system

Abstract

We provide the system. [Solution] A means of receiving input data for generating job postings, Means for validating the aforementioned input data, A means for inputting the validated data into a generative model to generate job information, The means for storing the generated job information, The means for displaying the generated job information to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When creating recruitment information, specialized knowledge and a lot of time are required, which is a great burden for personnel managers in small and medium-sized enterprises. Also, it is not easy to ensure the quality and consistency of recruitment information. It is necessary to solve these problems and quickly create more attractive and effective recruitment information.

Means for Solving the Problems

[0005] This invention provides a system for generating job postings that receives input data, performs validation, and then automatically generates job postings using a generation model. This system stores the generated job postings in a database and includes means for displaying them to the user, thereby streamlining the job posting creation process and enabling the provision of consistent, high-quality job postings at a low cost.

[0006] "Input data" refers to information provided by the user to generate job postings, such as job description, required experience, work location, and salary.

[0007] "Validation" refers to the process of verifying the accuracy and completeness of input data received by a system, and confirming that there are no errors or deficiencies.

[0008] "Generative models" refer to AI technology that uses machine learning algorithms to generate natural and appealing job postings based on input data.

[0009] "Job postings" refer to documents containing detailed information such as job descriptions and salary conditions that companies should provide to job seekers.

[0010] A "database" refers to an information storage system used to efficiently store and manage generated job postings and user-entered data.

[0011] "User" refers to HR personnel or administrators who operate the system for the purpose of creating and using job postings. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor and 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), or Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system for automatically generating job postings, which generates attractive and consistent job postings based on data entered by the user. The embodiments thereof are described below.

[0034] First, the user accesses a web form for generating job postings via their device. This form includes categories such as "Job Description," "Required Experience," and "Work Location." The user fills in the necessary data in these input fields and clicks the "Submit" button.

[0035] Next, the server receives the input data submitted by the user. The server validates the data to ensure it is accurate and contains all the necessary information. If there are errors in the data, it returns an error message to the terminal, giving the user an opportunity to correct the input.

[0036] Once validation is complete, the data is sent by the server to a generative model. This generative model uses machine learning algorithms to automatically generate natural and effective job postings based on the given input data. For example, it might create job postings that align with a company's needs, such as "We are looking for an experienced professional to plan and implement our marketing strategy."

[0037] The generated job postings are stored in a database by the server and used for future reference and management. The stored information is sent back to the terminal, where the user can review it and make adjustments as needed.

[0038] This system significantly streamlines the job posting process, allowing users to quickly obtain high-quality job postings even without specialized expertise. It will particularly benefit HR personnel in small and medium-sized enterprises, saving time and effort and contributing to more efficient recruitment activities.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device displays a form for generating job postings via a web browser. The user fills in information such as "job description," "required experience," and "work location" in the input fields and clicks the "submit" button.

[0042] Step 2:

[0043] The device sends the data that the user has sent to the server. The server receives this data.

[0044] Step 3:

[0045] The server validates the received data. It checks whether all required fields are filled in and whether the data format is correct. If an error is detected, it returns an error message to the terminal and prompts the user to correct it.

[0046] Step 4:

[0047] The server sends data that has passed validation to the generative model. The server then formats this data into a format that the AI ​​model can easily understand and passes it to the model via an API or other means.

[0048] Step 5:

[0049] The generative model generates job postings based on input data received from the server. For example, it might output text such as, "We are looking for a professional with over 3 years of experience in marketing strategy planning."

[0050] Step 6:

[0051] The server receives the generated job postings and saves them to the database. This allows the job postings to be referenced later.

[0052] Step 7:

[0053] The server sends the generated job information to the terminal and displays it to the user. The user can review the results and make further edits if necessary.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] Traditional job posting creation processes required significant time and specialized knowledge for information input and editing, making them a heavy burden for small and medium-sized enterprises and individuals who were not specialists. Furthermore, maintaining consistency and quality in job postings was difficult, potentially reducing a company's appeal to job seekers.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for acquiring information for generating job postings, means for inputting and verifying the information, and means for inputting the verified information into a generation algorithm to create job postings. This makes it possible for even non-experts to efficiently create high-quality job postings.

[0059] "Means for acquiring information" refers to methods or devices for collecting data necessary for job postings from system users.

[0060] "Means for verifying input" refers to a method or device for confirming whether the information obtained from the user is accurate and complete.

[0061] "Means for inputting into a generation algorithm and creating job information" refers to a method or apparatus that uses a machine learning model to automatically generate job postings based on validated input information.

[0062] "Means of storage" refers to a method or device for storing generated work information in a storage medium such as a database.

[0063] "Means of presenting to the user" refers to a method or device for displaying the generated work information on the user's terminal.

[0064] This invention relates to a system for automatically generating job postings. Specifically, it uses a user, a server, and a terminal to perform a series of processes.

[0065] First, the user accesses a web interface for generating job postings via their device. The user enters information such as "job description," "required experience," "work location," and "salary." This entered data is sent to the server via an HTTP request.

[0066] The server validates the received data. This validation process checks for incomplete or incorrectly formatted data. If there are any problems with the data, the server generates an error message and notifies the user.

[0067] Next, the data that passes validation is passed to a generative AI model. This generative AI model utilizes machine learning algorithms to generate effective and attractive job postings based on the input data. In this process, for example, if a prompt containing keywords such as "IT engineer" is entered, information such as "We are looking for an IT engineer with JavaScript® experience in Tokyo" will be generated.

[0068] The generated job postings are saved to a database by the server. This database will be used for future reference and editing. Users can view the generated job postings through their terminals and make adjustments as needed.

[0069] In this way, the system automates and streamlines the job posting generation process. Furthermore, this technology makes it possible to create high-quality, consistent job postings even without specialized knowledge.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] Users access a web form through their device and enter the data necessary to create a job posting. This form includes fields such as "Job Description," "Required Experience," "Work Location," and "Salary." The entered data is sent to the server via an HTTP request. Specifically, the user's actions include opening the form in a browser, entering information in each field, and clicking the "Submit" button.

[0073] Step 2:

[0074] The server receives data sent by the user. It analyzes the received data and performs input validation. Specifically, it runs a script to check if the data format is correct and if all required fields are filled in, ensuring input accuracy. If there are errors in the validation results, it generates an error message and sends it back to the terminal, prompting the user to make corrections.

[0075] Step 3:

[0076] Data that passes input validation is input to the generation AI model by the server. The server passes a prompt to the generation AI model, instructing it to generate job postings. The generation AI model creates appropriate job postings based on the input data. The generated job postings are obtained as output. For example, in response to the prompt requesting JavaScript experience, the output might be "We are recruiting an IT engineer located in Tokyo."

[0077] Step 4:

[0078] The generated job postings are stored in a database by the server. Storing them in a database is important for information persistence and for later reference and editing. The server accesses the database and stores the newly generated data in the appropriate tables and fields.

[0079] Step 5:

[0080] The server returns the saved job postings to the user's device. This allows the user to view the generated information via their browser. Furthermore, they can edit the job postings and make final adjustments as needed. The user operates their device to view the information in their browser and modify the content using the editing form.

[0081] (Application Example 1)

[0082] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0083] In recent years, with the increasing use of virtual spaces for business operations, conventional job posting generation systems face challenges in intuitively inputting data and verifying job details within a virtual environment. Furthermore, while there is a demand for rapid generation and presentation of job postings, there is a lack of systems that allow users to complete operations while fully immersed in the virtual space.

[0084] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0085] In this invention, the server includes a device for receiving input data for generating job postings, a device for verifying the input data, and a device for presenting the input data in a virtual space and interactively presenting the generated job postings. This allows users to intuitively generate and verify job postings by making voice and motion inputs in the virtual space.

[0086] A "device that receives input data for generating job postings" is part of a system that receives information such as job duties and requirements entered by users and processes that information.

[0087] "The device for verifying the input data" is part of a system for confirming that the received input data is accurate and complete.

[0088] A "generative model" is a program that uses machine learning algorithms to create appropriate job postings based on input data.

[0089] A "device that presents input data in a virtual space and interactively displays generated job information" is part of a system that uses virtual reality technology to allow users to visually and manually confirm and edit the information they input and the generated job information.

[0090] This invention is a job posting generation system that utilizes virtual reality technology, and in particular aims to streamline the job posting generation process within a virtual space. The system is implemented using a head-mounted display worn by the user.

[0091] The server receives input data to generate job postings transmitted from head-mounted displays such as the Oculus Quest 2. Users can interactively input data such as job descriptions and required skills through voice commands and hand tracking within the virtual space.

[0092] Subsequently, the server uses Python to validate the input data and check for defects. Once validation is complete, the data is input into a generative AI model using TENSORFLOW®, which generates effective job postings. The generated job postings are then visually presented in real time to the user's field of view within a virtual space running in the Unity environment.

[0093] Furthermore, users can visually review the generated job postings and, if necessary, modify the data by re-entering it via voice input within the virtual space. This system allows users to intuitively and quickly build and adjust job postings without being constrained by the limitations of the real world.

[0094] As a concrete example, let's assume a store in a virtual space is recruiting new staff. The user can input conditions such as "recruiting new staff" and "fitting assistant experience," and instantly receive optimized job information. An example of a prompt statement for giving instructions is as follows:

[0095] Example of a prompt:

[0096] Please generate a job posting for new staff recruitment based on the following conditions:

[0097] Job Description: Customer support and product explanation

[0098] Required experience: Practical work experience in previous job

[0099] Work location: Designated store within the virtual space

[0100] This invention dramatically simplifies the generation and management of job postings, and can particularly revolutionize job-seeking activities in virtual environments.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The user wears a head-mounted display and inputs data into an interface in a virtual space using voice or hand tracking.

[0104] The system takes user inputs such as job description and required experience, and captures user actions to record them as input data. The interface also analyzes hand movements and voice commands in real time to support the user's quick selection of desired items.

[0105] Step 2:

[0106] The server receives input data sent by the user.

[0107] The input data is validated using a Python script to check for missing or invalid data in the fields.

[0108] For example, if the required number of years of experience is unclear, an error message will be generated to prompt the user to re-enter the information.

[0109] Step 3:

[0110] The server inputs the validated data into the AI ​​model.

[0111] Using TensorFlow, we perform calculations to generate job postings based on data.

[0112] The output will generate specific and effective job postings such as, "We are seeking an experienced professional to plan and implement our marketing strategy."

[0113] Step 4:

[0114] The generated job postings are sent from the server to the terminal and presented to the user within the virtual space.

[0115] Using the Unity environment, information is displayed in a virtual interface so that the user can visually confirm it.

[0116] Based on the generated data, users can review the job postings displayed in the virtual space and make corrections as needed using voice input.

[0117] Step 5:

[0118] Once user-confirmed and corrected job postings are submitted, they are resubmitted to the server and stored in the database.

[0119] The saved information is managed so that it can be readily used in future recruitment activities, contributing to the user's recruitment strategy.

[0120] Through these operations, users can quickly and effectively complete the generation of job postings through intuitive operation within the virtual space.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention provides a system that automatically generates job postings and also includes an emotion engine that recognizes user emotions. The embodiments thereof are described below.

[0123] First, the user accesses an online form for generating job postings using their device. This form includes input fields such as "Job Description," "Required Experience," and "Work Location." The user enters the necessary information into these fields, and the emotion engine analyzes the user's emotional state in real time as they type. For example, if the user is relaxed while typing, that emotional data is also sent along with the form.

[0124] Next, the device sends input data and sentiment data to the server. The server first validates the input data to check for any errors. If errors are found, it returns an error message to the device, allowing the user to make corrections.

[0125] Once validation is complete, the data is passed from the server to a generative model. This generative model uses machine learning algorithms to generate job postings that take the user's emotional state into account. For example, if a user is showing positive emotions, the model will generate text with a positive tone that matches their mood.

[0126] The server saves the generated job postings to the database and simultaneously presents the final job postings to the user. The user can review this information and make further adjustments as needed.

[0127] This system has the advantage of generating more personalized job postings by taking into account the user's emotional state. This makes it possible to attract more suitable job seekers and streamline the company's recruitment process.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The terminal displays a form for generating job postings via a web browser. The user enters information such as "job description," "required experience," and "work location." Simultaneously, an emotion engine recognizes the user's emotional state and processes that information in real time.

[0131] Step 2:

[0132] The user completes the input and clicks the "Submit" button. The device sends the input data along with the sentiment data to the server.

[0133] Step 3:

[0134] The server validates the data it receives. It checks if all the necessary data is present and in the correct format, and if there are any deficiencies, it returns an error message to the terminal prompting the user to make corrections.

[0135] Step 4:

[0136] The server sends validated data to the generative model. The server also considers sentiment data and generates personalized job postings based on it.

[0137] Step 5:

[0138] The generative model generates job postings based on input data and sentiment data. For example, if the user's sentiment is positive, job postings using positive language will be created.

[0139] Step 6:

[0140] The server saves the generated job postings to a database. The saved data can be referenced and reused later.

[0141] Step 7:

[0142] The server sends the final job posting information to the terminal for the user to view. The user can then review this information and make any necessary edits.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] Conventional job posting generation systems are unable to generate personalized job postings that take user emotions into consideration, resulting in job postings that fail to sufficiently attract the interest of job seekers. Furthermore, the insufficient data validation to improve the reliability of the generated information is also a problem.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes means for analyzing input information and user sentiment information in real time, means for validating the input information, and means for inputting the validated information and sentiment information into a generative model to generate job postings. This enables the automatic generation of personalized job postings and the provision of highly reliable information.

[0148] "Input information" refers to the data that users provide to generate job postings, and includes data items such as job description, required skills, workplace location, and compensation.

[0149] "Emotional information" refers to data that represents the user's emotional state at the time of input, and is analyzed in real time based on the user's facial expressions and voice tone.

[0150] "Validation" is the process of verifying the accuracy and completeness of input information, and is performed in order to detect data errors in advance.

[0151] A "generative model" is a program that uses machine learning algorithms to automatically generate job postings from input information and sentiment information.

[0152] "Job postings" are information that describes the requirements and conditions related to the work, and include specific job details to attract job seekers.

[0153] This invention is a system that automatically generates job postings while taking into account the user's emotional state. First, the user accesses a dedicated online form using an information terminal and inputs the data necessary to create the job posting. The form includes fields for job description, required skills, workplace location, and compensation.

[0154] While the user is filling out a form, the device uses its built-in emotion engine to analyze the user's emotional state in real time. This emotional data is collected based on the user's facial expressions and tone of voice. For example, if the user is in a calm and positive emotional state, this information is recorded by the device as emotional data.

[0155] Once input is complete, the terminal sends the input information and sentiment information to the server. The server first validates the received information to confirm its accuracy. Then, using the validated data and sentiment information, it runs a generative AI model to automatically generate job postings. This generative model utilizes machine learning algorithms to provide personalized information based on the aforementioned data.

[0156] The generated job postings are stored in a central database by the server and then displayed on the user's terminal. Users can review the presented information and make corrections as needed, resulting in the generation of more suitable job postings.

[0157] One possible scenario is that when a user enters information such as "We are looking for a software engineer who can work remotely," if they are in a relaxed emotional state, the generated information might use a tone like, "We offer a flexible remote work environment and welcome those who want to participate in innovative projects!"

[0158] An example of a prompt message would be: "Generate a positive job posting based on the following data: Job description: Software Engineer, 3+ years of experience, remote work possible."

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1: The user uses their device to access an online form and enter the data necessary to generate the job posting. This data includes fields such as job description, required skills, work location, and compensation. Once the data is entered, the device prepares to send the data to the next step.

[0161] Step 2: The device analyzes the user's emotional state in real time using its built-in emotion engine, simultaneously with the user's input. The input for this emotion analysis is the user's facial expressions and voice tone, and the output is recorded as emotion data. This allows the user's emotional information to be obtained.

[0162] Step 3: The terminal combines the acquired input data and emotion data and sends them to the server. The transmitted information is used for processing in the next step.

[0163] Step 4: The server validates the received input data. It checks whether the input data is accurate and complete, and if there are any deficiencies, it generates an error message and returns it to the terminal. At this stage, the input is the submitted dataset, and the output is the validated data or the error message.

[0164] Step 5: The validated data is input into the AI ​​model generated by the server. At this stage, validated job postings and sentiment data are input, and the output is individually personalized job postings. Machine learning algorithms are used in this process.

[0165] Step 6: The server saves the generated job information to the database and simultaneously sends the job information to the terminal. The user receives this output, reviews the content, and can make corrections as needed.

[0166] Step 7: The user reviews the presented job information on their device. If necessary, the user adjusts the information and tone, and then confirms the final information. In this step, the user evaluates and gives final approval, after which the final version of the job information is output.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] In the current recruitment process, the provision of information tends to be limited to standard, one-way methods, which often results in insufficient appeal to applicants and makes efficient and appropriate talent acquisition difficult. Furthermore, while taking into account applicants' emotions and circumstances can lead to better matching, current systems lack the means to reflect emotional data in job postings.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] In this invention, the server includes means for receiving input data from a user using a terminal, means for validating the input data and the user's emotional data, and means for inputting the validated data into a generative model to generate customized job information based on the user's emotional state. This makes it possible to provide more personalized job information that grasps and reflects the user's emotional state in real time.

[0172] "Means of receiving input data using a device" refers to a function that provides an interface for users to input data necessary for job postings into online forms, etc., by operating a device such as a smartphone or computer.

[0173] "Means for validating input data and user sentiment data" refers to a process that not only verifies the accuracy and integrity of data entered by the user, but also analyzes the user's sentiment data using a sentiment engine and verifies its consistency.

[0174] "A means of inputting data into a generative model to generate customized job postings based on the user's emotional state" refers to an algorithm that uses a machine learning algorithm to input validated data into a generative model and adjusts and generates text while considering the user's emotional state.

[0175] "Means of storing data in data storage" refers to the function of saving generated job information in a digital format to a database or cloud storage so that it can be referenced later.

[0176] "Means of presenting to the user's terminal" refers to an interface that displays the final generated job information on the user's terminal, allowing them to review and adjust it.

[0177] This invention is a specific embodiment of a system that takes into account the emotional state of users in the job posting generation and interview process at a store.

[0178] First, the user provides input data using their smartphone. The device collects information such as "job requirements," "required experience," "work location," and "salary level" through an online form. During this process, an emotion engine is activated to evaluate the user's emotions in real time. For example, if the user is working in a relaxed state, the emotion engine detects this state and records it as data.

[0179] The terminal sends this input data and sentiment data to the server. The server performs validation of the input data using a program such as Python. If any missing items or inconsistencies between data are detected during validation, an error message is returned to the user's terminal, prompting them to make corrections.

[0180] The validated data is input into a generative AI model using machine learning algorithms to generate customized job postings that are appropriate to the user's emotional state. For example, if a user expresses positive emotions, the job posting will be generated with a friendly and appealing tone.

[0181] The generated job postings are saved to data storage and simultaneously displayed on the user's device. The user can review the displayed job postings and make further adjustments as needed.

[0182] As a concrete example, consider a case where a store owner is recruiting staff for a new cafe. Because this owner entered the information in a relaxed state when creating the job posting using the app, the resulting information has a friendly and warm tone. An example of a prompt message might be: "We are looking for staff who are looking for a comfortable work environment at our new cafe. If you have experience working in a cafe, why not put your skills to full use?"

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The user accesses an online form using their smartphone. The user enters data such as "job requirements," "required experience," "work location," and "salary level." While the user is entering data, an emotion engine activates, analyzing the user's emotional state in real time and preparing to send it as emotion data along with the input data.

[0186] Step 2:

[0187] The terminal sends input data and sentiment data to the server. The server validates the received data. Validation is performed using a Python script to check for missing input fields and string formatting, and any errors are sent to the terminal as error messages.

[0188] Step 3:

[0189] The server inputs the validated data into a generative AI model. The server structures the data and runs machine learning algorithms (e.g., PyTorch or TensorFlow) to generate customized job postings based on the input data and sentiment data. If the sentiment data is positive, adjustments are made, such as making the wording more approachable.

[0190] Step 4:

[0191] The server stores the generated job postings in data storage. During storage, the information is saved in an appropriate format (e.g., JSON) and an index is created to facilitate searching.

[0192] Step 5:

[0193] The server sends the generated job information as a response to the user's device. The user can review the suggested job information on their device and make any necessary corrections or adjustments via the UI. The device can also send these changes to the server as updates.

[0194] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0195] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0196] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0200] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0201] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0202] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0203] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0204] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0205] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0206] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0207] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0208] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0209] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0210] This invention provides a system for automatically generating job postings, which generates attractive and consistent job postings based on data entered by the user. The embodiments thereof are described below.

[0211] First, the user accesses a web form for generating job postings via their device. This form includes categories such as "Job Description," "Required Experience," and "Work Location." The user fills in the necessary data in these input fields and clicks the "Submit" button.

[0212] Next, the server receives the input data submitted by the user. The server validates the data to ensure it is accurate and contains all the necessary information. If there are errors in the data, it returns an error message to the terminal, giving the user an opportunity to correct the input.

[0213] Once validation is complete, the data is sent by the server to a generative model. This generative model uses machine learning algorithms to automatically generate natural and effective job postings based on the given input data. For example, it might create job postings that align with a company's needs, such as "We are looking for an experienced professional to plan and implement our marketing strategy."

[0214] The generated job postings are stored in a database by the server and used for future reference and management. The stored information is sent back to the terminal, where the user can review it and make adjustments as needed.

[0215] This system significantly streamlines the job posting process, allowing users to quickly obtain high-quality job postings even without specialized expertise. It will particularly benefit HR personnel in small and medium-sized enterprises, saving time and effort and contributing to more efficient recruitment activities.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The device displays a form for generating job postings via a web browser. The user fills in information such as "job description," "required experience," and "work location" in the input fields and clicks the "submit" button.

[0219] Step 2:

[0220] The device sends the data that the user has sent to the server. The server receives this data.

[0221] Step 3:

[0222] The server validates the received data. It checks whether all required fields are filled in and whether the data format is correct. If an error is detected, it returns an error message to the terminal and prompts the user to correct it.

[0223] Step 4:

[0224] The server sends data that has passed validation to the generative model. The server then formats this data into a format that the AI ​​model can easily understand and passes it to the model via an API or other means.

[0225] Step 5:

[0226] The generative model generates job postings based on input data received from the server. For example, it might output text such as, "We are looking for a professional with over 3 years of experience in marketing strategy planning."

[0227] Step 6:

[0228] The server receives the generated job postings and saves them to the database. This allows the job postings to be referenced later.

[0229] Step 7:

[0230] The server sends the generated job information to the terminal and displays it to the user. The user can review the results and make further edits if necessary.

[0231] (Example 1)

[0232] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0233] Traditional job posting creation processes required significant time and specialized knowledge for information input and editing, making them a heavy burden for small and medium-sized enterprises and individuals who were not specialists. Furthermore, maintaining consistency and quality in job postings was difficult, potentially reducing a company's appeal to job seekers.

[0234] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0235] In this invention, the server includes means for acquiring information for generating job postings, means for inputting and verifying the information, and means for inputting the verified information into a generation algorithm to create job postings. This makes it possible for even non-experts to efficiently create high-quality job postings.

[0236] "Means for acquiring information" refers to methods or devices for collecting data necessary for job postings from system users.

[0237] "Means for verifying input" refers to a method or device for confirming whether the information obtained from the user is accurate and complete.

[0238] "Means for inputting into a generation algorithm and creating job information" refers to a method or apparatus that uses a machine learning model to automatically generate job postings based on validated input information.

[0239] "Means of storage" refers to a method or device for storing generated work information in a storage medium such as a database.

[0240] "Means of presenting to the user" refers to a method or device for displaying the generated work information on the user's terminal.

[0241] This invention relates to a system for automatically generating job postings. Specifically, it uses a user, a server, and a terminal to perform a series of processes.

[0242] First, the user accesses a web interface for generating job postings via their device. The user enters information such as "job description," "required experience," "work location," and "salary." This entered data is sent to the server via an HTTP request.

[0243] The server validates the received data. This validation process checks for incomplete or incorrectly formatted data. If there are any problems with the data, the server generates an error message and notifies the user.

[0244] Next, the data that passes validation is passed to a generative AI model. This generative AI model utilizes machine learning algorithms to generate effective and attractive job postings based on the input data. In this process, for example, if a prompt containing keywords such as "IT engineer" is entered, information such as "We are looking for an IT engineer with JavaScript experience in Tokyo" will be generated.

[0245] The generated job postings are saved to a database by the server. This database will be used for future reference and editing. Users can view the generated job postings through their terminals and make adjustments as needed.

[0246] In this way, the system automates and streamlines the job posting generation process. Furthermore, this technology makes it possible to create high-quality, consistent job postings even without specialized knowledge.

[0247] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0248] Step 1:

[0249] Users access a web form through their device and enter the data necessary to create a job posting. This form includes fields such as "Job Description," "Required Experience," "Work Location," and "Salary." The entered data is sent to the server via an HTTP request. Specifically, the user's actions include opening the form in a browser, entering information in each field, and clicking the "Submit" button.

[0250] Step 2:

[0251] The server receives data sent by the user. It analyzes the received data and performs input validation. Specifically, it runs a script to check if the data format is correct and if all required fields are filled in, ensuring input accuracy. If there are errors in the validation results, it generates an error message and sends it back to the terminal, prompting the user to make corrections.

[0252] Step 3:

[0253] Data that passes input validation is input to the generation AI model by the server. The server passes a prompt to the generation AI model, instructing it to generate job postings. The generation AI model creates appropriate job postings based on the input data. The generated job postings are obtained as output. For example, in response to the prompt requesting JavaScript experience, the output might be "We are recruiting an IT engineer located in Tokyo."

[0254] Step 4:

[0255] The generated job postings are stored in a database by the server. Storing them in a database is important for information persistence and for later reference and editing. The server accesses the database and stores the newly generated data in the appropriate tables and fields.

[0256] Step 5:

[0257] The server returns the saved job postings to the user's device. This allows the user to view the generated information via their browser. Furthermore, they can edit the job postings and make final adjustments as needed. The user operates their device to view the information in their browser and modify the content using the editing form.

[0258] (Application Example 1)

[0259] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] In recent years, with the increasing use of virtual spaces for business operations, conventional job posting generation systems face challenges in intuitively inputting data and verifying job details within a virtual environment. Furthermore, while there is a demand for rapid generation and presentation of job postings, there is a lack of systems that allow users to complete operations while fully immersed in the virtual space.

[0261] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0262] In this invention, the server includes a device for receiving input data for generating job postings, a device for verifying the input data, and a device for presenting the input data in a virtual space and interactively presenting the generated job postings. This allows users to intuitively generate and verify job postings by making voice and motion inputs in the virtual space.

[0263] A "device that receives input data for generating job postings" is part of a system that receives information such as job duties and requirements entered by users and processes that information.

[0264] "The device for verifying the input data" is part of a system for confirming that the received input data is accurate and complete.

[0265] A "generative model" is a program that uses machine learning algorithms to create appropriate job postings based on input data.

[0266] A "device that presents input data in a virtual space and interactively displays generated job information" is part of a system that uses virtual reality technology to allow users to visually and manually confirm and edit the information they input and the generated job information.

[0267] This invention is a job posting generation system that utilizes virtual reality technology, and in particular aims to streamline the job posting generation process within a virtual space. The system is implemented using a head-mounted display worn by the user.

[0268] The server receives input data to generate job postings transmitted from head-mounted displays such as the Oculus Quest 2. Users can interactively input data such as job descriptions and required skills through voice commands and hand tracking within the virtual space.

[0269] Subsequently, the server uses Python to validate the input data and check for defects. Once validation is complete, the data is input into a generative AI model using TensorFlow, which generates effective job postings. The generated job postings are then visually presented in real time to the user's field of view within a virtual space running in the Unity environment.

[0270] Furthermore, users can visually review the generated job postings and, if necessary, modify the data by re-entering it via voice input within the virtual space. This system allows users to intuitively and quickly build and adjust job postings without being constrained by the limitations of the real world.

[0271] As a concrete example, let's assume a store in a virtual space is recruiting new staff. The user can input conditions such as "recruiting new staff" and "fitting assistant experience," and instantly receive optimized job information. An example of a prompt statement for giving instructions is as follows:

[0272] Example of a prompt:

[0273] Please generate a job posting for new staff recruitment based on the following conditions:

[0274] Job Description: Customer support and product explanation

[0275] Required experience: Practical work experience in previous job

[0276] Work location: Designated store within the virtual space

[0277] This invention dramatically simplifies the generation and management of job postings, and can particularly revolutionize job-seeking activities in virtual environments.

[0278] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0279] Step 1:

[0280] The user wears a head-mounted display and inputs data into an interface in a virtual space using voice or hand tracking.

[0281] The system takes user inputs such as job description and required experience, and captures user actions to record them as input data. The interface also analyzes hand movements and voice commands in real time to support the user's quick selection of desired items.

[0282] Step 2:

[0283] The server receives input data sent by the user.

[0284] Validate the input data using a Python script to check if there is any missing or invalid data in the fields.

[0285] For example, if the specific number of years of required experience is unclear, generate an error message and prompt for re - input.

[0286] Step 3:

[0287] The server inputs the validated data into the generative AI model.

[0288] Using TensorFlow, perform operations to generate job recruitment information based on the data.

[0289] As output, generate specific and effective job recruitment information such as "We are recruiting an experienced professional responsible for planning and implementing marketing strategies."

[0290] Step 4:

[0291] The generated job recruitment information is sent from the server to the terminal and presented to the user within the virtual space.

[0292] Using the Unity environment, display the information on a virtual interface so that the user can visually confirm it.

[0293] Based on the generated data, the user can check the content of the job recruitment information displayed in the virtual space and make corrections by voice input if necessary.

[0294] Step 5:

[0295] The job recruitment information confirmed and corrected by the user is resent to the server and stored in the database.

[0296] The saved information is managed so that it can be readily used in future recruitment activities, contributing to the user's recruitment strategy.

[0297] Through these operations, users can quickly and effectively complete the generation of job postings through intuitive operation within the virtual space.

[0298] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0299] This invention provides a system that automatically generates job postings and also includes an emotion engine that recognizes user emotions. The embodiments thereof are described below.

[0300] First, the user accesses an online form for generating job postings using their device. This form includes input fields such as "Job Description," "Required Experience," and "Work Location." The user enters the necessary information into these fields, and the emotion engine analyzes the user's emotional state in real time as they type. For example, if the user is relaxed while typing, that emotional data is also sent along with the form.

[0301] Next, the device sends input data and sentiment data to the server. The server first validates the input data to check for any errors. If errors are found, it returns an error message to the device, allowing the user to make corrections.

[0302] Once validation is complete, the data is passed from the server to a generative model. This generative model uses machine learning algorithms to generate job postings that take the user's emotional state into account. For example, if a user is showing positive emotions, the model will generate text with a positive tone that matches their mood.

[0303] The server saves the generated job information in the database and at the same time presents the final job information to the user. The user can check this information and make further adjustments if necessary.

[0304] This system has the advantage of generating more personalized job information by considering the user's emotional state. This makes it possible to attract more suitable job seekers and streamline the company's recruitment process.

[0305] The following describes the processing flow.

[0306] Step 1:

[0307] The terminal displays a form for generating job information through a web browser. The user enters information such as "job content", "required experience", "work location", etc. At the same time, the emotion engine recognizes the user's emotional state and processes this information in real time.

[0308] Step 2:

[0309] The user completes the input and clicks the "Send" button. The terminal sends the input data and emotion data to the server.

[0310] Step 3:

[0311] The server validates the received data. It checks whether the necessary data is complete and whether the format is appropriate. If there are deficiencies, it returns an error message to the terminal to prompt the user to make corrections.

[0312] Step 4:

[0313] The server sends the data that has passed validation to the generation model. The server also considers the emotion data and generates personalized job information based on this.

[0314] Step 5:

[0315] The generative model generates job postings based on input data and sentiment data. For example, if the user's sentiment is positive, job postings using positive language will be created.

[0316] Step 6:

[0317] The server saves the generated job postings to a database. The saved data can be referenced and reused later.

[0318] Step 7:

[0319] The server sends the final job posting information to the terminal for the user to view. The user can then review this information and make any necessary edits.

[0320] (Example 2)

[0321] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0322] Conventional job posting generation systems are unable to generate personalized job postings that take user emotions into consideration, resulting in job postings that fail to sufficiently attract the interest of job seekers. Furthermore, the insufficient data validation to improve the reliability of the generated information is also a problem.

[0323] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0324] In this invention, the server includes means for analyzing input information and user sentiment information in real time, means for validating the input information, and means for inputting the validated information and sentiment information into a generative model to generate job postings. This enables the automatic generation of personalized job postings and the provision of highly reliable information.

[0325] "Input information" refers to the data that users provide to generate job postings, and includes data items such as job description, required skills, workplace location, and compensation.

[0326] "Emotional information" refers to data that represents the user's emotional state at the time of input, and is analyzed in real time based on the user's facial expressions and voice tone.

[0327] "Validation" is the process of verifying the accuracy and completeness of input information, and is performed in order to detect data errors in advance.

[0328] A "generative model" is a program that uses machine learning algorithms to automatically generate job postings from input information and sentiment information.

[0329] "Job postings" are information that describes the requirements and conditions related to the work, and include specific job details to attract job seekers.

[0330] This invention is a system that automatically generates job postings while taking into account the user's emotional state. First, the user accesses a dedicated online form using an information terminal and inputs the data necessary to create the job posting. The form includes fields for job description, required skills, workplace location, and compensation.

[0331] While the user is filling out a form, the device uses its built-in emotion engine to analyze the user's emotional state in real time. This emotional data is collected based on the user's facial expressions and tone of voice. For example, if the user is in a calm and positive emotional state, this information is recorded by the device as emotional data.

[0332] Once input is complete, the terminal sends the input information and sentiment information to the server. The server first validates the received information to confirm its accuracy. Then, using the validated data and sentiment information, it runs a generative AI model to automatically generate job postings. This generative model utilizes machine learning algorithms to provide personalized information based on the aforementioned data.

[0333] The generated job postings are stored in a central database by the server and then displayed on the user's terminal. Users can review the presented information and make corrections as needed, resulting in the generation of more suitable job postings.

[0334] One possible scenario is that when a user enters information such as "We are looking for a software engineer who can work remotely," if they are in a relaxed emotional state, the generated information might use a tone like, "We offer a flexible remote work environment and welcome those who want to participate in innovative projects!"

[0335] An example of a prompt message would be: "Generate a positive job posting based on the following data: Job description: Software Engineer, 3+ years of experience, remote work possible."

[0336] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0337] Step 1: The user uses their device to access an online form and enter the data necessary to generate the job posting. This data includes fields such as job description, required skills, work location, and compensation. Once the data is entered, the device prepares to send the data to the next step.

[0338] Step 2: The device analyzes the user's emotional state in real time using its built-in emotion engine, simultaneously with the user's input. The input for this emotion analysis is the user's facial expressions and voice tone, and the output is recorded as emotion data. This allows the user's emotional information to be obtained.

[0339] Step 3: The terminal combines the acquired input data and emotion data and sends them to the server. The transmitted information is used for processing in the next step.

[0340] Step 4: The server validates the received input data. It checks whether the input data is accurate and complete, and if there are any deficiencies, it generates an error message and returns it to the terminal. At this stage, the input is the submitted dataset, and the output is the validated data or the error message.

[0341] Step 5: The validated data is input into the AI ​​model generated by the server. At this stage, validated job postings and sentiment data are input, and the output is individually personalized job postings. Machine learning algorithms are used in this process.

[0342] Step 6: The server saves the generated job information to the database and simultaneously sends the job information to the terminal. The user receives this output, reviews the content, and can make corrections as needed.

[0343] Step 7: The user reviews the presented job information on their device. If necessary, the user adjusts the information and tone, and then confirms the final information. In this step, the user evaluates and gives final approval, after which the final version of the job information is output.

[0344] (Application Example 2)

[0345] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0346] In the current recruitment process, the provision of information tends to be limited to standard, one-way methods, which often results in insufficient appeal to applicants and makes efficient and appropriate talent acquisition difficult. Furthermore, while taking into account applicants' emotions and circumstances can lead to better matching, current systems lack the means to reflect emotional data in job postings.

[0347] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0348] In this invention, the server includes means for receiving input data from a user using a terminal, means for validating the input data and the user's emotional data, and means for inputting the validated data into a generative model to generate customized job information based on the user's emotional state. This makes it possible to provide more personalized job information that grasps and reflects the user's emotional state in real time.

[0349] "Means of receiving input data using a device" refers to a function that provides an interface for users to input data necessary for job postings into online forms, etc., by operating a device such as a smartphone or computer.

[0350] "Means for validating input data and user sentiment data" refers to a process that not only verifies the accuracy and integrity of data entered by the user, but also analyzes the user's sentiment data using a sentiment engine and verifies its consistency.

[0351] "A means of inputting data into a generative model to generate customized job postings based on the user's emotional state" refers to an algorithm that uses a machine learning algorithm to input validated data into a generative model and adjusts and generates text while considering the user's emotional state.

[0352] "Means of storing data in data storage" refers to the function of saving generated job information in a digital format to a database or cloud storage so that it can be referenced later.

[0353] "Means of presenting to the user's terminal" refers to an interface that displays the final generated job information on the user's terminal, allowing them to review and adjust it.

[0354] This invention is a specific embodiment of a system that takes into account the emotional state of users in the job posting generation and interview process at a store.

[0355] First, the user provides input data using their smartphone. The device collects information such as "job requirements," "required experience," "work location," and "salary level" through an online form. During this process, an emotion engine is activated to evaluate the user's emotions in real time. For example, if the user is working in a relaxed state, the emotion engine detects this state and records it as data.

[0356] The terminal sends this input data and sentiment data to the server. The server performs validation of the input data using a program such as Python. If any missing items or inconsistencies between data are detected during validation, an error message is returned to the user's terminal, prompting them to make corrections.

[0357] The validated data is input into a generative AI model using machine learning algorithms to generate customized job postings that are appropriate to the user's emotional state. For example, if a user expresses positive emotions, the job posting will be generated with a friendly and appealing tone.

[0358] The generated job postings are saved to data storage and simultaneously displayed on the user's device. The user can review the displayed job postings and make further adjustments as needed.

[0359] As a concrete example, consider a case where a store owner is recruiting staff for a new cafe. Because this owner entered the information in a relaxed state when creating the job posting using the app, the resulting information has a friendly and warm tone. An example of a prompt message might be: "We are looking for staff who are looking for a comfortable work environment at our new cafe. If you have experience working in a cafe, why not put your skills to full use?"

[0360] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0361] Step 1:

[0362] The user accesses an online form using their smartphone. The user enters data such as "job requirements," "required experience," "work location," and "salary level." While the user is entering data, an emotion engine activates, analyzing the user's emotional state in real time and preparing to send it as emotion data along with the input data.

[0363] Step 2:

[0364] The terminal sends input data and sentiment data to the server. The server validates the received data. Validation is performed using a Python script to check for missing input fields and string formatting, and any errors are sent to the terminal as error messages.

[0365] Step 3:

[0366] The server inputs the validated data into a generative AI model. The server structures the data and runs machine learning algorithms (e.g., PyTorch or TensorFlow) to generate customized job postings based on the input data and sentiment data. If the sentiment data is positive, adjustments are made, such as making the wording more approachable.

[0367] Step 4:

[0368] The server stores the generated job postings in data storage. During storage, the information is saved in an appropriate format (e.g., JSON) and an index is created to facilitate searching.

[0369] Step 5:

[0370] The server sends the generated job information as a response to the user's device. The user can review the suggested job information on their device and make any necessary corrections or adjustments via the UI. The device can also send these changes to the server as updates.

[0371] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0372] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0373] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0374] [Third Embodiment]

[0375] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0376] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0377] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0378] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0379] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0380] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0381] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0382] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0383] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0384] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0385] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0386] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0387] This invention provides a system for automatically generating job postings, which generates attractive and consistent job postings based on data entered by the user. The embodiments thereof are described below.

[0388] First, the user accesses a web form for generating job postings via their device. This form includes categories such as "Job Description," "Required Experience," and "Work Location." The user fills in the necessary data in these input fields and clicks the "Submit" button.

[0389] Next, the server receives the input data submitted by the user. The server validates the data to ensure it is accurate and contains all the necessary information. If there are errors in the data, it returns an error message to the terminal, giving the user an opportunity to correct the input.

[0390] Once validation is complete, the data is sent by the server to a generative model. This generative model uses machine learning algorithms to automatically generate natural and effective job postings based on the given input data. For example, it might create job postings that align with a company's needs, such as "We are looking for an experienced professional to plan and implement our marketing strategy."

[0391] The generated job postings are stored in a database by the server and used for future reference and management. The stored information is sent back to the terminal, where the user can review it and make adjustments as needed.

[0392] This system significantly streamlines the job posting process, allowing users to quickly obtain high-quality job postings even without specialized expertise. It will particularly benefit HR personnel in small and medium-sized enterprises, saving time and effort and contributing to more efficient recruitment activities.

[0393] The following describes the processing flow.

[0394] Step 1:

[0395] The device displays a form for generating job postings via a web browser. The user fills in information such as "job description," "required experience," and "work location" in the input fields and clicks the "submit" button.

[0396] Step 2:

[0397] The device sends the data that the user has sent to the server. The server receives this data.

[0398] Step 3:

[0399] The server validates the received data. It checks whether all required fields are filled in and whether the data format is correct. If an error is detected, it returns an error message to the terminal and prompts the user to correct it.

[0400] Step 4:

[0401] The server sends data that has passed validation to the generative model. The server then formats this data into a format that the AI ​​model can easily understand and passes it to the model via an API or other means.

[0402] Step 5:

[0403] The generative model generates job postings based on input data received from the server. For example, it might output text such as, "We are looking for a professional with over 3 years of experience in marketing strategy planning."

[0404] Step 6:

[0405] The server receives the generated job postings and saves them to the database. This allows the job postings to be referenced later.

[0406] Step 7:

[0407] The server sends the generated job information to the terminal and displays it to the user. The user can review the results and make further edits if necessary.

[0408] (Example 1)

[0409] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0410] Traditional job posting creation processes required significant time and specialized knowledge for information input and editing, making them a heavy burden for small and medium-sized enterprises and individuals who were not specialists. Furthermore, maintaining consistency and quality in job postings was difficult, potentially reducing a company's appeal to job seekers.

[0411] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0412] In this invention, the server includes means for acquiring information for generating job postings, means for inputting and verifying the information, and means for inputting the verified information into a generation algorithm to create job postings. This makes it possible for even non-experts to efficiently create high-quality job postings.

[0413] "Means for acquiring information" refers to methods or devices for collecting data necessary for job postings from system users.

[0414] "Means for verifying input" refers to a method or device for confirming whether the information obtained from the user is accurate and complete.

[0415] "Means for inputting into a generation algorithm to create job information" refers to a method or device that uses a machine learning model to automatically generate job postings based on validated input information.

[0416] "Means of storage" refers to a method or device for storing generated work information in a storage medium such as a database.

[0417] "Means of presenting to the user" refers to a method or device for displaying the generated work information on the user's terminal.

[0418] This invention relates to a system for automatically generating job postings. Specifically, it uses a user, a server, and a terminal to perform a series of processes.

[0419] First, the user accesses a web interface for generating job postings via their device. The user enters information such as "job description," "required experience," "work location," and "salary." This entered data is sent to the server via an HTTP request.

[0420] The server validates the received data. This validation process checks for incomplete or incorrectly formatted data. If there are any problems with the data, the server generates an error message and notifies the user.

[0421] Next, the data that passes validation is passed to a generative AI model. This generative AI model utilizes machine learning algorithms to generate effective and attractive job postings based on the input data. In this process, for example, if a prompt containing keywords such as "IT engineer" is entered, information such as "We are looking for an IT engineer with JavaScript experience in Tokyo" will be generated.

[0422] The generated job postings are saved to a database by the server. This database will be used for future reference and editing. Users can view the generated job postings through their terminals and make adjustments as needed.

[0423] In this way, the system automates and streamlines the job posting generation process. Furthermore, this technology makes it possible to create high-quality, consistent job postings even without specialized knowledge.

[0424] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0425] Step 1:

[0426] Users access a web form through their device and enter the data necessary to create a job posting. This form includes fields such as "Job Description," "Required Experience," "Work Location," and "Salary." The entered data is sent to the server via an HTTP request. Specifically, the user's actions include opening the form in a browser, entering information in each field, and clicking the "Submit" button.

[0427] Step 2:

[0428] The server receives data sent by the user. It analyzes the received data and performs input validation. Specifically, it runs a script to check if the data format is correct and if all required fields are filled in, ensuring input accuracy. If there are errors in the validation results, it generates an error message and sends it back to the terminal, prompting the user to make corrections.

[0429] Step 3:

[0430] Data that passes input validation is input to the generation AI model by the server. The server passes a prompt to the generation AI model, instructing it to generate job postings. The generation AI model creates appropriate job postings based on the input data. The generated job postings are obtained as output. For example, in response to the prompt requesting JavaScript experience, the output might be "We are recruiting an IT engineer located in Tokyo."

[0431] Step 4:

[0432] The generated job postings are stored in a database by the server. Storing them in a database is important for information persistence and for later reference and editing. The server accesses the database and stores the newly generated data in the appropriate tables and fields.

[0433] Step 5:

[0434] The server returns the saved job postings to the user's device. This allows the user to view the generated information via their browser. Furthermore, they can edit the job postings and make final adjustments as needed. The user operates their device to view the information in their browser and modify the content using the editing form.

[0435] (Application Example 1)

[0436] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0437] In recent years, with the increasing use of virtual spaces for business operations, conventional job posting generation systems face challenges in intuitively inputting data and verifying job details within a virtual environment. Furthermore, while there is a demand for rapid generation and presentation of job postings, there is a lack of systems that allow users to complete operations while fully immersed in the virtual space.

[0438] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0439] In this invention, the server includes a device for receiving input data for generating job postings, a device for verifying the input data, and a device for presenting the input data in a virtual space and interactively presenting the generated job postings. This allows users to intuitively generate and verify job postings by making voice and motion inputs in the virtual space.

[0440] A "device that receives input data for generating job postings" is part of a system that receives information such as job duties and requirements entered by users and processes that information.

[0441] "The device for verifying the input data" is part of a system for confirming that the received input data is accurate and complete.

[0442] A "generative model" is a program that uses machine learning algorithms to create appropriate job postings based on input data.

[0443] A "device that presents input data in a virtual space and interactively displays generated job information" is part of a system that uses virtual reality technology to allow users to visually and manually confirm and edit the information they input and the generated job information.

[0444] This invention is a job posting generation system that utilizes virtual reality technology, and in particular aims to streamline the job posting generation process within a virtual space. The system is implemented using a head-mounted display worn by the user.

[0445] The server receives input data to generate job postings transmitted from head-mounted displays such as the Oculus Quest 2. Users can interactively input data such as job descriptions and required skills through voice commands and hand tracking within the virtual space.

[0446] Subsequently, the server uses Python to validate the input data and check for defects. Once validation is complete, the data is input into a generative AI model using TensorFlow, which generates effective job postings. The generated job postings are then visually presented in real time to the user's field of view within a virtual space running in the Unity environment.

[0447] Furthermore, users can visually review the generated job postings and, if necessary, modify the data by re-entering it via voice input within the virtual space. This system allows users to intuitively and quickly build and adjust job postings without being constrained by the limitations of the real world.

[0448] As a concrete example, let's assume a store in a virtual space is recruiting new staff. The user can input conditions such as "recruiting new staff" and "fitting assistant experience," and instantly receive optimized job information. An example of a prompt statement for giving instructions is as follows:

[0449] Example of a prompt:

[0450] Please generate a job posting for new staff recruitment based on the following conditions:

[0451] Job Description: Customer support and product explanation

[0452] Required experience: Practical work experience in previous job

[0453] Work location: Designated store within the virtual space

[0454] This invention dramatically simplifies the generation and management of job postings, and can particularly revolutionize job-seeking activities in virtual environments.

[0455] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0456] Step 1:

[0457] The user wears a head-mounted display and inputs data into an interface in a virtual space using voice or hand tracking.

[0458] The system takes user inputs such as job description and required experience, and captures user actions to record them as input data. The interface also analyzes hand movements and voice commands in real time to support the user's quick selection of desired items.

[0459] Step 2:

[0460] The server receives input data sent by the user.

[0461] The input data is validated using a Python script to check for missing or invalid data in the fields.

[0462] For example, if the required number of years of experience is unclear, an error message will be generated to prompt the user to re-enter the information.

[0463] Step 3:

[0464] The server inputs the validated data into the AI ​​model.

[0465] Using TensorFlow, we perform calculations to generate job postings based on data.

[0466] The output will generate specific and effective job postings such as, "We are seeking an experienced professional to plan and implement our marketing strategy."

[0467] Step 4:

[0468] The generated job postings are sent from the server to the terminal and presented to the user within the virtual space.

[0469] Using the Unity environment, information is displayed in a virtual interface so that the user can visually confirm it.

[0470] Based on the generated data, users can review the job postings displayed in the virtual space and make corrections as needed using voice input.

[0471] Step 5:

[0472] Once user-confirmed and corrected job postings are submitted, they are resubmitted to the server and stored in the database.

[0473] The saved information is managed so that it can be readily used in future recruitment activities, contributing to the user's recruitment strategy.

[0474] Through these operations, users can quickly and effectively complete the generation of job postings through intuitive operation within the virtual space.

[0475] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0476] This invention provides a system that automatically generates job postings and also includes an emotion engine that recognizes user emotions. The embodiments thereof are described below.

[0477] First, the user accesses an online form for generating job postings using their device. This form includes input fields such as "Job Description," "Required Experience," and "Work Location." The user enters the necessary information into these fields, and the emotion engine analyzes the user's emotional state in real time as they type. For example, if the user is relaxed while typing, that emotional data is also sent along with the form.

[0478] Next, the device sends input data and sentiment data to the server. The server first validates the input data to check for any errors. If errors are found, it returns an error message to the device, allowing the user to make corrections.

[0479] Once validation is complete, the data is passed from the server to a generative model. This generative model uses machine learning algorithms to generate job postings that take the user's emotional state into account. For example, if a user is showing positive emotions, the model will generate text with a positive tone that matches their mood.

[0480] The server saves the generated job postings to the database and simultaneously presents the final job postings to the user. The user can review this information and make further adjustments as needed.

[0481] This system has the advantage of generating more personalized job postings by taking into account the user's emotional state. This makes it possible to attract more suitable job seekers and streamline the company's recruitment process.

[0482] The following describes the processing flow.

[0483] Step 1:

[0484] The terminal displays a form for generating job postings via a web browser. The user enters information such as "job description," "required experience," and "work location." Simultaneously, an emotion engine recognizes the user's emotional state and processes that information in real time.

[0485] Step 2:

[0486] The user completes the input and clicks the "Submit" button. The device sends the input data along with the sentiment data to the server.

[0487] Step 3:

[0488] The server validates the data it receives. It checks if all the necessary data is present and in the correct format, and if there are any deficiencies, it returns an error message to the terminal prompting the user to make corrections.

[0489] Step 4:

[0490] The server sends validated data to the generative model. The server also considers sentiment data and generates personalized job postings based on it.

[0491] Step 5:

[0492] The generative model generates job postings based on input data and sentiment data. For example, if the user's sentiment is positive, job postings using positive language will be created.

[0493] Step 6:

[0494] The server saves the generated job postings to a database. The saved data can be referenced and reused later.

[0495] Step 7:

[0496] The server sends the final job posting information to the terminal for the user to view. The user can then review this information and make any necessary edits.

[0497] (Example 2)

[0498] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0499] Conventional job posting generation systems are unable to generate personalized job postings that take user emotions into consideration, resulting in job postings that fail to sufficiently attract the interest of job seekers. Furthermore, the insufficient data validation to improve the reliability of the generated information is also a problem.

[0500] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0501] In this invention, the server includes means for analyzing input information and user sentiment information in real time, means for validating the input information, and means for inputting the validated information and sentiment information into a generative model to generate job postings. This enables the automatic generation of personalized job postings and the provision of highly reliable information.

[0502] "Input information" refers to the data that users provide to generate job postings, and includes data items such as job description, required skills, workplace location, and compensation.

[0503] "Emotional information" refers to data that represents the user's emotional state at the time of input, and is analyzed in real time based on the user's facial expressions and voice tone.

[0504] "Validation" is the process of verifying the accuracy and completeness of input information, and is performed in order to detect data errors in advance.

[0505] A "generative model" is a program that uses machine learning algorithms to automatically generate job postings from input information and sentiment information.

[0506] "Job postings" are information that describes the requirements and conditions related to the work, and include specific job details to attract job seekers.

[0507] This invention is a system that automatically generates job postings while taking into account the user's emotional state. First, the user accesses a dedicated online form using an information terminal and inputs the data necessary to create the job posting. The form includes fields for job description, required skills, workplace location, and compensation.

[0508] While the user is filling out a form, the device uses its built-in emotion engine to analyze the user's emotional state in real time. This emotional data is collected based on the user's facial expressions and tone of voice. For example, if the user is in a calm and positive emotional state, this information is recorded by the device as emotional data.

[0509] Once input is complete, the terminal sends the input information and sentiment information to the server. The server first validates the received information to confirm its accuracy. Then, using the validated data and sentiment information, it runs a generative AI model to automatically generate job postings. This generative model utilizes machine learning algorithms to provide personalized information based on the aforementioned data.

[0510] The generated job postings are stored in a central database by the server and then displayed on the user's terminal. Users can review the presented information and make corrections as needed, resulting in the generation of more suitable job postings.

[0511] One possible scenario is that when a user enters information such as "We are looking for a software engineer who can work remotely," if they are in a relaxed emotional state, the generated information might use a tone like, "We offer a flexible remote work environment and welcome those who want to participate in innovative projects!"

[0512] An example of a prompt message would be: "Generate a positive job posting based on the following data: Job description: Software Engineer, 3+ years of experience, remote work possible."

[0513] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0514] Step 1: The user uses their device to access an online form and enter the data necessary to generate the job posting. This data includes fields such as job description, required skills, work location, and compensation. Once the data is entered, the device prepares to send the data to the next step.

[0515] Step 2: The device analyzes the user's emotional state in real time using its built-in emotion engine, simultaneously with the user's input. The input for this emotion analysis is the user's facial expressions and voice tone, and the output is recorded as emotion data. This allows the user's emotional information to be obtained.

[0516] Step 3: The terminal combines the acquired input data and emotion data and sends them to the server. The transmitted information is used for processing in the next step.

[0517] Step 4: The server validates the received input data. It checks whether the input data is accurate and complete, and if there are any deficiencies, it generates an error message and returns it to the terminal. At this stage, the input is the submitted dataset, and the output is the validated data or the error message.

[0518] Step 5: The validated data is input into the AI ​​model generated by the server. At this stage, validated job postings and sentiment data are input, and the output is individually personalized job postings. Machine learning algorithms are used in this process.

[0519] Step 6: The server saves the generated job information to the database and simultaneously sends the job information to the terminal. The user receives this output, reviews the content, and can make corrections as needed.

[0520] Step 7: The user reviews the presented job information on their device. If necessary, the user adjusts the information and tone, and then confirms the final information. In this step, the user evaluates and gives final approval, after which the final version of the job information is output.

[0521] (Application Example 2)

[0522] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] In the current recruitment process, the provision of information tends to be limited to standard, one-way methods, which often results in insufficient appeal to applicants and makes efficient and appropriate talent acquisition difficult. Furthermore, while taking into account applicants' emotions and circumstances can lead to better matching, current systems lack the means to reflect emotional data in job postings.

[0524] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0525] In this invention, the server includes means for receiving input data from a user using a terminal, means for validating the input data and the user's emotional data, and means for inputting the validated data into a generative model to generate customized job information based on the user's emotional state. This makes it possible to provide more personalized job information that grasps and reflects the user's emotional state in real time.

[0526] "Means of receiving input data using a device" refers to a function that provides an interface for users to input data necessary for job postings into online forms, etc., by operating a device such as a smartphone or computer.

[0527] "Means for validating input data and user sentiment data" refers to a process that not only verifies the accuracy and integrity of data entered by the user, but also analyzes the user's sentiment data using a sentiment engine and verifies its consistency.

[0528] "A means of inputting data into a generative model to generate customized job postings based on the user's emotional state" refers to an algorithm that uses a machine learning algorithm to input validated data into a generative model and adjusts and generates text while considering the user's emotional state.

[0529] "Means of storing data in data storage" refers to the function of saving generated job information in a digital format to a database or cloud storage so that it can be referenced later.

[0530] "Means of presenting to the user's terminal" refers to an interface that displays the final generated job information on the user's terminal, allowing them to review and adjust it.

[0531] This invention is a specific embodiment of a system that takes into account the emotional state of users in the job posting generation and interview process at a store.

[0532] First, the user provides input data using their smartphone. The device collects information such as "job requirements," "required experience," "work location," and "salary level" through an online form. During this process, an emotion engine is activated to evaluate the user's emotions in real time. For example, if the user is working in a relaxed state, the emotion engine detects this state and records it as data.

[0533] The terminal sends this input data and sentiment data to the server. The server performs validation of the input data using a program such as Python. If any missing items or inconsistencies between data are detected during validation, an error message is returned to the user's terminal, prompting them to make corrections.

[0534] The validated data is input into a generative AI model using machine learning algorithms to generate customized job postings that are appropriate to the user's emotional state. For example, if a user expresses positive emotions, the job posting will be generated with a friendly and appealing tone.

[0535] The generated job postings are saved to data storage and simultaneously displayed on the user's device. The user can review the displayed job postings and make further adjustments as needed.

[0536] As a concrete example, consider a case where a store owner is recruiting staff for a new cafe. Because this owner entered the information in a relaxed state when creating the job posting using the app, the resulting information has a friendly and warm tone. An example of a prompt message might be: "We are looking for staff who are looking for a comfortable work environment at our new cafe. If you have experience working in a cafe, why not put your skills to full use?"

[0537] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0538] Step 1:

[0539] The user accesses an online form using their smartphone. The user enters data such as "job requirements," "required experience," "work location," and "salary level." While the user is entering data, an emotion engine activates, analyzing the user's emotional state in real time and preparing to send it as emotion data along with the input data.

[0540] Step 2:

[0541] The terminal sends input data and sentiment data to the server. The server validates the received data. Validation is performed using a Python script to check for missing input fields and string formatting, and any errors are sent to the terminal as error messages.

[0542] Step 3:

[0543] The server inputs the validated data into a generative AI model. The server structures the data and runs machine learning algorithms (e.g., PyTorch or TensorFlow) to generate customized job postings based on the input data and sentiment data. If the sentiment data is positive, adjustments are made, such as making the wording more approachable.

[0544] Step 4:

[0545] The server stores the generated job postings in data storage. During storage, the information is saved in an appropriate format (e.g., JSON) and an index is created to facilitate searching.

[0546] Step 5:

[0547] The server sends the generated job information as a response to the user's device. The user can review the suggested job information on their device and make any necessary corrections or adjustments via the UI. The device can also send these changes to the server as updates.

[0548] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0549] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0550] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0551] [Fourth Embodiment]

[0552] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0553] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0554] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0555] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0556] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0557] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0558] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0559] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0560] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0561] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0562] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0563] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0564] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0565] This invention provides a system for automatically generating job postings, which generates attractive and consistent job postings based on data entered by the user. The embodiments thereof are described below.

[0566] First, the user accesses a web form for generating job postings via their device. This form includes categories such as "Job Description," "Required Experience," and "Work Location." The user fills in the necessary data in these input fields and clicks the "Submit" button.

[0567] Next, the server receives the input data submitted by the user. The server validates the data to ensure it is accurate and contains all the necessary information. If there are errors in the data, it returns an error message to the terminal, giving the user an opportunity to correct the input.

[0568] Once validation is complete, the data is sent by the server to a generative model. This generative model uses machine learning algorithms to automatically generate natural and effective job postings based on the given input data. For example, it might create job postings that align with a company's needs, such as "We are looking for an experienced professional to plan and implement our marketing strategy."

[0569] The generated job postings are stored in a database by the server and used for future reference and management. The stored information is sent back to the terminal, where the user can review it and make adjustments as needed.

[0570] This system significantly streamlines the job posting process, allowing users to quickly obtain high-quality job postings even without specialized expertise. It will particularly benefit HR personnel in small and medium-sized enterprises, saving time and effort and contributing to more efficient recruitment activities.

[0571] The following describes the processing flow.

[0572] Step 1:

[0573] The device displays a form for generating job postings via a web browser. The user fills in information such as "job description," "required experience," and "work location" in the input fields and clicks the "submit" button.

[0574] Step 2:

[0575] The device sends the data that the user has sent to the server. The server receives this data.

[0576] Step 3:

[0577] The server validates the received data. It checks whether all required fields are filled in and whether the data format is correct. If an error is detected, it returns an error message to the terminal and prompts the user to correct it.

[0578] Step 4:

[0579] The server sends data that has passed validation to the generative model. The server then formats this data into a format that the AI ​​model can easily understand and passes it to the model via an API or other means.

[0580] Step 5:

[0581] The generative model generates job postings based on input data received from the server. For example, it might output text such as, "We are looking for a professional with over 3 years of experience in marketing strategy planning."

[0582] Step 6:

[0583] The server receives the generated job postings and saves them to the database. This allows the job postings to be referenced later.

[0584] Step 7:

[0585] The server sends the generated job information to the terminal and displays it to the user. The user can review the results and make further edits if necessary.

[0586] (Example 1)

[0587] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0588] Traditional job posting creation processes required significant time and specialized knowledge for information input and editing, making them a heavy burden for small and medium-sized enterprises and individuals who were not specialists. Furthermore, maintaining consistency and quality in job postings was difficult, potentially reducing a company's appeal to job seekers.

[0589] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0590] In this invention, the server includes means for acquiring information for generating job postings, means for inputting and verifying the information, and means for inputting the verified information into a generation algorithm to create job postings. This makes it possible for even non-experts to efficiently create high-quality job postings.

[0591] "Means for acquiring information" refers to methods or devices for collecting data necessary for job postings from system users.

[0592] "Means for verifying input" refers to a method or device for confirming whether the information obtained from the user is accurate and complete.

[0593] "Means for inputting into a generation algorithm and creating job information" refers to a method or apparatus that uses a machine learning model to automatically generate job postings based on validated input information.

[0594] "Means of storage" refers to a method or device for storing generated work information in a storage medium such as a database.

[0595] "Means of presenting to the user" refers to a method or device for displaying the generated work information on the user's terminal.

[0596] This invention relates to a system for automatically generating job postings. Specifically, it uses a user, a server, and a terminal to perform a series of processes.

[0597] First, the user accesses a web interface for generating job postings via their device. The user enters information such as "job description," "required experience," "work location," and "salary." This entered data is sent to the server via an HTTP request.

[0598] The server validates the received data. This validation process checks for incomplete or incorrectly formatted data. If there are any problems with the data, the server generates an error message and notifies the user.

[0599] Next, the data that passes validation is passed to a generative AI model. This generative AI model utilizes machine learning algorithms to generate effective and attractive job postings based on the input data. In this process, for example, if a prompt containing keywords such as "IT engineer" is entered, information such as "We are looking for an IT engineer with JavaScript experience in Tokyo" will be generated.

[0600] The generated job postings are saved to a database by the server. This database will be used for future reference and editing. Users can view the generated job postings through their terminals and make adjustments as needed.

[0601] In this way, the system automates and streamlines the job posting generation process. Furthermore, this technology makes it possible to create high-quality, consistent job postings even without specialized knowledge.

[0602] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0603] Step 1:

[0604] Users access a web form through their device and enter the data necessary to create a job posting. This form includes fields such as "Job Description," "Required Experience," "Work Location," and "Salary." The entered data is sent to the server via an HTTP request. Specifically, the user's actions include opening the form in a browser, entering information in each field, and clicking the "Submit" button.

[0605] Step 2:

[0606] The server receives data sent by the user. It analyzes the received data and performs input validation. Specifically, it runs a script to check if the data format is correct and if all required fields are filled in, ensuring input accuracy. If there are errors in the validation results, it generates an error message and sends it back to the terminal, prompting the user to make corrections.

[0607] Step 3:

[0608] Data that passes input validation is input to the generation AI model by the server. The server passes a prompt to the generation AI model, instructing it to generate job postings. The generation AI model creates appropriate job postings based on the input data. The generated job postings are obtained as output. For example, in response to the prompt requesting JavaScript experience, the output might be "We are recruiting an IT engineer located in Tokyo."

[0609] Step 4:

[0610] The generated job postings are stored in a database by the server. Storing them in a database is important for information persistence and for later reference and editing. The server accesses the database and stores the newly generated data in the appropriate tables and fields.

[0611] Step 5:

[0612] The server returns the saved job postings to the user's device. This allows the user to view the generated information via their browser. Furthermore, they can edit the job postings and make final adjustments as needed. The user operates their device to view the information in their browser and modify the content using the editing form.

[0613] (Application Example 1)

[0614] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0615] In recent years, with the increasing use of virtual spaces for business operations, conventional job posting generation systems face challenges in intuitively inputting data and verifying job details within a virtual environment. Furthermore, while there is a demand for rapid generation and presentation of job postings, there is a lack of systems that allow users to complete operations while fully immersed in the virtual space.

[0616] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0617] In this invention, the server includes a device for receiving input data for generating job postings, a device for verifying the input data, and a device for presenting the input data in a virtual space and interactively presenting the generated job postings. This allows users to intuitively generate and verify job postings by making voice and motion inputs in the virtual space.

[0618] A "device that receives input data for generating job postings" is part of a system that receives information such as job duties and requirements entered by users and processes that information.

[0619] "The device for verifying the input data" is part of a system for confirming that the received input data is accurate and complete.

[0620] A "generative model" is a program that uses machine learning algorithms to create appropriate job postings based on input data.

[0621] A "device that presents input data in a virtual space and interactively displays generated job information" is part of a system that uses virtual reality technology to allow users to visually and manually confirm and edit the information they input and the generated job information.

[0622] This invention is a job posting generation system that utilizes virtual reality technology, and in particular aims to streamline the job posting generation process within a virtual space. The system is implemented using a head-mounted display worn by the user.

[0623] The server receives input data to generate job postings transmitted from head-mounted displays such as the Oculus Quest 2. Users can interactively input data such as job descriptions and required skills through voice commands and hand tracking within the virtual space.

[0624] Subsequently, the server uses Python to validate the input data and check for defects. Once validation is complete, the data is input into a generative AI model using TensorFlow, which generates effective job postings. The generated job postings are then visually presented in real time to the user's field of view within a virtual space running in the Unity environment.

[0625] Furthermore, users can visually review the generated job postings and, if necessary, modify the data by re-entering it via voice input within the virtual space. This system allows users to intuitively and quickly build and adjust job postings without being constrained by the limitations of the real world.

[0626] As a concrete example, let's assume a store in a virtual space is recruiting new staff. The user can input conditions such as "recruiting new staff" and "fitting assistant experience," and instantly receive optimized job information. An example of a prompt statement for giving instructions is as follows:

[0627] Example of a prompt:

[0628] Please generate a job posting for new staff recruitment based on the following conditions:

[0629] Job Description: Customer support and product explanation

[0630] Required experience: Practical work experience in previous job

[0631] Work location: Designated store within the virtual space

[0632] This invention dramatically simplifies the generation and management of job postings, and can particularly revolutionize job-seeking activities in virtual environments.

[0633] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0634] Step 1:

[0635] The user wears a head-mounted display and inputs data into an interface in a virtual space using voice or hand tracking.

[0636] The system takes user inputs such as job description and required experience, and captures user actions to record them as input data. The interface also analyzes hand movements and voice commands in real time to support the user's quick selection of desired items.

[0637] Step 2:

[0638] The server receives input data sent by the user.

[0639] The input data is validated using a Python script to check for missing or invalid data in the fields.

[0640] For example, if the required number of years of experience is unclear, an error message will be generated to prompt the user to re-enter the information.

[0641] Step 3:

[0642] The server inputs the validated data into the AI ​​model.

[0643] Using TensorFlow, we perform calculations to generate job postings based on data.

[0644] The output will generate specific and effective job postings such as, "We are seeking an experienced professional to plan and implement our marketing strategy."

[0645] Step 4:

[0646] The generated job postings are sent from the server to the terminal and presented to the user within the virtual space.

[0647] Using the Unity environment, information is displayed in a virtual interface so that the user can visually confirm it.

[0648] Based on the generated data, users can review the job postings displayed in the virtual space and make corrections as needed using voice input.

[0649] Step 5:

[0650] Once user-confirmed and corrected job postings are submitted, they are resubmitted to the server and stored in the database.

[0651] The saved information is managed so that it can be readily used in future recruitment activities, contributing to the user's recruitment strategy.

[0652] Through these operations, users can quickly and effectively complete the generation of job postings through intuitive operation within the virtual space.

[0653] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0654] This invention provides a system that automatically generates job postings and also includes an emotion engine that recognizes user emotions. The embodiments thereof are described below.

[0655] First, the user accesses an online form for generating job postings using their device. This form includes input fields such as "Job Description," "Required Experience," and "Work Location." The user enters the necessary information into these fields, and the emotion engine analyzes the user's emotional state in real time as they type. For example, if the user is relaxed while typing, that emotional data is also sent along with the form.

[0656] Next, the device sends input data and sentiment data to the server. The server first validates the input data to check for any errors. If errors are found, it returns an error message to the device, allowing the user to make corrections.

[0657] Once validation is complete, the data is passed from the server to a generative model. This generative model uses machine learning algorithms to generate job postings that take the user's emotional state into account. For example, if a user is showing positive emotions, the model will generate text with a positive tone that matches their mood.

[0658] The server saves the generated job postings to the database and simultaneously presents the final job postings to the user. The user can review this information and make further adjustments as needed.

[0659] This system has the advantage of generating more personalized job postings by taking into account the user's emotional state. This makes it possible to attract more suitable job seekers and streamline the company's recruitment process.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] The terminal displays a form for generating job postings via a web browser. The user enters information such as "job description," "required experience," and "work location." Simultaneously, an emotion engine recognizes the user's emotional state and processes that information in real time.

[0663] Step 2:

[0664] The user completes the input and clicks the "Submit" button. The device sends the input data along with the sentiment data to the server.

[0665] Step 3:

[0666] The server validates the data it receives. It checks if all the necessary data is present and in the correct format, and if there are any deficiencies, it returns an error message to the terminal prompting the user to make corrections.

[0667] Step 4:

[0668] The server sends validated data to the generative model. The server also considers sentiment data and generates personalized job postings based on it.

[0669] Step 5:

[0670] The generative model generates job postings based on input data and sentiment data. For example, if the user's sentiment is positive, job postings using positive language will be created.

[0671] Step 6:

[0672] The server saves the generated job postings to a database. The saved data can be referenced and reused later.

[0673] Step 7:

[0674] The server sends the final job posting information to the terminal for the user to view. The user can then review this information and make any necessary edits.

[0675] (Example 2)

[0676] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0677] Conventional job posting generation systems are unable to generate personalized job postings that take user emotions into consideration, resulting in job postings that fail to sufficiently attract the interest of job seekers. Furthermore, the insufficient data validation to improve the reliability of the generated information is also a problem.

[0678] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0679] In this invention, the server includes means for analyzing input information and user sentiment information in real time, means for validating the input information, and means for inputting the validated information and sentiment information into a generative model to generate job postings. This enables the automatic generation of personalized job postings and the provision of highly reliable information.

[0680] "Input information" refers to the data that users provide to generate job postings, and includes data items such as job description, required skills, workplace location, and compensation.

[0681] "Emotional information" refers to data that represents the user's emotional state at the time of input, and is analyzed in real time based on the user's facial expressions and voice tone.

[0682] "Validation" is the process of verifying the accuracy and completeness of input information, and is performed in order to detect data errors in advance.

[0683] A "generative model" is a program that uses machine learning algorithms to automatically generate job postings from input information and sentiment information.

[0684] "Job postings" are information that describes the requirements and conditions related to the work, and include specific job details to attract job seekers.

[0685] This invention is a system that automatically generates job postings while taking into account the user's emotional state. First, the user accesses a dedicated online form using an information terminal and inputs the data necessary to create the job posting. The form includes fields for job description, required skills, workplace location, and compensation.

[0686] While the user is filling out a form, the device uses its built-in emotion engine to analyze the user's emotional state in real time. This emotional data is collected based on the user's facial expressions and tone of voice. For example, if the user is in a calm and positive emotional state, this information is recorded by the device as emotional data.

[0687] Once input is complete, the terminal sends the input information and sentiment information to the server. The server first validates the received information to confirm its accuracy. Then, using the validated data and sentiment information, it runs a generative AI model to automatically generate job postings. This generative model utilizes machine learning algorithms to provide personalized information based on the aforementioned data.

[0688] The generated job postings are stored in a central database by the server and then displayed on the user's terminal. Users can review the presented information and make corrections as needed, resulting in the generation of more suitable job postings.

[0689] One possible scenario is that when a user enters information such as "We are looking for a software engineer who can work remotely," if they are in a relaxed emotional state, the generated information might use a tone like, "We offer a flexible remote work environment and welcome those who want to participate in innovative projects!"

[0690] An example of a prompt message would be: "Generate a positive job posting based on the following data: Job description: Software Engineer, 3+ years of experience, remote work possible."

[0691] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0692] Step 1: The user uses their device to access an online form and enter the data necessary to generate the job posting. This data includes fields such as job description, required skills, work location, and compensation. Once the data is entered, the device prepares to send the data to the next step.

[0693] Step 2: The device analyzes the user's emotional state in real time using its built-in emotion engine, simultaneously with the user's input. The input for this emotion analysis is the user's facial expressions and voice tone, and the output is recorded as emotion data. This allows the user's emotional information to be obtained.

[0694] Step 3: The terminal combines the acquired input data and emotion data and sends them to the server. The transmitted information is used for processing in the next step.

[0695] Step 4: The server validates the received input data. It checks whether the input data is accurate and complete, and if there are any deficiencies, it generates an error message and returns it to the terminal. At this stage, the input is the submitted dataset, and the output is the validated data or the error message.

[0696] Step 5: The validated data is input into the AI ​​model generated by the server. At this stage, validated job postings and sentiment data are input, and the output is individually personalized job postings. Machine learning algorithms are used in this process.

[0697] Step 6: The server saves the generated job information to the database and simultaneously sends the job information to the terminal. The user receives this output, reviews the content, and can make corrections as needed.

[0698] Step 7: The user reviews the presented job information on their device. If necessary, the user adjusts the information and tone, and then confirms the final information. In this step, the user evaluates and gives final approval, after which the final version of the job information is output.

[0699] (Application Example 2)

[0700] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] In the current recruitment process, the provision of information tends to be limited to standard, one-way methods, which often results in insufficient appeal to applicants and makes efficient and appropriate talent acquisition difficult. Furthermore, while taking into account applicants' emotions and circumstances can lead to better matching, current systems lack the means to reflect emotional data in job postings.

[0702] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0703] In this invention, the server includes means for receiving input data from a user using a terminal, means for validating the input data and the user's emotional data, and means for inputting the validated data into a generative model to generate customized job information based on the user's emotional state. This makes it possible to provide more personalized job information that grasps and reflects the user's emotional state in real time.

[0704] "Means of receiving input data using a device" refers to a function that provides an interface for users to input data necessary for job postings into online forms, etc., by operating a device such as a smartphone or computer.

[0705] "Means for validating input data and user sentiment data" refers to a process that not only verifies the accuracy and integrity of data entered by the user, but also analyzes the user's sentiment data using a sentiment engine and verifies its consistency.

[0706] "A means of inputting data into a generative model to generate customized job postings based on the user's emotional state" refers to an algorithm that uses a machine learning algorithm to input validated data into a generative model and adjusts and generates text while considering the user's emotional state.

[0707] "Means of storing data in data storage" refers to the function of saving generated job information in a digital format to a database or cloud storage so that it can be referenced later.

[0708] "Means of presenting to the user's terminal" refers to an interface that displays the final generated job information on the user's terminal, allowing them to review and adjust it.

[0709] This invention is a specific embodiment of a system that takes into account the emotional state of users in the job posting generation and interview process at a store.

[0710] First, the user provides input data using their smartphone. The device collects information such as "job requirements," "required experience," "work location," and "salary level" through an online form. During this process, an emotion engine is activated to evaluate the user's emotions in real time. For example, if the user is working in a relaxed state, the emotion engine detects this state and records it as data.

[0711] The terminal sends this input data and sentiment data to the server. The server performs validation of the input data using a program such as Python. If any missing items or inconsistencies between data are detected during validation, an error message is returned to the user's terminal, prompting them to make corrections.

[0712] The validated data is input into a generative AI model using machine learning algorithms to generate customized job postings that are appropriate to the user's emotional state. For example, if a user expresses positive emotions, the job posting will be generated with a friendly and appealing tone.

[0713] The generated job postings are saved to data storage and simultaneously displayed on the user's device. The user can review the displayed job postings and make further adjustments as needed.

[0714] As a concrete example, consider a case where a store owner is recruiting staff for a new cafe. Because this owner entered the information in a relaxed state when creating the job posting using the app, the resulting information has a friendly and warm tone. An example of a prompt message might be: "We are looking for staff who are looking for a comfortable work environment at our new cafe. If you have experience working in a cafe, why not put your skills to full use?"

[0715] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0716] Step 1:

[0717] The user accesses an online form using their smartphone. The user enters data such as "job requirements," "required experience," "work location," and "salary level." While the user is entering data, an emotion engine activates, analyzing the user's emotional state in real time and preparing to send it as emotion data along with the input data.

[0718] Step 2:

[0719] The terminal sends input data and sentiment data to the server. The server validates the received data. Validation is performed using a Python script to check for missing input fields and string formatting, and any errors are sent to the terminal as error messages.

[0720] Step 3:

[0721] The server inputs the validated data into a generative AI model. The server structures the data and runs machine learning algorithms (e.g., PyTorch or TensorFlow) to generate customized job postings based on the input data and sentiment data. If the sentiment data is positive, adjustments are made, such as making the wording more approachable.

[0722] Step 4:

[0723] The server stores the generated job postings in data storage. During storage, the information is saved in an appropriate format (e.g., JSON) and an index is created to facilitate searching.

[0724] Step 5:

[0725] The server sends the generated job information as a response to the user's device. The user can review the suggested job information on their device and make any necessary corrections or adjustments via the UI. The device can also send these changes to the server as updates.

[0726] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0727] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0728] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0729] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0730] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0731] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0732] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0733] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0734] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0735] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0736] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0737] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0738] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0739] 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.

[0740] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0741] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0742] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0743] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0744] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0745] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0746] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0747] The following is further disclosed regarding the embodiments described above.

[0748] (Claim 1)

[0749] A means of receiving input data for generating job postings,

[0750] Means for validating the aforementioned input data,

[0751] A means for inputting the validated data into a generative model to generate job information,

[0752] The means for storing the generated job information,

[0753] The means for displaying the generated job information to the user,

[0754] A system that includes this.

[0755] (Claim 2)

[0756] The system according to claim 1, wherein the generative model generates job information using a machine learning algorithm.

[0757] (Claim 3)

[0758] The system according to claim 1, wherein the input data comprises items including job description, required experience, work location, and salary.

[0759] "Example 1"

[0760] (Claim 1)

[0761] Means for obtaining information to generate job postings,

[0762] A means for inputting and verifying the aforementioned information,

[0763] A means for inputting the verified input information into a generation algorithm to create work information,

[0764] means for storing the created work information,

[0765] A means of presenting the aforementioned created work information to the user,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, wherein the generation algorithm creates work information using a data representation model.

[0769] (Claim 3)

[0770] The system according to claim 1, wherein the aforementioned information consists of items including work content, required skills, work location, and compensation.

[0771] "Application Example 1"

[0772] (Claim 1)

[0773] A device that receives input data for generating job postings,

[0774] A device for verifying the aforementioned input data,

[0775] A device that uses the verified data to input into a generative model and generates job postings,

[0776] A device for storing the generated job information,

[0777] A device that presents the generated job information to the user,

[0778] A device that presents input data in a virtual space and interactively displays the generated job information,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, wherein the generative model generates job postings using a machine learning algorithm, and the user can provide data by voice and motion input using virtual reality technology.

[0782] (Claim 3)

[0783] The system according to claim 1, wherein the input data comprises items including job description, required skills, work location, compensation, and a specific location in a virtual space.

[0784] "Example 2 of combining an emotion engine"

[0785] (Claim 1)

[0786] A means of receiving input information for generating job postings,

[0787] A means for analyzing the aforementioned input information and user sentiment information in real time,

[0788] Means for validating the aforementioned input information,

[0789] A means for inputting the validated information and sentiment information into a generative model to generate job postings,

[0790] Means for storing the generated job information in a storage device,

[0791] The means for displaying the generated job information on a terminal,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, wherein the generative model generates job postings using a machine learning algorithm and takes into account the user's sentiment information.

[0795] (Claim 3)

[0796] The system according to claim 1, wherein the input information comprises items including job description, required skills, workplace location, and compensation.

[0797] "Application example 2 when combining with an emotional engine"

[0798] (Claim 1)

[0799] A means by which the user receives input data using a terminal,

[0800] Means for validating the aforementioned input data and user sentiment data,

[0801] A means for inputting the validated data into a generative model and generating customized job information based on the user's emotional state,

[0802] Means for storing the generated job information in data storage,

[0803] A means for presenting the generated job information to the user terminal,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the generation model generates job information corresponding to the user's emotional state using a machine learning algorithm.

[0807] (Claim 3)

[0808] The system according to claim 1, wherein the input data comprises items including job requirements, required experience, work location, and salary level, and further includes emotional state analyzed in real time by a smart device. [Explanation of Symbols]

[0809] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving input data for generating job postings, Means for validating the aforementioned input data, A means for inputting the validated data into a generative model to generate job information, The means for storing the generated job information, The means for displaying the generated job information to the user, A system that includes this.

2. The system according to claim 1, wherein the generation model generates job information using a machine learning algorithm.

3. The system according to claim 1, wherein the input data consists of items including job description, required experience, work location, and salary.

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