advice generated by the generative AI model and customizing it based on the characteristics of the school and feedback from the career guidance office. This allows high school students to receive specific and personalized career advice, significantly reducing the burden of career choice. Customization based on feedback from the career guidance office is also possible, reducing the burden on teachers and counselors. Furthermore, by reflecting student feedback in real time and updating the learning data of the generative AI model, the accuracy of advice can be improved.

[0609] "Users" refers to those who use the system to receive career advice, such as high school students.

[0610] "Interests" refer to the academic subjects or fields of activity in which a user is particularly interested.

[0611] "Academic ability" refers to an indicator that shows a user's academic performance and level of knowledge.

[0612] "Special skills" refer to skills or abilities that a user has that are particularly superior to others.

[0613] "Personality" refers to a user's personal characteristics and behavioral traits.

[0614] "Hope and conditions" refers to the hopes and constraints that the user has regarding future education or employment.

[0615] "Terminal" refers to a device such as a smartphone or computer that a user uses to input data and communicate with a server or generative AI model.

[0616] "Server" refers to the computer system that receives, analyzes, and provides data submitted by users to the generative AI model.

[0617] "Database" refers to a storage device for storing user input data, generated advice, feedback, etc.

[0618] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal career advice based on data such as a user's interests and academic ability.

[0619] "Advice" refers to the advisory information generated by the generative AI model regarding the user's best future career, further education, expected annual income, points to note, etc.

[0620] "Feedback" refers to opinions and requests made by users in response to advice provided.

[0621] "Customization" refers to adjusting the generated advice to take into account the characteristics of the school and feedback from the career guidance office.

[0622] "Display" refers to visually presenting the customized advice on the device screen.

[0623] This invention relates to a digital career guidance system that helps high school students resolve their concerns about their career choices. This system uses a generative AI model to provide optimal career advice based on user (high school student) inputs of their interests, academic ability, special skills, personality, and hopes and requirements for further education and employment, and improves the accuracy of the system based on feedback.

[0624] First, a user uses a device such as a smartphone or PC to input their interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment. For example, User A inputs his / her interests as "biology," academic ability as "high," special skills as "experiment," personality as "curious," and desired education as "medical school."

[0625] Next, the device receives input data from the user, converts it into the required format (e.g., JSON format), and sends it to the server, which receives the data and stores it in a database. The server then formats the data into an analyzable format and provides it to the generative AI model.

[0626] The generative AI model analyzes a user's interests, academic ability, special skills, personality, and hopes and conditions to generate optimal career advice. For example, it analyzes User A's data and generates the following advice: "Future occupation: doctor or researcher," "Expected annual income: 5 million yen or more from the first year," and "Caution: Be careful of long working hours and excessive stress."

[0627] The generated advice is returned to the server and customized based on the characteristics of the school and feedback from the career guidance office. For example, advice optimized for User A is created based on the school's designated recommendation quota and local characteristics.

[0628] The server sends the customized advice to the user's terminal, which displays the advice, allowing the user to review the advice and use it to help them choose their own path.

[0629] The user can also input feedback on the advice provided, for example, "I would like information on scholarships related to this occupation." The terminal then sends this feedback back to the server, which then stores it in a database.

[0630] Finally, the generative AI model retrains based on new feedback data to improve the accuracy of future advice. In this way, the system of the present invention enables high school students to receive specific and realistic career advice optimized for their individual characteristics, significantly reducing the stress of career choice. It also reduces the burden on career guidance offices, enabling more students to receive highly accurate individualized guidance.

[0631] (Example of a prompt)

[0632] As a concrete example, let's use the following prompt: "If User A enters interests: biology, academic ability: high, special skills: experimentation, personality: curious, and educational aspirations: medical school, provide the best career advice."

[0633] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0634] Step 1:

[0635] User data entry

[0636] Using a device (such as a smartphone or PC), the user inputs their interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment. Specifically, the user inputs information such as "Interests: Biology," "Academic ability: High," "Special skills: Experiments," "Personality: Very curious," and "Aspirations for further education: Medical school" into the device's input form. The input data is entered in "text format," for example.

[0637] Input: Data such as user interests, academic ability, special skills, personality, and educational aspirations

[0638] Output: Input data formatted on the terminal

[0639] Step 2:

[0640] Data transmission by the terminal

[0641] The device internally converts the collected user data into an appropriate format (for example, JSON format). After conversion, the device sends this data to a server via the Internet. As a specific example, User A's data is converted into a JSON-formatted string and sent as "{"interest": "biology", "academic_level": "high", "skills": "experiment", "personality": "curious", "education_wish": "medical school"}".

[0642] Input: formatted user data

[0643] Output: JSON format data sent to the server

[0644] Step 3:

[0645] Data reception by the server

[0646] The server receives the data sent from the device. Specifically, the server's data reception API is called and the received data is saved in the appropriate database.

[0647] Input: User data in JSON format

[0648] Output: User data stored in the database

[0649] Step 4:

[0650] Data formatting and provision by the server

[0651] The server then formats the received data into an analyzable format, extracting it from the database, splitting it into fields, and cleansing the data as needed.Then, it provides the formatted data to the generative AI model.

[0652] Input: User data in the database

[0653] Output: The formatted data that is fed into the generative AI model.

[0654] Step 5:

[0655] Advice generation using generative AI models

[0656] The generative AI model analyzes the provided user data and generates optimal career advice. Specifically, it analyzes information such as interests, academic ability, special skills, personality, and educational aspirations, and generates specific advice such as "Future occupation: doctor or researcher," "Expected annual income: 5 million yen or more from the first year," and "Points to note: be careful of long working hours and excessive stress."

[0657] Input: formatted user data

[0658] Output: Generated career advice

[0659] Step 6:

[0660] Server-customized advice

[0661] The server receives advice from the generative AI model and customizes it based on the characteristics of the school and feedback from the career guidance office. For example, it creates advice optimized for User A, taking into account the school's specific recommendation quotas and the local job market.

[0662] Input: Advice from a generative AI model

[0663] Output: Customized advice

[0664] Step 7:

[0665] Server-based advice sending

[0666] The server transmits the customized advice to the user's terminal. Specifically, the server converts the customized advice into an appropriate format and transmits it to the user's terminal.

[0667] Input: Customized Advice

[0668] Output: Advice sent to the user's terminal

[0669] Step 8:

[0670] Advice display on the device

[0671] The user's device displays the advice received from the server. Specifically, the advice is displayed in a list or card format, providing an interface that the user can understand visually.

[0672] Input: Customized advice sent by the server

[0673] Output: Advice displayed on the screen

[0674] Step 9:

[0675] User feedback input

[0676] The user inputs feedback on the advice provided, for example, by inputting a specific request such as "I would like information on scholarships related to this occupation."

[0677] Input: User feedback on advice

[0678] Output: Feedback typed into the terminal

[0679] Step 10:

[0680] Sending feedback via device

[0681] The terminal receives the user's feedback and transmits it back to the server.

[0682] Input: Feedback entered into the device

[0683] Output: Feedback sent to the server

[0684] Step 11:

[0685] Server receives and stores feedback

[0686] The server receives the feedback and stores it in the database. Specifically, the server receives the feedback data via the receiving API and stores it in the database.

[0687] Input: Feedback sent to the server

[0688] Output: Feedback stored in a database

[0689] Step 12:

[0690] Retraining generative AI models

[0691] The generative AI model will re-train based on new feedback data to improve the accuracy of advice from the next time onwards. For example, new feedback data will be incorporated and the AI ​​model algorithm will be updated.

[0692] Input: Feedback stored in the database

[0693] Output: Improved advice from an updated generative AI model

[0694] In this way, the system of the present invention enables high school students to receive optimal career advice and reduces the worries of career selection.

[0695] (Application example 1)

[0696] 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."

[0697] With conventional career guidance systems, it is difficult to efficiently provide optimal career advice based on individual user characteristics, and they are also unable to meet the needs of career counseling in a virtual environment.It is also difficult to provide accurate individual guidance to many students while reducing the burden on career guidance offices.

[0698] 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.

[0699] In this invention, the server includes means for inputting a user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment, means for transmitting the input data to the server, means for providing the data to a generating AI and generating advice such as optimal future careers, further education, expected annual income, and points to note for the user, and means for accessing the virtual career counseling counter via a smartphone, tablet, smart glasses, or head-mounted display. This makes it possible to provide optimal career advice based on individual user characteristics even in a virtual environment.

[0700] Definitions of important terms included in the claims

[0701] "Users" refer to individuals who use the system, especially high school students who are struggling to choose their career path.

[0702] "Interests" refers to the academic subjects, fields, or activities in which a user is particularly interested.

[0703] "Academic ability" refers to the user's academic performance and level of knowledge.

[0704] "Special skills" refer to skills or techniques that a user is particularly good at.

[0705] "Personality" refers to the user's characteristics and behavioral patterns.

[0706] "Hope and conditions regarding further education or employment" refers to the specific goals and requirements that the user has regarding further education or employment.

[0707] "Means" refers to the methods or tools used to achieve a particular goal.

[0708] "Server" refers to a computer or system that receives, processes, stores, and distributes data sent by users.

[0709] "Database" refers to a collection of information that receives information and stores it in an organized manner so that it can be easily searched and used.

[0710] "Generative AI" refers to an artificial intelligence model that generates optimal advice based on data provided by the user.

[0711] "Terminal" refers to a device, such as a smartphone or computer, that allows a user to access the system and input and receive data.

[0712] A "virtual career counseling counter" refers to a virtual window set up online or in a virtual space that accepts career-related consultations.

[0713] "Feedback" refers to the user's thoughts, opinions, or requests for improvement regarding the advice provided.

[0714] MODE FOR CARRYING OUT THE INVENTION

[0715] System Program

[0716] The present invention is a digital career guidance system for high school students to resolve their worries about career choices. This system is configured as follows.

[0717] Hardware and Software

[0718] 1. User's device

[0719] It uses a smartphone, tablet, smart glasses, or head-mounted display, and these devices have the ability to receive input data from the user and send it to a server.

[0720] 2. Server

[0721] The server is a computer that has the ability to store data received from users and save it in a database. The server also provides data to the generative AI and generates optimal advice.

[0722] 3. Generation AI

[0723] This is an artificial intelligence model that analyzes a user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment, and generates optimal career advice.

[0724] Data processing and calculation

[0725] 1. Data Receipt and Storage

[0726] The server receives the data sent from the user's device and stores it in a database, which allows the systematic management of the user's interests, academic ability, special skills, personality, and hopes and requirements for further education and employment.

[0727] 2. Data Analysis

[0728] The generative AI analyzes the user's characteristics based on data provided by the server. For example, if a user's interests are "biology," their academic ability is "high," their special skill is "experimentation," and their personality is "curious," it will recommend appropriate schools and careers.

[0729] 3. Generating Advice

[0730] The AI ​​generates career advice based on the results of analyzing the user's data. Specific examples are as follows:

[0731] Future career: Doctor or researcher

[0732] Expected annual income: Over 5 million yen from the first year

[0733] Caution: Be careful of long working hours and excessive stress

[0734] 4. Customize your advice

[0735] The server then customizes the advice received from the AI ​​generator to suit the characteristics of the school and sends it to the user's device, providing optimal career advice based on the individual user's characteristics.

[0736] 5. Incorporating feedback

[0737] The server receives user feedback and reflects it in the learning data of the AI ​​generator. For example, if a user provides feedback such as "I would like information about scholarships related to this occupation," the AI ​​generator's algorithm will take this into consideration to improve the accuracy of future advice.

[0738] Examples and prompts

[0739] Examples:

[0740] "User A has entered the following information: Interests: Biology, Academic ability: High, Special skills: Experiments, Personality: Curious, Aspirations: Medical school. Please generate the most appropriate career advice based on the above information."

[0741] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0742] Processing Steps

[0743] Step 1:

[0744] (Input) Using a smartphone, tablet, smart glasses, or head-mounted display, the user inputs their interests, academic ability, special skills, personality, and hopes and requirements for further education or employment.

[0745] The (operational) terminal allows these data to be entered through a user interface.

[0746] (Output) The entered data is stored on the terminal and is ready to be sent.

[0747] Step 2:

[0748] (Input) Data entered by the user.

[0749] (Operation) The terminal converts the input data into a predetermined format and sends it to the server.

[0750] (Output) Data is sent to the server.

[0751] Step 3:

[0752] (Input) The server receives the data sent from the terminal.

[0753] (Operation) The server stores and organizes the received data in a database.

[0754] (Output) The data is saved in the database.

[0755] Step 4:

[0756] (Input) Data received and stored by the server.

[0757] (Operation) The server formats this data and provides it to the generation AI.

[0758] (Output) The data is prepared in the format provided to the generation AI.

[0759] Step 5:

[0760] (Input) Generative AI is organized user data.

[0761] (Operation) The generation AI analyzes the user's interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment.

[0762] (Output) The optimal career advice is generated.

[0763] Step 6:

[0764] (Input) The generated career advice.

[0765] (Operation) The server receives career advice from the generating AI and customizes it based on the characteristics of each school and feedback from the career guidance office.

[0766] (Output) Customized advice is prepared.

[0767] Step 7:

[0768] (Input) Customized career advice.

[0769] (Operation) The server sends the customized advice to the user's terminal.

[0770] (Output) Advice is displayed on the terminal.

[0771] Step 8:

[0772] (Input) User feedback.

[0773] (Action) The user inputs feedback on the advice provided, for example, asking for information on scholarships.

[0774] (Output) Feedback is input to the terminal.

[0775] Step 9:

[0776] (Input) Feedback entered by the user.

[0777] (Operation) The terminal transmits the input feedback to the server.

[0778] (Output) Feedback is sent to the server.

[0779] Step 10:

[0780] (Input) The server receives the user's feedback.

[0781] (Operation) The server stores the feedback in a database and provides it to the generating AI.

[0782] (Output) Feedback is provided to the generating AI.

[0783] Step 11:

[0784] (Input) Generative AI is new feedback data.

[0785] The (behavior) generative AI re-learns based on feedback data to improve the accuracy of its advice.

[0786] (Output) The advice from next time onwards will be more optimized.

[0787] 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.

[0788] This invention relates to a digital career guidance system that provides optimal career advice that takes emotions into consideration for high school students struggling with career choices. This system uses a generative AI and emotion engine to provide optimal career advice based on the interests, academic ability, special skills, personality, emotions, and hopes and conditions for further education and employment entered by the user (high school student), and improves the accuracy of the system based on feedback.

[0789] Program processing

[0790] Data entry and submission

[0791] 1. User: The user uses a device (smartphone or PC) to input their interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment. As a specific example, "User B" inputs his / her interests "engineering," academic ability "average," special skill "programming," personality "logical," desired education "engineering," and current emotional state "motivated."

[0792] 2. Terminal: The terminal stores the user's input data locally, formats it into a transmission format, and sends it to the server.

[0793] Data reception and analysis

[0794] 3. Server: The server receives the data sent from the device, analyzes the data format, and stores the received data in a database.

[0795] 4. Server: The server converts the data stored in the database into an analytical format and provides it to the emotion engine and generative AI.

[0796] Emotion Recognition and Advice Generation

[0797] 5. Emotion Engine: The emotion engine analyzes the user's emotions from the received data and provides the results to the generative AI. For example, the emotion engine extracts the emotional state of "motivated" as the analysis result.

[0798] 6. Generative AI: The Generative AI begins analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and their hopes and requirements for further education and employment. The Generative AI generates optimal career advice and sends it back to the server. As a concrete example, it analyzes the data of User B and generates the following advice:

[0799] Future job: Software engineer

[0800] Expected annual income: Over 4 million yen from the first year

[0801] Important note: Programming skills need to be continually improved.

[0802] Customize and deliver advice

[0803] 7. Server: The server analyzes the advice data received from the generation AI and customizes it based on feedback from the career guidance office and the characteristics of the school.

[0804] 8. Server: Sends customized advice data to the device.

[0805] 9. Terminal: The terminal analyzes the advice data sent from the server and displays it in the user interface. For example, User B checks the career path as a software engineer, related universities, expected annual salary, and points to consider.

[0806] Feedback and Updates

[0807] 10. User: The user enters feedback on the advice provided. For example, "I would like to know about internship information related to this occupation."

[0808] 11. Terminal: The terminal sends the user feedback to the server.

[0809] 12. Server: The server receives the feedback, stores it in a database, and provides the feedback data to the generative AI to reflect in its training data.

[0810] 13. Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[0811] In this way, the system of the present invention utilizes an emotion engine to provide specific, personalized career advice that takes into account the user's emotions, and incorporates feedback to continuously improve the service.

[0812] The processing flow will be explained below.

[0813] Step 1:

[0814] User: A user (high school student) uses a device (smartphone or PC) to input their interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment. As a specific example, "User B" inputs his / her interests of "engineering," academic ability of "average," special skill of "programming," personality of "logical," desired education of "engineering," and recent emotional state of "motivated."

[0815] Step 2:

[0816] Terminal: The terminal stores the user's input data locally and formats it for transmission. Specifically, it converts the user's input into a data format such as JSON.

[0817] Step 3:

[0818] Terminal: Sends the formatted data to the server as an HTTP request (e.g., POST request). The data sent includes information about the user's interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education or employment.

[0819] Step 4:

[0820] Server: The server receives the data sent from the device, analyzes the data format, extracts the necessary information, and temporarily stores the received data in memory.

[0821] Step 5:

[0822] Server: The analyzed data is stored in a database, including the user's interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education and employment.

[0823] Step 6:

[0824] Server: Converts the data stored in the database into an analytical format and provides it to the emotion engine and generation AI. Specifically, emotion information is sent to the emotion engine, and other data is sent to the generation AI.

[0825] Step 7:

[0826] Emotion engine: The emotion engine analyzes the user's emotions from the received data and provides the results to the generation AI. For example, the emotional state of "motivated" is extracted as the analysis result.

[0827] Step 8:

[0828] Generative AI: The Generative AI begins analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and hopes and conditions regarding further education and employment. The Generative AI generates optimal career advice and sends the results back to the server. As a concrete example, it analyzes User B's data and generates the following advice:

[0829] Future job: Software engineer

[0830] Expected annual income: Over 4 million yen from the first year

[0831] Important note: Programming skills need to be continually improved.

[0832] Step 9:

[0833] Server: The server analyzes the advice data received from the AI ​​generator and customizes it as needed based on feedback from the career guidance office and the characteristics of the school.

[0834] Step 10:

[0835] Server: Prepares customized advice data as an HTTP response to send to the device.

[0836] Step 11:

[0837] Device: The device analyzes the advice data received from the server and displays it through the user interface. For example, User B checks the career path as a software engineer, related universities, expected annual salary, and points to consider.

[0838] Step 12:

[0839] User: The user enters feedback on the advice provided. For example, the user enters additional information such as "I would like to know about scholarships related to this occupation."

[0840] Step 13:

[0841] Device: The device sends the user feedback to the server again.

[0842] Step 14:

[0843] Server: The server receives the feedback, stores it in a database, and provides the feedback data to be reflected in the learning data of the generative AI.

[0844] Step 15:

[0845] Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[0846] In this way, the system of the present invention utilizes an emotion engine to provide specific, personalized career advice that takes into account the user's emotions, and incorporates feedback to continuously improve the service.

[0847] Example 2

[0848] 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."

[0849] While conventional career guidance systems could take into account a user's interests, academic ability, special skills, personality, and hopes and requirements for further education or employment, they were unable to provide career advice that reflected the user's emotional state. Furthermore, they lacked the ability to effectively incorporate user feedback to improve the accuracy of the system. This made it difficult for users to receive advice that they were completely satisfied with when choosing a career path.

[0850] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting a user's interests, academic ability, special skills, personality, emotions, and wishes and conditions regarding further education or employment, means for temporarily saving the input data in the terminal and shaping it in local storage, means for transmitting the shaped data to the server, means for the server to receive the data, analyze the format, and store it in a database, means for converting the data into an analysis format and providing it to an emotion engine and a generation AI, means for the emotion engine to analyze the user's emotions and provide the result to the generation AI, means for the generation AI to generate optimal career advice based on the user's interests, academic ability, special skills, personality, and emotion analysis results and return it to the server, means for the server to receive the generated advice and customize it to suit the characteristics of each school, means for transmitting the customized advice to the terminal and displaying it to the user, and means for transmitting user feedback on the advice back to the server and reflecting it in the learning data of the generation AI. This makes it possible to provide personalized career advice that takes into account the user's emotions and to continuously improve the accuracy of the system by reflecting user feedback.

[0851] A "user" is a person who utilizes the system to input information and provide feedback about their path.

[0852] A "terminal" is a device that a user uses to input and output information, and includes smartphones and personal computers.

[0853] The "server" is a central processing unit that receives and analyzes user input data and provides the data to the generation AI and emotion engine.

[0854] A "database" is a system for storing and managing user input data and analysis results.

[0855] An "emotion engine" is an algorithm or software that analyzes emotions from user input data and provides the results to generative AI.

[0856] "Generative AI" is an artificial intelligence model that generates optimal career advice based on a user's interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education and employment.

[0857] "Advice" refers to recommendations such as future careers, further education, expected annual income, and points to note that are generated by the AI ​​based on the user's input data.

[0858] "Customization" refers to adjusting and changing the advice provided by the generating AI based on the characteristics of each school and feedback from the career guidance office.

[0859] "Feedback" refers to opinions or requests that a user inputs in response to advice provided, and is used as learning data for generating subsequent advice.

[0860] "Local storage" refers to a function and area for temporarily storing data within a terminal.

[0861] The "analysis format" is a format used to format data so that it is easy for the emotion engine and generative AI to understand.

[0862] This invention relates to a digital career guidance system that provides optimal career advice that takes emotions into consideration to individuals struggling with career choices. In this system, a server processes data entered by a user on a terminal, and an emotion engine and generation AI are used to generate optimal career advice and provide it to the user.

[0863] Specific system configuration

[0864] The system includes the following major hardware and software components:

[0865] Terminal: A device such as a smartphone or PC that a user uses to input information. The terminal temporarily stores the input data in local storage and then transmits the data.

[0866] Server: A central processing unit that receives, analyzes, stores, and provides data to the emotion engine and generative AI. Python and Node.js are typical backend technologies used.

[0867] Database: A system that stores and manages received data. Database management systems such as MySQL and PostgreSQL are used.

[0868] Emotion engine: An algorithm or software for analyzing emotions from user input data. An example of this is an emotion analysis model.

[0869] Generative AI: An artificial intelligence model that generates optimal career advice based on a user's interests, academic ability, special skills, personality, emotions, and their hopes and requirements for further education and employment. Specifically, it uses language models such as GPT-3.

[0870] Data entry and submission

[0871] 1. User behavior: The user uses a device to enter information about their interests, academic ability, special skills, personality, feelings, and hopes and requirements for further education or employment using a dedicated app or web form. When entering information, the system is designed to allow users to easily select information using pull-down menus and check boxes.

[0872] Example: User B inputs his / her interests as "Engineering", academic ability as "Average", special skill as "Programming", personality as "Logical", desired education as "Engineering", and emotional state as "Motivated".

[0873] 2. Device operation: The device uses JavaScript to check form input values ​​in real time to prevent input errors, converts the input information into a data format such as JSON, and securely sends it to the server using HTTPS.

[0874] Data reception and analysis

[0875] 3. Server operation: The server receives the data sent from the device, analyzes the format of the received data, and stores it in a database. After analysis, the data is converted into an analytical format that can be understood by the emotion engine and generation AI.

[0876] Emotion Recognition and Advice Generation

[0877] 4. Operation of the emotion engine: The emotion engine analyzes the data provided by the server and recognizes the user's emotional state. For example, it extracts the emotional state of "motivated" and sends it to the generation AI.

[0878] 5. How the Generative AI works: The Generative AI begins its analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and hopes and conditions regarding further education and employment. For example, it creates the following advice based on User B's data:

[0879] Future job: Software engineer

[0880] Expected annual income: Over 4 million yen from the first year

[0881] Important note: Programming skills need to be continually improved.

[0882] The generated advice data is returned to the server.

[0883] Customizing and displaying advice

[0884] 6. Server operation: The server analyzes the advice data received from the generation AI and customizes it based on feedback from the career guidance office and school characteristics, for example, adding information about specific university programs or local employment opportunities.

[0885] 7. Terminal operation: The terminal receives the advice data sent from the server and displays it in a user interface, sometimes using graphics and charts to make it easier for the user to understand intuitively.

[0886] Example: User B reviews software engineer career paths, relevant universities, salary expectations, and caveats to consider.

[0887] Feedback and Updates

[0888] 8. User action: The user enters feedback on the advice provided. For example, the user might enter, "I would like to know about internship information related to this occupation."

[0889] 9. Terminal operation: The terminal formats the feedback data and sends it to the server.

[0890] 10. Server operation: The server receives the feedback, stores it in a database, and provides it to the AI ​​generator to use as learning data for future advice generation.

[0891] 11. How the Generative AI works: The Generative AI retrains based on new feedback data to improve the accuracy of advice generation from the next time onwards. For example, it may include internship information in the advice.

[0892] In this way, the system can provide personalized navigation advice while taking into account the user's emotions and incorporate feedback to continuously improve the system's accuracy.

[0893] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0894] Step 1:

[0895] User Action:

[0896] Using a device, users input their interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education or employment. Specifically, they select information using check boxes and pull-down menus on a dedicated smartphone or PC app or web form, and enter detailed information in text fields. For example, they input information such as "Engineering," "Middle School," "Programming," "Logical," "Faculty of Engineering," and "Motivated."

[0897] Input: Interests, academic ability, special skills, personality, emotions, and desired conditions for further education or employment.

[0898] Output: The information entered by the user is temporarily stored in the device's local storage.

[0899] Step 2:

[0900] Terminal behavior:

[0901] The device temporarily stores the data entered by the user in local storage and converts it to a data format such as JSON. Next, it checks the input data for errors in real time using JavaScript or other methods and displays an error message. If there are no errors, it securely sends the data to the server using HTTPS.

[0902] Input: Data entered by the user.

[0903] Output: The data is converted to JSON format and sent to the server.

[0904] Step 3:

[0905] Server behavior:

[0906] The server receives the data sent from the device and analyzes the data format. Specifically, it uses backend technologies such as Python and Node.js to parse the received data and convert it into a format that can be saved in a database. The analyzed data is then stored in the database.

[0907] Input: JSON format data sent from the terminal.

[0908] Output: The parsed data is stored in a database.

[0909] Step 4:

[0910] Server behavior:

[0911] The server re-analyzes the data stored in the database and formats it to be provided to the emotion engine and generation AI. Specifically, it selects the necessary information and converts it into an appropriate format so that the emotion engine can accurately analyze the user's emotions. The data is then provided to the emotion engine and generation AI.

[0912] Input: User data stored in the database.

[0913] Output: Formatted data to feed into the emotion engine and generative AI.

[0914] Step 5:

[0915] Emotion Engine in action:

[0916] The emotion engine analyzes the data provided by the server and recognizes the user's emotional state. Using an emotion analysis model, it identifies the emotional state, for example, "motivated," and sends the results in JSON format to the generation AI.

[0917] Input: Formatted data provided by the server.

[0918] Output: Parsed emotion data is sent to the generation AI in JSON format.

[0919] Step 6:

[0920] Generative AI behavior:

[0921] The generation AI generates optimal career advice based on the emotion data provided by the emotion engine and other user data provided by the server. For example, it suggests a career path such as "software engineer" to User B and generates advice including expected annual salary and points to note. This generated advice is then sent back to the server.

[0922] Input: Emotion data provided by the emotion engine, other user data provided by the server.

[0923] Output: The generated career advice data.

[0924] Step 7:

[0925] Server behavior:

[0926] The server analyzes and customizes the career advice data received from the generation AI. Specifically, it adjusts the advice content to match feedback from the career guidance office and the characteristics of the university. For example, it adds information about specific university recommendation programs and regional specializations. This customized advice data is then sent to the device.

[0927] Input: Career advice data received from the generation AI.

[0928] Output: Customized career advice data.

[0929] Step 8:

[0930] Terminal behavior:

[0931] The device receives the customized advice data sent from the server and displays it in a user interface. Specifically, it uses a responsive design, displaying advice content according to the screen size of a smartphone or PC. Graphics and charts may be used to make the advice easier for users to understand intuitively.

[0932] Input: Customized advice data sent from the server.

[0933] Output: Career advice displayed in the user interface.

[0934] Step 9:

[0935] User Action:

[0936] The user inputs feedback on the advice provided, for example, a request or opinion such as "I would like to know more about internships related to this occupation."

[0937] Input: Feedback on career advice provided.

[0938] Output: The feedback data is saved to the device.

[0939] Step 10:

[0940] Terminal behavior:

[0941] The terminal formats the user's feedback data and sends it back to the server, where the feedback data is an important factor for subsequent advice generation.

[0942] Input: User feedback data.

[0943] Output: The formatted feedback data is sent to the server.

[0944] Step 11:

[0945] Server behavior:

[0946] The server receives the feedback data, stores it in a database, and provides it to the generation AI to update the advice generation algorithm.

[0947] Input: Formatted feedback data.

[0948] Output: Feedback data stored in a database, providing feedback to the generative AI.

[0949] Step 12:

[0950] Generative AI behavior:

[0951] The AI ​​will retrain based on new feedback data to improve the accuracy of future career advice generation. For example, based on the feedback, it will be able to include internship information in the next advice.

[0952] Input: Feedback data provided by the server.

[0953] Output: The updated advice generation algorithm.

[0954] (Application example 2)

[0955] 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."

[0956] In factories and industrial sites, improving employees' skills and choosing career paths is important, but providing individual advice that takes into account the characteristics and feelings of each employee is difficult. Furthermore, there are currently no effective ways to provide advice on the skills and careers that employees need. To solve these issues, a system is needed that can accurately grasp employees' interests, current skills, desired career paths, and motivation for skill improvement, and provide optimal advice.

[0957] 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.

[0958] In this invention, the server includes: means for inputting a user's interests, academic ability, special skills, personality, emotions, and hopes and requirements regarding further education and employment; means for transmitting the input data to the server; means for the server to receive the data and store it in a database; means for providing the data to a generation AI and generating advice on the user's optimal future career, future education, expected annual income, points to note, etc.; means for the server to receive the generated advice and customize it to suit the characteristics of each organization; means for transmitting the customized advice to a terminal and displaying it to the user; means for transmitting user feedback on the advice back to the server and reflecting it in the learning data of the generation AI; means for the user to input and provide the feedback using a smart terminal; and means for installing the system on a robot to provide career advice to factory employees. This enables personalized advice that takes into account the characteristics and emotions of each employee.

[0959] A "user" is a person or employee who uses the system to input information about interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment.

[0960] "Interests" refers to information about areas or activities in which a user is particularly interested.

[0961] "Academic ability" refers to information indicating the user's current educational level and learning progress.

[0962] "Special skills" refers to information about skills or abilities that a user excels at compared to others.

[0963] "Personality" refers to internal characteristics that indicate a user's tendencies in behavior and reactions.

[0964] "Emotion" refers to information that indicates the user's psychological state or sensation at that time.

[0965] "Aspirations and conditions regarding further education and employment" refers to the desired educational destination or employment destination of the user and related conditions (such as work location and working hours).

[0966] "Server" means a computer system that receives, stores, and analyzes data submitted by users and provides customized generated advice.

[0967] "Database" refers to an information storage system for storing and managing user data and feedback data received by the server.

[0968] "Generative AI" refers to an artificial intelligence model that generates optimal advice based on user data.

[0969] "Customizing" means optimizing the generated advice based on the characteristics of each organization and user feedback.

[0970] A "user interface" is a screen or device through which a user inputs data, receives advice, and provides feedback.

[0971] "Feedback" refers to information that a user sends to the server, such as their thoughts on the advice provided and suggestions for improvement.

[0972] "Robots" are automated machines that install and provide information on career advice systems.

[0973] This invention relates to a career consulting system for factory employees that applies a digital career guidance system. This system uses generative AI and an emotion engine to provide optimal career advice based on the interests, skills, desired career path, and emotional state input by the user (employee).

[0974] First, a user inputs their interests, current skills, special abilities, personality, emotions, and career aspirations and requirements from a smart terminal or interface device. For example, they might enter data such as "I want to improve my welding skills" or "I have a high motivation to work hard." The terminal then stores this data locally, formats it into a transmission format, and sends it to the server.

[0975] The server receives the data sent from the device, analyzes the data format, and stores it in a database. The received data is converted into an analytical format and provided to the emotion engine and generation AI. The emotion engine analyzes the user's emotions from the received data and provides the results to the generation AI. As a specific example, the emotion engine extracts the emotional state of "high motivation to make an effort" as the analysis result.

[0976] The AI ​​begins its analysis based on the user's interests, skills, special abilities, personality, emotional analysis results, and career aspirations and requirements. The AI ​​generates optimal career advice and sends it back to the server. For example, it might suggest a curriculum or course to improve specific skills to become a senior welding engineer.

[0977] The server analyzes the advice data received from the AI ​​generator and customizes the advice based on each organization's characteristics and past feedback. Specifically, it adds appropriate training programs and learning resources. This customized advice is then sent back to the device and displayed in the user interface.

[0978] Users can input feedback on the advice provided, adding information such as "I would like to know about internships related to this occupation." This feedback is sent from the device to the server and stored in a database. The AI ​​then retrains itself based on the new feedback data, improving the accuracy of its advice generation from the next time onwards.

[0979] The hardware used includes smart devices and tablets used by each user, and server systems that perform analysis and calculation processing, while the software used includes databases (e.g., MySQL or PostgreSQL), emotion engines (e.g., Google Cloud Natural Language API), and generative AI (e.g., OpenAI GPT-4).

[0980] Examples of specific prompts include:

[0981] "Interests: Welding, Current Skills: Intermediate, Desired Career Path: Senior Welder, Emotions: Highly motivated to work hard."

[0982] In this way, the system of the present invention provides optimal career advice to factory employees that takes their emotions into consideration, helping users improve their skills. Furthermore, by incorporating user feedback, the system can continuously improve the accuracy and effectiveness of its services.

[0983] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0984] Step 1:

[0985] Users input their interests, skills, special abilities, personality, emotions, and career aspirations and requirements using a smart terminal or interface device. Examples of input data include "Interests: Welding," "Skills: Intermediate," "Desired career path: Senior welding engineer," and "Emotions: Highly motivated to work hard."

[0986] Step 2:

[0987] The terminal locally stores the information entered by the user. After saving, the data is formatted for transmission and sent to the server. Here, the input is the user's information and the output is the formatted data.

[0988] Step 3:

[0989] The server receives data sent from the terminal and analyzes the data format. The analyzed data is stored in a database. The input is the received data, and the output is the data stored in the database.

[0990] Step 4:

[0991] The server converts the data stored in the database into an analytical format and provides it to the emotion engine and generation AI. The input is the data in the database, and the output is the data in the analytical format.

[0992] Step 5:

[0993] The emotion engine analyzes the user's emotions from the data in the analysis format. For example, it extracts the emotional state of "high motivation to make an effort." The input is the data in the analysis format, and the output is the emotion analysis result.

[0994] Step 6:

[0995] The generation AI begins analysis based on the user's interests, skills, special abilities, personality, emotional analysis results, and career-related aspirations and requirements. The generation AI generates optimal career advice and sends it back to the server. For example, it might suggest "curriculum and courses for becoming a senior welding engineer." The input is user data and the results of emotional analysis, and the output is career advice.

[0996] Step 7:

[0997] The server analyzes the advice data received from the generative AI and customizes the advice based on each organization's characteristics and past feedback. For example, it adds specific training programs and learning resources. The input is career advice from the generative AI, and the output is customized advice.

[0998] Step 8:

[0999] The server sends the customized advice to the terminal and displays it to the user. The input is the customized advice, and the output is the advice displayed on the user terminal.

[1000] Step 9:

[1001] The user inputs feedback for the provided advice. For example, the user adds a comment such as, "I would like to know about internship information related to this occupation." The input is the user's feedback, and the output is the transmission of the feedback data by the terminal.

[1002] Step 10:

[1003] The terminal sends the user's feedback to the server and stores it in a database. The input is the feedback data, and the output is the storage in the database.

[1004] Step 11:

[1005] The server provides feedback data to the AI ​​generator, which then retrains based on the new data. This improves the accuracy of advice generation from the next time onwards. The input is the feedback data, and the output is an improved algorithm of the AI ​​generator.

[1006] 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.

[1007] 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.

[1008] 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.

[1009] [Third embodiment]

[1010] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1011] 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.

[1012] 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).

[1013] 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.

[1014] 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.

[1015] 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).

[1016] 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.

[1017] 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.

[1018] 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.

[1019] 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.

[1020] 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.

[1021] 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."

[1022] This invention relates to a digital career guidance system that helps high school students resolve their concerns about their career choices. This system uses a generation AI to provide optimal career advice based on the user's (high school student's) input of their interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment, and improves the accuracy of the system based on feedback.

[1023] Program processing

[1024] Data entry and submission

[1025] 1. User:

[1026] Users use a device (such as a smartphone or computer) to input their interests, academic ability, special skills, personality, and hopes and requirements regarding further education or employment.

[1027] As a specific example, "User A" inputs his / her interests "biology," academic ability "high," special skill "experiment," personality "intense curiosity," and desired educational goal "medical school."

[1028] 2. Terminal:

[1029] The terminal receives input data from the user, converts it into the required format, and sends it to the server.

[1030] Data reception and analysis

[1031] 3. Server:

[1032] The server receives the data sent from the terminal and stores it in a database.

[1033] The received data is formatted into an analyzable format and provided to the generation AI.

[1034] Advice Generation

[1035] 4. Generation AI:

[1036] The generative AI analyzes the user's interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment.

[1037] As a concrete example, we analyze the data of User A and generate the following advice:

[1038] Future career: Doctor or researcher

[1039] Expected annual income: Over 5 million yen from the first year

[1040] Caution: Be careful of long working hours and excessive stress

[1041] Customize and deliver advice

[1042] 5. Server:

[1043] Advice is received from the generating AI and customized taking into account the characteristics of the school and feedback from the career guidance office.

[1044] The completed advice is optimized for the user and sent to the terminal.

[1045] 6. Terminal:

[1046] The user's terminal receives the advice sent from the server and displays it through a user interface.

[1047] As a specific example, user A checks the career path as a doctor or researcher, related universities, expected annual salary, and points to consider.

[1048] Feedback and Updates

[1049] 7. Users:

[1050] The user provides feedback on the advice provided, for example, by inputting "I would like information on scholarships related to this occupation."

[1051] 8. Terminal:

[1052] The terminal transmits the user's feedback to the server.

[1053] 9. Server:

[1054] The server receives the feedback and stores it in a database.

[1055] Feedback data is provided to the generation AI, and the learning data of the generation AI is updated.

[1056] 10. Generation AI:

[1057] The generating AI will re-learn based on new feedback data, improving the accuracy of advice from the next time onwards.

[1058] In this way, the system of the present invention allows high school students to receive specific and realistic career advice that is optimized for their own characteristics, significantly reducing the worries of career selection. It also reduces the burden on career guidance offices and allows more students to receive highly accurate individual guidance.

[1059] The processing flow will be explained below.

[1060] Step 1:

[1061] User: The user (high school student) uses a web form or application on the device to enter information about their interests, academic ability, special skills, personality, and hopes and requirements for further education or employment.

[1062] Step 2:

[1063] Terminal: The terminal temporarily stores the user's input data locally and then formats it into a data format to send to the server.

[1064] Step 3:

[1065] Terminal: Sends the formatted data to the server as an HTTP request (e.g., POST request).

[1066] Step 4:

[1067] Server: The server receives the data sent from the device, analyzes the data format, and extracts the necessary information.

[1068] Step 5:

[1069] Server: Stores the received data in a database.

[1070] Step 6:

[1071] Server: Converts the data stored in the database into an analytical format and provides it as input data to the generation AI.

[1072] Step 7:

[1073] Generative AI: The generative AI begins analysis based on the provided data. It analyzes the user's interests, academic ability, special skills, and personality data to generate optimal career advice.

[1074] Step 8:

[1075] Generative AI: The generated advice is formatted and sent back to the server. The advice includes future careers, further education, expected annual income, and points to note.

[1076] Step 9:

[1077] Server: The server analyzes the advice data received from the generated AI and customizes it based on feedback from each school's career guidance office and the characteristics of the school.

[1078] Step 10:

[1079] Server: Prepares customized advice data as an HTTP response to send to the device.

[1080] Step 11:

[1081] Device: The device analyzes the advice data received from the server and displays it to the user. For example, the user can check career paths as a doctor or researcher, related universities, expected annual salary, and points to consider.

[1082] Step 12:

[1083] User: The user enters feedback on the advice provided, for example, entering additional information such as "I would also like to know about scholarship information related to this occupation."

[1084] Step 13:

[1085] Device: The device sends the user feedback to the server.

[1086] Step 14:

[1087] Server: The server receives the feedback, stores it in a database, and provides the feedback data to be reflected in the learning data of the generative AI.

[1088] Step 15:

[1089] Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[1090] In this way, the system provides users with specific, personalized career advice and incorporates feedback to continuously improve the service.

[1091] Example 1

[1092] 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."

[1093] There is a need for a method to provide specific, individualized career advice quickly and accurately to high school students struggling with career choices. Traditional career guidance relies on the experience and subjectivity of teachers and counselors, and can lack consistency for all students. Another problem is that it is difficult to customize advice to reflect the characteristics of a specific school or feedback from the career guidance office. Furthermore, there is a lack of a way to incorporate student feedback in real time and improve the accuracy of career advice.

[1094] 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.

[1095] In this invention, the server includes a means for receiving user data and storing it in a database, a means for converting the received data into an analyzable format and providing it to the generative AI model, and a means for receiving advice generated by the generative AI model and customizing it based on the characteristics of the school and feedback from the career guidance office. This allows high school students to receive specific and personalized career advice, significantly reducing the burden of career choice. Customization based on feedback from the career guidance office is also possible, reducing the burden on teachers and counselors. Furthermore, by reflecting student feedback in real time and updating the learning data of the generative AI model, the accuracy of advice can be improved.

[1096] "Users" refers to those who use the system to receive career advice, such as high school students.

[1097] "Interests" refer to the academic subjects or fields of activity in which a user is particularly interested.

[1098] "Academic ability" refers to an indicator that shows a user's academic performance and level of knowledge.

[1099] "Special skills" refer to skills or abilities that a user has that are particularly superior to others.

[1100] "Personality" refers to a user's personal characteristics and behavioral traits.

[1101] "Hope and conditions" refers to the hopes and constraints that the user has regarding future education or employment.

[1102] "Terminal" refers to a device such as a smartphone or computer that a user uses to input data and communicate with a server or generative AI model.

[1103] "Server" refers to the computer system that receives, analyzes, and provides data submitted by users to the generative AI model.

[1104] "Database" refers to a storage device for storing user input data, generated advice, feedback, etc.

[1105] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal career advice based on data such as a user's interests and academic ability.

[1106] "Advice" refers to the advisory information generated by the generative AI model regarding the user's best future career, further education, expected annual income, points to note, etc.

[1107] "Feedback" refers to opinions and requests made by users in response to advice provided.

[1108] "Customization" refers to adjusting the generated advice to take into account the characteristics of the school and feedback from the career guidance office.

[1109] "Display" refers to visually presenting the customized advice on the device screen.

[1110] This invention relates to a digital career guidance system that helps high school students resolve their concerns about their career choices. This system uses a generative AI model to provide optimal career advice based on user (high school student) inputs of their interests, academic ability, special skills, personality, and hopes and requirements for further education and employment, and improves the accuracy of the system based on feedback.

[1111] First, a user uses a device such as a smartphone or PC to input their interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment. For example, User A inputs his / her interests as "biology," academic ability as "high," special skills as "experiment," personality as "curious," and desired education as "medical school."

[1112] Next, the device receives input data from the user, converts it into the required format (e.g., JSON format), and sends it to the server, which receives the data and stores it in a database. The server then formats the data into an analyzable format and provides it to the generative AI model.

[1113] The generative AI model analyzes a user's interests, academic ability, special skills, personality, and hopes and conditions to generate optimal career advice. For example, it analyzes User A's data and generates the following advice: "Future occupation: doctor or researcher," "Expected annual income: 5 million yen or more from the first year," and "Caution: Be careful of long working hours and excessive stress."

[1114] The generated advice is returned to the server and customized based on the characteristics of the school and feedback from the career guidance office. For example, advice optimized for User A is created based on the school's designated recommendation quota and local characteristics.

[1115] The server sends the customized advice to the user's terminal, which displays the advice, allowing the user to review the advice and use it to help them choose their own path.

[1116] The user can also input feedback on the advice provided, for example, "I would like information on scholarships related to this occupation." The terminal then sends this feedback back to the server, which then stores it in a database.

[1117] Finally, the generative AI model retrains based on new feedback data to improve the accuracy of future advice. In this way, the system of the present invention enables high school students to receive specific and realistic career advice optimized for their individual characteristics, significantly reducing the stress of career choice. It also reduces the burden on career guidance offices, enabling more students to receive highly accurate individualized guidance.

[1118] (Example of a prompt)

[1119] As a concrete example, let's use the following prompt: "If User A enters interests: biology, academic ability: high, special skills: experimentation, personality: curious, and educational aspirations: medical school, provide the best career advice."

[1120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1121] Step 1:

[1122] User data entry

[1123] Using a device (such as a smartphone or PC), the user inputs their interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment. Specifically, the user inputs information such as "Interests: Biology," "Academic ability: High," "Special skills: Experiments," "Personality: Very curious," and "Aspirations for further education: Medical school" into the device's input form. The input data is entered in "text format," for example.

[1124] Input: Data such as user interests, academic ability, special skills, personality, and educational aspirations

[1125] Output: Input data formatted on the terminal

[1126] Step 2:

[1127] Data transmission by the terminal

[1128] The device internally converts the collected user data into an appropriate format (for example, JSON format). After conversion, the device sends this data to a server via the Internet. As a specific example, User A's data is converted into a JSON-formatted string and sent as "{"interest": "biology", "academic_level": "high", "skills": "experiment", "personality": "curious", "education_wish": "medical school"}".

[1129] Input: formatted user data

[1130] Output: JSON format data sent to the server

[1131] Step 3:

[1132] Data reception by the server

[1133] The server receives the data sent from the device. Specifically, the server's data reception API is called and the received data is saved in the appropriate database.

[1134] Input: User data in JSON format

[1135] Output: User data stored in the database

[1136] Step 4:

[1137] Data formatting and provision by the server

[1138] The server then formats the received data into an analyzable format, extracting it from the database, splitting it into fields, and cleansing the data as needed.Then, it provides the formatted data to the generative AI model.

[1139] Input: User data in the database

[1140] Output: The formatted data that is fed into the generative AI model.

[1141] Step 5:

[1142] Advice generation using generative AI models

[1143] The generative AI model analyzes the provided user data and generates optimal career advice. Specifically, it analyzes information such as interests, academic ability, special skills, personality, and educational aspirations, and generates specific advice such as "Future occupation: doctor or researcher," "Expected annual income: 5 million yen or more from the first year," and "Points to note: be careful of long working hours and excessive stress."

[1144] Input: formatted user data

[1145] Output: Generated career advice

[1146] Step 6:

[1147] Server-customized advice

[1148] The server receives advice from the generative AI model and customizes it based on the characteristics of the school and feedback from the career guidance office. For example, it creates advice optimized for User A, taking into account the school's specific recommendation quotas and the local job market.

[1149] Input: Advice from a generative AI model

[1150] Output: Customized advice

[1151] Step 7:

[1152] Server-based advice sending

[1153] The server transmits the customized advice to the user's terminal. Specifically, the server converts the customized advice into an appropriate format and transmits it to the user's terminal.

[1154] Input: Customized Advice

[1155] Output: Advice sent to the user's terminal

[1156] Step 8:

[1157] Advice display on the device

[1158] The user's device displays the advice received from the server. Specifically, the advice is displayed in a list or card format, providing an interface that the user can understand visually.

[1159] Input: Customized advice sent by the server

[1160] Output: Advice displayed on the screen

[1161] Step 9:

[1162] User feedback input

[1163] The user inputs feedback on the advice provided, for example, by inputting a specific request such as "I would like information on scholarships related to this occupation."

[1164] Input: User feedback on advice

[1165] Output: Feedback typed into the terminal

[1166] Step 10:

[1167] Sending feedback via device

[1168] The terminal receives the user's feedback and transmits it back to the server.

[1169] Input: Feedback entered into the device

[1170] Output: Feedback sent to the server

[1171] Step 11:

[1172] Server receives and stores feedback

[1173] The server receives the feedback and stores it in the database. Specifically, the server receives the feedback data via the receiving API and stores it in the database.

[1174] Input: Feedback sent to the server

[1175] Output: Feedback stored in a database

[1176] Step 12:

[1177] Retraining generative AI models

[1178] The generative AI model will re-train based on new feedback data to improve the accuracy of advice from the next time onwards. For example, new feedback data will be incorporated and the AI ​​model algorithm will be updated.

[1179] Input: Feedback stored in the database

[1180] Output: Improved advice from an updated generative AI model

[1181] In this way, the system of the present invention enables high school students to receive optimal career advice and reduces the worries of career selection.

[1182] (Application example 1)

[1183] 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."

[1184] With conventional career guidance systems, it is difficult to efficiently provide optimal career advice based on individual user characteristics, and they are also unable to meet the needs of career counseling in a virtual environment.It is also difficult to provide accurate individual guidance to many students while reducing the burden on career guidance offices.

[1185] 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.

[1186] In this invention, the server includes means for inputting a user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment, means for transmitting the input data to the server, means for providing the data to a generating AI and generating advice such as optimal future careers, further education, expected annual income, and points to note for the user, and means for accessing the virtual career counseling counter via a smartphone, tablet, smart glasses, or head-mounted display. This makes it possible to provide optimal career advice based on individual user characteristics even in a virtual environment.

[1187] Definitions of important terms included in the claims

[1188] "Users" refer to individuals who use the system, especially high school students who are struggling to choose their career path.

[1189] "Interests" refers to the academic subjects, fields, or activities in which a user is particularly interested.

[1190] "Academic ability" refers to the user's academic performance and level of knowledge.

[1191] "Special skills" refer to skills or techniques that a user is particularly good at.

[1192] "Personality" refers to the user's characteristics and behavioral patterns.

[1193] "Hope and conditions regarding further education or employment" refers to the specific goals and requirements that the user has regarding further education or employment.

[1194] "Means" refers to the methods or tools used to achieve a particular goal.

[1195] "Server" refers to a computer or system that receives, processes, stores, and distributes data sent by users.

[1196] "Database" refers to a collection of information that receives information and stores it in an organized manner so that it can be easily searched and used.

[1197] "Generative AI" refers to an artificial intelligence model that generates optimal advice based on data provided by the user.

[1198] "Terminal" refers to a device, such as a smartphone or computer, that allows a user to access the system and input and receive data.

[1199] A "virtual career counseling counter" refers to a virtual window set up online or in a virtual space that accepts career-related consultations.

[1200] "Feedback" refers to the user's thoughts, opinions, or requests for improvement regarding the advice provided.

[1201] MODE FOR CARRYING OUT THE INVENTION

[1202] System Program

[1203] The present invention is a digital career guidance system for high school students to resolve their worries about career choices. This system is configured as follows.

[1204] Hardware and Software

[1205] 1. User's device

[1206] It uses a smartphone, tablet, smart glasses, or head-mounted display, and these devices have the ability to receive input data from the user and send it to a server.

[1207] 2. Server

[1208] The server is a computer that has the ability to store data received from users and save it in a database. The server also provides data to the generative AI and generates optimal advice.

[1209] 3. Generation AI

[1210] This is an artificial intelligence model that analyzes a user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment, and generates optimal career advice.

[1211] Data processing and calculation

[1212] 1. Data Receipt and Storage

[1213] The server receives the data sent from the user's device and stores it in a database, which allows the systematic management of the user's interests, academic ability, special skills, personality, and hopes and requirements for further education and employment.

[1214] 2. Data Analysis

[1215] The generative AI analyzes the user's characteristics based on data provided by the server. For example, if a user's interests are "biology," their academic ability is "high," their special skill is "experimentation," and their personality is "curious," it will recommend appropriate schools and careers.

[1216] 3. Generating Advice

[1217] The AI ​​generates career advice based on the results of analyzing the user's data. Specific examples are as follows:

[1218] Future career: Doctor or researcher

[1219] Expected annual income: Over 5 million yen from the first year

[1220] Caution: Be careful of long working hours and excessive stress

[1221] 4. Customize your advice

[1222] The server then customizes the advice received from the AI ​​generator to suit the characteristics of the school and sends it to the user's device, providing optimal career advice based on the individual user's characteristics.

[1223] 5. Incorporating feedback

[1224] The server receives user feedback and reflects it in the learning data of the AI ​​generator. For example, if a user provides feedback such as "I would like information about scholarships related to this occupation," the AI ​​generator's algorithm will take this into consideration to improve the accuracy of future advice.

[1225] Examples and prompts

[1226] Examples:

[1227] "User A has entered the following information: Interests: Biology, Academic ability: High, Special skills: Experiments, Personality: Curious, Aspirations: Medical school. Please generate the most appropriate career advice based on the above information."

[1228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1229] Processing Steps

[1230] Step 1:

[1231] (Input) Using a smartphone, tablet, smart glasses, or head-mounted display, the user inputs their interests, academic ability, special skills, personality, and hopes and requirements for further education or employment.

[1232] The (operational) terminal allows these data to be entered through a user interface.

[1233] (Output) The entered data is stored on the terminal and is ready to be sent.

[1234] Step 2:

[1235] (Input) Data entered by the user.

[1236] (Operation) The terminal converts the input data into a predetermined format and sends it to the server.

[1237] (Output) Data is sent to the server.

[1238] Step 3:

[1239] (Input) The server receives the data sent from the terminal.

[1240] (Operation) The server stores and organizes the received data in a database.

[1241] (Output) The data is saved in the database.

[1242] Step 4:

[1243] (Input) Data received and stored by the server.

[1244] (Operation) The server formats this data and provides it to the generation AI.

[1245] (Output) The data is prepared in the format provided to the generation AI.

[1246] Step 5:

[1247] (Input) Generative AI is organized user data.

[1248] (Operation) The generation AI analyzes the user's interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment.

[1249] (Output) The optimal career advice is generated.

[1250] Step 6:

[1251] (Input) The generated career advice.

[1252] (Operation) The server receives career advice from the generating AI and customizes it based on the characteristics of each school and feedback from the career guidance office.

[1253] (Output) Customized advice is prepared.

[1254] Step 7:

[1255] (Input) Customized career advice.

[1256] (Operation) The server sends the customized advice to the user's terminal.

[1257] (Output) Advice is displayed on the terminal.

[1258] Step 8:

[1259] (Input) User feedback.

[1260] (Action) The user inputs feedback on the advice provided, for example, asking for information on scholarships.

[1261] (Output) Feedback is input to the terminal.

[1262] Step 9:

[1263] (Input) Feedback entered by the user.

[1264] (Operation) The terminal transmits the input feedback to the server.

[1265] (Output) Feedback is sent to the server.

[1266] Step 10:

[1267] (Input) The server receives the user's feedback.

[1268] (Operation) The server stores the feedback in a database and provides it to the generating AI.

[1269] (Output) Feedback is provided to the generating AI.

[1270] Step 11:

[1271] (Input) Generative AI is new feedback data.

[1272] The (behavior) generative AI re-learns based on feedback data to improve the accuracy of its advice.

[1273] (Output) The advice from next time onwards will be more optimized.

[1274] 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.

[1275] This invention relates to a digital career guidance system that provides optimal career advice that takes emotions into consideration for high school students struggling with career choices. This system uses a generative AI and emotion engine to provide optimal career advice based on the interests, academic ability, special skills, personality, emotions, and hopes and conditions for further education and employment entered by the user (high school student), and improves the accuracy of the system based on feedback.

[1276] Program processing

[1277] Data entry and submission

[1278] 1. User: The user uses a device (smartphone or PC) to input their interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment. As a specific example, "User B" inputs his / her interests "engineering," academic ability "average," special skill "programming," personality "logical," desired education "engineering," and current emotional state "motivated."

[1279] 2. Terminal: The terminal stores the user's input data locally, formats it into a transmission format, and sends it to the server.

[1280] Data reception and analysis

[1281] 3. Server: The server receives the data sent from the device, analyzes the data format, and stores the received data in a database.

[1282] 4. Server: The server converts the data stored in the database into an analytical format and provides it to the emotion engine and generative AI.

[1283] Emotion Recognition and Advice Generation

[1284] 5. Emotion Engine: The emotion engine analyzes the user's emotions from the received data and provides the results to the generative AI. For example, the emotion engine extracts the emotional state of "motivated" as the analysis result.

[1285] 6. Generative AI: The Generative AI begins analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and their hopes and requirements for further education and employment. The Generative AI generates optimal career advice and sends it back to the server. As a concrete example, it analyzes the data of User B and generates the following advice:

[1286] Future job: Software engineer

[1287] Expected annual income: Over 4 million yen from the first year

[1288] Important note: Programming skills need to be continually improved.

[1289] Customize and deliver advice

[1290] 7. Server: The server analyzes the advice data received from the generation AI and customizes it based on feedback from the career guidance office and the characteristics of the school.

[1291] 8. Server: Sends customized advice data to the device.

[1292] 9. Terminal: The terminal analyzes the advice data sent from the server and displays it in the user interface. For example, User B checks the career path as a software engineer, related universities, expected annual salary, and points to consider.

[1293] Feedback and Updates

[1294] 10. User: The user enters feedback on the advice provided. For example, "I would like to know about internship information related to this occupation."

[1295] 11. Terminal: The terminal sends the user feedback to the server.

[1296] 12. Server: The server receives the feedback, stores it in a database, and provides the feedback data to the generative AI to reflect in its training data.

[1297] 13. Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[1298] In this way, the system of the present invention utilizes an emotion engine to provide specific, personalized career advice that takes into account the user's emotions, and incorporates feedback to continuously improve the service.

[1299] The processing flow will be explained below.

[1300] Step 1:

[1301] User: A user (high school student) uses a device (smartphone or PC) to input their interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment. As a specific example, "User B" inputs his / her interests of "engineering," academic ability of "average," special skill of "programming," personality of "logical," desired education of "engineering," and recent emotional state of "motivated."

[1302] Step 2:

[1303] Terminal: The terminal stores the user's input data locally and formats it for transmission. Specifically, it converts the user's input into a data format such as JSON.

[1304] Step 3:

[1305] Terminal: Sends the formatted data to the server as an HTTP request (e.g., POST request). The data sent includes information about the user's interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education or employment.

[1306] Step 4:

[1307] Server: The server receives the data sent from the device, analyzes the data format, extracts the necessary information, and temporarily stores the received data in memory.

[1308] Step 5:

[1309] Server: The analyzed data is stored in a database, including the user's interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education and employment.

[1310] Step 6:

[1311] Server: Converts the data stored in the database into an analytical format and provides it to the emotion engine and generation AI. Specifically, emotion information is sent to the emotion engine, and other data is sent to the generation AI.

[1312] Step 7:

[1313] Emotion engine: The emotion engine analyzes the user's emotions from the received data and provides the results to the generation AI. For example, the emotional state of "motivated" is extracted as the analysis result.

[1314] Step 8:

[1315] Generative AI: The Generative AI begins analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and hopes and conditions regarding further education and employment. The Generative AI generates optimal career advice and sends the results back to the server. As a concrete example, it analyzes User B's data and generates the following advice:

[1316] Future job: Software engineer

[1317] Expected annual income: Over 4 million yen from the first year

[1318] Important note: Programming skills need to be continually improved.

[1319] Step 9:

[1320] Server: The server analyzes the advice data received from the AI ​​generator and customizes it as needed based on feedback from the career guidance office and the characteristics of the school.

[1321] Step 10:

[1322] Server: Prepares customized advice data as an HTTP response to send to the device.

[1323] Step 11:

[1324] Device: The device analyzes the advice data received from the server and displays it through the user interface. For example, User B checks the career path as a software engineer, related universities, expected annual salary, and points to consider.

[1325] Step 12:

[1326] User: The user enters feedback on the advice provided. For example, the user enters additional information such as "I would like to know about scholarships related to this occupation."

[1327] Step 13:

[1328] Device: The device sends the user feedback to the server again.

[1329] Step 14:

[1330] Server: The server receives the feedback, stores it in a database, and provides the feedback data to be reflected in the learning data of the generative AI.

[1331] Step 15:

[1332] Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[1333] In this way, the system of the present invention utilizes an emotion engine to provide specific, personalized career advice that takes into account the user's emotions, and incorporates feedback to continuously improve the service.

[1334] Example 2

[1335] 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."

[1336] While conventional career guidance systems could take into account a user's interests, academic ability, special skills, personality, and hopes and requirements for further education or employment, they were unable to provide career advice that reflected the user's emotional state. Furthermore, they lacked the ability to effectively incorporate user feedback to improve the accuracy of the system. This made it difficult for users to receive advice that they were completely satisfied with when choosing a career path.

[1337] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting a user's interests, academic ability, special skills, personality, emotions, and wishes and conditions regarding further education or employment, means for temporarily saving the input data in the terminal and shaping it in local storage, means for transmitting the shaped data to the server, means for the server to receive the data, analyze the format, and store it in a database, means for converting the data into an analysis format and providing it to an emotion engine and a generation AI, means for the emotion engine to analyze the user's emotions and provide the result to the generation AI, means for the generation AI to generate optimal career advice based on the user's interests, academic ability, special skills, personality, and emotion analysis results and return it to the server, means for the server to receive the generated advice and customize it to suit the characteristics of each school, means for transmitting the customized advice to the terminal and displaying it to the user, and means for transmitting user feedback on the advice back to the server and reflecting it in the learning data of the generation AI. This makes it possible to provide personalized career advice that takes into account the user's emotions and to continuously improve the accuracy of the system by reflecting user feedback.

[1338] A "user" is a person who utilizes the system to input information and provide feedback about their path.

[1339] A "terminal" is a device that a user uses to input and output information, and includes smartphones and personal computers.

[1340] The "server" is a central processing unit that receives and analyzes user input data and provides the data to the generation AI and emotion engine.

[1341] A "database" is a system for storing and managing user input data and analysis results.

[1342] An "emotion engine" is an algorithm or software that analyzes emotions from user input data and provides the results to generative AI.

[1343] "Generative AI" is an artificial intelligence model that generates optimal career advice based on a user's interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education and employment.

[1344] "Advice" refers to recommendations such as future careers, further education, expected annual income, and points to note that are generated by the AI ​​based on the user's input data.

[1345] "Customization" refers to adjusting and changing the advice provided by the generating AI based on the characteristics of each school and feedback from the career guidance office.

[1346] "Feedback" refers to opinions or requests that a user inputs in response to advice provided, and is used as learning data for generating subsequent advice.

[1347] "Local storage" refers to a function and area for temporarily storing data within a terminal.

[1348] The "analysis format" is a format used to format data so that it is easy for the emotion engine and generative AI to understand.

[1349] This invention relates to a digital career guidance system that provides optimal career advice that takes emotions into consideration to individuals struggling with career choices. In this system, a server processes data entered by a user on a terminal, and an emotion engine and generation AI are used to generate optimal career advice and provide it to the user.

[1350] Specific system configuration

[1351] The system includes the following major hardware and software components:

[1352] Terminal: A device such as a smartphone or PC that a user uses to input information. The terminal temporarily stores the input data in local storage and then transmits the data.

[1353] Server: A central processing unit that receives, analyzes, stores, and provides data to the emotion engine and generative AI. Python and Node.js are typical backend technologies used.

[1354] Database: A system that stores and manages received data. Database management systems such as MySQL and PostgreSQL are used.

[1355] Emotion engine: An algorithm or software for analyzing emotions from user input data. An example of this is an emotion analysis model.

[1356] Generative AI: An artificial intelligence model that generates optimal career advice based on a user's interests, academic ability, special skills, personality, emotions, and their hopes and requirements for further education and employment. Specifically, it uses language models such as GPT-3.

[1357] Data entry and submission

[1358] 1. User behavior: The user uses a device to enter information about their interests, academic ability, special skills, personality, feelings, and hopes and requirements for further education or employment using a dedicated app or web form. When entering information, the system is designed to allow users to easily select information using pull-down menus and check boxes.

[1359] Example: User B inputs his / her interests as "Engineering", academic ability as "Average", special skill as "Programming", personality as "Logical", desired education as "Engineering", and emotional state as "Motivated".

[1360] 2. Device operation: The device uses JavaScript to check form input values ​​in real time to prevent input errors, converts the input information into a data format such as JSON, and securely sends it to the server using HTTPS.

[1361] Data reception and analysis

[1362] 3. Server operation: The server receives the data sent from the device, analyzes the format of the received data, and stores it in a database. After analysis, the data is converted into an analytical format that can be understood by the emotion engine and generation AI.

[1363] Emotion Recognition and Advice Generation

[1364] 4. Operation of the emotion engine: The emotion engine analyzes the data provided by the server and recognizes the user's emotional state. For example, it extracts the emotional state of "motivated" and sends it to the generation AI.

[1365] 5. How the Generative AI works: The Generative AI begins its analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and hopes and conditions regarding further education and employment. For example, it creates the following advice based on User B's data:

[1366] Future job: Software engineer

[1367] Expected annual income: Over 4 million yen from the first year

[1368] Important note: Programming skills need to be continually improved.

[1369] The generated advice data is returned to the server.

[1370] Customizing and displaying advice

[1371] 6. Server operation: The server analyzes the advice data received from the generation AI and customizes it based on feedback from the career guidance office and school characteristics, for example, adding information about specific university programs or local employment opportunities.

[1372] 7. Terminal operation: The terminal receives the advice data sent from the server and displays it in a user interface, sometimes using graphics and charts to make it easier for the user to understand intuitively.

[1373] Example: User B reviews software engineer career paths, relevant universities, salary expectations, and caveats to consider.

[1374] Feedback and Updates

[1375] 8. User action: The user enters feedback on the advice provided. For example, the user might enter, "I would like to know about internship information related to this occupation."

[1376] 9. Terminal operation: The terminal formats the feedback data and sends it to the server.

[1377] 10. Server operation: The server receives the feedback, stores it in a database, and provides it to the AI ​​generator to use as learning data for future advice generation.

[1378] 11. How the Generative AI works: The Generative AI retrains based on new feedback data to improve the accuracy of advice generation from the next time onwards. For example, it may include internship information in the advice.

[1379] In this way, the system can provide personalized navigation advice while taking into account the user's emotions and incorporate feedback to continuously improve the system's accuracy.

[1380] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1381] Step 1:

[1382] User Action:

[1383] Using a device, users input their interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education or employment. Specifically, they select information using check boxes and pull-down menus on a dedicated smartphone or PC app or web form, and enter detailed information in text fields. For example, they input information such as "Engineering," "Middle School," "Programming," "Logical," "Faculty of Engineering," and "Motivated."

[1384] Input: Interests, academic ability, special skills, personality, emotions, and desired conditions for further education or employment.

[1385] Output: The information entered by the user is temporarily stored in the device's local storage.

[1386] Step 2:

[1387] Terminal behavior:

[1388] The device temporarily stores the data entered by the user in local storage and converts it to a data format such as JSON. Next, it checks the input data for errors in real time using JavaScript or other methods and displays an error message. If there are no errors, it securely sends the data to the server using HTTPS.

[1389] Input: Data entered by the user.

[1390] Output: The data is converted to JSON format and sent to the server.

[1391] Step 3:

[1392] Server behavior:

[1393] The server receives the data sent from the device and analyzes the data format. Specifically, it uses backend technologies such as Python and Node.js to parse the received data and convert it into a format that can be saved in a database. The analyzed data is then stored in the database.

[1394] Input: JSON format data sent from the terminal.

[1395] Output: The parsed data is stored in a database.

[1396] Step 4:

[1397] Server behavior:

[1398] The server re-analyzes the data stored in the database and formats it to be provided to the emotion engine and generation AI. Specifically, it selects the necessary information and converts it into an appropriate format so that the emotion engine can accurately analyze the user's emotions. The data is then provided to the emotion engine and generation AI.

[1399] Input: User data stored in the database.

[1400] Output: Formatted data to feed into the emotion engine and generative AI.

[1401] Step 5:

[1402] Emotion Engine in action:

[1403] The emotion engine analyzes the data provided by the server and recognizes the user's emotional state. Using an emotion analysis model, it identifies the emotional state, for example, "motivated," and sends the results in JSON format to the generation AI.

[1404] Input: Formatted data provided by the server.

[1405] Output: Parsed emotion data is sent to the generation AI in JSON format.

[1406] Step 6:

[1407] Generative AI behavior:

[1408] The generation AI generates optimal career advice based on the emotion data provided by the emotion engine and other user data provided by the server. For example, it suggests a career path such as "software engineer" to User B and generates advice including expected annual salary and points to note. This generated advice is then sent back to the server.

[1409] Input: Emotion data provided by the emotion engine, other user data provided by the server.

[1410] Output: The generated career advice data.

[1411] Step 7:

[1412] Server behavior:

[1413] The server analyzes and customizes the career advice data received from the generation AI. Specifically, it adjusts the advice content to match feedback from the career guidance office and the characteristics of the university. For example, it adds information about specific university recommendation programs and regional specializations. This customized advice data is then sent to the device.

[1414] Input: Career advice data received from the generation AI.

[1415] Output: Customized career advice data.

[1416] Step 8:

[1417] Terminal behavior:

[1418] The device receives the customized advice data sent from the server and displays it in a user interface. Specifically, it uses a responsive design, displaying advice content according to the screen size of a smartphone or PC. Graphics and charts may be used to make the advice easier for users to understand intuitively.

[1419] Input: Customized advice data sent from the server.

[1420] Output: Career advice displayed in the user interface.

[1421] Step 9:

[1422] User Action:

[1423] The user inputs feedback on the advice provided, for example, a request or opinion such as "I would like to know more about internships related to this occupation."

[1424] Input: Feedback on career advice provided.

[1425] Output: The feedback data is saved to the device.

[1426] Step 10:

[1427] Terminal behavior:

[1428] The terminal formats the user's feedback data and sends it back to the server, where the feedback data is an important factor for subsequent advice generation.

[1429] Input: User feedback data.

[1430] Output: The formatted feedback data is sent to the server.

[1431] Step 11:

[1432] Server behavior:

[1433] The server receives the feedback data, stores it in a database, and provides it to the generation AI to update the advice generation algorithm.

[1434] Input: Formatted feedback data.

[1435] Output: Feedback data stored in a database, providing feedback to the generative AI.

[1436] Step 12:

[1437] Generative AI behavior:

[1438] The AI ​​will retrain based on new feedback data to improve the accuracy of future career advice generation. For example, based on the feedback, it will be able to include internship information in the next advice.

[1439] Input: Feedback data provided by the server.

[1440] Output: The updated advice generation algorithm.

[1441] (Application example 2)

[1442] 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."

[1443] In factories and industrial sites, improving employees' skills and choosing career paths is important, but providing individual advice that takes into account the characteristics and feelings of each employee is difficult. Furthermore, there are currently no effective ways to provide advice on the skills and careers that employees need. To solve these issues, a system is needed that can accurately grasp employees' interests, current skills, desired career paths, and motivation for skill improvement, and provide optimal advice.

[1444] 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.

[1445] In this invention, the server includes: means for inputting a user's interests, academic ability, special skills, personality, emotions, and hopes and requirements regarding further education and employment; means for transmitting the input data to the server; means for the server to receive the data and store it in a database; means for providing the data to a generation AI and generating advice on the user's optimal future career, future education, expected annual income, points to note, etc.; means for the server to receive the generated advice and customize it to suit the characteristics of each organization; means for transmitting the customized advice to a terminal and displaying it to the user; means for transmitting user feedback on the advice back to the server and reflecting it in the learning data of the generation AI; means for the user to input and provide the feedback using a smart terminal; and means for installing the system on a robot to provide career advice to factory employees. This enables personalized advice that takes into account the characteristics and emotions of each employee.

[1446] A "user" is a person or employee who uses the system to input information about interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment.

[1447] "Interests" refers to information about areas or activities in which a user is particularly interested.

[1448] "Academic ability" refers to information indicating the user's current educational level and learning progress.

[1449] "Special skills" refers to information about skills or abilities that a user excels at compared to others.

[1450] "Personality" refers to internal characteristics that indicate a user's tendencies in behavior and reactions.

[1451] "Emotion" refers to information that indicates the user's psychological state or sensation at that time.

[1452] "Aspirations and conditions regarding further education and employment" refers to the desired educational destination or employment destination of the user and related conditions (such as work location and working hours).

[1453] "Server" means a computer system that receives, stores, and analyzes data submitted by users and provides customized generated advice.

[1454] "Database" refers to an information storage system for storing and managing user data and feedback data received by the server.

[1455] "Generative AI" refers to an artificial intelligence model that generates optimal advice based on user data.

[1456] "Customizing" means optimizing the generated advice based on the characteristics of each organization and user feedback.

[1457] A "user interface" is a screen or device through which a user inputs data, receives advice, and provides feedback.

[1458] "Feedback" refers to information that a user sends to the server, such as their thoughts on the advice provided and suggestions for improvement.

[1459] "Robots" are automated machines that install and provide information on career advice systems.

[1460] This invention relates to a career consulting system for factory employees that applies a digital career guidance system. This system uses generative AI and an emotion engine to provide optimal career advice based on the interests, skills, desired career path, and emotional state input by the user (employee).

[1461] First, a user inputs their interests, current skills, special abilities, personality, emotions, and career aspirations and requirements from a smart terminal or interface device. For example, they might enter data such as "I want to improve my welding skills" or "I have a high motivation to work hard." The terminal then stores this data locally, formats it into a transmission format, and sends it to the server.

[1462] The server receives the data sent from the device, analyzes the data format, and stores it in a database. The received data is converted into an analytical format and provided to the emotion engine and generation AI. The emotion engine analyzes the user's emotions from the received data and provides the results to the generation AI. As a specific example, the emotion engine extracts the emotional state of "high motivation to make an effort" as the analysis result.

[1463] The AI ​​begins its analysis based on the user's interests, skills, special abilities, personality, emotional analysis results, and career aspirations and requirements. The AI ​​generates optimal career advice and sends it back to the server. For example, it might suggest a curriculum or course to improve specific skills to become a senior welding engineer.

[1464] The server analyzes the advice data received from the AI ​​generator and customizes the advice based on each organization's characteristics and past feedback. Specifically, it adds appropriate training programs and learning resources. This customized advice is then sent back to the device and displayed in the user interface.

[1465] Users can input feedback on the advice provided, adding information such as "I would like to know about internships related to this occupation." This feedback is sent from the device to the server and stored in a database. The AI ​​then retrains itself based on the new feedback data, improving the accuracy of its advice generation from the next time onwards.

[1466] The hardware used includes smart devices and tablets used by each user, and server systems that perform analysis and calculation processing, while the software used includes databases (e.g., MySQL or PostgreSQL), emotion engines (e.g., Google Cloud Natural Language API), and generative AI (e.g., OpenAI GPT-4).

[1467] Examples of specific prompts include:

[1468] "Interests: Welding, Current Skills: Intermediate, Desired Career Path: Senior Welder, Emotions: Highly motivated to work hard."

[1469] In this way, the system of the present invention provides optimal career advice to factory employees that takes their emotions into consideration, helping users improve their skills. Furthermore, by incorporating user feedback, the system can continuously improve the accuracy and effectiveness of its services.

[1470] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1471] Step 1:

[1472] Users input their interests, skills, special abilities, personality, emotions, and career aspirations and requirements using a smart terminal or interface device. Examples of input data include "Interests: Welding," "Skills: Intermediate," "Desired career path: Senior welding engineer," and "Emotions: Highly motivated to work hard."

[1473] Step 2:

[1474] The terminal locally stores the information entered by the user. After saving, the data is formatted for transmission and sent to the server. Here, the input is the user's information and the output is the formatted data.

[1475] Step 3:

[1476] The server receives data sent from the terminal and analyzes the data format. The analyzed data is stored in a database. The input is the received data, and the output is the data stored in the database.

[1477] Step 4:

[1478] The server converts the data stored in the database into an analytical format and provides it to the emotion engine and generation AI. The input is the data in the database, and the output is the data in the analytical format.

[1479] Step 5:

[1480] The emotion engine analyzes the user's emotions from the data in the analysis format. For example, it extracts the emotional state of "high motivation to make an effort." The input is the data in the analysis format, and the output is the emotion analysis result.

[1481] Step 6:

[1482] The generation AI begins analysis based on the user's interests, skills, special abilities, personality, emotional analysis results, and career-related aspirations and requirements. The generation AI generates optimal career advice and sends it back to the server. For example, it might suggest "curriculum and courses for becoming a senior welding engineer." The input is user data and the results of emotional analysis, and the output is career advice.

[1483] Step 7:

[1484] The server analyzes the advice data received from the generative AI and customizes the advice based on each organization's characteristics and past feedback. For example, it adds specific training programs and learning resources. The input is career advice from the generative AI, and the output is customized advice.

[1485] Step 8:

[1486] The server sends the customized advice to the terminal and displays it to the user. The input is the customized advice, and the output is the advice displayed on the user terminal.

[1487] Step 9:

[1488] The user inputs feedback for the provided advice. For example, the user adds a comment such as, "I would like to know about internship information related to this occupation." The input is the user's feedback, and the output is the transmission of the feedback data by the terminal.

[1489] Step 10:

[1490] The terminal sends the user's feedback to the server and stores it in a database. The input is the feedback data, and the output is the storage in the database.

[1491] Step 11:

[1492] The server provides feedback data to the AI ​​generator, which then retrains based on the new data. This improves the accuracy of advice generation from the next time onwards. The input is the feedback data, and the output is an improved algorithm of the AI ​​generator.

[1493] 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.

[1494] 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.

[1495] 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.

[1496] [Fourth embodiment]

[1497] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1498] 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.

[1499] 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).

[1500] 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.

[1501] 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.

[1502] 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).

[1503] 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.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] 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.

[1508] 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.

[1509] 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."

[1510] This invention relates to a digital career guidance system that helps high school students resolve their concerns about their career choices. This system uses a generation AI to provide optimal career advice based on the user's (high school student's) input of their interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment, and improves the accuracy of the system based on feedback.

[1511] Program processing

[1512] Data entry and submission

[1513] 1. User:

[1514] Users use a device (such as a smartphone or computer) to input their interests, academic ability, special skills, personality, and hopes and requirements regarding further education or employment.

[1515] As a specific example, "User A" inputs his / her interests "biology," academic ability "high," special skill "experiment," personality "intense curiosity," and desired educational goal "medical school."

[1516] 2. Terminal:

[1517] The terminal receives input data from the user, converts it into the required format, and sends it to the server.

[1518] Data reception and analysis

[1519] 3. Server:

[1520] The server receives the data sent from the terminal and stores it in a database.

[1521] The received data is formatted into an analyzable format and provided to the generation AI.

[1522] Advice Generation

[1523] 4. Generation AI:

[1524] The generative AI analyzes the user's interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment.

[1525] As a concrete example, we analyze the data of User A and generate the following advice:

[1526] Future career: Doctor or researcher

[1527] Expected annual income: Over 5 million yen from the first year

[1528] Caution: Be careful of long working hours and excessive stress

[1529] Customize and deliver advice

[1530] 5. Server:

[1531] Advice is received from the generating AI and customized taking into account the characteristics of the school and feedback from the career guidance office.

[1532] The completed advice is optimized for the user and sent to the terminal.

[1533] 6. Terminal:

[1534] The user's terminal receives the advice sent from the server and displays it through a user interface.

[1535] As a specific example, user A checks the career path as a doctor or researcher, related universities, expected annual salary, and points to consider.

[1536] Feedback and Updates

[1537] 7. Users:

[1538] The user provides feedback on the advice provided, for example, by inputting "I would like information on scholarships related to this occupation."

[1539] 8. Terminal:

[1540] The terminal transmits the user's feedback to the server.

[1541] 9. Server:

[1542] The server receives the feedback and stores it in a database.

[1543] Feedback data is provided to the generation AI, and the learning data of the generation AI is updated.

[1544] 10. Generation AI:

[1545] The generating AI will re-learn based on new feedback data, improving the accuracy of advice from the next time onwards.

[1546] In this way, the system of the present invention allows high school students to receive specific and realistic career advice that is optimized for their own characteristics, significantly reducing the worries of career selection. It also reduces the burden on career guidance offices and allows more students to receive highly accurate individual guidance.

[1547] The processing flow will be explained below.

[1548] Step 1:

[1549] User: The user (high school student) uses a web form or application on the device to enter information about their interests, academic ability, special skills, personality, and hopes and requirements for further education or employment.

[1550] Step 2:

[1551] Terminal: The terminal temporarily stores the user's input data locally and then formats it into a data format to send to the server.

[1552] Step 3:

[1553] Terminal: Sends the formatted data to the server as an HTTP request (e.g., POST request).

[1554] Step 4:

[1555] Server: The server receives the data sent from the device, analyzes the data format, and extracts the necessary information.

[1556] Step 5:

[1557] Server: Stores the received data in a database.

[1558] Step 6:

[1559] Server: Converts the data stored in the database into an analytical format and provides it as input data to the generation AI.

[1560] Step 7:

[1561] Generative AI: The generative AI begins analysis based on the provided data. It analyzes the user's interests, academic ability, special skills, and personality data to generate optimal career advice.

[1562] Step 8:

[1563] Generative AI: The generated advice is formatted and sent back to the server. The advice includes future careers, further education, expected annual income, and points to note.

[1564] Step 9:

[1565] Server: The server analyzes the advice data received from the generated AI and customizes it based on feedback from each school's career guidance office and the characteristics of the school.

[1566] Step 10:

[1567] Server: Prepares customized advice data as an HTTP response to send to the device.

[1568] Step 11:

[1569] Device: The device analyzes the advice data received from the server and displays it to the user. For example, the user can check career paths as a doctor or researcher, related universities, expected annual salary, and points to consider.

[1570] Step 12:

[1571] User: The user enters feedback on the advice provided, for example, entering additional information such as "I would also like to know about scholarship information related to this occupation."

[1572] Step 13:

[1573] Device: The device sends the user feedback to the server.

[1574] Step 14:

[1575] Server: The server receives the feedback, stores it in a database, and provides the feedback data to be reflected in the learning data of the generative AI.

[1576] Step 15:

[1577] Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[1578] In this way, the system provides users with specific, personalized career advice and incorporates feedback to continuously improve the service.

[1579] Example 1

[1580] 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."

[1581] There is a need for a method to provide specific, individualized career advice quickly and accurately to high school students struggling with career choices. Traditional career guidance relies on the experience and subjectivity of teachers and counselors, and can lack consistency for all students. Another problem is that it is difficult to customize advice to reflect the characteristics of a specific school or feedback from the career guidance office. Furthermore, there is a lack of a way to incorporate student feedback in real time and improve the accuracy of career advice.

[1582] 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.

[1583] In this invention, the server includes a means for receiving user data and storing it in a database, a means for converting the received data into an analyzable format and providing it to the generative AI model, and a means for receiving advice generated by the generative AI model and customizing it based on the characteristics of the school and feedback from the career guidance office. This allows high school students to receive specific and personalized career advice, significantly reducing the burden of career choice. Customization based on feedback from the career guidance office is also possible, reducing the burden on teachers and counselors. Furthermore, by reflecting student feedback in real time and updating the learning data of the generative AI model, the accuracy of advice can be improved.

[1584] "Users" refers to those who use the system to receive career advice, such as high school students.

[1585] "Interests" refer to the academic subjects or fields of activity in which a user is particularly interested.

[1586] "Academic ability" refers to an indicator that shows a user's academic performance and level of knowledge.

[1587] "Special skills" refer to skills or abilities that a user has that are particularly superior to others.

[1588] "Personality" refers to a user's personal characteristics and behavioral traits.

[1589] "Hope and conditions" refers to the hopes and constraints that the user has regarding future education or employment.

[1590] "Terminal" refers to a device such as a smartphone or computer that a user uses to input data and communicate with a server or generative AI model.

[1591] "Server" refers to the computer system that receives, analyzes, and provides data submitted by users to the generative AI model.

[1592] "Database" refers to a storage device for storing user input data, generated advice, feedback, etc.

[1593] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal career advice based on data such as a user's interests and academic ability.

[1594] "Advice" refers to the advisory information generated by the generative AI model regarding the user's best future career, further education, expected annual income, points to note, etc.

[1595] "Feedback" refers to opinions and requests made by users in response to advice provided.

[1596] "Customization" refers to adjusting the generated advice to take into account the characteristics of the school and feedback from the career guidance office.

[1597] "Display" refers to visually presenting the customized advice on the device screen.

[1598] This invention relates to a digital career guidance system that helps high school students resolve their concerns about their career choices. This system uses a generative AI model to provide optimal career advice based on user (high school student) inputs of their interests, academic ability, special skills, personality, and hopes and requirements for further education and employment, and improves the accuracy of the system based on feedback.

[1599] First, a user uses a device such as a smartphone or PC to input their interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment. For example, User A inputs his / her interests as "biology," academic ability as "high," special skills as "experiment," personality as "curious," and desired education as "medical school."

[1600] Next, the device receives input data from the user, converts it into the required format (e.g., JSON format), and sends it to the server, which receives the data and stores it in a database. The server then formats the data into an analyzable format and provides it to the generative AI model.

[1601] The generative AI model analyzes a user's interests, academic ability, special skills, personality, and hopes and conditions to generate optimal career advice. For example, it analyzes User A's data and generates the following advice: "Future occupation: doctor or researcher," "Expected annual income: 5 million yen or more from the first year," and "Caution: Be careful of long working hours and excessive stress."

[1602] The generated advice is returned to the server and customized based on the characteristics of the school and feedback from the career guidance office. For example, advice optimized for User A is created based on the school's designated recommendation quota and local characteristics.

[1603] The server sends the customized advice to the user's terminal, which displays the advice, allowing the user to review the advice and use it to help them choose their own path.

[1604] The user can also input feedback on the advice provided, for example, "I would like information on scholarships related to this occupation." The terminal then sends this feedback back to the server, which then stores it in a database.

[1605] Finally, the generative AI model retrains based on new feedback data to improve the accuracy of future advice. In this way, the system of the present invention enables high school students to receive specific and realistic career advice optimized for their individual characteristics, significantly reducing the stress of career choice. It also reduces the burden on career guidance offices, enabling more students to receive highly accurate individualized guidance.

[1606] (Example of a prompt)

[1607] As a concrete example, let's use the following prompt: "If User A enters interests: biology, academic ability: high, special skills: experimentation, personality: curious, and educational aspirations: medical school, provide the best career advice."

[1608] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1609] Step 1:

[1610] User data entry

[1611] Using a device (such as a smartphone or PC), the user inputs their interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment. Specifically, the user inputs information such as "Interests: Biology," "Academic ability: High," "Special skills: Experiments," "Personality: Very curious," and "Aspirations for further education: Medical school" into the device's input form. The input data is entered in "text format," for example.

[1612] Input: Data such as user interests, academic ability, special skills, personality, and educational aspirations

[1613] Output: Input data formatted on the terminal

[1614] Step 2:

[1615] Data transmission by the terminal

[1616] The device internally converts the collected user data into an appropriate format (for example, JSON format). After conversion, the device sends this data to a server via the Internet. As a specific example, User A's data is converted into a JSON-formatted string and sent as "{"interest": "biology", "academic_level": "high", "skills": "experiment", "personality": "curious", "education_wish": "medical school"}".

[1617] Input: formatted user data

[1618] Output: JSON format data sent to the server

[1619] Step 3:

[1620] Data reception by the server

[1621] The server receives the data sent from the device. Specifically, the server's data reception API is called and the received data is saved in the appropriate database.

[1622] Input: User data in JSON format

[1623] Output: User data stored in the database

[1624] Step 4:

[1625] Data formatting and provision by the server

[1626] The server then formats the received data into an analyzable format, extracting it from the database, splitting it into fields, and cleansing the data as needed.Then, it provides the formatted data to the generative AI model.

[1627] Input: User data in the database

[1628] Output: The formatted data that is fed into the generative AI model.

[1629] Step 5:

[1630] Advice generation using generative AI models

[1631] The generative AI model analyzes the provided user data and generates optimal career advice. Specifically, it analyzes information such as interests, academic ability, special skills, personality, and educational aspirations, and generates specific advice such as "Future occupation: doctor or researcher," "Expected annual income: 5 million yen or more from the first year," and "Points to note: be careful of long working hours and excessive stress."

[1632] Input: formatted user data

[1633] Output: Generated career advice

[1634] Step 6:

[1635] Server-customized advice

[1636] The server receives advice from the generative AI model and customizes it based on the characteristics of the school and feedback from the career guidance office. For example, it creates advice optimized for User A, taking into account the school's specific recommendation quotas and the local job market.

[1637] Input: Advice from a generative AI model

[1638] Output: Customized advice

[1639] Step 7:

[1640] Server-based advice sending

[1641] The server transmits the customized advice to the user's terminal. Specifically, the server converts the customized advice into an appropriate format and transmits it to the user's terminal.

[1642] Input: Customized Advice

[1643] Output: Advice sent to the user's terminal

[1644] Step 8:

[1645] Advice display on the device

[1646] The user's device displays the advice received from the server. Specifically, the advice is displayed in a list or card format, providing an interface that the user can understand visually.

[1647] Input: Customized advice sent by the server

[1648] Output: Advice displayed on the screen

[1649] Step 9:

[1650] User feedback input

[1651] The user inputs feedback on the advice provided, for example, by inputting a specific request such as "I would like information on scholarships related to this occupation."

[1652] Input: User feedback on advice

[1653] Output: Feedback typed into the terminal

[1654] Step 10:

[1655] Sending feedback via device

[1656] The terminal receives the user's feedback and transmits it back to the server.

[1657] Input: Feedback entered into the device

[1658] Output: Feedback sent to the server

[1659] Step 11:

[1660] Server receives and stores feedback

[1661] The server receives the feedback and stores it in the database. Specifically, the server receives the feedback data via the receiving API and stores it in the database.

[1662] Input: Feedback sent to the server

[1663] Output: Feedback stored in a database

[1664] Step 12:

[1665] Retraining generative AI models

[1666] The generative AI model will re-train based on new feedback data to improve the accuracy of advice from the next time onwards. For example, new feedback data will be incorporated and the AI ​​model algorithm will be updated.

[1667] Input: Feedback stored in the database

[1668] Output: Improved advice from an updated generative AI model

[1669] In this way, the system of the present invention enables high school students to receive optimal career advice and reduces the worries of career selection.

[1670] (Application example 1)

[1671] 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."

[1672] With conventional career guidance systems, it is difficult to efficiently provide optimal career advice based on individual user characteristics, and they are also unable to meet the needs of career counseling in a virtual environment.It is also difficult to provide accurate individual guidance to many students while reducing the burden on career guidance offices.

[1673] 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.

[1674] In this invention, the server includes means for inputting a user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment, means for transmitting the input data to the server, means for providing the data to a generating AI and generating advice such as optimal future careers, further education, expected annual income, and points to note for the user, and means for accessing the virtual career counseling counter via a smartphone, tablet, smart glasses, or head-mounted display. This makes it possible to provide optimal career advice based on individual user characteristics even in a virtual environment.

[1675] Definitions of important terms included in the claims

[1676] "Users" refer to individuals who use the system, especially high school students who are struggling to choose their career path.

[1677] "Interests" refers to the academic subjects, fields, or activities in which a user is particularly interested.

[1678] "Academic ability" refers to the user's academic performance and level of knowledge.

[1679] "Special skills" refer to skills or techniques that a user is particularly good at.

[1680] "Personality" refers to the user's characteristics and behavioral patterns.

[1681] "Hope and conditions regarding further education or employment" refers to the specific goals and requirements that the user has regarding further education or employment.

[1682] "Means" refers to the methods or tools used to achieve a particular goal.

[1683] "Server" refers to a computer or system that receives, processes, stores, and distributes data sent by users.

[1684] "Database" refers to a collection of information that receives information and stores it in an organized manner so that it can be easily searched and used.

[1685] "Generative AI" refers to an artificial intelligence model that generates optimal advice based on data provided by the user.

[1686] "Terminal" refers to a device, such as a smartphone or computer, that allows a user to access the system and input and receive data.

[1687] A "virtual career counseling counter" refers to a virtual window set up online or in a virtual space that accepts career-related consultations.

[1688] "Feedback" refers to the user's thoughts, opinions, or requests for improvement regarding the advice provided.

[1689] MODE FOR CARRYING OUT THE INVENTION

[1690] System Program

[1691] The present invention is a digital career guidance system for high school students to resolve their worries about career choices. This system is configured as follows.

[1692] Hardware and Software

[1693] 1. User's device

[1694] It uses a smartphone, tablet, smart glasses, or head-mounted display, and these devices have the ability to receive input data from the user and send it to a server.

[1695] 2. Server

[1696] The server is a computer that has the ability to store data received from users and save it in a database. The server also provides data to the generative AI and generates optimal advice.

[1697] 3. Generation AI

[1698] This is an artificial intelligence model that analyzes a user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment, and generates optimal career advice.

[1699] Data processing and calculation

[1700] 1. Data Receipt and Storage

[1701] The server receives the data sent from the user's device and stores it in a database, which allows the systematic management of the user's interests, academic ability, special skills, personality, and hopes and requirements for further education and employment.

[1702] 2. Data Analysis

[1703] The generative AI analyzes the user's characteristics based on data provided by the server. For example, if a user's interests are "biology," their academic ability is "high," their special skill is "experimentation," and their personality is "curious," it will recommend appropriate schools and careers.

[1704] 3. Generating Advice

[1705] The AI ​​generates career advice based on the results of analyzing the user's data. Specific examples are as follows:

[1706] Future career: Doctor or researcher

[1707] Expected annual income: Over 5 million yen from the first year

[1708] Caution: Be careful of long working hours and excessive stress

[1709] 4. Customize your advice

[1710] The server then customizes the advice received from the AI ​​generator to suit the characteristics of the school and sends it to the user's device, providing optimal career advice based on the individual user's characteristics.

[1711] 5. Incorporating feedback

[1712] The server receives user feedback and reflects it in the learning data of the AI ​​generator. For example, if a user provides feedback such as "I would like information about scholarships related to this occupation," the AI ​​generator's algorithm will take this into consideration to improve the accuracy of future advice.

[1713] Examples and prompts

[1714] Examples:

[1715] "User A has entered the following information: Interests: Biology, Academic ability: High, Special skills: Experiments, Personality: Curious, Aspirations: Medical school. Please generate the most appropriate career advice based on the above information."

[1716] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1717] Processing Steps

[1718] Step 1:

[1719] (Input) Using a smartphone, tablet, smart glasses, or head-mounted display, the user inputs their interests, academic ability, special skills, personality, and hopes and requirements for further education or employment.

[1720] The (operational) terminal allows these data to be entered through a user interface.

[1721] (Output) The entered data is stored on the terminal and is ready to be sent.

[1722] Step 2:

[1723] (Input) Data entered by the user.

[1724] (Operation) The terminal converts the input data into a predetermined format and sends it to the server.

[1725] (Output) Data is sent to the server.

[1726] Step 3:

[1727] (Input) The server receives the data sent from the terminal.

[1728] (Operation) The server stores and organizes the received data in a database.

[1729] (Output) The data is saved in the database.

[1730] Step 4:

[1731] (Input) Data received and stored by the server.

[1732] (Operation) The server formats this data and provides it to the generation AI.

[1733] (Output) The data is prepared in the format provided to the generation AI.

[1734] Step 5:

[1735] (Input) Generative AI is organized user data.

[1736] (Operation) The generation AI analyzes the user's interests, academic ability, special skills, personality, and hopes and requirements regarding further education and employment.

[1737] (Output) The optimal career advice is generated.

[1738] Step 6:

[1739] (Input) The generated career advice.

[1740] (Operation) The server receives career advice from the generating AI and customizes it based on the characteristics of each school and feedback from the career guidance office.

[1741] (Output) Customized advice is prepared.

[1742] Step 7:

[1743] (Input) Customized career advice.

[1744] (Operation) The server sends the customized advice to the user's terminal.

[1745] (Output) Advice is displayed on the terminal.

[1746] Step 8:

[1747] (Input) User feedback.

[1748] (Action) The user inputs feedback on the advice provided, for example, asking for information on scholarships.

[1749] (Output) Feedback is input to the terminal.

[1750] Step 9:

[1751] (Input) Feedback entered by the user.

[1752] (Operation) The terminal transmits the input feedback to the server.

[1753] (Output) Feedback is sent to the server.

[1754] Step 10:

[1755] (Input) The server receives the user's feedback.

[1756] (Operation) The server stores the feedback in a database and provides it to the generating AI.

[1757] (Output) Feedback is provided to the generating AI.

[1758] Step 11:

[1759] (Input) Generative AI is new feedback data.

[1760] The (behavior) generative AI re-learns based on feedback data to improve the accuracy of its advice.

[1761] (Output) The advice from next time onwards will be more optimized.

[1762] 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.

[1763] This invention relates to a digital career guidance system that provides optimal career advice that takes emotions into consideration for high school students struggling with career choices. This system uses a generative AI and emotion engine to provide optimal career advice based on the interests, academic ability, special skills, personality, emotions, and hopes and conditions for further education and employment entered by the user (high school student), and improves the accuracy of the system based on feedback.

[1764] Program processing

[1765] Data entry and submission

[1766] 1. User: The user uses a device (smartphone or PC) to input their interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment. As a specific example, "User B" inputs his / her interests "engineering," academic ability "average," special skill "programming," personality "logical," desired education "engineering," and current emotional state "motivated."

[1767] 2. Terminal: The terminal stores the user's input data locally, formats it into a transmission format, and sends it to the server.

[1768] Data reception and analysis

[1769] 3. Server: The server receives the data sent from the device, analyzes the data format, and stores the received data in a database.

[1770] 4. Server: The server converts the data stored in the database into an analytical format and provides it to the emotion engine and generative AI.

[1771] Emotion Recognition and Advice Generation

[1772] 5. Emotion Engine: The emotion engine analyzes the user's emotions from the received data and provides the results to the generative AI. For example, the emotion engine extracts the emotional state of "motivated" as the analysis result.

[1773] 6. Generative AI: The Generative AI begins analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and their hopes and requirements for further education and employment. The Generative AI generates optimal career advice and sends it back to the server. As a concrete example, it analyzes the data of User B and generates the following advice:

[1774] Future job: Software engineer

[1775] Expected annual income: Over 4 million yen from the first year

[1776] Important note: Programming skills need to be continually improved.

[1777] Customize and deliver advice

[1778] 7. Server: The server analyzes the advice data received from the generation AI and customizes it based on feedback from the career guidance office and the characteristics of the school.

[1779] 8. Server: Sends customized advice data to the device.

[1780] 9. Terminal: The terminal analyzes the advice data sent from the server and displays it in the user interface. For example, User B checks the career path as a software engineer, related universities, expected annual salary, and points to consider.

[1781] Feedback and Updates

[1782] 10. User: The user enters feedback on the advice provided. For example, "I would like to know about internship information related to this occupation."

[1783] 11. Terminal: The terminal sends the user feedback to the server.

[1784] 12. Server: The server receives the feedback, stores it in a database, and provides the feedback data to the generative AI to reflect in its training data.

[1785] 13. Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[1786] In this way, the system of the present invention utilizes an emotion engine to provide specific, personalized career advice that takes into account the user's emotions, and incorporates feedback to continuously improve the service.

[1787] The processing flow will be explained below.

[1788] Step 1:

[1789] User: A user (high school student) uses a device (smartphone or PC) to input their interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment. As a specific example, "User B" inputs his / her interests of "engineering," academic ability of "average," special skill of "programming," personality of "logical," desired education of "engineering," and recent emotional state of "motivated."

[1790] Step 2:

[1791] Terminal: The terminal stores the user's input data locally and formats it for transmission. Specifically, it converts the user's input into a data format such as JSON.

[1792] Step 3:

[1793] Terminal: Sends the formatted data to the server as an HTTP request (e.g., POST request). The data sent includes information about the user's interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education or employment.

[1794] Step 4:

[1795] Server: The server receives the data sent from the device, analyzes the data format, extracts the necessary information, and temporarily stores the received data in memory.

[1796] Step 5:

[1797] Server: The analyzed data is stored in a database, including the user's interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education and employment.

[1798] Step 6:

[1799] Server: Converts the data stored in the database into an analytical format and provides it to the emotion engine and generation AI. Specifically, emotion information is sent to the emotion engine, and other data is sent to the generation AI.

[1800] Step 7:

[1801] Emotion engine: The emotion engine analyzes the user's emotions from the received data and provides the results to the generation AI. For example, the emotional state of "motivated" is extracted as the analysis result.

[1802] Step 8:

[1803] Generative AI: The Generative AI begins analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and hopes and conditions regarding further education and employment. The Generative AI generates optimal career advice and sends the results back to the server. As a concrete example, it analyzes User B's data and generates the following advice:

[1804] Future job: Software engineer

[1805] Expected annual income: Over 4 million yen from the first year

[1806] Important note: Programming skills need to be continually improved.

[1807] Step 9:

[1808] Server: The server analyzes the advice data received from the AI ​​generator and customizes it as needed based on feedback from the career guidance office and the characteristics of the school.

[1809] Step 10:

[1810] Server: Prepares customized advice data as an HTTP response to send to the device.

[1811] Step 11:

[1812] Device: The device analyzes the advice data received from the server and displays it through the user interface. For example, User B checks the career path as a software engineer, related universities, expected annual salary, and points to consider.

[1813] Step 12:

[1814] User: The user enters feedback on the advice provided. For example, the user enters additional information such as "I would like to know about scholarships related to this occupation."

[1815] Step 13:

[1816] Device: The device sends the user feedback to the server again.

[1817] Step 14:

[1818] Server: The server receives the feedback, stores it in a database, and provides the feedback data to be reflected in the learning data of the generative AI.

[1819] Step 15:

[1820] Generative AI: The generative AI re-learns based on new feedback data to improve the accuracy of advice generation from the next time onwards.

[1821] In this way, the system of the present invention utilizes an emotion engine to provide specific, personalized career advice that takes into account the user's emotions, and incorporates feedback to continuously improve the service.

[1822] Example 2

[1823] 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."

[1824] While conventional career guidance systems could take into account a user's interests, academic ability, special skills, personality, and hopes and requirements for further education or employment, they were unable to provide career advice that reflected the user's emotional state. Furthermore, they lacked the ability to effectively incorporate user feedback to improve the accuracy of the system. This made it difficult for users to receive advice that they were completely satisfied with when choosing a career path.

[1825] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting a user's interests, academic ability, special skills, personality, emotions, and wishes and conditions regarding further education or employment, means for temporarily saving the input data in the terminal and shaping it in local storage, means for transmitting the shaped data to the server, means for the server to receive the data, analyze the format, and store it in a database, means for converting the data into an analysis format and providing it to an emotion engine and a generation AI, means for the emotion engine to analyze the user's emotions and provide the result to the generation AI, means for the generation AI to generate optimal career advice based on the user's interests, academic ability, special skills, personality, and emotion analysis results and return it to the server, means for the server to receive the generated advice and customize it to suit the characteristics of each school, means for transmitting the customized advice to the terminal and displaying it to the user, and means for transmitting user feedback on the advice back to the server and reflecting it in the learning data of the generation AI. This makes it possible to provide personalized career advice that takes into account the user's emotions and to continuously improve the accuracy of the system by reflecting user feedback.

[1826] A "user" is a person who utilizes the system to input information and provide feedback about their path.

[1827] A "terminal" is a device that a user uses to input and output information, and includes smartphones and personal computers.

[1828] The "server" is a central processing unit that receives and analyzes user input data and provides the data to the generation AI and emotion engine.

[1829] A "database" is a system for storing and managing user input data and analysis results.

[1830] An "emotion engine" is an algorithm or software that analyzes emotions from user input data and provides the results to generative AI.

[1831] "Generative AI" is an artificial intelligence model that generates optimal career advice based on a user's interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education and employment.

[1832] "Advice" refers to recommendations such as future careers, further education, expected annual income, and points to note that are generated by the AI ​​based on the user's input data.

[1833] "Customization" refers to adjusting and changing the advice provided by the generating AI based on the characteristics of each school and feedback from the career guidance office.

[1834] "Feedback" refers to opinions or requests that a user inputs in response to advice provided, and is used as learning data for generating subsequent advice.

[1835] "Local storage" refers to a function and area for temporarily storing data within a terminal.

[1836] The "analysis format" is a format used to format data so that it is easy for the emotion engine and generative AI to understand.

[1837] This invention relates to a digital career guidance system that provides optimal career advice that takes emotions into consideration to individuals struggling with career choices. In this system, a server processes data entered by a user on a terminal, and an emotion engine and generation AI are used to generate optimal career advice and provide it to the user.

[1838] Specific system configuration

[1839] The system includes the following major hardware and software components:

[1840] Terminal: A device such as a smartphone or PC that a user uses to input information. The terminal temporarily stores the input data in local storage and then transmits the data.

[1841] Server: A central processing unit that receives, analyzes, stores, and provides data to the emotion engine and generative AI. Python and Node.js are typical backend technologies used.

[1842] Database: A system that stores and manages received data. Database management systems such as MySQL and PostgreSQL are used.

[1843] Emotion engine: An algorithm or software for analyzing emotions from user input data. An example of this is an emotion analysis model.

[1844] Generative AI: An artificial intelligence model that generates optimal career advice based on a user's interests, academic ability, special skills, personality, emotions, and their hopes and requirements for further education and employment. Specifically, it uses language models such as GPT-3.

[1845] Data entry and submission

[1846] 1. User behavior: The user uses a device to enter information about their interests, academic ability, special skills, personality, feelings, and hopes and requirements for further education or employment using a dedicated app or web form. When entering information, the system is designed to allow users to easily select information using pull-down menus and check boxes.

[1847] Example: User B inputs his / her interests as "Engineering", academic ability as "Average", special skill as "Programming", personality as "Logical", desired education as "Engineering", and emotional state as "Motivated".

[1848] 2. Device operation: The device uses JavaScript to check form input values ​​in real time to prevent input errors, converts the input information into a data format such as JSON, and securely sends it to the server using HTTPS.

[1849] Data reception and analysis

[1850] 3. Server operation: The server receives the data sent from the device, analyzes the format of the received data, and stores it in a database. After analysis, the data is converted into an analytical format that can be understood by the emotion engine and generation AI.

[1851] Emotion Recognition and Advice Generation

[1852] 4. Operation of the emotion engine: The emotion engine analyzes the data provided by the server and recognizes the user's emotional state. For example, it extracts the emotional state of "motivated" and sends it to the generation AI.

[1853] 5. How the Generative AI works: The Generative AI begins its analysis based on the user's interests, academic ability, special skills, personality, emotional analysis results, and hopes and conditions regarding further education and employment. For example, it creates the following advice based on User B's data:

[1854] Future job: Software engineer

[1855] Expected annual income: Over 4 million yen from the first year

[1856] Important note: Programming skills need to be continually improved.

[1857] The generated advice data is returned to the server.

[1858] Customizing and displaying advice

[1859] 6. Server operation: The server analyzes the advice data received from the generation AI and customizes it based on feedback from the career guidance office and school characteristics, for example, adding information about specific university programs or local employment opportunities.

[1860] 7. Terminal operation: The terminal receives the advice data sent from the server and displays it in a user interface, sometimes using graphics and charts to make it easier for the user to understand intuitively.

[1861] Example: User B reviews software engineer career paths, relevant universities, salary expectations, and caveats to consider.

[1862] Feedback and Updates

[1863] 8. User action: The user enters feedback on the advice provided. For example, the user might enter, "I would like to know about internship information related to this occupation."

[1864] 9. Terminal operation: The terminal formats the feedback data and sends it to the server.

[1865] 10. Server operation: The server receives the feedback, stores it in a database, and provides it to the AI ​​generator to use as learning data for future advice generation.

[1866] 11. How the Generative AI works: The Generative AI retrains based on new feedback data to improve the accuracy of advice generation from the next time onwards. For example, it may include internship information in the advice.

[1867] In this way, the system can provide personalized navigation advice while taking into account the user's emotions and incorporate feedback to continuously improve the system's accuracy.

[1868] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1869] Step 1:

[1870] User Action:

[1871] Using a device, users input their interests, academic ability, special skills, personality, emotions, and hopes and requirements for further education or employment. Specifically, they select information using check boxes and pull-down menus on a dedicated smartphone or PC app or web form, and enter detailed information in text fields. For example, they input information such as "Engineering," "Middle School," "Programming," "Logical," "Faculty of Engineering," and "Motivated."

[1872] Input: Interests, academic ability, special skills, personality, emotions, and desired conditions for further education or employment.

[1873] Output: The information entered by the user is temporarily stored in the device's local storage.

[1874] Step 2:

[1875] Terminal behavior:

[1876] The device temporarily stores the data entered by the user in local storage and converts it to a data format such as JSON. Next, it checks the input data for errors in real time using JavaScript or other methods and displays an error message. If there are no errors, it securely sends the data to the server using HTTPS.

[1877] Input: Data entered by the user.

[1878] Output: The data is converted to JSON format and sent to the server.

[1879] Step 3:

[1880] Server behavior:

[1881] The server receives the data sent from the device and analyzes the data format. Specifically, it uses backend technologies such as Python and Node.js to parse the received data and convert it into a format that can be saved in a database. The analyzed data is then stored in the database.

[1882] Input: JSON format data sent from the terminal.

[1883] Output: The parsed data is stored in a database.

[1884] Step 4:

[1885] Server behavior:

[1886] The server re-analyzes the data stored in the database and formats it to be provided to the emotion engine and generation AI. Specifically, it selects the necessary information and converts it into an appropriate format so that the emotion engine can accurately analyze the user's emotions. The data is then provided to the emotion engine and generation AI.

[1887] Input: User data stored in the database.

[1888] Output: Formatted data to feed into the emotion engine and generative AI.

[1889] Step 5:

[1890] Emotion Engine in action:

[1891] The emotion engine analyzes the data provided by the server and recognizes the user's emotional state. Using an emotion analysis model, it identifies the emotional state, for example, "motivated," and sends the results in JSON format to the generation AI.

[1892] Input: Formatted data provided by the server.

[1893] Output: Parsed emotion data is sent to the generation AI in JSON format.

[1894] Step 6:

[1895] Generative AI behavior:

[1896] The generation AI generates optimal career advice based on the emotion data provided by the emotion engine and other user data provided by the server. For example, it suggests a career path such as "software engineer" to User B and generates advice including expected annual salary and points to note. This generated advice is then sent back to the server.

[1897] Input: Emotion data provided by the emotion engine, other user data provided by the server.

[1898] Output: The generated career advice data.

[1899] Step 7:

[1900] Server behavior:

[1901] The server analyzes and customizes the career advice data received from the generation AI. Specifically, it adjusts the advice content to match feedback from the career guidance office and the characteristics of the university. For example, it adds information about specific university recommendation programs and regional specializations. This customized advice data is then sent to the device.

[1902] Input: Career advice data received from the generation AI.

[1903] Output: Customized career advice data.

[1904] Step 8:

[1905] Terminal behavior:

[1906] The device receives the customized advice data sent from the server and displays it in a user interface. Specifically, it uses a responsive design, displaying advice content according to the screen size of a smartphone or PC. Graphics and charts may be used to make the advice easier for users to understand intuitively.

[1907] Input: Customized advice data sent from the server.

[1908] Output: Career advice displayed in the user interface.

[1909] Step 9:

[1910] User Action:

[1911] The user inputs feedback on the advice provided, for example, a request or opinion such as "I would like to know more about internships related to this occupation."

[1912] Input: Feedback on career advice provided.

[1913] Output: The feedback data is saved to the device.

[1914] Step 10:

[1915] Terminal behavior:

[1916] The terminal formats the user's feedback data and sends it back to the server, where the feedback data is an important factor for subsequent advice generation.

[1917] Input: User feedback data.

[1918] Output: The formatted feedback data is sent to the server.

[1919] Step 11:

[1920] Server behavior:

[1921] The server receives the feedback data, stores it in a database, and provides it to the generation AI to update the advice generation algorithm.

[1922] Input: Formatted feedback data.

[1923] Output: Feedback data stored in a database, providing feedback to the generative AI.

[1924] Step 12:

[1925] Generative AI behavior:

[1926] The AI ​​will retrain based on new feedback data to improve the accuracy of future career advice generation. For example, based on the feedback, it will be able to include internship information in the next advice.

[1927] Input: Feedback data provided by the server.

[1928] Output: The updated advice generation algorithm.

[1929] (Application example 2)

[1930] 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."

[1931] In factories and industrial sites, improving employees' skills and choosing career paths is important, but providing individual advice that takes into account the characteristics and feelings of each employee is difficult. Furthermore, there are currently no effective ways to provide advice on the skills and careers that employees need. To solve these issues, a system is needed that can accurately grasp employees' interests, current skills, desired career paths, and motivation for skill improvement, and provide optimal advice.

[1932] 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.

[1933] In this invention, the server includes: means for inputting a user's interests, academic ability, special skills, personality, emotions, and hopes and requirements regarding further education and employment; means for transmitting the input data to the server; means for the server to receive the data and store it in a database; means for providing the data to a generation AI and generating advice on the user's optimal future career, future education, expected annual income, points to note, etc.; means for the server to receive the generated advice and customize it to suit the characteristics of each organization; means for transmitting the customized advice to a terminal and displaying it to the user; means for transmitting user feedback on the advice back to the server and reflecting it in the learning data of the generation AI; means for the user to input and provide the feedback using a smart terminal; and means for installing the system on a robot to provide career advice to factory employees. This enables personalized advice that takes into account the characteristics and emotions of each employee.

[1934] A "user" is a person or employee who uses the system to input information about interests, academic ability, special skills, personality, emotions, and hopes and conditions regarding further education or employment.

[1935] "Interests" refers to information about areas or activities in which a user is particularly interested.

[1936] "Academic ability" refers to information indicating the user's current educational level and learning progress.

[1937] "Special skills" refers to information about skills or abilities that a user excels at compared to others.

[1938] "Personality" refers to internal characteristics that indicate a user's tendencies in behavior and reactions.

[1939] "Emotion" refers to information that indicates the user's psychological state or sensation at that time.

[1940] "Aspirations and conditions regarding further education and employment" refers to the desired educational destination or employment destination of the user and related conditions (such as work location and working hours).

[1941] "Server" means a computer system that receives, stores, and analyzes data submitted by users and provides customized generated advice.

[1942] "Database" refers to an information storage system for storing and managing user data and feedback data received by the server.

[1943] "Generative AI" refers to an artificial intelligence model that generates optimal advice based on user data.

[1944] "Customizing" means optimizing the generated advice based on the characteristics of each organization and user feedback.

[1945] A "user interface" is a screen or device through which a user inputs data, receives advice, and provides feedback.

[1946] "Feedback" refers to information that a user sends to the server, such as their thoughts on the advice provided and suggestions for improvement.

[1947] "Robots" are automated machines that install and provide information on career advice systems.

[1948] This invention relates to a career consulting system for factory employees that applies a digital career guidance system. This system uses generative AI and an emotion engine to provide optimal career advice based on the interests, skills, desired career path, and emotional state input by the user (employee).

[1949] First, a user inputs their interests, current skills, special abilities, personality, emotions, and career aspirations and requirements from a smart terminal or interface device. For example, they might enter data such as "I want to improve my welding skills" or "I have a high motivation to work hard." The terminal then stores this data locally, formats it into a transmission format, and sends it to the server.

[1950] The server receives the data sent from the device, analyzes the data format, and stores it in a database. The received data is converted into an analytical format and provided to the emotion engine and generation AI. The emotion engine analyzes the user's emotions from the received data and provides the results to the generation AI. As a specific example, the emotion engine extracts the emotional state of "high motivation to make an effort" as the analysis result.

[1951] The AI ​​begins its analysis based on the user's interests, skills, special abilities, personality, emotional analysis results, and career aspirations and requirements. The AI ​​generates optimal career advice and sends it back to the server. For example, it might suggest a curriculum or course to improve specific skills to become a senior welding engineer.

[1952] The server analyzes the advice data received from the AI ​​generator and customizes the advice based on each organization's characteristics and past feedback. Specifically, it adds appropriate training programs and learning resources. This customized advice is then sent back to the device and displayed in the user interface.

[1953] Users can input feedback on the advice provided, adding information such as "I would like to know about internships related to this occupation." This feedback is sent from the device to the server and stored in a database. The AI ​​then retrains itself based on the new feedback data, improving the accuracy of its advice generation from the next time onwards.

[1954] The hardware used includes smart devices and tablets used by each user, and server systems that perform analysis and calculation processing, while the software used includes databases (e.g., MySQL or PostgreSQL), emotion engines (e.g., Google Cloud Natural Language API), and generative AI (e.g., OpenAI GPT-4).

[1955] Examples of specific prompts include:

[1956] "Interests: Welding, Current Skills: Intermediate, Desired Career Path: Senior Welder, Emotions: Highly motivated to work hard."

[1957] In this way, the system of the present invention provides optimal career advice to factory employees that takes their emotions into consideration, helping users improve their skills. Furthermore, by incorporating user feedback, the system can continuously improve the accuracy and effectiveness of its services.

[1958] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1959] Step 1:

[1960] Users input their interests, skills, special abilities, personality, emotions, and career aspirations and requirements using a smart terminal or interface device. Examples of input data include "Interests: Welding," "Skills: Intermediate," "Desired career path: Senior welding engineer," and "Emotions: Highly motivated to work hard."

[1961] Step 2:

[1962] The terminal locally stores the information entered by the user. After saving, the data is formatted for transmission and sent to the server. Here, the input is the user's information and the output is the formatted data.

[1963] Step 3:

[1964] The server receives data sent from the terminal and analyzes the data format. The analyzed data is stored in a database. The input is the received data, and the output is the data stored in the database.

[1965] Step 4:

[1966] The server converts the data stored in the database into an analytical format and provides it to the emotion engine and generation AI. The input is the data in the database, and the output is the data in the analytical format.

[1967] Step 5:

[1968] The emotion engine analyzes the user's emotions from the data in the analysis format. For example, it extracts the emotional state of "high motivation to make an effort." The input is the data in the analysis format, and the output is the emotion analysis result.

[1969] Step 6:

[1970] The generation AI begins analysis based on the user's interests, skills, special abilities, personality, emotional analysis results, and career-related aspirations and requirements. The generation AI generates optimal career advice and sends it back to the server. For example, it might suggest "curriculum and courses for becoming a senior welding engineer." The input is user data and the results of emotional analysis, and the output is career advice.

[1971] Step 7:

[1972] The server analyzes the advice data received from the generative AI and customizes the advice based on each organization's characteristics and past feedback. For example, it adds specific training programs and learning resources. The input is career advice from the generative AI, and the output is customized advice.

[1973] Step 8:

[1974] The server sends the customized advice to the terminal and displays it to the user. The input is the customized advice, and the output is the advice displayed on the user terminal.

[1975] Step 9:

[1976] The user inputs feedback for the provided advice. For example, the user adds a comment such as, "I would like to know about internship information related to this occupation." The input is the user's feedback, and the output is the transmission of the feedback data by the terminal.

[1977] Step 10:

[1978] The terminal sends the user's feedback to the server and stores it in a database. The input is the feedback data, and the output is the storage in the database.

[1979] Step 11:

[1980] The server provides feedback data to the AI ​​generator, which then retrains based on the new data. This improves the accuracy of advice generation from the next time onwards. The input is the feedback data, and the output is an improved algorithm of the AI ​​generator.

[1981] 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.

[1982] 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.

[1983] 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.

[1984] 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.

[1985] 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.

[1986] 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.

[1987] 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).

[1988] 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.

[1989] 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."

[1990] 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.

[1991] 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).

[1992] 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.

[1993] 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.

[1994] 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.

[1995] 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.

[1996] 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.

[1997] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1998] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1999] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2000] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2001] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2002] The following is further disclosed regarding the above embodiment.

[2003] (Claim 1)

[2004] A means for inputting the user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment;

[2005] means for transmitting the input data to a server;

[2006] means for the server to receive the data and store it in a database;

[2007] A means for providing the data to a generation AI and generating advice on the user's optimal future career, future education, expected annual income, points to note, etc.;

[2008] A server receives the generated advice and customizes it to suit the characteristics of each school;

[2009] means for transmitting the customized advice to the terminal and displaying the advice to the user;

[2010] A means for transmitting the user's feedback regarding the advice to the server again and reflecting it in the learning data of the generation AI;

[2011] A system including:

[2012] (Claim 2)

[2013] 2. The system according to claim 1, wherein the generated advice is customized based on the user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education or employment.

[2014] (Claim 3)

[2015] 10. The system of claim 1, wherein the generation AI includes means for receiving user feedback and updating the generation AI's algorithm.

[2016] "Example 1" ...

Claims

1. A means for inputting the user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education and employment; means for transmitting the input data to a server; means for the server to receive the data and store it in a database; A means for providing the data to a generation AI and generating advice on the user's optimal future career, future education, expected annual income, points to note, etc.; A server receives the generated advice and customizes it to suit the characteristics of each school; means for transmitting the customized advice to the terminal and displaying the advice to the user; A means for transmitting the user's feedback regarding the advice to the server again and reflecting it in the learning data of the generation AI; A system including:

2. 2. The system according to claim 1, wherein the generated advice is customized based on the user's interests, academic ability, special skills, personality, and hopes and conditions regarding further education or employment.

3. 10. The system of claim 1, wherein the generating AI includes means for receiving user feedback and updating the generating AI's algorithms.

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