system

Abstract

We provide the system. [Solution] An input means for job seekers or workers to answer multiple questions, A terminal device that collects response data obtained by the aforementioned input means and displays it on a glasses-type display device, A communication means for transmitting the collected response data to a processing device, A determination means that analyzes the aforementioned response data using a data set of past successful individuals to determine the appropriate occupation or job, A result generation means that generates a recommendation result for an occupation or job determined by the determination means, A result display means for transmitting and displaying the aforementioned recommendation results to the aforementioned glasses-type display device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In career selection, there is a problem that it usually takes a lot of time and effort for job seekers to find the most suitable career for their aptitude. Also, it is difficult to objectively evaluate the individual characteristics of job seekers and present suitable careers based on them. Therefore, it is necessary to solve the problem that it is difficult for job seekers to quickly and accurately find a suitable career and efficiently proceed with job-hopping activities.

Means for Solving the Problems

[0005] This invention provides a system that determines suitable occupations by statistically analyzing collected response data from multiple questions answered by job seekers against data from past successful individuals. In this system, job seekers input their answers to assess their vocational aptitude via an input means, and transmit this response data to a server device via a communication means. The server device uses this data as a basis for a determination means, which analyzes it using a database of past successful individuals to determine suitable occupations. Furthermore, a result generation means calculates a match score for each occupation, generates a list including recommendations for the most suitable occupations for the job seeker, and presents it to the job seeker through a result display means. This system enables job seekers to quickly find occupations that match their characteristics, facilitating efficient job search support.

[0006] A "job seeker" is a person who is looking for a job or wants to find out if they are suited to a job.

[0007] A "question" refers to the content that job seekers should answer in order to clarify their own characteristics and skills, and is mainly composed of multiple-choice or free-response formats.

[0008] "Input method" refers to an interface used by job seekers to input data into a terminal device, and primarily refers to a keyboard, touch panel, or voice input device.

[0009] "Terminal equipment" refers to electronic devices such as computers, smartphones, or tablets that job seekers need to use the system.

[0010] "Communication means" refers to a network connection used to transmit data from a terminal device to a server device, utilizing either wired or wireless protocols.

[0011] A "server device" is a computer system located in the center of a system that performs data analysis and storage.

[0012] "Determination means" refers to an algorithm or program that performs analysis within a server device to determine suitable occupations based on the job seeker's response data.

[0013] A "database of past successes" is an information resource that stores data on the characteristics and experiences of people who have achieved success in a particular profession.

[0014] "Statistical analysis" refers to the process of analyzing data using statistical methods to derive correlations and relationships.

[0015] "Result generation means" refers to the process or system that creates job recommendation results for job seekers based on the judgment results.

[0016] "Result display means" refers to a screen display or interface used to visually show the recommendation results determined on the terminal device.

[0017] A "match score" is a numerical value or percentage that indicates how well a particular occupation is suited to a job seeker.

[0018] "Job recommendation results" refer to a list of occupations that have been determined to be suitable for the job seeker based on the evaluation criteria. [Brief explanation of the drawing]

[0019] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5]It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0022] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0027] [First Embodiment]

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

[0029] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0032] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0036] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0039] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0040] This invention is constructed as a system for determining suitable occupations for job seekers. This system includes three main elements: a user, a terminal, and a server, each performing a specific function.

[0041] Users first log into the system and enter their basic information. Next, they answer a series of questions and tests provided on the terminal. These questions are designed to assess the job seeker's interests, skills, personality traits, etc., and based on this, users enter detailed information about their skills and preferences.

[0042] The terminal collects information entered by the user and transmits it to the server using communication methods. This transmission utilizes encryption technology to maintain data accuracy and prevent information leakage.

[0043] The server analyzes the received data and uses a determination tool to statistically compare it with a database of past successes. This determines the occupation that is most suitable for the user. For example, the server matches the user's leadership skills with data of successful individuals in a specific occupation, and if it shows a high match score, it recommends that occupation.

[0044] The result generation mechanism creates a list of suggested occupations determined by the server, which includes a scored degree of matching and a brief description of each occupation. This result is displayed to the user via the terminal. The user can review these recommendations and consider which occupation best matches their interests. The terminal also provides features to assist with job postings and the application process related to the occupations the user is interested in.

[0045] For example, if a user is found to have "high communication skills," and this skill shows a high correlation with past data of success in "sales positions," the server can recommend a sales position to the user. In this case, the user can immediately access sales-related job postings and complete the application process on their device.

[0046] This allows job seekers to choose careers based on their own abilities and interests, supporting efficient and effective career development.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The user logs into the system and enters their basic profile information into the terminal. This information includes name, age, educational background, and work history.

[0050] Step 2:

[0051] The device displays a series of questions and tests on the user's screen to assess their career aptitude. These questions include topics related to areas of interest and skill assessment.

[0052] Step 3:

[0053] Users answer the presented questions and complete various aptitude tests. Answers can be entered in multiple-choice or text format.

[0054] Step 4:

[0055] The device temporarily stores the user's response data and prepares to send it to the server after the test is completed.

[0056] Step 5:

[0057] The user confirms the completion of the test and instructs the device to send the data.

[0058] Step 6:

[0059] The device uses a communication method to send the user's response data to the server. During this process, the data is encrypted before transmission.

[0060] Step 7:

[0061] The server decodes the received response data and begins the analysis process.

[0062] Step 8:

[0063] The server compares the received response data with a database of past success stories and uses statistical methods to determine a suitable occupation for the user. Specifically, it calculates a match score using correlation analysis and machine learning algorithms.

[0064] Step 9:

[0065] The server generates a list of recommended occupations for the user based on the assessment results. The list includes a match score and a summary for each occupation.

[0066] Step 10:

[0067] The server sends the job recommendation results it has generated to the terminal.

[0068] Step 11:

[0069] The device visually displays the received job recommendation results to the user. The user can then consider specific job options based on the provided information.

[0070] Step 12:

[0071] Users can view detailed information about occupations they are interested in and proceed with their job search as needed. The device also provides relevant job information and support for the application process.

[0072] (Example 1)

[0073] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0074] Current job placement systems struggle to accurately recommend occupations that reflect job seekers' abilities and interests. Furthermore, the reliability of the results and the timeliness of their display are sometimes lacking. Therefore, there is a need for a system with sufficient mechanisms and specific recommendations to provide job seekers with the information they need to choose the most suitable occupation.

[0075] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0076] In this invention, the server includes an information discrimination means that analyzes response information using information records about past successful individuals to determine suitable occupations, an information generation means that generates recommended occupations determined by the information discrimination means, and a result display means. This makes it possible to recommend appropriate occupations that match the job seeker's abilities and interests.

[0077] An "information input method" is an interface used by job seekers to input their response information.

[0078] An "electronic device" is a device that collects response information obtained from users and transmits it to a server for processing.

[0079] "Data communication means" refers to technologies for securely and accurately transmitting aggregated response information from electronic devices to a server.

[0080] An "information processing device" is a server that analyzes and interprets the submitted response information.

[0081] An "information record" is a database that stores information about past successful individuals.

[0082] An "information discrimination means" is a function within an information processing device that analyzes response information based on information about past successful individuals to determine suitable occupations.

[0083] "Information generation means" refers to a function that generates recommended occupation results based on the judgment results of the information discrimination means.

[0084] "Result display means" refers to a function for displaying the generated recommended results on the user's electronic device.

[0085] "Relevance" is an indicator that shows the degree of relationship between a user's response information and information from past successful users.

[0086] The "Recommended Occupation List" is a list generated by an information generation system that includes occupations recommended to job seekers and their degree of matching.

[0087] This invention relates to a job search system for job seekers to find suitable employment. This system consists of three main components: a user, a terminal, and a server, each performing a specific function.

[0088] About the user

[0089] Users log in to the system and enter basic information. They answer a series of questions and tests provided through the terminal. These questions are designed to assess the job seeker's interests, skills, and personality traits. Users can record detailed skills and preferences using checkboxes, radio buttons, and open-ended text boxes.

[0090] About the device

[0091] The terminal is responsible for aggregating information entered by the user and sending the aggregated data to the server. The terminal uses JavaScript (registered trademark) to check the integrity of the data and transmits it via HTTPS while protecting it with encryption technologies such as AES. The terminal also has the function of visually displaying recommended results using HTML and CSS.

[0092] About the server

[0093] The server analyzes the received data. It utilizes a generative AI model for analysis, executing machine learning algorithms using Python and Scikit-learn. Based on the prompt, the server compares the user's information with data from past successful individuals to determine the most suitable occupation. For example, the prompt "Determine an occupation based on the user's skills and interests, and provide relevant job postings" is input to the AI ​​model. Based on the analysis results, the server generates recommendations.

[0094] This invention provides a system that quickly and accurately recommends suitable occupations to users, supporting their decision-making in career choices. This system enables job seekers to efficiently find occupations that best match their characteristics and interests.

[0095] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0096] Step 1:

[0097] User

[0098] The user logs into the system and enters the requested basic information. This information includes name, age, and contact information. The user uses the keyboard to enter this information into the input form on the terminal and saves the information by clicking the "Submit" button.

[0099] Step 2:

[0100] User

[0101] Users answer questions and take tests displayed on their device. The questions are designed to assess the user's interests, skills, and personality traits. Users check the answer choices and enter their skills and preferences in the free-response field. Once finished, they confirm their answers by clicking the "Submit" button and send them to their device.

[0102] Step 3:

[0103] terminal

[0104] The terminal aggregates information obtained from the user. JavaScript is used for data format checks and validation during this aggregation. Next, the aggregated information is encrypted. AES encryption technology is used to encrypt the information, which is then securely transmitted to the server. The encrypted data is output from the terminal and sent to the server via HTTPS.

[0105] Step 4:

[0106] server

[0107] The server decrypts the encrypted data received from the terminal and prepares it for analysis. The decrypted data is input into the generating AI model. The server uses Python and Scikit-learn to perform clustering analysis using machine learning algorithms. As a result of this analysis, a list of possible occupations based on the user's interests and skills is generated.

[0108] Step 5:

[0109] server

[0110] The server generates a prompt message and uses an AI model to determine recommended occupations based on the analysis results. The prompt message used is, "Determine occupations based on the user's skills and interests, and provide relevant job postings." The resulting list of recommended occupations is then passed on to the next step.

[0111] Step 6:

[0112] terminal

[0113] The terminal displays a list of recommended occupations received from the server to the user. HTML and CSS are used to visualize the data and provide the results in a user-friendly format. The displayed results include recommended occupations and their matching scores, allowing the user to make an occupational selection.

[0114] (Application Example 1)

[0115] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0116] Conventional vocational aptitude assessment systems struggle to quickly and accurately identify detailed job suitability based on individual job seekers' skills and characteristics in the field. This makes it difficult for workers in physical stores to efficiently find the most suitable role for their daily tasks. Furthermore, the constant need to operate a terminal to access visualized information disrupts the workflow. To address these challenges, a new support system is needed that enables workers to adapt smoothly to their jobs.

[0117] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0118] In this invention, the server includes an input means for job seekers or workers to answer a series of questions, a means for using a terminal device to collect the answer data obtained by the input means and display it on a glasses-type display device, and a determination means for analyzing the answer data using a data set of past successful individuals to determine suitable occupations or tasks. This makes it possible for workers in physical stores to receive suggestions for optimal tasks based on their own characteristics, thereby improving the efficiency and effectiveness of their work.

[0119] A "job seeker or worker" is a person who requires an assessment of their suitability for a specific occupation or job.

[0120] "Input means" refers to a device or method used by a job seeker or worker to provide information to the system.

[0121] A "terminal device" is a device that processes collected data and presents that information to the user as needed.

[0122] A "glasses-type display device" is a display medium in the shape of glasses that provides information in a way that is visible to the user.

[0123] "Communication means" refers to technology or equipment for sending and receiving data between a terminal device and a server device.

[0124] A "processing device" is a device or system used to analyze collected data and generate or provide appropriate information.

[0125] A "dataset of past successes" is a dataset that collects information related to individuals who have achieved success in their profession or field.

[0126] "Determination means" refers to a function or device that analyzes input data to identify a suitable occupation or job.

[0127] "Result generation means" refers to a function or device that creates information, including recommended occupations or jobs, based on the analysis results from the determination means.

[0128] "Result display means" refers to a device or method for visually presenting the generated recommendation results to the user.

[0129] This invention is a system designed to support operational suitability in physical stores. The operation and implementation of the system are described below.

[0130] The system uses a glasses-type display device worn by job seekers or workers. Users first log in to the system via an input device and answer questions displayed on the terminal device. The answer data is sent to the server via a communication device. The server receives this data and performs analysis using a data set of past successful candidates. A determination device identifies suitable occupations or tasks based on the analysis, and a result generation device constructs the results.

[0131] These results are transmitted to a glasses-type display device and presented to the user in real time. This allows users to quickly understand recommended tasks that match their skills and interests and immediately apply them to their daily work.

[0132] As a concrete example, imagine a scenario where a store employee is assigned to handle a new product category during their shift and uses this system to assess their suitability. In this case, the system compares the employee's past successes with the database and suggests the most suitable category for them.

[0133] Another example of a prompt for the generating AI model is: "Design an application that diagnoses suitability for in-store tasks and suggests the most suitable roles. Based on actual work, it aims to identify staff best suited for specific tasks or product sales." This prompt allows the system to generate and visually display specific suggestions for the work.

[0134] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0135] Step 1:

[0136] The user puts on glasses-type display devices and logs into the terminal.

[0137] Input: User account information

[0138] Action: The initial authentication process begins, and the user account is verified.

[0139] Output: Authentication status of verified user accounts

[0140] Step 2:

[0141] The user answers a series of questions using input methods on their device.

[0142] Input: User's response information

[0143] Operation: The terminal collects user responses through the interface. These questions concern the job seeker's interests and skills.

[0144] Output: Collected response data

[0145] Step 3:

[0146] The device transmits the collected response data to the server using a communication method.

[0147] Input: Collected response data

[0148] Operation: This data is encrypted and securely transmitted to the server.

[0149] Output: Response data that reached the server

[0150] Step 4:

[0151] The server analyzes the response data using a dataset of past successful users.

[0152] Input: Collected response data, database of past success stories

[0153] Operation: The server generates target data, uses an AI model to analyze it, and identifies the characteristics of the respondents.

[0154] Output: Analyzed characteristic data

[0155] Step 5:

[0156] The server's determination mechanism identifies a suitable occupation or job based on the analysis results.

[0157] Input: Analyzed characteristic data

[0158] Operation: Characteristic data is cross-referenced with past success stories, and highly suitable tasks are presented based on statistical algorithms.

[0159] Output: Identified appropriate business information

[0160] Step 6:

[0161] The server's result generation mechanism constitutes the recommendation results for appropriate tasks.

[0162] Input: Identified appropriate business information

[0163] Operation: Calculates the suitability score and builds a list of suitable tasks.

[0164] Output: Recommended Task List

[0165] Step 7:

[0166] A list of recommended tasks is sent to a glasses-type display device and displayed to the user in real time.

[0167] Input: Recommended Task List

[0168] Function: The display device shows the user the results and provides visual feedback, helping them understand how to perform the task correctly.

[0169] Output: Visually presented recommended business information for the user.

[0170] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0171] This invention is a system that combines an emotional engine with a career aptitude system to provide career recommendations that take into account the psychological aspects of job seekers. This system consists of a user, a terminal, a server, and an emotional engine, with each element playing a specific role.

[0172] The user logs into the system and enters the necessary basic profile information into the terminal. Next, a series of questions to assess their vocational aptitude are presented on the terminal. As the user answers these questions, the emotion engine recognizes their emotional state in real time from their facial expressions and voice.

[0173] The device collects user responses and emotional data from the emotion engine, and sends this data to the server. The emotion engine recognizes the user's emotional state (e.g., tension, joy, interest) and provides this to the server as a dataset.

[0174] The server analyzes the received response data and sentiment data, and evaluates occupational aptitude using a judgment method. In this process, sentiment data is used as a factor that influences the judgment. For example, if a user shows interest in a particular question, that sentiment is evaluated positively, and occupations in that field are recommended preferentially.

[0175] Next, the results generation system creates a list of recommended occupations based on the information obtained from the analysis. The list includes matching scores and related information, as well as supplementary information based on sentiment data. This result is transmitted to the terminal and presented to the user visually.

[0176] For example, if a user smiles when answering the question "Do you enjoy creative work?" on their device, the emotion engine will record that response as positive. Based on this, the server will recommend professions such as advertising designer or game developer with a high match score.

[0177] In this way, users can receive career recommendations based on their interests and emotions, enabling them to make more personalized career choices. This invention allows job seekers to make career choices that consider emotional aptitude in addition to traditional skills and experience, which is expected to improve job satisfaction.

[0178] The following describes the processing flow.

[0179] Step 1:

[0180] The user logs into the system and enters their basic profile information into the device.

[0181] Step 2:

[0182] The device displays a series of questions on the screen to assess the user's aptitude for work. These questions include content designed to identify the user's interests and skills.

[0183] Step 3:

[0184] The emotion engine analyzes the user's facial expressions and voice in real time through the device's camera and microphone to determine the user's emotional state.

[0185] Step 4:

[0186] As the user answers the presented questions, their emotions during the process are recognized by an emotion engine. This information is collected on the device as emotion data.

[0187] Step 5:

[0188] The device temporarily stores the user's response data and aggregated sentiment data, and verifies the data's integrity.

[0189] Step 6:

[0190] The user completes the answers to the questions and instructs the data to be sent.

[0191] Step 7:

[0192] The device encrypts the response data and sentiment data when it sends them to the server using a communication method.

[0193] Step 8:

[0194] The server analyzes the received data and uses a judgment tool to evaluate the user's occupational aptitude. It compares this to past success data in the database and considers statistical and emotional factors.

[0195] Step 9:

[0196] The server generates a list of recommended occupations based on the analysis results. This list also includes supplementary information based on matching scores and sentiment data.

[0197] Step 10:

[0198] The server sends the final recommendation results to the terminal, where they are displayed visually to the user.

[0199] Step 11:

[0200] The user reviews the presented list of recommended occupations and considers the details of each occupation. If necessary, they can access relevant job postings through their device and proceed with the application process directly.

[0201] (Example 2)

[0202] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0203] Traditional job recommendation systems only assess job seekers' skills and experience, failing to consider their emotional and psychological aspects. This creates a problem where recommended jobs may not always align with the job seeker's inner interests and passions, potentially leading to decreased job satisfaction.

[0204] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0205] In this invention, the server includes an analysis means, a result generation means, and a result display means. This makes it possible to evaluate job suitability by considering both the job seeker's response data and emotional data, thereby realizing job recommendations that are more in line with individual emotions and interests, and improving job satisfaction.

[0206] An "input method" refers to a means by which job seekers input their answers to multiple questions.

[0207] A "terminal device" is a device that collects job applicants' response data and transmits it to a server device along with data obtained through emotion recognition.

[0208] An "emotion recognition method" is a means of analyzing a user's facial expressions and voice to acquire emotional data.

[0209] "Communication means" refers to the means of transmitting response data and sentiment data from a terminal device to a server device.

[0210] The "analysis means" refers to a method for analyzing the response data and sentiment data received by the server device to determine the suitable occupation for the job seeker.

[0211] The "result generation means" is a means for generating a list of recommended occupations suitable for job seekers based on the analysis results obtained by the analysis means.

[0212] The "result display means" is a means for transmitting the generated recommendation list to a terminal device and displaying it visually.

[0213] This is "partial text".

[0214] The system of this invention is designed to evaluate a job seeker's vocational aptitude based on their emotions and recommend a suitable job. The system mainly consists of a terminal device operated by the user, a server device that processes data, and an emotion recognition means that analyzes the user's emotional state.

[0215] The user first logs into the system using a terminal device and enters basic profile information. This terminal device provides an input mechanism for collecting response data. Next, the terminal device presents the user with a series of questions designed to assess their vocational aptitude. The user answers these questions through the input mechanism.

[0216] On the other hand, the emotion recognition means analyzes the user's facial expressions and voice as they answer questions and acquires emotion data in real time. This uses sensors such as cameras and microphones. After the terminal device collects the answer data and emotion data, it uses communication means to transmit them to the server device.

[0217] The server processes the received data using analysis tools to determine the most suitable occupation for the job seeker. Since both response data and sentiment data are considered in this determination, the job recommendations become more personalized and accurate. The generated analysis results are formatted into a recommendation list constructed by a results generation tool. This list is transmitted to the terminal device via a results display tool and visually displayed to the user.

[0218] For example, if a user answers "yes" with a smile to the question "Do you enjoy creative work?", the emotion recognition system records that positive response. Based on this, the server recommends professions such as advertising designer or game developer with a high degree of relevance.

[0219] Examples of prompts for a generative AI model include sentences like, "Use the job aptitude system to show how a user's emotions influence job recommendations."

[0220] This invention will enable job seekers to make career choices that take their emotional aptitudes into consideration, and is expected to improve job satisfaction.

[0221] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0222] Step 1:

[0223] The user logs into the terminal device and enters their profile information. The entered data includes basic personal information and work experience. Based on this information, the terminal initializes the user profile and prepares to send that data to the server.

[0224] Step 2:

[0225] The device displays questions on its screen to assess vocational aptitude. The user enters their answers to these questions, and the device records them. Simultaneously, the device uses its camera and microphone to capture the user's facial expressions and voice. Emotion recognition measures analyze this data to evaluate the user's emotional state in real time. This process uses facial recognition algorithms and voice analysis technology to obtain emotional data such as tension and joy.

[0226] Step 3:

[0227] The terminal packets the collected response data and obtained sentiment data and sends them to the server. The communication method transmits this data to the server using a secure protocol (e.g., HTTPS). Encryption technology is used throughout this process to ensure data integrity and privacy.

[0228] Step 4:

[0229] The server processes the received response data and sentiment data using analytical tools. It utilizes a generative AI model to analyze the correlation between the response data and sentiment data. Specifically, it evaluates how sentiment data influences the response content and scores that influence. This allows for a quantitative assessment of the user's occupational aptitude.

[0230] Step 5:

[0231] The results generation mechanism generates a list of recommended occupations most suitable for the user based on the analysis results. This list includes supplementary evaluations based on agreement scores and sentiment data. The generated list serves as an important factor in the user's decision-making process when choosing an occupation.

[0232] Step 6:

[0233] The server sends the recommendation list generated using the results display means to the terminal. The terminal displays the received recommendation list on the user interface, making it easy for the user to review. This allows the user to access information based on their own feelings and career aptitudes, enabling them to make more appropriate career choices.

[0234] (Application Example 2)

[0235] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0236] Traditional vocational aptitude systems primarily considered information based on job seekers' skills and experience, failing to adequately evaluate their psychological aspects, particularly their emotional suitability. As a result, it was difficult to make job recommendations that reflected job seekers' potential interests and passions, thus hindering their career satisfaction. Furthermore, the lack of techniques for appropriately recording and utilizing naturally occurring emotional responses in daily life limited the ability to assess job seekers' overall vocational aptitude.

[0237] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0238] In this invention, the server includes an information input means for job seekers to answer multiple questions, an observation and recording means for accumulating emotional data by observing the user's daily behavior and recording emotional responses, and an evaluation means for analyzing the response data and emotional data using a set of information on successful candidates from the past to determine suitable occupations. This enables personalized job recommendations that reflect the job seeker's potential interests and passions, thereby improving career satisfaction.

[0239] An "information input method" is an interface for job seekers to input their answers to questions.

[0240] An "electronic system" is a device for processing and transmitting data collected from information input sources.

[0241] "Transmission means" refers to a communication method for transmitting collected response data and sentiment data to an information processing device.

[0242] An "information processing device" is a device that analyzes and evaluates received data and generates results.

[0243] A "collection of information on successful precedents" is a database of data on individuals who have achieved success in the past.

[0244] "Evaluation means" refers to a device or program that has the function of determining occupational aptitude by analyzing response data and emotional data.

[0245] The "result generation means" refers to a function for generating recommendation results based on the determined occupation.

[0246] "Result display means" refers to a device or program that has the function of visualizing the generated recommendation results and presenting them to the user.

[0247] The "observation and recording means" refers to a function for observing the user's daily behavior and recording their emotional responses during that time.

[0248] The "match score" is a numerical representation of how well each job in the generated list of recommended jobs matches the job seeker.

[0249] The "Recommended Occupation List" is a list that shows the recommended order of occupations, generated based on the evaluation method.

[0250] To implement this invention, first, an interface is required in which job seekers answer multiple questions. The response data collected through the information input means is then stored in an electronic system. This electronic system includes observation and recording means that observe the user's daily behavior and record their facial expressions and voice tone in real time. This observation and recording means uses a terminal equipped with a camera and microphone.

[0251] The server analyzes this response data and emotion data through an information processing device. Real-time processing hardware such as NVIDIA Jetson Nano and Raspberry Pi is used for the analysis, and facial recognition and voice analysis are performed using software such as OpenCV and Python. The emotion data quantified by the emotion engine and the job seeker's response data are compared with a cloud-based database of successful precedents, and occupational aptitude is determined by an evaluation method.

[0252] Once the evaluation is complete, the results generation system generates a list of recommended occupations, including the degree of matching score. The server sends these recommendations to the terminal and presents them visually to the job seeker using the results display system.

[0253] For example, if a device engages in a casual conversation with a user and the user smiles and says they are interested in creative work, the emotion engine will record this response as positive. The server will then use this information to recommend professions such as advertising designer or game developer with a high match score. An example of a prompt might be, "When a user smiles and talks about movies, which professions should be recommended?"

[0254] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0255] Step 1:

[0256] The terminal provides a means for job seekers to answer multiple questions through an interface, thereby inputting response data. The response data is temporarily stored on the terminal. The input response data is structured based on the prompt text and prepared to be sent to the server.

[0257] Step 2:

[0258] The device observes the user's daily activities and acquires facial expression and voice data using its camera and microphone. The acquired data is analyzed in real time using an emotion engine. Based on the analysis, the user's emotional state is output as numerical emotion data, which is then prepared to be sent to the server.

[0259] Step 3:

[0260] The server receives response data and sentiment data sent from the terminal. An information processing device analyzes this data and compares it with a set of information on successful individuals in the past. Using NVIDIA Jetson Nano and OpenCV, the data is statistically processed based on indicators derived from the prompt text to generate numerical data representing occupational aptitude.

[0261] Step 4:

[0262] The server calculates a match score using the results of the vocational aptitude assessment determined by the evaluation tool. The result generation tool generates a list of recommended occupations for the job seeker based on the calculated score. The list of recommended occupations is also corrected based on the job seeker's sentiment data.

[0263] Step 5:

[0264] The server sends the generated list of recommended occupations to the terminal. The terminal visually displays the recommended occupations and their matching scores to the job seeker through a results display device. The job seeker who receives the results can then obtain information to help them make informed decisions about their occupation selection.

[0265] Step 6:

[0266] Users can view a list of recommended occupations and enter additional information or new questions about occupations they are interested in into their terminal. This input is sent back to the server for further analysis and re-evaluation, resulting in more accurate recommendations.

[0267] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0268] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0269] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0270] [Second Embodiment]

[0271] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0272] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0273] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0274] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0275] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0276] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0277] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0278] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0279] The specific processing program 56 is an example of the "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 operating as the specific processing unit 290 according to the specific processing program 56 executed by the processor 28 on the RAM 30.

[0280] 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 specific processing unit 290.

[0281] In the smart glasses 214, the processor 46 performs reception and output processing. The storage 50 stores a reception and output program 60. The processor 46 reads the reception and output program 60 from the storage 50 and executes the read reception and output program 60 on the RAM 48. The reception and output processing is realized by operating as the control unit 46A according to the reception and output program 60 executed by the processor 46 on the RAM 48.

[0282] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0283] The present invention is constructed as a system for determining a suitable occupation for a job seeker. This system includes three main elements: a user, a terminal, and a server, each of which performs a specific function.

[0284] The user first logs in to the system and enters their basic information. Next, the user answers a series of questions and tests provided on the terminal. These questions are designed to evaluate the interests, skills, and personality traits of the job seeker, and the user enters their skills and aspirations in detail based on them.

[0285] The terminal aggregates the information input by the user and transmits it to the server using communication means. Encryption technology is used for this transmission to ensure data accuracy and prevent information leakage.

[0286] The server analyzes the received data and statistically compares it with the database of past successful candidates using a determination means. This determines the most suitable occupation for the user. For example, the server matches the user's leadership skills with the data of successful candidates in a specific occupation, and if a high degree of match score is shown, it recommends that occupation.

[0287] The result generation means creates a list of proposed occupations determined by the server, which includes the scored degree of match and a brief description of each occupation. This result is displayed to the user via the terminal. The user can check this recommendation result and consider the occupation that best suits their interests. Also, the terminal provides a function to assist with job information and the application process related to the occupation the user is interested in.

[0288] As a specific example, when the user obtains the result of "having high communication skills", since this skill shows a high correlation with the past data of successful candidates in "sales positions", the server can recommend a sales position to the user. At this time, the user can directly access job offers related to sales and complete the specific application procedures on the terminal.

[0289] This enables job seekers to make career choices based on their own abilities and interests, and can support efficient and effective career formation.

[0290] The following describes the processing flow.

[0291] Step 1:

[0292] The user logs in to the system and enters their basic profile information into the terminal. This information includes name, age, educational background, work experience, etc.

[0293] Step 2:

[0294] The terminal displays on the screen questions and tests for evaluating a series of vocational aptitudes for the user. The questions include content related to fields of interest and skill evaluations.

[0295] Step 3:

[0296] The user answers the presented questions and completes various aptitude tests. The answers are input in the form of options or text.

[0297] Step 4:

[0298] The terminal temporarily stores the user's answer data and prepares to send this to the server after the test ends.

[0299] Step 5:

[0300] The user confirms the completion of the test and instructs the terminal to send the data.

[0301] Step 6:

[0302] The terminal uses the communication means to send the user's answer data to the server. The data is encrypted and sent in this process.

[0303] Step 7:

[0304] The server decodes the received answer data and starts the analysis process.

[0305] Step 8:

[0306] The server compares the received answer data with the database of past successful candidates and determines the occupations suitable for the user using statistical methods. Specifically, a degree of match score is calculated by correlation analysis or machine learning algorithms.

[0307] Step 9:

[0308] The server generates a list of recommended occupations for the user based on the assessment results. The list includes a match score and a summary for each occupation.

[0309] Step 10:

[0310] The server sends the job recommendation results it has generated to the terminal.

[0311] Step 11:

[0312] The device visually displays the received job recommendation results to the user. The user can then consider specific job options based on the provided information.

[0313] Step 12:

[0314] Users can view detailed information about occupations they are interested in and proceed with their job search as needed. The device also provides relevant job information and support for the application process.

[0315] (Example 1)

[0316] Next, we will describe Example 1. 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."

[0317] Current job placement systems struggle to accurately recommend occupations that reflect job seekers' abilities and interests. Furthermore, the reliability of the results and the timeliness of their display are sometimes lacking. Therefore, there is a need for a system with sufficient mechanisms and specific recommendations to provide job seekers with the information they need to choose the most suitable occupation.

[0318] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0319] In this invention, the server includes an information discrimination means that analyzes response information using information records about past successful individuals to determine suitable occupations, an information generation means that generates recommended occupations determined by the information discrimination means, and a result display means. This makes it possible to recommend appropriate occupations that match the job seeker's abilities and interests.

[0320] An "information input method" is an interface used by job seekers to input their response information.

[0321] An "electronic device" is a device that collects response information obtained from users and transmits it to a server for processing.

[0322] "Data communication means" refers to technologies for securely and accurately transmitting aggregated response information from electronic devices to a server.

[0323] An "information processing device" is a server that analyzes and interprets the submitted response information.

[0324] An "information record" is a database that stores information about past successful individuals.

[0325] An "information discrimination means" is a function within an information processing device that analyzes response information based on information about past successful individuals to determine suitable occupations.

[0326] "Information generation means" refers to a function that generates recommended occupation results based on the judgment results of the information discrimination means.

[0327] "Result display means" refers to a function for displaying the generated recommended results on the user's electronic device.

[0328] "Relevance" is an indicator that shows the degree of relationship between a user's response information and information from past successful users.

[0329] The "Recommended Occupation List" is a list generated by an information generation system that includes occupations recommended to job seekers and their degree of matching.

[0330] This invention relates to a job search system for job seekers to find suitable employment. This system consists of three main components: a user, a terminal, and a server, each performing a specific function.

[0331] About the user

[0332] Users log in to the system and enter basic information. They answer a series of questions and tests provided through the terminal. These questions are designed to assess the job seeker's interests, skills, and personality traits. Users can record detailed skills and preferences using checkboxes, radio buttons, and open-ended text boxes.

[0333] About the device

[0334] The terminal is responsible for aggregating information entered by the user and sending the aggregated data to the server. The terminal uses JavaScript to check the integrity of the data and transmits it via HTTPS while protecting it with encryption technologies such as AES. The terminal also has the functionality to visually display recommended results using HTML and CSS.

[0335] About the server

[0336] The server analyzes the received data. It utilizes a generative AI model for analysis, executing machine learning algorithms using Python and Scikit-learn. Based on the prompt, the server compares the user's information with data from past successful individuals to determine the most suitable occupation. For example, the prompt "Determine an occupation based on the user's skills and interests, and provide relevant job postings" is input to the AI ​​model. Based on the analysis results, the server generates recommendations.

[0337] This invention provides a system that quickly and accurately recommends suitable occupations to users, supporting their decision-making in career choices. This system enables job seekers to efficiently find occupations that best match their characteristics and interests.

[0338] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0339] Step 1:

[0340] User

[0341] The user logs into the system and enters the requested basic information. This information includes name, age, and contact information. The user uses the keyboard to enter this information into the input form on the terminal and saves the information by clicking the "Submit" button.

[0342] Step 2:

[0343] User

[0344] Users answer questions and take tests displayed on their device. The questions are designed to assess the user's interests, skills, and personality traits. Users check the answer choices and enter their skills and preferences in the free-response field. Once finished, they confirm their answers by clicking the "Submit" button and send them to their device.

[0345] Step 3:

[0346] terminal

[0347] The terminal aggregates information obtained from the user. JavaScript is used for data format checks and validation during this aggregation. Next, the aggregated information is encrypted. AES encryption technology is used to encrypt the information, which is then securely transmitted to the server. The encrypted data is output from the terminal and sent to the server via HTTPS.

[0348] Step 4:

[0349] server

[0350] The server decrypts the encrypted data received from the terminal and prepares it for analysis. The decrypted data is input into the generating AI model. The server uses Python and Scikit-learn to perform clustering analysis using machine learning algorithms. As a result of this analysis, a list of possible occupations based on the user's interests and skills is generated.

[0351] Step 5:

[0352] server

[0353] The server generates a prompt message and uses an AI model to determine recommended occupations based on the analysis results. The prompt message used is, "Determine occupations based on the user's skills and interests, and provide relevant job postings." The resulting list of recommended occupations is then passed on to the next step.

[0354] Step 6:

[0355] terminal

[0356] The terminal displays a list of recommended occupations received from the server to the user. HTML and CSS are used to visualize the data and provide the results in a user-friendly format. The displayed results include recommended occupations and their matching scores, allowing the user to make an occupational selection.

[0357] (Application Example 1)

[0358] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0359] Conventional vocational aptitude assessment systems struggle to quickly and accurately identify detailed job suitability based on individual job seekers' skills and characteristics in the field. This makes it difficult for workers in physical stores to efficiently find the most suitable role for their daily tasks. Furthermore, the constant need to operate a terminal to access visualized information disrupts the workflow. To address these challenges, a new support system is needed that enables workers to adapt smoothly to their jobs.

[0360] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0361] In this invention, the server includes an input means for job seekers or workers to answer a series of questions, a means for using a terminal device to collect the answer data obtained by the input means and display it on a glasses-type display device, and a determination means for analyzing the answer data using a data set of past successful individuals to determine suitable occupations or tasks. This makes it possible for workers in physical stores to receive suggestions for optimal tasks based on their own characteristics, thereby improving the efficiency and effectiveness of their work.

[0362] A "job seeker or worker" is a person who requires an assessment of their suitability for a specific occupation or job.

[0363] "Input means" refers to a device or method used by a job seeker or worker to provide information to the system.

[0364] A "terminal device" is a device that processes collected data and presents that information to the user as needed.

[0365] A "glasses-type display device" is a display medium in the shape of glasses that provides information in a way that is visible to the user.

[0366] "Communication means" refers to technology or equipment for sending and receiving data between a terminal device and a server device.

[0367] A "processing device" is a device or system used to analyze collected data and generate or provide appropriate information.

[0368] A "dataset of past successes" is a dataset that collects information related to individuals who have achieved success in their profession or field.

[0369] "Determination means" refers to a function or device that analyzes input data to identify a suitable occupation or job.

[0370] "Result generation means" refers to a function or device that creates information, including recommended occupations or jobs, based on the analysis results from the determination means.

[0371] "Result display means" refers to a device or method for visually presenting the generated recommendation results to the user.

[0372] This invention is a system designed to support operational suitability in physical stores. The operation and implementation of the system are described below.

[0373] The system uses a glasses-type display device worn by job seekers or workers. Users first log in to the system via an input device and answer questions displayed on the terminal device. The answer data is sent to the server via a communication device. The server receives this data and performs analysis using a data set of past successful candidates. A determination device identifies suitable occupations or tasks based on the analysis, and a result generation device constructs the results.

[0374] These results are transmitted to a glasses-type display device and presented to the user in real time. This allows users to quickly understand recommended tasks that match their skills and interests and immediately apply them to their daily work.

[0375] As a concrete example, imagine a scenario where a store employee is assigned to handle a new product category during their shift and uses this system to assess their suitability. In this case, the system compares the employee's past successes with the database and suggests the most suitable category for them.

[0376] Another example of a prompt for the generating AI model is: "Design an application that diagnoses suitability for in-store tasks and suggests the most suitable roles. Based on actual work, it aims to identify staff best suited for specific tasks or product sales." This prompt allows the system to generate and visually display specific suggestions for the work.

[0377] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0378] Step 1:

[0379] The user puts on glasses-type display devices and logs into the terminal.

[0380] Input: User account information

[0381] Action: The initial authentication process begins, and the user account is verified.

[0382] Output: Authentication status of verified user accounts

[0383] Step 2:

[0384] The user answers a series of questions using input methods on their device.

[0385] Input: User's response information

[0386] Operation: The terminal collects user responses through the interface. These questions concern the job seeker's interests and skills.

[0387] Output: Collected response data

[0388] Step 3:

[0389] The device transmits the collected response data to the server using a communication method.

[0390] Input: Collected response data

[0391] Operation: This data is encrypted and securely transmitted to the server.

[0392] Output: Response data that reached the server

[0393] Step 4:

[0394] The server analyzes the response data using a dataset of past successful users.

[0395] Input: Collected response data, database of past success stories

[0396] Operation: The server generates target data, uses an AI model to analyze it, and identifies the characteristics of the respondents.

[0397] Output: Analyzed characteristic data

[0398] Step 5:

[0399] The server's determination mechanism identifies a suitable occupation or job based on the analysis results.

[0400] Input: Analyzed characteristic data

[0401] Operation: Characteristic data is cross-referenced with past success stories, and highly suitable tasks are presented based on statistical algorithms.

[0402] Output: Identified appropriate business information

[0403] Step 6:

[0404] The server's result generation mechanism constitutes the recommendation results for appropriate tasks.

[0405] Input: Identified appropriate business information

[0406] Operation: Calculates the suitability score and builds a list of suitable tasks.

[0407] Output: Recommended Task List

[0408] Step 7:

[0409] A list of recommended tasks is sent to a glasses-type display device and displayed to the user in real time.

[0410] Input: Recommended Task List

[0411] Function: The display device shows the user the results and provides visual feedback, helping them understand how to perform the task correctly.

[0412] Output: Visually presented recommended business information for the user.

[0413] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0414] This invention is a system that combines an emotional engine with a career aptitude system to provide career recommendations that take into account the psychological aspects of job seekers. This system consists of a user, a terminal, a server, and an emotional engine, with each element playing a specific role.

[0415] The user logs into the system and enters the necessary basic profile information into the terminal. Next, a series of questions to assess their vocational aptitude are presented on the terminal. As the user answers these questions, the emotion engine recognizes their emotional state in real time from their facial expressions and voice.

[0416] The device collects user responses and emotional data from the emotion engine, and sends this data to the server. The emotion engine recognizes the user's emotional state (e.g., tension, joy, interest) and provides this to the server as a dataset.

[0417] The server analyzes the received response data and sentiment data, and evaluates occupational aptitude using a judgment method. In this process, sentiment data is used as a factor that influences the judgment. For example, if a user shows interest in a particular question, that sentiment is evaluated positively, and occupations in that field are recommended preferentially.

[0418] Next, the results generation system creates a list of recommended occupations based on the information obtained from the analysis. The list includes matching scores and related information, as well as supplementary information based on sentiment data. This result is transmitted to the terminal and presented to the user visually.

[0419] For example, if a user smiles when answering the question "Do you enjoy creative work?" on their device, the emotion engine will record that response as positive. Based on this, the server will recommend professions such as advertising designer or game developer with a high match score.

[0420] In this way, users can receive career recommendations based on their interests and emotions, enabling them to make more personalized career choices. This invention allows job seekers to make career choices that consider emotional aptitude in addition to traditional skills and experience, which is expected to improve job satisfaction.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] The user logs into the system and enters their basic profile information into the device.

[0424] Step 2:

[0425] The device displays a series of questions on the screen to assess the user's aptitude for work. These questions include content designed to identify the user's interests and skills.

[0426] Step 3:

[0427] The emotion engine analyzes the user's facial expressions and voice in real time through the device's camera and microphone to determine the user's emotional state.

[0428] Step 4:

[0429] As the user answers the presented questions, their emotions during the process are recognized by an emotion engine. This information is collected on the device as emotion data.

[0430] Step 5:

[0431] The device temporarily stores the user's response data and aggregated sentiment data, and verifies the data's integrity.

[0432] Step 6:

[0433] The user completes the answers to the questions and instructs the data to be sent.

[0434] Step 7:

[0435] The device encrypts the response data and sentiment data when it sends them to the server using a communication method.

[0436] Step 8:

[0437] The server analyzes the received data and uses a judgment tool to evaluate the user's occupational aptitude. It compares this to past success data in the database and considers statistical and emotional factors.

[0438] Step 9:

[0439] The server generates a list of recommended occupations based on the analysis results. This list also includes supplementary information based on matching scores and sentiment data.

[0440] Step 10:

[0441] The server sends the final recommendation results to the terminal, where they are displayed visually to the user.

[0442] Step 11:

[0443] The user reviews the presented list of recommended occupations and considers the details of each occupation. If necessary, they can access relevant job postings through their device and proceed with the application process directly.

[0444] (Example 2)

[0445] Next, we will describe Example 2. 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".

[0446] Traditional job recommendation systems only assess job seekers' skills and experience, failing to consider their emotional and psychological aspects. This creates a problem where recommended jobs may not always align with the job seeker's inner interests and passions, potentially leading to decreased job satisfaction.

[0447] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0448] In this invention, the server includes an analysis means, a result generation means, and a result display means. This makes it possible to evaluate job suitability by considering both the job seeker's response data and emotional data, thereby realizing job recommendations that are more in line with individual emotions and interests, and improving job satisfaction.

[0449] An "input method" refers to a means by which job seekers input their answers to multiple questions.

[0450] A "terminal device" is a device that collects job applicants' response data and transmits it to a server device along with data obtained through emotion recognition.

[0451] An "emotion recognition method" is a means of analyzing a user's facial expressions and voice to acquire emotional data.

[0452] "Communication means" refers to the means of transmitting response data and sentiment data from a terminal device to a server device.

[0453] The "analysis means" refers to a method for analyzing the response data and sentiment data received by the server device to determine the suitable occupation for the job seeker.

[0454] The "result generation means" is a means for generating a list of recommended occupations suitable for job seekers based on the analysis results obtained by the analysis means.

[0455] The "result display means" is a means for transmitting the generated recommendation list to a terminal device and displaying it visually.

[0456] This is "partial text".

[0457] The system of this invention is designed to evaluate a job seeker's vocational aptitude based on their emotions and recommend a suitable job. The system mainly consists of a terminal device operated by the user, a server device that processes data, and an emotion recognition means that analyzes the user's emotional state.

[0458] The user first logs into the system using a terminal device and enters basic profile information. This terminal device provides an input mechanism for collecting response data. Next, the terminal device presents the user with a series of questions designed to assess their vocational aptitude. The user answers these questions through the input mechanism.

[0459] On the other hand, the emotion recognition means analyzes the user's facial expressions and voice as they answer questions and acquires emotion data in real time. This uses sensors such as cameras and microphones. After the terminal device collects the answer data and emotion data, it uses communication means to transmit them to the server device.

[0460] The server processes the received data using analysis tools to determine the most suitable occupation for the job seeker. Since both response data and sentiment data are considered in this determination, the job recommendations become more personalized and accurate. The generated analysis results are formatted into a recommendation list constructed by a results generation tool. This list is transmitted to the terminal device via a results display tool and visually displayed to the user.

[0461] For example, if a user answers "yes" with a smile to the question "Do you enjoy creative work?", the emotion recognition system records that positive response. Based on this, the server recommends professions such as advertising designer or game developer with a high degree of relevance.

[0462] Examples of prompts for a generative AI model include sentences like, "Use the job aptitude system to show how a user's emotions influence job recommendations."

[0463] This invention will enable job seekers to make career choices that take their emotional aptitudes into consideration, and is expected to improve job satisfaction.

[0464] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0465] Step 1:

[0466] The user logs into the terminal device and enters their profile information. The entered data includes basic personal information and work experience. Based on this information, the terminal initializes the user profile and prepares to send that data to the server.

[0467] Step 2:

[0468] The device displays questions on its screen to assess vocational aptitude. The user enters their answers to these questions, and the device records them. Simultaneously, the device uses its camera and microphone to capture the user's facial expressions and voice. Emotion recognition measures analyze this data to evaluate the user's emotional state in real time. This process uses facial recognition algorithms and voice analysis technology to obtain emotional data such as tension and joy.

[0469] Step 3:

[0470] The terminal packets the collected response data and obtained sentiment data and sends them to the server. The communication method transmits this data to the server using a secure protocol (e.g., HTTPS). Encryption technology is used throughout this process to ensure data integrity and privacy.

[0471] Step 4:

[0472] The server processes the received response data and sentiment data using analytical tools. It utilizes a generative AI model to analyze the correlation between the response data and sentiment data. Specifically, it evaluates how sentiment data influences the response content and scores that influence. This allows for a quantitative assessment of the user's occupational aptitude.

[0473] Step 5:

[0474] The results generation mechanism generates a list of recommended occupations most suitable for the user based on the analysis results. This list includes supplementary evaluations based on agreement scores and sentiment data. The generated list serves as an important factor in the user's decision-making process when choosing an occupation.

[0475] Step 6:

[0476] The server sends the recommendation list generated using the results display means to the terminal. The terminal displays the received recommendation list on the user interface, making it easy for the user to review. This allows the user to access information based on their own feelings and career aptitudes, enabling them to make more appropriate career choices.

[0477] (Application Example 2)

[0478] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0479] Traditional vocational aptitude systems primarily considered information based on job seekers' skills and experience, failing to adequately evaluate their psychological aspects, particularly their emotional suitability. As a result, it was difficult to make job recommendations that reflected job seekers' potential interests and passions, thus hindering their career satisfaction. Furthermore, the lack of techniques for appropriately recording and utilizing naturally occurring emotional responses in daily life limited the ability to assess job seekers' overall vocational aptitude.

[0480] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0481] In this invention, the server includes an information input means for job seekers to answer multiple questions, an observation and recording means for accumulating emotional data by observing the user's daily behavior and recording emotional responses, and an evaluation means for analyzing the response data and emotional data using a set of information on successful candidates from the past to determine suitable occupations. This enables personalized job recommendations that reflect the job seeker's potential interests and passions, thereby improving career satisfaction.

[0482] An "information input method" is an interface for job seekers to input their answers to questions.

[0483] An "electronic system" is a device for processing and transmitting data collected from information input sources.

[0484] "Transmission means" refers to a communication method for transmitting collected response data and sentiment data to an information processing device.

[0485] An "information processing device" is a device that analyzes and evaluates received data and generates results.

[0486] A "collection of information on successful precedents" is a database of data on individuals who have achieved success in the past.

[0487] "Evaluation means" refers to a device or program that has the function of determining occupational aptitude by analyzing response data and emotional data.

[0488] The "result generation means" refers to a function for generating recommendation results based on the determined occupation.

[0489] "Result display means" refers to a device or program that has the function of visualizing the generated recommendation results and presenting them to the user.

[0490] The "observation and recording means" refers to a function for observing the user's daily behavior and recording their emotional responses during that time.

[0491] The "match score" is a numerical representation of how well each job in the generated list of recommended jobs matches the job seeker.

[0492] The "Recommended Occupation List" is a list that shows the recommended order of occupations, generated based on the evaluation method.

[0493] To implement this invention, first, an interface is required in which job seekers answer multiple questions. The response data collected through the information input means is then stored in an electronic system. This electronic system includes observation and recording means that observe the user's daily behavior and record their facial expressions and voice tone in real time. This observation and recording means uses a terminal equipped with a camera and microphone.

[0494] The server analyzes this response data and emotion data through an information processing device. Real-time processing hardware such as NVIDIA Jetson Nano and Raspberry Pi is used for the analysis, and facial recognition and voice analysis are performed using software such as OpenCV and Python. The emotion data quantified by the emotion engine and the job seeker's response data are compared with a cloud-based database of successful precedents, and occupational aptitude is determined by an evaluation method.

[0495] Once the evaluation is complete, the results generation system generates a list of recommended occupations, including the degree of matching score. The server sends these recommendations to the terminal and presents them visually to the job seeker using the results display system.

[0496] For example, if a device engages in a casual conversation with a user and the user smiles and says they are interested in creative work, the emotion engine will record this response as positive. The server will then use this information to recommend professions such as advertising designer or game developer with a high match score. An example of a prompt might be, "When a user smiles and talks about movies, which professions should be recommended?"

[0497] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0498] Step 1:

[0499] The terminal provides a means for job seekers to answer multiple questions through an interface, thereby inputting response data. The response data is temporarily stored on the terminal. The input response data is structured based on the prompt text and prepared to be sent to the server.

[0500] Step 2:

[0501] The device observes the user's daily activities and acquires facial expression and voice data using its camera and microphone. The acquired data is analyzed in real time using an emotion engine. Based on the analysis, the user's emotional state is output as numerical emotion data, which is then prepared to be sent to the server.

[0502] Step 3:

[0503] The server receives response data and sentiment data sent from the terminal. An information processing device analyzes this data and compares it with a set of information on successful individuals in the past. Using NVIDIA Jetson Nano and OpenCV, the data is statistically processed based on indicators derived from the prompt text to generate numerical data representing occupational aptitude.

[0504] Step 4:

[0505] The server calculates a match score using the results of the vocational aptitude assessment determined by the evaluation tool. The result generation tool generates a list of recommended occupations for the job seeker based on the calculated score. The list of recommended occupations is also corrected based on the job seeker's sentiment data.

[0506] Step 5:

[0507] The server sends the generated list of recommended occupations to the terminal. The terminal visually displays the recommended occupations and their matching scores to the job seeker through a results display device. The job seeker who receives the results can then obtain information to help them make informed decisions about their occupation selection.

[0508] Step 6:

[0509] Users can view a list of recommended occupations and enter additional information or new questions about occupations they are interested in into their terminal. This input is sent back to the server for further analysis and re-evaluation, resulting in more accurate recommendations.

[0510] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0511] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0512] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0513] [Third Embodiment]

[0514] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0515] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0516] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0517] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0518] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0519] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0520] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0521] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0522] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0523] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0524] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0525] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0526] This invention is constructed as a system for determining suitable occupations for job seekers. This system includes three main elements: a user, a terminal, and a server, each performing a specific function.

[0527] Users first log into the system and enter their basic information. Next, they answer a series of questions and tests provided on the terminal. These questions are designed to assess the job seeker's interests, skills, personality traits, etc., and based on this, users enter detailed information about their skills and preferences.

[0528] The terminal collects information entered by the user and transmits it to the server using communication methods. This transmission utilizes encryption technology to maintain data accuracy and prevent information leakage.

[0529] The server analyzes the received data and uses a determination tool to statistically compare it with a database of past successes. This determines the occupation that is most suitable for the user. For example, the server matches the user's leadership skills with data of successful individuals in a specific occupation, and if it shows a high match score, it recommends that occupation.

[0530] The result generation mechanism creates a list of suggested occupations determined by the server, which includes a scored degree of matching and a brief description of each occupation. This result is displayed to the user via the terminal. The user can review these recommendations and consider which occupation best matches their interests. The terminal also provides features to assist with job postings and the application process related to the occupations the user is interested in.

[0531] For example, if a user is found to have "high communication skills," and this skill shows a high correlation with past data of success in "sales positions," the server can recommend a sales position to the user. In this case, the user can immediately access sales-related job postings and complete the application process on their device.

[0532] This allows job seekers to choose careers based on their own abilities and interests, supporting efficient and effective career development.

[0533] The following describes the processing flow.

[0534] Step 1:

[0535] The user logs into the system and enters their basic profile information into the terminal. This information includes name, age, educational background, and work history.

[0536] Step 2:

[0537] The device displays a series of questions and tests on the user's screen to assess their career aptitude. These questions include topics related to areas of interest and skill assessment.

[0538] Step 3:

[0539] Users answer the presented questions and complete various aptitude tests. Answers can be entered in multiple-choice or text format.

[0540] Step 4:

[0541] The device temporarily stores the user's response data and prepares to send it to the server after the test is completed.

[0542] Step 5:

[0543] The user confirms the completion of the test and instructs the device to send the data.

[0544] Step 6:

[0545] The device uses a communication method to send the user's response data to the server. During this process, the data is encrypted before transmission.

[0546] Step 7:

[0547] The server decodes the received response data and begins the analysis process.

[0548] Step 8:

[0549] The server compares the received response data with a database of past success stories and uses statistical methods to determine a suitable occupation for the user. Specifically, it calculates a match score using correlation analysis and machine learning algorithms.

[0550] Step 9:

[0551] The server generates a list of recommended occupations for the user based on the assessment results. The list includes a match score and a summary for each occupation.

[0552] Step 10:

[0553] The server sends the job recommendation results it has generated to the terminal.

[0554] Step 11:

[0555] The device visually displays the received job recommendation results to the user. The user can then consider specific job options based on the provided information.

[0556] Step 12:

[0557] Users can view detailed information about occupations they are interested in and proceed with their job search as needed. The device also provides relevant job information and support for the application process.

[0558] (Example 1)

[0559] Next, we will describe Example 1. 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."

[0560] Current job placement systems struggle to accurately recommend occupations that reflect job seekers' abilities and interests. Furthermore, the reliability of the results and the timeliness of their display are sometimes lacking. Therefore, there is a need for a system with sufficient mechanisms and specific recommendations to provide job seekers with the information they need to choose the most suitable occupation.

[0561] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0562] In this invention, the server includes an information discrimination means that analyzes response information using information records about past successful individuals to determine suitable occupations, an information generation means that generates recommended occupations determined by the information discrimination means, and a result display means. This makes it possible to recommend appropriate occupations that match the job seeker's abilities and interests.

[0563] An "information input method" is an interface used by job seekers to input their response information.

[0564] An "electronic device" is a device that collects response information obtained from users and transmits it to a server for processing.

[0565] "Data communication means" refers to technologies for securely and accurately transmitting aggregated response information from electronic devices to a server.

[0566] An "information processing device" is a server that analyzes and interprets the submitted response information.

[0567] An "information record" is a database that stores information about past successful individuals.

[0568] An "information discrimination means" is a function within an information processing device that analyzes response information based on information about past successful individuals to determine suitable occupations.

[0569] "Information generation means" refers to a function that generates recommended occupation results based on the judgment results of the information discrimination means.

[0570] "Result display means" refers to a function for displaying the generated recommended results on the user's electronic device.

[0571] "Relevance" is an indicator that shows the degree of relationship between a user's response information and information from past successful users.

[0572] The "Recommended Occupation List" is a list generated by an information generation system that includes occupations recommended to job seekers and their degree of matching.

[0573] This invention relates to a job search system for job seekers to find suitable employment. This system consists of three main components: a user, a terminal, and a server, each performing a specific function.

[0574] About the user

[0575] Users log in to the system and enter basic information. They answer a series of questions and tests provided through the terminal. These questions are designed to assess the job seeker's interests, skills, and personality traits. Users can record detailed skills and preferences using checkboxes, radio buttons, and open-ended text boxes.

[0576] About the device

[0577] The terminal is responsible for aggregating information entered by the user and sending the aggregated data to the server. The terminal uses JavaScript to check the integrity of the data and transmits it via HTTPS while protecting it with encryption technologies such as AES. The terminal also has the functionality to visually display recommended results using HTML and CSS.

[0578] About the server

[0579] The server analyzes the received data. It utilizes a generative AI model for analysis, executing machine learning algorithms using Python and Scikit-learn. Based on the prompt, the server compares the user's information with data from past successful individuals to determine the most suitable occupation. For example, the prompt "Determine an occupation based on the user's skills and interests, and provide relevant job postings" is input to the AI ​​model. Based on the analysis results, the server generates recommendations.

[0580] This invention provides a system that quickly and accurately recommends suitable occupations to users, supporting their decision-making in career choices. This system enables job seekers to efficiently find occupations that best match their characteristics and interests.

[0581] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0582] Step 1:

[0583] User

[0584] The user logs into the system and enters the requested basic information. This information includes name, age, and contact information. The user uses the keyboard to enter this information into the input form on the terminal and saves the information by clicking the "Submit" button.

[0585] Step 2:

[0586] User

[0587] Users answer questions and take tests displayed on their device. The questions are designed to assess the user's interests, skills, and personality traits. Users check the answer choices and enter their skills and preferences in the free-response field. Once finished, they confirm their answers by clicking the "Submit" button and send them to their device.

[0588] Step 3:

[0589] terminal

[0590] The terminal aggregates information obtained from the user. JavaScript is used for data format checks and validation during this aggregation. Next, the aggregated information is encrypted. AES encryption technology is used to encrypt the information, which is then securely transmitted to the server. The encrypted data is output from the terminal and sent to the server via HTTPS.

[0591] Step 4:

[0592] server

[0593] The server decrypts the encrypted data received from the terminal and prepares it for analysis. The decrypted data is input into the generating AI model. The server uses Python and Scikit-learn to perform clustering analysis using machine learning algorithms. As a result of this analysis, a list of possible occupations based on the user's interests and skills is generated.

[0594] Step 5:

[0595] server

[0596] The server generates a prompt message and uses an AI model to determine recommended occupations based on the analysis results. The prompt message used is, "Determine occupations based on the user's skills and interests, and provide relevant job postings." The resulting list of recommended occupations is then passed on to the next step.

[0597] Step 6:

[0598] terminal

[0599] The terminal displays a list of recommended occupations received from the server to the user. HTML and CSS are used to visualize the data and provide the results in a user-friendly format. The displayed results include recommended occupations and their matching scores, allowing the user to make an occupational selection.

[0600] (Application Example 1)

[0601] Next, we will explain Application Example 1. In the following explanation, 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."

[0602] Conventional vocational aptitude assessment systems struggle to quickly and accurately identify detailed job suitability based on individual job seekers' skills and characteristics in the field. This makes it difficult for workers in physical stores to efficiently find the most suitable role for their daily tasks. Furthermore, the constant need to operate a terminal to access visualized information disrupts the workflow. To address these challenges, a new support system is needed that enables workers to adapt smoothly to their jobs.

[0603] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0604] In this invention, the server includes an input means for job seekers or workers to answer a series of questions, a means for using a terminal device to collect the answer data obtained by the input means and display it on a glasses-type display device, and a determination means for analyzing the answer data using a data set of past successful individuals to determine suitable occupations or tasks. This makes it possible for workers in physical stores to receive suggestions for optimal tasks based on their own characteristics, thereby improving the efficiency and effectiveness of their work.

[0605] A "job seeker or worker" is a person who requires an assessment of their suitability for a specific occupation or job.

[0606] "Input means" refers to a device or method used by a job seeker or worker to provide information to the system.

[0607] A "terminal device" is a device that processes collected data and presents that information to the user as needed.

[0608] A "glasses-type display device" is a display medium in the shape of glasses that provides information in a way that is visible to the user.

[0609] "Communication means" refers to technology or equipment for sending and receiving data between a terminal device and a server device.

[0610] A "processing device" is a device or system used to analyze collected data and generate or provide appropriate information.

[0611] A "dataset of past successes" is a dataset that collects information related to individuals who have achieved success in their profession or field.

[0612] "Determination means" refers to a function or device that analyzes input data to identify a suitable occupation or job.

[0613] "Result generation means" refers to a function or device that creates information, including recommended occupations or jobs, based on the analysis results from the determination means.

[0614] "Result display means" refers to a device or method for visually presenting the generated recommendation results to the user.

[0615] This invention is a system designed to support operational suitability in physical stores. The operation and implementation of the system are described below.

[0616] The system uses a glasses-type display device worn by job seekers or workers. Users first log in to the system via an input device and answer questions displayed on the terminal device. The answer data is sent to the server via a communication device. The server receives this data and performs analysis using a data set of past successful candidates. A determination device identifies suitable occupations or tasks based on the analysis, and a result generation device constructs the results.

[0617] These results are transmitted to a glasses-type display device and presented to the user in real time. This allows users to quickly understand recommended tasks that match their skills and interests and immediately apply them to their daily work.

[0618] As a concrete example, imagine a scenario where a store employee is assigned to handle a new product category during their shift and uses this system to assess their suitability. In this case, the system compares the employee's past successes with the database and suggests the most suitable category for them.

[0619] Another example of a prompt for the generating AI model is: "Design an application that diagnoses suitability for in-store tasks and suggests the most suitable roles. Based on actual work, it aims to identify staff best suited for specific tasks or product sales." This prompt allows the system to generate and visually display specific suggestions for the work.

[0620] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0621] Step 1:

[0622] The user puts on glasses-type display devices and logs into the terminal.

[0623] Input: User account information

[0624] Action: The initial authentication process begins, and the user account is verified.

[0625] Output: Authentication status of verified user accounts

[0626] Step 2:

[0627] The user answers a series of questions using input methods on their device.

[0628] Input: User's response information

[0629] Operation: The terminal collects user responses through the interface. These questions concern the job seeker's interests and skills.

[0630] Output: Collected response data

[0631] Step 3:

[0632] The device transmits the collected response data to the server using a communication method.

[0633] Input: Collected response data

[0634] Operation: This data is encrypted and securely transmitted to the server.

[0635] Output: Response data that reached the server

[0636] Step 4:

[0637] The server analyzes the response data using a dataset of past successful users.

[0638] Input: Collected response data, database of past success stories

[0639] Operation: The server generates target data, uses an AI model to analyze it, and identifies the characteristics of the respondents.

[0640] Output: Analyzed characteristic data

[0641] Step 5:

[0642] The server's determination mechanism identifies a suitable occupation or job based on the analysis results.

[0643] Input: Analyzed characteristic data

[0644] Operation: Characteristic data is cross-referenced with past success stories, and highly suitable tasks are presented based on statistical algorithms.

[0645] Output: Identified appropriate business information

[0646] Step 6:

[0647] The server's result generation mechanism constitutes the recommendation results for appropriate tasks.

[0648] Input: Identified appropriate business information

[0649] Operation: Calculates the suitability score and builds a list of suitable tasks.

[0650] Output: Recommended Task List

[0651] Step 7:

[0652] A list of recommended tasks is sent to a glasses-type display device and displayed to the user in real time.

[0653] Input: Recommended Task List

[0654] Function: The display device shows the user the results and provides visual feedback, helping them understand how to perform the task correctly.

[0655] Output: Visually presented recommended business information for the user.

[0656] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0657] This invention is a system that combines an emotional engine with a career aptitude system to provide career recommendations that take into account the psychological aspects of job seekers. This system consists of a user, a terminal, a server, and an emotional engine, with each element playing a specific role.

[0658] The user logs into the system and enters the necessary basic profile information into the terminal. Next, a series of questions to assess their vocational aptitude are presented on the terminal. As the user answers these questions, the emotion engine recognizes their emotional state in real time from their facial expressions and voice.

[0659] The device collects user responses and emotional data from the emotion engine, and sends this data to the server. The emotion engine recognizes the user's emotional state (e.g., tension, joy, interest) and provides this to the server as a dataset.

[0660] The server analyzes the received response data and sentiment data, and evaluates occupational aptitude using a judgment method. In this process, sentiment data is used as a factor that influences the judgment. For example, if a user shows interest in a particular question, that sentiment is evaluated positively, and occupations in that field are recommended preferentially.

[0661] Next, the results generation system creates a list of recommended occupations based on the information obtained from the analysis. The list includes matching scores and related information, as well as supplementary information based on sentiment data. This result is transmitted to the terminal and presented to the user visually.

[0662] For example, if a user smiles when answering the question "Do you enjoy creative work?" on their device, the emotion engine will record that response as positive. Based on this, the server will recommend professions such as advertising designer or game developer with a high match score.

[0663] In this way, users can receive career recommendations based on their interests and emotions, enabling them to make more personalized career choices. This invention allows job seekers to make career choices that consider emotional aptitude in addition to traditional skills and experience, which is expected to improve job satisfaction.

[0664] The following describes the processing flow.

[0665] Step 1:

[0666] The user logs into the system and enters their basic profile information into the device.

[0667] Step 2:

[0668] The device displays a series of questions on the screen to assess the user's aptitude for work. These questions include content designed to identify the user's interests and skills.

[0669] Step 3:

[0670] The emotion engine analyzes the user's facial expressions and voice in real time through the device's camera and microphone to determine the user's emotional state.

[0671] Step 4:

[0672] As the user answers the presented questions, their emotions during the process are recognized by an emotion engine. This information is collected on the device as emotion data.

[0673] Step 5:

[0674] The device temporarily stores the user's response data and aggregated sentiment data, and verifies the data's integrity.

[0675] Step 6:

[0676] The user completes the answers to the questions and instructs the data to be sent.

[0677] Step 7:

[0678] The device encrypts the response data and sentiment data when it sends them to the server using a communication method.

[0679] Step 8:

[0680] The server analyzes the received data and uses a judgment tool to evaluate the user's occupational aptitude. It compares this to past success data in the database and considers statistical and emotional factors.

[0681] Step 9:

[0682] The server generates a list of recommended occupations based on the analysis results. This list also includes supplementary information based on matching scores and sentiment data.

[0683] Step 10:

[0684] The server sends the final recommendation results to the terminal, where they are displayed visually to the user.

[0685] Step 11:

[0686] The user reviews the presented list of recommended occupations and considers the details of each occupation. If necessary, they can access relevant job postings through their device and proceed with the application process directly.

[0687] (Example 2)

[0688] Next, we will describe Example 2. 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."

[0689] Traditional job recommendation systems only assess job seekers' skills and experience, failing to consider their emotional and psychological aspects. This creates a problem where recommended jobs may not always align with the job seeker's inner interests and passions, potentially leading to decreased job satisfaction.

[0690] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0691] In this invention, the server includes an analysis means, a result generation means, and a result display means. This makes it possible to evaluate job suitability by considering both the job seeker's response data and emotional data, thereby realizing job recommendations that are more in line with individual emotions and interests, and improving job satisfaction.

[0692] An "input method" refers to a means by which job seekers input their answers to multiple questions.

[0693] A "terminal device" is a device that collects job applicants' response data and transmits it to a server device along with data obtained through emotion recognition.

[0694] An "emotion recognition method" is a means of analyzing a user's facial expressions and voice to acquire emotional data.

[0695] "Communication means" refers to the means of transmitting response data and sentiment data from a terminal device to a server device.

[0696] The "analysis means" refers to a method for analyzing the response data and sentiment data received by the server device to determine the suitable occupation for the job seeker.

[0697] The "result generation means" is a means for generating a list of recommended occupations suitable for job seekers based on the analysis results obtained by the analysis means.

[0698] The "result display means" is a means for transmitting the generated recommendation list to a terminal device and displaying it visually.

[0699] This is "partial text".

[0700] The system of this invention is designed to evaluate a job seeker's vocational aptitude based on their emotions and recommend a suitable job. The system mainly consists of a terminal device operated by the user, a server device that processes data, and an emotion recognition means that analyzes the user's emotional state.

[0701] The user first logs into the system using a terminal device and enters basic profile information. This terminal device provides an input mechanism for collecting response data. Next, the terminal device presents the user with a series of questions designed to assess their vocational aptitude. The user answers these questions through the input mechanism.

[0702] On the other hand, the emotion recognition means analyzes the user's facial expressions and voice as they answer questions and acquires emotion data in real time. This uses sensors such as cameras and microphones. After the terminal device collects the answer data and emotion data, it uses communication means to transmit them to the server device.

[0703] The server processes the received data using analysis tools to determine the most suitable occupation for the job seeker. Since both response data and sentiment data are considered in this determination, the job recommendations become more personalized and accurate. The generated analysis results are formatted into a recommendation list constructed by a results generation tool. This list is transmitted to the terminal device via a results display tool and visually displayed to the user.

[0704] For example, if a user answers "yes" with a smile to the question "Do you enjoy creative work?", the emotion recognition system records that positive response. Based on this, the server recommends professions such as advertising designer or game developer with a high degree of relevance.

[0705] Examples of prompts for a generative AI model include sentences like, "Use the job aptitude system to show how a user's emotions influence job recommendations."

[0706] This invention will enable job seekers to make career choices that take their emotional aptitudes into consideration, and is expected to improve job satisfaction.

[0707] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0708] Step 1:

[0709] The user logs into the terminal device and enters their profile information. The entered data includes basic personal information and work experience. Based on this information, the terminal initializes the user profile and prepares to send that data to the server.

[0710] Step 2:

[0711] The device displays questions on its screen to assess vocational aptitude. The user enters their answers to these questions, and the device records them. Simultaneously, the device uses its camera and microphone to capture the user's facial expressions and voice. Emotion recognition measures analyze this data to evaluate the user's emotional state in real time. This process uses facial recognition algorithms and voice analysis technology to obtain emotional data such as tension and joy.

[0712] Step 3:

[0713] The terminal packets the collected response data and obtained sentiment data and sends them to the server. The communication method transmits this data to the server using a secure protocol (e.g., HTTPS). Encryption technology is used throughout this process to ensure data integrity and privacy.

[0714] Step 4:

[0715] The server processes the received response data and sentiment data using analytical tools. It utilizes a generative AI model to analyze the correlation between the response data and sentiment data. Specifically, it evaluates how sentiment data influences the response content and scores that influence. This allows for a quantitative assessment of the user's occupational aptitude.

[0716] Step 5:

[0717] The results generation mechanism generates a list of recommended occupations most suitable for the user based on the analysis results. This list includes supplementary evaluations based on agreement scores and sentiment data. The generated list serves as an important factor in the user's decision-making process when choosing an occupation.

[0718] Step 6:

[0719] The server sends the recommendation list generated using the results display means to the terminal. The terminal displays the received recommendation list on the user interface, making it easy for the user to review. This allows the user to access information based on their own feelings and career aptitudes, enabling them to make more appropriate career choices.

[0720] (Application Example 2)

[0721] Next, we will explain application example 2. In the following explanation, 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."

[0722] Traditional vocational aptitude systems primarily considered information based on job seekers' skills and experience, failing to adequately evaluate their psychological aspects, particularly their emotional suitability. As a result, it was difficult to make job recommendations that reflected job seekers' potential interests and passions, thus hindering their career satisfaction. Furthermore, the lack of techniques for appropriately recording and utilizing naturally occurring emotional responses in daily life limited the ability to assess job seekers' overall vocational aptitude.

[0723] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0724] In this invention, the server includes an information input means for job seekers to answer multiple questions, an observation and recording means for accumulating emotional data by observing the user's daily behavior and recording emotional responses, and an evaluation means for analyzing the response data and emotional data using a set of information on successful candidates from the past to determine suitable occupations. This enables personalized job recommendations that reflect the job seeker's potential interests and passions, thereby improving career satisfaction.

[0725] An "information input method" is an interface for job seekers to input their answers to questions.

[0726] An "electronic system" is a device for processing and transmitting data collected from information input sources.

[0727] "Transmission means" refers to a communication method for transmitting collected response data and sentiment data to an information processing device.

[0728] An "information processing device" is a device that analyzes and evaluates received data and generates results.

[0729] A "collection of information on successful precedents" is a database of data on individuals who have achieved success in the past.

[0730] "Evaluation means" refers to a device or program that has the function of determining occupational aptitude by analyzing response data and emotional data.

[0731] The "result generation means" refers to a function for generating recommendation results based on the determined occupation.

[0732] "Result display means" refers to a device or program that has the function of visualizing the generated recommendation results and presenting them to the user.

[0733] The "observation and recording means" refers to a function for observing the user's daily behavior and recording their emotional responses during that time.

[0734] The "match score" is a numerical representation of how well each job in the generated list of recommended jobs matches the job seeker.

[0735] The "Recommended Occupation List" is a list that shows the recommended order of occupations, generated based on the evaluation method.

[0736] To implement this invention, first, an interface is required in which job seekers answer multiple questions. The response data collected through the information input means is then stored in an electronic system. This electronic system includes observation and recording means that observe the user's daily behavior and record their facial expressions and voice tone in real time. This observation and recording means uses a terminal equipped with a camera and microphone.

[0737] The server analyzes this response data and emotion data through an information processing device. Real-time processing hardware such as NVIDIA Jetson Nano and Raspberry Pi is used for the analysis, and facial recognition and voice analysis are performed using software such as OpenCV and Python. The emotion data quantified by the emotion engine and the job seeker's response data are compared with a cloud-based database of successful precedents, and occupational aptitude is determined by an evaluation method.

[0738] Once the evaluation is complete, the results generation system generates a list of recommended occupations, including the degree of matching score. The server sends these recommendations to the terminal and presents them visually to the job seeker using the results display system.

[0739] For example, if a device engages in a casual conversation with a user and the user smiles and says they are interested in creative work, the emotion engine will record this response as positive. The server will then use this information to recommend professions such as advertising designer or game developer with a high match score. An example of a prompt might be, "When a user smiles and talks about movies, which professions should be recommended?"

[0740] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0741] Step 1:

[0742] The terminal provides a means for job seekers to answer multiple questions through an interface, thereby inputting response data. The response data is temporarily stored on the terminal. The input response data is structured based on the prompt text and prepared to be sent to the server.

[0743] Step 2:

[0744] The device observes the user's daily activities and acquires facial expression and voice data using its camera and microphone. The acquired data is analyzed in real time using an emotion engine. Based on the analysis, the user's emotional state is output as numerical emotion data, which is then prepared to be sent to the server.

[0745] Step 3:

[0746] The server receives response data and sentiment data sent from the terminal. An information processing device analyzes this data and compares it with a set of information on successful individuals in the past. Using NVIDIA Jetson Nano and OpenCV, the data is statistically processed based on indicators derived from the prompt text to generate numerical data representing occupational aptitude.

[0747] Step 4:

[0748] The server calculates a match score using the results of the vocational aptitude assessment determined by the evaluation tool. The result generation tool generates a list of recommended occupations for the job seeker based on the calculated score. The list of recommended occupations is also corrected based on the job seeker's sentiment data.

[0749] Step 5:

[0750] The server sends the generated list of recommended occupations to the terminal. The terminal visually displays the recommended occupations and their matching scores to the job seeker through a results display device. The job seeker who receives the results can then obtain information to help them make informed decisions about their occupation selection.

[0751] Step 6:

[0752] Users can view a list of recommended occupations and enter additional information or new questions about occupations they are interested in into their terminal. This input is sent back to the server for further analysis and re-evaluation, resulting in more accurate recommendations.

[0753] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0754] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0755] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0756] [Fourth Embodiment]

[0757] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0758] As shown in Figure 7, the 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.

[0759] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0760] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0761] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0762] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0763] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0764] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0765] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0766] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0767] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0768] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0769] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0770] This invention is constructed as a system for determining suitable occupations for job seekers. This system includes three main elements: a user, a terminal, and a server, each performing a specific function.

[0771] Users first log into the system and enter their basic information. Next, they answer a series of questions and tests provided on the terminal. These questions are designed to assess the job seeker's interests, skills, personality traits, etc., and based on this, users enter detailed information about their skills and preferences.

[0772] The terminal collects information entered by the user and transmits it to the server using communication methods. This transmission utilizes encryption technology to maintain data accuracy and prevent information leakage.

[0773] The server analyzes the received data and uses a determination tool to statistically compare it with a database of past successes. This determines the occupation that is most suitable for the user. For example, the server matches the user's leadership skills with data of successful individuals in a specific occupation, and if it shows a high match score, it recommends that occupation.

[0774] The result generation mechanism creates a list of suggested occupations determined by the server, which includes a scored degree of matching and a brief description of each occupation. This result is displayed to the user via the terminal. The user can review these recommendations and consider which occupation best matches their interests. The terminal also provides features to assist with job postings and the application process related to the occupations the user is interested in.

[0775] For example, if a user is found to have "high communication skills," and this skill shows a high correlation with past data of success in "sales positions," the server can recommend a sales position to the user. In this case, the user can immediately access sales-related job postings and complete the application process on their device.

[0776] This allows job seekers to choose careers based on their own abilities and interests, supporting efficient and effective career development.

[0777] The following describes the processing flow.

[0778] Step 1:

[0779] The user logs into the system and enters their basic profile information into the terminal. This information includes name, age, educational background, and work history.

[0780] Step 2:

[0781] The device displays a series of questions and tests on the user's screen to assess their career aptitude. These questions include topics related to areas of interest and skill assessment.

[0782] Step 3:

[0783] Users answer the presented questions and complete various aptitude tests. Answers can be entered in multiple-choice or text format.

[0784] Step 4:

[0785] The device temporarily stores the user's response data and prepares to send it to the server after the test is completed.

[0786] Step 5:

[0787] The user confirms the completion of the test and instructs the device to send the data.

[0788] Step 6:

[0789] The device uses a communication method to send the user's response data to the server. During this process, the data is encrypted before transmission.

[0790] Step 7:

[0791] The server decodes the received response data and begins the analysis process.

[0792] Step 8:

[0793] The server compares the received response data with a database of past success stories and uses statistical methods to determine a suitable occupation for the user. Specifically, it calculates a match score using correlation analysis and machine learning algorithms.

[0794] Step 9:

[0795] The server generates a list of recommended occupations for the user based on the assessment results. The list includes a match score and a summary for each occupation.

[0796] Step 10:

[0797] The server sends the job recommendation results it has generated to the terminal.

[0798] Step 11:

[0799] The device visually displays the received job recommendation results to the user. The user can then consider specific job options based on the provided information.

[0800] Step 12:

[0801] Users can view detailed information about occupations they are interested in and proceed with their job search as needed. The device also provides relevant job information and support for the application process.

[0802] (Example 1)

[0803] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0804] Current job placement systems struggle to accurately recommend occupations that reflect job seekers' abilities and interests. Furthermore, the reliability of the results and the timeliness of their display are sometimes lacking. Therefore, there is a need for a system with sufficient mechanisms and specific recommendations to provide job seekers with the information they need to choose the most suitable occupation.

[0805] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0806] In this invention, the server includes an information discrimination means that analyzes response information using information records about past successful individuals to determine suitable occupations, an information generation means that generates recommended occupations determined by the information discrimination means, and a result display means. This makes it possible to recommend appropriate occupations that match the job seeker's abilities and interests.

[0807] An "information input method" is an interface used by job seekers to input their response information.

[0808] An "electronic device" is a device that collects response information obtained from users and transmits it to a server for processing.

[0809] "Data communication means" refers to technologies for securely and accurately transmitting aggregated response information from electronic devices to a server.

[0810] An "information processing device" is a server that analyzes and interprets the submitted response information.

[0811] An "information record" is a database that stores information about past successful individuals.

[0812] An "information discrimination means" is a function within an information processing device that analyzes response information based on information about past successful individuals to determine suitable occupations.

[0813] "Information generation means" refers to a function that generates recommended occupation results based on the judgment results of the information discrimination means.

[0814] "Result display means" refers to a function for displaying the generated recommended results on the user's electronic device.

[0815] "Relevance" is an indicator that shows the degree of relationship between a user's response information and information from past successful users.

[0816] The "Recommended Occupation List" is a list generated by an information generation system that includes occupations recommended to job seekers and their degree of matching.

[0817] This invention relates to a job search system for job seekers to find suitable employment. This system consists of three main components: a user, a terminal, and a server, each performing a specific function.

[0818] About the user

[0819] Users log in to the system and enter basic information. They answer a series of questions and tests provided through the terminal. These questions are designed to assess the job seeker's interests, skills, and personality traits. Users can record detailed skills and preferences using checkboxes, radio buttons, and open-ended text boxes.

[0820] About the device

[0821] The terminal is responsible for aggregating information entered by the user and sending the aggregated data to the server. The terminal uses JavaScript to check the integrity of the data and transmits it via HTTPS while protecting it with encryption technologies such as AES. The terminal also has the functionality to visually display recommended results using HTML and CSS.

[0822] About the server

[0823] The server analyzes the received data. It utilizes a generative AI model for analysis, executing machine learning algorithms using Python and Scikit-learn. Based on the prompt, the server compares the user's information with data from past successful individuals to determine the most suitable occupation. For example, the prompt "Determine an occupation based on the user's skills and interests, and provide relevant job postings" is input to the AI ​​model. Based on the analysis results, the server generates recommendations.

[0824] This invention provides a system that quickly and accurately recommends suitable occupations to users, supporting their decision-making in career choices. This system enables job seekers to efficiently find occupations that best match their characteristics and interests.

[0825] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0826] Step 1:

[0827] User

[0828] The user logs into the system and enters the requested basic information. This information includes name, age, and contact information. The user uses the keyboard to enter this information into the input form on the terminal and saves the information by clicking the "Submit" button.

[0829] Step 2:

[0830] User

[0831] Users answer questions and take tests displayed on their device. The questions are designed to assess the user's interests, skills, and personality traits. Users check the answer choices and enter their skills and preferences in the free-response field. Once finished, they confirm their answers by clicking the "Submit" button and send them to their device.

[0832] Step 3:

[0833] terminal

[0834] The terminal aggregates information obtained from the user. JavaScript is used for data format checks and validation during this aggregation. Next, the aggregated information is encrypted. AES encryption technology is used to encrypt the information, which is then securely transmitted to the server. The encrypted data is output from the terminal and sent to the server via HTTPS.

[0835] Step 4:

[0836] server

[0837] The server decrypts the encrypted data received from the terminal and prepares it for analysis. The decrypted data is input into the generating AI model. The server uses Python and Scikit-learn to perform clustering analysis using machine learning algorithms. As a result of this analysis, a list of possible occupations based on the user's interests and skills is generated.

[0838] Step 5:

[0839] server

[0840] The server generates a prompt message and uses an AI model to determine recommended occupations based on the analysis results. The prompt message used is, "Determine occupations based on the user's skills and interests, and provide relevant job postings." The resulting list of recommended occupations is then passed on to the next step.

[0841] Step 6:

[0842] terminal

[0843] The terminal displays a list of recommended occupations received from the server to the user. HTML and CSS are used to visualize the data and provide the results in a user-friendly format. The displayed results include recommended occupations and their matching scores, allowing the user to make an occupational selection.

[0844] (Application Example 1)

[0845] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0846] Conventional vocational aptitude assessment systems struggle to quickly and accurately identify detailed job suitability based on individual job seekers' skills and characteristics in the field. This makes it difficult for workers in physical stores to efficiently find the most suitable role for their daily tasks. Furthermore, the constant need to operate a terminal to access visualized information disrupts the workflow. To address these challenges, a new support system is needed that enables workers to adapt smoothly to their jobs.

[0847] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0848] In this invention, the server includes an input means for job seekers or workers to answer a series of questions, a means for using a terminal device to collect the answer data obtained by the input means and display it on a glasses-type display device, and a determination means for analyzing the answer data using a data set of past successful individuals to determine suitable occupations or tasks. This makes it possible for workers in physical stores to receive suggestions for optimal tasks based on their own characteristics, thereby improving the efficiency and effectiveness of their work.

[0849] A "job seeker or worker" is a person who requires an assessment of their suitability for a specific occupation or job.

[0850] "Input means" refers to a device or method used by a job seeker or worker to provide information to the system.

[0851] A "terminal device" is a device that processes collected data and presents that information to the user as needed.

[0852] A "glasses-type display device" is a display medium in the shape of glasses that provides information in a way that is visible to the user.

[0853] "Communication means" refers to technology or equipment for sending and receiving data between a terminal device and a server device.

[0854] A "processing device" is a device or system used to analyze collected data and generate or provide appropriate information.

[0855] A "dataset of past successes" is a dataset that collects information related to individuals who have achieved success in their profession or field.

[0856] "Determination means" refers to a function or device that analyzes input data to identify a suitable occupation or job.

[0857] "Result generation means" refers to a function or device that creates information, including recommended occupations or jobs, based on the analysis results from the determination means.

[0858] "Result display means" refers to a device or method for visually presenting the generated recommendation results to the user.

[0859] This invention is a system designed to support operational suitability in physical stores. The operation and implementation of the system are described below.

[0860] The system uses a glasses-type display device worn by job seekers or workers. Users first log in to the system via an input device and answer questions displayed on the terminal device. The answer data is sent to the server via a communication device. The server receives this data and performs analysis using a data set of past successful candidates. A determination device identifies suitable occupations or tasks based on the analysis, and a result generation device constructs the results.

[0861] These results are transmitted to a glasses-type display device and presented to the user in real time. This allows users to quickly understand recommended tasks that match their skills and interests and immediately apply them to their daily work.

[0862] As a concrete example, imagine a scenario where a store employee is assigned to handle a new product category during their shift and uses this system to assess their suitability. In this case, the system compares the employee's past successes with the database and suggests the most suitable category for them.

[0863] Another example of a prompt for the generating AI model is: "Design an application that diagnoses suitability for in-store tasks and suggests the most suitable roles. Based on actual work, it aims to identify staff best suited for specific tasks or product sales." This prompt allows the system to generate and visually display specific suggestions for the work.

[0864] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0865] Step 1:

[0866] The user puts on glasses-type display devices and logs into the terminal.

[0867] Input: User account information

[0868] Action: The initial authentication process begins, and the user account is verified.

[0869] Output: Authentication status of verified user accounts

[0870] Step 2:

[0871] The user answers a series of questions using input methods on their device.

[0872] Input: User's response information

[0873] Operation: The terminal collects user responses through the interface. These questions concern the job seeker's interests and skills.

[0874] Output: Collected response data

[0875] Step 3:

[0876] The device transmits the collected response data to the server using a communication method.

[0877] Input: Collected response data

[0878] Operation: This data is encrypted and securely transmitted to the server.

[0879] Output: Response data that reached the server

[0880] Step 4:

[0881] The server analyzes the response data using a dataset of past successful users.

[0882] Input: Collected response data, database of past success stories

[0883] Operation: The server generates target data, uses an AI model to analyze it, and identifies the characteristics of the respondents.

[0884] Output: Analyzed characteristic data

[0885] Step 5:

[0886] The server's determination mechanism identifies a suitable occupation or job based on the analysis results.

[0887] Input: Analyzed characteristic data

[0888] Operation: Characteristic data is cross-referenced with past success stories, and highly suitable tasks are presented based on statistical algorithms.

[0889] Output: Identified appropriate business information

[0890] Step 6:

[0891] The server's result generation mechanism constitutes the recommendation results for appropriate tasks.

[0892] Input: Identified appropriate business information

[0893] Operation: Calculates the suitability score and builds a list of suitable tasks.

[0894] Output: Recommended Task List

[0895] Step 7:

[0896] A list of recommended tasks is sent to a glasses-type display device and displayed to the user in real time.

[0897] Input: Recommended Task List

[0898] Function: The display device shows the user the results and provides visual feedback, helping them understand how to perform the task correctly.

[0899] Output: Visually presented recommended business information for the user.

[0900] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0901] This invention is a system that combines an emotional engine with a career aptitude system to provide career recommendations that take into account the psychological aspects of job seekers. This system consists of a user, a terminal, a server, and an emotional engine, with each element playing a specific role.

[0902] The user logs into the system and enters the necessary basic profile information into the terminal. Next, a series of questions to assess their vocational aptitude are presented on the terminal. As the user answers these questions, the emotion engine recognizes their emotional state in real time from their facial expressions and voice.

[0903] The device collects user responses and emotional data from the emotion engine, and sends this data to the server. The emotion engine recognizes the user's emotional state (e.g., tension, joy, interest) and provides this to the server as a dataset.

[0904] The server analyzes the received response data and sentiment data, and evaluates occupational aptitude using a judgment method. In this process, sentiment data is used as a factor that influences the judgment. For example, if a user shows interest in a particular question, that sentiment is evaluated positively, and occupations in that field are recommended preferentially.

[0905] Next, the results generation system creates a list of recommended occupations based on the information obtained from the analysis. The list includes matching scores and related information, as well as supplementary information based on sentiment data. This result is transmitted to the terminal and presented to the user visually.

[0906] For example, if a user smiles when answering the question "Do you enjoy creative work?" on their device, the emotion engine will record that response as positive. Based on this, the server will recommend professions such as advertising designer or game developer with a high match score.

[0907] In this way, users can receive career recommendations based on their interests and emotions, enabling them to make more personalized career choices. This invention allows job seekers to make career choices that consider emotional aptitude in addition to traditional skills and experience, which is expected to improve job satisfaction.

[0908] The following describes the processing flow.

[0909] Step 1:

[0910] The user logs into the system and enters their basic profile information into the device.

[0911] Step 2:

[0912] The device displays a series of questions on the screen to assess the user's aptitude for work. These questions include content designed to identify the user's interests and skills.

[0913] Step 3:

[0914] The emotion engine analyzes the user's facial expressions and voice in real time through the device's camera and microphone to determine the user's emotional state.

[0915] Step 4:

[0916] As the user answers the presented questions, their emotions during the process are recognized by an emotion engine. This information is collected on the device as emotion data.

[0917] Step 5:

[0918] The device temporarily stores the user's response data and aggregated sentiment data, and verifies the data's integrity.

[0919] Step 6:

[0920] The user completes the answers to the questions and instructs the data to be sent.

[0921] Step 7:

[0922] The device encrypts the response data and sentiment data when it sends them to the server using a communication method.

[0923] Step 8:

[0924] The server analyzes the received data and uses a judgment tool to evaluate the user's occupational aptitude. It compares this to past success data in the database and considers statistical and emotional factors.

[0925] Step 9:

[0926] The server generates a list of recommended occupations based on the analysis results. This list also includes supplementary information based on matching scores and sentiment data.

[0927] Step 10:

[0928] The server sends the final recommendation results to the terminal, where they are displayed visually to the user.

[0929] Step 11:

[0930] The user reviews the presented list of recommended occupations and considers the details of each occupation. If necessary, they can access relevant job postings through their device and proceed with the application process directly.

[0931] (Example 2)

[0932] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0933] Traditional job recommendation systems only assess job seekers' skills and experience, failing to consider their emotional and psychological aspects. This creates a problem where recommended jobs may not always align with the job seeker's inner interests and passions, potentially leading to decreased job satisfaction.

[0934] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0935] In this invention, the server includes an analysis means, a result generation means, and a result display means. This makes it possible to evaluate job suitability by considering both the job seeker's response data and emotional data, thereby realizing job recommendations that are more in line with individual emotions and interests, and improving job satisfaction.

[0936] An "input method" refers to a means by which job seekers input their answers to multiple questions.

[0937] A "terminal device" is a device that collects job applicants' response data and transmits it to a server device along with data obtained through emotion recognition.

[0938] An "emotion recognition method" is a means of analyzing a user's facial expressions and voice to acquire emotional data.

[0939] "Communication means" refers to the means of transmitting response data and sentiment data from a terminal device to a server device.

[0940] The "analysis means" refers to a method for analyzing the response data and sentiment data received by the server device to determine the suitable occupation for the job seeker.

[0941] The "result generation means" is a means for generating a list of recommended occupations suitable for job seekers based on the analysis results obtained by the analysis means.

[0942] The "result display means" is a means for transmitting the generated recommendation list to a terminal device and displaying it visually.

[0943] This is "partial text".

[0944] The system of this invention is designed to evaluate a job seeker's vocational aptitude based on their emotions and recommend a suitable job. The system mainly consists of a terminal device operated by the user, a server device that processes data, and an emotion recognition means that analyzes the user's emotional state.

[0945] The user first logs into the system using a terminal device and enters basic profile information. This terminal device provides an input mechanism for collecting response data. Next, the terminal device presents the user with a series of questions designed to assess their vocational aptitude. The user answers these questions through the input mechanism.

[0946] On the other hand, the emotion recognition means analyzes the user's facial expressions and voice as they answer questions and acquires emotion data in real time. This uses sensors such as cameras and microphones. After the terminal device collects the answer data and emotion data, it uses communication means to transmit them to the server device.

[0947] The server processes the received data using analysis tools to determine the most suitable occupation for the job seeker. Since both response data and sentiment data are considered in this determination, the job recommendations become more personalized and accurate. The generated analysis results are formatted into a recommendation list constructed by a results generation tool. This list is transmitted to the terminal device via a results display tool and visually displayed to the user.

[0948] For example, if a user answers "yes" with a smile to the question "Do you enjoy creative work?", the emotion recognition system records that positive response. Based on this, the server recommends professions such as advertising designer or game developer with a high degree of relevance.

[0949] Examples of prompts for a generative AI model include sentences like, "Use the job aptitude system to show how a user's emotions influence job recommendations."

[0950] This invention will enable job seekers to make career choices that take their emotional aptitudes into consideration, and is expected to improve job satisfaction.

[0951] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0952] Step 1:

[0953] The user logs into the terminal device and enters their profile information. The entered data includes basic personal information and work experience. Based on this information, the terminal initializes the user profile and prepares to send that data to the server.

[0954] Step 2:

[0955] The device displays questions on its screen to assess vocational aptitude. The user enters their answers to these questions, and the device records them. Simultaneously, the device uses its camera and microphone to capture the user's facial expressions and voice. Emotion recognition measures analyze this data to evaluate the user's emotional state in real time. This process uses facial recognition algorithms and voice analysis technology to obtain emotional data such as tension and joy.

[0956] Step 3:

[0957] The terminal packets the collected response data and obtained sentiment data and sends them to the server. The communication method transmits this data to the server using a secure protocol (e.g., HTTPS). Encryption technology is used throughout this process to ensure data integrity and privacy.

[0958] Step 4:

[0959] The server processes the received response data and sentiment data using analytical tools. It utilizes a generative AI model to analyze the correlation between the response data and sentiment data. Specifically, it evaluates how sentiment data influences the response content and scores that influence. This allows for a quantitative assessment of the user's occupational aptitude.

[0960] Step 5:

[0961] The results generation mechanism generates a list of recommended occupations most suitable for the user based on the analysis results. This list includes supplementary evaluations based on agreement scores and sentiment data. The generated list serves as an important factor in the user's decision-making process when choosing an occupation.

[0962] Step 6:

[0963] The server sends the recommendation list generated using the results display means to the terminal. The terminal displays the received recommendation list on the user interface, making it easy for the user to review. This allows the user to access information based on their own feelings and career aptitudes, enabling them to make more appropriate career choices.

[0964] (Application Example 2)

[0965] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0966] Traditional vocational aptitude systems primarily considered information based on job seekers' skills and experience, failing to adequately evaluate their psychological aspects, particularly their emotional suitability. As a result, it was difficult to make job recommendations that reflected job seekers' potential interests and passions, thus hindering their career satisfaction. Furthermore, the lack of techniques for appropriately recording and utilizing naturally occurring emotional responses in daily life limited the ability to assess job seekers' overall vocational aptitude.

[0967] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0968] In this invention, the server includes an information input means for job seekers to answer multiple questions, an observation and recording means for accumulating emotional data by observing the user's daily behavior and recording emotional responses, and an evaluation means for analyzing the response data and emotional data using a set of information on successful candidates from the past to determine suitable occupations. This enables personalized job recommendations that reflect the job seeker's potential interests and passions, thereby improving career satisfaction.

[0969] An "information input method" is an interface for job seekers to input their answers to questions.

[0970] An "electronic system" is a device for processing and transmitting data collected from information input sources.

[0971] "Transmission means" refers to a communication method for transmitting collected response data and sentiment data to an information processing device.

[0972] An "information processing device" is a device that analyzes and evaluates received data and generates results.

[0973] A "collection of information on successful precedents" is a database of data on individuals who have achieved success in the past.

[0974] "Evaluation means" refers to a device or program that has the function of determining occupational aptitude by analyzing response data and emotional data.

[0975] The "result generation means" refers to a function for generating recommendation results based on the determined occupation.

[0976] "Result display means" refers to a device or program that has the function of visualizing the generated recommendation results and presenting them to the user.

[0977] The "observation and recording means" refers to a function for observing the user's daily behavior and recording their emotional responses during that time.

[0978] The "match score" is a numerical representation of how well each job in the generated list of recommended jobs matches the job seeker.

[0979] The "Recommended Occupation List" is a list that shows the recommended order of occupations, generated based on the evaluation method.

[0980] To implement this invention, first, an interface is required in which job seekers answer multiple questions. The response data collected through the information input means is then stored in an electronic system. This electronic system includes observation and recording means that observe the user's daily behavior and record their facial expressions and voice tone in real time. This observation and recording means uses a terminal equipped with a camera and microphone.

[0981] The server analyzes this response data and emotion data through an information processing device. Real-time processing hardware such as NVIDIA Jetson Nano and Raspberry Pi is used for the analysis, and facial recognition and voice analysis are performed using software such as OpenCV and Python. The emotion data quantified by the emotion engine and the job seeker's response data are compared with a cloud-based database of successful precedents, and occupational aptitude is determined by an evaluation method.

[0982] Once the evaluation is complete, the results generation system generates a list of recommended occupations, including the degree of matching score. The server sends these recommendations to the terminal and presents them visually to the job seeker using the results display system.

[0983] For example, if a device engages in a casual conversation with a user and the user smiles and says they are interested in creative work, the emotion engine will record this response as positive. The server will then use this information to recommend professions such as advertising designer or game developer with a high match score. An example of a prompt might be, "When a user smiles and talks about movies, which professions should be recommended?"

[0984] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0985] Step 1:

[0986] The terminal provides a means for job seekers to answer multiple questions through an interface, thereby inputting response data. The response data is temporarily stored on the terminal. The input response data is structured based on the prompt text and prepared to be sent to the server.

[0987] Step 2:

[0988] The device observes the user's daily activities and acquires facial expression and voice data using its camera and microphone. The acquired data is analyzed in real time using an emotion engine. Based on the analysis, the user's emotional state is output as numerical emotion data, which is then prepared to be sent to the server.

[0989] Step 3:

[0990] The server receives response data and sentiment data sent from the terminal. An information processing device analyzes this data and compares it with a set of information on successful individuals in the past. Using NVIDIA Jetson Nano and OpenCV, the data is statistically processed based on indicators derived from the prompt text to generate numerical data representing occupational aptitude.

[0991] Step 4:

[0992] The server calculates a match score using the results of the vocational aptitude assessment determined by the evaluation tool. The result generation tool generates a list of recommended occupations for the job seeker based on the calculated score. The list of recommended occupations is also corrected based on the job seeker's sentiment data.

[0993] Step 5:

[0994] The server sends the generated list of recommended occupations to the terminal. The terminal visually displays the recommended occupations and their matching scores to the job seeker through a results display device. The job seeker who receives the results can then obtain information to help them make informed decisions about their occupation selection.

[0995] Step 6:

[0996] Users can view a list of recommended occupations and enter additional information or new questions about occupations they are interested in into their terminal. This input is sent back to the server for further analysis and re-evaluation, resulting in more accurate recommendations.

[0997] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0998] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0999] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1000] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1001] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1002] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1003] The inside of the Emotion Map 400 represents what's in your mind, while the outside represents what you're doing. Therefore, the further you go out the 400-coordinate scale, the more visible your emotions become (the more they manifest in your actions).

[1004] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1005] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1006] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1007] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1008] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1009] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1010] 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.

[1011] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1012] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1013] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1014] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1015] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1016] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1017] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1018] The following is further disclosed regarding the embodiments described above.

[1019] (Claim 1)

[1020] An input method for job seekers to answer multiple questions,

[1021] A terminal device for collecting response data obtained by the input means,

[1022] A communication means for transmitting the collected response data to a server device,

[1023] A determination means that analyzes the aforementioned response data using a database of past successful individuals to determine suitable occupations,

[1024] A result generation means that generates a recommendation result for the occupation determined by the determination means,

[1025] A result display means for transmitting and displaying the aforementioned recommendation results to the terminal device,

[1026] A system that includes this.

[1027] (Claim 2)

[1028] The system according to claim 1, wherein the determination means comprises an analysis means for statistically comparing the response data with data of past successful individuals.

[1029] (Claim 3)

[1030] The system according to claim 1, wherein the result generation means comprises means for calculating a degree of agreement score for each occupation, and generates a list of recommended occupations including the degree of agreement score.

[1031] "Example 1"

[1032] (Claim 1)

[1033] A means of inputting information for job seekers to answer numerous questions,

[1034] An electronic device that aggregates the response information obtained by the aforementioned information input means,

[1035] A data communication means for transmitting the aggregated response information to an information processing device,

[1036] An information discrimination means that analyzes the aforementioned response information using information records about past successful individuals to determine suitable occupations,

[1037] Information generation means for generating occupation recommendation results determined by the information discrimination means,

[1038] A result display means for transmitting and displaying the aforementioned recommended results to the electronic device,

[1039] A system that includes this.

[1040] (Claim 2)

[1041] The system according to claim 1, wherein the information discrimination means comprises an information analysis means for statistically comparing the response information with information of past successful individuals.

[1042] (Claim 3)

[1043] The system according to claim 1, wherein the information generation means comprises means for calculating the degree of agreement for each occupation, and generates a list of recommended occupations including the degree of agreement.

[1044] "Application Example 1"

[1045] (Claim 1)

[1046] An input means for job seekers or workers to answer multiple questions,

[1047] A terminal device that collects response data obtained by the aforementioned input means and displays it on a glasses-type display device,

[1048] A communication means for transmitting the collected response data to a processing device,

[1049] A determination means that analyzes the aforementioned response data using a data set of past successful individuals to determine the appropriate occupation or job,

[1050] A result generation means that generates a recommendation result for an occupation or job determined by the determination means,

[1051] A result display means for transmitting and displaying the aforementioned recommendation results to the aforementioned glasses-type display device,

[1052] A system that includes this.

[1053] (Claim 2)

[1054] The system according to claim 1, wherein the determination means comprises an analysis means for statistically comparing the response data with data from past successful individuals to identify the optimal business or product for sale.

[1055] (Claim 3)

[1056] The system according to claim 1, wherein the result generation means comprises means for calculating an aptitude score for each occupation or task, and generates a list of recommended occupations or tasks including the aptitude scores.

[1057] "Example 2 of combining an emotion engine"

[1058] (Claim 1)

[1059] An input method for job seekers to answer multiple questions,

[1060] A terminal device for collecting response data obtained by the input means,

[1061] An emotion recognition method that analyzes the user's facial expressions and voice to acquire emotion data,

[1062] A communication means for transmitting the aforementioned response data and emotion data to a server device,

[1063] The server device includes an analysis means for analyzing the response data and emotional data to determine suitable occupations,

[1064] A result generation means that generates a list of recommended occupations suitable for the user based on the generated analysis results,

[1065] A result display means that transmits and displays the aforementioned recommendation list to the terminal device,

[1066] A system that includes this.

[1067] (Claim 2)

[1068] The system according to claim 1, wherein the analysis means comprises means for statistically analyzing the response data and sentiment data and using them for evaluation.

[1069] (Claim 3)

[1070] The system according to claim 1, wherein the result generation means calculates a degree of agreement score for each occupation and generates a list of recommended occupations including supplementary information based on sentiment data.

[1071] "Application example 2 when combining with an emotional engine"

[1072] (Claim 1)

[1073] A means of inputting information for job seekers to answer multiple questions,

[1074] An electronic system for collecting response data and emotion data obtained by the aforementioned information input means,

[1075] A transmission means for transmitting the collected response data and emotion data to an information processing device,

[1076] An evaluation means for determining suitable occupations by analyzing the aforementioned response data and emotional data using a set of information on successful precedents,

[1077] A result generation means for generating a recommendation result for the occupation determined by the evaluation means,

[1078] A result display means for transmitting and displaying the recommendation results to the electronic system,

[1079] An observation and recording means that accumulates emotional data by observing the user's daily behavior and recording their emotional responses,

[1080] A system that includes this.

[1081] (Claim 2)

[1082] The system according to claim 1, wherein the evaluation means comprises an analysis means for statistically comparing the response data and emotional data with information of previous successful individuals.

[1083] (Claim 3)

[1084] The system according to claim 1, wherein the result creation means comprises means for calculating a degree of agreement score for each occupation, and generates a list of recommended occupations including the degree of agreement score. [Explanation of symbols]

[1085] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An input means for job seekers or workers to answer multiple questions, A terminal device that collects response data obtained by the aforementioned input means and displays it on a glasses-type display device, A communication means for transmitting the collected response data to a processing device, A determination means that analyzes the aforementioned response data using a data set of past successful individuals to determine the appropriate occupation or job, A result generation means that generates a recommendation result for an occupation or job determined by the determination means, A result display means for transmitting and displaying the aforementioned recommendation results to the aforementioned glasses-type display device, A system that includes this.

2. The system according to claim 1, wherein the determination means comprises an analysis means for statistically comparing the response data with data from past successful individuals to identify the optimal business or product for sale.

3. The system according to claim 1, wherein the result generation means comprises means for calculating an aptitude score for each occupation or task, and generates a list of recommended occupations or tasks including the aptitude scores.

Images