System

Abstract

A system is provided.SOLUTION: A system comprising: means for causing a user to input information on learning experience, skill, knowledge, interest, and goal; means for converting the information into a data format and transmitting the data format to a server; means for detecting an appropriate occupation or company based on the information and executing a process of giving a score; means for generating a list of appropriate occupations or companies based on the score; and means for displaying the list to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] This invention relates to a system that helps students who are job-hunting understand their own aptitudes and find the right job. Conventional job information and employment agencies fail to fully consider students' diverse learning experiences, skills, interests, and future goals, making it difficult for students to find the job or company that best suits them. There is a need for a system that solves this problem and helps students efficiently find the job or company that best suits their characteristics. [Means for solving the problem]

[0005] The present invention solves the aforementioned problems by providing a system that includes: a means for allowing a user to input information about learning experience, skills, knowledge, interests, and goals; a means for converting the information into a data format and transmitting it to a server; a means for detecting suitable occupations or companies based on the information and assigning scores; a means for generating a list of suitable occupations or companies based on the scores; and a means for displaying the list to the user, thereby helping students find the occupation or company that best suits them. Furthermore, by introducing a means for assigning scores using different weightings for learning experience, skills, knowledge, interests, and goals, more accurate job recommendations are possible. Furthermore, by adding a means for visually displaying the scores of recommended companies based on the user's input information, the system allows users to easily understand the system visually.

[0006] "User" refers to a person who uses the system to enter information about suitable occupations and companies.

[0007] "Learning experience" refers to the knowledge and skills a user has acquired in life, particularly the subjects and specialties they studied during their student life.

[0008] "Skills" refer to specific skills or abilities that a user possesses, such as programming, data analysis, leadership, etc.

[0009] "Knowledge" refers to a user's understanding and accumulation of information about a particular field or topic.

[0010] "Interests" refer to subjects, activities, particular fields or hobbies that a user is interested in.

[0011] "Goals" refers to the goals and aspirations that the user wants to achieve in the future, such as aiming to become a manager or wanting to work overseas.

[0012] A "means" refers to a method or process, or a function or device used to achieve a particular purpose.

[0013] "Aptitude" refers to the degree of match or compatibility that indicates how suitable a particular occupation or company is based on the user's characteristics.

[0014] "Score" refers to a numerical value calculated as a result of matching the user's information with the characteristics of their occupation and company.

[0015] A "list" refers to a list of occupations or companies arranged in order of suitability based on scores. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The system of the present invention is designed to help students find the jobs and companies that best suit their characteristics. The main components of the system are users, terminals, and servers. Specific embodiments of each part of the system are described below.

[0038] User Input

[0039] A web form or mobile application interface is provided for users to enter their information. Users enter their learning experiences, skills, knowledge, interests, and future goals into the form. For example, if a user studies economics, has skills in data analysis, is interested in leadership, and aspires to a managerial position, they would enter that information.

[0040] Data transmission from the device

[0041] The terminal receives the information entered by the user and converts it into a data format (for example, JSON format). The converted data is then sent to the server. The terminal has an application or script for data transmission built in, and the data is sent automatically once the user has completed entering the information.

[0042] Server data processing and analysis

[0043] When the server receives the data sent from the device, it first analyzes the received data. The algorithm used in the analysis compares the received data with the characteristics of jobs and companies stored in the database. The server assigns different weights to learning experience, skills, knowledge, interests, and goals, and calculates the degree of suitability for each job and company. This suitability is calculated as a score, and the most suitable jobs and companies are given higher scores.

[0044] Generate and send a list based on the score

[0045] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, based on the user's input data, a list may be generated in which Company A has a score of 90, Company B has a score of 78, and Company C has a score of 66. This list is then converted back to JSON format and sent to the device.

[0046] Displaying results on a terminal

[0047] The device visualizes the received list and displays it to the user, who can then browse the displayed list to find out more information about the companies and jobs that suit them. For example, with the help of a good UI design, the results can be displayed in a ranking format, with links to access more detailed information about each company or job.

[0048] Specific examples

[0049] For example, suppose student Taro Yamada uses a device to enter the following information:

[0050] Learning experience: Economics, Programming

[0051] Skills: Data analysis, leadership

[0052] Knowledge: Basic knowledge of IT, economics

[0053] Interests: Technology, startups

[0054] Goals: Aspiring to become a manager and want to work overseas

[0055] When Taro Yamada submits his input information, the server analyzes the data and calculates suitable jobs and companies. For example, technology companies might score 90, financial consulting firms 78, and startups 66. The results are sent to his device, where Taro Yamada can visually check the rankings and access detailed information about each company.

[0056] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find jobs and companies that best suit their characteristics.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] Users enter information using their own devices. Specifically, they fill out an input form with items such as their learning experience, skills, knowledge, interests, and future goals. For example, they enter information such as economics, data analysis, leadership, technology, or aspiring to a management position.

[0060] Step 2:

[0061] The terminal acquires the information entered by the user and converts it into a data format (for example, JSON format). The converted data will be in the following format, for example.

[0062] json

[0063] {

[0064] "Things Learned": ["Economics", "Programming"],

[0065] "Skills": ["Data Analysis", "Leadership"],

[0066] "Knowledge": ["Basic knowledge of IT", "Economics"],

[0067] "Interests": ["Technology", "Startups"],

[0068] "Goals": ["I want to become a manager", "I want to work overseas"]

[0069] }

[0070] Step 3:

[0071] The terminal sends the converted data to the server using an HTTP POST request, which allows the server to receive the user information.

[0072] Step 4:

[0073] The server receives the data sent from the device. After receiving the data, the server begins analyzing the data. The analysis calculates a score for suitability for each job or company based on learning experience, skills, knowledge, interests, and goals.

[0074] Step 5:

[0075] The server uses an analytical algorithm to match the user's information with job and company information in a database, weighting it and calculating a score for each item (learning experience, skills, knowledge, interests, and goals). For example, if knowledge of economics matches the requirements of a financial company, a high score will be awarded.

[0076] Step 6:

[0077] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, the list might look like this: "Company A: 90 points," "Company B: 78 points," "Company C: 66 points."

[0078] Step 7:

[0079] The server sends the generated list to the terminal. The data sent is in JSON format, for example, as follows:

[0080] json

[0081] [

[0082] {"Company": "Company A", "Score": 90},

[0083] {"Company": "Company B", "Score": 78},

[0084] {"Company": "Company C", "Score": 66}

[0085] ]

[0086] Step 8:

[0087] The terminal visualizes the list received from the server and displays it to the user. The user can view the displayed list and check detailed information about each company or occupation. The display is provided in a visually easy-to-understand interface.

[0088] Step 9:

[0089] Users can select jobs and companies that fit their profile from the displayed list, obtain more information, and access links and contact information to further their application process.

[0090] The above are the specific processing steps of the system.

[0091] Example 1

[0092] 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."

[0093] Conventional career aptitude analysis systems have the problem that they are unable to fully reflect a user's individual learning experience, skills, knowledge, preferences, and goals, and therefore have low accuracy in proposing suitable occupations and companies, making it difficult for users to find the optimal career path. In particular, since it is difficult to analyze taking into account the diverse backgrounds and goals of users, there is a demand for highly accurate recommendations that are tailored to individual characteristics.

[0094] 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.

[0095] In this invention, the server includes means for allowing a user to input information about their learning experiences, skills, knowledge, preferences, and goals, means for converting the information into a standard data format and transmitting it to the server, means for detecting suitable occupations or companies based on the information and assigning scores, means for generating a list of suitable occupations or companies based on the scores, and means for visually displaying the list to the user, thereby enabling highly accurate personalized recommendations based on the user's diverse characteristics.

[0096] "User" refers to a person who uses the system to input information about their learning experiences, skills, knowledge, preferences, and goals.

[0097] "Learning experience" refers to information including the knowledge and skills that the user has acquired so far, educational history, and the like.

[0098] "Skills" refers to specific abilities or expertise possessed by a user, including programming skills and data analysis abilities.

[0099] "Knowledge" refers to the level of knowledge or understanding a user has in a particular field.

[0100] "Preferences" refer to the fields, occupations, and company characteristics that the user is interested in.

[0101] "Goals" refer to the professional objectives or career paths that a user wants to achieve in the future.

[0102] "Standard data format" refers to a common data representation format such as JSON that is suitable for sending, receiving, and analyzing data.

[0103] "Server" refers to the central computer system that processes the information received from users, identifies suitable jobs and companies, assigns scores, and generates a list.

[0104] The "score" refers to the numerical value of the server's evaluation of the suitability of a job or company based on the user's information.

[0105] A "list" refers to a ranking of occupations or companies based on scores.

[0106] "Visually displaying" refers to presenting results to the user in a graphical format that makes them easier to understand.

[0107] "Analysis" refers to the process by which the server evaluates suitability based on the information entered by the user.

[0108] "Means for performing processing" refers to the functionality of the server to analyze user information using a specific algorithm and generate a result.

[0109] "Weighting" refers to the method of calculating points based on different weightings assigned to learning experiences, skills, knowledge, preferences, and goals.

[0110] The system of the present invention allows users to input information about their learning experiences, skills, knowledge, preferences, and goals, and then analyzes the data to recommend suitable jobs and companies. The system consists of three main components: the user, the terminal, and the server.

[0111] Enter user information

[0112] To enter their information, users use a web form or mobile application interface that allows them to enter the following information:

[0113] Learning experience (e.g., economics, programming)

[0114] Skills (e.g., data analysis, leadership)

[0115] Knowledge (e.g., basic knowledge of IT, economics)

[0116] Preferences (e.g., technology, startups)

[0117] Goals (e.g., wanting to become a manager or working overseas)

[0118] The user enters the information and clicks the "Submit" button to proceed to the next step.

[0119] Data conversion and transmission on the device

[0120] The terminal receives the information entered by the user and converts it into a standard data format (for example, JSON format).The converted data is then sent to the server using an HTTP POST request.Scripts such as JavaScript or Python are implemented on the terminal, and the information transmission process is carried out automatically.

[0121] Data reception and analysis on the server

[0122] The server receives the JSON data sent from the device. The server uses the Python Flask framework to manage the data reception and analysis process. The server then uses machine learning algorithms to match the received data with occupation and company characteristics stored in the database.

[0123] Generate a list of jobs and companies based on scores

[0124] The server assigns weights to each data item (learning experience, skills, knowledge, preferences, and goals) and calculates the degree of suitability for each job or company. Based on the calculated suitability, the server generates a list of jobs or companies in descending order of scores. For example, the server generates the following list:

[0125] Company A: 90 points

[0126] Company B: 78 points

[0127] Company C: 66 points

[0128] The generated list is converted back to JSON format and sent to the terminal as an HTTP response.

[0129] Displaying results on your device

[0130] The device parses the received JSON data and visually displays the results in a ranking format using JavaScript frameworks such as React and Vue.js. Users can review the displayed list to identify career and company options that suit them. Links to access more information about each company and career are also provided.

[0131] Specific examples

[0132] For example, if a student enters the following information:

[0133] Learning experience: Economics, Programming

[0134] Skills: Data analysis, leadership

[0135] Knowledge: Basic knowledge of IT, economics

[0136] Preferences: Technology, startups

[0137] Goals: Aspiring to become a manager and want to work overseas

[0138] Based on this information, the server performs analysis and calculates the companies and jobs that are suitable for the student, as follows:

[0139] Technology companies: 90 points

[0140] Financial Consulting Firm: 78 points

[0141] Startups: 66 points

[0142] The results are sent to a device where students can visually view the rankings and access detailed information about each company.

[0143] Prompt Sentence Examples

[0144] "Use a generative AI model to generate a list of suitable jobs and companies based on user inputs about their learning experience, skills, knowledge, preferences, and goals. For example, if a user studies economics, has data analysis skills, is interested in leadership, and aspires to a management position, consider that information when outputting results."

[0145] The above is a specific embodiment for carrying out the present invention. This system allows users to efficiently find jobs and companies that best suit their characteristics.

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

[0147] Step 1: User Enters Information

[0148] Users enter their learning experience, skills, knowledge, preferences, and goals using a web form or mobile application. For example, a user might enter information such as "Economics," "Data Analysis," "IT Basics," "Technology," or "Aiming for a Managerial Position." This information becomes input data for subsequent analysis.

[0149] Step 2: The device converts the user data

[0150] The terminal converts the received user data into a standard data format (for example, JSON format). Specifically, the data is structured using a JavaScript or Python script, and the converted JSON data is generated. This JSON data is then prepared for transmission to the server.

[0151] Step 3: The device sends the data to the server

[0152] The device sends the converted JSON data to the server using an HTTP POST request, setting the appropriate content type (e.g., application / json) in the HTTP header to ensure the data reaches the server correctly. If the data is successfully sent, the server returns a receipt confirmation as an HTTP response.

[0153] Step 4: The server receives the data

[0154] The server receives the JSON data sent from the device. It uses the Python Flask framework to parse the received data. The server parses the request, converts the received data into the appropriate format, and prepares it for processing.

[0155] Step 5: The server parses the data

[0156] The server performs an analysis process based on the received data to identify suitable occupations and companies. Specifically, it assigns different weights to learning experience, skills, knowledge, preferences, and goals, and calculates the suitability for each occupation and company. The analysis is performed using a machine learning algorithm, and a score is calculated.

[0157] Step 6: The server generates a list based on the scores

[0158] The server generates a list of suitable jobs and companies for the user based on the obtained scores. For example, it ranks the jobs and companies with the highest scores. The generated list is then converted back to JSON format and prepared for transmission to the device.

[0159] Step 7: The server sends the list to the device

[0160] The server sends the generated list to the device as an HTTP response. The response contains a list of ranked occupations and companies in JSON data format. Once the device receives this data, it can proceed to the next step.

[0161] Step 8: The terminal displays the results visually

[0162] The device parses the received JSON data and displays it visually to the user, using JavaScript frameworks such as React and Vue.js to display a ranking list, with links to access more information about each job or company.

[0163] Step 9: User accesses additional information

[0164] Users can select the jobs or companies they are interested in from the displayed list and click to access more information. Clicking on a link opens a new page or modal window with more information, allowing users to gain a deeper understanding of the jobs and companies that are best suited to them.

[0165] (Application example 1)

[0166] 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."

[0167] In today's labor market, it is difficult for factory workers to find the job or department that best suits their skills and experience. Furthermore, if workers are not assigned to the appropriate job or department, there is a concern that their motivation and efficiency will decline. Particularly in a factory environment with a wide variety of jobs, it is important to identify the job that best suits each worker's characteristics. This will maximize worker performance and improve the working environment.

[0168] 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.

[0169] In this invention, the server includes means for allowing users to input information about their learning experiences, skills, knowledge, interests, and goals, means for detecting suitable occupations or companies based on the information and assigning scores, and means for evaluating the job aptitude of factory workers and quantifying the degree of suitability, thereby enabling factory workers to find the job or department that best suits their characteristics.

[0170] A "user" is someone who uses the system to input information.

[0171] "Learning experience" is information about the subjects the user has studied and the knowledge they have acquired.

[0172] "Skills" is information about specific techniques and abilities that a user has acquired.

[0173] "Knowledge" is the extent of a user's general or specialized understanding or information.

[0174] "Interests" are information about areas or topics that a user is personally interested in.

[0175] "Goals" are information about the job or career that the user wants to achieve in the future.

[0176] A "data format" is a standardized format for handling information electronically.

[0177] A "server" is a computer system that receives information entered by a user and analyzes and processes it.

[0178] The "score" is a numerical representation of the degree of compatibility between the user's characteristics and the occupation or company.

[0179] "Occupation or company" refers to the job the user is looking to work for or the organization for which the user works.

[0180] A "list" is a ranked collection of jobs and companies organized based on scores.

[0181] "Factory workers" are workers who perform various tasks in factories.

[0182] "Job fit" is an assessment of how well a worker's characteristics suit a particular job.

[0183] A "visually displayed result" is a due diligence result that is presented to a user graphically.

[0184] The system of this invention helps users find the jobs and companies that best suit their characteristics. It has a function to recommend jobs that are particularly suitable for factory workers. The system is mainly composed of users, terminals, and a server.

[0185] User Input Process

[0186] First, the user enters information about themselves using a device. This information includes learning experiences, skills, knowledge, interests, and goals. A dedicated application is installed on the device and collects the information entered by the user. The information is entered using a device such as a smartphone, tablet, or PC.

[0187] Data transmission by the terminal

[0188] The terminal first converts the information entered by the user into a data format. Specifically, data is generated in JSON format. The converted data is automatically sent to the server. Scripts and programs for sending the data are built into the terminal.

[0189] Server data processing and analysis

[0190] The server receives data sent from the device. The received data is first analyzed and evaluated based on factors such as learning experience, skills, knowledge, interests, and goals. Based on this, the server calculates a score for suitable occupations and companies, and also evaluates job suitability, especially for factory workers. Python + Flask is used for the data processing algorithm.

[0191] Generate and send a list based on the score

[0192] The server generates a list of jobs and companies based on the calculated score, in order of suitability. For factory workers, a list of the most suitable jobs and departments is generated. The generated list is then sent to the device in JSON format.

[0193] Displaying results on a terminal

[0194] The terminal receives the list sent from the server and visually displays it to the user. The user can check the list in a ranked format and access detailed information about each occupation and company. Visualizing recommended jobs, especially for factory workers, makes it easier for users to make appropriate choices. This is expected to improve worker performance and job satisfaction.

[0195] Specific examples

[0196] For example, a factory worker enters the following information into a terminal:

[0197] Learning experience: Line work, maintenance

[0198] Skills: screw tightening, welding, product inspection

[0199] Knowledge: Seven QC Tools

[0200] Interests: Quality control, safety management

[0201] Goals: Quality control manager, small group activity leader

[0202] Based on the information sent, the server analyzes it and assigns a score to each suitable job or department: a quality control department might get 90 points, a production line supervisor 80 points, and a maintenance technician 70 points. This information is returned to the terminal, where the user can view the results in a visual format.

[0203] Prompt Sentence Examples

[0204] The information entered by the user is in the following format:

[0205] Learning experience: Line work, maintenance

[0206] Skills: screw tightening, welding, product inspection

[0207] Knowledge: Seven QC Tools

[0208] Interests: Quality control, safety management

[0209] Goals: Quality control manager, small group activity leader

[0210] In this way, the system allows factory workers to easily find the job or department that best suits their characteristics.

[0211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0212] Step 1:

[0213] Users use a device to input information about their learning experiences, skills, knowledge, interests, and goals. The input information is collected through web forms or mobile application interfaces. Specific examples of information input include users entering work experience such as "line work" or "maintenance," or skills such as "screw tightening" or "welding."

[0214] Input: Learning experiences, skills, knowledge, interests, goals

[0215] Output: Input data (text format)

[0216] Step 2:

[0217] The terminal converts the information entered by the user into JSON format, and the converted JSON data is sent to the server by a script or program for data transmission.

[0218] Input: Input data (text format)

[0219] Output: Input data (JSON format)

[0220] Step 3:

[0221] The server receives the JSON data sent from the device, which is first parsed and divided into categories (learning experience, skills, knowledge, interests, and goals).

[0222] Input: Input data (JSON format)

[0223] Output: Analysis data (categorized data)

[0224] Step 4:

[0225] The server then uses the analytics data to generate a score for each category. The algorithm then assigns different weights to learning experiences, skills, interests, and goals, and assigns points to jobs and companies that are suitable for the candidate. For factory workers, the system also evaluates job suitability.

[0226] Input: Analysis data

[0227] Output: Score data (scores for each job and company)

[0228] Step 5:

[0229] The server generates a list of jobs and companies in order of suitability based on the calculated score. In particular, for factory workers, a list of the most suitable jobs and departments is generated. The generated list is again sent to the terminal in JSON format.

[0230] Input: Score data

[0231] Output: Eligibility list (JSON format)

[0232] Step 6:

[0233] The terminal receives the suitability list sent from the server and visually displays it to the user. The user can check the list in a ranked format and access detailed information about each occupation and company. As a result, factory workers can find the job or department that best suits them.

[0234] Input: Eligibility list (JSON format)

[0235] Output: Visual display (ranked list)

[0236] 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.

[0237] The system of the present invention helps students find the occupation or company that best suits their characteristics. It is particularly distinctive in that it incorporates an emotion engine that recognizes the user's emotions and suggests suitable careers based on those emotions. The main components of the system are the user, terminal, server, and emotion engine. Specific embodiments of each part of the system are described below.

[0238] User Input

[0239] A web form or mobile application interface is provided for users to enter their information. Users enter their learning experiences, skills, knowledge, interests, and future goals into the form. For example, if they studied economics, have skills in data analysis, are interested in leadership, and aspire to a managerial position, they enter that information.

[0240] Data transmission from the device

[0241] The terminal receives the information entered by the user and converts it into a data format (for example, JSON format). The converted data is then sent to the server. The terminal has an application or script for data transmission built in, and the data is sent automatically once the user has completed entering the information.

[0242] Server data processing and analysis

[0243] When the server receives the data sent from the device, it first begins analyzing the data and calculates a score based on the user's learning experience, skills, knowledge, interests, and goals to determine their suitability for each job or company.

[0244] Emotion Engine Operation

[0245] The emotion engine recognizes emotions from user input information and, in some cases, from the user's facial expression, tone of voice, and typing speed. This emotional information influences the suggestions of suitable jobs and companies. For example, the suggestions of suitable jobs and companies will differ depending on whether the user is relaxed or stressed.

[0246] Generate and send a list based on the score

[0247] The server calculates the score taking into account the information from the emotion engine and generates a list of occupations and companies in order of suitability. For example, a list might be generated such as "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points." This list is then converted back to JSON format and sent to the device.

[0248] Displaying results on a terminal

[0249] The device visualizes the received list and displays it to the user, who can then view the list and find out more information about the companies and jobs that suit them. For example, the device can display the results in a ranking format through a well-designed UI, and provide links to access more detailed information about each company or job.

[0250] Specific examples

[0251] For example, suppose student Taro Yamada uses a device to enter the following information:

[0252] Learning experience: Economics, Programming

[0253] Skills: Data analysis, leadership

[0254] Knowledge: Basic knowledge of IT, economics

[0255] Interests: Technology, startups

[0256] Goals: Aspiring to become a manager and want to work overseas

[0257] When Taro Yamada submits his input, the server analyzes the data and the emotion engine recognizes his emotional state. For example, if he is in a relaxed state, companies that offer a challenging environment might be given higher scores. As a result, technology companies might receive a score of 90, financial consulting firms a score of 78, and startups a score of 66. The results are sent to his device, where Taro Yamada can visually check the rankings and access detailed information about each company.

[0258] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find the occupations and companies that best suit their characteristics and emotional state.

[0259] The processing flow will be explained below.

[0260] Step 1:

[0261] Users input information using their own devices, specifically by filling out an input form about their learning experiences, skills, knowledge, interests, and future goals. If necessary, the emotion engine also captures emotional data through a camera and microphone that captures the user's facial expressions and tone of voice.

[0262] Step 2:

[0263] The device receives information entered by the user and converts it into a data format (e.g., JSON format). The emotion engine also converts the emotion data it receives into a data format, and all information is integrated into a single data set.

[0264] Step 3:

[0265] The device sends the converted data to the server using an HTTP POST request, allowing the server to receive the user's information and emotion data.

[0266] Step 4:

[0267] The server receives the data sent from the device. After receiving the data, it begins analyzing it. The analysis calculates a score based on the device's learning experience, skills, knowledge, interests, and goals to indicate its suitability for each job or company.

[0268] Step 5:

[0269] The server analyzes the emotion data from the emotion engine and recognizes the user's emotional state. The emotional state is classified into categories such as stress, relaxation, and excitement. This allows the server to adjust the match points for jobs and companies according to the user's emotions.

[0270] Step 6:

[0271] The server uses an analytical algorithm to match the user's information with occupation and company information in the database. It then weights the emotional data and calculates a score for each item (learning experience, skills, knowledge, interests, and goals). For example, if a user's knowledge of economics matches the requirements of a financial company and the user is relaxed, the server will assign a high score.

[0272] Step 7:

[0273] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, a list like "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points" is generated. This list is then converted back to JSON format and sent to the device.

[0274] Step 8:

[0275] The device visualizes the list received from the server and displays it to the user. The user can then view the displayed list and find out more information about the companies and jobs that suit them. For example, the results can be displayed in a ranking format, with links to access more detailed information about each company or job.

[0276] Step 9:

[0277] From the displayed list, users can select the job or company that best suits their characteristics and feelings, obtain more information, and access links and contact information to further their application process.

[0278] The above is the specific processing flow of the present invention combined with the emotion engine. This system allows students to find the most suitable job or company that takes into consideration not only their characteristics but also their emotional state.

[0279] Example 2

[0280] 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."

[0281] Conventional career recommendation systems rely on static information such as a user's learning experience, skills, knowledge, interests, and goals, and are therefore unable to recommend appropriate jobs or companies that take into account the user's current emotional state. This has resulted in problems such as not reflecting aptitude assessments based on the user's emotional state, and not recommending optimal jobs or companies. Furthermore, the process of entering information and displaying results is not intuitive, making them difficult for users to use.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0283] In this invention, the server includes: means for allowing the user to input information regarding their educational history, skills, knowledge, interests, and goals; means for converting the information into a data format and transmitting it to the server; means for detecting suitable positions or companies based on the information and assigning scores; means for generating a list of suitable positions or companies based on the scores; means for displaying the list to the user; and means for recognizing the user's emotions and reflecting the information in the aptitude evaluation. This enables appropriate recommendations that take the user's current emotional state into consideration. Furthermore, displaying the recommendation results to the user in a visually easy-to-understand format also improves the system's ease of use.

[0284] "Learning history" refers to the subjects and fields of study that the user has studied to date.

[0285] "Skills" refer to specific skills or abilities that a user possesses.

[0286] "Knowledge" refers to the totality of information and wisdom that a user has acquired.

[0287] "Interests" refer to areas or things that a user is interested in.

[0288] "Goals" refer to the occupation or position that the user wants to achieve in the future.

[0289] "Data format" refers to the way data is organized into a form that a computer can process.

[0290] A "position" refers to a specific position or job within a company.

[0291] "Enterprise" refers to an organization or entity established to provide goods or services.

[0292] A "list" refers to a collection of items ordered based on a particular criterion.

[0293] "Emotion" indicates the user's psychological state, such as whether they are relaxed or tense.

[0294] "Aptitude assessment" refers to assessing the suitability of a user for a job or company based on their characteristics and feelings.

[0295] The system of the present invention helps users find the most suitable job or company based on their characteristics and emotional state. The main components of the system are the user, the terminal, the server, and the emotion engine. Specific embodiments of each part of the system are described below.

[0296] User Input

[0297] Users enter their information through a web form or mobile application, such as their educational background (e.g., economics, programming), skills (e.g., data analysis, leadership), knowledge (e.g., IT basics, economics), interests (e.g., technology, startups), and goals (e.g., aspiring to a management position, wanting to work abroad). Once the user has entered their information and clicked the "Submit" button, the next processing step begins.

[0298] Data transmission from the device

[0299] The terminal converts the information entered by the user into JSON format and sends it to the server. For this purpose, the terminal is equipped with an application or script for data transmission. The HTTPS protocol is used for communication, ensuring data security.

[0300] Server data reception and analysis

[0301] The server receives the JSON format data sent from the device. The received data is stored appropriately on the server and analyzed using analysis tools (e.g., Python scripts, data analysis libraries). This analysis process calculates the user's suitability for each job and company based on their educational background, skills, knowledge, interests, and goals.

[0302] Emotion Engine Operation

[0303] The emotion engine analyzes user input information and, if necessary, the user's facial expressions, tone of voice, and input speed to recognize emotions. This emotion recognition uses pre-trained machine learning models (e.g., TensorFlow, PyTorch). Emotional information influences the suitability assessment for a job or company.

[0304] Generate and send a list based on the score

[0305] The server recalculates the suitability of occupations and companies based on the information from the emotion engine, and generates a list sorted in order of suitability. The list is then converted back to JSON format and sent to the device as an HTTP response.

[0306] Displaying results on a terminal

[0307] The device then visualizes the received JSON-formatted list and displays it to the user using a Javascript and HTML5 interface, allowing the user to view the list in a ranked format and providing links to access more information about each company or occupation.

[0308] Specific examples

[0309] For example, if a student uses a device to enter the following information:

[0310] Educational background: Economics, Programming

[0311] Skills: Data analysis, leadership

[0312] Knowledge: Basic knowledge of IT, economics

[0313] Interests: Technology, startups

[0314] Goals: Aspiring to become a manager and want to work overseas

[0315] Once the user submits their input, the server analyzes the data and an emotion engine recognizes the user's emotional state. For example, if the user is relaxed, companies that offer a challenging environment may be given higher scores. This results in a list with, say, 90 points for technology companies, 78 points for financial consulting firms, and 66 points for startups. This list is then sent to the device, where the user can visually view the rankings and access detailed information about each company.

[0316] Example prompts for generative AI models

[0317] "Describe a system that suggests suitable jobs and companies based on a student's characteristics and emotions. Explain in natural language the process of processing data using user input and evaluating suitability using an emotion engine."

[0318] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find the occupations and companies that best suit their characteristics and emotional state.

[0319] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0320] Step 1: User Input

[0321] Through a web form or a mobile application, users enter information about their learning history, skills, knowledge, interests, and goals. The information entered must be accurate and detailed, as it will be used in the next steps. For example, a user enters their learning history, such as "Economics, Programming," and their skills, such as "Data Analysis, Leadership." This forms the basis for the data analysis that will be performed later. The output is the data entered by the user.

[0322] Step 2: Send data from your device

[0323] The terminal converts the information entered by the user into JSON format. To perform this conversion, the terminal uses an internal script or application. The converted JSON data is sent to the server via the HTTPS protocol. The user input information obtained in step 1 is used as input, and the data converted into JSON format is obtained as output. For example, the following JSON data is generated:

[0324] json

[0325] {

[0326] "Educational background": "Economics, Programming",

[0327] "Skills": "Data analysis, leadership",

[0328] "Knowledge": "Basic knowledge of IT, economics",

[0329] "Interests": "Technology, Startups",

[0330] "Goal": "I want to become a manager and work overseas"

[0331] }

[0332] Step 3: Server receives data

[0333] The server receives the JSON formatted data sent from the device. The received data is stored in the server and prepared for analysis. The JSON data generated in step 2 is used as input, and the data stored in the server is obtained as output. The server obtains the data via an API endpoint.

[0334] Step 4: Server Data Analysis

[0335] The server analyzes the received data using analytical tools (e.g., Python scripts, data analysis libraries). This analysis calculates the suitability for occupations and companies based on each item (learning history, skills, knowledge, interests, and goals). The user data stored on the server is used as input, and the suitability for each occupation and company is obtained as output. For example, scores such as "Company A: 85 points," "Company B: 72 points," and "Company C: 60 points" are calculated.

[0336] Step 5: Emotion Engine in Action

[0337] The emotion engine recognizes emotions based on user input. To do this, it uses pre-trained machine learning models (e.g., TensorFlow, PyTorch). Optionally, it also performs facial expression recognition and voice tone analysis. The input is the user's input and emotion data, and the output is the user's emotional state. For example, emotion information such as "relaxed" or "stressed" can be recognized.

[0338] Step 6: Generate a list based on the scores

[0339] The server takes the information from the emotion engine, modifies the suitability of jobs and companies, and calculates new scores. This generates a list sorted in order of suitability. The analysis results and emotion data are used as input, and the output is a list of jobs and companies sorted in order of suitability. For example, a list like "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points" might be generated.

[0340] Step 7: Send the list to your device

[0341] The server reconverts the generated list into JSON format and sends it to the terminal using the HTTPS protocol. The generated list is used as input, and the JSON format data is sent to the terminal as output. The server sends the data via an API endpoint.

[0342] Step 8: Viewing the results in the terminal

[0343] The device visualizes the received JSON data and displays it to the user. The display uses an interface using Javascript and HTML5. The received JSON data is used as input, and the results are displayed to the user in a visually easy-to-understand format as output. The user can not only view the list in ranking format, but also click on links for each company or occupation to access more information.

[0344] (Application example 2)

[0345] 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."

[0346] In modern virtual stores, product recommendations based on users' emotional state and past preferences are not fully implemented. This can lead to a lack of personalized shopping experiences and lower satisfaction. Furthermore, the lack of dynamic product suggestions utilizing emotion recognition technology makes it difficult for users to efficiently find the products that best suit them.

[0347] 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.

[0348] In this invention, the server includes means for allowing a user to input information about their learning experiences, skills, knowledge, interests, and goals, means for converting the information into a data format and transmitting it to the server, means for detecting suitable products or services based on the information and the user's emotional state and executing a process for assigning scores, means for generating a list of suitable products or services based on the scores, and means for displaying the list to the user, thereby enabling dynamic and personalized product recommendations based on the user's emotional state and past preferences.

[0349] "User" refers to a person who uses the system to input information and receive results.

[0350] "Learning experience" is a general term for knowledge and skills that a user has learned and acquired in the past.

[0351] "Skill" refers to a user's ability or technique to perform a specific task or activity.

[0352] "Knowledge" refers to the accumulation of information and understanding that a user has.

[0353] "Interests" refer to a user's interest in a particular field or activity.

[0354] A "goal" refers to a specific purpose or aspiration that a user wants to achieve in the future.

[0355] "Emotional state" refers to the psychological and mental state that a user is feeling at a particular moment, as recognized by the emotion engine.

[0356] "Data format" refers to a particular format in which digital information is organized for transmission and processing. An example is the JSON format.

[0357] A "server" refers to a computer system that receives and processes data sent from a user terminal.

[0358] "Suitable products or services" refer to products or services that are determined to be optimal for the user based on the user's input information and emotional state.

[0359] A "score" refers to an evaluation value given to a suitable product or service based on the user's information and emotional state.

[0360] "List" refers to the order of products or services generated based on scores and provided to the user.

[0361] "Means for displaying" refers to the way in which a user can visually view the list or information, typically a display or monitor.

[0362] This invention is a virtual store enhancer system that helps users find the products and services that are best suited to them. It recommends products based on the user's learning experience, skills, knowledge, interests, goals, and emotional state. This system mainly uses the following hardware and software:

[0363] 1. Hardware

[0364] Smart glasses (e.g. Google Glass)

[0365] Facial Recognition Camera

[0366] 2. Software

[0367] facial_recognition library: for recognizing emotions from a user's face

[0368] recommendation_engine library: for recommending products based on emotions and preferences

[0369] json module: for saving and loading user data

[0370] The server uses a means for allowing the user to input information about learning experience, skills, knowledge, interests, and goals, and a means for converting the information into a data format and transmitting it. This allows the user to easily provide their information to the system. The server also performs a process of detecting suitable products or services based on the information and the user's emotional state and assigning a score to them. This allows for dynamic product recommendations based on the user's emotional state. The server then generates a list of suitable products or services based on the score and displays the list to the user. This allows the user to visually identify the products and services that are best suited to them.

[0371] As a concrete example, suppose a user, Hanako Sato, inputs information using the virtual store enhancer. The emotion engine recognizes that Hanako Sato has purchased many health foods and fitness products in the past and is currently in a relaxed state. Based on this information, the server suggests yoga mats and health supplements to Hanako Sato.

[0372] Examples of prompts using generative AI models include:

[0373] "Recommend appropriate products based on the user's past purchase history and real-time emotional state. For example, if the user is relaxed, suggest health-related products, and if the user is stressed, suggest relaxation goods."

[0374] This results in a system that provides a highly personalized shopping experience based on the user's emotional state and preferences.

[0375] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0376] Step 1:

[0377] Users access the system by wearing smart glasses. They provide information about their learning experience, skills, knowledge, interests, and goals through input forms or voice input. The input information is converted into JSON format by the device.

[0378] Input: Information about your learning experience, skills, knowledge, interests, and goals

[0379] Output: User information in JSON format

[0380] Step 2:

[0381] The terminal transmits the converted user information to the server, which includes processing for securely transmitting the data using a data communication protocol.

[0382] Input: User information in JSON format

[0383] Output: User information sent to the server

[0384] Step 3:

[0385] The server receives the user information from the device, stores it in a database, and then analyzes the data to evaluate each user's aptitude based on their learning experience, skills, knowledge, interests, and goals, and assigns them a score.

[0386] Input: User information in JSON format

[0387] Output: Aptitude score for each item

[0388] Step 4:

[0389] The user captures a facial image through a facial recognition camera in the smart glasses, which is then analyzed in real time using the facial_recognition library to recognize the user's emotional state.

[0390] Input: Face image data

[0391] Output: Emotional state data

[0392] Step 5:

[0393] The server combines the emotional state data with the user's past purchase history and preference data and uses the recommendation_engine to calculate a score to recommend the most suitable product or service.

[0394] Input: Emotional state data, user's past purchase history and preference data

[0395] Output: List of recommended products or services and their scores

[0396] Step 6:

[0397] The server converts the list of recommended products obtained into JSON format and sends it to the device, using an encryption protocol to ensure security.

[0398] Input: List of recommended products or services and their ratings

[0399] Output: Recommended product list in JSON format

[0400] Step 7:

[0401] The device parses the received JSON-formatted recommended product list and displays it visually on the smart glasses display, allowing the user to visually review the list and access detailed information about the suggested products and services.

[0402] Input: Recommended product list in JSON format

[0403] Output: A list of recommended products displayed on the smart glasses display.

[0404] The above are the processing steps and specific operations of the system that realizes the application example.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Second embodiment]

[0409] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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."

[0421] The system of the present invention is designed to help students find the jobs and companies that best suit their characteristics. The main components of the system are users, terminals, and servers. Specific embodiments of each part of the system are described below.

[0422] User Input

[0423] A web form or mobile application interface is provided for users to enter their information. Users enter their learning experiences, skills, knowledge, interests, and future goals into the form. For example, if a user studies economics, has skills in data analysis, is interested in leadership, and aspires to a managerial position, they would enter that information.

[0424] Data transmission from the device

[0425] The terminal receives the information entered by the user and converts it into a data format (for example, JSON format). The converted data is then sent to the server. The terminal has an application or script for data transmission built in, and the data is sent automatically once the user has completed entering the information.

[0426] Server data processing and analysis

[0427] When the server receives the data sent from the device, it first analyzes the received data. The algorithm used in the analysis compares the received data with the characteristics of jobs and companies stored in the database. The server assigns different weights to learning experience, skills, knowledge, interests, and goals, and calculates the degree of suitability for each job and company. This suitability is calculated as a score, and the most suitable jobs and companies are given higher scores.

[0428] Generate and send a list based on the score

[0429] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, based on the user's input data, a list may be generated in which Company A has a score of 90, Company B has a score of 78, and Company C has a score of 66. This list is then converted back to JSON format and sent to the device.

[0430] Displaying results on a terminal

[0431] The device visualizes the received list and displays it to the user, who can then browse the displayed list to find out more information about the companies and jobs that suit them. For example, with the help of a good UI design, the results can be displayed in a ranking format, with links to access more detailed information about each company or job.

[0432] Specific examples

[0433] For example, suppose student Taro Yamada uses a device to enter the following information:

[0434] Learning experience: Economics, Programming

[0435] Skills: Data analysis, leadership

[0436] Knowledge: Basic knowledge of IT, economics

[0437] Interests: Technology, startups

[0438] Goals: Aspiring to become a manager and want to work overseas

[0439] When Taro Yamada submits his input information, the server analyzes the data and calculates suitable jobs and companies. For example, technology companies might score 90, financial consulting firms 78, and startups 66. The results are sent to his device, where Taro Yamada can visually check the rankings and access detailed information about each company.

[0440] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find jobs and companies that best suit their characteristics.

[0441] The processing flow will be explained below.

[0442] Step 1:

[0443] Users enter information using their own devices. Specifically, they fill out an input form with items such as their learning experience, skills, knowledge, interests, and future goals. For example, they enter information such as economics, data analysis, leadership, technology, or aspiring to a management position.

[0444] Step 2:

[0445] The terminal acquires the information entered by the user and converts it into a data format (for example, JSON format). The converted data will be in the following format, for example.

[0446] json

[0447] {

[0448] "Things Learned": ["Economics", "Programming"],

[0449] "Skills": ["Data Analysis", "Leadership"],

[0450] "Knowledge": ["Basic knowledge of IT", "Economics"],

[0451] "Interests": ["Technology", "Startups"],

[0452] "Goals": ["I want to become a manager", "I want to work overseas"]

[0453] }

[0454] Step 3:

[0455] The terminal sends the converted data to the server using an HTTP POST request, which allows the server to receive the user information.

[0456] Step 4:

[0457] The server receives the data sent from the device. After receiving the data, the server begins analyzing the data. The analysis calculates a score for suitability for each job or company based on learning experience, skills, knowledge, interests, and goals.

[0458] Step 5:

[0459] The server uses an analytical algorithm to match the user's information with job and company information in a database, weighting it and calculating a score for each item (learning experience, skills, knowledge, interests, and goals). For example, if knowledge of economics matches the requirements of a financial company, a high score will be awarded.

[0460] Step 6:

[0461] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, the list might look like this: "Company A: 90 points," "Company B: 78 points," "Company C: 66 points."

[0462] Step 7:

[0463] The server sends the generated list to the terminal. The data sent is in JSON format, for example, as follows:

[0464] json

[0465] [

[0466] {"Company": "Company A", "Score": 90},

[0467] {"Company": "Company B", "Score": 78},

[0468] {"Company": "Company C", "Score": 66}

[0469] ]

[0470] Step 8:

[0471] The terminal visualizes the list received from the server and displays it to the user. The user can view the displayed list and check detailed information about each company or occupation. The display is provided in a visually easy-to-understand interface.

[0472] Step 9:

[0473] Users can select jobs and companies that fit their profile from the displayed list, obtain more information, and access links and contact information to further their application process.

[0474] The above are the specific processing steps of the system.

[0475] Example 1

[0476] 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."

[0477] Conventional career aptitude analysis systems have the problem that they are unable to fully reflect a user's individual learning experience, skills, knowledge, preferences, and goals, and therefore have low accuracy in proposing suitable occupations and companies, making it difficult for users to find the optimal career path. In particular, since it is difficult to analyze taking into account the diverse backgrounds and goals of users, there is a demand for highly accurate recommendations that are tailored to individual characteristics.

[0478] 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.

[0479] In this invention, the server includes means for allowing a user to input information about their learning experiences, skills, knowledge, preferences, and goals, means for converting the information into a standard data format and transmitting it to the server, means for detecting suitable occupations or companies based on the information and assigning scores, means for generating a list of suitable occupations or companies based on the scores, and means for visually displaying the list to the user, thereby enabling highly accurate personalized recommendations based on the user's diverse characteristics.

[0480] "User" refers to a person who uses the system to input information about their learning experiences, skills, knowledge, preferences, and goals.

[0481] "Learning experience" refers to information including the knowledge and skills that the user has acquired so far, educational history, and the like.

[0482] "Skills" refers to specific abilities or expertise possessed by a user, including programming skills and data analysis abilities.

[0483] "Knowledge" refers to the level of knowledge or understanding a user has in a particular field.

[0484] "Preferences" refer to the fields, occupations, and company characteristics that the user is interested in.

[0485] "Goals" refer to the professional objectives or career paths that a user wants to achieve in the future.

[0486] "Standard data format" refers to a common data representation format such as JSON that is suitable for sending, receiving, and analyzing data.

[0487] "Server" refers to the central computer system that processes the information received from users, identifies suitable jobs and companies, assigns scores, and generates a list.

[0488] The "score" refers to the numerical value of the server's evaluation of the suitability of a job or company based on the user's information.

[0489] A "list" refers to a ranking of occupations or companies based on scores.

[0490] "Visually displaying" refers to presenting results to the user in a graphical format that makes them easier to understand.

[0491] "Analysis" refers to the process by which the server evaluates suitability based on the information entered by the user.

[0492] "Means for performing processing" refers to the functionality of the server to analyze user information using a specific algorithm and generate a result.

[0493] "Weighting" refers to the method of calculating points based on different weightings assigned to learning experiences, skills, knowledge, preferences, and goals.

[0494] The system of the present invention allows users to input information about their learning experiences, skills, knowledge, preferences, and goals, and then analyzes the data to recommend suitable jobs and companies. The system consists of three main components: the user, the terminal, and the server.

[0495] Enter user information

[0496] To enter their information, users use a web form or mobile application interface that allows them to enter the following information:

[0497] Learning experience (e.g., economics, programming)

[0498] Skills (e.g., data analysis, leadership)

[0499] Knowledge (e.g., basic knowledge of IT, economics)

[0500] Preferences (e.g., technology, startups)

[0501] Goals (e.g., wanting to become a manager or working overseas)

[0502] The user enters the information and clicks the "Submit" button to proceed to the next step.

[0503] Data conversion and transmission on the device

[0504] The terminal receives the information entered by the user and converts it into a standard data format (for example, JSON format).The converted data is then sent to the server using an HTTP POST request.Scripts such as JavaScript or Python are implemented on the terminal, and the information transmission process is carried out automatically.

[0505] Data reception and analysis on the server

[0506] The server receives the JSON data sent from the device. The server uses the Python Flask framework to manage the data reception and analysis process. The server then uses machine learning algorithms to match the received data with occupation and company characteristics stored in the database.

[0507] Generate a list of jobs and companies based on scores

[0508] The server assigns weights to each data item (learning experience, skills, knowledge, preferences, and goals) and calculates the degree of suitability for each job or company. Based on the calculated suitability, the server generates a list of jobs or companies in descending order of scores. For example, the server generates the following list:

[0509] Company A: 90 points

[0510] Company B: 78 points

[0511] Company C: 66 points

[0512] The generated list is converted back to JSON format and sent to the terminal as an HTTP response.

[0513] Displaying results on your device

[0514] The device parses the received JSON data and visually displays the results in a ranking format using JavaScript frameworks such as React and Vue.js. Users can review the displayed list to identify career and company options that suit them. Links to access more information about each company and career are also provided.

[0515] Specific examples

[0516] For example, if a student enters the following information:

[0517] Learning experience: Economics, Programming

[0518] Skills: Data analysis, leadership

[0519] Knowledge: Basic knowledge of IT, economics

[0520] Preferences: Technology, startups

[0521] Goals: Aspiring to become a manager and want to work overseas

[0522] Based on this information, the server performs analysis and calculates the companies and jobs that are suitable for the student, as follows:

[0523] Technology companies: 90 points

[0524] Financial Consulting Firm: 78 points

[0525] Startups: 66 points

[0526] The results are sent to a device where students can visually view the rankings and access detailed information about each company.

[0527] Prompt Sentence Examples

[0528] "Use a generative AI model to generate a list of suitable jobs and companies based on user inputs about their learning experience, skills, knowledge, preferences, and goals. For example, if a user studies economics, has data analysis skills, is interested in leadership, and aspires to a management position, consider that information when outputting results."

[0529] The above is a specific embodiment for carrying out the present invention. This system allows users to efficiently find jobs and companies that best suit their characteristics.

[0530] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0531] Step 1: User Enters Information

[0532] Users enter their learning experience, skills, knowledge, preferences, and goals using a web form or mobile application. For example, a user might enter information such as "Economics," "Data Analysis," "IT Basics," "Technology," or "Aiming for a Managerial Position." This information becomes input data for subsequent analysis.

[0533] Step 2: The device converts the user data

[0534] The terminal converts the received user data into a standard data format (for example, JSON format). Specifically, the data is structured using a JavaScript or Python script, and the converted JSON data is generated. This JSON data is then prepared for transmission to the server.

[0535] Step 3: The device sends the data to the server

[0536] The device sends the converted JSON data to the server using an HTTP POST request, setting the appropriate content type (e.g., application / json) in the HTTP header to ensure the data reaches the server correctly. If the data is successfully sent, the server returns a receipt confirmation as an HTTP response.

[0537] Step 4: The server receives the data

[0538] The server receives the JSON data sent from the device. It uses the Python Flask framework to parse the received data. The server parses the request, converts the received data into the appropriate format, and prepares it for processing.

[0539] Step 5: The server parses the data

[0540] The server performs an analysis process based on the received data to identify suitable occupations and companies. Specifically, it assigns different weights to learning experience, skills, knowledge, preferences, and goals, and calculates the suitability for each occupation and company. The analysis is performed using a machine learning algorithm, and a score is calculated.

[0541] Step 6: The server generates a list based on the scores

[0542] The server generates a list of suitable jobs and companies for the user based on the obtained scores. For example, it ranks the jobs and companies with the highest scores. The generated list is then converted back to JSON format and prepared for transmission to the device.

[0543] Step 7: The server sends the list to the device

[0544] The server sends the generated list to the device as an HTTP response. The response contains a list of ranked occupations and companies in JSON data format. Once the device receives this data, it can proceed to the next step.

[0545] Step 8: The terminal displays the results visually

[0546] The device parses the received JSON data and displays it visually to the user, using JavaScript frameworks such as React and Vue.js to display a ranking list, with links to access more information about each job or company.

[0547] Step 9: User accesses additional information

[0548] Users can select the jobs or companies they are interested in from the displayed list and click to access more information. Clicking on a link opens a new page or modal window with more information, allowing users to gain a deeper understanding of the jobs and companies that are best suited to them.

[0549] (Application example 1)

[0550] 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."

[0551] In today's labor market, it is difficult for factory workers to find the job or department that best suits their skills and experience. Furthermore, if workers are not assigned to the appropriate job or department, there is a concern that their motivation and efficiency will decline. Particularly in a factory environment with a wide variety of jobs, it is important to identify the job that best suits each worker's characteristics. This will maximize worker performance and improve the working environment.

[0552] 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.

[0553] In this invention, the server includes means for allowing users to input information about their learning experiences, skills, knowledge, interests, and goals, means for detecting suitable occupations or companies based on the information and assigning scores, and means for evaluating the job aptitude of factory workers and quantifying the degree of suitability, thereby enabling factory workers to find the job or department that best suits their characteristics.

[0554] A "user" is someone who uses the system to input information.

[0555] "Learning experience" is information about the subjects the user has studied and the knowledge they have acquired.

[0556] "Skills" is information about specific techniques and abilities that a user has acquired.

[0557] "Knowledge" is the extent of a user's general or specialized understanding or information.

[0558] "Interests" are information about areas or topics that a user is personally interested in.

[0559] "Goals" are information about the job or career that the user wants to achieve in the future.

[0560] A "data format" is a standardized format for handling information electronically.

[0561] A "server" is a computer system that receives information entered by a user and analyzes and processes it.

[0562] The "score" is a numerical representation of the degree of compatibility between the user's characteristics and the occupation or company.

[0563] "Occupation or company" refers to the job the user is looking to work for or the organization for which the user works.

[0564] A "list" is a ranked collection of jobs and companies organized based on scores.

[0565] "Factory workers" are workers who perform various tasks in factories.

[0566] "Job fit" is an assessment of how well a worker's characteristics suit a particular job.

[0567] A "visually displayed result" is a due diligence result that is presented to a user graphically.

[0568] The system of this invention helps users find the jobs and companies that best suit their characteristics. It has a function to recommend jobs that are particularly suitable for factory workers. The system is mainly composed of users, terminals, and a server.

[0569] User Input Process

[0570] First, the user enters information about themselves using a device. This information includes learning experiences, skills, knowledge, interests, and goals. A dedicated application is installed on the device and collects the information entered by the user. The information is entered using a device such as a smartphone, tablet, or PC.

[0571] Data transmission by the terminal

[0572] The terminal first converts the information entered by the user into a data format. Specifically, data is generated in JSON format. The converted data is automatically sent to the server. Scripts and programs for sending the data are built into the terminal.

[0573] Server data processing and analysis

[0574] The server receives data sent from the device. The received data is first analyzed and evaluated based on factors such as learning experience, skills, knowledge, interests, and goals. Based on this, the server calculates a score for suitable occupations and companies, and also evaluates job suitability, especially for factory workers. Python + Flask is used for the data processing algorithm.

[0575] Generate and send a list based on the score

[0576] The server generates a list of jobs and companies based on the calculated score, in order of suitability. For factory workers, a list of the most suitable jobs and departments is generated. The generated list is then sent to the device in JSON format.

[0577] Displaying results on a terminal

[0578] The terminal receives the list sent from the server and visually displays it to the user. The user can check the list in a ranked format and access detailed information about each occupation and company. Visualizing recommended jobs, especially for factory workers, makes it easier for users to make appropriate choices. This is expected to improve worker performance and job satisfaction.

[0579] Specific examples

[0580] For example, a factory worker enters the following information into a terminal:

[0581] Learning experience: Line work, maintenance

[0582] Skills: screw tightening, welding, product inspection

[0583] Knowledge: Seven QC Tools

[0584] Interests: Quality control, safety management

[0585] Goals: Quality control manager, small group activity leader

[0586] Based on the information sent, the server analyzes it and assigns a score to each suitable job or department: a quality control department might get 90 points, a production line supervisor 80 points, and a maintenance technician 70 points. This information is returned to the terminal, where the user can view the results in a visual format.

[0587] Prompt Sentence Examples

[0588] The information entered by the user is in the following format:

[0589] Learning experience: Line work, maintenance

[0590] Skills: screw tightening, welding, product inspection

[0591] Knowledge: Seven QC Tools

[0592] Interests: Quality control, safety management

[0593] Goals: Quality control manager, small group activity leader

[0594] In this way, the system allows factory workers to easily find the job or department that best suits their characteristics.

[0595] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0596] Step 1:

[0597] Users use a device to input information about their learning experiences, skills, knowledge, interests, and goals. The input information is collected through web forms or mobile application interfaces. Specific examples of information input include users entering work experience such as "line work" or "maintenance," or skills such as "screw tightening" or "welding."

[0598] Input: Learning experiences, skills, knowledge, interests, goals

[0599] Output: Input data (text format)

[0600] Step 2:

[0601] The terminal converts the information entered by the user into JSON format, and the converted JSON data is sent to the server by a script or program for data transmission.

[0602] Input: Input data (text format)

[0603] Output: Input data (JSON format)

[0604] Step 3:

[0605] The server receives the JSON data sent from the device, which is first parsed and divided into categories (learning experience, skills, knowledge, interests, and goals).

[0606] Input: Input data (JSON format)

[0607] Output: Analysis data (categorized data)

[0608] Step 4:

[0609] The server then uses the analytics data to generate a score for each category. The algorithm then assigns different weights to learning experiences, skills, interests, and goals, and assigns points to jobs and companies that are suitable for the candidate. For factory workers, the system also evaluates job suitability.

[0610] Input: Analysis data

[0611] Output: Score data (scores for each job and company)

[0612] Step 5:

[0613] The server generates a list of jobs and companies in order of suitability based on the calculated score. In particular, for factory workers, a list of the most suitable jobs and departments is generated. The generated list is again sent to the terminal in JSON format.

[0614] Input: Score data

[0615] Output: Eligibility list (JSON format)

[0616] Step 6:

[0617] The terminal receives the suitability list sent from the server and visually displays it to the user. The user can check the list in a ranked format and access detailed information about each occupation and company. As a result, factory workers can find the job or department that best suits them.

[0618] Input: Eligibility list (JSON format)

[0619] Output: Visual display (ranked list)

[0620] 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.

[0621] The system of the present invention helps students find the occupation or company that best suits their characteristics. It is particularly distinctive in that it incorporates an emotion engine that recognizes the user's emotions and suggests suitable careers based on those emotions. The main components of the system are the user, terminal, server, and emotion engine. Specific embodiments of each part of the system are described below.

[0622] User Input

[0623] A web form or mobile application interface is provided for users to enter their information. Users enter their learning experiences, skills, knowledge, interests, and future goals into the form. For example, if they studied economics, have skills in data analysis, are interested in leadership, and aspire to a managerial position, they enter that information.

[0624] Data transmission from the device

[0625] The terminal receives the information entered by the user and converts it into a data format (for example, JSON format). The converted data is then sent to the server. The terminal has an application or script for data transmission built in, and the data is sent automatically once the user has completed entering the information.

[0626] Server data processing and analysis

[0627] When the server receives the data sent from the device, it first begins analyzing the data and calculates a score based on the user's learning experience, skills, knowledge, interests, and goals to determine their suitability for each job or company.

[0628] Emotion Engine Operation

[0629] The emotion engine recognizes emotions from user input information and, in some cases, from the user's facial expression, tone of voice, and typing speed. This emotional information influences the suggestions of suitable jobs and companies. For example, the suggestions of suitable jobs and companies will differ depending on whether the user is relaxed or stressed.

[0630] Generate and send a list based on the score

[0631] The server calculates the score taking into account the information from the emotion engine and generates a list of occupations and companies in order of suitability. For example, a list might be generated such as "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points." This list is then converted back to JSON format and sent to the device.

[0632] Displaying results on a terminal

[0633] The device visualizes the received list and displays it to the user, who can then view the list and find out more information about the companies and jobs that suit them. For example, the device can display the results in a ranking format through a well-designed UI, and provide links to access more detailed information about each company or job.

[0634] Specific examples

[0635] For example, suppose student Taro Yamada uses a device to enter the following information:

[0636] Learning experience: Economics, Programming

[0637] Skills: Data analysis, leadership

[0638] Knowledge: Basic knowledge of IT, economics

[0639] Interests: Technology, startups

[0640] Goals: Aspiring to become a manager and want to work overseas

[0641] When Taro Yamada submits his input, the server analyzes the data and the emotion engine recognizes his emotional state. For example, if he is in a relaxed state, companies that offer a challenging environment might be given higher scores. As a result, technology companies might receive a score of 90, financial consulting firms a score of 78, and startups a score of 66. The results are sent to his device, where Taro Yamada can visually check the rankings and access detailed information about each company.

[0642] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find the occupations and companies that best suit their characteristics and emotional state.

[0643] The processing flow will be explained below.

[0644] Step 1:

[0645] Users input information using their own devices, specifically by filling out an input form about their learning experiences, skills, knowledge, interests, and future goals. If necessary, the emotion engine also captures emotional data through a camera and microphone that captures the user's facial expressions and tone of voice.

[0646] Step 2:

[0647] The device receives information entered by the user and converts it into a data format (e.g., JSON format). The emotion engine also converts the emotion data it receives into a data format, and all information is integrated into a single data set.

[0648] Step 3:

[0649] The device sends the converted data to the server using an HTTP POST request, allowing the server to receive the user's information and emotion data.

[0650] Step 4:

[0651] The server receives the data sent from the device. After receiving the data, it begins analyzing it. The analysis calculates a score based on the device's learning experience, skills, knowledge, interests, and goals to indicate its suitability for each job or company.

[0652] Step 5:

[0653] The server analyzes the emotion data from the emotion engine and recognizes the user's emotional state. The emotional state is classified into categories such as stress, relaxation, and excitement. This allows the server to adjust the match points for jobs and companies according to the user's emotions.

[0654] Step 6:

[0655] The server uses an analytical algorithm to match the user's information with occupation and company information in the database. It then weights the emotional data and calculates a score for each item (learning experience, skills, knowledge, interests, and goals). For example, if a user's knowledge of economics matches the requirements of a financial company and the user is relaxed, the server will assign a high score.

[0656] Step 7:

[0657] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, a list like "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points" is generated. This list is then converted back to JSON format and sent to the device.

[0658] Step 8:

[0659] The device visualizes the list received from the server and displays it to the user. The user can then view the displayed list and find out more information about the companies and jobs that suit them. For example, the results can be displayed in a ranking format, with links to access more detailed information about each company or job.

[0660] Step 9:

[0661] From the displayed list, users can select the job or company that best suits their characteristics and feelings, obtain more information, and access links and contact information to further their application process.

[0662] The above is the specific processing flow of the present invention combined with the emotion engine. This system allows students to find the most suitable job or company that takes into consideration not only their characteristics but also their emotional state.

[0663] Example 2

[0664] 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."

[0665] Conventional career recommendation systems rely on static information such as a user's learning experience, skills, knowledge, interests, and goals, and are therefore unable to recommend appropriate jobs or companies that take into account the user's current emotional state. This has resulted in problems such as not reflecting aptitude assessments based on the user's emotional state, and not recommending optimal jobs or companies. Furthermore, the process of entering information and displaying results is not intuitive, making them difficult for users to use.

[0666] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0667] In this invention, the server includes: means for allowing the user to input information regarding their educational history, skills, knowledge, interests, and goals; means for converting the information into a data format and transmitting it to the server; means for detecting suitable positions or companies based on the information and assigning scores; means for generating a list of suitable positions or companies based on the scores; means for displaying the list to the user; and means for recognizing the user's emotions and reflecting the information in the aptitude evaluation. This enables appropriate recommendations that take the user's current emotional state into consideration. Furthermore, displaying the recommendation results to the user in a visually easy-to-understand format also improves the system's ease of use.

[0668] "Learning history" refers to the subjects and fields of study that the user has studied to date.

[0669] "Skills" refer to specific skills or abilities that a user possesses.

[0670] "Knowledge" refers to the totality of information and wisdom that a user has acquired.

[0671] "Interests" refer to areas or things that a user is interested in.

[0672] "Goals" refer to the occupation or position that the user wants to achieve in the future.

[0673] "Data format" refers to the way data is organized into a form that a computer can process.

[0674] A "position" refers to a specific position or job within a company.

[0675] "Enterprise" refers to an organization or entity established to provide goods or services.

[0676] A "list" refers to a collection of items ordered based on a particular criterion.

[0677] "Emotion" indicates the user's psychological state, such as whether they are relaxed or tense.

[0678] "Aptitude assessment" refers to assessing the suitability of a user for a job or company based on their characteristics and feelings.

[0679] The system of the present invention helps users find the most suitable job or company based on their characteristics and emotional state. The main components of the system are the user, the terminal, the server, and the emotion engine. Specific embodiments of each part of the system are described below.

[0680] User Input

[0681] Users enter their information through a web form or mobile application, such as their educational background (e.g., economics, programming), skills (e.g., data analysis, leadership), knowledge (e.g., IT basics, economics), interests (e.g., technology, startups), and goals (e.g., aspiring to a management position, wanting to work abroad). Once the user has entered their information and clicked the "Submit" button, the next processing step begins.

[0682] Data transmission from the device

[0683] The terminal converts the information entered by the user into JSON format and sends it to the server. For this purpose, the terminal is equipped with an application or script for data transmission. The HTTPS protocol is used for communication, ensuring data security.

[0684] Server data reception and analysis

[0685] The server receives the JSON format data sent from the device. The received data is stored appropriately on the server and analyzed using analysis tools (e.g., Python scripts, data analysis libraries). This analysis process calculates the user's suitability for each job and company based on their educational background, skills, knowledge, interests, and goals.

[0686] Emotion Engine Operation

[0687] The emotion engine analyzes user input information and, if necessary, the user's facial expressions, tone of voice, and input speed to recognize emotions. This emotion recognition uses pre-trained machine learning models (e.g., TensorFlow, PyTorch). Emotional information influences the suitability assessment for a job or company.

[0688] Generate and send a list based on the score

[0689] The server recalculates the suitability of occupations and companies based on the information from the emotion engine, and generates a list sorted in order of suitability. The list is then converted back to JSON format and sent to the device as an HTTP response.

[0690] Displaying results on a terminal

[0691] The device then visualizes the received JSON-formatted list and displays it to the user using a Javascript and HTML5 interface, allowing the user to view the list in a ranked format and providing links to access more information about each company or occupation.

[0692] Specific examples

[0693] For example, if a student uses a device to enter the following information:

[0694] Educational background: Economics, Programming

[0695] Skills: Data analysis, leadership

[0696] Knowledge: Basic knowledge of IT, economics

[0697] Interests: Technology, startups

[0698] Goals: Aspiring to become a manager and want to work overseas

[0699] Once the user submits their input, the server analyzes the data and an emotion engine recognizes the user's emotional state. For example, if the user is relaxed, companies that offer a challenging environment may be given higher scores. This results in a list with, say, 90 points for technology companies, 78 points for financial consulting firms, and 66 points for startups. This list is then sent to the device, where the user can visually view the rankings and access detailed information about each company.

[0700] Example prompts for generative AI models

[0701] "Describe a system that suggests suitable jobs and companies based on a student's characteristics and emotions. Explain in natural language the process of processing data using user input and evaluating suitability using an emotion engine."

[0702] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find the occupations and companies that best suit their characteristics and emotional state.

[0703] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0704] Step 1: User Input

[0705] Through a web form or a mobile application, users enter information about their learning history, skills, knowledge, interests, and goals. The information entered must be accurate and detailed, as it will be used in the next steps. For example, a user enters their learning history, such as "Economics, Programming," and their skills, such as "Data Analysis, Leadership." This forms the basis for the data analysis that will be performed later. The output is the data entered by the user.

[0706] Step 2: Send data from your device

[0707] The terminal converts the information entered by the user into JSON format. To perform this conversion, the terminal uses an internal script or application. The converted JSON data is sent to the server via the HTTPS protocol. The user input information obtained in step 1 is used as input, and the data converted into JSON format is obtained as output. For example, the following JSON data is generated:

[0708] json

[0709] {

[0710] "Educational background": "Economics, Programming",

[0711] "Skills": "Data analysis, leadership",

[0712] "Knowledge": "Basic knowledge of IT, economics",

[0713] "Interests": "Technology, Startups",

[0714] "Goal": "I want to become a manager and work overseas"

[0715] }

[0716] Step 3: Server receives data

[0717] The server receives the JSON formatted data sent from the device. The received data is stored in the server and prepared for analysis. The JSON data generated in step 2 is used as input, and the data stored in the server is obtained as output. The server obtains the data via an API endpoint.

[0718] Step 4: Server Data Analysis

[0719] The server analyzes the received data using analytical tools (e.g., Python scripts, data analysis libraries). This analysis calculates the suitability for occupations and companies based on each item (learning history, skills, knowledge, interests, and goals). The user data stored on the server is used as input, and the suitability for each occupation and company is obtained as output. For example, scores such as "Company A: 85 points," "Company B: 72 points," and "Company C: 60 points" are calculated.

[0720] Step 5: Emotion Engine in Action

[0721] The emotion engine recognizes emotions based on user input. To do this, it uses pre-trained machine learning models (e.g., TensorFlow, PyTorch). Optionally, it also performs facial expression recognition and voice tone analysis. The input is the user's input and emotion data, and the output is the user's emotional state. For example, emotion information such as "relaxed" or "stressed" can be recognized.

[0722] Step 6: Generate a list based on the scores

[0723] The server takes the information from the emotion engine, modifies the suitability of jobs and companies, and calculates new scores. This generates a list sorted in order of suitability. The analysis results and emotion data are used as input, and the output is a list of jobs and companies sorted in order of suitability. For example, a list like "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points" might be generated.

[0724] Step 7: Send the list to your device

[0725] The server reconverts the generated list into JSON format and sends it to the terminal using the HTTPS protocol. The generated list is used as input, and the JSON format data is sent to the terminal as output. The server sends the data via an API endpoint.

[0726] Step 8: Viewing the results in the terminal

[0727] The device visualizes the received JSON data and displays it to the user. The display uses an interface using Javascript and HTML5. The received JSON data is used as input, and the results are displayed to the user in a visually easy-to-understand format as output. The user can not only view the list in ranking format, but also click on links for each company or occupation to access more information.

[0728] (Application example 2)

[0729] 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."

[0730] In modern virtual stores, product recommendations based on users' emotional state and past preferences are not fully implemented. This can lead to a lack of personalized shopping experiences and lower satisfaction. Furthermore, the lack of dynamic product suggestions utilizing emotion recognition technology makes it difficult for users to efficiently find the products that best suit them.

[0731] 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.

[0732] In this invention, the server includes means for allowing a user to input information about their learning experiences, skills, knowledge, interests, and goals, means for converting the information into a data format and transmitting it to the server, means for detecting suitable products or services based on the information and the user's emotional state and executing a process for assigning scores, means for generating a list of suitable products or services based on the scores, and means for displaying the list to the user, thereby enabling dynamic and personalized product recommendations based on the user's emotional state and past preferences.

[0733] "User" refers to a person who uses the system to input information and receive results.

[0734] "Learning experience" is a general term for knowledge and skills that a user has learned and acquired in the past.

[0735] "Skill" refers to a user's ability or technique to perform a specific task or activity.

[0736] "Knowledge" refers to the accumulation of information and understanding that a user has.

[0737] "Interests" refer to a user's interest in a particular field or activity.

[0738] A "goal" refers to a specific purpose or aspiration that a user wants to achieve in the future.

[0739] "Emotional state" refers to the psychological and mental state that a user is feeling at a particular moment, as recognized by the emotion engine.

[0740] "Data format" refers to a particular format in which digital information is organized for transmission and processing. An example is the JSON format.

[0741] A "server" refers to a computer system that receives and processes data sent from a user terminal.

[0742] "Suitable products or services" refer to products or services that are determined to be optimal for the user based on the user's input information and emotional state.

[0743] A "score" refers to an evaluation value given to a suitable product or service based on the user's information and emotional state.

[0744] "List" refers to the order of products or services generated based on scores and provided to the user.

[0745] "Means for displaying" refers to the way in which a user can visually view the list or information, typically a display or monitor.

[0746] This invention is a virtual store enhancer system that helps users find the products and services that are best suited to them. It recommends products based on the user's learning experience, skills, knowledge, interests, goals, and emotional state. This system mainly uses the following hardware and software:

[0747] 1. Hardware

[0748] Smart glasses (e.g. Google Glass)

[0749] Facial Recognition Camera

[0750] 2. Software

[0751] facial_recognition library: for recognizing emotions from a user's face

[0752] recommendation_engine library: for recommending products based on emotions and preferences

[0753] json module: for saving and loading user data

[0754] The server uses a means for allowing the user to input information about learning experience, skills, knowledge, interests, and goals, and a means for converting the information into a data format and transmitting it. This allows the user to easily provide their information to the system. The server also performs a process of detecting suitable products or services based on the information and the user's emotional state and assigning a score to them. This allows for dynamic product recommendations based on the user's emotional state. The server then generates a list of suitable products or services based on the score and displays the list to the user. This allows the user to visually identify the products and services that are best suited to them.

[0755] As a concrete example, suppose a user, Hanako Sato, inputs information using the virtual store enhancer. The emotion engine recognizes that Hanako Sato has purchased many health foods and fitness products in the past and is currently in a relaxed state. Based on this information, the server suggests yoga mats and health supplements to Hanako Sato.

[0756] Examples of prompts using generative AI models include:

[0757] "Recommend appropriate products based on the user's past purchase history and real-time emotional state. For example, if the user is relaxed, suggest health-related products, and if the user is stressed, suggest relaxation goods."

[0758] This results in a system that provides a highly personalized shopping experience based on the user's emotional state and preferences.

[0759] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0760] Step 1:

[0761] Users access the system by wearing smart glasses. They provide information about their learning experience, skills, knowledge, interests, and goals through input forms or voice input. The input information is converted into JSON format by the device.

[0762] Input: Information about your learning experience, skills, knowledge, interests, and goals

[0763] Output: User information in JSON format

[0764] Step 2:

[0765] The terminal transmits the converted user information to the server, which includes processing for securely transmitting the data using a data communication protocol.

[0766] Input: User information in JSON format

[0767] Output: User information sent to the server

[0768] Step 3:

[0769] The server receives the user information from the device, stores it in a database, and then analyzes the data to evaluate each user's aptitude based on their learning experience, skills, knowledge, interests, and goals, and assigns them a score.

[0770] Input: User information in JSON format

[0771] Output: Aptitude score for each item

[0772] Step 4:

[0773] The user captures a facial image through a facial recognition camera in the smart glasses, which is then analyzed in real time using the facial_recognition library to recognize the user's emotional state.

[0774] Input: Face image data

[0775] Output: Emotional state data

[0776] Step 5:

[0777] The server combines the emotional state data with the user's past purchase history and preference data and uses the recommendation_engine to calculate a score to recommend the most suitable product or service.

[0778] Input: Emotional state data, user's past purchase history and preference data

[0779] Output: List of recommended products or services and their scores

[0780] Step 6:

[0781] The server converts the list of recommended products obtained into JSON format and sends it to the device, using an encryption protocol to ensure security.

[0782] Input: List of recommended products or services and their ratings

[0783] Output: Recommended product list in JSON format

[0784] Step 7:

[0785] The device parses the received JSON-formatted recommended product list and displays it visually on the smart glasses display, allowing the user to visually review the list and access detailed information about the suggested products and services.

[0786] Input: Recommended product list in JSON format

[0787] Output: A list of recommended products displayed on the smart glasses display.

[0788] The above are the processing steps and specific operations of the system that realizes the application example.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] [Third embodiment]

[0793] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0794] 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.

[0795] 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).

[0796] 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.

[0797] 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.

[0798] 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).

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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."

[0805] The system of the present invention is designed to help students find the jobs and companies that best suit their characteristics. The main components of the system are users, terminals, and servers. Specific embodiments of each part of the system are described below.

[0806] User Input

[0807] A web form or mobile application interface is provided for users to enter their information. Users enter their learning experiences, skills, knowledge, interests, and future goals into the form. For example, if a user studies economics, has skills in data analysis, is interested in leadership, and aspires to a managerial position, they would enter that information.

[0808] Data transmission from the device

[0809] The terminal receives the information entered by the user and converts it into a data format (for example, JSON format). The converted data is then sent to the server. The terminal has an application or script for data transmission built in, and the data is sent automatically once the user has completed entering the information.

[0810] Server data processing and analysis

[0811] When the server receives the data sent from the device, it first analyzes the received data. The algorithm used in the analysis compares the received data with the characteristics of jobs and companies stored in the database. The server assigns different weights to learning experience, skills, knowledge, interests, and goals, and calculates the degree of suitability for each job and company. This suitability is calculated as a score, and the most suitable jobs and companies are given higher scores.

[0812] Generate and send a list based on the score

[0813] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, based on the user's input data, a list may be generated in which Company A has a score of 90, Company B has a score of 78, and Company C has a score of 66. This list is then converted back to JSON format and sent to the device.

[0814] Displaying results on a terminal

[0815] The device visualizes the received list and displays it to the user, who can then browse the displayed list to find out more information about the companies and jobs that suit them. For example, with the help of a good UI design, the results can be displayed in a ranking format, with links to access more detailed information about each company or job.

[0816] Specific examples

[0817] For example, suppose student Taro Yamada uses a device to enter the following information:

[0818] Learning experience: Economics, Programming

[0819] Skills: Data analysis, leadership

[0820] Knowledge: Basic knowledge of IT, economics

[0821] Interests: Technology, startups

[0822] Goals: Aspiring to become a manager and want to work overseas

[0823] When Taro Yamada submits his input information, the server analyzes the data and calculates suitable jobs and companies. For example, technology companies might score 90, financial consulting firms 78, and startups 66. The results are sent to his device, where Taro Yamada can visually check the rankings and access detailed information about each company.

[0824] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find jobs and companies that best suit their characteristics.

[0825] The processing flow will be explained below.

[0826] Step 1:

[0827] Users enter information using their own devices. Specifically, they fill out an input form with items such as their learning experience, skills, knowledge, interests, and future goals. For example, they enter information such as economics, data analysis, leadership, technology, or aspiring to a management position.

[0828] Step 2:

[0829] The terminal acquires the information entered by the user and converts it into a data format (for example, JSON format). The converted data will be in the following format, for example.

[0830] json

[0831] {

[0832] "Things Learned": ["Economics", "Programming"],

[0833] "Skills": ["Data Analysis", "Leadership"],

[0834] "Knowledge": ["Basic knowledge of IT", "Economics"],

[0835] "Interests": ["Technology", "Startups"],

[0836] "Goals": ["I want to become a manager", "I want to work overseas"]

[0837] }

[0838] Step 3:

[0839] The terminal sends the converted data to the server using an HTTP POST request, which allows the server to receive the user information.

[0840] Step 4:

[0841] The server receives the data sent from the device. After receiving the data, the server begins analyzing the data. The analysis calculates a score for suitability for each job or company based on learning experience, skills, knowledge, interests, and goals.

[0842] Step 5:

[0843] The server uses an analytical algorithm to match the user's information with job and company information in a database, weighting it and calculating a score for each item (learning experience, skills, knowledge, interests, and goals). For example, if knowledge of economics matches the requirements of a financial company, a high score will be awarded.

[0844] Step 6:

[0845] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, the list might look like this: "Company A: 90 points," "Company B: 78 points," "Company C: 66 points."

[0846] Step 7:

[0847] The server sends the generated list to the terminal. The data sent is in JSON format, for example, as follows:

[0848] json

[0849] [

[0850] {"Company": "Company A", "Score": 90},

[0851] {"Company": "Company B", "Score": 78},

[0852] {"Company": "Company C", "Score": 66}

[0853] ]

[0854] Step 8:

[0855] The terminal visualizes the list received from the server and displays it to the user. The user can view the displayed list and check detailed information about each company or occupation. The display is provided in a visually easy-to-understand interface.

[0856] Step 9:

[0857] Users can select jobs and companies that fit their profile from the displayed list, obtain more information, and access links and contact information to further their application process.

[0858] The above are the specific processing steps of the system.

[0859] Example 1

[0860] 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."

[0861] Conventional career aptitude analysis systems have the problem that they are unable to fully reflect a user's individual learning experience, skills, knowledge, preferences, and goals, and therefore have low accuracy in proposing suitable occupations and companies, making it difficult for users to find the optimal career path. In particular, since it is difficult to analyze taking into account the diverse backgrounds and goals of users, there is a demand for highly accurate recommendations that are tailored to individual characteristics.

[0862] 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.

[0863] In this invention, the server includes means for allowing a user to input information about their learning experiences, skills, knowledge, preferences, and goals, means for converting the information into a standard data format and transmitting it to the server, means for detecting suitable occupations or companies based on the information and assigning scores, means for generating a list of suitable occupations or companies based on the scores, and means for visually displaying the list to the user, thereby enabling highly accurate personalized recommendations based on the user's diverse characteristics.

[0864] "User" refers to a person who uses the system to input information about their learning experiences, skills, knowledge, preferences, and goals.

[0865] "Learning experience" refers to information including the knowledge and skills that the user has acquired so far, educational history, and the like.

[0866] "Skills" refers to specific abilities or expertise possessed by a user, including programming skills and data analysis abilities.

[0867] "Knowledge" refers to the level of knowledge or understanding a user has in a particular field.

[0868] "Preferences" refer to the fields, occupations, and company characteristics that the user is interested in.

[0869] "Goals" refer to the professional objectives or career paths that a user wants to achieve in the future.

[0870] "Standard data format" refers to a common data representation format such as JSON that is suitable for sending, receiving, and analyzing data.

[0871] "Server" refers to the central computer system that processes the information received from users, identifies suitable jobs and companies, assigns scores, and generates a list.

[0872] The "score" refers to the numerical value of the server's evaluation of the suitability of a job or company based on the user's information.

[0873] A "list" refers to a ranking of occupations or companies based on scores.

[0874] "Visually displaying" refers to presenting results to the user in a graphical format that makes them easier to understand.

[0875] "Analysis" refers to the process by which the server evaluates suitability based on the information entered by the user.

[0876] "Means for performing processing" refers to the functionality of the server to analyze user information using a specific algorithm and generate a result.

[0877] "Weighting" refers to the method of calculating points based on different weightings assigned to learning experiences, skills, knowledge, preferences, and goals.

[0878] The system of the present invention allows users to input information about their learning experiences, skills, knowledge, preferences, and goals, and then analyzes the data to recommend suitable jobs and companies. The system consists of three main components: the user, the terminal, and the server.

[0879] Enter user information

[0880] To enter their information, users use a web form or mobile application interface that allows them to enter the following information:

[0881] Learning experience (e.g., economics, programming)

[0882] Skills (e.g., data analysis, leadership)

[0883] Knowledge (e.g., basic knowledge of IT, economics)

[0884] Preferences (e.g., technology, startups)

[0885] Goals (e.g., wanting to become a manager or working overseas)

[0886] The user enters the information and clicks the "Submit" button to proceed to the next step.

[0887] Data conversion and transmission on the device

[0888] The terminal receives the information entered by the user and converts it into a standard data format (for example, JSON format).The converted data is then sent to the server using an HTTP POST request.Scripts such as JavaScript or Python are implemented on the terminal, and the information transmission process is carried out automatically.

[0889] Data reception and analysis on the server

[0890] The server receives the JSON data sent from the device. The server uses the Python Flask framework to manage the data reception and analysis process. The server then uses machine learning algorithms to match the received data with occupation and company characteristics stored in the database.

[0891] Generate a list of jobs and companies based on scores

[0892] The server assigns weights to each data item (learning experience, skills, knowledge, preferences, and goals) and calculates the degree of suitability for each job or company. Based on the calculated suitability, the server generates a list of jobs or companies in descending order of scores. For example, the server generates the following list:

[0893] Company A: 90 points

[0894] Company B: 78 points

[0895] Company C: 66 points

[0896] The generated list is converted back to JSON format and sent to the terminal as an HTTP response.

[0897] Displaying results on your device

[0898] The device parses the received JSON data and visually displays the results in a ranking format using JavaScript frameworks such as React and Vue.js. Users can review the displayed list to identify career and company options that suit them. Links to access more information about each company and career are also provided.

[0899] Specific examples

[0900] For example, if a student enters the following information:

[0901] Learning experience: Economics, Programming

[0902] Skills: Data analysis, leadership

[0903] Knowledge: Basic knowledge of IT, economics

[0904] Preferences: Technology, startups

[0905] Goals: Aspiring to become a manager and want to work overseas

[0906] Based on this information, the server performs analysis and calculates the companies and jobs that are suitable for the student, as follows:

[0907] Technology companies: 90 points

[0908] Financial Consulting Firm: 78 points

[0909] Startups: 66 points

[0910] The results are sent to a device where students can visually view the rankings and access detailed information about each company.

[0911] Prompt Sentence Examples

[0912] "Use a generative AI model to generate a list of suitable jobs and companies based on user inputs about their learning experience, skills, knowledge, preferences, and goals. For example, if a user studies economics, has data analysis skills, is interested in leadership, and aspires to a management position, consider that information when outputting results."

[0913] The above is a specific embodiment for carrying out the present invention. This system allows users to efficiently find jobs and companies that best suit their characteristics.

[0914] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0915] Step 1: User Enters Information

[0916] Users enter their learning experience, skills, knowledge, preferences, and goals using a web form or mobile application. For example, a user might enter information such as "Economics," "Data Analysis," "IT Basics," "Technology," or "Aiming for a Managerial Position." This information becomes input data for subsequent analysis.

[0917] Step 2: The device converts the user data

[0918] The terminal converts the received user data into a standard data format (for example, JSON format). Specifically, the data is structured using a JavaScript or Python script, and the converted JSON data is generated. This JSON data is then prepared for transmission to the server.

[0919] Step 3: The device sends the data to the server

[0920] The device sends the converted JSON data to the server using an HTTP POST request, setting the appropriate content type (e.g., application / json) in the HTTP header to ensure the data reaches the server correctly. If the data is successfully sent, the server returns a receipt confirmation as an HTTP response.

[0921] Step 4: The server receives the data

[0922] The server receives the JSON data sent from the device. It uses the Python Flask framework to parse the received data. The server parses the request, converts the received data into the appropriate format, and prepares it for processing.

[0923] Step 5: The server parses the data

[0924] The server performs an analysis process based on the received data to identify suitable occupations and companies. Specifically, it assigns different weights to learning experience, skills, knowledge, preferences, and goals, and calculates the suitability for each occupation and company. The analysis is performed using a machine learning algorithm, and a score is calculated.

[0925] Step 6: The server generates a list based on the scores

[0926] The server generates a list of suitable jobs and companies for the user based on the obtained scores. For example, it ranks the jobs and companies with the highest scores. The generated list is then converted back to JSON format and prepared for transmission to the device.

[0927] Step 7: The server sends the list to the device

[0928] The server sends the generated list to the device as an HTTP response. The response contains a list of ranked occupations and companies in JSON data format. Once the device receives this data, it can proceed to the next step.

[0929] Step 8: The terminal displays the results visually

[0930] The device parses the received JSON data and displays it visually to the user, using JavaScript frameworks such as React and Vue.js to display a ranking list, with links to access more information about each job or company.

[0931] Step 9: User accesses additional information

[0932] Users can select the jobs or companies they are interested in from the displayed list and click to access more information. Clicking on a link opens a new page or modal window with more information, allowing users to gain a deeper understanding of the jobs and companies that are best suited to them.

[0933] (Application example 1)

[0934] 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."

[0935] In today's labor market, it is difficult for factory workers to find the job or department that best suits their skills and experience. Furthermore, if workers are not assigned to the appropriate job or department, there is a concern that their motivation and efficiency will decline. Particularly in a factory environment with a wide variety of jobs, it is important to identify the job that best suits each worker's characteristics. This will maximize worker performance and improve the working environment.

[0936] 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.

[0937] In this invention, the server includes means for allowing users to input information about their learning experiences, skills, knowledge, interests, and goals, means for detecting suitable occupations or companies based on the information and assigning scores, and means for evaluating the job aptitude of factory workers and quantifying the degree of suitability, thereby enabling factory workers to find the job or department that best suits their characteristics.

[0938] A "user" is someone who uses the system to input information.

[0939] "Learning experience" is information about the subjects the user has studied and the knowledge they have acquired.

[0940] "Skills" is information about specific techniques and abilities that a user has acquired.

[0941] "Knowledge" is the extent of a user's general or specialized understanding or information.

[0942] "Interests" are information about areas or topics that a user is personally interested in.

[0943] "Goals" are information about the job or career that the user wants to achieve in the future.

[0944] A "data format" is a standardized format for handling information electronically.

[0945] A "server" is a computer system that receives information entered by a user and analyzes and processes it.

[0946] The "score" is a numerical representation of the degree of compatibility between the user's characteristics and the occupation or company.

[0947] "Occupation or company" refers to the job the user is looking to work for or the organization for which the user works.

[0948] A "list" is a ranked collection of jobs and companies organized based on scores.

[0949] "Factory workers" are workers who perform various tasks in factories.

[0950] "Job fit" is an assessment of how well a worker's characteristics suit a particular job.

[0951] A "visually displayed result" is a due diligence result that is presented to a user graphically.

[0952] The system of this invention helps users find the jobs and companies that best suit their characteristics. It has a function to recommend jobs that are particularly suitable for factory workers. The system is mainly composed of users, terminals, and a server.

[0953] User Input Process

[0954] First, the user enters information about themselves using a device. This information includes learning experiences, skills, knowledge, interests, and goals. A dedicated application is installed on the device and collects the information entered by the user. The information is entered using a device such as a smartphone, tablet, or PC.

[0955] Data transmission by the terminal

[0956] The terminal first converts the information entered by the user into a data format. Specifically, data is generated in JSON format. The converted data is automatically sent to the server. Scripts and programs for sending the data are built into the terminal.

[0957] Server data processing and analysis

[0958] The server receives data sent from the device. The received data is first analyzed and evaluated based on factors such as learning experience, skills, knowledge, interests, and goals. Based on this, the server calculates a score for suitable occupations and companies, and also evaluates job suitability, especially for factory workers. Python + Flask is used for the data processing algorithm.

[0959] Generate and send a list based on the score

[0960] The server generates a list of jobs and companies based on the calculated score, in order of suitability. For factory workers, a list of the most suitable jobs and departments is generated. The generated list is then sent to the device in JSON format.

[0961] Displaying results on a terminal

[0962] The terminal receives the list sent from the server and visually displays it to the user. The user can check the list in a ranked format and access detailed information about each occupation and company. Visualizing recommended jobs, especially for factory workers, makes it easier for users to make appropriate choices. This is expected to improve worker performance and job satisfaction.

[0963] Specific examples

[0964] For example, a factory worker enters the following information into a terminal:

[0965] Learning experience: Line work, maintenance

[0966] Skills: screw tightening, welding, product inspection

[0967] Knowledge: Seven QC Tools

[0968] Interests: Quality control, safety management

[0969] Goals: Quality control manager, small group activity leader

[0970] Based on the information sent, the server analyzes it and assigns a score to each suitable job or department: a quality control department might get 90 points, a production line supervisor 80 points, and a maintenance technician 70 points. This information is returned to the terminal, where the user can view the results in a visual format.

[0971] Prompt Sentence Examples

[0972] The information entered by the user is in the following format:

[0973] Learning experience: Line work, maintenance

[0974] Skills: screw tightening, welding, product inspection

[0975] Knowledge: Seven QC Tools

[0976] Interests: Quality control, safety management

[0977] Goals: Quality control manager, small group activity leader

[0978] In this way, the system allows factory workers to easily find the job or department that best suits their characteristics.

[0979] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0980] Step 1:

[0981] Users use a device to input information about their learning experiences, skills, knowledge, interests, and goals. The input information is collected through web forms or mobile application interfaces. Specific examples of information input include users entering work experience such as "line work" or "maintenance," or skills such as "screw tightening" or "welding."

[0982] Input: Learning experiences, skills, knowledge, interests, goals

[0983] Output: Input data (text format)

[0984] Step 2:

[0985] The terminal converts the information entered by the user into JSON format, and the converted JSON data is sent to the server by a script or program for data transmission.

[0986] Input: Input data (text format)

[0987] Output: Input data (JSON format)

[0988] Step 3:

[0989] The server receives the JSON data sent from the device, which is first parsed and divided into categories (learning experience, skills, knowledge, interests, and goals).

[0990] Input: Input data (JSON format)

[0991] Output: Analysis data (categorized data)

[0992] Step 4:

[0993] The server then uses the analytics data to generate a score for each category. The algorithm then assigns different weights to learning experiences, skills, interests, and goals, and assigns points to jobs and companies that are suitable for the candidate. For factory workers, the system also evaluates job suitability.

[0994] Input: Analysis data

[0995] Output: Score data (scores for each job and company)

[0996] Step 5:

[0997] The server generates a list of jobs and companies in order of suitability based on the calculated score. In particular, for factory workers, a list of the most suitable jobs and departments is generated. The generated list is again sent to the terminal in JSON format.

[0998] Input: Score data

[0999] Output: Eligibility list (JSON format)

[1000] Step 6:

[1001] The terminal receives the suitability list sent from the server and visually displays it to the user. The user can check the list in a ranked format and access detailed information about each occupation and company. As a result, factory workers can find the job or department that best suits them.

[1002] Input: Eligibility list (JSON format)

[1003] Output: Visual display (ranked list)

[1004] 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.

[1005] The system of the present invention helps students find the occupation or company that best suits their characteristics. It is particularly distinctive in that it incorporates an emotion engine that recognizes the user's emotions and suggests suitable careers based on those emotions. The main components of the system are the user, terminal, server, and emotion engine. Specific embodiments of each part of the system are described below.

[1006] User Input

[1007] A web form or mobile application interface is provided for users to enter their information. Users enter their learning experiences, skills, knowledge, interests, and future goals into the form. For example, if they studied economics, have skills in data analysis, are interested in leadership, and aspire to a managerial position, they enter that information.

[1008] Data transmission from the device

[1009] The terminal receives the information entered by the user and converts it into a data format (for example, JSON format). The converted data is then sent to the server. The terminal has an application or script for data transmission built in, and the data is sent automatically once the user has completed entering the information.

[1010] Server data processing and analysis

[1011] When the server receives the data sent from the device, it first begins analyzing the data and calculates a score based on the user's learning experience, skills, knowledge, interests, and goals to determine their suitability for each job or company.

[1012] Emotion Engine Operation

[1013] The emotion engine recognizes emotions from user input information and, in some cases, from the user's facial expression, tone of voice, and typing speed. This emotional information influences the suggestions of suitable jobs and companies. For example, the suggestions of suitable jobs and companies will differ depending on whether the user is relaxed or stressed.

[1014] Generate and send a list based on the score

[1015] The server calculates the score taking into account the information from the emotion engine and generates a list of occupations and companies in order of suitability. For example, a list might be generated such as "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points." This list is then converted back to JSON format and sent to the device.

[1016] Displaying results on a terminal

[1017] The device visualizes the received list and displays it to the user, who can then view the list and find out more information about the companies and jobs that suit them. For example, the device can display the results in a ranking format through a well-designed UI, and provide links to access more detailed information about each company or job.

[1018] Specific examples

[1019] For example, suppose student Taro Yamada uses a device to enter the following information:

[1020] Learning experience: Economics, Programming

[1021] Skills: Data analysis, leadership

[1022] Knowledge: Basic knowledge of IT, economics

[1023] Interests: Technology, startups

[1024] Goals: Aspiring to become a manager and want to work overseas

[1025] When Taro Yamada submits his input, the server analyzes the data and the emotion engine recognizes his emotional state. For example, if he is in a relaxed state, companies that offer a challenging environment might be given higher scores. As a result, technology companies might receive a score of 90, financial consulting firms a score of 78, and startups a score of 66. The results are sent to his device, where Taro Yamada can visually check the rankings and access detailed information about each company.

[1026] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find the occupations and companies that best suit their characteristics and emotional state.

[1027] The processing flow will be explained below.

[1028] Step 1:

[1029] Users input information using their own devices, specifically by filling out an input form about their learning experiences, skills, knowledge, interests, and future goals. If necessary, the emotion engine also captures emotional data through a camera and microphone that captures the user's facial expressions and tone of voice.

[1030] Step 2:

[1031] The device receives information entered by the user and converts it into a data format (e.g., JSON format). The emotion engine also converts the emotion data it receives into a data format, and all information is integrated into a single data set.

[1032] Step 3:

[1033] The device sends the converted data to the server using an HTTP POST request, allowing the server to receive the user's information and emotion data.

[1034] Step 4:

[1035] The server receives the data sent from the device. After receiving the data, it begins analyzing it. The analysis calculates a score based on the device's learning experience, skills, knowledge, interests, and goals to indicate its suitability for each job or company.

[1036] Step 5:

[1037] The server analyzes the emotion data from the emotion engine and recognizes the user's emotional state. The emotional state is classified into categories such as stress, relaxation, and excitement. This allows the server to adjust the match points for jobs and companies according to the user's emotions.

[1038] Step 6:

[1039] The server uses an analytical algorithm to match the user's information with occupation and company information in the database. It then weights the emotional data and calculates a score for each item (learning experience, skills, knowledge, interests, and goals). For example, if a user's knowledge of economics matches the requirements of a financial company and the user is relaxed, the server will assign a high score.

[1040] Step 7:

[1041] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, a list like "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points" is generated. This list is then converted back to JSON format and sent to the device.

[1042] Step 8:

[1043] The device visualizes the list received from the server and displays it to the user. The user can then view the displayed list and find out more information about the companies and jobs that suit them. For example, the results can be displayed in a ranking format, with links to access more detailed information about each company or job.

[1044] Step 9:

[1045] From the displayed list, users can select the job or company that best suits their characteristics and feelings, obtain more information, and access links and contact information to further their application process.

[1046] The above is the specific processing flow of the present invention combined with the emotion engine. This system allows students to find the most suitable job or company that takes into consideration not only their characteristics but also their emotional state.

[1047] Example 2

[1048] 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."

[1049] Conventional career recommendation systems rely on static information such as a user's learning experience, skills, knowledge, interests, and goals, and are therefore unable to recommend appropriate jobs or companies that take into account the user's current emotional state. This has resulted in problems such as not reflecting aptitude assessments based on the user's emotional state, and not recommending optimal jobs or companies. Furthermore, the process of entering information and displaying results is not intuitive, making them difficult for users to use.

[1050] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1051] In this invention, the server includes: means for allowing the user to input information regarding their educational history, skills, knowledge, interests, and goals; means for converting the information into a data format and transmitting it to the server; means for detecting suitable positions or companies based on the information and assigning scores; means for generating a list of suitable positions or companies based on the scores; means for displaying the list to the user; and means for recognizing the user's emotions and reflecting the information in the aptitude evaluation. This enables appropriate recommendations that take the user's current emotional state into consideration. Furthermore, displaying the recommendation results to the user in a visually easy-to-understand format also improves the system's ease of use.

[1052] "Learning history" refers to the subjects and fields of study that the user has studied to date.

[1053] "Skills" refer to specific skills or abilities that a user possesses.

[1054] "Knowledge" refers to the totality of information and wisdom that a user has acquired.

[1055] "Interests" refer to areas or things that a user is interested in.

[1056] "Goals" refer to the occupation or position that the user wants to achieve in the future.

[1057] "Data format" refers to the way data is organized into a form that a computer can process.

[1058] A "position" refers to a specific position or job within a company.

[1059] "Enterprise" refers to an organization or entity established to provide goods or services.

[1060] A "list" refers to a collection of items ordered based on a particular criterion.

[1061] "Emotion" indicates the user's psychological state, such as whether they are relaxed or tense.

[1062] "Aptitude assessment" refers to assessing the suitability of a user for a job or company based on their characteristics and feelings.

[1063] The system of the present invention helps users find the most suitable job or company based on their characteristics and emotional state. The main components of the system are the user, the terminal, the server, and the emotion engine. Specific embodiments of each part of the system are described below.

[1064] User Input

[1065] Users enter their information through a web form or mobile application, such as their educational background (e.g., economics, programming), skills (e.g., data analysis, leadership), knowledge (e.g., IT basics, economics), interests (e.g., technology, startups), and goals (e.g., aspiring to a management position, wanting to work abroad). Once the user has entered their information and clicked the "Submit" button, the next processing step begins.

[1066] Data transmission from the device

[1067] The terminal converts the information entered by the user into JSON format and sends it to the server. For this purpose, the terminal is equipped with an application or script for data transmission. The HTTPS protocol is used for communication, ensuring data security.

[1068] Server data reception and analysis

[1069] The server receives the JSON format data sent from the device. The received data is stored appropriately on the server and analyzed using analysis tools (e.g., Python scripts, data analysis libraries). This analysis process calculates the user's suitability for each job and company based on their educational background, skills, knowledge, interests, and goals.

[1070] Emotion Engine Operation

[1071] The emotion engine analyzes user input information and, if necessary, the user's facial expressions, tone of voice, and input speed to recognize emotions. This emotion recognition uses pre-trained machine learning models (e.g., TensorFlow, PyTorch). Emotional information influences the suitability assessment for a job or company.

[1072] Generate and send a list based on the score

[1073] The server recalculates the suitability of occupations and companies based on the information from the emotion engine, and generates a list sorted in order of suitability. The list is then converted back to JSON format and sent to the device as an HTTP response.

[1074] Displaying results on a terminal

[1075] The device then visualizes the received JSON-formatted list and displays it to the user using a Javascript and HTML5 interface, allowing the user to view the list in a ranked format and providing links to access more information about each company or occupation.

[1076] Specific examples

[1077] For example, if a student uses a device to enter the following information:

[1078] Educational background: Economics, Programming

[1079] Skills: Data analysis, leadership

[1080] Knowledge: Basic knowledge of IT, economics

[1081] Interests: Technology, startups

[1082] Goals: Aspiring to become a manager and want to work overseas

[1083] Once the user submits their input, the server analyzes the data and an emotion engine recognizes the user's emotional state. For example, if the user is relaxed, companies that offer a challenging environment may be given higher scores. This results in a list with, say, 90 points for technology companies, 78 points for financial consulting firms, and 66 points for startups. This list is then sent to the device, where the user can visually view the rankings and access detailed information about each company.

[1084] Example prompts for generative AI models

[1085] "Describe a system that suggests suitable jobs and companies based on a student's characteristics and emotions. Explain in natural language the process of processing data using user input and evaluating suitability using an emotion engine."

[1086] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find the occupations and companies that best suit their characteristics and emotional state.

[1087] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1088] Step 1: User Input

[1089] Through a web form or a mobile application, users enter information about their learning history, skills, knowledge, interests, and goals. The information entered must be accurate and detailed, as it will be used in the next steps. For example, a user enters their learning history, such as "Economics, Programming," and their skills, such as "Data Analysis, Leadership." This forms the basis for the data analysis that will be performed later. The output is the data entered by the user.

[1090] Step 2: Send data from your device

[1091] The terminal converts the information entered by the user into JSON format. To perform this conversion, the terminal uses an internal script or application. The converted JSON data is sent to the server via the HTTPS protocol. The user input information obtained in step 1 is used as input, and the data converted into JSON format is obtained as output. For example, the following JSON data is generated:

[1092] json

[1093] {

[1094] "Educational background": "Economics, Programming",

[1095] "Skills": "Data analysis, leadership",

[1096] "Knowledge": "Basic knowledge of IT, economics",

[1097] "Interests": "Technology, Startups",

[1098] "Goal": "I want to become a manager and work overseas"

[1099] }

[1100] Step 3: Server receives data

[1101] The server receives the JSON formatted data sent from the device. The received data is stored in the server and prepared for analysis. The JSON data generated in step 2 is used as input, and the data stored in the server is obtained as output. The server obtains the data via an API endpoint.

[1102] Step 4: Server Data Analysis

[1103] The server analyzes the received data using analytical tools (e.g., Python scripts, data analysis libraries). This analysis calculates the suitability for occupations and companies based on each item (learning history, skills, knowledge, interests, and goals). The user data stored on the server is used as input, and the suitability for each occupation and company is obtained as output. For example, scores such as "Company A: 85 points," "Company B: 72 points," and "Company C: 60 points" are calculated.

[1104] Step 5: Emotion Engine in Action

[1105] The emotion engine recognizes emotions based on user input. To do this, it uses pre-trained machine learning models (e.g., TensorFlow, PyTorch). Optionally, it also performs facial expression recognition and voice tone analysis. The input is the user's input and emotion data, and the output is the user's emotional state. For example, emotion information such as "relaxed" or "stressed" can be recognized.

[1106] Step 6: Generate a list based on the scores

[1107] The server takes the information from the emotion engine, modifies the suitability of jobs and companies, and calculates new scores. This generates a list sorted in order of suitability. The analysis results and emotion data are used as input, and the output is a list of jobs and companies sorted in order of suitability. For example, a list like "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points" might be generated.

[1108] Step 7: Send the list to your device

[1109] The server reconverts the generated list into JSON format and sends it to the terminal using the HTTPS protocol. The generated list is used as input, and the JSON format data is sent to the terminal as output. The server sends the data via an API endpoint.

[1110] Step 8: Viewing the results in the terminal

[1111] The device visualizes the received JSON data and displays it to the user. The display uses an interface using Javascript and HTML5. The received JSON data is used as input, and the results are displayed to the user in a visually easy-to-understand format as output. The user can not only view the list in ranking format, but also click on links for each company or occupation to access more information.

[1112] (Application example 2)

[1113] 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."

[1114] In modern virtual stores, product recommendations based on users' emotional state and past preferences are not fully implemented. This can lead to a lack of personalized shopping experiences and lower satisfaction. Furthermore, the lack of dynamic product suggestions utilizing emotion recognition technology makes it difficult for users to efficiently find the products that best suit them.

[1115] 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.

[1116] In this invention, the server includes means for allowing a user to input information about their learning experiences, skills, knowledge, interests, and goals, means for converting the information into a data format and transmitting it to the server, means for detecting suitable products or services based on the information and the user's emotional state and executing a process for assigning scores, means for generating a list of suitable products or services based on the scores, and means for displaying the list to the user, thereby enabling dynamic and personalized product recommendations based on the user's emotional state and past preferences.

[1117] "User" refers to a person who uses the system to input information and receive results.

[1118] "Learning experience" is a general term for knowledge and skills that a user has learned and acquired in the past.

[1119] "Skill" refers to a user's ability or technique to perform a specific task or activity.

[1120] "Knowledge" refers to the accumulation of information and understanding that a user has.

[1121] "Interests" refer to a user's interest in a particular field or activity.

[1122] A "goal" refers to a specific purpose or aspiration that a user wants to achieve in the future.

[1123] "Emotional state" refers to the psychological and mental state that a user is feeling at a particular moment, as recognized by the emotion engine.

[1124] "Data format" refers to a particular format in which digital information is organized for transmission and processing. An example is the JSON format.

[1125] A "server" refers to a computer system that receives and processes data sent from a user terminal.

[1126] "Suitable products or services" refer to products or services that are determined to be optimal for the user based on the user's input information and emotional state.

[1127] A "score" refers to an evaluation value given to a suitable product or service based on the user's information and emotional state.

[1128] "List" refers to the order of products or services generated based on scores and provided to the user.

[1129] "Means for displaying" refers to the way in which a user can visually view the list or information, typically a display or monitor.

[1130] This invention is a virtual store enhancer system that helps users find the products and services that are best suited to them. It recommends products based on the user's learning experience, skills, knowledge, interests, goals, and emotional state. This system mainly uses the following hardware and software:

[1131] 1. Hardware

[1132] Smart glasses (e.g. Google Glass)

[1133] Facial Recognition Camera

[1134] 2. Software

[1135] facial_recognition library: for recognizing emotions from a user's face

[1136] recommendation_engine library: for recommending products based on emotions and preferences

[1137] json module: for saving and loading user data

[1138] The server uses a means for allowing the user to input information about learning experience, skills, knowledge, interests, and goals, and a means for converting the information into a data format and transmitting it. This allows the user to easily provide their information to the system. The server also performs a process of detecting suitable products or services based on the information and the user's emotional state and assigning a score to them. This allows for dynamic product recommendations based on the user's emotional state. The server then generates a list of suitable products or services based on the score and displays the list to the user. This allows the user to visually identify the products and services that are best suited to them.

[1139] As a concrete example, suppose a user, Hanako Sato, inputs information using the virtual store enhancer. The emotion engine recognizes that Hanako Sato has purchased many health foods and fitness products in the past and is currently in a relaxed state. Based on this information, the server suggests yoga mats and health supplements to Hanako Sato.

[1140] Examples of prompts using generative AI models include:

[1141] "Recommend appropriate products based on the user's past purchase history and real-time emotional state. For example, if the user is relaxed, suggest health-related products, and if the user is stressed, suggest relaxation goods."

[1142] This results in a system that provides a highly personalized shopping experience based on the user's emotional state and preferences.

[1143] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1144] Step 1:

[1145] Users access the system by wearing smart glasses. They provide information about their learning experience, skills, knowledge, interests, and goals through input forms or voice input. The input information is converted into JSON format by the device.

[1146] Input: Information about your learning experience, skills, knowledge, interests, and goals

[1147] Output: User information in JSON format

[1148] Step 2:

[1149] The terminal transmits the converted user information to the server, which includes processing for securely transmitting the data using a data communication protocol.

[1150] Input: User information in JSON format

[1151] Output: User information sent to the server

[1152] Step 3:

[1153] The server receives the user information from the device, stores it in a database, and then analyzes the data to evaluate each user's aptitude based on their learning experience, skills, knowledge, interests, and goals, and assigns them a score.

[1154] Input: User information in JSON format

[1155] Output: Aptitude score for each item

[1156] Step 4:

[1157] The user captures a facial image through a facial recognition camera in the smart glasses, which is then analyzed in real time using the facial_recognition library to recognize the user's emotional state.

[1158] Input: Face image data

[1159] Output: Emotional state data

[1160] Step 5:

[1161] The server combines the emotional state data with the user's past purchase history and preference data and uses the recommendation_engine to calculate a score to recommend the most suitable product or service.

[1162] Input: Emotional state data, user's past purchase history and preference data

[1163] Output: List of recommended products or services and their scores

[1164] Step 6:

[1165] The server converts the list of recommended products obtained into JSON format and sends it to the device, using an encryption protocol to ensure security.

[1166] Input: List of recommended products or services and their ratings

[1167] Output: Recommended product list in JSON format

[1168] Step 7:

[1169] The device parses the received JSON-formatted recommended product list and displays it visually on the smart glasses display, allowing the user to visually review the list and access detailed information about the suggested products and services.

[1170] Input: Recommended product list in JSON format

[1171] Output: A list of recommended products displayed on the smart glasses display.

[1172] The above are the processing steps and specific operations of the system that realizes the application example.

[1173] 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.

[1174] 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.

[1175] 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.

[1176] [Fourth embodiment]

[1177] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1178] 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.

[1179] 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).

[1180] 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.

[1181] 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.

[1182] 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).

[1183] 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.

[1184] 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.

[1185] 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.

[1186] 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.

[1187] 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.

[1188] 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.

[1189] 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."

[1190] The system of the present invention is designed to help students find the jobs and companies that best suit their characteristics. The main components of the system are users, terminals, and servers. Specific embodiments of each part of the system are described below.

[1191] User Input

[1192] A web form or mobile application interface is provided for users to enter their information. Users enter their learning experiences, skills, knowledge, interests, and future goals into the form. For example, if a user studies economics, has skills in data analysis, is interested in leadership, and aspires to a managerial position, they would enter that information.

[1193] Data transmission from the device

[1194] The terminal receives the information entered by the user and converts it into a data format (for example, JSON format). The converted data is then sent to the server. The terminal has an application or script for data transmission built in, and the data is sent automatically once the user has completed entering the information.

[1195] Server data processing and analysis

[1196] When the server receives the data sent from the device, it first analyzes the received data. The algorithm used in the analysis compares the received data with the characteristics of jobs and companies stored in the database. The server assigns different weights to learning experience, skills, knowledge, interests, and goals, and calculates the degree of suitability for each job and company. This suitability is calculated as a score, and the most suitable jobs and companies are given higher scores.

[1197] Generate and send a list based on the score

[1198] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, based on the user's input data, a list may be generated in which Company A has a score of 90, Company B has a score of 78, and Company C has a score of 66. This list is then converted back to JSON format and sent to the device.

[1199] Displaying results on a terminal

[1200] The device visualizes the received list and displays it to the user, who can then browse the displayed list to find out more information about the companies and jobs that suit them. For example, with the help of a good UI design, the results can be displayed in a ranking format, with links to access more detailed information about each company or job.

[1201] Specific examples

[1202] For example, suppose student Taro Yamada uses a device to enter the following information:

[1203] Learning experience: Economics, Programming

[1204] Skills: Data analysis, leadership

[1205] Knowledge: Basic knowledge of IT, economics

[1206] Interests: Technology, startups

[1207] Goals: Aspiring to become a manager and want to work overseas

[1208] When Taro Yamada submits his input information, the server analyzes the data and calculates suitable jobs and companies. For example, technology companies might score 90, financial consulting firms 78, and startups 66. The results are sent to his device, where Taro Yamada can visually check the rankings and access detailed information about each company.

[1209] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find jobs and companies that best suit their characteristics.

[1210] The processing flow will be explained below.

[1211] Step 1:

[1212] Users enter information using their own devices. Specifically, they fill out an input form with items such as their learning experience, skills, knowledge, interests, and future goals. For example, they enter information such as economics, data analysis, leadership, technology, or aspiring to a management position.

[1213] Step 2:

[1214] The terminal acquires the information entered by the user and converts it into a data format (for example, JSON format). The converted data will be in the following format, for example.

[1215] json

[1216] {

[1217] "Things Learned": ["Economics", "Programming"],

[1218] "Skills": ["Data Analysis", "Leadership"],

[1219] "Knowledge": ["Basic knowledge of IT", "Economics"],

[1220] "Interests": ["Technology", "Startups"],

[1221] "Goals": ["I want to become a manager", "I want to work overseas"]

[1222] }

[1223] Step 3:

[1224] The terminal sends the converted data to the server using an HTTP POST request, which allows the server to receive the user information.

[1225] Step 4:

[1226] The server receives the data sent from the device. After receiving the data, the server begins analyzing the data. The analysis calculates a score for suitability for each job or company based on learning experience, skills, knowledge, interests, and goals.

[1227] Step 5:

[1228] The server uses an analytical algorithm to match the user's information with job and company information in a database, weighting it and calculating a score for each item (learning experience, skills, knowledge, interests, and goals). For example, if knowledge of economics matches the requirements of a financial company, a high score will be awarded.

[1229] Step 6:

[1230] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, the list might look like this: "Company A: 90 points," "Company B: 78 points," "Company C: 66 points."

[1231] Step 7:

[1232] The server sends the generated list to the terminal. The data sent is in JSON format, for example, as follows:

[1233] json

[1234] [

[1235] {"Company": "Company A", "Score": 90},

[1236] {"Company": "Company B", "Score": 78},

[1237] {"Company": "Company C", "Score": 66}

[1238] ]

[1239] Step 8:

[1240] The terminal visualizes the list received from the server and displays it to the user. The user can view the displayed list and check detailed information about each company or occupation. The display is provided in a visually easy-to-understand interface.

[1241] Step 9:

[1242] Users can select jobs and companies that fit their profile from the displayed list, obtain more information, and access links and contact information to further their application process.

[1243] The above are the specific processing steps of the system.

[1244] Example 1

[1245] 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."

[1246] Conventional career aptitude analysis systems have the problem that they are unable to fully reflect a user's individual learning experience, skills, knowledge, preferences, and goals, and therefore have low accuracy in proposing suitable occupations and companies, making it difficult for users to find the optimal career path. In particular, since it is difficult to analyze taking into account the diverse backgrounds and goals of users, there is a demand for highly accurate recommendations that are tailored to individual characteristics.

[1247] 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.

[1248] In this invention, the server includes means for allowing a user to input information about their learning experiences, skills, knowledge, preferences, and goals, means for converting the information into a standard data format and transmitting it to the server, means for detecting suitable occupations or companies based on the information and assigning scores, means for generating a list of suitable occupations or companies based on the scores, and means for visually displaying the list to the user, thereby enabling highly accurate personalized recommendations based on the user's diverse characteristics.

[1249] "User" refers to a person who uses the system to input information about their learning experiences, skills, knowledge, preferences, and goals.

[1250] "Learning experience" refers to information including the knowledge and skills that the user has acquired so far, educational history, and the like.

[1251] "Skills" refers to specific abilities or expertise possessed by a user, including programming skills and data analysis abilities.

[1252] "Knowledge" refers to the level of knowledge or understanding a user has in a particular field.

[1253] "Preferences" refer to the fields, occupations, and company characteristics that the user is interested in.

[1254] "Goals" refer to the professional objectives or career paths that a user wants to achieve in the future.

[1255] "Standard data format" refers to a common data representation format such as JSON that is suitable for sending, receiving, and analyzing data.

[1256] "Server" refers to the central computer system that processes the information received from users, identifies suitable jobs and companies, assigns scores, and generates a list.

[1257] The "score" refers to the numerical value of the server's evaluation of the suitability of a job or company based on the user's information.

[1258] A "list" refers to a ranking of occupations or companies based on scores.

[1259] "Visually displaying" refers to presenting results to the user in a graphical format that makes them easier to understand.

[1260] "Analysis" refers to the process by which the server evaluates suitability based on the information entered by the user.

[1261] "Means for performing processing" refers to the functionality of the server to analyze user information using a specific algorithm and generate a result.

[1262] "Weighting" refers to the method of calculating points based on different weightings assigned to learning experiences, skills, knowledge, preferences, and goals.

[1263] The system of the present invention allows users to input information about their learning experiences, skills, knowledge, preferences, and goals, and then analyzes the data to recommend suitable jobs and companies. The system consists of three main components: the user, the terminal, and the server.

[1264] Enter user information

[1265] To enter their information, users use a web form or mobile application interface that allows them to enter the following information:

[1266] Learning experience (e.g., economics, programming)

[1267] Skills (e.g., data analysis, leadership)

[1268] Knowledge (e.g., basic knowledge of IT, economics)

[1269] Preferences (e.g., technology, startups)

[1270] Goals (e.g., wanting to become a manager or working overseas)

[1271] The user enters the information and clicks the "Submit" button to proceed to the next step.

[1272] Data conversion and transmission on the device

[1273] The terminal receives the information entered by the user and converts it into a standard data format (for example, JSON format).The converted data is then sent to the server using an HTTP POST request.Scripts such as JavaScript or Python are implemented on the terminal, and the information transmission process is carried out automatically.

[1274] Data reception and analysis on the server

[1275] The server receives the JSON data sent from the device. The server uses the Python Flask framework to manage the data reception and analysis process. The server then uses machine learning algorithms to match the received data with occupation and company characteristics stored in the database.

[1276] Generate a list of jobs and companies based on scores

[1277] The server assigns weights to each data item (learning experience, skills, knowledge, preferences, and goals) and calculates the degree of suitability for each job or company. Based on the calculated suitability, the server generates a list of jobs or companies in descending order of scores. For example, the server generates the following list:

[1278] Company A: 90 points

[1279] Company B: 78 points

[1280] Company C: 66 points

[1281] The generated list is converted back to JSON format and sent to the terminal as an HTTP response.

[1282] Displaying results on your device

[1283] The device parses the received JSON data and visually displays the results in a ranking format using JavaScript frameworks such as React and Vue.js. Users can review the displayed list to identify career and company options that suit them. Links to access more information about each company and career are also provided.

[1284] Specific examples

[1285] For example, if a student enters the following information:

[1286] Learning experience: Economics, Programming

[1287] Skills: Data analysis, leadership

[1288] Knowledge: Basic knowledge of IT, economics

[1289] Preferences: Technology, startups

[1290] Goals: Aspiring to become a manager and want to work overseas

[1291] Based on this information, the server performs analysis and calculates the companies and jobs that are suitable for the student, as follows:

[1292] Technology companies: 90 points

[1293] Financial Consulting Firm: 78 points

[1294] Startups: 66 points

[1295] The results are sent to a device where students can visually view the rankings and access detailed information about each company.

[1296] Prompt Sentence Examples

[1297] "Use a generative AI model to generate a list of suitable jobs and companies based on user inputs about their learning experience, skills, knowledge, preferences, and goals. For example, if a user studies economics, has data analysis skills, is interested in leadership, and aspires to a management position, consider that information when outputting results."

[1298] The above is a specific embodiment for carrying out the present invention. This system allows users to efficiently find jobs and companies that best suit their characteristics.

[1299] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1300] Step 1: User Enters Information

[1301] Users enter their learning experience, skills, knowledge, preferences, and goals using a web form or mobile application. For example, a user might enter information such as "Economics," "Data Analysis," "IT Basics," "Technology," or "Aiming for a Managerial Position." This information becomes input data for subsequent analysis.

[1302] Step 2: The device converts the user data

[1303] The terminal converts the received user data into a standard data format (for example, JSON format). Specifically, the data is structured using a JavaScript or Python script, and the converted JSON data is generated. This JSON data is then prepared for transmission to the server.

[1304] Step 3: The device sends the data to the server

[1305] The device sends the converted JSON data to the server using an HTTP POST request, setting the appropriate content type (e.g., application / json) in the HTTP header to ensure the data reaches the server correctly. If the data is successfully sent, the server returns a receipt confirmation as an HTTP response.

[1306] Step 4: The server receives the data

[1307] The server receives the JSON data sent from the device. It uses the Python Flask framework to parse the received data. The server parses the request, converts the received data into the appropriate format, and prepares it for processing.

[1308] Step 5: The server parses the data

[1309] The server performs an analysis process based on the received data to identify suitable occupations and companies. Specifically, it assigns different weights to learning experience, skills, knowledge, preferences, and goals, and calculates the suitability for each occupation and company. The analysis is performed using a machine learning algorithm, and a score is calculated.

[1310] Step 6: The server generates a list based on the scores

[1311] The server generates a list of suitable jobs and companies for the user based on the obtained scores. For example, it ranks the jobs and companies with the highest scores. The generated list is then converted back to JSON format and prepared for transmission to the device.

[1312] Step 7: The server sends the list to the device

[1313] The server sends the generated list to the device as an HTTP response. The response contains a list of ranked occupations and companies in JSON data format. Once the device receives this data, it can proceed to the next step.

[1314] Step 8: The terminal displays the results visually

[1315] The device parses the received JSON data and displays it visually to the user, using JavaScript frameworks such as React and Vue.js to display a ranking list, with links to access more information about each job or company.

[1316] Step 9: User accesses additional information

[1317] Users can select the jobs or companies they are interested in from the displayed list and click to access more information. Clicking on a link opens a new page or modal window with more information, allowing users to gain a deeper understanding of the jobs and companies that are best suited to them.

[1318] (Application example 1)

[1319] 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."

[1320] In today's labor market, it is difficult for factory workers to find the job or department that best suits their skills and experience. Furthermore, if workers are not assigned to the appropriate job or department, there is a concern that their motivation and efficiency will decline. Particularly in a factory environment with a wide variety of jobs, it is important to identify the job that best suits each worker's characteristics. This will maximize worker performance and improve the working environment.

[1321] 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.

[1322] In this invention, the server includes means for allowing users to input information about their learning experiences, skills, knowledge, interests, and goals, means for detecting suitable occupations or companies based on the information and assigning scores, and means for evaluating the job aptitude of factory workers and quantifying the degree of suitability, thereby enabling factory workers to find the job or department that best suits their characteristics.

[1323] A "user" is someone who uses the system to input information.

[1324] "Learning experience" is information about the subjects the user has studied and the knowledge they have acquired.

[1325] "Skills" is information about specific techniques and abilities that a user has acquired.

[1326] "Knowledge" is the extent of a user's general or specialized understanding or information.

[1327] "Interests" are information about areas or topics that a user is personally interested in.

[1328] "Goals" are information about the job or career that the user wants to achieve in the future.

[1329] A "data format" is a standardized format for handling information electronically.

[1330] A "server" is a computer system that receives information entered by a user and analyzes and processes it.

[1331] The "score" is a numerical representation of the degree of compatibility between the user's characteristics and the occupation or company.

[1332] "Occupation or company" refers to the job the user is looking to work for or the organization for which the user works.

[1333] A "list" is a ranked collection of jobs and companies organized based on scores.

[1334] "Factory workers" are workers who perform various tasks in factories.

[1335] "Job fit" is an assessment of how well a worker's characteristics suit a particular job.

[1336] A "visually displayed result" is a due diligence result that is presented to a user graphically.

[1337] The system of this invention helps users find the jobs and companies that best suit their characteristics. It has a function to recommend jobs that are particularly suitable for factory workers. The system is mainly composed of users, terminals, and a server.

[1338] User Input Process

[1339] First, the user enters information about themselves using a device. This information includes learning experiences, skills, knowledge, interests, and goals. A dedicated application is installed on the device and collects the information entered by the user. The information is entered using a device such as a smartphone, tablet, or PC.

[1340] Data transmission by the terminal

[1341] The terminal first converts the information entered by the user into a data format. Specifically, data is generated in JSON format. The converted data is automatically sent to the server. Scripts and programs for sending the data are built into the terminal.

[1342] Server data processing and analysis

[1343] The server receives data sent from the device. The received data is first analyzed and evaluated based on factors such as learning experience, skills, knowledge, interests, and goals. Based on this, the server calculates a score for suitable occupations and companies, and also evaluates job suitability, especially for factory workers. Python + Flask is used for the data processing algorithm.

[1344] Generate and send a list based on the score

[1345] The server generates a list of jobs and companies based on the calculated score, in order of suitability. For factory workers, a list of the most suitable jobs and departments is generated. The generated list is then sent to the device in JSON format.

[1346] Displaying results on a terminal

[1347] The terminal receives the list sent from the server and visually displays it to the user. The user can check the list in a ranked format and access detailed information about each occupation and company. Visualizing recommended jobs, especially for factory workers, makes it easier for users to make appropriate choices. This is expected to improve worker performance and job satisfaction.

[1348] Specific examples

[1349] For example, a factory worker enters the following information into a terminal:

[1350] Learning experience: Line work, maintenance

[1351] Skills: screw tightening, welding, product inspection

[1352] Knowledge: Seven QC Tools

[1353] Interests: Quality control, safety management

[1354] Goals: Quality control manager, small group activity leader

[1355] Based on the information sent, the server analyzes it and assigns a score to each suitable job or department: a quality control department might get 90 points, a production line supervisor 80 points, and a maintenance technician 70 points. This information is returned to the terminal, where the user can view the results in a visual format.

[1356] Prompt Sentence Examples

[1357] The information entered by the user is in the following format:

[1358] Learning experience: Line work, maintenance

[1359] Skills: screw tightening, welding, product inspection

[1360] Knowledge: Seven QC Tools

[1361] Interests: Quality control, safety management

[1362] Goals: Quality control manager, small group activity leader

[1363] In this way, the system allows factory workers to easily find the job or department that best suits their characteristics.

[1364] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1365] Step 1:

[1366] Users use a device to input information about their learning experiences, skills, knowledge, interests, and goals. The input information is collected through web forms or mobile application interfaces. Specific examples of information input include users entering work experience such as "line work" or "maintenance," or skills such as "screw tightening" or "welding."

[1367] Input: Learning experiences, skills, knowledge, interests, goals

[1368] Output: Input data (text format)

[1369] Step 2:

[1370] The terminal converts the information entered by the user into JSON format, and the converted JSON data is sent to the server by a script or program for data transmission.

[1371] Input: Input data (text format)

[1372] Output: Input data (JSON format)

[1373] Step 3:

[1374] The server receives the JSON data sent from the device, which is first parsed and divided into categories (learning experience, skills, knowledge, interests, and goals).

[1375] Input: Input data (JSON format)

[1376] Output: Analysis data (categorized data)

[1377] Step 4:

[1378] The server then uses the analytics data to generate a score for each category. The algorithm then assigns different weights to learning experiences, skills, interests, and goals, and assigns points to jobs and companies that are suitable for the candidate. For factory workers, the system also evaluates job suitability.

[1379] Input: Analysis data

[1380] Output: Score data (scores for each job and company)

[1381] Step 5:

[1382] The server generates a list of jobs and companies in order of suitability based on the calculated score. In particular, for factory workers, a list of the most suitable jobs and departments is generated. The generated list is again sent to the terminal in JSON format.

[1383] Input: Score data

[1384] Output: Eligibility list (JSON format)

[1385] Step 6:

[1386] The terminal receives the suitability list sent from the server and visually displays it to the user. The user can check the list in a ranked format and access detailed information about each occupation and company. As a result, factory workers can find the job or department that best suits them.

[1387] Input: Eligibility list (JSON format)

[1388] Output: Visual display (ranked list)

[1389] 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.

[1390] The system of the present invention helps students find the occupation or company that best suits their characteristics. It is particularly distinctive in that it incorporates an emotion engine that recognizes the user's emotions and suggests suitable careers based on those emotions. The main components of the system are the user, terminal, server, and emotion engine. Specific embodiments of each part of the system are described below.

[1391] User Input

[1392] A web form or mobile application interface is provided for users to enter their information. Users enter their learning experiences, skills, knowledge, interests, and future goals into the form. For example, if they studied economics, have skills in data analysis, are interested in leadership, and aspire to a managerial position, they enter that information.

[1393] Data transmission from the device

[1394] The terminal receives the information entered by the user and converts it into a data format (for example, JSON format). The converted data is then sent to the server. The terminal has an application or script for data transmission built in, and the data is sent automatically once the user has completed entering the information.

[1395] Server data processing and analysis

[1396] When the server receives the data sent from the device, it first begins analyzing the data and calculates a score based on the user's learning experience, skills, knowledge, interests, and goals to determine their suitability for each job or company.

[1397] Emotion Engine Operation

[1398] The emotion engine recognizes emotions from user input information and, in some cases, from the user's facial expression, tone of voice, and typing speed. This emotional information influences the suggestions of suitable jobs and companies. For example, the suggestions of suitable jobs and companies will differ depending on whether the user is relaxed or stressed.

[1399] Generate and send a list based on the score

[1400] The server calculates the score taking into account the information from the emotion engine and generates a list of occupations and companies in order of suitability. For example, a list might be generated such as "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points." This list is then converted back to JSON format and sent to the device.

[1401] Displaying results on a terminal

[1402] The device visualizes the received list and displays it to the user, who can then view the list and find out more information about the companies and jobs that suit them. For example, the device can display the results in a ranking format through a well-designed UI, and provide links to access more detailed information about each company or job.

[1403] Specific examples

[1404] For example, suppose student Taro Yamada uses a device to enter the following information:

[1405] Learning experience: Economics, Programming

[1406] Skills: Data analysis, leadership

[1407] Knowledge: Basic knowledge of IT, economics

[1408] Interests: Technology, startups

[1409] Goals: Aspiring to become a manager and want to work overseas

[1410] When Taro Yamada submits his input, the server analyzes the data and the emotion engine recognizes his emotional state. For example, if he is in a relaxed state, companies that offer a challenging environment might be given higher scores. As a result, technology companies might receive a score of 90, financial consulting firms a score of 78, and startups a score of 66. The results are sent to his device, where Taro Yamada can visually check the rankings and access detailed information about each company.

[1411] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find the occupations and companies that best suit their characteristics and emotional state.

[1412] The processing flow will be explained below.

[1413] Step 1:

[1414] Users input information using their own devices, specifically by filling out an input form about their learning experiences, skills, knowledge, interests, and future goals. If necessary, the emotion engine also captures emotional data through a camera and microphone that captures the user's facial expressions and tone of voice.

[1415] Step 2:

[1416] The device receives information entered by the user and converts it into a data format (e.g., JSON format). The emotion engine also converts the emotion data it receives into a data format, and all information is integrated into a single data set.

[1417] Step 3:

[1418] The device sends the converted data to the server using an HTTP POST request, allowing the server to receive the user's information and emotion data.

[1419] Step 4:

[1420] The server receives the data sent from the device. After receiving the data, it begins analyzing it. The analysis calculates a score based on the device's learning experience, skills, knowledge, interests, and goals to indicate its suitability for each job or company.

[1421] Step 5:

[1422] The server analyzes the emotion data from the emotion engine and recognizes the user's emotional state. The emotional state is classified into categories such as stress, relaxation, and excitement. This allows the server to adjust the match points for jobs and companies according to the user's emotions.

[1423] Step 6:

[1424] The server uses an analytical algorithm to match the user's information with occupation and company information in the database. It then weights the emotional data and calculates a score for each item (learning experience, skills, knowledge, interests, and goals). For example, if a user's knowledge of economics matches the requirements of a financial company and the user is relaxed, the server will assign a high score.

[1425] Step 7:

[1426] Based on the scores calculated by the server, a list of occupations and companies is generated in order of suitability. For example, a list like "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points" is generated. This list is then converted back to JSON format and sent to the device.

[1427] Step 8:

[1428] The device visualizes the list received from the server and displays it to the user. The user can then view the displayed list and find out more information about the companies and jobs that suit them. For example, the results can be displayed in a ranking format, with links to access more detailed information about each company or job.

[1429] Step 9:

[1430] From the displayed list, users can select the job or company that best suits their characteristics and feelings, obtain more information, and access links and contact information to further their application process.

[1431] The above is the specific processing flow of the present invention combined with the emotion engine. This system allows students to find the most suitable job or company that takes into consideration not only their characteristics but also their emotional state.

[1432] Example 2

[1433] 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."

[1434] Conventional career recommendation systems rely on static information such as a user's learning experience, skills, knowledge, interests, and goals, and are therefore unable to recommend appropriate jobs or companies that take into account the user's current emotional state. This has resulted in problems such as not reflecting aptitude assessments based on the user's emotional state, and not recommending optimal jobs or companies. Furthermore, the process of entering information and displaying results is not intuitive, making them difficult for users to use.

[1435] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1436] In this invention, the server includes: means for allowing the user to input information regarding their educational history, skills, knowledge, interests, and goals; means for converting the information into a data format and transmitting it to the server; means for detecting suitable positions or companies based on the information and assigning scores; means for generating a list of suitable positions or companies based on the scores; means for displaying the list to the user; and means for recognizing the user's emotions and reflecting the information in the aptitude evaluation. This enables appropriate recommendations that take the user's current emotional state into consideration. Furthermore, displaying the recommendation results to the user in a visually easy-to-understand format also improves the system's ease of use.

[1437] "Learning history" refers to the subjects and fields of study that the user has studied to date.

[1438] "Skills" refer to specific skills or abilities that a user possesses.

[1439] "Knowledge" refers to the totality of information and wisdom that a user has acquired.

[1440] "Interests" refer to areas or things that a user is interested in.

[1441] "Goals" refer to the occupation or position that the user wants to achieve in the future.

[1442] "Data format" refers to the way data is organized into a form that a computer can process.

[1443] A "position" refers to a specific position or job within a company.

[1444] "Enterprise" refers to an organization or entity established to provide goods or services.

[1445] A "list" refers to a collection of items ordered based on a particular criterion.

[1446] "Emotion" indicates the user's psychological state, such as whether they are relaxed or tense.

[1447] "Aptitude assessment" refers to assessing the suitability of a user for a job or company based on their characteristics and feelings.

[1448] The system of the present invention helps users find the most suitable job or company based on their characteristics and emotional state. The main components of the system are the user, the terminal, the server, and the emotion engine. Specific embodiments of each part of the system are described below.

[1449] User Input

[1450] Users enter their information through a web form or mobile application, such as their educational background (e.g., economics, programming), skills (e.g., data analysis, leadership), knowledge (e.g., IT basics, economics), interests (e.g., technology, startups), and goals (e.g., aspiring to a management position, wanting to work abroad). Once the user has entered their information and clicked the "Submit" button, the next processing step begins.

[1451] Data transmission from the device

[1452] The terminal converts the information entered by the user into JSON format and sends it to the server. For this purpose, the terminal is equipped with an application or script for data transmission. The HTTPS protocol is used for communication, ensuring data security.

[1453] Server data reception and analysis

[1454] The server receives the JSON format data sent from the device. The received data is stored appropriately on the server and analyzed using analysis tools (e.g., Python scripts, data analysis libraries). This analysis process calculates the user's suitability for each job and company based on their educational background, skills, knowledge, interests, and goals.

[1455] Emotion Engine Operation

[1456] The emotion engine analyzes user input information and, if necessary, the user's facial expressions, tone of voice, and input speed to recognize emotions. This emotion recognition uses pre-trained machine learning models (e.g., TensorFlow, PyTorch). Emotional information influences the suitability assessment for a job or company.

[1457] Generate and send a list based on the score

[1458] The server recalculates the suitability of occupations and companies based on the information from the emotion engine, and generates a list sorted in order of suitability. The list is then converted back to JSON format and sent to the device as an HTTP response.

[1459] Displaying results on a terminal

[1460] The device then visualizes the received JSON-formatted list and displays it to the user using a Javascript and HTML5 interface, allowing the user to view the list in a ranked format and providing links to access more information about each company or occupation.

[1461] Specific examples

[1462] For example, if a student uses a device to enter the following information:

[1463] Educational background: Economics, Programming

[1464] Skills: Data analysis, leadership

[1465] Knowledge: Basic knowledge of IT, economics

[1466] Interests: Technology, startups

[1467] Goals: Aspiring to become a manager and want to work overseas

[1468] Once the user submits their input, the server analyzes the data and an emotion engine recognizes the user's emotional state. For example, if the user is relaxed, companies that offer a challenging environment may be given higher scores. This results in a list with, say, 90 points for technology companies, 78 points for financial consulting firms, and 66 points for startups. This list is then sent to the device, where the user can visually view the rankings and access detailed information about each company.

[1469] Example prompts for generative AI models

[1470] "Describe a system that suggests suitable jobs and companies based on a student's characteristics and emotions. Explain in natural language the process of processing data using user input and evaluating suitability using an emotion engine."

[1471] The above is a specific embodiment for carrying out the present invention. This system allows students to efficiently find the occupations and companies that best suit their characteristics and emotional state.

[1472] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1473] Step 1: User Input

[1474] Through a web form or a mobile application, users enter information about their learning history, skills, knowledge, interests, and goals. The information entered must be accurate and detailed, as it will be used in the next steps. For example, a user enters their learning history, such as "Economics, Programming," and their skills, such as "Data Analysis, Leadership." This forms the basis for the data analysis that will be performed later. The output is the data entered by the user.

[1475] Step 2: Send data from your device

[1476] The terminal converts the information entered by the user into JSON format. To perform this conversion, the terminal uses an internal script or application. The converted JSON data is sent to the server via the HTTPS protocol. The user input information obtained in step 1 is used as input, and the data converted into JSON format is obtained as output. For example, the following JSON data is generated:

[1477] json

[1478] {

[1479] "Educational background": "Economics, Programming",

[1480] "Skills": "Data analysis, leadership",

[1481] "Knowledge": "Basic knowledge of IT, economics",

[1482] "Interests": "Technology, Startups",

[1483] "Goal": "I want to become a manager and work overseas"

[1484] }

[1485] Step 3: Server receives data

[1486] The server receives the JSON formatted data sent from the device. The received data is stored in the server and prepared for analysis. The JSON data generated in step 2 is used as input, and the data stored in the server is obtained as output. The server obtains the data via an API endpoint.

[1487] Step 4: Server Data Analysis

[1488] The server analyzes the received data using analytical tools (e.g., Python scripts, data analysis libraries). This analysis calculates the suitability for occupations and companies based on each item (learning history, skills, knowledge, interests, and goals). The user data stored on the server is used as input, and the suitability for each occupation and company is obtained as output. For example, scores such as "Company A: 85 points," "Company B: 72 points," and "Company C: 60 points" are calculated.

[1489] Step 5: Emotion Engine in Action

[1490] The emotion engine recognizes emotions based on user input. To do this, it uses pre-trained machine learning models (e.g., TensorFlow, PyTorch). Optionally, it also performs facial expression recognition and voice tone analysis. The input is the user's input and emotion data, and the output is the user's emotional state. For example, emotion information such as "relaxed" or "stressed" can be recognized.

[1491] Step 6: Generate a list based on the scores

[1492] The server takes the information from the emotion engine, modifies the suitability of jobs and companies, and calculates new scores. This generates a list sorted in order of suitability. The analysis results and emotion data are used as input, and the output is a list of jobs and companies sorted in order of suitability. For example, a list like "Company A: 90 points," "Company B: 78 points," and "Company C: 66 points" might be generated.

[1493] Step 7: Send the list to your device

[1494] The server reconverts the generated list into JSON format and sends it to the terminal using the HTTPS protocol. The generated list is used as input, and the JSON format data is sent to the terminal as output. The server sends the data via an API endpoint.

[1495] Step 8: Viewing the results in the terminal

[1496] The device visualizes the received JSON data and displays it to the user. The display uses an interface using Javascript and HTML5. The received JSON data is used as input, and the results are displayed to the user in a visually easy-to-understand format as output. The user can not only view the list in ranking format, but also click on links for each company or occupation to access more information.

[1497] (Application example 2)

[1498] 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."

[1499] In modern virtual stores, product recommendations based on users' emotional state and past preferences are not fully implemented. This can lead to a lack of personalized shopping experiences and lower satisfaction. Furthermore, the lack of dynamic product suggestions utilizing emotion recognition technology makes it difficult for users to efficiently find the products that best suit them.

[1500] 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.

[1501] In this invention, the server includes means for allowing a user to input information about their learning experiences, skills, knowledge, interests, and goals, means for converting the information into a data format and transmitting it to the server, means for detecting suitable products or services based on the information and the user's emotional state and executing a process for assigning scores, means for generating a list of suitable products or services based on the scores, and means for displaying the list to the user, thereby enabling dynamic and personalized product recommendations based on the user's emotional state and past preferences.

[1502] "User" refers to a person who uses the system to input information and receive results.

[1503] "Learning experience" is a general term for knowledge and skills that a user has learned and acquired in the past.

[1504] "Skill" refers to a user's ability or technique to perform a specific task or activity.

[1505] "Knowledge" refers to the accumulation of information and understanding that a user has.

[1506] "Interests" refer to a user's interest in a particular field or activity.

[1507] A "goal" refers to a specific purpose or aspiration that a user wants to achieve in the future.

[1508] "Emotional state" refers to the psychological and mental state that a user is feeling at a particular moment, as recognized by the emotion engine.

[1509] "Data format" refers to a particular format in which digital information is organized for transmission and processing. An example is the JSON format.

[1510] A "server" refers to a computer system that receives and processes data sent from a user terminal.

[1511] "Suitable products or services" refer to products or services that are determined to be optimal for the user based on the user's input information and emotional state.

[1512] A "score" refers to an evaluation value given to a suitable product or service based on the user's information and emotional state.

[1513] "List" refers to the order of products or services generated based on scores and provided to the user.

[1514] "Means for displaying" refers to the way in which a user can visually view the list or information, typically a display or monitor.

[1515] This invention is a virtual store enhancer system that helps users find the products and services that are best suited to them. It recommends products based on the user's learning experience, skills, knowledge, interests, goals, and emotional state. This system mainly uses the following hardware and software:

[1516] 1. Hardware

[1517] Smart glasses (e.g. Google Glass)

[1518] Facial Recognition Camera

[1519] 2. Software

[1520] facial_recognition library: for recognizing emotions from a user's face

[1521] recommendation_engine library: for recommending products based on emotions and preferences

[1522] json module: for saving and loading user data

[1523] The server uses a means for allowing the user to input information about learning experience, skills, knowledge, interests, and goals, and a means for converting the information into a data format and transmitting it. This allows the user to easily provide their information to the system. The server also performs a process of detecting suitable products or services based on the information and the user's emotional state and assigning a score to them. This allows for dynamic product recommendations based on the user's emotional state. The server then generates a list of suitable products or services based on the score and displays the list to the user. This allows the user to visually identify the products and services that are best suited to them.

[1524] As a concrete example, suppose a user, Hanako Sato, inputs information using the virtual store enhancer. The emotion engine recognizes that Hanako Sato has purchased many health foods and fitness products in the past and is currently in a relaxed state. Based on this information, the server suggests yoga mats and health supplements to Hanako Sato.

[1525] Examples of prompts using generative AI models include:

[1526] "Recommend appropriate products based on the user's past purchase history and real-time emotional state. For example, if the user is relaxed, suggest health-related products, and if the user is stressed, suggest relaxation goods."

[1527] This results in a system that provides a highly personalized shopping experience based on the user's emotional state and preferences.

[1528] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1529] Step 1:

[1530] Users access the system by wearing smart glasses. They provide information about their learning experience, skills, knowledge, interests, and goals through input forms or voice input. The input information is converted into JSON format by the device.

[1531] Input: Information about your learning experience, skills, knowledge, interests, and goals

[1532] Output: User information in JSON format

[1533] Step 2:

[1534] The terminal transmits the converted user information to the server, which includes processing for securely transmitting the data using a data communication protocol.

[1535] Input: User information in JSON format

[1536] Output: User information sent to the server

[1537] Step 3:

[1538] The server receives the user information from the device, stores it in a database, and then analyzes the data to evaluate each user's aptitude based on their learning experience, skills, knowledge, interests, and goals, and assigns them a score.

[1539] Input: User information in JSON format

[1540] Output: Aptitude score for each item

[1541] Step 4:

[1542] The user captures a facial image through a facial recognition camera in the smart glasses, which is then analyzed in real time using the facial_recognition library to recognize the user's emotional state.

[1543] Input: Face image data

[1544] Output: Emotional state data

[1545] Step 5:

[1546] The server combines the emotional state data with the user's past purchase history and preference data and uses the recommendation_engine to calculate a score to recommend the most suitable product or service.

[1547] Input: Emotional state data, user's past purchase history and preference data

[1548] Output: List of recommended products or services and their scores

[1549] Step 6:

[1550] The server converts the list of recommended products obtained into JSON format and sends it to the device, using an encryption protocol to ensure security.

[1551] Input: List of recommended products or services and their ratings

[1552] Output: Recommended product list in JSON format

[1553] Step 7:

[1554] The device parses the received JSON-formatted recommended product list and displays it visually on the smart glasses display, allowing the user to visually review the list and access detailed information about the suggested products and services.

[1555] Input: Recommended product list in JSON format

[1556] Output: A list of recommended products displayed on the smart glasses display.

[1557] The above are the processing steps and specific operations of the system that realizes the application example.

[1558] 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.

[1559] 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.

[1560] 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 robot 414.

[1561] 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.

[1562] 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.

[1563] 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.

[1564] 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).

[1565] 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.

[1566] 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."

[1567] 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.

[1568] 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).

[1569] 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.

[1570] 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.

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] The following is further disclosed regarding the above embodiment.

[1580] (Claim 1)

[1581] means for obtaining input from a user about information regarding learning experiences, skills, knowledge, interests, and goals;

[1582] means for converting the information into a data format and transmitting the data to a server;

[1583] A means for detecting suitable occupations or companies based on the information and assigning points to the occupations or companies;

[1584] A means for generating a list of suitable occupations or companies based on the score;

[1585] means for displaying the list to a user;

[1586] A system including:

[1587] (Claim 2)

[1588] 10. The system of claim 1, further comprising means for performing a process of assigning points using different weightings for learning experiences, skills, knowledge, interests, and goals.

[1589] (Claim 3)

[1590] 2. The system according to claim 1, further comprising means for visually displaying scores of recommended companies based on information input by the user.

[1591] "Example 1"

[1592] (Claim 1)

[1593] means for allowing a user to input information regarding their learning experiences, skills, knowledge, preferences, and goals;

[1594] means for converting the information into a standard data format and transmitting the converted information to a server;

[1595] A means for detecting suitable occupations or companies based on the information and assigning points to the occupations or companies;

[1596] A means for generating a list of suitable occupations or companies based on the score;

[1597] means for visually displaying the list to a user;

[1598] A system including:

[1599] (Claim 2)

[1600] 10. The system of claim 1, further comprising means for performing a process of assigning scores using different weightings for learning experiences, skills, knowledge, preferences, and goals.

[1601] (Claim 3)

[1602] 10. The system of claim 1, further comprising means for visually displaying the list of recommended jobs or businesses sent from the server.

[1603] "Application Example 1"

[1604] (Claim 1)

[1605] means for obtaining input from a user about information regarding learning experiences, skills, knowledge, interests, and goals;

[1606] means for converting the information into a data format and transmitting the data to a server;

[1607] A means for detecting suitable occupations or companies based on the information and assigning points to the occupations or companies;

[1608] A means for generating a list of suitable occupations or companies based on the score;

[1609] means for displaying the list to a user;

[1610] A means for evaluating the job suitability of factory workers and quantifying the degree of suitability;

[1611] A means for the user to view the visually displayed results;

[1612] A system including:

[1613] (Claim 2)

[1614] 10. The system of claim 1, further comprising means for performing a process of assigning points using different weightings for learning experiences, skills, knowledge, interests, and goals.

[1615] (Claim 3)

[1616] 2. The system according to claim 1, further comprising means for visually displaying scores of recommended companies based on information input by the user.

[1617] "Example 2: Combining Emotion Engines"

[1618] (Claim 1)

[1619] means for allowing a user to input information regarding their educational background, skills, knowledge, interests, and goals;

[1620] means for converting the information into a data format and transmitting the data to a server;

[1621] A means for detecting suitable positions or companies based on the information and assigning points;

[1622] A means for generating a list of suitable positions or companies based on the score;

[1623] means for displaying the list to a user;

[1624] A means for recognizing a user's emotions and reflecting the information in an aptitude assessment;

[1625] A system including:

[1626] (Claim 2)

[1627] 10. The system of claim 1, further comprising means for performing a process of assigning scores using different weightings for learning history, skills, knowledge, interests, and goals.

[1628] (Claim 3)

[1629] 10. The system of claim 1, further comprising means for visually displaying a score for the recommended company based on the user's input information and emotional state.

[1630] "Application example 2 when combining emotion engines"

[1631] (Claim 1)

[1632] means for obtaining input from a user about information regarding learning experiences, skills, knowledge, interests, and goals;

[1633] means for converting the information into a data format and transmitting the data to a server;

[1634] means for detecting suitable products or services based on the information and the user's emotional state and assigning a score to the products or services;

[1635] A means for generating a list of suitable products or services based on the score;

[1636] means for displaying the list to a user;

[1637] A system including:

[1638] (Claim 2)

[1639] 10. The system of claim 1, further comprising means for performing a process of assigning points using different weightings for learning experiences, skills, knowledge, interests, and goals.

[1640] (Claim 3)

[1641] 10. The system of claim 1, further comprising means for visually displaying a score for a recommended product or service based on the user's input information and emotional state. [Explanation of symbols]

[1642] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for obtaining input from a user about information regarding learning experiences, skills, knowledge, interests, and goals; means for converting the information into a data format and transmitting the data to a server; A means for detecting suitable occupations or companies based on the information and assigning points to the occupations or companies; A means for generating a list of suitable occupations or companies based on the score; means for displaying the list to a user; A system including:

2. 10. The system of claim 1, further comprising means for performing a process of assigning scores using different weightings for learning experiences, skills, knowledge, interests, and goals.

3. 2. The system according to claim 1, further comprising means for visually displaying scores of recommended companies based on information input by the user.

Images