System

Abstract

A system is provided.SOLUTION: This system includes an input means for inputting the outline of job offer information, an analysis means for analyzing data received from the input means, an automatic generation means for automatically generating the job offer information on the basis of the data analyzed by the analysis means, and a display means for displaying the job offer information generated by the automatic generation means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Human resource managers at small and medium-sized enterprises spend a great deal of time and effort creating job postings. Furthermore, they lack specialized skills in creating job posting copy, making it difficult for them to create professional, appealing job postings. This increases the cost and time required to create job postings, resulting in lower productivity. [Means for solving the problem]

[0005] The present invention provides a system including an input means for inputting an outline of a job posting, an analysis means for analyzing data received from the input means, an automatic generation means for automatically generating job postings based on the data analyzed by the analysis means, and a display means for displaying the job postings generated by the automatic generation means. The analysis means validates the data and categorizes it based on the job type and required skills, and the automatic generation means selects an appropriate generation model to generate the job postings. In this way, the efficiency of job posting creation can be improved and time and costs can be reduced.

[0006] The "input means" is a means for a user to input an outline of the job information, such as the type of job and required skills.

[0007] The "analysis means" is a means for analyzing data received from the input means, validating the data, and classifying the data into categories based on job type and required skills.

[0008] The "automatic generation means" is a means for automatically generating job information based on the analyzed data.

[0009] The "display means" is a means for displaying the job information generated by the automatic generation means to the user.

[0010] "Data validation" is the process of verifying the accuracy and appropriateness of data received from input means.

[0011] "Category classification" is the process of appropriately classifying analyzed data based on job type and required skills.

[0012] A "generative model" refers to a pre-trained algorithm or template used to automatically generate job listings. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention relates to a system for enabling a company's human resources personnel to create job information efficiently and at low cost. The system includes the following elements:

[0035] 1. Input method:

[0036] It provides a web form for users to enter a job description, such as the job title, required skills, etc. For example, a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience" into the form.

[0037] 2. Data transmission:

[0038] The terminal transmits the user's input data to the server.

[0039] 3. Data Analysis:

[0040] The server parses the data it receives, which includes:

[0041] Data validation: Ensuring that the information entered is correct and accurate.

[0042] Categorization: Classify items such as job types and required skills into appropriate categories.

[0043] 4. Generative model selection:

[0044] The server selects an appropriate generative model based on the analyzed data, for example, selecting a specific AI model to generate job postings for "software engineers."

[0045] 5. Automatic Job Creation:

[0046] The AI ​​generates job postings based on the analysis results. For example, it generates the following job postings:

[0047] Job title: Software Engineer

[0048] Required skills:

[0049] Python development experience

[0050] Practical use of the Django framework

[0051] 5+ years of work experience

[0052] job description:

[0053] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[0054] Location: Shibuya-ku, Tokyo

[0055] Salary: Annual salary (determined according to experience and ability)

[0056] Benefits: Full social insurance, transportation expenses provided, remote work available

[0057] 6. Displaying data:

[0058] The server sends the generated job information to the terminal.

[0059] 7. Displaying Information:

[0060] The terminal displays the received job information to the user.

[0061] 8. Check and correct the content:

[0062] The user checks the generated job information and makes corrections as necessary. For example, the "work location" is changed from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo."

[0063] 9. Updates:

[0064] The terminal retransmits the corrections to the server, and the server incorporates the corrections and updates the final job information.

[0065] 10. Saving and Publishing Job Postings:

[0066] The terminal saves the final job information that reflects the changes and makes it available for publication. After that, it processes the recruitment information to make it public.

[0067] By using this system, the process of creating job information can be made more efficient, reducing time and costs. The generated job information is also more professional and attractive to job seekers.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The user enters a summary of the job information, such as the job type and required skills, into a web form.

[0071] Step 2:

[0072] The terminal sends the input data to the server, where it is converted into the appropriate format.

[0073] Step 3:

[0074] The server parses the data it receives, which includes validating the data to ensure it is valid.

[0075] Step 4:

[0076] The server categorizes the data by job type and required skills, which allows it to be organized into appropriate categories.

[0077] Step 5:

[0078] The server selects an appropriate generative model based on the analysis results. For example, it selects a generative AI model suitable for "software engineer."

[0079] Step 6:

[0080] Based on the selected model, the generative AI automatically generates job postings, embedding data into job posting templates and generating fluent sentences using natural language processing technology.

[0081] Step 7:

[0082] The server sends the generated job information to the terminal.

[0083] Step 8:

[0084] The terminal displays the received job information to the user, and the generated job information can be checked through a user interface.

[0085] Step 9:

[0086] The user reviews the displayed job listing and makes any necessary changes, such as changing the location or salary information.

[0087] Step 10:

[0088] The device resends the modifications to the server.

[0089] Step 11:

[0090] The server incorporates the modifications and updates the final job listing.

[0091] Step 12:

[0092] The terminal saves the final job information that reflects the changes, makes it available for publication, and then performs the publication process for the job information.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] Conventional job information creation systems require a lot of manual work and specialized knowledge after inputting the job information summary, making it difficult to create job information efficiently and at low cost. Also, checking and correcting the generated job information is time-consuming, resulting in issues of labor and time. Therefore, there is a need for a system that can create job information more efficiently and at low cost, while also providing specialized content.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes an input means for inputting an outline of a job posting, a transmission means for transmitting data received from the input means, an analysis means for validating the data received by the transmission means and classifying the data into categories based on the job type and required skills, a model selection means for selecting an appropriate generative model based on the data analyzed by the analysis means, an automatic generation means for automatically generating job postings using the generative model selected by the model selection means, and a display means for displaying the job postings generated by the automatic generation means. This automates the creation of job postings, enabling the efficient, low-cost generation of highly specialized job postings. Furthermore, users can confirm and modify the generated job postings, improving the accuracy of the final job postings.

[0098] "Input means for entering a job summary" refers to the interface used by users to enter a summary of the job information, such as the job type and required skills, and specifically refers to a web form or input field.

[0099] The "transmission means for transmitting data received from the input means" is a means having the function of transmitting data input from the input means to a server, and transmits data using a protocol such as an HTTP POST request.

[0100] "Analysis means for validating the data received by the transmission means and classifying it into categories based on the job type and required skills" refers to a means that has the function of checking whether the received data is appropriate and accurate, and further classifying the data into appropriate categories based on the job type and required skills.

[0101] "Model selection means for selecting an appropriate generative model based on the data analyzed by the analysis means" refers to a means having the function of selecting the optimal generative AI model based on the analyzed data.

[0102] "Automatic generation means for automatically generating job information using the generative model selected by the model selection means" refers to a means having the function of automatically generating job information using the selected generative AI model.

[0103] "Display means for displaying job information generated by the automatic generation means" refers to a means that has the function of visually presenting the automatically generated job information to the user, and specifically refers to display on a web browser or application.

[0104] The present invention relates to a system that enables a company's human resources personnel to create job information efficiently and at low cost. This system includes the following elements. The specific processing is as follows.

[0105] System configuration

[0106] 1. Input Method

[0107] It provides an interface for users to enter job summary information such as job type and required skills. It is recommended that this interface be implemented as a web form using front-end technology such as React.js. Users can use this form to enter detailed information.

[0108] Example: A user fills out a form with the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience."

[0109] 2. Transmission Method

[0110] The device sends the user input data to the server using an HTTP POST request, with the data sent in JSON format.

[0111] Example: When the user clicks the "Submit" button, the entered data is sent to the server.

[0112] 3. Analysis method

[0113] The server validates the data received and categorizes it based on job type and required skills. Validation is performed using a Python library (e.g., Cerberus), and an AI model (e.g., scikit-learn) is used to classify the data into the appropriate category.

[0114] Example: Checking data integrity on the server side and classifying "Software Engineer" and "Python, Django, 5+ years experience" into their respective categories.

[0115] 4. Model Selection Method

[0116] The server selects an appropriate generative AI model based on the parsed data, for example, selecting a specific generative AI model (e.g., OpenAI GPT-3) to generate job postings for "software engineers."

[0117] Example: Based on the analysis results, the server selects GPT-3 to generate job listings for "Software Engineer."

[0118] 5. Automatic generation means

[0119] The generative AI generates job information based on the analysis results. The generative AI model receives the selected prompt and generates information based on it.

[0120] Example prompt sentence:

[0121] Job title: Software Engineer

[0122] Required skills: Python, Django, 5+ years of experience

[0123] Job Summary:

[0124] You will be responsible for the design and development of new products, addressing technical challenges, and working with a team using agile development methodologies.

[0125] Example of a generated job posting:

[0126] Job title: Software Engineer

[0127] Required skills:

[0128] Python development experience

[0129] Practical use of the Django framework

[0130] 5+ years of work experience

[0131] job description:

[0132] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[0133] Location: Shibuya-ku, Tokyo

[0134] Salary: Annual salary (determined according to experience and ability)

[0135] Benefits: Full social insurance, transportation expenses provided, remote work available

[0136] 6. Display means

[0137] The server sends the generated job information to the device, which then displays the received information to the user, typically via a web browser or application.

[0138] Example: The server sends job information generated in JSON format to the device, and JavaScript on the device parses the received data and inserts it into the page as HTML elements.

[0139] System Benefits

[0140] This system automates the creation of job listings, enabling the efficient, low-cost generation of highly specialized job listings. Furthermore, users can check and edit the generated job listings, improving the accuracy of the final job listings.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] User Input

[0144] The user enters information such as job type and required skills into a web form. This web form is implemented using front-end technologies such as React.js. The user uses this form to enter detailed information about the job type and required skills.

[0145] Input: User enters job title "Software Engineer" and required skills "Python, Django, 5+ years experience".

[0146] Output: Job title and required skill details as form data.

[0147] Specific behavior:

[0148] A user accesses a web form in a browser and enters the job title and required skills into the input fields.

[0149] The user clicks the "Submit" button.

[0150] Step 2:

[0151] Data transmission

[0152] The device sends the user input data to the server using an HTTP POST request, with the data sent in JSON format.

[0153] Input: Data entered by a user into a form.

[0154] Output: JSON formatted data sent to the server.

[0155] Specific behavior:

[0156] When the "Submit" button is clicked, JavaScript reads the data and converts it to JSON format.

[0157] The terminal (web browser) sends data to the server using an HTTP POST request.

[0158] Step 3:

[0159] Data Validation

[0160] Validate the data received by the server. This process checks for incorrect data or missing fields. Use a Python library (e.g., Cerberus).

[0161] Input: JSON format data sent from the terminal.

[0162] Output: The validated data or error messages.

[0163] Specific behavior:

[0164] On the server side, Django receives the HTTP POST request and deserializes the data.

[0165] Uses the Cerberus library to validate data and returns error messages if data is invalid.

[0166] Step 4:

[0167] Data categorization

[0168] The server classifies input data such as job type and required skills into appropriate categories using an AI model (e.g., scikit-learn).

[0169] Input: Validated data.

[0170] Output: Categorized data.

[0171] Specific behavior:

[0172] Data that passes validation is input into the AI ​​model.

[0173] Using models such as scikit-learn, the job title "Software Engineer" and the required skills "Python, Django, 5+ years of experience" are classified into corresponding categories.

[0174] Step 5:

[0175] Generative Model Selection

[0176] The server selects an appropriate generative AI model based on the parsed data, for example, selecting a specific generative AI model (e.g., OpenAI GPT-3) to generate job postings for "software engineers."

[0177] Input: Categorical data.

[0178] Output: The selected generative model.

[0179] Specific behavior:

[0180] Based on the data analysis results, the server selects an appropriate generative AI model (e.g., GPT-3).

[0181] Read API key and endpoint information.

[0182] Step 6:

[0183] Automatic generation of job postings

[0184] The generative AI generates job information based on the analysis results. A prompt is generated based on the input data and sent to the generative AI model.

[0185] Input: A prompt based on the selected generative AI model and analysis results.

[0186] Output: Auto-generated job listing.

[0187] Specific behavior:

[0188] Create a prompt sentence based on the analysis results.

[0189] Send prompt text to the generative AI model to generate job information.

[0190] Step 7:

[0191] Viewing Data

[0192] The server transmits the generated job information to the terminal, and the terminal displays the received job information to the user.

[0193] Input: Auto-generated job posting.

[0194] Output: The job listing displayed to the user.

[0195] Specific behavior:

[0196] The server serializes the generated job information in JSON format and sends it as an HTTP response.

[0197] The device receives this response and prepares to render.

[0198] JavaScript parses the incoming data and generates HTML elements to insert into the page so that the user can view the job listings in their browser.

[0199] Step 8:

[0200] Check and correct the contents

[0201] The user checks the generated job information and makes corrections as necessary.

[0202] Input: Auto-generated job posting.

[0203] Output: The job posting as modified by the user.

[0204] Specific behavior:

[0205] The user reviews the job listing generated on the web page and clicks the "Edit" button.

[0206] Modify the content in the text boxes and form fields.

[0207] Step 9:

[0208] Updates

[0209] The terminal retransmits the corrections to the server, and the server incorporates the corrections and updates the final job information.

[0210] Input: The job posting modified by the user.

[0211] Output: The final updated job listing.

[0212] Specific behavior:

[0213] The device sends the corrected data back to the server in JSON format.

[0214] The server re-validates and parses the data and updates the database.

[0215] Step 10:

[0216] Saving and publishing job listings

[0217] The device saves the final job listing with the changes and makes it available for publication.

[0218] Input: Last updated job posting.

[0219] Output: A saved, publicly available job posting.

[0220] Specific behavior:

[0221] The final job listings are stored in a database and displayed on a website via a public API or similar.

[0222] Clicking the publish button will make the job posting publicly available.

[0223] (Application example 1)

[0224] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0225] Conventional systems lack the means to efficiently and cost-effectively create job listings. Furthermore, creating the various information required by companies newly adopting electronic payment services is time-consuming, making it difficult to provide the information accurately and quickly. The present invention aims to solve these problems by providing a system that can efficiently and automatically generate job listings and information for new companies.

[0226] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0227] In this invention, the server includes input means for inputting an outline of the job information, analysis means for analyzing data received from the input means, automatic generation means for automatically generating job information based on the data analyzed by the analysis means, display means for displaying the job information generated by the automatic generation means, means for inputting information related to requirements sought by introducing companies, and automatic generation means for analyzing the information related to the requirements and automatically generating the necessary information. This makes it possible to efficiently and automatically generate job information and information for new introducing companies.

[0228] An "input means" is a device or system through which a user inputs information.

[0229] The "analysis means" is a device or system that analyzes the received data and performs the necessary processing.

[0230] "Automatic generation means" refers to a device or system that automatically generates information based on analyzed data.

[0231] A "display means" is a device or system that visually displays the generated information to a user.

[0232] The "means for inputting information regarding requirements" refers to a device or system for inputting information regarding the requirements desired by the adopting company.

[0233] "Automatic generation means for analyzing information related to requirements and automatically generating necessary information" refers to a device or system that analyzes and automatically generates necessary information based on the requirements desired by the adopting company.

[0234] The present invention relates to a system for automatically generating job information and information for new companies efficiently and at low cost. This system includes an input means for a user to input information, an analysis means for analyzing data, an automatic generation means, and a display means.

[0235] First, the user uses an input method to enter information about the job summary and the requirements of the company. The input method can be a web form or a mobile application. For example, the user might enter the job title "Software Engineer" and the required skills of "Python, Django, and 5+ years of experience," or the company's security standards and required API information into the form.

[0236] The server then validates the input data using analytical methods and classifies it into categories based on job type, skills, requirements, etc. This analysis uses data validation software and machine learning algorithms (e.g., TensorFlow, PyTorch). Based on the category, an appropriate generative AI model is selected. For example, if the data corresponds to "software engineer," a dedicated generative model is selected.

[0237] The automated generation method then uses the selected generative AI model to automatically generate job listings and information for potential adopters based on the analyzed data. The generative AI model includes a generative model that has learned from past data. For example, a job listing for a "software engineer" is automatically generated as follows:

[0238] Job title: Software Engineer

[0239] Required skills:

[0240] Python development experience

[0241] Practical use of the Django framework

[0242] 5+ years of work experience

[0243] job description:

[0244] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[0245] Location: Shibuya-ku, Tokyo

[0246] Salary: Annual salary (determined according to experience and ability)

[0247] Benefits: Full social insurance, transportation expenses provided, remote work available

[0248] In addition, information for adopting companies is automatically generated as follows:

[0249] Company name: XYZ Corp.

[0250] Required APIs:

[0251] Payment API: REST-based interface. Documentation is available at the following URL: https: / / api.example.com / docs / payment

[0252] Authentication API: OAuth2.0 compatible. Documentation is available at the URL: https: / / api.example.com / docs / auth

[0253] Security Standards:

[0254] PCI-DSS: Payment Card Industry Data Security Standard compliant.

[0255] The terminal (user's device) then displays the generated information to the user. The user checks the displayed information and makes any necessary corrections. Once the corrections are complete, the user resubmits the final information to the server, which then saves the updated information. This series of processes streamlines the process of generating job information and information for adopting companies.

[0256] Based on the above explanation, the functions of each element of the present invention and their cooperation with each other enable users to easily generate high-quality job information and information for adopting companies.

[0257] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0258] Step 1:

[0259] Users use input methods to enter information about the job posting summary and the requirements of the company. Input methods include web forms and mobile applications. Input includes, for example, the job title "Software Engineer" and skill information such as "Python, Django, 5+ years of experience," as well as required API information and security standards. This input data is sent from the device to the server.

[0260] Step 2:

[0261] The server analyzes the received data using analytical methods. Specifically, it validates the data to ensure that the input information is appropriate. For example, it checks whether all input fields are filled in and the format is correct. Once validated, the data is classified into categories based on job type, required skills, and requirements. This process uses data analysis libraries (e.g., pandas) and machine learning algorithms (e.g., scikit-learn).

[0262] Step 3:

[0263] The server selects the appropriate generative AI model based on the analyzed data. It uses a large number of pre-trained models to determine which model to use based on the category. It selects a model that matches the specific skill set and requirements. For example, if the category corresponds to "software engineer," a software-related job generation model is selected. This process is performed using a model management tool (e.g., MLflow).

[0264] Step 4:

[0265] The server uses the selected generative AI model to automatically generate job postings and information for adopting companies based on the analysis results. The generative AI model formats the generated text and creates job postings and requirements information in the required format. For example, the generated job postings include fields such as "job type," "required skills," "job description," and "work location." A natural language generation library (e.g., GPT-3) is used for this process. The following are specific examples of prompt sentences:

[0266] Generate the information needed to implement new electronic payment services for businesses such as:

[0267] Company name: XYZ Corp.

[0268] Required APIs: Payment API, Authentication API

[0269] Security Standard: PCI-DSS

[0270] Step 5:

[0271] The server sends the generated information to the terminal. The terminal displays the received information to the user, allowing the user to check the generated job information and requirements information. A web browser or mobile app is used as the display method. The displayed information is provided in a format that the user can easily check visually.

[0272] Step 6:

[0273] The user checks the displayed information and makes any necessary corrections. For example, if the generated work location is changed from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo," the user makes the corrections through the input field. The device then sends the corrections back to the server.

[0274] Step 7:

[0275] The server then reparses the modified data and stores it as the final information. This results in a complete job or requirement information reflecting the modifications. This process is performed using a database system (e.g., PostgreSQL). The final information is stored and can be made publicly available if desired.

[0276] By performing the above steps, the system of the present invention can efficiently and automatically generate job information and information for companies newly introducing electronic payment services, and provide it to users.

[0277] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0278] The present invention relates to a system that enables a company's human resources personnel to efficiently create job postings and adjust the job postings based on user sentiment. The system includes the following elements:

[0279] 1. Input method:

[0280] It provides a web form for users to enter a job description, such as the job title, required skills, etc. For example, a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience" into the form.

[0281] 2. Data transmission:

[0282] The terminal transmits the user's input data to the server.

[0283] 3. Data Analysis:

[0284] The server analyzes the received data, which includes:

[0285] Data validation: Checking the accuracy and appropriateness of input data.

[0286] Categorization: Classify items such as job types and required skills into appropriate categories.

[0287] 4. Generative model selection:

[0288] The server selects an appropriate generative model based on the analysis results. For example, it selects a generative AI model suitable for "software engineer."

[0289] 5. Automatic Job Creation:

[0290] Based on the selected model, the generative AI automatically generates job postings, embedding data into job posting templates and generating fluent sentences using natural language processing technology.

[0291] 6. Emotion Recognition with Emotion Engine:

[0292] The emotion engine analyzes the user's facial expressions and tone of voice as they type to recognize their emotions, for example identifying when they are tired or particularly excited.

[0293] 7. Applying emotional information:

[0294] The adjustment means adjusts the job posting based on the recognized emotional information, for example, if it is recognized that the user is nervous, the job posting text is changed to a more relaxed tone.

[0295] 8. Displaying Job Postings:

[0296] The server sends the generated job information and the adjusted text to the terminal.

[0297] 9. Display of information:

[0298] The terminal displays the received job information to the user, and the generated job information can be checked through a user interface.

[0299] 10. Checking and correcting content:

[0300] The user reviews the displayed job listing and makes any necessary changes, such as changing the location or salary information.

[0301] 11. Updates:

[0302] The device resends the modifications to the server.

[0303] 12. Saving and Publishing Final Job Postings:

[0304] The server retrieves the corrections and updates the final job listing. The terminal saves the final job listing with the corrections reflected and makes it available for publication. The job listing is then published.

[0305] This system will not only streamline the process of creating job listings, but also make it possible to provide job listings that take into account the user's emotions, thereby improving the quality of job listings and making them more attractive to job seekers.

[0306] The processing flow will be explained below.

[0307] Step 1:

[0308] A user enters job information summary such as job type and required skills into a web form. The form contains fields for entering basic information such as job type, required skills, work location, and salary.

[0309] Step 2:

[0310] The device sends the input data to the server, where it is converted into an appropriate format such as JSON or XML.

[0311] Step 3:

[0312] The server analyzes the received data, which includes the following processes:

[0313] Data validation: Checking whether the information entered is appropriate and accurate, for example, whether required skills exist or salary information is valid.

[0314] Categorization: Classifying items such as job types and required skills into specific categories.

[0315] Step 4:

[0316] The server selects an appropriate generative model based on the analysis results. For example, it selects an AI model for technical jobs for the job title "software engineer."

[0317] Step 5:

[0318] The Generative AI automatically generates job postings based on the selected model. It does the following:

[0319] Choose a template: Choose a job posting template based on the job type.

[0320] Embedding information: Embed analytical data in templates.

[0321] Natural language processing: Performs language processing to generate fluent sentences.

[0322] Step 6:

[0323] The emotion engine analyzes the user's facial expressions and tone of voice when inputting and recognizes emotions. For example, it uses a camera and microphone to analyze the user's facial expressions and voice in real time.

[0324] Step 7:

[0325] The server receives the analysis results from the emotion engine and adjusts the job posting based on the recognized emotion information. For example, if it determines that the user is nervous, it will change the tone of the message to be more friendly and relaxed.

[0326] Step 8:

[0327] The server sends the generated job information and the adjusted job information to the terminal.

[0328] Step 9:

[0329] The terminal displays the received job information to the user, including the function of visually displaying the generated job information using a GUI (Graphical User Interface).

[0330] Step 10:

[0331] The user checks the displayed job information and modifies it as necessary. For example, the user changes the work location from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo."

[0332] Step 11:

[0333] The terminal retransmits the modified information to the server, where the modified information is reformatted.

[0334] Step 12:

[0335] The server takes the modifications and updates the final job listing, ensuring the most up-to-date information is stored.

[0336] Step 13:

[0337] The device saves the final job listing with the changes reflected and makes it available for publication. It then processes the job listing to be published. The published job listing is posted on job sites, internal bulletin boards, etc.

[0338] Example 2

[0339] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0340] Conventional methods for creating job information require a lot of time and effort from the user, and they also face the problem of difficulty in providing job information that reflects the user's feelings. This leads to a decline in the quality of job information provided by companies, making it difficult for the content to be attractive to job seekers. Furthermore, automatic generation of job information also has the problem of a decline in user satisfaction, as it is not possible to take the user's feelings into account.

[0341] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0342] In this invention, the server includes input means for inputting an outline of a job listing, analysis means for analyzing data received from the input means, automatic generation means for automatically generating job listings based on the data analyzed by the analysis means, display means for displaying the job listings generated by the automatic generation means, emotion recognition means for recognizing a user's emotion, and adjustment means for adjusting the job listings based on the emotion information recognized by the emotion recognition means. This enables efficient creation and adjustment of job listings, making it possible to provide high-quality job listings that reflect the user's emotions.

[0343] "Input means" refers to a device or system that allows a user to input an outline of job information such as the type of job and required skills.

[0344] The "analysis means" is a device or system that analyzes data received from the input means and performs validation and categorization of the content.

[0345] The "automatic generation means" is a device or system that automatically generates job information based on the data analyzed by the analysis means.

[0346] The "display means" is a device or system that displays the job information generated by the automatic generation means to the user.

[0347] The "emotion recognition means" is a device or system that recognizes the user's emotions from facial expressions, tone of voice, etc.

[0348] The "adjustment means" is a device or system that adjusts the generated job information based on the emotion information recognized by the emotion recognition means.

[0349] The present invention relates to a system that enables a company's human resources personnel to efficiently create job information and adjust the job information based on user sentiment. This system is implemented using the following hardware and software.

[0350] 1. Hardware and Software Used

[0351] Web browser: Used by users to enter job description information such as job title and required skills.

[0352] Server: Performs data analysis, generative AI model selection, sentiment analysis, and final storage and publishing of job listings.

[0353] Generative AI: A system that automatically generates job information using natural language processing technology.

[0354] Emotion recognition engine: A system that analyzes user emotions and adjusts job information based on the results.

[0355] Terminal: A device that interfaces with the user, submits input data, displays job information, and resubmits corrected data.

[0356] 2. Explanation of program processing

[0357] First, the user accesses a company's job posting creation page using a web browser. The user enters a summary of the job posting, including the job type and required skills, and sends the data from their device to the server. The server analyzes the received data and checks for accuracy and appropriateness. As a result of the analysis, the job type and skills are classified into appropriate categories.

[0358] Based on the analysis results, the server selects an appropriate generation AI model and begins the automatic generation process. The generation AI embeds the data entered by the user into a template and generates a job posting in fluent natural language. An emotion recognition engine then analyzes the user's facial expressions and tone of voice when entering information to recognize the user's emotions. Based on this recognized emotional information, an adjustment method adjusts the job posting.

[0359] For example, if the user is nervous, the tone of the job posting can be changed to a more relaxed tone. The server sends the generated and adjusted job posting to the device, which displays it to the user. The user checks the displayed job posting and makes changes to information such as work location and salary as necessary. The device then sends the changes back to the server, which incorporates the changes and saves the final job posting. This information is then made available to job seekers.

[0360] 3. Adding concrete examples and prompt sentence examples

[0361] As a concrete example, consider the case where a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years of experience." The device sends this data to the server, which analyzes the data, categorizes it, and selects a generative AI model. The generative AI generates a job posting such as "Our company is hiring a software engineer with 5+ years of Python and Django experience." The emotion recognition engine analyzes the user's emotions and adjusts the wording as necessary.

[0362] Example prompts for generative AI models

[0363] "Generate a compelling software engineer job posting based on the following data: Job Title: Software Engineer, Required Skills: Python, Django, 5+ years of experience."

[0364] This system allows for efficient creation and publication of job postings, and provides high-quality job postings that take user sentiment into consideration. This improves the quality of job postings for companies and makes them more attractive to job seekers.

[0365] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0366] Step 1:

[0367] The user enters a job summary

[0368] A user uses a web browser to enter a job description, such as the job title and required skills. Specifically, the user enters "Software Engineer" and "Python, Django, 5+ years of experience." This input data is sent to the terminal and temporarily saved.

[0369] Input: User inputs job title and required skills

[0370] Output: Input data saved on the device

[0371] Step 2:

[0372] The device sends the data to the server

[0373] The terminal sends the data entered by the user to the server. At this time, the data is encrypted and sent using a secure communication method (e.g., HTTPS).

[0374] Input: Input data stored on the device

[0375] Output: Data sent to the server

[0376] Step 3:

[0377] The server analyzes the data

[0378] The server analyzes the received data. First, it validates the data to ensure that the job titles and required skills are accurate and appropriate. Then it categorizes the job titles and skills into categories.

[0379] Input: Data sent to the server

[0380] Output: Validated and classified data

[0381] Step 4:

[0382] The server selects the generative model

[0383] The server selects an appropriate generative AI model based on the analysis results. For example, for the job title "software engineer," it references past data to select the optimal model.

[0384] Input: Validated and classified data

[0385] Output: The selected generative AI model

[0386] Step 5:

[0387] Generative AI automatically generates job information

[0388] Based on the selected model, the generative AI automatically generates a job posting, such as, "Our company is looking for a software engineer with 5+ years of experience in Python and Django."

[0389] Input: Validated and classified data, selected generative AI model

[0390] Output: Generated job listings

[0391] Step 6:

[0392] Emotion recognition engine recognizes emotions

[0393] The emotion recognition engine analyzes the user's facial expressions and tone of voice when typing to recognize the user's emotions, for example, whether the user is nervous or relaxed.

[0394] Input: User's facial expression data and tone of voice

[0395] Output: Recognized emotion information

[0396] Step 7:

[0397] Regulatory measures apply emotional information

[0398] The adjustment means adjusts the generated job posting based on the recognized emotion information. For example, if the user is nervous, the adjustment means changes the posting to "This is an opportunity to work in a relaxed office environment."

[0399] Input: Generated job information, recognized emotion information

[0400] Output: Adjusted job listings

[0401] Step 8:

[0402] The server sends the job information to the device.

[0403] The server prepares the adjusted job information for transmission to the terminal and sends the encoded data to the terminal.

[0404] Input: Adjusted Job Posting

[0405] Output: Data sent to the terminal

[0406] Step 9:

[0407] The device displays the information

[0408] The device decodes the received data and displays the adjusted job information on the GUI.

[0409] Input: Data sent to the terminal

[0410] Output: Job information displayed in GUI

[0411] Step 10:

[0412] The user checks and corrects the content

[0413] The user can check the displayed job information and modify it as necessary, for example, adjusting the work location or salary conditions. The modified data is saved on the device.

[0414] Input: Job information displayed on the GUI

[0415] Output: Corrected data saved on the device

[0416] Step 11:

[0417] The device resends the corrected data to the server

[0418] The terminal transmits the data modified by the user back to the server.

[0419] Input: Correction data saved on the device

[0420] Output: Data resent to the server

[0421] Step 12:

[0422] The server stores and publishes the final job listings.

[0423] The server takes the modifications and saves them as the final job listing, which is then made available to job seekers.

[0424] Input: Data resubmitted to server

[0425] Output: Final job posting saved and published

[0426] (Application example 2)

[0427] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0428] Conventional job information creation systems simply display the job information entered by the user as is, and have the problem of being unable to adjust the text to take the user's feelings into account. Furthermore, there is a lack of support for content creators to efficiently create video titles and descriptions. This has led to the problem of not being able to provide content that is more appealing to job seekers and viewers.

[0429] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for inputting an outline of the job information, an analysis means for analyzing data received from the input means, an automatic generation means for automatically generating job information based on the analyzed data, an adjustment means for adjusting the text of the generated job information, and a display means for displaying the generated job information. This makes it possible to adjust the text of the job information based on user emotional information and to efficiently generate and adjust video titles and descriptions for content creators.

[0430] "Job information summary" refers to basic information such as the type of job required of applicants, necessary skills, and working conditions.

[0431] "Input means" refers to an interface for a user to input data, and specifically includes a keyboard, a mouse, a touch screen, and the like.

[0432] "Analysis means" refers to a device or program that has the function of analyzing input data and verifying its appropriateness and accuracy.

[0433] "Validation" refers to the process of ensuring that entered data is correct and appropriate.

[0434] "Automatic generation means" refers to a device or program that automatically generates job listings and other content based on analyzed data.

[0435] "Display means" refers to a device or interface for displaying generated information in a form visible to a user.

[0436] The "adjustment means" refers to a device or program that adjusts the generated text based on the user's emotional information.

[0437] "Emotional information" refers to data related to emotions obtained from the user's facial expressions and voice.

[0438] A "face recognition camera" refers to a camera that captures a user's face and analyzes their facial expressions.

[0439] A "content creator" is someone who produces content such as videos and articles.

[0440] "Video title" refers to a name that succinctly expresses the content of the video.

[0441] "Description" refers to text that provides a detailed explanation of the content of a video or other content.

[0442] "Generative model" refers to an artificial intelligence model that generates natural language or other content based on specific input data.

[0443] A "prompt sentence" refers to an input sentence that instructs a generative model to produce a specific output.

[0444] To implement this invention, content creators must first provide an interface for inputting video titles and description summaries. Users input data through this interface, and the data is sent from the terminal to the server.

[0445] The server then analyzes the received data. Analysis methods include data validation and categorization to ensure the appropriateness and accuracy of the input. For example, data such as "Video Title: Introducing new React trends" and "Description: Introducing the latest React features and how to apply them" may be analyzed.

[0446] Based on the analyzed data, the server selects an appropriate generative AI model and automatically generates the video title and description. Generative AI models such as the OpenAI GPT series can be used. The generated data is displayed to the user, who can review it and make corrections if necessary.

[0447] Furthermore, the adjustment means plays an important role in this invention. The adjustment means acquires emotional information from the user's facial expressions and voice, and adjusts the generated text based on this information. A facial recognition camera and voice analysis software are used to acquire emotional information. For example, if the user is nervous, the video title can be changed to a more relaxed tone, such as "Improve your skills! A clear explanation of the new React trend."

[0448] Finally, the adjusted data is sent back to the server and the final information is saved, allowing content creators to efficiently create titles and descriptions for their videos and provide more engaging content to viewers.

[0449] For example, the following prompt sentence can be used:

[0450] Example prompt:

[0451] "Generate an inspiring and clear title and description for an introduction to a new React trend."

[0452] In this way, by implementing the present invention, it becomes possible to generate effective content that takes into account the user's emotional information.

[0453] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0454] Step 1: Enter data using the input method

[0455] The user inputs the video title and description summary via the interface, such as "Video title: Introducing new React trends" and "Description: Introducing the latest React features and how to apply them," and sends this information from the device to the server.

[0456] Input: Video title, description

[0457] Output: Input data

[0458] Step 2: Receiving and validating data

[0459] The server receives the input data sent from the terminal, and then validates the data using analytical means, checking the accuracy and appropriateness of the input.

[0460] Input: Input data

[0461] Output: Validated data

[0462] Step 3: Categorization

[0463] Based on the validated data, the server classifies items such as job type and required skills into appropriate categories.

[0464] Input: Validated data

[0465] Output: Classified data

[0466] Step 4: Selecting a Generative Model

[0467] The server selects an appropriate generative AI model based on the classified data, using, for example, the OpenAI GPT series as the generative AI model.

[0468] Input: Classified data

[0469] Output: The selected generative AI model

[0470] Step 5: Automatically generate job postings

[0471] Using the selected generative AI model, the server automatically generates a video title and description, using the specific prompt "Generate an inspiring and easy-to-understand title and description for introducing a new React trend."

[0472] Input: Generative AI model, prompt, classified data

[0473] Output: Generated title and description

[0474] Step 6: Acquiring emotional information

[0475] Using a facial recognition camera and voice analysis equipment, the server obtains the user's emotional information, which includes analyzing the user's facial expressions and voice tone.

[0476] Input: User's facial expression data, voice data

[0477] Output: Emotional information

[0478] Step 7: Adjust the text

[0479] Based on the acquired emotional information, the server adjusts the automatically generated title and description text, for example, changing the tone to a more relaxed one if the user is nervous.

[0480] Input: Generated title and description, sentiment information

[0481] Output: Adjusted title and description

[0482] Step 8: Viewing generated data

[0483] The server sends the final adjusted title and description to the terminal and displays them through the user interface.

[0484] Input: Adjusted title and description

[0485] Output: Display data

[0486] Step 9: Check and correct the content

[0487] The user checks the displayed title and description, makes any necessary corrections, and then sends the corrections back to the server.

[0488] Input: Display data

[0489] Output: Corrected data

[0490] Step 10: Save and publish your final information

[0491] The server takes the corrected data, updates the final information, and stores it, after which it is made available for publication.

[0492] Input: Correction data

[0493] Output: Saved data, public data

[0494] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0495] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0496] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0497] [Second embodiment]

[0498] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0499] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0500] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0501] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0502] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0503] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0504] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0505] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0506] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0507] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0508] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0509] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0510] The present invention relates to a system for enabling a company's human resources personnel to create job information efficiently and at low cost. The system includes the following elements:

[0511] 1. Input method:

[0512] It provides a web form for users to enter a job description, such as the job title, required skills, etc. For example, a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience" into the form.

[0513] 2. Data transmission:

[0514] The terminal transmits the user's input data to the server.

[0515] 3. Data Analysis:

[0516] The server parses the data it receives, which includes:

[0517] Data validation: Ensuring that the information entered is correct and accurate.

[0518] Categorization: Classify items such as job types and required skills into appropriate categories.

[0519] 4. Generative model selection:

[0520] The server selects an appropriate generative model based on the analyzed data, for example, selecting a specific AI model to generate job postings for "software engineers."

[0521] 5. Automatic Job Creation:

[0522] The AI ​​generates job postings based on the analysis results. For example, it generates the following job postings:

[0523] Job title: Software Engineer

[0524] Required skills:

[0525] Python development experience

[0526] Practical use of the Django framework

[0527] 5+ years of work experience

[0528] job description:

[0529] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[0530] Location: Shibuya-ku, Tokyo

[0531] Salary: Annual salary (determined according to experience and ability)

[0532] Benefits: Full social insurance, transportation expenses provided, remote work available

[0533] 6. Displaying data:

[0534] The server sends the generated job information to the terminal.

[0535] 7. Displaying Information:

[0536] The terminal displays the received job information to the user.

[0537] 8. Check and correct the content:

[0538] The user checks the generated job information and makes corrections as necessary. For example, the "work location" is changed from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo."

[0539] 9. Updates:

[0540] The terminal retransmits the corrections to the server, and the server incorporates the corrections and updates the final job information.

[0541] 10. Saving and Publishing Job Postings:

[0542] The terminal saves the final job information that reflects the changes and makes it available for publication. After that, it processes the recruitment information to make it public.

[0543] By using this system, the process of creating job information can be made more efficient, reducing time and costs. The generated job information is also more professional and attractive to job seekers.

[0544] The processing flow will be explained below.

[0545] Step 1:

[0546] The user enters a summary of the job information, such as the job type and required skills, into a web form.

[0547] Step 2:

[0548] The terminal sends the input data to the server, where it is converted into the appropriate format.

[0549] Step 3:

[0550] The server parses the data it receives, which includes validating the data to ensure it is valid.

[0551] Step 4:

[0552] The server categorizes the data by job type and required skills, which allows it to be organized into appropriate categories.

[0553] Step 5:

[0554] The server selects an appropriate generative model based on the analysis results. For example, it selects a generative AI model suitable for "software engineer."

[0555] Step 6:

[0556] Based on the selected model, the generative AI automatically generates job postings, embedding data into job posting templates and generating fluent sentences using natural language processing technology.

[0557] Step 7:

[0558] The server sends the generated job information to the terminal.

[0559] Step 8:

[0560] The terminal displays the received job information to the user, and the generated job information can be checked through a user interface.

[0561] Step 9:

[0562] The user reviews the displayed job listing and makes any necessary changes, such as changing the location or salary information.

[0563] Step 10:

[0564] The device resends the modifications to the server.

[0565] Step 11:

[0566] The server incorporates the modifications and updates the final job listing.

[0567] Step 12:

[0568] The terminal saves the final job information that reflects the changes, makes it available for publication, and then performs the publication process for the job information.

[0569] Example 1

[0570] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0571] Conventional job information creation systems require a lot of manual work and specialized knowledge after inputting the job information summary, making it difficult to create job information efficiently and at low cost. Also, checking and correcting the generated job information is time-consuming, resulting in issues of labor and time. Therefore, there is a need for a system that can create job information more efficiently and at low cost, while also providing specialized content.

[0572] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0573] In this invention, the server includes an input means for inputting an outline of a job posting, a transmission means for transmitting data received from the input means, an analysis means for validating the data received by the transmission means and classifying the data into categories based on the job type and required skills, a model selection means for selecting an appropriate generative model based on the data analyzed by the analysis means, an automatic generation means for automatically generating job postings using the generative model selected by the model selection means, and a display means for displaying the job postings generated by the automatic generation means. This automates the creation of job postings, enabling the efficient, low-cost generation of highly specialized job postings. Furthermore, users can confirm and modify the generated job postings, improving the accuracy of the final job postings.

[0574] "Input means for entering a job summary" refers to the interface used by users to enter a summary of the job information, such as the job type and required skills, and specifically refers to a web form or input field.

[0575] The "transmission means for transmitting data received from the input means" is a means having the function of transmitting data input from the input means to a server, and transmits data using a protocol such as an HTTP POST request.

[0576] "Analysis means for validating the data received by the transmission means and classifying it into categories based on the job type and required skills" refers to a means that has the function of checking whether the received data is appropriate and accurate, and further classifying the data into appropriate categories based on the job type and required skills.

[0577] "Model selection means for selecting an appropriate generative model based on the data analyzed by the analysis means" refers to a means having the function of selecting the optimal generative AI model based on the analyzed data.

[0578] "Automatic generation means for automatically generating job information using the generative model selected by the model selection means" refers to a means having the function of automatically generating job information using the selected generative AI model.

[0579] "Display means for displaying job information generated by the automatic generation means" refers to a means that has the function of visually presenting the automatically generated job information to the user, and specifically refers to display on a web browser or application.

[0580] The present invention relates to a system that enables a company's human resources personnel to create job information efficiently and at low cost. This system includes the following elements. The specific processing is as follows.

[0581] System configuration

[0582] 1. Input Method

[0583] It provides an interface for users to enter job summary information such as job type and required skills. It is recommended that this interface be implemented as a web form using front-end technology such as React.js. Users can use this form to enter detailed information.

[0584] Example: A user fills out a form with the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience."

[0585] 2. Transmission Method

[0586] The device sends the user input data to the server using an HTTP POST request, with the data sent in JSON format.

[0587] Example: When the user clicks the "Submit" button, the entered data is sent to the server.

[0588] 3. Analysis method

[0589] The server validates the data received and categorizes it based on job type and required skills. Validation is performed using a Python library (e.g., Cerberus), and an AI model (e.g., scikit-learn) is used to classify the data into the appropriate category.

[0590] Example: Checking data integrity on the server side and classifying "Software Engineer" and "Python, Django, 5+ years experience" into their respective categories.

[0591] 4. Model Selection Method

[0592] The server selects an appropriate generative AI model based on the parsed data, for example, selecting a specific generative AI model (e.g., OpenAI GPT-3) to generate job postings for "software engineers."

[0593] Example: Based on the analysis results, the server selects GPT-3 to generate job listings for "Software Engineer."

[0594] 5. Automatic generation means

[0595] The generative AI generates job information based on the analysis results. The generative AI model receives the selected prompt and generates information based on it.

[0596] Example prompt sentence:

[0597] Job title: Software Engineer

[0598] Required skills: Python, Django, 5+ years of experience

[0599] Job Summary:

[0600] You will be responsible for the design and development of new products, addressing technical challenges, and working with a team using agile development methodologies.

[0601] Example of a generated job posting:

[0602] Job title: Software Engineer

[0603] Required skills:

[0604] Python development experience

[0605] Practical use of the Django framework

[0606] 5+ years of work experience

[0607] job description:

[0608] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[0609] Location: Shibuya-ku, Tokyo

[0610] Salary: Annual salary (determined according to experience and ability)

[0611] Benefits: Full social insurance, transportation expenses provided, remote work available

[0612] 6. Display means

[0613] The server sends the generated job information to the device, which then displays the received information to the user, typically via a web browser or application.

[0614] Example: The server sends job information generated in JSON format to the device, and JavaScript on the device parses the received data and inserts it into the page as HTML elements.

[0615] System Benefits

[0616] This system automates the creation of job listings, enabling the efficient, low-cost generation of highly specialized job listings. Furthermore, users can check and edit the generated job listings, improving the accuracy of the final job listings.

[0617] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0618] Step 1:

[0619] User Input

[0620] The user enters information such as job type and required skills into a web form. This web form is implemented using front-end technologies such as React.js. The user uses this form to enter detailed information about the job type and required skills.

[0621] Input: User enters job title "Software Engineer" and required skills "Python, Django, 5+ years experience".

[0622] Output: Job title and required skill details as form data.

[0623] Specific behavior:

[0624] A user accesses a web form in a browser and enters the job title and required skills into the input fields.

[0625] The user clicks the "Submit" button.

[0626] Step 2:

[0627] Data transmission

[0628] The device sends the user input data to the server using an HTTP POST request, with the data sent in JSON format.

[0629] Input: Data entered by a user into a form.

[0630] Output: JSON formatted data sent to the server.

[0631] Specific behavior:

[0632] When the "Submit" button is clicked, JavaScript reads the data and converts it to JSON format.

[0633] The terminal (web browser) sends data to the server using an HTTP POST request.

[0634] Step 3:

[0635] Data Validation

[0636] Validate the data received by the server. This process checks for incorrect data or missing fields. Use a Python library (e.g., Cerberus).

[0637] Input: JSON format data sent from the terminal.

[0638] Output: The validated data or error messages.

[0639] Specific behavior:

[0640] On the server side, Django receives the HTTP POST request and deserializes the data.

[0641] Uses the Cerberus library to validate data and returns error messages if data is invalid.

[0642] Step 4:

[0643] Data categorization

[0644] The server classifies input data such as job type and required skills into appropriate categories using an AI model (e.g., scikit-learn).

[0645] Input: Validated data.

[0646] Output: Categorized data.

[0647] Specific behavior:

[0648] Data that passes validation is input into the AI ​​model.

[0649] Using models such as scikit-learn, the job title "Software Engineer" and the required skills "Python, Django, 5+ years of experience" are classified into corresponding categories.

[0650] Step 5:

[0651] Generative Model Selection

[0652] The server selects an appropriate generative AI model based on the parsed data, for example, selecting a specific generative AI model (e.g., OpenAI GPT-3) to generate job postings for "software engineers."

[0653] Input: Categorical data.

[0654] Output: The selected generative model.

[0655] Specific behavior:

[0656] Based on the data analysis results, the server selects an appropriate generative AI model (e.g., GPT-3).

[0657] Read API key and endpoint information.

[0658] Step 6:

[0659] Automatic generation of job postings

[0660] The generative AI generates job information based on the analysis results. A prompt is generated based on the input data and sent to the generative AI model.

[0661] Input: A prompt based on the selected generative AI model and analysis results.

[0662] Output: Auto-generated job listing.

[0663] Specific behavior:

[0664] Create a prompt sentence based on the analysis results.

[0665] Send prompt text to the generative AI model to generate job information.

[0666] Step 7:

[0667] Viewing Data

[0668] The server transmits the generated job information to the terminal, and the terminal displays the received job information to the user.

[0669] Input: Auto-generated job posting.

[0670] Output: The job listing displayed to the user.

[0671] Specific behavior:

[0672] The server serializes the generated job information in JSON format and sends it as an HTTP response.

[0673] The device receives this response and prepares to render.

[0674] JavaScript parses the incoming data and generates HTML elements to insert into the page so that the user can view the job listings in their browser.

[0675] Step 8:

[0676] Check and correct the contents

[0677] The user checks the generated job information and makes corrections as necessary.

[0678] Input: Auto-generated job posting.

[0679] Output: The job posting as modified by the user.

[0680] Specific behavior:

[0681] The user reviews the job listing generated on the web page and clicks the "Edit" button.

[0682] Modify the content in the text boxes and form fields.

[0683] Step 9:

[0684] Updates

[0685] The terminal retransmits the corrections to the server, and the server incorporates the corrections and updates the final job information.

[0686] Input: The job posting modified by the user.

[0687] Output: The final updated job listing.

[0688] Specific behavior:

[0689] The device sends the corrected data back to the server in JSON format.

[0690] The server re-validates and parses the data and updates the database.

[0691] Step 10:

[0692] Saving and publishing job listings

[0693] The device saves the final job listing with the changes and makes it available for publication.

[0694] Input: Last updated job posting.

[0695] Output: A saved, publicly available job posting.

[0696] Specific behavior:

[0697] The final job listings are stored in a database and displayed on a website via a public API or similar.

[0698] Clicking the publish button will make the job posting publicly available.

[0699] (Application example 1)

[0700] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0701] Conventional systems lack the means to efficiently and cost-effectively create job listings. Furthermore, creating the various information required by companies newly adopting electronic payment services is time-consuming, making it difficult to provide the information accurately and quickly. The present invention aims to solve these problems by providing a system that can efficiently and automatically generate job listings and information for new companies.

[0702] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0703] In this invention, the server includes input means for inputting an outline of the job information, analysis means for analyzing data received from the input means, automatic generation means for automatically generating job information based on the data analyzed by the analysis means, display means for displaying the job information generated by the automatic generation means, means for inputting information related to requirements sought by introducing companies, and automatic generation means for analyzing the information related to the requirements and automatically generating the necessary information. This makes it possible to efficiently and automatically generate job information and information for new introducing companies.

[0704] An "input means" is a device or system through which a user inputs information.

[0705] The "analysis means" is a device or system that analyzes the received data and performs the necessary processing.

[0706] "Automatic generation means" refers to a device or system that automatically generates information based on analyzed data.

[0707] A "display means" is a device or system that visually displays the generated information to a user.

[0708] The "means for inputting information regarding requirements" refers to a device or system for inputting information regarding the requirements desired by the adopting company.

[0709] "Automatic generation means for analyzing information related to requirements and automatically generating necessary information" refers to a device or system that analyzes and automatically generates necessary information based on the requirements desired by the adopting company.

[0710] The present invention relates to a system for automatically generating job information and information for new companies efficiently and at low cost. This system includes an input means for a user to input information, an analysis means for analyzing data, an automatic generation means, and a display means.

[0711] First, the user uses an input method to enter information about the job summary and the requirements of the company. The input method can be a web form or a mobile application. For example, the user might enter the job title "Software Engineer" and the required skills of "Python, Django, and 5+ years of experience," or the company's security standards and required API information into the form.

[0712] The server then validates the input data using analytical methods and classifies it into categories based on job type, skills, requirements, etc. This analysis uses data validation software and machine learning algorithms (e.g., TensorFlow, PyTorch). Based on the category, an appropriate generative AI model is selected. For example, if the data corresponds to "software engineer," a dedicated generative model is selected.

[0713] The automated generation method then uses the selected generative AI model to automatically generate job listings and information for potential adopters based on the analyzed data. The generative AI model includes a generative model that has learned from past data. For example, a job listing for a "software engineer" is automatically generated as follows:

[0714] Job title: Software Engineer

[0715] Required skills:

[0716] Python development experience

[0717] Practical use of the Django framework

[0718] 5+ years of work experience

[0719] job description:

[0720] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[0721] Location: Shibuya-ku, Tokyo

[0722] Salary: Annual salary (determined according to experience and ability)

[0723] Benefits: Full social insurance, transportation expenses provided, remote work available

[0724] In addition, information for adopting companies is automatically generated as follows:

[0725] Company name: XYZ Corp.

[0726] Required APIs:

[0727] Payment API: REST-based interface. Documentation is available at the following URL: https: / / api.example.com / docs / payment

[0728] Authentication API: OAuth2.0 compatible. Documentation is available at the URL: https: / / api.example.com / docs / auth

[0729] Security Standards:

[0730] PCI-DSS: Payment Card Industry Data Security Standard compliant.

[0731] The terminal (user's device) then displays the generated information to the user. The user checks the displayed information and makes any necessary corrections. Once the corrections are complete, the user resubmits the final information to the server, which then saves the updated information. This series of processes streamlines the process of generating job information and information for adopting companies.

[0732] Based on the above explanation, the functions of each element of the present invention and their cooperation with each other enable users to easily generate high-quality job information and information for adopting companies.

[0733] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0734] Step 1:

[0735] Users use input methods to enter information about the job posting summary and the requirements of the company. Input methods include web forms and mobile applications. Input includes, for example, the job title "Software Engineer" and skill information such as "Python, Django, 5+ years of experience," as well as required API information and security standards. This input data is sent from the device to the server.

[0736] Step 2:

[0737] The server analyzes the received data using analytical methods. Specifically, it validates the data to ensure that the input information is appropriate. For example, it checks whether all input fields are filled in and the format is correct. Once validated, the data is classified into categories based on job type, required skills, and requirements. This process uses data analysis libraries (e.g., pandas) and machine learning algorithms (e.g., scikit-learn).

[0738] Step 3:

[0739] The server selects the appropriate generative AI model based on the analyzed data. It uses a large number of pre-trained models to determine which model to use based on the category. It selects a model that matches the specific skill set and requirements. For example, if the category corresponds to "software engineer," a software-related job generation model is selected. This process is performed using a model management tool (e.g., MLflow).

[0740] Step 4:

[0741] The server uses the selected generative AI model to automatically generate job postings and information for adopting companies based on the analysis results. The generative AI model formats the generated text and creates job postings and requirements information in the required format. For example, the generated job postings include fields such as "job type," "required skills," "job description," and "work location." A natural language generation library (e.g., GPT-3) is used for this process. The following are specific examples of prompt sentences:

[0742] Generate the information needed to implement new electronic payment services for businesses such as:

[0743] Company name: XYZ Corp.

[0744] Required APIs: Payment API, Authentication API

[0745] Security Standard: PCI-DSS

[0746] Step 5:

[0747] The server sends the generated information to the terminal. The terminal displays the received information to the user, allowing the user to check the generated job information and requirements information. A web browser or mobile app is used as the display method. The displayed information is provided in a format that the user can easily check visually.

[0748] Step 6:

[0749] The user checks the displayed information and makes any necessary corrections. For example, if the generated work location is changed from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo," the user makes the corrections through the input field. The device then sends the corrections back to the server.

[0750] Step 7:

[0751] The server then reparses the modified data and stores it as the final information. This results in a complete job or requirement information reflecting the modifications. This process is performed using a database system (e.g., PostgreSQL). The final information is stored and can be made publicly available if desired.

[0752] By performing the above steps, the system of the present invention can efficiently and automatically generate job information and information for companies newly introducing electronic payment services, and provide it to users.

[0753] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0754] The present invention relates to a system that enables a company's human resources personnel to efficiently create job postings and adjust the job postings based on user sentiment. The system includes the following elements:

[0755] 1. Input method:

[0756] It provides a web form for users to enter a job description, such as the job title, required skills, etc. For example, a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience" into the form.

[0757] 2. Data transmission:

[0758] The terminal transmits the user's input data to the server.

[0759] 3. Data Analysis:

[0760] The server analyzes the received data, which includes:

[0761] Data validation: Checking the accuracy and appropriateness of input data.

[0762] Categorization: Classify items such as job types and required skills into appropriate categories.

[0763] 4. Generative model selection:

[0764] The server selects an appropriate generative model based on the analysis results. For example, it selects a generative AI model suitable for "software engineer."

[0765] 5. Automatic Job Creation:

[0766] Based on the selected model, the generative AI automatically generates job postings, embedding data into job posting templates and generating fluent sentences using natural language processing technology.

[0767] 6. Emotion Recognition with Emotion Engine:

[0768] The emotion engine analyzes the user's facial expressions and tone of voice as they type to recognize their emotions, for example identifying when they are tired or particularly excited.

[0769] 7. Applying emotional information:

[0770] The adjustment means adjusts the job posting based on the recognized emotional information, for example, if it is recognized that the user is nervous, the job posting text is changed to a more relaxed tone.

[0771] 8. Displaying Job Postings:

[0772] The server sends the generated job information and the adjusted text to the terminal.

[0773] 9. Display of information:

[0774] The terminal displays the received job information to the user, and the generated job information can be checked through a user interface.

[0775] 10. Checking and correcting content:

[0776] The user reviews the displayed job listing and makes any necessary changes, such as changing the location or salary information.

[0777] 11. Updates:

[0778] The device resends the modifications to the server.

[0779] 12. Saving and Publishing Final Job Postings:

[0780] The server retrieves the corrections and updates the final job listing. The terminal saves the final job listing with the corrections reflected and makes it available for publication. The job listing is then published.

[0781] This system will not only streamline the process of creating job listings, but also make it possible to provide job listings that take into account the user's emotions, thereby improving the quality of job listings and making them more attractive to job seekers.

[0782] The processing flow will be explained below.

[0783] Step 1:

[0784] A user enters job information summary such as job type and required skills into a web form. The form contains fields for entering basic information such as job type, required skills, work location, and salary.

[0785] Step 2:

[0786] The device sends the input data to the server, where it is converted into an appropriate format such as JSON or XML.

[0787] Step 3:

[0788] The server analyzes the received data, which includes the following processes:

[0789] Data validation: Checking whether the information entered is appropriate and accurate, for example, whether required skills exist or salary information is valid.

[0790] Categorization: Classifying items such as job types and required skills into specific categories.

[0791] Step 4:

[0792] The server selects an appropriate generative model based on the analysis results. For example, it selects an AI model for technical jobs for the job title "software engineer."

[0793] Step 5:

[0794] The Generative AI automatically generates job postings based on the selected model. It does the following:

[0795] Choose a template: Choose a job posting template based on the job type.

[0796] Embedding information: Embed analytical data in templates.

[0797] Natural language processing: Performs language processing to generate fluent sentences.

[0798] Step 6:

[0799] The emotion engine analyzes the user's facial expressions and tone of voice when inputting and recognizes emotions. For example, it uses a camera and microphone to analyze the user's facial expressions and voice in real time.

[0800] Step 7:

[0801] The server receives the analysis results from the emotion engine and adjusts the job posting based on the recognized emotion information. For example, if it determines that the user is nervous, it will change the tone of the message to be more friendly and relaxed.

[0802] Step 8:

[0803] The server sends the generated job information and the adjusted job information to the terminal.

[0804] Step 9:

[0805] The terminal displays the received job information to the user, including the function of visually displaying the generated job information using a GUI (Graphical User Interface).

[0806] Step 10:

[0807] The user checks the displayed job information and modifies it as necessary. For example, the user changes the work location from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo."

[0808] Step 11:

[0809] The terminal retransmits the modified information to the server, where the modified information is reformatted.

[0810] Step 12:

[0811] The server takes the modifications and updates the final job listing, ensuring the most up-to-date information is stored.

[0812] Step 13:

[0813] The device saves the final job listing with the changes reflected and makes it available for publication. It then processes the job listing to be published. The published job listing is posted on job sites, internal bulletin boards, etc.

[0814] Example 2

[0815] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0816] Conventional methods for creating job information require a lot of time and effort from the user, and they also face the problem of difficulty in providing job information that reflects the user's feelings. This leads to a decline in the quality of job information provided by companies, making it difficult for the content to be attractive to job seekers. Furthermore, automatic generation of job information also has the problem of a decline in user satisfaction, as it is not possible to take the user's feelings into account.

[0817] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0818] In this invention, the server includes input means for inputting an outline of a job listing, analysis means for analyzing data received from the input means, automatic generation means for automatically generating job listings based on the data analyzed by the analysis means, display means for displaying the job listings generated by the automatic generation means, emotion recognition means for recognizing a user's emotion, and adjustment means for adjusting the job listings based on the emotion information recognized by the emotion recognition means. This enables efficient creation and adjustment of job listings, making it possible to provide high-quality job listings that reflect the user's emotions.

[0819] "Input means" refers to a device or system that allows a user to input an outline of job information such as the type of job and required skills.

[0820] The "analysis means" is a device or system that analyzes data received from the input means and performs validation and categorization of the content.

[0821] The "automatic generation means" is a device or system that automatically generates job information based on the data analyzed by the analysis means.

[0822] The "display means" is a device or system that displays the job information generated by the automatic generation means to the user.

[0823] The "emotion recognition means" is a device or system that recognizes the user's emotions from facial expressions, tone of voice, etc.

[0824] The "adjustment means" is a device or system that adjusts the generated job information based on the emotion information recognized by the emotion recognition means.

[0825] The present invention relates to a system that enables a company's human resources personnel to efficiently create job information and adjust the job information based on user sentiment. This system is implemented using the following hardware and software.

[0826] 1. Hardware and Software Used

[0827] Web browser: Used by users to enter job description information such as job title and required skills.

[0828] Server: Performs data analysis, generative AI model selection, sentiment analysis, and final storage and publishing of job listings.

[0829] Generative AI: A system that automatically generates job information using natural language processing technology.

[0830] Emotion recognition engine: A system that analyzes user emotions and adjusts job information based on the results.

[0831] Terminal: A device that interfaces with the user, submits input data, displays job information, and resubmits corrected data.

[0832] 2. Explanation of program processing

[0833] First, the user accesses a company's job posting creation page using a web browser. The user enters a summary of the job posting, including the job type and required skills, and sends the data from their device to the server. The server analyzes the received data and checks for accuracy and appropriateness. As a result of the analysis, the job type and skills are classified into appropriate categories.

[0834] Based on the analysis results, the server selects an appropriate generation AI model and begins the automatic generation process. The generation AI embeds the data entered by the user into a template and generates a job posting in fluent natural language. An emotion recognition engine then analyzes the user's facial expressions and tone of voice when entering information to recognize the user's emotions. Based on this recognized emotional information, an adjustment method adjusts the job posting.

[0835] For example, if the user is nervous, the tone of the job posting can be changed to a more relaxed tone. The server sends the generated and adjusted job posting to the device, which displays it to the user. The user checks the displayed job posting and makes changes to information such as work location and salary as necessary. The device then sends the changes back to the server, which incorporates the changes and saves the final job posting. This information is then made available to job seekers.

[0836] 3. Adding concrete examples and prompt sentence examples

[0837] As a concrete example, consider the case where a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years of experience." The device sends this data to the server, which analyzes the data, categorizes it, and selects a generative AI model. The generative AI generates a job posting such as "Our company is hiring a software engineer with 5+ years of Python and Django experience." The emotion recognition engine analyzes the user's emotions and adjusts the wording as necessary.

[0838] Example prompts for generative AI models

[0839] "Generate a compelling software engineer job posting based on the following data: Job Title: Software Engineer, Required Skills: Python, Django, 5+ years of experience."

[0840] This system allows for efficient creation and publication of job postings, and provides high-quality job postings that take user sentiment into consideration. This improves the quality of job postings for companies and makes them more attractive to job seekers.

[0841] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0842] Step 1:

[0843] The user enters a job summary

[0844] A user uses a web browser to enter a job description, such as the job title and required skills. Specifically, the user enters "Software Engineer" and "Python, Django, 5+ years of experience." This input data is sent to the terminal and temporarily saved.

[0845] Input: User inputs job title and required skills

[0846] Output: Input data saved on the device

[0847] Step 2:

[0848] The device sends the data to the server

[0849] The terminal sends the data entered by the user to the server. At this time, the data is encrypted and sent using a secure communication method (e.g., HTTPS).

[0850] Input: Input data stored on the device

[0851] Output: Data sent to the server

[0852] Step 3:

[0853] The server analyzes the data

[0854] The server analyzes the received data. First, it validates the data to ensure that the job titles and required skills are accurate and appropriate. Then it categorizes the job titles and skills into categories.

[0855] Input: Data sent to the server

[0856] Output: Validated and classified data

[0857] Step 4:

[0858] The server selects the generative model

[0859] The server selects an appropriate generative AI model based on the analysis results. For example, for the job title "software engineer," it references past data to select the optimal model.

[0860] Input: Validated and classified data

[0861] Output: The selected generative AI model

[0862] Step 5:

[0863] Generative AI automatically generates job information

[0864] Based on the selected model, the generative AI automatically generates a job posting, such as, "Our company is looking for a software engineer with 5+ years of experience in Python and Django."

[0865] Input: Validated and classified data, selected generative AI model

[0866] Output: Generated job listings

[0867] Step 6:

[0868] Emotion recognition engine recognizes emotions

[0869] The emotion recognition engine analyzes the user's facial expressions and tone of voice when typing to recognize the user's emotions, for example, whether the user is nervous or relaxed.

[0870] Input: User's facial expression data and tone of voice

[0871] Output: Recognized emotion information

[0872] Step 7:

[0873] Regulatory measures apply emotional information

[0874] The adjustment means adjusts the generated job posting based on the recognized emotion information. For example, if the user is nervous, the adjustment means changes the posting to "This is an opportunity to work in a relaxed office environment."

[0875] Input: Generated job information, recognized emotion information

[0876] Output: Adjusted job listings

[0877] Step 8:

[0878] The server sends the job information to the device.

[0879] The server prepares the adjusted job information for transmission to the terminal and sends the encoded data to the terminal.

[0880] Input: Adjusted Job Posting

[0881] Output: Data sent to the terminal

[0882] Step 9:

[0883] The device displays the information

[0884] The device decodes the received data and displays the adjusted job information on the GUI.

[0885] Input: Data sent to the terminal

[0886] Output: Job information displayed in GUI

[0887] Step 10:

[0888] The user checks and corrects the content

[0889] The user can check the displayed job information and modify it as necessary, for example, adjusting the work location or salary conditions. The modified data is saved on the device.

[0890] Input: Job information displayed on the GUI

[0891] Output: Corrected data saved on the device

[0892] Step 11:

[0893] The device resends the corrected data to the server

[0894] The terminal transmits the data modified by the user back to the server.

[0895] Input: Correction data saved on the device

[0896] Output: Data resent to the server

[0897] Step 12:

[0898] The server stores and publishes the final job listings.

[0899] The server takes the modifications and saves them as the final job listing, which is then made available to job seekers.

[0900] Input: Data resubmitted to server

[0901] Output: Final job posting saved and published

[0902] (Application example 2)

[0903] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0904] Conventional job information creation systems simply display the job information entered by the user as is, and have the problem of being unable to adjust the text to take the user's feelings into account. Furthermore, there is a lack of support for content creators to efficiently create video titles and descriptions. This has led to the problem of not being able to provide content that is more appealing to job seekers and viewers.

[0905] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for inputting an outline of the job information, an analysis means for analyzing data received from the input means, an automatic generation means for automatically generating job information based on the analyzed data, an adjustment means for adjusting the text of the generated job information, and a display means for displaying the generated job information. This makes it possible to adjust the text of the job information based on user emotional information and to efficiently generate and adjust video titles and descriptions for content creators.

[0906] "Job information summary" refers to basic information such as the type of job required of applicants, necessary skills, and working conditions.

[0907] "Input means" refers to an interface for a user to input data, and specifically includes a keyboard, a mouse, a touch screen, and the like.

[0908] "Analysis means" refers to a device or program that has the function of analyzing input data and verifying its appropriateness and accuracy.

[0909] "Validation" refers to the process of ensuring that entered data is correct and appropriate.

[0910] "Automatic generation means" refers to a device or program that automatically generates job listings and other content based on analyzed data.

[0911] "Display means" refers to a device or interface for displaying generated information in a form visible to a user.

[0912] The "adjustment means" refers to a device or program that adjusts the generated text based on the user's emotional information.

[0913] "Emotional information" refers to data related to emotions obtained from the user's facial expressions and voice.

[0914] A "face recognition camera" refers to a camera that captures a user's face and analyzes their facial expressions.

[0915] A "content creator" is someone who produces content such as videos and articles.

[0916] "Video title" refers to a name that succinctly expresses the content of the video.

[0917] "Description" refers to text that provides a detailed explanation of the content of a video or other content.

[0918] "Generative model" refers to an artificial intelligence model that generates natural language or other content based on specific input data.

[0919] A "prompt sentence" refers to an input sentence that instructs a generative model to produce a specific output.

[0920] To implement this invention, content creators must first provide an interface for inputting video titles and description summaries. Users input data through this interface, and the data is sent from the terminal to the server.

[0921] The server then analyzes the received data. Analysis methods include data validation and categorization to ensure the appropriateness and accuracy of the input. For example, data such as "Video Title: Introducing new React trends" and "Description: Introducing the latest React features and how to apply them" may be analyzed.

[0922] Based on the analyzed data, the server selects an appropriate generative AI model and automatically generates the video title and description. Generative AI models such as the OpenAI GPT series can be used. The generated data is displayed to the user, who can review it and make corrections if necessary.

[0923] Furthermore, the adjustment means plays an important role in this invention. The adjustment means acquires emotional information from the user's facial expressions and voice, and adjusts the generated text based on this information. A facial recognition camera and voice analysis software are used to acquire emotional information. For example, if the user is nervous, the video title can be changed to a more relaxed tone, such as "Improve your skills! A clear explanation of the new React trend."

[0924] Finally, the adjusted data is sent back to the server and the final information is saved, allowing content creators to efficiently create titles and descriptions for their videos and provide more engaging content to viewers.

[0925] For example, the following prompt sentence can be used:

[0926] Example prompt:

[0927] "Generate an inspiring and clear title and description for an introduction to a new React trend."

[0928] In this way, by implementing the present invention, it becomes possible to generate effective content that takes into account the user's emotional information.

[0929] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0930] Step 1: Enter data using the input method

[0931] The user inputs the video title and description summary via the interface, such as "Video title: Introducing new React trends" and "Description: Introducing the latest React features and how to apply them," and sends this information from the device to the server.

[0932] Input: Video title, description

[0933] Output: Input data

[0934] Step 2: Receiving and validating data

[0935] The server receives the input data sent from the terminal, and then validates the data using analytical means, checking the accuracy and appropriateness of the input.

[0936] Input: Input data

[0937] Output: Validated data

[0938] Step 3: Categorization

[0939] Based on the validated data, the server classifies items such as job type and required skills into appropriate categories.

[0940] Input: Validated data

[0941] Output: Classified data

[0942] Step 4: Selecting a Generative Model

[0943] The server selects an appropriate generative AI model based on the classified data, using, for example, the OpenAI GPT series as the generative AI model.

[0944] Input: Classified data

[0945] Output: The selected generative AI model

[0946] Step 5: Automatically generate job postings

[0947] Using the selected generative AI model, the server automatically generates a video title and description, using the specific prompt "Generate an inspiring and easy-to-understand title and description for introducing a new React trend."

[0948] Input: Generative AI model, prompt, classified data

[0949] Output: Generated title and description

[0950] Step 6: Acquiring emotional information

[0951] Using a facial recognition camera and voice analysis equipment, the server obtains the user's emotional information, which includes analyzing the user's facial expressions and voice tone.

[0952] Input: User's facial expression data, voice data

[0953] Output: Emotional information

[0954] Step 7: Adjust the text

[0955] Based on the acquired emotional information, the server adjusts the automatically generated title and description text, for example, changing the tone to a more relaxed one if the user is nervous.

[0956] Input: Generated title and description, sentiment information

[0957] Output: Adjusted title and description

[0958] Step 8: Viewing generated data

[0959] The server sends the final adjusted title and description to the terminal and displays them through the user interface.

[0960] Input: Adjusted title and description

[0961] Output: Display data

[0962] Step 9: Check and correct the content

[0963] The user checks the displayed title and description, makes any necessary corrections, and then sends the corrections back to the server.

[0964] Input: Display data

[0965] Output: Corrected data

[0966] Step 10: Save and publish your final information

[0967] The server takes the corrected data, updates the final information, and stores it, after which it is made available for publication.

[0968] Input: Correction data

[0969] Output: Saved data, public data

[0970] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0971] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0972] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0973] [Third embodiment]

[0974] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0975] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0976] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0977] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0978] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0979] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0980] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0981] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0982] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0983] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0984] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0985] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0986] The present invention relates to a system for enabling a company's human resources personnel to create job information efficiently and at low cost. The system includes the following elements:

[0987] 1. Input method:

[0988] It provides a web form for users to enter a job description, such as the job title, required skills, etc. For example, a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience" into the form.

[0989] 2. Data transmission:

[0990] The terminal transmits the user's input data to the server.

[0991] 3. Data Analysis:

[0992] The server parses the data it receives, which includes:

[0993] Data validation: Ensuring that the information entered is correct and accurate.

[0994] Categorization: Classify items such as job types and required skills into appropriate categories.

[0995] 4. Generative model selection:

[0996] The server selects an appropriate generative model based on the analyzed data, for example, selecting a specific AI model to generate job postings for "software engineers."

[0997] 5. Automatic Job Creation:

[0998] The AI ​​generates job postings based on the analysis results. For example, it generates the following job postings:

[0999] Job title: Software Engineer

[1000] Required skills:

[1001] Python development experience

[1002] Practical use of the Django framework

[1003] 5+ years of work experience

[1004] job description:

[1005] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[1006] Location: Shibuya-ku, Tokyo

[1007] Salary: Annual salary (determined according to experience and ability)

[1008] Benefits: Full social insurance, transportation expenses provided, remote work available

[1009] 6. Displaying data:

[1010] The server sends the generated job information to the terminal.

[1011] 7. Displaying Information:

[1012] The terminal displays the received job information to the user.

[1013] 8. Check and correct the content:

[1014] The user checks the generated job information and makes corrections as necessary. For example, the "work location" is changed from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo."

[1015] 9. Updates:

[1016] The terminal retransmits the corrections to the server, and the server incorporates the corrections and updates the final job information.

[1017] 10. Saving and Publishing Job Postings:

[1018] The terminal saves the final job information that reflects the changes and makes it available for publication. After that, it processes the recruitment information to make it public.

[1019] By using this system, the process of creating job information can be made more efficient, reducing time and costs. The generated job information is also more professional and attractive to job seekers.

[1020] The processing flow will be explained below.

[1021] Step 1:

[1022] The user enters a summary of the job information, such as the job type and required skills, into a web form.

[1023] Step 2:

[1024] The terminal sends the input data to the server, where it is converted into the appropriate format.

[1025] Step 3:

[1026] The server parses the data it receives, which includes validating the data to ensure it is valid.

[1027] Step 4:

[1028] The server categorizes the data by job type and required skills, which allows it to be organized into appropriate categories.

[1029] Step 5:

[1030] The server selects an appropriate generative model based on the analysis results. For example, it selects a generative AI model suitable for "software engineer."

[1031] Step 6:

[1032] Based on the selected model, the generative AI automatically generates job postings, embedding data into job posting templates and generating fluent sentences using natural language processing technology.

[1033] Step 7:

[1034] The server sends the generated job information to the terminal.

[1035] Step 8:

[1036] The terminal displays the received job information to the user, and the generated job information can be checked through a user interface.

[1037] Step 9:

[1038] The user reviews the displayed job listing and makes any necessary changes, such as changing the location or salary information.

[1039] Step 10:

[1040] The device resends the modifications to the server.

[1041] Step 11:

[1042] The server incorporates the modifications and updates the final job listing.

[1043] Step 12:

[1044] The terminal saves the final job information that reflects the changes, makes it available for publication, and then performs the publication process for the job information.

[1045] Example 1

[1046] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1047] Conventional job information creation systems require a lot of manual work and specialized knowledge after inputting the job information summary, making it difficult to create job information efficiently and at low cost. Also, checking and correcting the generated job information is time-consuming, resulting in issues of labor and time. Therefore, there is a need for a system that can create job information more efficiently and at low cost, while also providing specialized content.

[1048] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1049] In this invention, the server includes an input means for inputting an outline of a job posting, a transmission means for transmitting data received from the input means, an analysis means for validating the data received by the transmission means and classifying the data into categories based on the job type and required skills, a model selection means for selecting an appropriate generative model based on the data analyzed by the analysis means, an automatic generation means for automatically generating job postings using the generative model selected by the model selection means, and a display means for displaying the job postings generated by the automatic generation means. This automates the creation of job postings, enabling the efficient, low-cost generation of highly specialized job postings. Furthermore, users can confirm and modify the generated job postings, improving the accuracy of the final job postings.

[1050] "Input means for entering a job summary" refers to the interface used by users to enter a summary of the job information, such as the job type and required skills, and specifically refers to a web form or input field.

[1051] The "transmission means for transmitting data received from the input means" is a means having the function of transmitting data input from the input means to a server, and transmits data using a protocol such as an HTTP POST request.

[1052] "Analysis means for validating the data received by the transmission means and classifying it into categories based on the job type and required skills" refers to a means that has the function of checking whether the received data is appropriate and accurate, and further classifying the data into appropriate categories based on the job type and required skills.

[1053] "Model selection means for selecting an appropriate generative model based on the data analyzed by the analysis means" refers to a means having the function of selecting the optimal generative AI model based on the analyzed data.

[1054] "Automatic generation means for automatically generating job information using the generative model selected by the model selection means" refers to a means having the function of automatically generating job information using the selected generative AI model.

[1055] "Display means for displaying job information generated by the automatic generation means" refers to a means that has the function of visually presenting the automatically generated job information to the user, and specifically refers to display on a web browser or application.

[1056] The present invention relates to a system that enables a company's human resources personnel to create job information efficiently and at low cost. This system includes the following elements. The specific processing is as follows.

[1057] System configuration

[1058] 1. Input Method

[1059] It provides an interface for users to enter job summary information such as job type and required skills. It is recommended that this interface be implemented as a web form using front-end technology such as React.js. Users can use this form to enter detailed information.

[1060] Example: A user fills out a form with the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience."

[1061] 2. Transmission Method

[1062] The device sends the user input data to the server using an HTTP POST request, with the data sent in JSON format.

[1063] Example: When the user clicks the "Submit" button, the entered data is sent to the server.

[1064] 3. Analysis method

[1065] The server validates the data received and categorizes it based on job type and required skills. Validation is performed using a Python library (e.g., Cerberus), and an AI model (e.g., scikit-learn) is used to classify the data into the appropriate category.

[1066] Example: Checking data integrity on the server side and classifying "Software Engineer" and "Python, Django, 5+ years experience" into their respective categories.

[1067] 4. Model Selection Method

[1068] The server selects an appropriate generative AI model based on the parsed data, for example, selecting a specific generative AI model (e.g., OpenAI GPT-3) to generate job postings for "software engineers."

[1069] Example: Based on the analysis results, the server selects GPT-3 to generate job listings for "Software Engineer."

[1070] 5. Automatic generation means

[1071] The generative AI generates job information based on the analysis results. The generative AI model receives the selected prompt and generates information based on it.

[1072] Example prompt sentence:

[1073] Job title: Software Engineer

[1074] Required skills: Python, Django, 5+ years of experience

[1075] Job Summary:

[1076] You will be responsible for the design and development of new products, addressing technical challenges, and working with a team using agile development methodologies.

[1077] Example of a generated job posting:

[1078] Job title: Software Engineer

[1079] Required skills:

[1080] Python development experience

[1081] Practical use of the Django framework

[1082] 5+ years of work experience

[1083] job description:

[1084] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[1085] Location: Shibuya-ku, Tokyo

[1086] Salary: Annual salary (determined according to experience and ability)

[1087] Benefits: Full social insurance, transportation expenses provided, remote work available

[1088] 6. Display means

[1089] The server sends the generated job information to the device, which then displays the received information to the user, typically via a web browser or application.

[1090] Example: The server sends job information generated in JSON format to the device, and JavaScript on the device parses the received data and inserts it into the page as HTML elements.

[1091] System Benefits

[1092] This system automates the creation of job listings, enabling the efficient, low-cost generation of highly specialized job listings. Furthermore, users can check and edit the generated job listings, improving the accuracy of the final job listings.

[1093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1094] Step 1:

[1095] User Input

[1096] The user enters information such as job type and required skills into a web form. This web form is implemented using front-end technologies such as React.js. The user uses this form to enter detailed information about the job type and required skills.

[1097] Input: User enters job title "Software Engineer" and required skills "Python, Django, 5+ years experience".

[1098] Output: Job title and required skill details as form data.

[1099] Specific behavior:

[1100] A user accesses a web form in a browser and enters the job title and required skills into the input fields.

[1101] The user clicks the "Submit" button.

[1102] Step 2:

[1103] Data transmission

[1104] The device sends the user input data to the server using an HTTP POST request, with the data sent in JSON format.

[1105] Input: Data entered by a user into a form.

[1106] Output: JSON formatted data sent to the server.

[1107] Specific behavior:

[1108] When the "Submit" button is clicked, JavaScript reads the data and converts it to JSON format.

[1109] The terminal (web browser) sends data to the server using an HTTP POST request.

[1110] Step 3:

[1111] Data Validation

[1112] Validate the data received by the server. This process checks for incorrect data or missing fields. Use a Python library (e.g., Cerberus).

[1113] Input: JSON format data sent from the terminal.

[1114] Output: The validated data or error messages.

[1115] Specific behavior:

[1116] On the server side, Django receives the HTTP POST request and deserializes the data.

[1117] Uses the Cerberus library to validate data and returns error messages if data is invalid.

[1118] Step 4:

[1119] Data categorization

[1120] The server classifies input data such as job type and required skills into appropriate categories using an AI model (e.g., scikit-learn).

[1121] Input: Validated data.

[1122] Output: Categorized data.

[1123] Specific behavior:

[1124] Data that passes validation is input into the AI ​​model.

[1125] Using models such as scikit-learn, the job title "Software Engineer" and the required skills "Python, Django, 5+ years of experience" are classified into corresponding categories.

[1126] Step 5:

[1127] Generative Model Selection

[1128] The server selects an appropriate generative AI model based on the parsed data, for example, selecting a specific generative AI model (e.g., OpenAI GPT-3) to generate job postings for "software engineers."

[1129] Input: Categorical data.

[1130] Output: The selected generative model.

[1131] Specific behavior:

[1132] Based on the data analysis results, the server selects an appropriate generative AI model (e.g., GPT-3).

[1133] Read API key and endpoint information.

[1134] Step 6:

[1135] Automatic generation of job postings

[1136] The generative AI generates job information based on the analysis results. A prompt is generated based on the input data and sent to the generative AI model.

[1137] Input: A prompt based on the selected generative AI model and analysis results.

[1138] Output: Auto-generated job listing.

[1139] Specific behavior:

[1140] Create a prompt sentence based on the analysis results.

[1141] Send prompt text to the generative AI model to generate job information.

[1142] Step 7:

[1143] Viewing Data

[1144] The server transmits the generated job information to the terminal, and the terminal displays the received job information to the user.

[1145] Input: Auto-generated job posting.

[1146] Output: The job listing displayed to the user.

[1147] Specific behavior:

[1148] The server serializes the generated job information in JSON format and sends it as an HTTP response.

[1149] The device receives this response and prepares to render.

[1150] JavaScript parses the incoming data and generates HTML elements to insert into the page so that the user can view the job listings in their browser.

[1151] Step 8:

[1152] Check and correct the contents

[1153] The user checks the generated job information and makes corrections as necessary.

[1154] Input: Auto-generated job posting.

[1155] Output: The job posting as modified by the user.

[1156] Specific behavior:

[1157] The user reviews the job listing generated on the web page and clicks the "Edit" button.

[1158] Modify the content in the text boxes and form fields.

[1159] Step 9:

[1160] Updates

[1161] The terminal retransmits the corrections to the server, and the server incorporates the corrections and updates the final job information.

[1162] Input: The job posting modified by the user.

[1163] Output: The final updated job listing.

[1164] Specific behavior:

[1165] The device sends the corrected data back to the server in JSON format.

[1166] The server re-validates and parses the data and updates the database.

[1167] Step 10:

[1168] Saving and publishing job listings

[1169] The device saves the final job listing with the changes and makes it available for publication.

[1170] Input: Last updated job posting.

[1171] Output: A saved, publicly available job posting.

[1172] Specific behavior:

[1173] The final job listings are stored in a database and displayed on a website via a public API or similar.

[1174] Clicking the publish button will make the job posting publicly available.

[1175] (Application example 1)

[1176] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1177] Conventional systems lack the means to efficiently and cost-effectively create job listings. Furthermore, creating the various information required by companies newly adopting electronic payment services is time-consuming, making it difficult to provide the information accurately and quickly. The present invention aims to solve these problems by providing a system that can efficiently and automatically generate job listings and information for new companies.

[1178] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1179] In this invention, the server includes input means for inputting an outline of the job information, analysis means for analyzing data received from the input means, automatic generation means for automatically generating job information based on the data analyzed by the analysis means, display means for displaying the job information generated by the automatic generation means, means for inputting information related to requirements sought by introducing companies, and automatic generation means for analyzing the information related to the requirements and automatically generating the necessary information. This makes it possible to efficiently and automatically generate job information and information for new introducing companies.

[1180] An "input means" is a device or system through which a user inputs information.

[1181] The "analysis means" is a device or system that analyzes the received data and performs the necessary processing.

[1182] "Automatic generation means" refers to a device or system that automatically generates information based on analyzed data.

[1183] A "display means" is a device or system that visually displays the generated information to a user.

[1184] The "means for inputting information regarding requirements" refers to a device or system for inputting information regarding the requirements desired by the adopting company.

[1185] "Automatic generation means for analyzing information related to requirements and automatically generating necessary information" refers to a device or system that analyzes and automatically generates necessary information based on the requirements desired by the adopting company.

[1186] The present invention relates to a system for automatically generating job information and information for new companies efficiently and at low cost. This system includes an input means for a user to input information, an analysis means for analyzing data, an automatic generation means, and a display means.

[1187] First, the user uses an input method to enter information about the job summary and the requirements of the company. The input method can be a web form or a mobile application. For example, the user might enter the job title "Software Engineer" and the required skills of "Python, Django, and 5+ years of experience," or the company's security standards and required API information into the form.

[1188] The server then validates the input data using analytical methods and classifies it into categories based on job type, skills, requirements, etc. This analysis uses data validation software and machine learning algorithms (e.g., TensorFlow, PyTorch). Based on the category, an appropriate generative AI model is selected. For example, if the data corresponds to "software engineer," a dedicated generative model is selected.

[1189] The automated generation method then uses the selected generative AI model to automatically generate job listings and information for potential adopters based on the analyzed data. The generative AI model includes a generative model that has learned from past data. For example, a job listing for a "software engineer" is automatically generated as follows:

[1190] Job title: Software Engineer

[1191] Required skills:

[1192] Python development experience

[1193] Practical use of the Django framework

[1194] 5+ years of work experience

[1195] job description:

[1196] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[1197] Location: Shibuya-ku, Tokyo

[1198] Salary: Annual salary (determined according to experience and ability)

[1199] Benefits: Full social insurance, transportation expenses provided, remote work available

[1200] In addition, information for adopting companies is automatically generated as follows:

[1201] Company name: XYZ Corp.

[1202] Required APIs:

[1203] Payment API: REST-based interface. Documentation is available at the following URL: https: / / api.example.com / docs / payment

[1204] Authentication API: OAuth2.0 compatible. Documentation is available at the URL: https: / / api.example.com / docs / auth

[1205] Security Standards:

[1206] PCI-DSS: Payment Card Industry Data Security Standard compliant.

[1207] The terminal (user's device) then displays the generated information to the user. The user checks the displayed information and makes any necessary corrections. Once the corrections are complete, the user resubmits the final information to the server, which then saves the updated information. This series of processes streamlines the process of generating job information and information for adopting companies.

[1208] Based on the above explanation, the functions of each element of the present invention and their cooperation with each other enable users to easily generate high-quality job information and information for adopting companies.

[1209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1210] Step 1:

[1211] Users use input methods to enter information about the job posting summary and the requirements of the company. Input methods include web forms and mobile applications. Input includes, for example, the job title "Software Engineer" and skill information such as "Python, Django, 5+ years of experience," as well as required API information and security standards. This input data is sent from the device to the server.

[1212] Step 2:

[1213] The server analyzes the received data using analytical methods. Specifically, it validates the data to ensure that the input information is appropriate. For example, it checks whether all input fields are filled in and the format is correct. Once validated, the data is classified into categories based on job type, required skills, and requirements. This process uses data analysis libraries (e.g., pandas) and machine learning algorithms (e.g., scikit-learn).

[1214] Step 3:

[1215] The server selects the appropriate generative AI model based on the analyzed data. It uses a large number of pre-trained models to determine which model to use based on the category. It selects a model that matches the specific skill set and requirements. For example, if the category corresponds to "software engineer," a software-related job generation model is selected. This process is performed using a model management tool (e.g., MLflow).

[1216] Step 4:

[1217] The server uses the selected generative AI model to automatically generate job postings and information for adopting companies based on the analysis results. The generative AI model formats the generated text and creates job postings and requirements information in the required format. For example, the generated job postings include fields such as "job type," "required skills," "job description," and "work location." A natural language generation library (e.g., GPT-3) is used for this process. The following are specific examples of prompt sentences:

[1218] Generate the information needed to implement new electronic payment services for businesses such as:

[1219] Company name: XYZ Corp.

[1220] Required APIs: Payment API, Authentication API

[1221] Security Standard: PCI-DSS

[1222] Step 5:

[1223] The server sends the generated information to the terminal. The terminal displays the received information to the user, allowing the user to check the generated job information and requirements information. A web browser or mobile app is used as the display method. The displayed information is provided in a format that the user can easily check visually.

[1224] Step 6:

[1225] The user checks the displayed information and makes any necessary corrections. For example, if the generated work location is changed from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo," the user makes the corrections through the input field. The device then sends the corrections back to the server.

[1226] Step 7:

[1227] The server then reparses the modified data and stores it as the final information. This results in a complete job or requirement information reflecting the modifications. This process is performed using a database system (e.g., PostgreSQL). The final information is stored and can be made publicly available if desired.

[1228] By performing the above steps, the system of the present invention can efficiently and automatically generate job information and information for companies newly introducing electronic payment services, and provide it to users.

[1229] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1230] The present invention relates to a system that enables a company's human resources personnel to efficiently create job postings and adjust the job postings based on user sentiment. The system includes the following elements:

[1231] 1. Input method:

[1232] It provides a web form for users to enter a job description, such as the job title, required skills, etc. For example, a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience" into the form.

[1233] 2. Data transmission:

[1234] The terminal transmits the user's input data to the server.

[1235] 3. Data Analysis:

[1236] The server analyzes the received data, which includes:

[1237] Data validation: Checking the accuracy and appropriateness of input data.

[1238] Categorization: Classify items such as job types and required skills into appropriate categories.

[1239] 4. Generative model selection:

[1240] The server selects an appropriate generative model based on the analysis results. For example, it selects a generative AI model suitable for "software engineer."

[1241] 5. Automatic Job Creation:

[1242] Based on the selected model, the generative AI automatically generates job postings, embedding data into job posting templates and generating fluent sentences using natural language processing technology.

[1243] 6. Emotion Recognition with Emotion Engine:

[1244] The emotion engine analyzes the user's facial expressions and tone of voice as they type to recognize their emotions, for example identifying when they are tired or particularly excited.

[1245] 7. Applying emotional information:

[1246] The adjustment means adjusts the job posting based on the recognized emotional information, for example, if it is recognized that the user is nervous, the job posting text is changed to a more relaxed tone.

[1247] 8. Displaying Job Postings:

[1248] The server sends the generated job information and the adjusted text to the terminal.

[1249] 9. Display of information:

[1250] The terminal displays the received job information to the user, and the generated job information can be checked through a user interface.

[1251] 10. Checking and correcting content:

[1252] The user reviews the displayed job listing and makes any necessary changes, such as changing the location or salary information.

[1253] 11. Updates:

[1254] The device resends the modifications to the server.

[1255] 12. Saving and Publishing Final Job Postings:

[1256] The server retrieves the corrections and updates the final job listing. The terminal saves the final job listing with the corrections reflected and makes it available for publication. The job listing is then published.

[1257] This system will not only streamline the process of creating job listings, but also make it possible to provide job listings that take into account the user's emotions, thereby improving the quality of job listings and making them more attractive to job seekers.

[1258] The processing flow will be explained below.

[1259] Step 1:

[1260] A user enters job information summary such as job type and required skills into a web form. The form contains fields for entering basic information such as job type, required skills, work location, and salary.

[1261] Step 2:

[1262] The device sends the input data to the server, where it is converted into an appropriate format such as JSON or XML.

[1263] Step 3:

[1264] The server analyzes the received data, which includes the following processes:

[1265] Data validation: Checking whether the information entered is appropriate and accurate, for example, whether required skills exist or salary information is valid.

[1266] Categorization: Classifying items such as job types and required skills into specific categories.

[1267] Step 4:

[1268] The server selects an appropriate generative model based on the analysis results. For example, it selects an AI model for technical jobs for the job title "software engineer."

[1269] Step 5:

[1270] The Generative AI automatically generates job postings based on the selected model. It does the following:

[1271] Choose a template: Choose a job posting template based on the job type.

[1272] Embedding information: Embed analytical data in templates.

[1273] Natural language processing: Performs language processing to generate fluent sentences.

[1274] Step 6:

[1275] The emotion engine analyzes the user's facial expressions and tone of voice when inputting and recognizes emotions. For example, it uses a camera and microphone to analyze the user's facial expressions and voice in real time.

[1276] Step 7:

[1277] The server receives the analysis results from the emotion engine and adjusts the job posting based on the recognized emotion information. For example, if it determines that the user is nervous, it will change the tone of the message to be more friendly and relaxed.

[1278] Step 8:

[1279] The server sends the generated job information and the adjusted job information to the terminal.

[1280] Step 9:

[1281] The terminal displays the received job information to the user, including the function of visually displaying the generated job information using a GUI (Graphical User Interface).

[1282] Step 10:

[1283] The user checks the displayed job information and modifies it as necessary. For example, the user changes the work location from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo."

[1284] Step 11:

[1285] The terminal retransmits the modified information to the server, where the modified information is reformatted.

[1286] Step 12:

[1287] The server takes the modifications and updates the final job listing, ensuring the most up-to-date information is stored.

[1288] Step 13:

[1289] The device saves the final job listing with the changes reflected and makes it available for publication. It then processes the job listing to be published. The published job listing is posted on job sites, internal bulletin boards, etc.

[1290] Example 2

[1291] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1292] Conventional methods for creating job information require a lot of time and effort from the user, and they also face the problem of difficulty in providing job information that reflects the user's feelings. This leads to a decline in the quality of job information provided by companies, making it difficult for the content to be attractive to job seekers. Furthermore, automatic generation of job information also has the problem of a decline in user satisfaction, as it is not possible to take the user's feelings into account.

[1293] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1294] In this invention, the server includes input means for inputting an outline of a job listing, analysis means for analyzing data received from the input means, automatic generation means for automatically generating job listings based on the data analyzed by the analysis means, display means for displaying the job listings generated by the automatic generation means, emotion recognition means for recognizing a user's emotion, and adjustment means for adjusting the job listings based on the emotion information recognized by the emotion recognition means. This enables efficient creation and adjustment of job listings, making it possible to provide high-quality job listings that reflect the user's emotions.

[1295] "Input means" refers to a device or system that allows a user to input an outline of job information such as the type of job and required skills.

[1296] The "analysis means" is a device or system that analyzes data received from the input means and performs validation and categorization of the content.

[1297] The "automatic generation means" is a device or system that automatically generates job information based on the data analyzed by the analysis means.

[1298] The "display means" is a device or system that displays the job information generated by the automatic generation means to the user.

[1299] The "emotion recognition means" is a device or system that recognizes the user's emotions from facial expressions, tone of voice, etc.

[1300] The "adjustment means" is a device or system that adjusts the generated job information based on the emotion information recognized by the emotion recognition means.

[1301] The present invention relates to a system that enables a company's human resources personnel to efficiently create job information and adjust the job information based on user sentiment. This system is implemented using the following hardware and software.

[1302] 1. Hardware and Software Used

[1303] Web browser: Used by users to enter job description information such as job title and required skills.

[1304] Server: Performs data analysis, generative AI model selection, sentiment analysis, and final storage and publishing of job listings.

[1305] Generative AI: A system that automatically generates job information using natural language processing technology.

[1306] Emotion recognition engine: A system that analyzes user emotions and adjusts job information based on the results.

[1307] Terminal: A device that interfaces with the user, submits input data, displays job information, and resubmits corrected data.

[1308] 2. Explanation of program processing

[1309] First, the user accesses a company's job posting creation page using a web browser. The user enters a summary of the job posting, including the job type and required skills, and sends the data from their device to the server. The server analyzes the received data and checks for accuracy and appropriateness. As a result of the analysis, the job type and skills are classified into appropriate categories.

[1310] Based on the analysis results, the server selects an appropriate generation AI model and begins the automatic generation process. The generation AI embeds the data entered by the user into a template and generates a job posting in fluent natural language. An emotion recognition engine then analyzes the user's facial expressions and tone of voice when entering information to recognize the user's emotions. Based on this recognized emotional information, an adjustment method adjusts the job posting.

[1311] For example, if the user is nervous, the tone of the job posting can be changed to a more relaxed tone. The server sends the generated and adjusted job posting to the device, which displays it to the user. The user checks the displayed job posting and makes changes to information such as work location and salary as necessary. The device then sends the changes back to the server, which incorporates the changes and saves the final job posting. This information is then made available to job seekers.

[1312] 3. Adding concrete examples and prompt sentence examples

[1313] As a concrete example, consider the case where a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years of experience." The device sends this data to the server, which analyzes the data, categorizes it, and selects a generative AI model. The generative AI generates a job posting such as "Our company is hiring a software engineer with 5+ years of Python and Django experience." The emotion recognition engine analyzes the user's emotions and adjusts the wording as necessary.

[1314] Example prompts for generative AI models

[1315] "Generate a compelling software engineer job posting based on the following data: Job Title: Software Engineer, Required Skills: Python, Django, 5+ years of experience."

[1316] This system allows for efficient creation and publication of job postings, and provides high-quality job postings that take user sentiment into consideration. This improves the quality of job postings for companies and makes them more attractive to job seekers.

[1317] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1318] Step 1:

[1319] The user enters a job summary

[1320] A user uses a web browser to enter a job description, such as the job title and required skills. Specifically, the user enters "Software Engineer" and "Python, Django, 5+ years of experience." This input data is sent to the terminal and temporarily saved.

[1321] Input: User inputs job title and required skills

[1322] Output: Input data saved on the device

[1323] Step 2:

[1324] The device sends the data to the server

[1325] The terminal sends the data entered by the user to the server. At this time, the data is encrypted and sent using a secure communication method (e.g., HTTPS).

[1326] Input: Input data stored on the device

[1327] Output: Data sent to the server

[1328] Step 3:

[1329] The server analyzes the data

[1330] The server analyzes the received data. First, it validates the data to ensure that the job titles and required skills are accurate and appropriate. Then it categorizes the job titles and skills into categories.

[1331] Input: Data sent to the server

[1332] Output: Validated and classified data

[1333] Step 4:

[1334] The server selects the generative model

[1335] The server selects an appropriate generative AI model based on the analysis results. For example, for the job title "software engineer," it references past data to select the optimal model.

[1336] Input: Validated and classified data

[1337] Output: The selected generative AI model

[1338] Step 5:

[1339] Generative AI automatically generates job information

[1340] Based on the selected model, the generative AI automatically generates a job posting, such as, "Our company is looking for a software engineer with 5+ years of experience in Python and Django."

[1341] Input: Validated and classified data, selected generative AI model

[1342] Output: Generated job listings

[1343] Step 6:

[1344] Emotion recognition engine recognizes emotions

[1345] The emotion recognition engine analyzes the user's facial expressions and tone of voice when typing to recognize the user's emotions, for example, whether the user is nervous or relaxed.

[1346] Input: User's facial expression data and tone of voice

[1347] Output: Recognized emotion information

[1348] Step 7:

[1349] Regulatory measures apply emotional information

[1350] The adjustment means adjusts the generated job posting based on the recognized emotion information. For example, if the user is nervous, the adjustment means changes the posting to "This is an opportunity to work in a relaxed office environment."

[1351] Input: Generated job information, recognized emotion information

[1352] Output: Adjusted job listings

[1353] Step 8:

[1354] The server sends the job information to the device.

[1355] The server prepares the adjusted job information for transmission to the terminal and sends the encoded data to the terminal.

[1356] Input: Adjusted Job Posting

[1357] Output: Data sent to the terminal

[1358] Step 9:

[1359] The device displays the information

[1360] The device decodes the received data and displays the adjusted job information on the GUI.

[1361] Input: Data sent to the terminal

[1362] Output: Job information displayed in GUI

[1363] Step 10:

[1364] The user checks and corrects the content

[1365] The user can check the displayed job information and modify it as necessary, for example, adjusting the work location or salary conditions. The modified data is saved on the device.

[1366] Input: Job information displayed on the GUI

[1367] Output: Corrected data saved on the device

[1368] Step 11:

[1369] The device resends the corrected data to the server

[1370] The terminal transmits the data modified by the user back to the server.

[1371] Input: Correction data saved on the device

[1372] Output: Data resent to the server

[1373] Step 12:

[1374] The server stores and publishes the final job listings.

[1375] The server takes the modifications and saves them as the final job listing, which is then made available to job seekers.

[1376] Input: Data resubmitted to server

[1377] Output: Final job posting saved and published

[1378] (Application example 2)

[1379] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1380] Conventional job information creation systems simply display the job information entered by the user as is, and have the problem of being unable to adjust the text to take the user's feelings into account. Furthermore, there is a lack of support for content creators to efficiently create video titles and descriptions. This has led to the problem of not being able to provide content that is more appealing to job seekers and viewers.

[1381] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for inputting an outline of the job information, an analysis means for analyzing data received from the input means, an automatic generation means for automatically generating job information based on the analyzed data, an adjustment means for adjusting the text of the generated job information, and a display means for displaying the generated job information. This makes it possible to adjust the text of the job information based on user emotional information and to efficiently generate and adjust video titles and descriptions for content creators.

[1382] "Job information summary" refers to basic information such as the type of job required of applicants, necessary skills, and working conditions.

[1383] "Input means" refers to an interface for a user to input data, and specifically includes a keyboard, a mouse, a touch screen, and the like.

[1384] "Analysis means" refers to a device or program that has the function of analyzing input data and verifying its appropriateness and accuracy.

[1385] "Validation" refers to the process of ensuring that entered data is correct and appropriate.

[1386] "Automatic generation means" refers to a device or program that automatically generates job listings and other content based on analyzed data.

[1387] "Display means" refers to a device or interface for displaying generated information in a form visible to a user.

[1388] The "adjustment means" refers to a device or program that adjusts the generated text based on the user's emotional information.

[1389] "Emotional information" refers to data related to emotions obtained from the user's facial expressions and voice.

[1390] A "face recognition camera" refers to a camera that captures a user's face and analyzes their facial expressions.

[1391] A "content creator" is someone who produces content such as videos and articles.

[1392] "Video title" refers to a name that succinctly expresses the content of the video.

[1393] "Description" refers to text that provides a detailed explanation of the content of a video or other content.

[1394] "Generative model" refers to an artificial intelligence model that generates natural language or other content based on specific input data.

[1395] A "prompt sentence" refers to an input sentence that instructs a generative model to produce a specific output.

[1396] To implement this invention, content creators must first provide an interface for inputting video titles and description summaries. Users input data through this interface, and the data is sent from the terminal to the server.

[1397] The server then analyzes the received data. Analysis methods include data validation and categorization to ensure the appropriateness and accuracy of the input. For example, data such as "Video Title: Introducing new React trends" and "Description: Introducing the latest React features and how to apply them" may be analyzed.

[1398] Based on the analyzed data, the server selects an appropriate generative AI model and automatically generates the video title and description. Generative AI models such as the OpenAI GPT series can be used. The generated data is displayed to the user, who can review it and make corrections if necessary.

[1399] Furthermore, the adjustment means plays an important role in this invention. The adjustment means acquires emotional information from the user's facial expressions and voice, and adjusts the generated text based on this information. A facial recognition camera and voice analysis software are used to acquire emotional information. For example, if the user is nervous, the video title can be changed to a more relaxed tone, such as "Improve your skills! A clear explanation of the new React trend."

[1400] Finally, the adjusted data is sent back to the server and the final information is saved, allowing content creators to efficiently create titles and descriptions for their videos and provide more engaging content to viewers.

[1401] For example, the following prompt sentence can be used:

[1402] Example prompt:

[1403] "Generate an inspiring and clear title and description for an introduction to a new React trend."

[1404] In this way, by implementing the present invention, it becomes possible to generate effective content that takes into account the user's emotional information.

[1405] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1406] Step 1: Enter data using the input method

[1407] The user inputs the video title and description summary via the interface, such as "Video title: Introducing new React trends" and "Description: Introducing the latest React features and how to apply them," and sends this information from the device to the server.

[1408] Input: Video title, description

[1409] Output: Input data

[1410] Step 2: Receiving and validating data

[1411] The server receives the input data sent from the terminal, and then validates the data using analytical means, checking the accuracy and appropriateness of the input.

[1412] Input: Input data

[1413] Output: Validated data

[1414] Step 3: Categorization

[1415] Based on the validated data, the server classifies items such as job type and required skills into appropriate categories.

[1416] Input: Validated data

[1417] Output: Classified data

[1418] Step 4: Selecting a Generative Model

[1419] The server selects an appropriate generative AI model based on the classified data, using, for example, the OpenAI GPT series as the generative AI model.

[1420] Input: Classified data

[1421] Output: The selected generative AI model

[1422] Step 5: Automatically generate job postings

[1423] Using the selected generative AI model, the server automatically generates a video title and description, using the specific prompt "Generate an inspiring and easy-to-understand title and description for introducing a new React trend."

[1424] Input: Generative AI model, prompt, classified data

[1425] Output: Generated title and description

[1426] Step 6: Acquiring emotional information

[1427] Using a facial recognition camera and voice analysis equipment, the server obtains the user's emotional information, which includes analyzing the user's facial expressions and voice tone.

[1428] Input: User's facial expression data, voice data

[1429] Output: Emotional information

[1430] Step 7: Adjust the text

[1431] Based on the acquired emotional information, the server adjusts the automatically generated title and description text, for example, changing the tone to a more relaxed one if the user is nervous.

[1432] Input: Generated title and description, sentiment information

[1433] Output: Adjusted title and description

[1434] Step 8: Viewing generated data

[1435] The server sends the final adjusted title and description to the terminal and displays them through the user interface.

[1436] Input: Adjusted title and description

[1437] Output: Display data

[1438] Step 9: Check and correct the content

[1439] The user checks the displayed title and description, makes any necessary corrections, and then sends the corrections back to the server.

[1440] Input: Display data

[1441] Output: Corrected data

[1442] Step 10: Save and publish your final information

[1443] The server takes the corrected data, updates the final information, and stores it, after which it is made available for publication.

[1444] Input: Correction data

[1445] Output: Saved data, public data

[1446] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1447] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1448] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1449] [Fourth embodiment]

[1450] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1451] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1452] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1453] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1454] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1455] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1456] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1457] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1458] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1459] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1460] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1461] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1462] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1463] The present invention relates to a system for enabling a company's human resources personnel to create job information efficiently and at low cost. The system includes the following elements:

[1464] 1. Input method:

[1465] It provides a web form for users to enter a job description, such as the job title, required skills, etc. For example, a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience" into the form.

[1466] 2. Data transmission:

[1467] The terminal transmits the user's input data to the server.

[1468] 3. Data Analysis:

[1469] The server parses the data it receives, which includes:

[1470] Data validation: Ensuring that the information entered is correct and accurate.

[1471] Categorization: Classify items such as job types and required skills into appropriate categories.

[1472] 4. Generative model selection:

[1473] The server selects an appropriate generative model based on the analyzed data, for example, selecting a specific AI model to generate job postings for "software engineers."

[1474] 5. Automatic Job Creation:

[1475] The AI ​​generates job postings based on the analysis results. For example, it generates the following job postings:

[1476] Job title: Software Engineer

[1477] Required skills:

[1478] Python development experience

[1479] Practical use of the Django framework

[1480] 5+ years of work experience

[1481] job description:

[1482] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[1483] Location: Shibuya-ku, Tokyo

[1484] Salary: Annual salary (determined according to experience and ability)

[1485] Benefits: Full social insurance, transportation expenses provided, remote work available

[1486] 6. Displaying data:

[1487] The server sends the generated job information to the terminal.

[1488] 7. Displaying Information:

[1489] The terminal displays the received job information to the user.

[1490] 8. Check and correct the content:

[1491] The user checks the generated job information and makes corrections as necessary. For example, the "work location" is changed from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo."

[1492] 9. Updates:

[1493] The terminal retransmits the corrections to the server, and the server incorporates the corrections and updates the final job information.

[1494] 10. Saving and Publishing Job Postings:

[1495] The terminal saves the final job information that reflects the changes and makes it available for publication. After that, it processes the recruitment information to make it public.

[1496] By using this system, the process of creating job information can be made more efficient, reducing time and costs. The generated job information is also more professional and attractive to job seekers.

[1497] The processing flow will be explained below.

[1498] Step 1:

[1499] The user enters a summary of the job information, such as the job type and required skills, into a web form.

[1500] Step 2:

[1501] The terminal sends the input data to the server, where it is converted into the appropriate format.

[1502] Step 3:

[1503] The server parses the data it receives, which includes validating the data to ensure it is valid.

[1504] Step 4:

[1505] The server categorizes the data by job type and required skills, which allows it to be organized into appropriate categories.

[1506] Step 5:

[1507] The server selects an appropriate generative model based on the analysis results. For example, it selects a generative AI model suitable for "software engineer."

[1508] Step 6:

[1509] Based on the selected model, the generative AI automatically generates job postings, embedding data into job posting templates and generating fluent sentences using natural language processing technology.

[1510] Step 7:

[1511] The server sends the generated job information to the terminal.

[1512] Step 8:

[1513] The terminal displays the received job information to the user, and the generated job information can be checked through a user interface.

[1514] Step 9:

[1515] The user reviews the displayed job listing and makes any necessary changes, such as changing the location or salary information.

[1516] Step 10:

[1517] The device resends the modifications to the server.

[1518] Step 11:

[1519] The server incorporates the modifications and updates the final job listing.

[1520] Step 12:

[1521] The terminal saves the final job information that reflects the changes, makes it available for publication, and then performs the publication process for the job information.

[1522] Example 1

[1523] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1524] Conventional job information creation systems require a lot of manual work and specialized knowledge after inputting the job information summary, making it difficult to create job information efficiently and at low cost. Also, checking and correcting the generated job information is time-consuming, resulting in issues of labor and time. Therefore, there is a need for a system that can create job information more efficiently and at low cost, while also providing specialized content.

[1525] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1526] In this invention, the server includes an input means for inputting an outline of a job posting, a transmission means for transmitting data received from the input means, an analysis means for validating the data received by the transmission means and classifying the data into categories based on the job type and required skills, a model selection means for selecting an appropriate generative model based on the data analyzed by the analysis means, an automatic generation means for automatically generating job postings using the generative model selected by the model selection means, and a display means for displaying the job postings generated by the automatic generation means. This automates the creation of job postings, enabling the efficient, low-cost generation of highly specialized job postings. Furthermore, users can confirm and modify the generated job postings, improving the accuracy of the final job postings.

[1527] "Input means for entering a job summary" refers to the interface used by users to enter a summary of the job information, such as the job type and required skills, and specifically refers to a web form or input field.

[1528] The "transmission means for transmitting data received from the input means" is a means having the function of transmitting data input from the input means to a server, and transmits data using a protocol such as an HTTP POST request.

[1529] "Analysis means for validating the data received by the transmission means and classifying it into categories based on the job type and required skills" refers to a means that has the function of checking whether the received data is appropriate and accurate, and further classifying the data into appropriate categories based on the job type and required skills.

[1530] "Model selection means for selecting an appropriate generative model based on the data analyzed by the analysis means" refers to a means having the function of selecting the optimal generative AI model based on the analyzed data.

[1531] "Automatic generation means for automatically generating job information using the generative model selected by the model selection means" refers to a means having the function of automatically generating job information using the selected generative AI model.

[1532] "Display means for displaying job information generated by the automatic generation means" refers to a means that has the function of visually presenting the automatically generated job information to the user, and specifically refers to display on a web browser or application.

[1533] The present invention relates to a system that enables a company's human resources personnel to create job information efficiently and at low cost. This system includes the following elements. The specific processing is as follows.

[1534] System configuration

[1535] 1. Input Method

[1536] It provides an interface for users to enter job summary information such as job type and required skills. It is recommended that this interface be implemented as a web form using front-end technology such as React.js. Users can use this form to enter detailed information.

[1537] Example: A user fills out a form with the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience."

[1538] 2. Transmission Method

[1539] The device sends the user input data to the server using an HTTP POST request, with the data sent in JSON format.

[1540] Example: When the user clicks the "Submit" button, the entered data is sent to the server.

[1541] 3. Analysis method

[1542] The server validates the data received and categorizes it based on job type and required skills. Validation is performed using a Python library (e.g., Cerberus), and an AI model (e.g., scikit-learn) is used to classify the data into the appropriate category.

[1543] Example: Checking data integrity on the server side and classifying "Software Engineer" and "Python, Django, 5+ years experience" into their respective categories.

[1544] 4. Model Selection Method

[1545] The server selects an appropriate generative AI model based on the parsed data, for example, selecting a specific generative AI model (e.g., OpenAI GPT-3) to generate job postings for "software engineers."

[1546] Example: Based on the analysis results, the server selects GPT-3 to generate job listings for "Software Engineer."

[1547] 5. Automatic generation means

[1548] The generative AI generates job information based on the analysis results. The generative AI model receives the selected prompt and generates information based on it.

[1549] Example prompt sentence:

[1550] Job title: Software Engineer

[1551] Required skills: Python, Django, 5+ years of experience

[1552] Job Summary:

[1553] You will be responsible for the design and development of new products, addressing technical challenges, and working with a team using agile development methodologies.

[1554] Example of a generated job posting:

[1555] Job title: Software Engineer

[1556] Required skills:

[1557] Python development experience

[1558] Practical use of the Django framework

[1559] 5+ years of work experience

[1560] job description:

[1561] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[1562] Location: Shibuya-ku, Tokyo

[1563] Salary: Annual salary (determined according to experience and ability)

[1564] Benefits: Full social insurance, transportation expenses provided, remote work available

[1565] 6. Display means

[1566] The server sends the generated job information to the device, which then displays the received information to the user, typically via a web browser or application.

[1567] Example: The server sends job information generated in JSON format to the device, and JavaScript on the device parses the received data and inserts it into the page as HTML elements.

[1568] System Benefits

[1569] This system automates the creation of job listings, enabling the efficient, low-cost generation of highly specialized job listings. Furthermore, users can check and edit the generated job listings, improving the accuracy of the final job listings.

[1570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1571] Step 1:

[1572] User Input

[1573] The user enters information such as job type and required skills into a web form. This web form is implemented using front-end technologies such as React.js. The user uses this form to enter detailed information about the job type and required skills.

[1574] Input: User enters job title "Software Engineer" and required skills "Python, Django, 5+ years experience".

[1575] Output: Job title and required skill details as form data.

[1576] Specific behavior:

[1577] A user accesses a web form in a browser and enters the job title and required skills into the input fields.

[1578] The user clicks the "Submit" button.

[1579] Step 2:

[1580] Data transmission

[1581] The device sends the user input data to the server using an HTTP POST request, with the data sent in JSON format.

[1582] Input: Data entered by a user into a form.

[1583] Output: JSON formatted data sent to the server.

[1584] Specific behavior:

[1585] When the "Submit" button is clicked, JavaScript reads the data and converts it to JSON format.

[1586] The terminal (web browser) sends data to the server using an HTTP POST request.

[1587] Step 3:

[1588] Data Validation

[1589] Validate the data received by the server. This process checks for incorrect data or missing fields. Use a Python library (e.g., Cerberus).

[1590] Input: JSON format data sent from the terminal.

[1591] Output: The validated data or error messages.

[1592] Specific behavior:

[1593] On the server side, Django receives the HTTP POST request and deserializes the data.

[1594] Uses the Cerberus library to validate data and returns error messages if data is invalid.

[1595] Step 4:

[1596] Data categorization

[1597] The server classifies input data such as job type and required skills into appropriate categories using an AI model (e.g., scikit-learn).

[1598] Input: Validated data.

[1599] Output: Categorized data.

[1600] Specific behavior:

[1601] Data that passes validation is input into the AI ​​model.

[1602] Using models such as scikit-learn, the job title "Software Engineer" and the required skills "Python, Django, 5+ years of experience" are classified into corresponding categories.

[1603] Step 5:

[1604] Generative Model Selection

[1605] The server selects an appropriate generative AI model based on the parsed data, for example, selecting a specific generative AI model (e.g., OpenAI GPT-3) to generate job postings for "software engineers."

[1606] Input: Categorical data.

[1607] Output: The selected generative model.

[1608] Specific behavior:

[1609] Based on the data analysis results, the server selects an appropriate generative AI model (e.g., GPT-3).

[1610] Read API key and endpoint information.

[1611] Step 6:

[1612] Automatic generation of job postings

[1613] The generative AI generates job information based on the analysis results. A prompt is generated based on the input data and sent to the generative AI model.

[1614] Input: A prompt based on the selected generative AI model and analysis results.

[1615] Output: Auto-generated job listing.

[1616] Specific behavior:

[1617] Create a prompt sentence based on the analysis results.

[1618] Send prompt text to the generative AI model to generate job information.

[1619] Step 7:

[1620] Viewing Data

[1621] The server transmits the generated job information to the terminal, and the terminal displays the received job information to the user.

[1622] Input: Auto-generated job posting.

[1623] Output: The job listing displayed to the user.

[1624] Specific behavior:

[1625] The server serializes the generated job information in JSON format and sends it as an HTTP response.

[1626] The device receives this response and prepares to render.

[1627] JavaScript parses the incoming data and generates HTML elements to insert into the page so that the user can view the job listings in their browser.

[1628] Step 8:

[1629] Check and correct the contents

[1630] The user checks the generated job information and makes corrections as necessary.

[1631] Input: Auto-generated job posting.

[1632] Output: The job posting as modified by the user.

[1633] Specific behavior:

[1634] The user reviews the job listing generated on the web page and clicks the "Edit" button.

[1635] Modify the content in the text boxes and form fields.

[1636] Step 9:

[1637] Updates

[1638] The terminal retransmits the corrections to the server, and the server incorporates the corrections and updates the final job information.

[1639] Input: The job posting modified by the user.

[1640] Output: The final updated job listing.

[1641] Specific behavior:

[1642] The device sends the corrected data back to the server in JSON format.

[1643] The server re-validates and parses the data and updates the database.

[1644] Step 10:

[1645] Saving and publishing job listings

[1646] The device saves the final job listing with the changes and makes it available for publication.

[1647] Input: Last updated job posting.

[1648] Output: A saved, publicly available job posting.

[1649] Specific behavior:

[1650] The final job listings are stored in a database and displayed on a website via a public API or similar.

[1651] Clicking the publish button will make the job posting publicly available.

[1652] (Application example 1)

[1653] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1654] Conventional systems lack the means to efficiently and cost-effectively create job listings. Furthermore, creating the various information required by companies newly adopting electronic payment services is time-consuming, making it difficult to provide the information accurately and quickly. The present invention aims to solve these problems by providing a system that can efficiently and automatically generate job listings and information for new companies.

[1655] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1656] In this invention, the server includes input means for inputting an outline of the job information, analysis means for analyzing data received from the input means, automatic generation means for automatically generating job information based on the data analyzed by the analysis means, display means for displaying the job information generated by the automatic generation means, means for inputting information related to requirements sought by introducing companies, and automatic generation means for analyzing the information related to the requirements and automatically generating the necessary information. This makes it possible to efficiently and automatically generate job information and information for new introducing companies.

[1657] An "input means" is a device or system through which a user inputs information.

[1658] The "analysis means" is a device or system that analyzes the received data and performs the necessary processing.

[1659] "Automatic generation means" refers to a device or system that automatically generates information based on analyzed data.

[1660] A "display means" is a device or system that visually displays the generated information to a user.

[1661] The "means for inputting information regarding requirements" refers to a device or system for inputting information regarding the requirements desired by the adopting company.

[1662] "Automatic generation means for analyzing information related to requirements and automatically generating necessary information" refers to a device or system that analyzes and automatically generates necessary information based on the requirements desired by the adopting company.

[1663] The present invention relates to a system for automatically generating job information and information for new companies efficiently and at low cost. This system includes an input means for a user to input information, an analysis means for analyzing data, an automatic generation means, and a display means.

[1664] First, the user uses an input method to enter information about the job summary and the requirements of the company. The input method can be a web form or a mobile application. For example, the user might enter the job title "Software Engineer" and the required skills of "Python, Django, and 5+ years of experience," or the company's security standards and required API information into the form.

[1665] The server then validates the input data using analytical methods and classifies it into categories based on job type, skills, requirements, etc. This analysis uses data validation software and machine learning algorithms (e.g., TensorFlow, PyTorch). Based on the category, an appropriate generative AI model is selected. For example, if the data corresponds to "software engineer," a dedicated generative model is selected.

[1666] The automated generation method then uses the selected generative AI model to automatically generate job listings and information for potential adopters based on the analyzed data. The generative AI model includes a generative model that has learned from past data. For example, a job listing for a "software engineer" is automatically generated as follows:

[1667] Job title: Software Engineer

[1668] Required skills:

[1669] Python development experience

[1670] Practical use of the Django framework

[1671] 5+ years of work experience

[1672] job description:

[1673] You will be responsible for the design and development of new products, solving technical problems, and working with a team in sprints using agile development methodologies.

[1674] Location: Shibuya-ku, Tokyo

[1675] Salary: Annual salary (determined according to experience and ability)

[1676] Benefits: Full social insurance, transportation expenses provided, remote work available

[1677] In addition, information for adopting companies is automatically generated as follows:

[1678] Company name: XYZ Corp.

[1679] Required APIs:

[1680] Payment API: REST-based interface. Documentation is available at the following URL: https: / / api.example.com / docs / payment

[1681] Authentication API: OAuth2.0 compatible. Documentation is available at the URL: https: / / api.example.com / docs / auth

[1682] Security Standards:

[1683] PCI-DSS: Payment Card Industry Data Security Standard compliant.

[1684] The terminal (user's device) then displays the generated information to the user. The user checks the displayed information and makes any necessary corrections. Once the corrections are complete, the user resubmits the final information to the server, which then saves the updated information. This series of processes streamlines the process of generating job information and information for adopting companies.

[1685] Based on the above explanation, the functions of each element of the present invention and their cooperation with each other enable users to easily generate high-quality job information and information for adopting companies.

[1686] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1687] Step 1:

[1688] Users use input methods to enter information about the job posting summary and the requirements of the company. Input methods include web forms and mobile applications. Input includes, for example, the job title "Software Engineer" and skill information such as "Python, Django, 5+ years of experience," as well as required API information and security standards. This input data is sent from the device to the server.

[1689] Step 2:

[1690] The server analyzes the received data using analytical methods. Specifically, it validates the data to ensure that the input information is appropriate. For example, it checks whether all input fields are filled in and the format is correct. Once validated, the data is classified into categories based on job type, required skills, and requirements. This process uses data analysis libraries (e.g., pandas) and machine learning algorithms (e.g., scikit-learn).

[1691] Step 3:

[1692] The server selects the appropriate generative AI model based on the analyzed data. It uses a large number of pre-trained models to determine which model to use based on the category. It selects a model that matches the specific skill set and requirements. For example, if the category corresponds to "software engineer," a software-related job generation model is selected. This process is performed using a model management tool (e.g., MLflow).

[1693] Step 4:

[1694] The server uses the selected generative AI model to automatically generate job postings and information for adopting companies based on the analysis results. The generative AI model formats the generated text and creates job postings and requirements information in the required format. For example, the generated job postings include fields such as "job type," "required skills," "job description," and "work location." A natural language generation library (e.g., GPT-3) is used for this process. The following are specific examples of prompt sentences:

[1695] Generate the information needed to implement new electronic payment services for businesses such as:

[1696] Company name: XYZ Corp.

[1697] Required APIs: Payment API, Authentication API

[1698] Security Standard: PCI-DSS

[1699] Step 5:

[1700] The server sends the generated information to the terminal. The terminal displays the received information to the user, allowing the user to check the generated job information and requirements information. A web browser or mobile app is used as the display method. The displayed information is provided in a format that the user can easily check visually.

[1701] Step 6:

[1702] The user checks the displayed information and makes any necessary corrections. For example, if the generated work location is changed from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo," the user makes the corrections through the input field. The device then sends the corrections back to the server.

[1703] Step 7:

[1704] The server then reparses the modified data and stores it as the final information. This results in a complete job or requirement information reflecting the modifications. This process is performed using a database system (e.g., PostgreSQL). The final information is stored and can be made publicly available if desired.

[1705] By performing the above steps, the system of the present invention can efficiently and automatically generate job information and information for companies newly introducing electronic payment services, and provide it to users.

[1706] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1707] The present invention relates to a system that enables a company's human resources personnel to efficiently create job postings and adjust the job postings based on user sentiment. The system includes the following elements:

[1708] 1. Input method:

[1709] It provides a web form for users to enter a job description, such as the job title, required skills, etc. For example, a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years experience" into the form.

[1710] 2. Data transmission:

[1711] The terminal transmits the user's input data to the server.

[1712] 3. Data Analysis:

[1713] The server analyzes the received data, which includes:

[1714] Data validation: Checking the accuracy and appropriateness of input data.

[1715] Categorization: Classify items such as job types and required skills into appropriate categories.

[1716] 4. Generative model selection:

[1717] The server selects an appropriate generative model based on the analysis results. For example, it selects a generative AI model suitable for "software engineer."

[1718] 5. Automatic Job Creation:

[1719] Based on the selected model, the generative AI automatically generates job postings, embedding data into job posting templates and generating fluent sentences using natural language processing technology.

[1720] 6. Emotion Recognition with Emotion Engine:

[1721] The emotion engine analyzes the user's facial expressions and tone of voice as they type to recognize their emotions, for example identifying when they are tired or particularly excited.

[1722] 7. Applying emotional information:

[1723] The adjustment means adjusts the job posting based on the recognized emotional information, for example, if it is recognized that the user is nervous, the job posting text is changed to a more relaxed tone.

[1724] 8. Displaying Job Postings:

[1725] The server sends the generated job information and the adjusted text to the terminal.

[1726] 9. Display of information:

[1727] The terminal displays the received job information to the user, and the generated job information can be checked through a user interface.

[1728] 10. Checking and correcting content:

[1729] The user reviews the displayed job listing and makes any necessary changes, such as changing the location or salary information.

[1730] 11. Updates:

[1731] The device resends the modifications to the server.

[1732] 12. Saving and Publishing Final Job Postings:

[1733] The server retrieves the corrections and updates the final job listing. The terminal saves the final job listing with the corrections reflected and makes it available for publication. The job listing is then published.

[1734] This system will not only streamline the process of creating job listings, but also make it possible to provide job listings that take into account the user's emotions, thereby improving the quality of job listings and making them more attractive to job seekers.

[1735] The processing flow will be explained below.

[1736] Step 1:

[1737] A user enters job information summary such as job type and required skills into a web form. The form contains fields for entering basic information such as job type, required skills, work location, and salary.

[1738] Step 2:

[1739] The device sends the input data to the server, where it is converted into an appropriate format such as JSON or XML.

[1740] Step 3:

[1741] The server analyzes the received data, which includes the following processes:

[1742] Data validation: Checking whether the information entered is appropriate and accurate, for example, whether required skills exist or salary information is valid.

[1743] Categorization: Classifying items such as job types and required skills into specific categories.

[1744] Step 4:

[1745] The server selects an appropriate generative model based on the analysis results. For example, it selects an AI model for technical jobs for the job title "software engineer."

[1746] Step 5:

[1747] The Generative AI automatically generates job postings based on the selected model. It does the following:

[1748] Choose a template: Choose a job posting template based on the job type.

[1749] Embedding information: Embed analytical data in templates.

[1750] Natural language processing: Performs language processing to generate fluent sentences.

[1751] Step 6:

[1752] The emotion engine analyzes the user's facial expressions and tone of voice when inputting and recognizes emotions. For example, it uses a camera and microphone to analyze the user's facial expressions and voice in real time.

[1753] Step 7:

[1754] The server receives the analysis results from the emotion engine and adjusts the job posting based on the recognized emotion information. For example, if it determines that the user is nervous, it will change the tone of the message to be more friendly and relaxed.

[1755] Step 8:

[1756] The server sends the generated job information and the adjusted job information to the terminal.

[1757] Step 9:

[1758] The terminal displays the received job information to the user, including the function of visually displaying the generated job information using a GUI (Graphical User Interface).

[1759] Step 10:

[1760] The user checks the displayed job information and modifies it as necessary. For example, the user changes the work location from "Shibuya-ku, Tokyo" to "Minato-ku, Tokyo."

[1761] Step 11:

[1762] The terminal retransmits the modified information to the server, where the modified information is reformatted.

[1763] Step 12:

[1764] The server takes the modifications and updates the final job listing, ensuring the most up-to-date information is stored.

[1765] Step 13:

[1766] The device saves the final job listing with the changes reflected and makes it available for publication. It then processes the job listing to be published. The published job listing is posted on job sites, internal bulletin boards, etc.

[1767] Example 2

[1768] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1769] Conventional methods for creating job information require a lot of time and effort from the user, and they also face the problem of difficulty in providing job information that reflects the user's feelings. This leads to a decline in the quality of job information provided by companies, making it difficult for the content to be attractive to job seekers. Furthermore, automatic generation of job information also has the problem of a decline in user satisfaction, as it is not possible to take the user's feelings into account.

[1770] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1771] In this invention, the server includes input means for inputting an outline of a job listing, analysis means for analyzing data received from the input means, automatic generation means for automatically generating job listings based on the data analyzed by the analysis means, display means for displaying the job listings generated by the automatic generation means, emotion recognition means for recognizing a user's emotion, and adjustment means for adjusting the job listings based on the emotion information recognized by the emotion recognition means. This enables efficient creation and adjustment of job listings, making it possible to provide high-quality job listings that reflect the user's emotions.

[1772] "Input means" refers to a device or system that allows a user to input an outline of job information such as the type of job and required skills.

[1773] The "analysis means" is a device or system that analyzes data received from the input means and performs validation and categorization of the content.

[1774] The "automatic generation means" is a device or system that automatically generates job information based on the data analyzed by the analysis means.

[1775] The "display means" is a device or system that displays the job information generated by the automatic generation means to the user.

[1776] The "emotion recognition means" is a device or system that recognizes the user's emotions from facial expressions, tone of voice, etc.

[1777] The "adjustment means" is a device or system that adjusts the generated job information based on the emotion information recognized by the emotion recognition means.

[1778] The present invention relates to a system that enables a company's human resources personnel to efficiently create job information and adjust the job information based on user sentiment. This system is implemented using the following hardware and software.

[1779] 1. Hardware and Software Used

[1780] Web browser: Used by users to enter job description information such as job title and required skills.

[1781] Server: Performs data analysis, generative AI model selection, sentiment analysis, and final storage and publishing of job listings.

[1782] Generative AI: A system that automatically generates job information using natural language processing technology.

[1783] Emotion recognition engine: A system that analyzes user emotions and adjusts job information based on the results.

[1784] Terminal: A device that interfaces with the user, submits input data, displays job information, and resubmits corrected data.

[1785] 2. Explanation of program processing

[1786] First, the user accesses a company's job posting creation page using a web browser. The user enters a summary of the job posting, including the job type and required skills, and sends the data from their device to the server. The server analyzes the received data and checks for accuracy and appropriateness. As a result of the analysis, the job type and skills are classified into appropriate categories.

[1787] Based on the analysis results, the server selects an appropriate generation AI model and begins the automatic generation process. The generation AI embeds the data entered by the user into a template and generates a job posting in fluent natural language. An emotion recognition engine then analyzes the user's facial expressions and tone of voice when entering information to recognize the user's emotions. Based on this recognized emotional information, an adjustment method adjusts the job posting.

[1788] For example, if the user is nervous, the tone of the job posting can be changed to a more relaxed tone. The server sends the generated and adjusted job posting to the device, which displays it to the user. The user checks the displayed job posting and makes changes to information such as work location and salary as necessary. The device then sends the changes back to the server, which incorporates the changes and saves the final job posting. This information is then made available to job seekers.

[1789] 3. Adding concrete examples and prompt sentence examples

[1790] As a concrete example, consider the case where a user enters the job title "Software Engineer" and the required skills "Python, Django, 5+ years of experience." The device sends this data to the server, which analyzes the data, categorizes it, and selects a generative AI model. The generative AI generates a job posting such as "Our company is hiring a software engineer with 5+ years of Python and Django experience." The emotion recognition engine analyzes the user's emotions and adjusts the wording as necessary.

[1791] Example prompts for generative AI models

[1792] "Generate a compelling software engineer job posting based on the following data: Job Title: Software Engineer, Required Skills: Python, Django, 5+ years of experience."

[1793] This system allows for efficient creation and publication of job postings, and provides high-quality job postings that take user sentiment into consideration. This improves the quality of job postings for companies and makes them more attractive to job seekers.

[1794] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1795] Step 1:

[1796] The user enters a job summary

[1797] A user uses a web browser to enter a job description, such as the job title and required skills. Specifically, the user enters "Software Engineer" and "Python, Django, 5+ years of experience." This input data is sent to the terminal and temporarily saved.

[1798] Input: User inputs job title and required skills

[1799] Output: Input data saved on the device

[1800] Step 2:

[1801] The device sends the data to the server

[1802] The terminal sends the data entered by the user to the server. At this time, the data is encrypted and sent using a secure communication method (e.g., HTTPS).

[1803] Input: Input data stored on the device

[1804] Output: Data sent to the server

[1805] Step 3:

[1806] The server analyzes the data

[1807] The server analyzes the received data. First, it validates the data to ensure that the job titles and required skills are accurate and appropriate. Then it categorizes the job titles and skills into categories.

[1808] Input: Data sent to the server

[1809] Output: Validated and classified data

[1810] Step 4:

[1811] The server selects the generative model

[1812] The server selects an appropriate generative AI model based on the analysis results. For example, for the job title "software engineer," it references past data to select the optimal model.

[1813] Input: Validated and classified data

[1814] Output: The selected generative AI model

[1815] Step 5:

[1816] Generative AI automatically generates job information

[1817] Based on the selected model, the generative AI automatically generates a job posting, such as, "Our company is looking for a software engineer with 5+ years of experience in Python and Django."

[1818] Input: Validated and classified data, selected generative AI model

[1819] Output: Generated job listings

[1820] Step 6:

[1821] Emotion recognition engine recognizes emotions

[1822] The emotion recognition engine analyzes the user's facial expressions and tone of voice when typing to recognize the user's emotions, for example, whether the user is nervous or relaxed.

[1823] Input: User's facial expression data and tone of voice

[1824] Output: Recognized emotion information

[1825] Step 7:

[1826] Regulatory measures apply emotional information

[1827] The adjustment means adjusts the generated job posting based on the recognized emotion information. For example, if the user is nervous, the adjustment means changes the posting to "This is an opportunity to work in a relaxed office environment."

[1828] Input: Generated job information, recognized emotion information

[1829] Output: Adjusted job listings

[1830] Step 8:

[1831] The server sends the job information to the device.

[1832] The server prepares the adjusted job information for transmission to the terminal and sends the encoded data to the terminal.

[1833] Input: Adjusted Job Posting

[1834] Output: Data sent to the terminal

[1835] Step 9:

[1836] The device displays the information

[1837] The device decodes the received data and displays the adjusted job information on the GUI.

[1838] Input: Data sent to the terminal

[1839] Output: Job information displayed in GUI

[1840] Step 10:

[1841] The user checks and corrects the content

[1842] The user can check the displayed job information and modify it as necessary, for example, adjusting the work location or salary conditions. The modified data is saved on the device.

[1843] Input: Job information displayed on the GUI

[1844] Output: Corrected data saved on the device

[1845] Step 11:

[1846] The device resends the corrected data to the server

[1847] The terminal transmits the data modified by the user back to the server.

[1848] Input: Correction data saved on the device

[1849] Output: Data resent to the server

[1850] Step 12:

[1851] The server stores and publishes the final job listings.

[1852] The server takes the modifications and saves them as the final job listing, which is then made available to job seekers.

[1853] Input: Data resubmitted to server

[1854] Output: Final job posting saved and published

[1855] (Application example 2)

[1856] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1857] Conventional job information creation systems simply display the job information entered by the user as is, and have the problem of being unable to adjust the text to take the user's feelings into account. Furthermore, there is a lack of support for content creators to efficiently create video titles and descriptions. This has led to the problem of not being able to provide content that is more appealing to job seekers and viewers.

[1858] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means for inputting an outline of the job information, an analysis means for analyzing data received from the input means, an automatic generation means for automatically generating job information based on the analyzed data, an adjustment means for adjusting the text of the generated job information, and a display means for displaying the generated job information. This makes it possible to adjust the text of the job information based on user emotional information and to efficiently generate and adjust video titles and descriptions for content creators.

[1859] "Job information summary" refers to basic information such as the type of job required of applicants, necessary skills, and working conditions.

[1860] "Input means" refers to an interface for a user to input data, and specifically includes a keyboard, a mouse, a touch screen, and the like.

[1861] "Analysis means" refers to a device or program that has the function of analyzing input data and verifying its appropriateness and accuracy.

[1862] "Validation" refers to the process of ensuring that entered data is correct and appropriate.

[1863] "Automatic generation means" refers to a device or program that automatically generates job listings and other content based on analyzed data.

[1864] "Display means" refers to a device or interface for displaying generated information in a form visible to a user.

[1865] The "adjustment means" refers to a device or program that adjusts the generated text based on the user's emotional information.

[1866] "Emotional information" refers to data related to emotions obtained from the user's facial expressions and voice.

[1867] A "face recognition camera" refers to a camera that captures a user's face and analyzes their facial expressions.

[1868] A "content creator" is someone who produces content such as videos and articles.

[1869] "Video title" refers to a name that succinctly expresses the content of the video.

[1870] "Description" refers to text that provides a detailed explanation of the content of a video or other content.

[1871] "Generative model" refers to an artificial intelligence model that generates natural language or other content based on specific input data.

[1872] A "prompt sentence" refers to an input sentence that instructs a generative model to produce a specific output.

[1873] To implement this invention, content creators must first provide an interface for inputting video titles and description summaries. Users input data through this interface, and the data is sent from the terminal to the server.

[1874] The server then analyzes the received data. Analysis methods include data validation and categorization to ensure the appropriateness and accuracy of the input. For example, data such as "Video Title: Introducing new React trends" and "Description: Introducing the latest React features and how to apply them" may be analyzed.

[1875] Based on the analyzed data, the server selects an appropriate generative AI model and automatically generates the video title and description. Generative AI models such as the OpenAI GPT series can be used. The generated data is displayed to the user, who can review it and make corrections if necessary.

[1876] Furthermore, the adjustment means plays an important role in this invention. The adjustment means acquires emotional information from the user's facial expressions and voice, and adjusts the generated text based on this information. A facial recognition camera and voice analysis software are used to acquire emotional information. For example, if the user is nervous, the video title can be changed to a more relaxed tone, such as "Improve your skills! A clear explanation of the new React trend."

[1877] Finally, the adjusted data is sent back to the server and the final information is saved, allowing content creators to efficiently create titles and descriptions for their videos and provide more engaging content to viewers.

[1878] For example, the following prompt sentence can be used:

[1879] Example prompt:

[1880] "Generate an inspiring and clear title and description for an introduction to a new React trend."

[1881] In this way, by implementing the present invention, it becomes possible to generate effective content that takes into account the user's emotional information.

[1882] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1883] Step 1: Enter data using the input method

[1884] The user inputs the video title and description summary via the interface, such as "Video title: Introducing new React trends" and "Description: Introducing the latest React features and how to apply them," and sends this information from the device to the server.

[1885] Input: Video title, description

[1886] Output: Input data

[1887] Step 2: Receiving and validating data

[1888] The server receives the input data sent from the terminal, and then validates the data using analytical means, checking the accuracy and appropriateness of the input.

[1889] Input: Input data

[1890] Output: Validated data

[1891] Step 3: Categorization

[1892] Based on the validated data, the server classifies items such as job type and required skills into appropriate categories.

[1893] Input: Validated data

[1894] Output: Classified data

[1895] Step 4: Selecting a Generative Model

[1896] The server selects an appropriate generative AI model based on the classified data, using, for example, the OpenAI GPT series as the generative AI model.

[1897] Input: Classified data

[1898] Output: The selected generative AI model

[1899] Step 5: Automatically generate job postings

[1900] Using the selected generative AI model, the server automatically generates a video title and description, using the specific prompt "Generate an inspiring and easy-to-understand title and description for introducing a new React trend."

[1901] Input: Generative AI model, prompt, classified data

[1902] Output: Generated title and description

[1903] Step 6: Acquiring emotional information

[1904] Using a facial recognition camera and voice analysis equipment, the server obtains the user's emotional information, which includes analyzing the user's facial expressions and voice tone.

[1905] Input: User's facial expression data, voice data

[1906] Output: Emotional information

[1907] Step 7: Adjust the text

[1908] Based on the acquired emotional information, the server adjusts the automatically generated title and description text, for example, changing the tone to a more relaxed one if the user is nervous.

[1909] Input: Generated title and description, sentiment information

[1910] Output: Adjusted title and description

[1911] Step 8: Viewing generated data

[1912] The server sends the final adjusted title and description to the terminal and displays them through the user interface.

[1913] Input: Adjusted title and description

[1914] Output: Display data

[1915] Step 9: Check and correct the content

[1916] The user checks the displayed title and description, makes any necessary corrections, and then sends the corrections back to the server.

[1917] Input: Display data

[1918] Output: Corrected data

[1919] Step 10: Save and publish your final information

[1920] The server takes the corrected data, updates the final information, and stores it, after which it is made available for publication.

[1921] Input: Correction data

[1922] Output: Saved data, public data

[1923] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1924] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1925] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1926] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1927] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1928] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1929] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1930] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1931] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1932] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1933] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1934] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1935] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1936] 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.

[1937] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1938] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1939] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1940] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1941] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1942] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1943] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1944] The following is further disclosed regarding the above embodiment.

[1945] (Claim 1)

[1946] an input means for inputting a summary of job information;

[1947] analysis means for analyzing the data received from the input means;

[1948] an automatic generation means for automatically generating job information based on the data analyzed by the analysis means;

[1949] a display means for displaying the job information generated by the automatic generation means;

[1950] A system including:

[1951] (Claim 2)

[1952] 2. The system according to claim 1, wherein the analysis means validates the data and classifies it into categories based on job type and required skills.

[1953] (Claim 3)

[1954] 2. The system according to claim 1, wherein the automatic generation means selects an appropriate generation model based on the analyzed data and generates job information.

[1955] "Example 1"

[1956] (Claim 1)

[1957] an input means for inputting a summary of job information;

[1958] a transmitting means for transmitting the data received from the input means;

[1959] an analysis means for validating the data received by the transmission means and classifying the data into categories based on the job type and required skills;

[1960] a model selection means for selecting an appropriate generative model based on the data analyzed by the analysis means;

[1961] an automatic generation means for automatically generating job information using the generation model selected by the model selection means;

[1962] a display means for displaying the job information generated by the automatic generation means;

[1963] A system including:

[1964] (Claim 2)

[1965] 2. The system according to claim 1, wherein the automatic generation means transmits the generated job information to a terminal and displays it.

[1966] (Claim 3)

[1967] 2. The system of claim 1, wherein the user checks the generated job information, modifies it if necessary, and resends the modified content to the server.

[1968] "Application Example 1"

[1969] (Claim 1)

[1970] an input means for inputting a summary of job information;

[1971] analysis means for analyzing the data received from the input means;

[1972] an automatic generation means for automatically generating job information based on the data analyzed by the analysis means;

[1973] a display means for displaying the job information generated by the automatic generation means;

[1974] A means to input information about the requirements required by the adopting company,

[1975] an automatic generation means for analyzing information relating to the requirements and automatically generating necessary information;

[1976] A system including:

[1977] (Claim 2)

[1978] 2. The system according to claim 1, wherein the analysis means validates the data and classifies it into categories based on job type and required skills.

[1979] (Claim 3)

[1980] 2. The system according to claim 1, wherein the automatic generation means selects an appropriate generation model based on the analyzed data and generates job information and information for adopting companies.

[1981] "Example 2: Combining Emotion Engines"

[1982] (Claim 1)

[1983] an input means for inputting a summary of job information;

[1984] analysis means for analyzing the data received from the input means;

[1985] an automatic generation means for automatically generating job information based on the data analyzed by the analysis means;

[1986] a display means for displaying the job information generated by the automatic generation means;

[1987] emotion recognition means for recognizing an emotion of a user;

[1988] an adjustment means for adjusting job information based on the emotion information recognized by the emotion recognition means;

[1989] A system including:

[1990] (Claim 2)

[1991] 2. The system according to claim 1, wherein the analysis means validates the data and classifies it into categories based on job type and required skills.

[1992] (Claim 3)

[1993] 2. The system according to claim 1, wherein the automatic generation means selects an appropriate generation model based on the analyzed data and generates job information.

[1994] "Application example 2 when combining emotion engines"

[1995] (Claim 1)

[1996] an input means for inputting a summary of job information;

[1997] analysis means for analyzing the data received from the input means;

[1998] an automatic generation means for automatically generating job information based on the data analyzed by the analysis means;

[1999] a display means for displaying the job information generated by the automatic generation means;

[2000] an adjustment means for adjusting the wording of the generated job information;

[2001] A system including:

[2002] (Claim 2)

[2003] 2. The system according to claim 1, wherein the analysis means validates the data and classifies it into categories based on job type and required skills.

[2004] (Claim 3)

[2005] 2. The system according to claim 1, wherein the automatic generation means selects an appropriate generation model based on the analyzed data and generates job information.

[2006] (Claim 4)

[2007] 2. The system according to claim 1, wherein the adjusting means adjusts the wording of the job information based on the user's emotional information.

[2008] (Claim 5)

[2009] 5. The system according to claim 4, wherein the adjustment means acquires the user's emotional information using a face recognition camera and a voice analysis means.

[2010] (Claim 6)

[2011] 2. The system of claim 1, wherein the adjusting means adjusts video titles and descriptions for content creators. [Explanation of symbols]

[2012] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting a summary of job information; analysis means for analyzing the data received from the input means; an automatic generation means for automatically generating job information based on the data analyzed by the analysis means; a display means for displaying the job information generated by the automatic generation means; A system including:

2. 2. The system according to claim 1, wherein the analysis means validates the data and classifies it into categories based on job type and required skills.

3. The system according to claim 1 , wherein the automatic generation means selects an appropriate generation model based on the analyzed data and generates job information.

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