System

Abstract

A system is provided.SOLUTION: This system includes a means for allowing a job applicant to submit an application form, a means for allowing a generation system AI to analyze the submitted application form, and to select a candidate, and a means for notifying a personnel department of the information of the selected candidate.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The problems this invention aims to solve are reducing the workload of human resources departments in the recruitment industry, realizing an efficient recruitment process, improving customer service, automating interviews, enhancing employee training, and providing career advice. Traditional manual processes require a great deal of time and effort, resulting in a decline in work efficiency and quality. Other challenges include consideration of neurodiversity and the elimination of bias. [Means for solving the problem]

[0005] The present invention provides a system that utilizes generative AI to automate and streamline various tasks in the human resources industry. Specifically, the system includes a means for job seekers to submit application documents, a means for a generative AI to analyze the submitted application documents and select candidates, and a means for notifying the human resources department of the information on the selected candidates. The system also includes a means for accepting customer inquiries, generating responses using a generative AI, and providing the generated responses to the customer. The system also includes a means for scheduling interviews with job seekers, generating and presenting questions using a generative AI, and a means for analyzing and evaluating the job seeker's responses. The system also includes a means for employees to access a training platform and input questions, and a means for a generative AI to generate and display answers. The system also includes a means for collecting user profile information, generating and proposing career paths using a generative AI, and a means for creating and providing career advice reports to users. This enables efficient and consistent business operations, reducing the burden on human resources and related departments and improving the quality of work.

[0006] A "job seeker" is an individual who submits an application to apply for a particular job or position.

[0007] "Application documents" are documents such as resumes and curriculum vitae submitted by job seekers that show the job seeker's skills, experience, and qualifications.

[0008] "Generative AI" is artificial intelligence that uses machine learning and natural language processing technology to automatically analyze data and generate optimal answers and suggestions.

[0009] The "human resources department" is the department in a company or organization that is responsible for managing employee recruitment, evaluation, training, etc.

[0010] A "customer" is an individual or entity that uses the services or products of a company or organization.

[0011] An "Inquiry" is a question or request that a customer submits to a business or organization seeking specific information or support.

[0012] An "interview" is an interactive evaluation process between a job seeker and an interviewer, conducted to ascertain the job seeker's aptitude and skills.

[0013] An "employee" is an individual employed by a business or organization to perform a specific task or function.

[0014] "Training Platform" means an online learning environment used by employees to improve their skills and knowledge.

[0015] "User" refers to an individual or member of an organization who uses the system, and in this context refers specifically to employees and job seekers.

[0016] "Profile Information" is detailed personal information about a user, such as their skills, experience, qualifications, and goals.

[0017] A "career path" is a plan or path that shows the steps and skill sets required to reach a particular occupation or position.

[0018] "Career advice" is advice that suggests optimal career paths and skill development based on the user's goals and current situation.

[0019] A "report" is a documented version of the career advice and suggestions that the generative AI provides to the user. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] Automated Application Screening

[0042] One form of this system is automated application screening.

[0043] System Overview

[0044] It provides a web portal for users to submit their applications, and the server analyzes the applications using generative AI. Based on the analysis results, it creates a list of the most suitable candidates and notifies the HR department of this information.

[0045] Program processing

[0046] 1. User: Job seekers upload their resumes and CVs through a web portal.

[0047] 2. Server: Receives uploaded documents and stores them in a database. It then launches generative AI to analyze these documents. For example, if a job candidate applies for an IT engineer position, it evaluates their skill set, years of experience, and specific project experience.

[0048] 3. Server: Scores the analysis results and generates a list of optimal candidates.

[0049] 4. Server: Finally, notify the HR department of the generated list and move on to the next step in the hiring process.

[0050] Improved customer service

[0051] Next, there is the use of generative AI to improve customer service.

[0052] System Overview

[0053] When a customer makes an inquiry, generative AI generates an appropriate response and provides it through a virtual assistant.

[0054] Program processing

[0055] 1. User: A customer submits a question through a chat widget or contact form.

[0056] 2. Terminal: The virtual assistant receives the query and uses generative AI to analyze the required information.

[0057] 3. On the device: Generative AI generates appropriate answers and provides them to users. For example, if a customer asks about how to use a product, a virtual assistant will guide them to the manual or related videos.

[0058] 4. Server: Logs all queries and responses for further analysis.

[0059] Interview automation

[0060] Additionally, the automation of interviews using generative AI is also included in the embodiments.

[0061] System Overview

[0062] An interview session is held between job seekers and generative AI, and the responses are analyzed and evaluated in real time.

[0063] Program processing

[0064] 1. User: A job seeker books an interview through an online platform.

[0065] 2. Server: When the scheduled time arrives, the generative AI generates interview questions and presents them to the job seeker.

[0066] 3. User: The job seeker answers the questions by typing or speaking.

[0067] 4. Server: Generative AI analyzes and scores answers in real time, for example, case study questions to assess suitability for a consulting position.

[0068] 5. Server: Determines whether the application is successful and notifies the result.

[0069] Enhanced employee training

[0070] In some embodiments, employee training is enhanced.

[0071] System Overview

[0072] Employees access a training platform and receive assistance with answers from generative AI.

[0073] Program processing

[0074] 1. User: An employee logs into the training platform and types in a question.

[0075] 2. Terminal: Sends the question to the generative AI and requests the required answer.

[0076] 3. Server: The generative AI generates the optimal answer to the question and returns it to the device.

[0077] 4. Terminal: Generate answers and display them on the employee's screen, for example, step-by-step instructions for a question about how to use new software.

[0078] Providing career advice

[0079] Finally, there are embodiments that provide career advice.

[0080] System Overview

[0081] Generative AI analyzes users' profile information and suggests career paths and skill development.

[0082] Program processing

[0083] 1. User: An employee or job seeker applies for career counseling.

[0084] 2. Server: Collects user profile information and analyzes it using generative AI.

[0085] 3. Server: Generates optimal career paths and creates reports. For example, it suggests the skills and experience required for a user who wants to become a project manager within five years.

[0086] 4. User: Receives the generated report and checks its contents.

[0087] The above-described embodiment enables efficient business operations in the human resources industry, reduces the burden on the human resources department and related departments, and improves the quality of work.

[0088] The processing flow will be explained below.

[0089] Automated Application Screening

[0090] Program processing

[0091] Step 1:

[0092] A user uploads their application documents (resume, curriculum vitae, etc.) through a web portal.

[0093] Step 2:

[0094] The server receives the uploaded application documents and stores them in a database.

[0095] Step 3:

[0096] The server launches a generative AI and extracts the contents of the application documents stored in the database as text.

[0097] Step 4:

[0098] The server analyzes the text data and evaluates skill sets, experience, qualifications, etc.

[0099] Step 5:

[0100] The server scores the candidates based on the analysis results and generates a list of optimal candidates.

[0101] Step 6:

[0102] The server notifies the human resources personnel of the generated candidate list.

[0103] Improved customer service

[0104] Program processing

[0105] Step 1:

[0106] A user (customer) submits a question via a chat widget or contact form.

[0107] Step 2:

[0108] The on-device virtual assistant receives inquiries in real time.

[0109] Step 3:

[0110] The device analyzes the inquiry and sends the data to the generative AI.

[0111] Step 4:

[0112] The server uses generative AI to generate the best answer to the query.

[0113] Step 5:

[0114] The server generates a response and sends it back to the terminal.

[0115] Step 6:

[0116] The device displays the answer to the user and provides it in a chat window or via email.

[0117] Step 7:

[0118] The server logs all queries and responses.

[0119] Interview automation

[0120] Program processing

[0121] Step 1:

[0122] A user schedules an interview through an online platform.

[0123] Step 2:

[0124] The server starts the scheduled interview session.

[0125] Step 3:

[0126] The server runs a generative AI to generate interview questions based on the user's profile.

[0127] Step 4:

[0128] The server presents the generated question to the user.

[0129] Step 5:

[0130] The user answers the questions by typing or speaking.

[0131] Step 6:

[0132] The server uses generative AI to analyze user responses in real time.

[0133] Step 7:

[0134] The server scores the analysis results and generates an evaluation result.

[0135] Step 8:

[0136] The server determines whether the application passes or fails based on the evaluation results and notifies the user of the result.

[0137] Enhanced employee training

[0138] Program processing

[0139] Step 1:

[0140] A user (employee) logs into the training platform.

[0141] Step 2:

[0142] A user enters a question into a question form within the training platform.

[0143] Step 3:

[0144] The device sends the entered question to the generative AI.

[0145] Step 4:

[0146] The server uses generative AI to generate the best answer to the question.

[0147] Step 5:

[0148] The server sends the generated response to the terminal.

[0149] Step 6:

[0150] The terminal displays the answer to the user.

[0151] Providing career advice

[0152] Program processing

[0153] Step 1:

[0154] A user (employee or job seeker) applies for career counseling through an online platform.

[0155] Step 2:

[0156] The server collects user profile information.

[0157] Step 3:

[0158] The profile information collected by the server is sent to the generative AI for analysis.

[0159] Step 4:

[0160] The server uses generative AI to generate the optimal career path based on the user's goals.

[0161] Step 5:

[0162] The server documents the generated career path and creates a career advice report.

[0163] Step 6:

[0164] The server provides the reports to the user and makes them available for download from an online platform.

[0165] In this way, by performing specific actions at each processing step, efficient and consistent business operations can be achieved using generative AI.

[0166] Example 1

[0167] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0168] Traditional recruitment and customer service processes require a lot of manual work and lack advanced analysis and automation. This has led to problems that reduce recruitment efficiency and the quality of customer service. In particular, processing large volumes of application documents and customer inquiries takes time and effort, increasing the risk of human error. Furthermore, interview automation and the provision of career advice are not fully implemented, creating challenges for improving job seeker and employee satisfaction. A new system is needed to solve these problems and provide efficient, high-quality services.

[0169] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0170] In this invention, the server includes a means for job seekers to submit application documents, a means for a generative AI to analyze the submitted application documents and select candidates, a means for notifying the human resources department of information on the selected candidates, a means for saving the submitted application documents in a database and analyzing them, a means for generating a candidate list based on the analysis results, and a means for notifying the human resources department of the generated list and advancing the hiring process. This reduces manual work in hiring activities and makes it more efficient through advanced analysis.

[0171] "Means of submitting application documents" refers to the means by which job seekers submit application documents, such as resumes and curriculum vitae, through an online platform.

[0172] "Generative AI" is a type of artificial intelligence model that generates and analyzes advanced information through natural language processing and data analysis.

[0173] "Means for analyzing application documents" refers to using generative AI to analyze the contents of submitted application documents and extract information such as skills and experience.

[0174] The "means of selecting candidates" refers to the means of evaluating and selecting appropriate candidates based on the information in the application documents analyzed by the generative AI.

[0175] "Means for notifying the human resources department of the candidate's information" refers to the means for notifying the human resources department of the information about the selected candidate, including sending a notification email and displaying the information on the management screen.

[0176] "Means of storing in a database" refers to the means of storing submitted application documents in a digital format so that they can be accessed and analyzed at a later date.

[0177] "Means for generating a candidate list" refers to the means for creating a list of appropriate candidates based on the results of analysis and scoring by the generative AI.

[0178] The "means for notifying the human resources department of the generated list" refers to a means for notifying the human resources department of the generated candidate list and for proceeding to the next step in the recruitment process.

[0179] "Means for accepting customer inquiries" refers to the means by which customers can submit questions, such as through a chat widget or a contact form.

[0180] "Means for generating responses to customer inquiries" refers to means for using generative AI to analyze the content of customer inquiries and generate appropriate responses.

[0181] "Means for providing the generated response to the customer" refers to means for providing the response generated by the generative AI to the customer, including via a chat widget or email.

[0182] "Means for scheduling an interview" means the means by which a job seeker schedules an interview on the online platform.

[0183] "Means for generating and presenting interview questions" refers to the means by which generative AI generates interview questions and presents them to job seekers.

[0184] "Means for analyzing and evaluating job seeker responses in real time" refers to a means for using generative AI to analyze responses provided by job seekers in interviews in real time and evaluate their content.

[0185] "Means for scoring the analysis results and determining pass / fail" refers to means for scoring the analyzed answers and determining pass / fail.

[0186] "Means of notifying results" refers to the means used to notify job seekers of the results of their application, including sending emails and displaying the results on the platform.

[0187] This invention is a system that uses generative AI models to automatically screen job applications, improve customer service, automate interviews, and streamline employee training and career advice. To implement this system, a server, terminals, and users must all work together.

[0188] Automated application screening

[0189] The system begins with a user submitting an application through a web portal. Job seekers upload their resumes and CVs in PDF format. The server stores the uploaded documents in a database and runs a generative AI model (e.g., OpenAI GPT-4) to analyze them. Based on the analysis results, the server scores candidates and generates a list of the best candidates. This list is automatically notified to the HR department.

[0190] As a concrete example, taking the position of an IT engineer, the server inputs the following prompt sentence into the generative AI: "From this application document, please generate a list of technical skills and experience that are suitable for the position of IT engineer."

[0191] Improved customer service

[0192] When a customer submits a question using a website chat widget or contact form, the device receives the inquiry. The generative AI model analyzes the information and generates an appropriate response. The generated response is provided to the user from the device, and all inquiries and responses are logged on the server.

[0193] For example, if a customer inquires, "I don't know how to use the new product," the device will send the following prompt to the generation AI: "Please generate a sentence that provides detailed instructions on how to use the new product."

[0194] Interview automation

[0195] When a job seeker makes an interview reservation through the online platform, the server uses generative AI to generate appropriate questions based on the reservation time. The job seeker answers the questions via text or voice, and the answers are analyzed in real time. The server scores the analysis results and notifies the job seeker of their success or failure.

[0196] For example, send the following prompt to the generation AI: "Generate case study questions to assess suitability as a consultant."

[0197] Employee training and career advice

[0198] When an employee accesses the training platform and enters a question, the device sends the question to the generative AI, which generates the best answer, and the device displays the answer on the employee's screen.

[0199] For example, if asked how to use new project management software, the device would send the following prompt to the generating AI: "Please provide a step-by-step explanation of the basics of how to use the new project management software."

[0200] In the career advice service, employees and job seekers apply for career counseling, and the server analyzes the user's profile information using AI to generate an optimal career path and prepares a report to provide to the user.

[0201] For example, send the following prompt to the generation AI: "Generate a skills and experience development plan for a user who wants to become a project manager within five years."

[0202] As described above, by having the server, terminals, and users all work together, the system can efficiently and effectively perform automated screening of application documents, improve customer service, automate interviews, train employees, and provide career advice.

[0203] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0204] Automated Application Screening

[0205] Step 1:

[0206] User: Job seekers upload their resumes and CVs through a web portal.

[0207] Input: PDF resume or curriculum vitae selected from local disk

[0208] Specific operation: The user clicks the "Upload documents" button on the web portal, selects application documents in the file selection dialog, and then presses the "Upload" button, which sends the selected documents to the server.

[0209] Output: The web portal sends the file to the server.

[0210] Step 2:

[0211] Server: Receives uploaded documents, stores them in a database, and runs a generative AI to analyze these documents.

[0212] Input: Application file submitted by the user

[0213] What happens: The server receives the HTTP POST request and saves the application in a specific folder. It then records the file path and associated metadata in a database. It then generates and sends a prompt to a generative AI model (e.g., OpenAI GPT-4) to analyze the application.

[0214] Output: Prompt for analysis and analysis results

[0215] Step 3:

[0216] Server: Scores the analysis results and generates a list of optimal candidates.

[0217] Input: Analysis results obtained from generative AI

[0218] Specific operation: The server scores the analysis results (skills, years of experience, project details, etc.) obtained from the generative AI model based on evaluation criteria. For example, it assigns points to "years of Java experience" and "project leadership experience" and calculates an overall score.

[0219] Output: A list of the best candidates

[0220] Step 4:

[0221] Server: Notifies the HR department of the generated list and moves on to the next step in the hiring process.

[0222] Input: Scored candidate list

[0223] What it does: The server generates a report containing the best candidates and automatically sends it to the HR department's email address. It also displays the list in the HR admin panel of the web portal so that HR personnel can log in and view it.

[0224] Output: Notify the HR department and display the data on the management screen

[0225] Improved customer service

[0226] Step 1:

[0227] User: A customer submits a question through a chat widget or contact form.

[0228] Input: Customer inquiry

[0229] What it does: A user accesses a chat widget on a website, enters a question in the text field, and clicks the "Send" button to send the question to their device.

[0230] Output: Send query to device

[0231] Step 2:

[0232] On the device: The virtual assistant receives the query and uses generative AI to parse the required information.

[0233] Input: Received customer inquiry

[0234] How it works: The device analyzes the query and generates and sends an appropriate prompt to a generative AI model (e.g., GPT-4), such as "Please generate an answer on how to use product X."

[0235] Output: The prompt sent to the AI ​​model and the answer it gives

[0236] Step 3:

[0237] Terminal: Generative AI generates appropriate answers and provides them to the user.

[0238] Input: Answer obtained from generative AI

[0239] What it does: The device receives a response from the generative AI model and displays the text response in the chat widget, such as a snippet from the product manual or a related link.

[0240] Output: The answer displayed to the user

[0241] Step 4:

[0242] Server: Logs all queries and responses for further analysis.

[0243] Input: History of inquiries received and responses generated

[0244] What it does: The server stores the query and its response in a database, allowing for later log analysis and retrieval of statistics.

[0245] Output: Saving and managing log data

[0246] Interview automation

[0247] Step 1:

[0248] User: A job seeker schedules an interview through an online platform.

[0249] Input: Reservation information (date and time, job seeker information, etc.)

[0250] Specific operation: The user accesses the platform's reservation page, selects the desired date and time from the calendar, and presses the "Confirm reservation" button, which sends the reservation information to the server.

[0251] Output: Send reservation information to the server

[0252] Step 2:

[0253] Server: When the scheduled time arrives, the generative AI generates interview questions and presents them to the job seeker.

[0254] Input: Booking information and job seeker profile

[0255] How it works: When the appointment time arrives, the server sends a prompt to the generative AI model to generate appropriate interview questions, which are then displayed on the job seeker's device.

[0256] Output: Generated interview questions

[0257] Step 3:

[0258] User: The job seeker answers questions by typing or speaking.

[0259] Input: Job seeker's response (text or voice)

[0260] Specific behavior: The user answers the questions using the text box or voice input function. Once the answer is complete, the user presses the "Submit" button to send the answer to the server.

[0261] Output: Send response to server

[0262] Step 4:

[0263] Server: Generative AI analyzes and scores answers in real time.

[0264] Input: Job seeker's answers

[0265] Specific operation: After receiving the answer, the server analyzes the answer using the generative AI model. The analysis results are scored based on evaluation criteria, such as logic, the presence or absence of specific examples, and the level of expertise.

[0266] Output: Scoring results

[0267] Step 5:

[0268] Server: Determines whether the application is successful and notifies the result.

[0269] Input: Scoring results

[0270] Specific operation: The server determines whether the job seeker has passed or failed based on the scoring results, automatically generates a notification email and sends it to the job seeker. The result is also displayed on the management screen.

[0271] Output: Notification of pass / fail result

[0272] Employee training and career advice

[0273] Step 1:

[0274] User: An employee logs into the training platform and types in a question.

[0275] Input: Employee question

[0276] What happens: A user accesses the training platform through a login form, selects the "Ask a Question" option from the dashboard, enters a question in the text field, and presses the "Submit" button to send a request to the server.

[0277] Output: Send the question

[0278] Step 2:

[0279] Terminal: Sends the question to the generative AI and requests the required answer.

[0280] Input: Question submitted by employee

[0281] What it does: The device converts the input question into a prompt and sends it to the generative AI model, for example, "Please tell me how to use my new accounting software."

[0282] Output: The prompt sent to the AI ​​model and the answer from the AI

[0283] Step 3:

[0284] Server: The generative AI generates the optimal answer to the question and returns it to the device.

[0285] Input: Answer obtained from generative AI

[0286] How it works: The server formats the answer received from the generative AI and sends it to the device. The answer may be in text format, but it may also include related materials and links.

[0287] Output: Sending formatted answers

[0288] Step 4:

[0289] Terminal: Displays the generated answers on the employee's screen.

[0290] Input: Answer sent from the server

[0291] What happens: The device displays the received answer in a user interface, for example, a step-by-step guide or a related video tutorial.

[0292] Output: The answer presented to the user

[0293] Providing career advice

[0294] Step 1:

[0295] Users: Employees and job seekers request career counseling.

[0296] Input: Career counseling application information

[0297] Specific operation: The user accesses the career counseling application form and enters the required information (current position, goals, skills, etc.). Presses the "Apply" button to send the information to the server.

[0298] Output: Send application details

[0299] Step 2:

[0300] Server: Collects user profile information and analyzes it using generative AI.

[0301] Input: Career counseling application information and profile information

[0302] What it does: The server stores the input information in a database and sends it to a generative AI model for analysis, which generates prompts that assess gaps in your current skill set, experience, and goals.

[0303] Output: Profile analysis results

[0304] Step 3:

[0305] Server: Generates optimal career paths and creates reports.

[0306] Input: Analysis results from generative AI

[0307] How it works: Based on the analysis results from the generative AI model, the server generates a report proposing the optimal career path, including required skills, recommended training courses, and specific steps to gain experience.

[0308] Output: Career Path Report

[0309] Step 4:

[0310] User: Receives the generated report and reviews its contents.

[0311] Input: Career path report sent from the server

[0312] What happens next: Users log in and download the report or receive it via email. They review the report and consider the suggested career paths.

[0313] Output: Review the report and review the contents

[0314] (Application example 1)

[0315] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0316] In physical stores, in order to improve the speed and accuracy of customer service and response to inquiries, a system that allows store staff to instantly provide appropriate information is required. There is also a need to reduce the workload of store staff while maintaining the quality of communication with customers.

[0317] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0318] In this invention, the server includes a means for customers to input and send questions by hand or voice, a means for the generative AI to analyze the received questions and generate appropriate answers, a means for providing the generated answers to customers or employees, and a means for recording the transmitted questions and the generated answers for further analysis, thereby improving the speed and accuracy of customer service in physical stores and reducing the workload of store employees.

[0319] "Customer" means any individual or entity that purchases goods or services at a physical store.

[0320] A "question" refers to information that a customer inputs to a store clerk or system in the form of a request for an answer.

[0321] "Input" refers to the act of a customer providing a question to the system via text or voice.

[0322] "Submit" refers to the act of sending the entered question to the system.

[0323] "Generative AI" refers to a system that uses artificial intelligence technology to analyze questions and generate appropriate answers.

[0324] "Answer" refers to the answer information provided by generative AI in response to a question.

[0325] "Providing" refers to the act of displaying the answer generated by generative AI to a customer or employee.

[0326] "Employee" refers to staff who provide customer service in physical stores.

[0327] "Devices" refers to electronic devices used by employees, such as smartphones, smart glasses, etc.

[0328] "Recording" refers to the act of storing submitted questions and generated answers in a database.

[0329] "Analysis" refers to the processing of recorded data for later review and analysis.

[0330] "Real-time" means that information is processed and provided immediately, without delay.

[0331] A system for realizing this invention is intended to improve the efficiency of customer service in brick-and-mortar stores, enabling employees to respond to customer questions quickly and accurately. Specific embodiments of the system are described below.

[0332] When a customer types or speaks a question in a physical store using a smartphone or smart glasses, the question is sent to a server via the device. The server analyzes the received question data and activates a generative AI (specifically, OpenAI's GPT-3 model). The generative AI understands the content of the question and generates an appropriate answer. This process uses software such as Google's TensorFlow and Flask.

[0333] The server then provides the generated answers in real time to the customer or employee's device, which could be a smartphone, smart glasses, or other electronic device. This allows employees to respond to customer questions quickly and accurately. The server also records the submitted questions and the generated answers for future analysis.

[0334] Examples:

[0335] 1. A customer asks a question about a specific product: "How many calories are in this cream puff?"

[0336] 2. The customer or employee enters the question into the application using the smart glasses.

[0337] 3. The application receives the query and sends it to the server.

[0338] 4. The server passes the question to OpenAI's GPT-3 model, and the generative AI generates an answer: "A cream puff has about 200 calories."

[0339] 5. The server immediately sends the generated response to the customer or employee device.

[0340] 6. The employee communicates the answer to the customer.

[0341] Example prompt sentence:

[0342] Q: How many calories are in this cream puff?

[0343] answer:

[0344] In this way, it is possible to improve the efficiency of customer service in physical stores and reduce the workload of employees. The above is a detailed description of the embodiment of the present invention.

[0345] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0346] Step 1:

[0347] A user (customer or employee) uses a smartphone or smart glasses to type or speak a question and submit it. The input is recorded as text or voice data and sent from the device to the server. The input includes specific questions such as "How many calories are in this cream puff?"

[0348] Step 2:

[0349] The server analyzes the received question data and converts it into text data if it is voice data. Specifically, it uses voice recognition technology (e.g., Google Speech-to-Text API) to convert the voice data into text data. The output is text-formatted question data.

[0350] Step 3:

[0351] The server sends text-based question data to the generative AI (OpenAI's GPT-3 model). The server passes the question as a prompt to the generative AI and waits for a response. The input is text-based question data, and an example prompt includes "Question: How many calories are in this cream puff?"

[0352] Step 4:

[0353] The generative AI generates an appropriate answer based on the prompt it receives. For data processing, the generative AI uses natural language processing technology to analyze the question and generate the optimal answer based on known information. The output is answer data in text format. As a specific example, the generated answer would be "Answer: A cream puff has approximately 200 calories."

[0354] Step 5:

[0355] The server receives the generated response data and sends it to the customer or employee's device. The input is the text-formatted response data output from the generative AI, and the output is the response information displayed on the device. The server performs the data transfer process.

[0356] Step 6:

[0357] The device then displays the received answer data to the user. Specifically, the screen of the smartphone or smart glasses displays "A cream puff has approximately 200 calories." The input is the answer data sent from the server, and the output is the answer information that the user can visually confirm.

[0358] Step 7:

[0359] The server records the submitted questions and generated answers in a database, which can then be used for future analysis and improvement. The input is the question data and the answer data, and the output is the records stored in the database. The recording process must be consistent and reliable.

[0360] The above is the specific processing flow of the program of this system.

[0361] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0362] Combining automated application screening with an emotion engine

[0363] One form of this system combines an emotion engine with automated application screening.

[0364] System Overview

[0365] When a user submits their application documents, the generative AI analyzes them. At the same time, the emotion engine also analyzes the user's emotions at the time of submission and reflects them in the analysis results.

[0366] Program processing

[0367] 1. User: Job seekers upload their resumes and CVs through a web portal.

[0368] 2. Server: Receives the uploaded documents and stores them in a database. The emotion engine analyzes the user's facial expressions, tone of voice, etc. to extract emotional data.

[0369] 3. Server: Uses generative AI and an emotion engine to analyze the text and emotion data of documents.

[0370] 4. Server: Scores the analysis results and generates a list of optimal candidates, including emotional data.

[0371] 5. Server: Finally, the generated candidate list is notified to the HR personnel.

[0372] Combining customer service improvement with emotion engines

[0373] There are also embodiments in which emotion engines are used to improve customer service.

[0374] System Overview

[0375] When a customer makes an inquiry, the emotion engine recognizes their emotion, and the generative AI generates an appropriate response based on that emotion.

[0376] Program processing

[0377] 1. User: A customer submits a question via the chat widget or contact form.

[0378] 2. Terminal: The virtual assistant receives the inquiry in real time, and the emotion engine analyzes the customer's input and voice data to determine their emotions.

[0379] 3. Terminal: Sends the analysis results of the emotion engine to the generative AI.

[0380] 4. Server: Generative AI generates responses that take emotional data into account, for example, if a customer is stressed, it will generate a more polite and reassuring response.

[0381] 5. Terminal: The generated response is displayed to the user and provided in a chat window or email.

[0382] 6. Server: Logs all queries and responses.

[0383] Combining automated interviews with an emotion engine

[0384] There are also embodiments in which interviews are automated using emotion engines.

[0385] System Overview

[0386] Generative AI is used to generate interview questions, and an emotion engine recognizes the job seeker's emotions and reflects them in the results.

[0387] Program processing

[0388] 1. User: A job seeker books an interview on an online platform.

[0389] 2. Server: Starts scheduled interview sessions. Generative AI generates interview questions and emotion engine analyzes job seeker reactions.

[0390] 3. User: The job seeker answers the questions by typing or speaking.

[0391] 4. Server: The emotion engine analyzes the job seeker's facial expressions and vocal changes, which the generative AI takes into account when analyzing the answers.

[0392] 5. Server: The generative AI combines the answers and emotional data to generate an evaluation result. For example, it can reflect whether the job seeker is feeling stressed.

[0393] 6. Server: Determines whether the application passes or fails based on the evaluation results and notifies the user of the result.

[0394] Combining enhanced employee training with the Emotion Engine

[0395] In some embodiments, employee training is enhanced with an emotion engine.

[0396] System Overview

[0397] The emotion engine recognizes employees' emotions, and generative AI provides appropriate training content based on those emotions.

[0398] Program processing

[0399] 1. User: An employee logs into the training platform and begins training.

[0400] 2. On the device: The emotion engine analyzes the employee's facial expressions and tone of voice to recognize their emotions.

[0401] 3. Terminal: Sends the recognized emotion data to the generative AI.

[0402] 4. Server: Generative AI customizes training content based on emotional data, for example, offering lighter training if an employee is tired.

[0403] 5. Terminal: Presents customized training content to employees and facilitates training.

[0404] Combining career advice and emotion engines

[0405] In some embodiments, career advice is combined with an emotion engine.

[0406] System Overview

[0407] The emotion engine recognizes the user's emotions, and the generative AI provides career paths and advice that take their emotional state into account.

[0408] Program processing

[0409] 1. User: An employee or job seeker applies for career counseling.

[0410] 2. Server: Collects the emotional state along with the user's profile information.

[0411] 3. Server: Analyzes the profile information and emotional data collected by the generative AI.

[0412] 4. Server: Generates career paths that take into account sentiment data and creates reports, for example adding specific advice to alleviate user anxiety.

[0413] 5. Server: Provides the generated reports to users and makes them available for download from the online platform.

[0414] In this way, combining emotion engines enables more precise data analysis and service provision that takes into account the user's emotional state, significantly improving overall efficiency and user satisfaction.

[0415] The processing flow will be explained below.

[0416] Combining automated application screening with an emotion engine

[0417] Program processing

[0418] Step 1:

[0419] A user uploads their application documents (resume, curriculum vitae, etc.) through a web portal.

[0420] Step 2:

[0421] The server receives the uploaded application documents and stores them in a database.

[0422] Step 3:

[0423] The device's built-in emotion engine extracts emotion data from the user's facial expressions and voice and sends it to the server.

[0424] Step 4:

[0425] The server launches a generative AI and analyzes the text data in the application documents.

[0426] Step 5:

[0427] The server integrates the emotional data into the analysis results and performs scoring. For example, positive emotional expressions receive a high score.

[0428] Step 6:

[0429] The server generates a list of the best candidates and notifies the HR personnel.

[0430] Combining customer service improvement with emotion engines

[0431] Program processing

[0432] Step 1:

[0433] A user (customer) submits a question via a chat widget or contact form.

[0434] Step 2:

[0435] The on-device virtual assistant receives inquiries in real time.

[0436] Step 3:

[0437] The emotion engine built into the device recognizes the customer's emotions from the input content and voice data and sends this to the server.

[0438] Step 4:

[0439] The server sends the emotion data and the query content to the generative AI.

[0440] Step 5:

[0441] The server takes emotional data into account and the generative AI generates the optimal response to the inquiry. For example, if the customer is feeling stressed, it will generate a more polite response.

[0442] Step 6:

[0443] The device displays the generated answer to the user and provides it in a chat window or via email.

[0444] Step 7:

[0445] The server logs all queries and responses.

[0446] Combining automated interviews with an emotion engine

[0447] Program processing

[0448] Step 1:

[0449] A user (job seeker) schedules an interview on an online platform.

[0450] Step 2:

[0451] The server starts the scheduled interview session.

[0452] Step 3:

[0453] The device's built-in emotion engine detects the job seeker's emotions from their facial expressions and voice and sends this information to the server.

[0454] Step 4:

[0455] The server uses generative AI to generate interview questions and present them to job seekers.

[0456] Step 5:

[0457] The user answers the questions by typing or speaking.

[0458] Step 6:

[0459] The server receives the emotion data and uses generative AI to comprehensively analyze the responses and emotion data. For example, if a job seeker is nervous, that will be reflected in the evaluation.

[0460] Step 7:

[0461] The server scores the evaluation results and determines whether the application passes or fails.

[0462] Step 8:

[0463] The server notifies the result and provides it to the user.

[0464] Combining enhanced employee training with the Emotion Engine

[0465] Program processing

[0466] Step 1:

[0467] A user (employee) logs into the training platform.

[0468] Step 2:

[0469] Employees enter their questions into a question form within the training platform.

[0470] Step 3:

[0471] The device's built-in emotion engine recognizes emotions from the employee's facial expressions and tone of voice and transmits this information to a server.

[0472] Step 4:

[0473] The server sends the emotional data to a generative AI, which generates the optimal answer to the question.

[0474] Step 5:

[0475] The server sends the generated response to the terminal.

[0476] Step 6:

[0477] The device displays customized responses to the user, for example, suggesting a lighter workout if the user is tired.

[0478] Combining career advice and emotion engines

[0479] Program processing

[0480] Step 1:

[0481] A user (employee or job seeker) applies for career counseling through an online platform.

[0482] Step 2:

[0483] The device's built-in emotion engine recognizes the user's emotions from their facial expressions and voice and transmits them to the server.

[0484] Step 3:

[0485] The server collects user profile information and emotional data and analyzes it using generative AI.

[0486] Step 4:

[0487] The server generates a report based on the emotional data, generating an optimal career path, adding specific advice to alleviate any anxiety the user may have, for example.

[0488] Step 5:

[0489] The server provides the generated reports to the user and makes them available for download from an online platform.

[0490] In this way, combining emotion engines enables more precise data analysis and service provision that takes into account the user's emotional state, significantly improving overall efficiency and user satisfaction.

[0491] Example 2

[0492] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0493] Traditional application screening cannot take into account the emotional state of job seekers, which can result in the overlooking of highly suitable candidates. Furthermore, in customer service and interview evaluations, responses and evaluations that ignore the emotional state of employees can be inaccurate. This can lead to issues such as a decline in the quality of a company's recruitment process and customer service.

[0494] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0495] In this invention, the server includes a means for job seekers to submit application documents, a means for analyzing facial expressions and tone of voice to extract emotional data, a means for a generative AI to analyze the submitted application documents and the extracted emotional data to select candidates, and a means for notifying the human resources department of the information on the selected candidates. This enables screening that takes into account the emotional state of applicants, allowing for the selection of more suitable candidates. Similarly, utilizing emotional data in customer service and interview evaluations allows for more accurate and effective responses.

[0496] "Application documents" refers to documents such as resumes and work histories that job seekers submit to companies seeking employment.

[0497] "Generative AI" refers to artificial intelligence that can analyze text and voice data using natural language processing technology and create new products.

[0498] "Emotional data" refers to data analyzed from facial expressions, vocal tone, etc., and is information that expresses an individual's emotional state in numerical or categorical terms.

[0499] "Means" refers to a device, system, method, etc. used to achieve a particular purpose.

[0500] "Candidate" refers to an applicant who has been assessed as suitable for a particular job or role.

[0501] "Notification" refers to the act of conveying specific information to a recipient.

[0502] "Analysis" refers to the process of examining data or information in detail to clarify its content and structure.

[0503] An "interview" refers to the process by which a job seeker and an interviewer evaluate the job seeker's aptitude and abilities through dialogue.

[0504] "Inquiry" means a question or request made by a Customer seeking information or support regarding a product or service.

[0505] Combining automated application screening with an emotion engine

[0506] In this embodiment of the invention, a user (job seeker) uploads a resume or work history through a web portal. The server that receives the application documents first stores the documents in a database, and then uses an emotion engine to analyze the user's facial expressions and voice tone to extract emotion data. Specific software that can be used includes a "face analysis API" and a "voice analysis API."

[0507] For example, when a user logs in to a job-seeking website and uploads a PDF resume, the server saves the document in cloud storage. The server then uses face analysis APIs and voice analysis APIs to extract emotional data from the uploaded data.

[0508] Next, generative AI is used to analyze the text data and extracted emotion data from the submitted application documents. Natural language processing AI can be used as generative AI. As a result of the analysis, the server scores the suitability of the candidates and generates a list of optimal candidates based on this. For scoring, a machine learning library is used to quantify the suitability of each applicant.

[0509] Finally, the server notifies the HR department of the generated candidate list using a messaging API, automating the process of efficiently selecting and notifying highly suitable candidates.

[0510] Career Services Prompt Examples

[0511] "Analyze the facial expressions and tone of voice of job applicants and evaluate them along with the content of their application documents. For example, if a job applicant shows signs of stress, reflect that emotion in your evaluation."

[0512] By inputting this prompt into the generative AI model, the emotion engine and generative AI work together to process the prompt. This enables highly accurate analysis and evaluation using emotion data, improving the efficiency and accuracy of the hiring process.

[0513] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0514] Specific processing steps of the program

[0515] Combining automated application screening with an emotion engine

[0516] Step 1:

[0517] User: A job seeker uploads their resume or CV through a web portal.

[0518] Input: A user-uploaded PDF resume or CV.

[0519] Output: The application data sent to the server.

[0520] Specific operation: Click the "Upload Documents" button on the web portal, select your resume from the file selection dialog, and upload it.

[0521] Step 2:

[0522] Server: Receives uploaded documents and stores them in a database.

[0523] Input: Application data submitted by the user.

[0524] Output: File path of application data saved in the database.

[0525] Specific operation: The server uploads the received file to "cloud storage" and writes the file metadata and storage location to the database.

[0526] Step 3:

[0527] Server: The emotion engine analyzes the user's facial expressions, tone of voice, etc. to extract emotional data.

[0528] Input: Application document data stored on the server and user's voice and facial image data.

[0529] Output: Emotion data obtained from the emotion engine.

[0530] Specific operation: Using the "face analysis API" and "voice analysis API," facial images and voice data are analyzed, and the emotional state is quantified and stored in a database.

[0531] Step 4:

[0532] Server: Uses a combination of generative AI and an emotion engine to analyze text data and emotion data from application documents.

[0533] Input: Text data and sentiment data from job applications.

[0534] Output: Analysis result data.

[0535] Specific operation: The content of the resume is analyzed using generative AI (natural language processing AI), emotional data obtained from the emotion engine is integrated, and the analysis results are stored in a database.

[0536] Step 5:

[0537] Server: Scores the analysis results and generates a list of optimal candidates, including emotional data.

[0538] Input: Analysis result data.

[0539] Output: A list of the best candidates.

[0540] Specific operation: Using a "machine learning library," it calculates the score for each applicant, and based on the results, generates a list of optimal candidates and stores them in a database.

[0541] Step 6:

[0542] Server: Notifies the HR department of the generated candidate list.

[0543] Input: A list of best candidates.

[0544] Output: Notification message to HR department.

[0545] Specific operation: Using the "Messaging API," a notification message is sent to a specific channel in the HR department, providing a link to the candidate list.

[0546] (Application example 2)

[0547] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0548] Conventional application screening, customer service, and automated interview systems are unable to take into account the user's emotional state, making it difficult to provide personalized services. Furthermore, evaluations and responses without emotion analysis have limited the potential for improving the user experience. Therefore, there is a need for technology that can recognize user emotions in real time and respond and evaluate accordingly.

[0549] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0550] In this invention, the server includes: a means for a job seeker to submit an application; a means for a generative AI to analyze the submitted application and select candidates; a means for notifying a human resources department of information about the selected candidates; a means including an emotion engine for analyzing the job seeker's emotions at the time of submission; a means for re-evaluating and scoring based on the emotion data; a means for accepting customer inquiries; a means for generating responses to customer inquiries using a generative AI; a means for providing the generated responses to the customers; a means including an emotion engine for analyzing customer emotions in real time; a means for adjusting responses based on the emotion data; a means for scheduling interviews with the job seeker; a means for generating interview questions using a generative AI and presenting them to the job seeker; a means for analyzing and evaluating the job seeker's answers; a means including an emotion engine for analyzing the job seeker's emotions during the interview; and a means for adjusting the evaluation results based on the emotion data. This enables more precise and personalized responses and evaluations while taking into account the user's emotional state.

[0551] "Means for submitting applications" refers to the interface that job seekers use to upload application documents, such as resumes and curriculum vitae, to the system.

[0552] "Generative AI" is artificial intelligence that uses natural language processing and machine learning techniques to analyze and generate responses to application documents and customer inquiries.

[0553] The "means of candidate selection" refers to the method for selecting suitable candidates based on the information in the application documents analyzed by the generative AI.

[0554] "Means of notifying the human resources department" refers to the means by which information about the selected candidate is communicated to the human resources department via email or internal systems.

[0555] The "emotion engine that analyzes job seekers' emotions at the time of submission" is a technology that recognizes emotions from facial expressions and voice when job seekers submit their application documents and extracts them as data.

[0556] "Means for re-evaluating and scoring based on emotional data" refers to a method for adjusting the evaluation of application documents and recalculating scores using recognized emotional data.

[0557] "Means for accepting customer inquiries" are interfaces such as chat widgets or forms that allow customers to submit questions or requests.

[0558] The "means of generating a response" is how the generative AI creates an appropriate response to a customer inquiry.

[0559] "Means of providing the generated response to the customer" refers to the method of communicating the answer created by the generative AI to the customer via a chat window, email, etc.

[0560] The "emotion engine that analyzes customer emotions in real time" is a technology that instantly recognizes emotions from customer text input, voice data, and even facial expressions.

[0561] "Means for adjusting responses based on emotional data" refers to a method in which generative AI changes the content and tone of responses based on recognized emotional data.

[0562] "Means for scheduling interviews" refers to a system that allows job seekers to schedule interview dates and times online.

[0563] "Means for generating interview questions and presenting them to job seekers" refers to a method in which generative AI generates specific interview questions and presents them to job seekers in text or audio.

[0564] "Means for analyzing and evaluating job seekers' responses" refers to the method by which generative AI analyzes the content of job seekers' responses and assigns an evaluation score.

[0565] The "emotion engine that analyzes the emotions of job seekers during interviews" is a technology that recognizes emotions in real time from the facial expressions and voice of job seekers during interviews.

[0566] "Means for adjusting evaluation results based on emotional data" refers to a method in which generative AI recalculates evaluation results by taking into account emotional data recognized during the interview.

[0567] This invention implements the following steps: We present specific procedures for combining generative AI and an emotion engine to screen job applicants' applications, conduct online interviews, provide customer support, and provide a recommendation system.

[0568] Screening job applicant applications

[0569] Hardware:

[0570] Users use a computer or smartphone to upload their application documents to a web portal.

[0571] software:

[0572] The server uses generative AI (e.g., OpenAI's GPT-3 model) and emotion engine (e.g., Microsoft's Azure Cognitive Services' Face API).

[0573] Data processing:

[0574] When an application is uploaded, a generative AI analyzes it and evaluates the job seeker's skills and experience. In parallel, an emotion engine recognizes emotions from video and audio data and adds that data to the analysis.

[0575] As a specific example, if a user is feeling angry or impatient, the generative AI will use the recognized emotional data to perform more flexible scoring.

[0576] Interview automation

[0577] Hardware:

[0578] A webcam and microphone are used when the interview is conducted between the job seeker and the server.

[0579] software:

[0580] The server conducts interviews using generative AI and an emotion engine. The generative AI generates interview questions, and the emotion engine analyzes the job seeker's emotions in real time as they answer.

[0581] Data processing:

[0582] The responses are re-evaluated based on the job seeker's emotional state to generate a final rating. For example, if the job seeker is nervous, the rating will adjust to reflect this.

[0583] An example prompt might be, "The user is nervous, so please keep your questions brief."

[0584] Customer Service

[0585] Hardware:

[0586] Customers use a chat widget or inquiry form on their computer or smartphone.

[0587] software:

[0588] Virtual assistants use generative AI to generate responses to customer queries, and here too, an emotion engine recognizes customer emotions in real time.

[0589] Data processing:

[0590] It generates and quickly delivers responses that reflect the customer's emotions. For example, if a customer expresses dissatisfaction, generative AI will generate an appropriate and comforting response.

[0591] An example would be, "The customer is unhappy, so please carefully explain the specific steps you will take to resolve the issue."

[0592] Product Recommendations

[0593] Hardware:

[0594] Users of the online shopping site access it from their smartphones or computers.

[0595] software:

[0596] The server uses generative AI and an emotion engine to analyze the product pages the user views and their emotions at the time.

[0597] Data processing:

[0598] The recommendation algorithm reflects the user's emotional data and recommends appropriate products. For example, if a user feels happy when looking at a product, it will suggest more products from that category.

[0599] An example prompt might be, "Since this user is happy, please recommend products in the same category."

[0600] These embodiments allow for application analysis, interviews, customer service, and product recommendations to take into account the user's emotional state, improving overall service quality.

[0601] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0602] Step 1:

[0603] A job seeker uploads their application to a web portal.

[0604] Operation:

[0605] Users (job seekers) upload their resumes and work histories along with the necessary documents to the system.

[0606] input:

[0607] Resume, work history

[0608] output:

[0609] Saved application data

[0610] Step 2:

[0611] The server receives the uploaded application documents and stores them in a database.

[0612] Operation:

[0613] The server receives the applications and stores the data in a database for further analysis.

[0614] input:

[0615] Application document data

[0616] output:

[0617] Document data stored in the database

[0618] Step 3:

[0619] The server uses a sentiment engine to analyze the job seeker's sentiment at the time of submission.

[0620] Operation:

[0621] The server activates an emotion engine and extracts emotional data by analyzing the job seeker's facial expressions and tone of voice.

[0622] input:

[0623] Video or audio data when submitting documents

[0624] output:

[0625] Emotional Data

[0626] Step 4:

[0627] Generative AI analyzes the text data of submitted application documents.

[0628] Operation:

[0629] Generative AI (such as the GPT-3 model) analyzes the content of application documents and evaluates skills and experience.

[0630] input:

[0631] Text data of application documents

[0632] output:

[0633] Initial evaluation data

[0634] Step 5:

[0635] Generative AI will readjust the evaluation of your application based on emotional data.

[0636] Operation:

[0637] The server recalculates the evaluation results based on the emotional data and adjusts the scoring. For example, if a job applicant is nervous, the server will take that into account and adjust the scoring accordingly.

[0638] input:

[0639] Initial evaluation data, emotion data

[0640] output:

[0641] Adjusted evaluation data

[0642] Step 6:

[0643] Based on the evaluation results, the server generates a list of optimal candidates.

[0644] Operation:

[0645] The server creates a list of appropriate candidates based on the evaluation data adjusted by the generative AI.

[0646] input:

[0647] Adjusted evaluation data

[0648] output:

[0649] Candidate List

[0650] Step 7:

[0651] The server notifies the HR department of the best candidates.

[0652] Operation:

[0653] The server notifies the human resources department of the generated candidate list via email or an internal company system.

[0654] input:

[0655] Candidate List

[0656] output:

[0657] Candidate list notified to HR department

[0658] Step 8:

[0659] Schedule candidate interviews.

[0660] Operation:

[0661] The user (job seeker) sets the date and time of the interview on the system.

[0662] input:

[0663] Interview reservation information

[0664] output:

[0665] Scheduled Interview Sessions

[0666] Step 9:

[0667] Generative AI generates interview questions and presents them to job seekers.

[0668] Operation:

[0669] The server uses generative AI to generate and present appropriate interview questions to job seekers.

[0670] input:

[0671] Interview Question Generation Prompts

[0672] output:

[0673] Generated interview questions

[0674] Step 10:

[0675] Generative AI analyzes job seekers' responses, and an emotion engine recognizes their emotions in real time.

[0676] Operation:

[0677] The server uses generative AI to analyze job seekers' responses, and an emotion engine analyzes emotions during the interview in real time.

[0678] input:

[0679] Job seeker response data, emotion data

[0680] output:

[0681] Analyzed response data, real-time sentiment data

[0682] Step 11:

[0683] Evaluation results are generated based on emotion data.

[0684] Operation:

[0685] The server evaluates the job seeker's responses taking into account the emotional data and synthesizes the results.

[0686] input:

[0687] Analyzed response data and emotion data

[0688] output:

[0689] Final evaluation results

[0690] Step 12:

[0691] The server determines whether the job seeker passes or fails based on the final evaluation results and notifies the job seeker of the result.

[0692] Operation:

[0693] The server determines whether the job seeker has passed or failed based on the generated final evaluation results and notifies the job seeker of the result via email or system notification.

[0694] input:

[0695] Final evaluation results

[0696] output:

[0697] The result of the job applicant's acceptance was notified

[0698] Example prompts:

[0699] "Please keep your questions brief as users are nervous."

[0700] "The customer is unhappy, so please carefully explain the specific steps you will take to resolve the issue."

[0701] "Recommend products in the same category because they bring joy to the user."

[0702] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0703] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0704] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0705] [Second embodiment]

[0706] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0707] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0708] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0709] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0710] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0711] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0712] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0713] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0714] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0715] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0716] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0717] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0718] Automated Application Screening

[0719] One form of this system is automated application screening.

[0720] System Overview

[0721] It provides a web portal for users to submit their applications, and the server analyzes the applications using generative AI. Based on the analysis results, it creates a list of the most suitable candidates and notifies the HR department of this information.

[0722] Program processing

[0723] 1. User: Job seekers upload their resumes and CVs through a web portal.

[0724] 2. Server: Receives uploaded documents and stores them in a database. It then launches generative AI to analyze these documents. For example, if a job candidate applies for an IT engineer position, it evaluates their skill set, years of experience, and specific project experience.

[0725] 3. Server: Scores the analysis results and generates a list of optimal candidates.

[0726] 4. Server: Finally, notify the HR department of the generated list and move on to the next step in the hiring process.

[0727] Improved customer service

[0728] Next, there is the use of generative AI to improve customer service.

[0729] System Overview

[0730] When a customer makes an inquiry, generative AI generates an appropriate response and provides it through a virtual assistant.

[0731] Program processing

[0732] 1. User: A customer submits a question through a chat widget or contact form.

[0733] 2. Terminal: The virtual assistant receives the query and uses generative AI to analyze the required information.

[0734] 3. On the device: Generative AI generates appropriate answers and provides them to users. For example, if a customer asks about how to use a product, a virtual assistant will guide them to the manual or related videos.

[0735] 4. Server: Logs all queries and responses for further analysis.

[0736] Interview automation

[0737] Additionally, the automation of interviews using generative AI is also included in the embodiments.

[0738] System Overview

[0739] An interview session is held between job seekers and generative AI, and the responses are analyzed and evaluated in real time.

[0740] Program processing

[0741] 1. User: A job seeker books an interview through an online platform.

[0742] 2. Server: When the scheduled time arrives, the generative AI generates interview questions and presents them to the job seeker.

[0743] 3. User: The job seeker answers the questions by typing or speaking.

[0744] 4. Server: Generative AI analyzes and scores answers in real time, for example, case study questions to assess suitability for a consulting position.

[0745] 5. Server: Determines whether the application is successful and notifies the result.

[0746] Enhanced employee training

[0747] In some embodiments, employee training is enhanced.

[0748] System Overview

[0749] Employees access a training platform and receive assistance with answers from generative AI.

[0750] Program processing

[0751] 1. User: An employee logs into the training platform and types in a question.

[0752] 2. Terminal: Sends the question to the generative AI and requests the required answer.

[0753] 3. Server: The generative AI generates the optimal answer to the question and returns it to the device.

[0754] 4. Terminal: Generate answers and display them on the employee's screen, for example, step-by-step instructions for a question about how to use new software.

[0755] Providing career advice

[0756] Finally, there are embodiments that provide career advice.

[0757] System Overview

[0758] Generative AI analyzes users' profile information and suggests career paths and skill development.

[0759] Program processing

[0760] 1. User: An employee or job seeker applies for career counseling.

[0761] 2. Server: Collects user profile information and analyzes it using generative AI.

[0762] 3. Server: Generates optimal career paths and creates reports. For example, it suggests the skills and experience required for a user who wants to become a project manager within five years.

[0763] 4. User: Receives the generated report and checks its contents.

[0764] The above-described embodiment enables efficient business operations in the human resources industry, reduces the burden on the human resources department and related departments, and improves the quality of work.

[0765] The processing flow will be explained below.

[0766] Automated Application Screening

[0767] Program processing

[0768] Step 1:

[0769] A user uploads their application documents (resume, curriculum vitae, etc.) through a web portal.

[0770] Step 2:

[0771] The server receives the uploaded application documents and stores them in a database.

[0772] Step 3:

[0773] The server launches a generative AI and extracts the contents of the application documents stored in the database as text.

[0774] Step 4:

[0775] The server analyzes the text data and evaluates skill sets, experience, qualifications, etc.

[0776] Step 5:

[0777] The server scores the candidates based on the analysis results and generates a list of optimal candidates.

[0778] Step 6:

[0779] The server notifies the human resources personnel of the generated candidate list.

[0780] Improved customer service

[0781] Program processing

[0782] Step 1:

[0783] A user (customer) submits a question via a chat widget or contact form.

[0784] Step 2:

[0785] The on-device virtual assistant receives inquiries in real time.

[0786] Step 3:

[0787] The device analyzes the inquiry and sends the data to the generative AI.

[0788] Step 4:

[0789] The server uses generative AI to generate the best answer to the query.

[0790] Step 5:

[0791] The server generates a response and sends it back to the terminal.

[0792] Step 6:

[0793] The device displays the answer to the user and provides it in a chat window or via email.

[0794] Step 7:

[0795] The server logs all queries and responses.

[0796] Interview automation

[0797] Program processing

[0798] Step 1:

[0799] A user schedules an interview through an online platform.

[0800] Step 2:

[0801] The server starts the scheduled interview session.

[0802] Step 3:

[0803] The server runs a generative AI to generate interview questions based on the user's profile.

[0804] Step 4:

[0805] The server presents the generated question to the user.

[0806] Step 5:

[0807] The user answers the questions by typing or speaking.

[0808] Step 6:

[0809] The server uses generative AI to analyze user responses in real time.

[0810] Step 7:

[0811] The server scores the analysis results and generates an evaluation result.

[0812] Step 8:

[0813] The server determines whether the application passes or fails based on the evaluation results and notifies the user of the result.

[0814] Enhanced employee training

[0815] Program processing

[0816] Step 1:

[0817] A user (employee) logs into the training platform.

[0818] Step 2:

[0819] A user enters a question into a question form within the training platform.

[0820] Step 3:

[0821] The device sends the entered question to the generative AI.

[0822] Step 4:

[0823] The server uses generative AI to generate the best answer to the question.

[0824] Step 5:

[0825] The server sends the generated response to the terminal.

[0826] Step 6:

[0827] The terminal displays the answer to the user.

[0828] Providing career advice

[0829] Program processing

[0830] Step 1:

[0831] A user (employee or job seeker) applies for career counseling through an online platform.

[0832] Step 2:

[0833] The server collects user profile information.

[0834] Step 3:

[0835] The profile information collected by the server is sent to the generative AI for analysis.

[0836] Step 4:

[0837] The server uses generative AI to generate the optimal career path based on the user's goals.

[0838] Step 5:

[0839] The server documents the generated career path and creates a career advice report.

[0840] Step 6:

[0841] The server provides the reports to the user and makes them available for download from an online platform.

[0842] In this way, by performing specific actions at each processing step, efficient and consistent business operations can be achieved using generative AI.

[0843] Example 1

[0844] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0845] Traditional recruitment and customer service processes require a lot of manual work and lack advanced analysis and automation. This has led to problems that reduce recruitment efficiency and the quality of customer service. In particular, processing large volumes of application documents and customer inquiries takes time and effort, increasing the risk of human error. Furthermore, interview automation and the provision of career advice are not fully implemented, creating challenges for improving job seeker and employee satisfaction. A new system is needed to solve these problems and provide efficient, high-quality services.

[0846] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0847] In this invention, the server includes a means for job seekers to submit application documents, a means for a generative AI to analyze the submitted application documents and select candidates, a means for notifying the human resources department of information on the selected candidates, a means for saving the submitted application documents in a database and analyzing them, a means for generating a candidate list based on the analysis results, and a means for notifying the human resources department of the generated list and advancing the hiring process. This reduces manual work in hiring activities and makes it more efficient through advanced analysis.

[0848] "Means of submitting application documents" refers to the means by which job seekers submit application documents, such as resumes and curriculum vitae, through an online platform.

[0849] "Generative AI" is a type of artificial intelligence model that generates and analyzes advanced information through natural language processing and data analysis.

[0850] "Means for analyzing application documents" refers to using generative AI to analyze the contents of submitted application documents and extract information such as skills and experience.

[0851] The "means of selecting candidates" refers to the means of evaluating and selecting appropriate candidates based on the information in the application documents analyzed by the generative AI.

[0852] "Means for notifying the human resources department of the candidate's information" refers to the means for notifying the human resources department of the information about the selected candidate, including sending a notification email and displaying the information on the management screen.

[0853] "Means of storing in a database" refers to the means of storing submitted application documents in a digital format so that they can be accessed and analyzed at a later date.

[0854] "Means for generating a candidate list" refers to the means for creating a list of appropriate candidates based on the results of analysis and scoring by the generative AI.

[0855] The "means for notifying the human resources department of the generated list" refers to a means for notifying the human resources department of the generated candidate list and for proceeding to the next step in the recruitment process.

[0856] "Means for accepting customer inquiries" refers to the means by which customers can submit questions, such as through a chat widget or a contact form.

[0857] "Means for generating responses to customer inquiries" refers to means for using generative AI to analyze the content of customer inquiries and generate appropriate responses.

[0858] "Means for providing the generated response to the customer" refers to means for providing the response generated by the generative AI to the customer, including via a chat widget or email.

[0859] "Means for scheduling an interview" means the means by which a job seeker schedules an interview on the online platform.

[0860] "Means for generating and presenting interview questions" refers to the means by which generative AI generates interview questions and presents them to job seekers.

[0861] "Means for analyzing and evaluating job seeker responses in real time" refers to a means for using generative AI to analyze responses provided by job seekers in interviews in real time and evaluate their content.

[0862] "Means for scoring the analysis results and determining pass / fail" refers to means for scoring the analyzed answers and determining pass / fail.

[0863] "Means of notifying results" refers to the means used to notify job seekers of the results of their application, including sending emails and displaying the results on the platform.

[0864] This invention is a system that uses generative AI models to automatically screen job applications, improve customer service, automate interviews, and streamline employee training and career advice. To implement this system, a server, terminals, and users must all work together.

[0865] Automated application screening

[0866] The system begins with a user submitting an application through a web portal. Job seekers upload their resumes and CVs in PDF format. The server stores the uploaded documents in a database and runs a generative AI model (e.g., OpenAI GPT-4) to analyze them. Based on the analysis results, the server scores candidates and generates a list of the best candidates. This list is automatically notified to the HR department.

[0867] As a concrete example, taking the position of an IT engineer, the server inputs the following prompt sentence into the generative AI: "From this application document, please generate a list of technical skills and experience that are suitable for the position of IT engineer."

[0868] Improved customer service

[0869] When a customer submits a question using a website chat widget or contact form, the device receives the inquiry. The generative AI model analyzes the information and generates an appropriate response. The generated response is provided to the user from the device, and all inquiries and responses are logged on the server.

[0870] For example, if a customer inquires, "I don't know how to use the new product," the device will send the following prompt to the generation AI: "Please generate a sentence that provides detailed instructions on how to use the new product."

[0871] Interview automation

[0872] When a job seeker makes an interview reservation through the online platform, the server uses generative AI to generate appropriate questions based on the reservation time. The job seeker answers the questions via text or voice, and the answers are analyzed in real time. The server scores the analysis results and notifies the job seeker of their success or failure.

[0873] For example, send the following prompt to the generation AI: "Generate case study questions to assess suitability as a consultant."

[0874] Employee training and career advice

[0875] When an employee accesses the training platform and enters a question, the device sends the question to the generative AI, which generates the best answer, and the device displays the answer on the employee's screen.

[0876] For example, if asked how to use new project management software, the device would send the following prompt to the generating AI: "Please provide a step-by-step explanation of the basics of how to use the new project management software."

[0877] In the career advice service, employees and job seekers apply for career counseling, and the server analyzes the user's profile information using AI to generate an optimal career path and prepares a report to provide to the user.

[0878] For example, send the following prompt to the generation AI: "Generate a skills and experience development plan for a user who wants to become a project manager within five years."

[0879] As described above, by having the server, terminals, and users all work together, the system can efficiently and effectively perform automated screening of application documents, improve customer service, automate interviews, train employees, and provide career advice.

[0880] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0881] Automated Application Screening

[0882] Step 1:

[0883] User: Job seekers upload their resumes and CVs through a web portal.

[0884] Input: PDF resume or curriculum vitae selected from local disk

[0885] Specific operation: The user clicks the "Upload documents" button on the web portal, selects application documents in the file selection dialog, and then presses the "Upload" button, which sends the selected documents to the server.

[0886] Output: The web portal sends the file to the server.

[0887] Step 2:

[0888] Server: Receives uploaded documents, stores them in a database, and runs a generative AI to analyze these documents.

[0889] Input: Application file submitted by the user

[0890] What happens: The server receives the HTTP POST request and saves the application in a specific folder. It then records the file path and associated metadata in a database. It then generates and sends a prompt to a generative AI model (e.g., OpenAI GPT-4) to analyze the application.

[0891] Output: Prompt for analysis and analysis results

[0892] Step 3:

[0893] Server: Scores the analysis results and generates a list of optimal candidates.

[0894] Input: Analysis results obtained from generative AI

[0895] Specific operation: The server scores the analysis results (skills, years of experience, project details, etc.) obtained from the generative AI model based on evaluation criteria. For example, it assigns points to "years of Java experience" and "project leadership experience" and calculates an overall score.

[0896] Output: A list of the best candidates

[0897] Step 4:

[0898] Server: Notifies the HR department of the generated list and moves on to the next step in the hiring process.

[0899] Input: Scored candidate list

[0900] What it does: The server generates a report containing the best candidates and automatically sends it to the HR department's email address. It also displays the list in the HR admin panel of the web portal so that HR personnel can log in and view it.

[0901] Output: Notify the HR department and display the data on the management screen

[0902] Improved customer service

[0903] Step 1:

[0904] User: A customer submits a question through a chat widget or contact form.

[0905] Input: Customer inquiry

[0906] What it does: A user accesses a chat widget on a website, enters a question in the text field, and clicks the "Send" button to send the question to their device.

[0907] Output: Send query to device

[0908] Step 2:

[0909] On the device: The virtual assistant receives the query and uses generative AI to parse the required information.

[0910] Input: Received customer inquiry

[0911] How it works: The device analyzes the query and generates and sends an appropriate prompt to a generative AI model (e.g., GPT-4), such as "Please generate an answer on how to use product X."

[0912] Output: The prompt sent to the AI ​​model and the answer it gives

[0913] Step 3:

[0914] Terminal: Generative AI generates appropriate answers and provides them to the user.

[0915] Input: Answer obtained from generative AI

[0916] What it does: The device receives a response from the generative AI model and displays the text response in the chat widget, such as a snippet from the product manual or a related link.

[0917] Output: The answer displayed to the user

[0918] Step 4:

[0919] Server: Logs all queries and responses for further analysis.

[0920] Input: History of inquiries received and responses generated

[0921] What it does: The server stores the query and its response in a database, allowing for later log analysis and retrieval of statistics.

[0922] Output: Saving and managing log data

[0923] Interview automation

[0924] Step 1:

[0925] User: A job seeker schedules an interview through an online platform.

[0926] Input: Reservation information (date and time, job seeker information, etc.)

[0927] Specific operation: The user accesses the platform's reservation page, selects the desired date and time from the calendar, and presses the "Confirm reservation" button, which sends the reservation information to the server.

[0928] Output: Send reservation information to the server

[0929] Step 2:

[0930] Server: When the scheduled time arrives, the generative AI generates interview questions and presents them to the job seeker.

[0931] Input: Booking information and job seeker profile

[0932] How it works: When the appointment time arrives, the server sends a prompt to the generative AI model to generate appropriate interview questions, which are then displayed on the job seeker's device.

[0933] Output: Generated interview questions

[0934] Step 3:

[0935] User: The job seeker answers questions by typing or speaking.

[0936] Input: Job seeker's response (text or voice)

[0937] Specific behavior: The user answers the questions using the text box or voice input function. Once the answer is complete, the user presses the "Submit" button to send the answer to the server.

[0938] Output: Send response to server

[0939] Step 4:

[0940] Server: Generative AI analyzes and scores answers in real time.

[0941] Input: Job seeker's answers

[0942] Specific operation: After receiving the answer, the server analyzes the answer using the generative AI model. The analysis results are scored based on evaluation criteria, such as logic, the presence or absence of specific examples, and the level of expertise.

[0943] Output: Scoring results

[0944] Step 5:

[0945] Server: Determines whether the application is successful and notifies the result.

[0946] Input: Scoring results

[0947] Specific operation: The server determines whether the job seeker has passed or failed based on the scoring results, automatically generates a notification email and sends it to the job seeker. The result is also displayed on the management screen.

[0948] Output: Notification of pass / fail result

[0949] Employee training and career advice

[0950] Step 1:

[0951] User: An employee logs into the training platform and types in a question.

[0952] Input: Employee question

[0953] What happens: A user accesses the training platform through a login form, selects the "Ask a Question" option from the dashboard, enters a question in the text field, and presses the "Submit" button to send a request to the server.

[0954] Output: Send the question

[0955] Step 2:

[0956] Terminal: Sends the question to the generative AI and requests the required answer.

[0957] Input: Question submitted by employee

[0958] What it does: The device converts the input question into a prompt and sends it to the generative AI model, for example, "Please tell me how to use my new accounting software."

[0959] Output: The prompt sent to the AI ​​model and the answer from the AI

[0960] Step 3:

[0961] Server: The generative AI generates the optimal answer to the question and returns it to the device.

[0962] Input: Answer obtained from generative AI

[0963] How it works: The server formats the answer received from the generative AI and sends it to the device. The answer may be in text format, but it may also include related materials and links.

[0964] Output: Sending formatted answers

[0965] Step 4:

[0966] Terminal: Displays the generated answers on the employee's screen.

[0967] Input: Answer sent from the server

[0968] What happens: The device displays the received answer in a user interface, for example, a step-by-step guide or a related video tutorial.

[0969] Output: The answer presented to the user

[0970] Providing career advice

[0971] Step 1:

[0972] Users: Employees and job seekers request career counseling.

[0973] Input: Career counseling application information

[0974] Specific operation: The user accesses the career counseling application form and enters the required information (current position, goals, skills, etc.). Presses the "Apply" button to send the information to the server.

[0975] Output: Send application details

[0976] Step 2:

[0977] Server: Collects user profile information and analyzes it using generative AI.

[0978] Input: Career counseling application information and profile information

[0979] What it does: The server stores the input information in a database and sends it to a generative AI model for analysis, which generates prompts that assess gaps in your current skill set, experience, and goals.

[0980] Output: Profile analysis results

[0981] Step 3:

[0982] Server: Generates optimal career paths and creates reports.

[0983] Input: Analysis results from generative AI

[0984] How it works: Based on the analysis results from the generative AI model, the server generates a report proposing the optimal career path, including required skills, recommended training courses, and specific steps to gain experience.

[0985] Output: Career Path Report

[0986] Step 4:

[0987] User: Receives the generated report and reviews its contents.

[0988] Input: Career path report sent from the server

[0989] What happens next: Users log in and download the report or receive it via email. They review the report and consider the suggested career paths.

[0990] Output: Review the report and review the contents

[0991] (Application example 1)

[0992] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0993] In physical stores, in order to improve the speed and accuracy of customer service and response to inquiries, a system that allows store staff to instantly provide appropriate information is required. There is also a need to reduce the workload of store staff while maintaining the quality of communication with customers.

[0994] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0995] In this invention, the server includes a means for customers to input and send questions by hand or voice, a means for the generative AI to analyze the received questions and generate appropriate answers, a means for providing the generated answers to customers or employees, and a means for recording the transmitted questions and the generated answers for further analysis, thereby improving the speed and accuracy of customer service in physical stores and reducing the workload of store employees.

[0996] "Customer" means any individual or entity that purchases goods or services at a physical store.

[0997] A "question" refers to information that a customer inputs to a store clerk or system in the form of a request for an answer.

[0998] "Input" refers to the act of a customer providing a question to the system via text or voice.

[0999] "Submit" refers to the act of sending the entered question to the system.

[1000] "Generative AI" refers to a system that uses artificial intelligence technology to analyze questions and generate appropriate answers.

[1001] "Answer" refers to the answer information provided by generative AI in response to a question.

[1002] "Providing" refers to the act of displaying the answer generated by generative AI to a customer or employee.

[1003] "Employee" refers to staff who provide customer service in physical stores.

[1004] "Devices" refers to electronic devices used by employees, such as smartphones, smart glasses, etc.

[1005] "Recording" refers to the act of storing submitted questions and generated answers in a database.

[1006] "Analysis" refers to the processing of recorded data for later review and analysis.

[1007] "Real-time" means that information is processed and provided immediately, without delay.

[1008] A system for realizing this invention is intended to improve the efficiency of customer service in brick-and-mortar stores, enabling employees to respond to customer questions quickly and accurately. Specific embodiments of the system are described below.

[1009] When a customer types or speaks a question in a physical store using a smartphone or smart glasses, the question is sent to a server via the device. The server analyzes the received question data and activates a generative AI (specifically, OpenAI's GPT-3 model). The generative AI understands the content of the question and generates an appropriate answer. This process uses software such as Google's TensorFlow and Flask.

[1010] The server then provides the generated answers in real time to the customer or employee's device, which could be a smartphone, smart glasses, or other electronic device. This allows employees to respond to customer questions quickly and accurately. The server also records the submitted questions and the generated answers for future analysis.

[1011] Examples:

[1012] 1. A customer asks a question about a specific product: "How many calories are in this cream puff?"

[1013] 2. The customer or employee enters the question into the application using the smart glasses.

[1014] 3. The application receives the query and sends it to the server.

[1015] 4. The server passes the question to OpenAI's GPT-3 model, and the generative AI generates an answer: "A cream puff has about 200 calories."

[1016] 5. The server immediately sends the generated response to the customer or employee device.

[1017] 6. The employee communicates the answer to the customer.

[1018] Example prompt sentence:

[1019] Q: How many calories are in this cream puff?

[1020] answer:

[1021] In this way, it is possible to improve the efficiency of customer service in physical stores and reduce the workload of employees. The above is a detailed description of the embodiment of the present invention.

[1022] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1023] Step 1:

[1024] A user (customer or employee) uses a smartphone or smart glasses to type or speak a question and submit it. The input is recorded as text or voice data and sent from the device to the server. The input includes specific questions such as "How many calories are in this cream puff?"

[1025] Step 2:

[1026] The server analyzes the received question data and converts it into text data if it is voice data. Specifically, it uses voice recognition technology (e.g., Google Speech-to-Text API) to convert the voice data into text data. The output is text-formatted question data.

[1027] Step 3:

[1028] The server sends text-based question data to the generative AI (OpenAI's GPT-3 model). The server passes the question as a prompt to the generative AI and waits for a response. The input is text-based question data, and an example prompt includes "Question: How many calories are in this cream puff?"

[1029] Step 4:

[1030] The generative AI generates an appropriate answer based on the prompt it receives. For data processing, the generative AI uses natural language processing technology to analyze the question and generate the optimal answer based on known information. The output is answer data in text format. As a specific example, the generated answer would be "Answer: A cream puff has approximately 200 calories."

[1031] Step 5:

[1032] The server receives the generated response data and sends it to the customer or employee's device. The input is the text-formatted response data output from the generative AI, and the output is the response information displayed on the device. The server performs the data transfer process.

[1033] Step 6:

[1034] The device then displays the received answer data to the user. Specifically, the screen of the smartphone or smart glasses displays "A cream puff has approximately 200 calories." The input is the answer data sent from the server, and the output is the answer information that the user can visually confirm.

[1035] Step 7:

[1036] The server records the submitted questions and generated answers in a database, which can then be used for future analysis and improvement. The input is the question data and the answer data, and the output is the records stored in the database. The recording process must be consistent and reliable.

[1037] The above is the specific processing flow of the program of this system.

[1038] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1039] Combining automated application screening with an emotion engine

[1040] One form of this system combines an emotion engine with automated application screening.

[1041] System Overview

[1042] When a user submits their application documents, the generative AI analyzes them. At the same time, the emotion engine also analyzes the user's emotions at the time of submission and reflects them in the analysis results.

[1043] Program processing

[1044] 1. User: Job seekers upload their resumes and CVs through a web portal.

[1045] 2. Server: Receives the uploaded documents and stores them in a database. The emotion engine analyzes the user's facial expressions, tone of voice, etc. to extract emotional data.

[1046] 3. Server: Uses generative AI and an emotion engine to analyze the text and emotion data of documents.

[1047] 4. Server: Scores the analysis results and generates a list of optimal candidates, including emotional data.

[1048] 5. Server: Finally, the generated candidate list is notified to the HR personnel.

[1049] Combining customer service improvement with emotion engines

[1050] There are also embodiments in which emotion engines are used to improve customer service.

[1051] System Overview

[1052] When a customer makes an inquiry, the emotion engine recognizes their emotion, and the generative AI generates an appropriate response based on that emotion.

[1053] Program processing

[1054] 1. User: A customer submits a question via the chat widget or contact form.

[1055] 2. Terminal: The virtual assistant receives the inquiry in real time, and the emotion engine analyzes the customer's input and voice data to determine their emotions.

[1056] 3. Terminal: Sends the analysis results of the emotion engine to the generative AI.

[1057] 4. Server: Generative AI generates responses that take emotional data into account, for example, if a customer is stressed, it will generate a more polite and reassuring response.

[1058] 5. Terminal: The generated response is displayed to the user and provided in a chat window or email.

[1059] 6. Server: Logs all queries and responses.

[1060] Combining automated interviews with an emotion engine

[1061] There are also embodiments in which interviews are automated using emotion engines.

[1062] System Overview

[1063] Generative AI is used to generate interview questions, and an emotion engine recognizes the job seeker's emotions and reflects them in the results.

[1064] Program processing

[1065] 1. User: A job seeker books an interview on an online platform.

[1066] 2. Server: Starts scheduled interview sessions. Generative AI generates interview questions and emotion engine analyzes job seeker reactions.

[1067] 3. User: The job seeker answers the questions by typing or speaking.

[1068] 4. Server: The emotion engine analyzes the job seeker's facial expressions and vocal changes, which the generative AI takes into account when analyzing the answers.

[1069] 5. Server: The generative AI combines the answers and emotional data to generate an evaluation result. For example, it can reflect whether the job seeker is feeling stressed.

[1070] 6. Server: Determines whether the application passes or fails based on the evaluation results and notifies the user of the result.

[1071] Combining enhanced employee training with the Emotion Engine

[1072] In some embodiments, employee training is enhanced with an emotion engine.

[1073] System Overview

[1074] The emotion engine recognizes employees' emotions, and generative AI provides appropriate training content based on those emotions.

[1075] Program processing

[1076] 1. User: An employee logs into the training platform and begins training.

[1077] 2. On the device: The emotion engine analyzes the employee's facial expressions and tone of voice to recognize their emotions.

[1078] 3. Terminal: Sends the recognized emotion data to the generative AI.

[1079] 4. Server: Generative AI customizes training content based on emotional data, for example, offering lighter training if an employee is tired.

[1080] 5. Terminal: Presents customized training content to employees and facilitates training.

[1081] Combining career advice and emotion engines

[1082] In some embodiments, career advice is combined with an emotion engine.

[1083] System Overview

[1084] The emotion engine recognizes the user's emotions, and the generative AI provides career paths and advice that take their emotional state into account.

[1085] Program processing

[1086] 1. User: An employee or job seeker applies for career counseling.

[1087] 2. Server: Collects the emotional state along with the user's profile information.

[1088] 3. Server: Analyzes the profile information and emotional data collected by the generative AI.

[1089] 4. Server: Generates career paths that take into account sentiment data and creates reports, for example adding specific advice to alleviate user anxiety.

[1090] 5. Server: Provides the generated reports to users and makes them available for download from the online platform.

[1091] In this way, combining emotion engines enables more precise data analysis and service provision that takes into account the user's emotional state, significantly improving overall efficiency and user satisfaction.

[1092] The processing flow will be explained below.

[1093] Combining automated application screening with an emotion engine

[1094] Program processing

[1095] Step 1:

[1096] A user uploads their application documents (resume, curriculum vitae, etc.) through a web portal.

[1097] Step 2:

[1098] The server receives the uploaded application documents and stores them in a database.

[1099] Step 3:

[1100] The device's built-in emotion engine extracts emotion data from the user's facial expressions and voice and sends it to the server.

[1101] Step 4:

[1102] The server launches a generative AI and analyzes the text data in the application documents.

[1103] Step 5:

[1104] The server integrates the emotional data into the analysis results and performs scoring. For example, positive emotional expressions receive a high score.

[1105] Step 6:

[1106] The server generates a list of the best candidates and notifies the HR personnel.

[1107] Combining customer service improvement with emotion engines

[1108] Program processing

[1109] Step 1:

[1110] A user (customer) submits a question via a chat widget or contact form.

[1111] Step 2:

[1112] The on-device virtual assistant receives inquiries in real time.

[1113] Step 3:

[1114] The emotion engine built into the device recognizes the customer's emotions from the input content and voice data and sends this to the server.

[1115] Step 4:

[1116] The server sends the emotion data and the query content to the generative AI.

[1117] Step 5:

[1118] The server takes emotional data into account and the generative AI generates the optimal response to the inquiry. For example, if the customer is feeling stressed, it will generate a more polite response.

[1119] Step 6:

[1120] The device displays the generated answer to the user and provides it in a chat window or via email.

[1121] Step 7:

[1122] The server logs all queries and responses.

[1123] Combining automated interviews with an emotion engine

[1124] Program processing

[1125] Step 1:

[1126] A user (job seeker) schedules an interview on an online platform.

[1127] Step 2:

[1128] The server starts the scheduled interview session.

[1129] Step 3:

[1130] The device's built-in emotion engine detects the job seeker's emotions from their facial expressions and voice and sends this information to the server.

[1131] Step 4:

[1132] The server uses generative AI to generate interview questions and present them to job seekers.

[1133] Step 5:

[1134] The user answers the questions by typing or speaking.

[1135] Step 6:

[1136] The server receives the emotion data and uses generative AI to comprehensively analyze the responses and emotion data. For example, if a job seeker is nervous, that will be reflected in the evaluation.

[1137] Step 7:

[1138] The server scores the evaluation results and determines whether the application passes or fails.

[1139] Step 8:

[1140] The server notifies the result and provides it to the user.

[1141] Combining enhanced employee training with the Emotion Engine

[1142] Program processing

[1143] Step 1:

[1144] A user (employee) logs into the training platform.

[1145] Step 2:

[1146] Employees enter their questions into a question form within the training platform.

[1147] Step 3:

[1148] The device's built-in emotion engine recognizes emotions from the employee's facial expressions and tone of voice and transmits this information to a server.

[1149] Step 4:

[1150] The server sends the emotional data to a generative AI, which generates the optimal answer to the question.

[1151] Step 5:

[1152] The server sends the generated response to the terminal.

[1153] Step 6:

[1154] The device displays customized responses to the user, for example, suggesting a lighter workout if the user is tired.

[1155] Combining career advice and emotion engines

[1156] Program processing

[1157] Step 1:

[1158] A user (employee or job seeker) applies for career counseling through an online platform.

[1159] Step 2:

[1160] The device's built-in emotion engine recognizes the user's emotions from their facial expressions and voice and transmits them to the server.

[1161] Step 3:

[1162] The server collects user profile information and emotional data and analyzes it using generative AI.

[1163] Step 4:

[1164] The server generates a report based on the emotional data, generating an optimal career path, adding specific advice to alleviate any anxiety the user may have, for example.

[1165] Step 5:

[1166] The server provides the generated reports to the user and makes them available for download from an online platform.

[1167] In this way, combining emotion engines enables more precise data analysis and service provision that takes into account the user's emotional state, significantly improving overall efficiency and user satisfaction.

[1168] Example 2

[1169] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1170] Traditional application screening cannot take into account the emotional state of job seekers, which can result in the overlooking of highly suitable candidates. Furthermore, in customer service and interview evaluations, responses and evaluations that ignore the emotional state of employees can be inaccurate. This can lead to issues such as a decline in the quality of a company's recruitment process and customer service.

[1171] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1172] In this invention, the server includes a means for job seekers to submit application documents, a means for analyzing facial expressions and tone of voice to extract emotional data, a means for a generative AI to analyze the submitted application documents and the extracted emotional data to select candidates, and a means for notifying the human resources department of the information on the selected candidates. This enables screening that takes into account the emotional state of applicants, allowing for the selection of more suitable candidates. Similarly, utilizing emotional data in customer service and interview evaluations allows for more accurate and effective responses.

[1173] "Application documents" refers to documents such as resumes and work histories that job seekers submit to companies seeking employment.

[1174] "Generative AI" refers to artificial intelligence that can analyze text and voice data using natural language processing technology and create new products.

[1175] "Emotional data" refers to data analyzed from facial expressions, vocal tone, etc., and is information that expresses an individual's emotional state in numerical or categorical terms.

[1176] "Means" refers to a device, system, method, etc. used to achieve a particular purpose.

[1177] "Candidate" refers to an applicant who has been assessed as suitable for a particular job or role.

[1178] "Notification" refers to the act of conveying specific information to a recipient.

[1179] "Analysis" refers to the process of examining data or information in detail to clarify its content and structure.

[1180] An "interview" refers to the process by which a job seeker and an interviewer evaluate the job seeker's aptitude and abilities through dialogue.

[1181] "Inquiry" means a question or request made by a Customer seeking information or support regarding a product or service.

[1182] Combining automated application screening with an emotion engine

[1183] In this embodiment of the invention, a user (job seeker) uploads a resume or work history through a web portal. The server that receives the application documents first stores the documents in a database, and then uses an emotion engine to analyze the user's facial expressions and voice tone to extract emotion data. Specific software that can be used includes a "face analysis API" and a "voice analysis API."

[1184] For example, when a user logs in to a job-seeking website and uploads a PDF resume, the server saves the document in cloud storage. The server then uses face analysis APIs and voice analysis APIs to extract emotional data from the uploaded data.

[1185] Next, generative AI is used to analyze the text data and extracted emotion data from the submitted application documents. Natural language processing AI can be used as generative AI. As a result of the analysis, the server scores the suitability of the candidates and generates a list of optimal candidates based on this. For scoring, a machine learning library is used to quantify the suitability of each applicant.

[1186] Finally, the server notifies the HR department of the generated candidate list using a messaging API, automating the process of efficiently selecting and notifying highly suitable candidates.

[1187] Career Services Prompt Examples

[1188] "Analyze the facial expressions and tone of voice of job applicants and evaluate them along with the content of their application documents. For example, if a job applicant shows signs of stress, reflect that emotion in your evaluation."

[1189] By inputting this prompt into the generative AI model, the emotion engine and generative AI work together to process the prompt. This enables highly accurate analysis and evaluation using emotion data, improving the efficiency and accuracy of the hiring process.

[1190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1191] Specific processing steps of the program

[1192] Combining automated application screening with an emotion engine

[1193] Step 1:

[1194] User: A job seeker uploads their resume or CV through a web portal.

[1195] Input: A user-uploaded PDF resume or CV.

[1196] Output: The application data sent to the server.

[1197] Specific operation: Click the "Upload Documents" button on the web portal, select your resume from the file selection dialog, and upload it.

[1198] Step 2:

[1199] Server: Receives uploaded documents and stores them in a database.

[1200] Input: Application data submitted by the user.

[1201] Output: File path of application data saved in the database.

[1202] Specific operation: The server uploads the received file to "cloud storage" and writes the file metadata and storage location to the database.

[1203] Step 3:

[1204] Server: The emotion engine analyzes the user's facial expressions, tone of voice, etc. to extract emotional data.

[1205] Input: Application document data stored on the server and user's voice and facial image data.

[1206] Output: Emotion data obtained from the emotion engine.

[1207] Specific operation: Using the "face analysis API" and "voice analysis API," facial images and voice data are analyzed, and the emotional state is quantified and stored in a database.

[1208] Step 4:

[1209] Server: Uses a combination of generative AI and an emotion engine to analyze text data and emotion data from application documents.

[1210] Input: Text data and sentiment data from job applications.

[1211] Output: Analysis result data.

[1212] Specific operation: The content of the resume is analyzed using generative AI (natural language processing AI), emotional data obtained from the emotion engine is integrated, and the analysis results are stored in a database.

[1213] Step 5:

[1214] Server: Scores the analysis results and generates a list of optimal candidates, including emotional data.

[1215] Input: Analysis result data.

[1216] Output: A list of the best candidates.

[1217] Specific operation: Using a "machine learning library," it calculates the score for each applicant, and based on the results, generates a list of optimal candidates and stores them in a database.

[1218] Step 6:

[1219] Server: Notifies the HR department of the generated candidate list.

[1220] Input: A list of best candidates.

[1221] Output: Notification message to HR department.

[1222] Specific operation: Using the "Messaging API," a notification message is sent to a specific channel in the HR department, providing a link to the candidate list.

[1223] (Application example 2)

[1224] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1225] Conventional application screening, customer service, and automated interview systems are unable to take into account the user's emotional state, making it difficult to provide personalized services. Furthermore, evaluations and responses without emotion analysis have limited the potential for improving the user experience. Therefore, there is a need for technology that can recognize user emotions in real time and respond and evaluate accordingly.

[1226] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1227] In this invention, the server includes: a means for a job seeker to submit an application; a means for a generative AI to analyze the submitted application and select candidates; a means for notifying a human resources department of information about the selected candidates; a means including an emotion engine for analyzing the job seeker's emotions at the time of submission; a means for re-evaluating and scoring based on the emotion data; a means for accepting customer inquiries; a means for generating responses to customer inquiries using a generative AI; a means for providing the generated responses to the customers; a means including an emotion engine for analyzing customer emotions in real time; a means for adjusting responses based on the emotion data; a means for scheduling interviews with the job seeker; a means for generating interview questions using a generative AI and presenting them to the job seeker; a means for analyzing and evaluating the job seeker's answers; a means including an emotion engine for analyzing the job seeker's emotions during the interview; and a means for adjusting the evaluation results based on the emotion data. This enables more precise and personalized responses and evaluations while taking into account the user's emotional state.

[1228] "Means for submitting applications" refers to the interface that job seekers use to upload application documents, such as resumes and curriculum vitae, to the system.

[1229] "Generative AI" is artificial intelligence that uses natural language processing and machine learning techniques to analyze and generate responses to application documents and customer inquiries.

[1230] The "means of candidate selection" refers to the method for selecting suitable candidates based on the information in the application documents analyzed by the generative AI.

[1231] "Means of notifying the human resources department" refers to the means by which information about the selected candidate is communicated to the human resources department via email or internal systems.

[1232] The "emotion engine that analyzes job seekers' emotions at the time of submission" is a technology that recognizes emotions from facial expressions and voice when job seekers submit their application documents and extracts them as data.

[1233] "Means for re-evaluating and scoring based on emotional data" refers to a method for adjusting the evaluation of application documents and recalculating scores using recognized emotional data.

[1234] "Means for accepting customer inquiries" are interfaces such as chat widgets or forms that allow customers to submit questions or requests.

[1235] The "means of generating a response" is how the generative AI creates an appropriate response to a customer inquiry.

[1236] "Means of providing the generated response to the customer" refers to the method of communicating the answer created by the generative AI to the customer via a chat window, email, etc.

[1237] The "emotion engine that analyzes customer emotions in real time" is a technology that instantly recognizes emotions from customer text input, voice data, and even facial expressions.

[1238] "Means for adjusting responses based on emotional data" refers to a method in which generative AI changes the content and tone of responses based on recognized emotional data.

[1239] "Means for scheduling interviews" refers to a system that allows job seekers to schedule interview dates and times online.

[1240] "Means for generating interview questions and presenting them to job seekers" refers to a method in which generative AI generates specific interview questions and presents them to job seekers in text or audio.

[1241] "Means for analyzing and evaluating job seekers' responses" refers to the method by which generative AI analyzes the content of job seekers' responses and assigns an evaluation score.

[1242] The "emotion engine that analyzes the emotions of job seekers during interviews" is a technology that recognizes emotions in real time from the facial expressions and voice of job seekers during interviews.

[1243] "Means for adjusting evaluation results based on emotional data" refers to a method in which generative AI recalculates evaluation results by taking into account emotional data recognized during the interview.

[1244] This invention implements the following steps: We present specific procedures for combining generative AI and an emotion engine to screen job applicants' applications, conduct online interviews, provide customer support, and provide a recommendation system.

[1245] Screening job applicant applications

[1246] Hardware:

[1247] Users use a computer or smartphone to upload their application documents to a web portal.

[1248] software:

[1249] The server uses generative AI (e.g., OpenAI's GPT-3 model) and emotion engine (e.g., Microsoft's Azure Cognitive Services' Face API).

[1250] Data processing:

[1251] When an application is uploaded, a generative AI analyzes it and evaluates the job seeker's skills and experience. In parallel, an emotion engine recognizes emotions from video and audio data and adds that data to the analysis.

[1252] As a specific example, if a user is feeling angry or impatient, the generative AI will use the recognized emotional data to perform more flexible scoring.

[1253] Interview automation

[1254] Hardware:

[1255] A webcam and microphone are used when the interview is conducted between the job seeker and the server.

[1256] software:

[1257] The server conducts interviews using generative AI and an emotion engine. The generative AI generates interview questions, and the emotion engine analyzes the job seeker's emotions in real time as they answer.

[1258] Data processing:

[1259] The responses are re-evaluated based on the job seeker's emotional state to generate a final rating. For example, if the job seeker is nervous, the rating will adjust to reflect this.

[1260] An example prompt might be, "The user is nervous, so please keep your questions brief."

[1261] Customer Service

[1262] Hardware:

[1263] Customers use a chat widget or inquiry form on their computer or smartphone.

[1264] software:

[1265] Virtual assistants use generative AI to generate responses to customer queries, and here too, an emotion engine recognizes customer emotions in real time.

[1266] Data processing:

[1267] It generates and quickly delivers responses that reflect the customer's emotions. For example, if a customer expresses dissatisfaction, generative AI will generate an appropriate and comforting response.

[1268] An example would be, "The customer is unhappy, so please carefully explain the specific steps you will take to resolve the issue."

[1269] Product Recommendations

[1270] Hardware:

[1271] Users of the online shopping site access it from their smartphones or computers.

[1272] software:

[1273] The server uses generative AI and an emotion engine to analyze the product pages the user views and their emotions at the time.

[1274] Data processing:

[1275] The recommendation algorithm reflects the user's emotional data and recommends appropriate products. For example, if a user feels happy when looking at a product, it will suggest more products from that category.

[1276] An example prompt might be, "Since this user is happy, please recommend products in the same category."

[1277] These embodiments allow for application analysis, interviews, customer service, and product recommendations to take into account the user's emotional state, improving overall service quality.

[1278] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1279] Step 1:

[1280] A job seeker uploads their application to a web portal.

[1281] Operation:

[1282] Users (job seekers) upload their resumes and work histories along with the necessary documents to the system.

[1283] input:

[1284] Resume, work history

[1285] output:

[1286] Saved application data

[1287] Step 2:

[1288] The server receives the uploaded application documents and stores them in a database.

[1289] Operation:

[1290] The server receives the applications and stores the data in a database for further analysis.

[1291] input:

[1292] Application document data

[1293] output:

[1294] Document data stored in the database

[1295] Step 3:

[1296] The server uses a sentiment engine to analyze the job seeker's sentiment at the time of submission.

[1297] Operation:

[1298] The server activates an emotion engine and extracts emotional data by analyzing the job seeker's facial expressions and tone of voice.

[1299] input:

[1300] Video or audio data when submitting documents

[1301] output:

[1302] Emotional Data

[1303] Step 4:

[1304] Generative AI analyzes the text data of submitted application documents.

[1305] Operation:

[1306] Generative AI (such as the GPT-3 model) analyzes the content of application documents and evaluates skills and experience.

[1307] input:

[1308] Text data of application documents

[1309] output:

[1310] Initial evaluation data

[1311] Step 5:

[1312] Generative AI will readjust the evaluation of your application based on emotional data.

[1313] Operation:

[1314] The server recalculates the evaluation results based on the emotional data and adjusts the scoring. For example, if a job applicant is nervous, the server will take that into account and adjust the scoring accordingly.

[1315] input:

[1316] Initial evaluation data, emotion data

[1317] output:

[1318] Adjusted evaluation data

[1319] Step 6:

[1320] Based on the evaluation results, the server generates a list of optimal candidates.

[1321] Operation:

[1322] The server creates a list of appropriate candidates based on the evaluation data adjusted by the generative AI.

[1323] input:

[1324] Adjusted evaluation data

[1325] output:

[1326] Candidate List

[1327] Step 7:

[1328] The server notifies the HR department of the best candidates.

[1329] Operation:

[1330] The server notifies the human resources department of the generated candidate list via email or an internal company system.

[1331] input:

[1332] Candidate List

[1333] output:

[1334] Candidate list notified to HR department

[1335] Step 8:

[1336] Schedule candidate interviews.

[1337] Operation:

[1338] The user (job seeker) sets the date and time of the interview on the system.

[1339] input:

[1340] Interview reservation information

[1341] output:

[1342] Scheduled Interview Sessions

[1343] Step 9:

[1344] Generative AI generates interview questions and presents them to job seekers.

[1345] Operation:

[1346] The server uses generative AI to generate and present appropriate interview questions to job seekers.

[1347] input:

[1348] Interview Question Generation Prompts

[1349] output:

[1350] Generated interview questions

[1351] Step 10:

[1352] Generative AI analyzes job seekers' responses, and an emotion engine recognizes their emotions in real time.

[1353] Operation:

[1354] The server uses generative AI to analyze job seekers' responses, and an emotion engine analyzes emotions during the interview in real time.

[1355] input:

[1356] Job seeker response data, emotion data

[1357] output:

[1358] Analyzed response data, real-time sentiment data

[1359] Step 11:

[1360] Evaluation results are generated based on emotion data.

[1361] Operation:

[1362] The server evaluates the job seeker's responses taking into account the emotional data and synthesizes the results.

[1363] input:

[1364] Analyzed response data and emotion data

[1365] output:

[1366] Final evaluation results

[1367] Step 12:

[1368] The server determines whether the job seeker passes or fails based on the final evaluation results and notifies the job seeker of the result.

[1369] Operation:

[1370] The server determines whether the job seeker has passed or failed based on the generated final evaluation results and notifies the job seeker of the result via email or system notification.

[1371] input:

[1372] Final evaluation results

[1373] output:

[1374] The result of the job applicant's acceptance was notified

[1375] Example prompts:

[1376] "Please keep your questions brief as users are nervous."

[1377] "The customer is unhappy, so please carefully explain the specific steps you will take to resolve the issue."

[1378] "Recommend products in the same category because they bring joy to the user."

[1379] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1380] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1381] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1382] [Third embodiment]

[1383] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1384] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1385] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1386] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1387] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1388] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1389] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1390] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1391] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1392] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1393] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1394] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1395] Automated Application Screening

[1396] One form of this system is automated application screening.

[1397] System Overview

[1398] It provides a web portal for users to submit their applications, and the server analyzes the applications using generative AI. Based on the analysis results, it creates a list of the most suitable candidates and notifies the HR department of this information.

[1399] Program processing

[1400] 1. User: Job seekers upload their resumes and CVs through a web portal.

[1401] 2. Server: Receives uploaded documents and stores them in a database. It then launches generative AI to analyze these documents. For example, if a job candidate applies for an IT engineer position, it evaluates their skill set, years of experience, and specific project experience.

[1402] 3. Server: Scores the analysis results and generates a list of optimal candidates.

[1403] 4. Server: Finally, notify the HR department of the generated list and move on to the next step in the hiring process.

[1404] Improved customer service

[1405] Next, there is the use of generative AI to improve customer service.

[1406] System Overview

[1407] When a customer makes an inquiry, generative AI generates an appropriate response and provides it through a virtual assistant.

[1408] Program processing

[1409] 1. User: A customer submits a question through a chat widget or contact form.

[1410] 2. Terminal: The virtual assistant receives the query and uses generative AI to analyze the required information.

[1411] 3. On the device: Generative AI generates appropriate answers and provides them to users. For example, if a customer asks about how to use a product, a virtual assistant will guide them to the manual or related videos.

[1412] 4. Server: Logs all queries and responses for further analysis.

[1413] Interview automation

[1414] Additionally, the automation of interviews using generative AI is also included in the embodiments.

[1415] System Overview

[1416] An interview session is held between job seekers and generative AI, and the responses are analyzed and evaluated in real time.

[1417] Program processing

[1418] 1. User: A job seeker books an interview through an online platform.

[1419] 2. Server: When the scheduled time arrives, the generative AI generates interview questions and presents them to the job seeker.

[1420] 3. User: The job seeker answers the questions by typing or speaking.

[1421] 4. Server: Generative AI analyzes and scores answers in real time, for example, case study questions to assess suitability for a consulting position.

[1422] 5. Server: Determines whether the application is successful and notifies the result.

[1423] Enhanced employee training

[1424] In some embodiments, employee training is enhanced.

[1425] System Overview

[1426] Employees access a training platform and receive assistance with answers from generative AI.

[1427] Program processing

[1428] 1. User: An employee logs into the training platform and types in a question.

[1429] 2. Terminal: Sends the question to the generative AI and requests the required answer.

[1430] 3. Server: The generative AI generates the optimal answer to the question and returns it to the device.

[1431] 4. Terminal: Generate answers and display them on the employee's screen, for example, step-by-step instructions for a question about how to use new software.

[1432] Providing career advice

[1433] Finally, there are embodiments that provide career advice.

[1434] System Overview

[1435] Generative AI analyzes users' profile information and suggests career paths and skill development.

[1436] Program processing

[1437] 1. User: An employee or job seeker applies for career counseling.

[1438] 2. Server: Collects user profile information and analyzes it using generative AI.

[1439] 3. Server: Generates optimal career paths and creates reports. For example, it suggests the skills and experience required for a user who wants to become a project manager within five years.

[1440] 4. User: Receives the generated report and checks its contents.

[1441] The above-described embodiment enables efficient business operations in the human resources industry, reduces the burden on the human resources department and related departments, and improves the quality of work.

[1442] The processing flow will be explained below.

[1443] Automated Application Screening

[1444] Program processing

[1445] Step 1:

[1446] A user uploads their application documents (resume, curriculum vitae, etc.) through a web portal.

[1447] Step 2:

[1448] The server receives the uploaded application documents and stores them in a database.

[1449] Step 3:

[1450] The server launches a generative AI and extracts the contents of the application documents stored in the database as text.

[1451] Step 4:

[1452] The server analyzes the text data and evaluates skill sets, experience, qualifications, etc.

[1453] Step 5:

[1454] The server scores the candidates based on the analysis results and generates a list of optimal candidates.

[1455] Step 6:

[1456] The server notifies the human resources personnel of the generated candidate list.

[1457] Improved customer service

[1458] Program processing

[1459] Step 1:

[1460] A user (customer) submits a question via a chat widget or contact form.

[1461] Step 2:

[1462] The on-device virtual assistant receives inquiries in real time.

[1463] Step 3:

[1464] The device analyzes the inquiry and sends the data to the generative AI.

[1465] Step 4:

[1466] The server uses generative AI to generate the best answer to the query.

[1467] Step 5:

[1468] The server generates a response and sends it back to the terminal.

[1469] Step 6:

[1470] The device displays the answer to the user and provides it in a chat window or via email.

[1471] Step 7:

[1472] The server logs all queries and responses.

[1473] Interview automation

[1474] Program processing

[1475] Step 1:

[1476] A user schedules an interview through an online platform.

[1477] Step 2:

[1478] The server starts the scheduled interview session.

[1479] Step 3:

[1480] The server runs a generative AI to generate interview questions based on the user's profile.

[1481] Step 4:

[1482] The server presents the generated question to the user.

[1483] Step 5:

[1484] The user answers the questions by typing or speaking.

[1485] Step 6:

[1486] The server uses generative AI to analyze user responses in real time.

[1487] Step 7:

[1488] The server scores the analysis results and generates an evaluation result.

[1489] Step 8:

[1490] The server determines whether the application passes or fails based on the evaluation results and notifies the user of the result.

[1491] Enhanced employee training

[1492] Program processing

[1493] Step 1:

[1494] A user (employee) logs into the training platform.

[1495] Step 2:

[1496] A user enters a question into a question form within the training platform.

[1497] Step 3:

[1498] The device sends the entered question to the generative AI.

[1499] Step 4:

[1500] The server uses generative AI to generate the best answer to the question.

[1501] Step 5:

[1502] The server sends the generated response to the terminal.

[1503] Step 6:

[1504] The terminal displays the answer to the user.

[1505] Providing career advice

[1506] Program processing

[1507] Step 1:

[1508] A user (employee or job seeker) applies for career counseling through an online platform.

[1509] Step 2:

[1510] The server collects user profile information.

[1511] Step 3:

[1512] The profile information collected by the server is sent to the generative AI for analysis.

[1513] Step 4:

[1514] The server uses generative AI to generate the optimal career path based on the user's goals.

[1515] Step 5:

[1516] The server documents the generated career path and creates a career advice report.

[1517] Step 6:

[1518] The server provides the reports to the user and makes them available for download from an online platform.

[1519] In this way, by performing specific actions at each processing step, efficient and consistent business operations can be achieved using generative AI.

[1520] Example 1

[1521] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1522] Traditional recruitment and customer service processes require a lot of manual work and lack advanced analysis and automation. This has led to problems that reduce recruitment efficiency and the quality of customer service. In particular, processing large volumes of application documents and customer inquiries takes time and effort, increasing the risk of human error. Furthermore, interview automation and the provision of career advice are not fully implemented, creating challenges for improving job seeker and employee satisfaction. A new system is needed to solve these problems and provide efficient, high-quality services.

[1523] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1524] In this invention, the server includes a means for job seekers to submit application documents, a means for a generative AI to analyze the submitted application documents and select candidates, a means for notifying the human resources department of information on the selected candidates, a means for saving the submitted application documents in a database and analyzing them, a means for generating a candidate list based on the analysis results, and a means for notifying the human resources department of the generated list and advancing the hiring process. This reduces manual work in hiring activities and makes it more efficient through advanced analysis.

[1525] "Means of submitting application documents" refers to the means by which job seekers submit application documents, such as resumes and curriculum vitae, through an online platform.

[1526] "Generative AI" is a type of artificial intelligence model that generates and analyzes advanced information through natural language processing and data analysis.

[1527] "Means for analyzing application documents" refers to using generative AI to analyze the contents of submitted application documents and extract information such as skills and experience.

[1528] The "means of selecting candidates" refers to the means of evaluating and selecting appropriate candidates based on the information in the application documents analyzed by the generative AI.

[1529] "Means for notifying the human resources department of the candidate's information" refers to the means for notifying the human resources department of the information about the selected candidate, including sending a notification email and displaying the information on the management screen.

[1530] "Means of storing in a database" refers to the means of storing submitted application documents in a digital format so that they can be accessed and analyzed at a later date.

[1531] "Means for generating a candidate list" refers to the means for creating a list of appropriate candidates based on the results of analysis and scoring by the generative AI.

[1532] The "means for notifying the human resources department of the generated list" refers to a means for notifying the human resources department of the generated candidate list and for proceeding to the next step in the recruitment process.

[1533] "Means for accepting customer inquiries" refers to the means by which customers can submit questions, such as through a chat widget or a contact form.

[1534] "Means for generating responses to customer inquiries" refers to means for using generative AI to analyze the content of customer inquiries and generate appropriate responses.

[1535] "Means for providing the generated response to the customer" refers to means for providing the response generated by the generative AI to the customer, including via a chat widget or email.

[1536] "Means for scheduling an interview" means the means by which a job seeker schedules an interview on the online platform.

[1537] "Means for generating and presenting interview questions" refers to the means by which generative AI generates interview questions and presents them to job seekers.

[1538] "Means for analyzing and evaluating job seeker responses in real time" refers to a means for using generative AI to analyze responses provided by job seekers in interviews in real time and evaluate their content.

[1539] "Means for scoring the analysis results and determining pass / fail" refers to means for scoring the analyzed answers and determining pass / fail.

[1540] "Means of notifying results" refers to the means used to notify job seekers of the results of their application, including sending emails and displaying the results on the platform.

[1541] This invention is a system that uses generative AI models to automatically screen job applications, improve customer service, automate interviews, and streamline employee training and career advice. To implement this system, a server, terminals, and users must all work together.

[1542] Automated application screening

[1543] The system begins with a user submitting an application through a web portal. Job seekers upload their resumes and CVs in PDF format. The server stores the uploaded documents in a database and runs a generative AI model (e.g., OpenAI GPT-4) to analyze them. Based on the analysis results, the server scores candidates and generates a list of the best candidates. This list is automatically notified to the HR department.

[1544] As a concrete example, taking the position of an IT engineer, the server inputs the following prompt sentence into the generative AI: "From this application document, please generate a list of technical skills and experience that are suitable for the position of IT engineer."

[1545] Improved customer service

[1546] When a customer submits a question using a website chat widget or contact form, the device receives the inquiry. The generative AI model analyzes the information and generates an appropriate response. The generated response is provided to the user from the device, and all inquiries and responses are logged on the server.

[1547] For example, if a customer inquires, "I don't know how to use the new product," the device will send the following prompt to the generation AI: "Please generate a sentence that provides detailed instructions on how to use the new product."

[1548] Interview automation

[1549] When a job seeker makes an interview reservation through the online platform, the server uses generative AI to generate appropriate questions based on the reservation time. The job seeker answers the questions via text or voice, and the answers are analyzed in real time. The server scores the analysis results and notifies the job seeker of their success or failure.

[1550] For example, send the following prompt to the generation AI: "Generate case study questions to assess suitability as a consultant."

[1551] Employee training and career advice

[1552] When an employee accesses the training platform and enters a question, the device sends the question to the generative AI, which generates the best answer, and the device displays the answer on the employee's screen.

[1553] For example, if asked how to use new project management software, the device would send the following prompt to the generating AI: "Please provide a step-by-step explanation of the basics of how to use the new project management software."

[1554] In the career advice service, employees and job seekers apply for career counseling, and the server analyzes the user's profile information using AI to generate an optimal career path and prepares a report to provide to the user.

[1555] For example, send the following prompt to the generation AI: "Generate a skills and experience development plan for a user who wants to become a project manager within five years."

[1556] As described above, by having the server, terminals, and users all work together, the system can efficiently and effectively perform automated screening of application documents, improve customer service, automate interviews, train employees, and provide career advice.

[1557] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1558] Automated Application Screening

[1559] Step 1:

[1560] User: Job seekers upload their resumes and CVs through a web portal.

[1561] Input: PDF resume or curriculum vitae selected from local disk

[1562] Specific operation: The user clicks the "Upload documents" button on the web portal, selects application documents in the file selection dialog, and then presses the "Upload" button, which sends the selected documents to the server.

[1563] Output: The web portal sends the file to the server.

[1564] Step 2:

[1565] Server: Receives uploaded documents, stores them in a database, and runs a generative AI to analyze these documents.

[1566] Input: Application file submitted by the user

[1567] What happens: The server receives the HTTP POST request and saves the application in a specific folder. It then records the file path and associated metadata in a database. It then generates and sends a prompt to a generative AI model (e.g., OpenAI GPT-4) to analyze the application.

[1568] Output: Prompt for analysis and analysis results

[1569] Step 3:

[1570] Server: Scores the analysis results and generates a list of optimal candidates.

[1571] Input: Analysis results obtained from generative AI

[1572] Specific operation: The server scores the analysis results (skills, years of experience, project details, etc.) obtained from the generative AI model based on evaluation criteria. For example, it assigns points to "years of Java experience" and "project leadership experience" and calculates an overall score.

[1573] Output: A list of the best candidates

[1574] Step 4:

[1575] Server: Notifies the HR department of the generated list and moves on to the next step in the hiring process.

[1576] Input: Scored candidate list

[1577] What it does: The server generates a report containing the best candidates and automatically sends it to the HR department's email address. It also displays the list in the HR admin panel of the web portal so that HR personnel can log in and view it.

[1578] Output: Notify the HR department and display the data on the management screen

[1579] Improved customer service

[1580] Step 1:

[1581] User: A customer submits a question through a chat widget or contact form.

[1582] Input: Customer inquiry

[1583] What it does: A user accesses a chat widget on a website, enters a question in the text field, and clicks the "Send" button to send the question to their device.

[1584] Output: Send query to device

[1585] Step 2:

[1586] On the device: The virtual assistant receives the query and uses generative AI to parse the required information.

[1587] Input: Received customer inquiry

[1588] How it works: The device analyzes the query and generates and sends an appropriate prompt to a generative AI model (e.g., GPT-4), such as "Please generate an answer on how to use product X."

[1589] Output: The prompt sent to the AI ​​model and the answer it gives

[1590] Step 3:

[1591] Terminal: Generative AI generates appropriate answers and provides them to the user.

[1592] Input: Answer obtained from generative AI

[1593] What it does: The device receives a response from the generative AI model and displays the text response in the chat widget, such as a snippet from the product manual or a related link.

[1594] Output: The answer displayed to the user

[1595] Step 4:

[1596] Server: Logs all queries and responses for further analysis.

[1597] Input: History of inquiries received and responses generated

[1598] What it does: The server stores the query and its response in a database, allowing for later log analysis and retrieval of statistics.

[1599] Output: Saving and managing log data

[1600] Interview automation

[1601] Step 1:

[1602] User: A job seeker schedules an interview through an online platform.

[1603] Input: Reservation information (date and time, job seeker information, etc.)

[1604] Specific operation: The user accesses the platform's reservation page, selects the desired date and time from the calendar, and presses the "Confirm reservation" button, which sends the reservation information to the server.

[1605] Output: Send reservation information to the server

[1606] Step 2:

[1607] Server: When the scheduled time arrives, the generative AI generates interview questions and presents them to the job seeker.

[1608] Input: Booking information and job seeker profile

[1609] How it works: When the appointment time arrives, the server sends a prompt to the generative AI model to generate appropriate interview questions, which are then displayed on the job seeker's device.

[1610] Output: Generated interview questions

[1611] Step 3:

[1612] User: The job seeker answers questions by typing or speaking.

[1613] Input: Job seeker's response (text or voice)

[1614] Specific behavior: The user answers the questions using the text box or voice input function. Once the answer is complete, the user presses the "Submit" button to send the answer to the server.

[1615] Output: Send response to server

[1616] Step 4:

[1617] Server: Generative AI analyzes and scores answers in real time.

[1618] Input: Job seeker's answers

[1619] Specific operation: After receiving the answer, the server analyzes the answer using the generative AI model. The analysis results are scored based on evaluation criteria, such as logic, the presence or absence of specific examples, and the level of expertise.

[1620] Output: Scoring results

[1621] Step 5:

[1622] Server: Determines whether the application is successful and notifies the result.

[1623] Input: Scoring results

[1624] Specific operation: The server determines whether the job seeker has passed or failed based on the scoring results, automatically generates a notification email and sends it to the job seeker. The result is also displayed on the management screen.

[1625] Output: Notification of pass / fail result

[1626] Employee training and career advice

[1627] Step 1:

[1628] User: An employee logs into the training platform and types in a question.

[1629] Input: Employee question

[1630] What happens: A user accesses the training platform through a login form, selects the "Ask a Question" option from the dashboard, enters a question in the text field, and presses the "Submit" button to send a request to the server.

[1631] Output: Send the question

[1632] Step 2:

[1633] Terminal: Sends the question to the generative AI and requests the required answer.

[1634] Input: Question submitted by employee

[1635] What it does: The device converts the input question into a prompt and sends it to the generative AI model, for example, "Please tell me how to use my new accounting software."

[1636] Output: The prompt sent to the AI ​​model and the answer from the AI

[1637] Step 3:

[1638] Server: The generative AI generates the optimal answer to the question and returns it to the device.

[1639] Input: Answer obtained from generative AI

[1640] How it works: The server formats the answer received from the generative AI and sends it to the device. The answer may be in text format, but it may also include related materials and links.

[1641] Output: Sending formatted answers

[1642] Step 4:

[1643] Terminal: Displays the generated answers on the employee's screen.

[1644] Input: Answer sent from the server

[1645] What happens: The device displays the received answer in a user interface, for example, a step-by-step guide or a related video tutorial.

[1646] Output: The answer presented to the user

[1647] Providing career advice

[1648] Step 1:

[1649] Users: Employees and job seekers request career counseling.

[1650] Input: Career counseling application information

[1651] Specific operation: The user accesses the career counseling application form and enters the required information (current position, goals, skills, etc.). Presses the "Apply" button to send the information to the server.

[1652] Output: Send application details

[1653] Step 2:

[1654] Server: Collects user profile information and analyzes it using generative AI.

[1655] Input: Career counseling application information and profile information

[1656] What it does: The server stores the input information in a database and sends it to a generative AI model for analysis, which generates prompts that assess gaps in your current skill set, experience, and goals.

[1657] Output: Profile analysis results

[1658] Step 3:

[1659] Server: Generates optimal career paths and creates reports.

[1660] Input: Analysis results from generative AI

[1661] How it works: Based on the analysis results from the generative AI model, the server generates a report proposing the optimal career path, including required skills, recommended training courses, and specific steps to gain experience.

[1662] Output: Career Path Report

[1663] Step 4:

[1664] User: Receives the generated report and reviews its contents.

[1665] Input: Career path report sent from the server

[1666] What happens next: Users log in and download the report or receive it via email. They review the report and consider the suggested career paths.

[1667] Output: Review the report and review the contents

[1668] (Application example 1)

[1669] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1670] In physical stores, in order to improve the speed and accuracy of customer service and response to inquiries, a system that allows store staff to instantly provide appropriate information is required. There is also a need to reduce the workload of store staff while maintaining the quality of communication with customers.

[1671] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1672] In this invention, the server includes a means for customers to input and send questions by hand or voice, a means for the generative AI to analyze the received questions and generate appropriate answers, a means for providing the generated answers to customers or employees, and a means for recording the transmitted questions and the generated answers for further analysis, thereby improving the speed and accuracy of customer service in physical stores and reducing the workload of store employees.

[1673] "Customer" means any individual or entity that purchases goods or services at a physical store.

[1674] A "question" refers to information that a customer inputs to a store clerk or system in the form of a request for an answer.

[1675] "Input" refers to the act of a customer providing a question to the system via text or voice.

[1676] "Submit" refers to the act of sending the entered question to the system.

[1677] "Generative AI" refers to a system that uses artificial intelligence technology to analyze questions and generate appropriate answers.

[1678] "Answer" refers to the answer information provided by generative AI in response to a question.

[1679] "Providing" refers to the act of displaying the answer generated by generative AI to a customer or employee.

[1680] "Employee" refers to staff who provide customer service in physical stores.

[1681] "Devices" refers to electronic devices used by employees, such as smartphones, smart glasses, etc.

[1682] "Recording" refers to the act of storing submitted questions and generated answers in a database.

[1683] "Analysis" refers to the processing of recorded data for later review and analysis.

[1684] "Real-time" means that information is processed and provided immediately, without delay.

[1685] A system for realizing this invention is intended to improve the efficiency of customer service in brick-and-mortar stores, enabling employees to respond to customer questions quickly and accurately. Specific embodiments of the system are described below.

[1686] When a customer types or speaks a question in a physical store using a smartphone or smart glasses, the question is sent to a server via the device. The server analyzes the received question data and activates a generative AI (specifically, OpenAI's GPT-3 model). The generative AI understands the content of the question and generates an appropriate answer. This process uses software such as Google's TensorFlow and Flask.

[1687] The server then provides the generated answers in real time to the customer or employee's device, which could be a smartphone, smart glasses, or other electronic device. This allows employees to respond to customer questions quickly and accurately. The server also records the submitted questions and the generated answers for future analysis.

[1688] Examples:

[1689] 1. A customer asks a question about a specific product: "How many calories are in this cream puff?"

[1690] 2. The customer or employee enters the question into the application using the smart glasses.

[1691] 3. The application receives the query and sends it to the server.

[1692] 4. The server passes the question to OpenAI's GPT-3 model, and the generative AI generates an answer: "A cream puff has about 200 calories."

[1693] 5. The server immediately sends the generated response to the customer or employee device.

[1694] 6. The employee communicates the answer to the customer.

[1695] Example prompt sentence:

[1696] Q: How many calories are in this cream puff?

[1697] answer:

[1698] In this way, it is possible to improve the efficiency of customer service in physical stores and reduce the workload of employees. The above is a detailed description of the embodiment of the present invention.

[1699] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1700] Step 1:

[1701] A user (customer or employee) uses a smartphone or smart glasses to type or speak a question and submit it. The input is recorded as text or voice data and sent from the device to the server. The input includes specific questions such as "How many calories are in this cream puff?"

[1702] Step 2:

[1703] The server analyzes the received question data and converts it into text data if it is voice data. Specifically, it uses voice recognition technology (e.g., Google Speech-to-Text API) to convert the voice data into text data. The output is text-formatted question data.

[1704] Step 3:

[1705] The server sends text-based question data to the generative AI (OpenAI's GPT-3 model). The server passes the question as a prompt to the generative AI and waits for a response. The input is text-based question data, and an example prompt includes "Question: How many calories are in this cream puff?"

[1706] Step 4:

[1707] The generative AI generates an appropriate answer based on the prompt it receives. For data processing, the generative AI uses natural language processing technology to analyze the question and generate the optimal answer based on known information. The output is answer data in text format. As a specific example, the generated answer would be "Answer: A cream puff has approximately 200 calories."

[1708] Step 5:

[1709] The server receives the generated response data and sends it to the customer or employee's device. The input is the text-formatted response data output from the generative AI, and the output is the response information displayed on the device. The server performs the data transfer process.

[1710] Step 6:

[1711] The device then displays the received answer data to the user. Specifically, the screen of the smartphone or smart glasses displays "A cream puff has approximately 200 calories." The input is the answer data sent from the server, and the output is the answer information that the user can visually confirm.

[1712] Step 7:

[1713] The server records the submitted questions and generated answers in a database, which can then be used for future analysis and improvement. The input is the question data and the answer data, and the output is the records stored in the database. The recording process must be consistent and reliable.

[1714] The above is the specific processing flow of the program of this system.

[1715] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1716] Combining automated application screening with an emotion engine

[1717] One form of this system combines an emotion engine with automated application screening.

[1718] System Overview

[1719] When a user submits their application documents, the generative AI analyzes them. At the same time, the emotion engine also analyzes the user's emotions at the time of submission and reflects them in the analysis results.

[1720] Program processing

[1721] 1. User: Job seekers upload their resumes and CVs through a web portal.

[1722] 2. Server: Receives the uploaded documents and stores them in a database. The emotion engine analyzes the user's facial expressions, tone of voice, etc. to extract emotional data.

[1723] 3. Server: Uses generative AI and an emotion engine to analyze the text and emotion data of documents.

[1724] 4. Server: Scores the analysis results and generates a list of optimal candidates, including emotional data.

[1725] 5. Server: Finally, the generated candidate list is notified to the HR personnel.

[1726] Combining customer service improvement with emotion engines

[1727] There are also embodiments in which emotion engines are used to improve customer service.

[1728] System Overview

[1729] When a customer makes an inquiry, the emotion engine recognizes their emotion, and the generative AI generates an appropriate response based on that emotion.

[1730] Program processing

[1731] 1. User: A customer submits a question via the chat widget or contact form.

[1732] 2. Terminal: The virtual assistant receives the inquiry in real time, and the emotion engine analyzes the customer's input and voice data to determine their emotions.

[1733] 3. Terminal: Sends the analysis results of the emotion engine to the generative AI.

[1734] 4. Server: Generative AI generates responses that take emotional data into account, for example, if a customer is stressed, it will generate a more polite and reassuring response.

[1735] 5. Terminal: The generated response is displayed to the user and provided in a chat window or email.

[1736] 6. Server: Logs all queries and responses.

[1737] Combining automated interviews with an emotion engine

[1738] There are also embodiments in which interviews are automated using emotion engines.

[1739] System Overview

[1740] Generative AI is used to generate interview questions, and an emotion engine recognizes the job seeker's emotions and reflects them in the results.

[1741] Program processing

[1742] 1. User: A job seeker books an interview on an online platform.

[1743] 2. Server: Starts scheduled interview sessions. Generative AI generates interview questions and emotion engine analyzes job seeker reactions.

[1744] 3. User: The job seeker answers the questions by typing or speaking.

[1745] 4. Server: The emotion engine analyzes the job seeker's facial expressions and vocal changes, which the generative AI takes into account when analyzing the answers.

[1746] 5. Server: The generative AI combines the answers and emotional data to generate an evaluation result. For example, it can reflect whether the job seeker is feeling stressed.

[1747] 6. Server: Determines whether the application passes or fails based on the evaluation results and notifies the user of the result.

[1748] Combining enhanced employee training with the Emotion Engine

[1749] In some embodiments, employee training is enhanced with an emotion engine.

[1750] System Overview

[1751] The emotion engine recognizes employees' emotions, and generative AI provides appropriate training content based on those emotions.

[1752] Program processing

[1753] 1. User: An employee logs into the training platform and begins training.

[1754] 2. On the device: The emotion engine analyzes the employee's facial expressions and tone of voice to recognize their emotions.

[1755] 3. Terminal: Sends the recognized emotion data to the generative AI.

[1756] 4. Server: Generative AI customizes training content based on emotional data, for example, offering lighter training if an employee is tired.

[1757] 5. Terminal: Presents customized training content to employees and facilitates training.

[1758] Combining career advice and emotion engines

[1759] In some embodiments, career advice is combined with an emotion engine.

[1760] System Overview

[1761] The emotion engine recognizes the user's emotions, and the generative AI provides career paths and advice that take their emotional state into account.

[1762] Program processing

[1763] 1. User: An employee or job seeker applies for career counseling.

[1764] 2. Server: Collects the emotional state along with the user's profile information.

[1765] 3. Server: Analyzes the profile information and emotional data collected by the generative AI.

[1766] 4. Server: Generates career paths that take into account sentiment data and creates reports, for example adding specific advice to alleviate user anxiety.

[1767] 5. Server: Provides the generated reports to users and makes them available for download from the online platform.

[1768] In this way, combining emotion engines enables more precise data analysis and service provision that takes into account the user's emotional state, significantly improving overall efficiency and user satisfaction.

[1769] The processing flow will be explained below.

[1770] Combining automated application screening with an emotion engine

[1771] Program processing

[1772] Step 1:

[1773] A user uploads their application documents (resume, curriculum vitae, etc.) through a web portal.

[1774] Step 2:

[1775] The server receives the uploaded application documents and stores them in a database.

[1776] Step 3:

[1777] The device's built-in emotion engine extracts emotion data from the user's facial expressions and voice and sends it to the server.

[1778] Step 4:

[1779] The server launches a generative AI and analyzes the text data in the application documents.

[1780] Step 5:

[1781] The server integrates the emotional data into the analysis results and performs scoring. For example, positive emotional expressions receive a high score.

[1782] Step 6:

[1783] The server generates a list of the best candidates and notifies the HR personnel.

[1784] Combining customer service improvement with emotion engines

[1785] Program processing

[1786] Step 1:

[1787] A user (customer) submits a question via a chat widget or contact form.

[1788] Step 2:

[1789] The on-device virtual assistant receives inquiries in real time.

[1790] Step 3:

[1791] The emotion engine built into the device recognizes the customer's emotions from the input content and voice data and sends this to the server.

[1792] Step 4:

[1793] The server sends the emotion data and the query content to the generative AI.

[1794] Step 5:

[1795] The server takes emotional data into account and the generative AI generates the optimal response to the inquiry. For example, if the customer is feeling stressed, it will generate a more polite response.

[1796] Step 6:

[1797] The device displays the generated answer to the user and provides it in a chat window or via email.

[1798] Step 7:

[1799] The server logs all queries and responses.

[1800] Combining automated interviews with an emotion engine

[1801] Program processing

[1802] Step 1:

[1803] A user (job seeker) schedules an interview on an online platform.

[1804] Step 2:

[1805] The server starts the scheduled interview session.

[1806] Step 3:

[1807] The device's built-in emotion engine detects the job seeker's emotions from their facial expressions and voice and sends this information to the server.

[1808] Step 4:

[1809] The server uses generative AI to generate interview questions and present them to job seekers.

[1810] Step 5:

[1811] The user answers the questions by typing or speaking.

[1812] Step 6:

[1813] The server receives the emotion data and uses generative AI to comprehensively analyze the responses and emotion data. For example, if a job seeker is nervous, that will be reflected in the evaluation.

[1814] Step 7:

[1815] The server scores the evaluation results and determines whether the application passes or fails.

[1816] Step 8:

[1817] The server notifies the result and provides it to the user.

[1818] Combining enhanced employee training with the Emotion Engine

[1819] Program processing

[1820] Step 1:

[1821] A user (employee) logs into the training platform.

[1822] Step 2:

[1823] Employees enter their questions into a question form within the training platform.

[1824] Step 3:

[1825] The device's built-in emotion engine recognizes emotions from the employee's facial expressions and tone of voice and transmits this information to a server.

[1826] Step 4:

[1827] The server sends the emotional data to a generative AI, which generates the optimal answer to the question.

[1828] Step 5:

[1829] The server sends the generated response to the terminal.

[1830] Step 6:

[1831] The device displays customized responses to the user, for example, suggesting a lighter workout if the user is tired.

[1832] Combining career advice and emotion engines

[1833] Program processing

[1834] Step 1:

[1835] A user (employee or job seeker) applies for career counseling through an online platform.

[1836] Step 2:

[1837] The device's built-in emotion engine recognizes the user's emotions from their facial expressions and voice and transmits them to the server.

[1838] Step 3:

[1839] The server collects user profile information and emotional data and analyzes it using generative AI.

[1840] Step 4:

[1841] The server generates a report based on the emotional data, generating an optimal career path, adding specific advice to alleviate any anxiety the user may have, for example.

[1842] Step 5:

[1843] The server provides the generated reports to the user and makes them available for download from an online platform.

[1844] In this way, combining emotion engines enables more precise data analysis and service provision that takes into account the user's emotional state, significantly improving overall efficiency and user satisfaction.

[1845] Example 2

[1846] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1847] Traditional application screening cannot take into account the emotional state of job seekers, which can result in the overlooking of highly suitable candidates. Furthermore, in customer service and interview evaluations, responses and evaluations that ignore the emotional state of employees can be inaccurate. This can lead to issues such as a decline in the quality of a company's recruitment process and customer service.

[1848] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1849] In this invention, the server includes a means for job seekers to submit application documents, a means for analyzing facial expressions and tone of voice to extract emotional data, a means for a generative AI to analyze the submitted application documents and the extracted emotional data to select candidates, and a means for notifying the human resources department of the information on the selected candidates. This enables screening that takes into account the emotional state of applicants, allowing for the selection of more suitable candidates. Similarly, utilizing emotional data in customer service and interview evaluations allows for more accurate and effective responses.

[1850] "Application documents" refers to documents such as resumes and work histories that job seekers submit to companies seeking employment.

[1851] "Generative AI" refers to artificial intelligence that can analyze text and voice data using natural language processing technology and create new products.

[1852] "Emotional data" refers to data analyzed from facial expressions, vocal tone, etc., and is information that expresses an individual's emotional state in numerical or categorical terms.

[1853] "Means" refers to a device, system, method, etc. used to achieve a particular purpose.

[1854] "Candidate" refers to an applicant who has been assessed as suitable for a particular job or role.

[1855] "Notification" refers to the act of conveying specific information to a recipient.

[1856] "Analysis" refers to the process of examining data or information in detail to clarify its content and structure.

[1857] An "interview" refers to the process by which a job seeker and an interviewer evaluate the job seeker's aptitude and abilities through dialogue.

[1858] "Inquiry" means a question or request made by a Customer seeking information or support regarding a product or service.

[1859] Combining automated application screening with an emotion engine

[1860] In this embodiment of the invention, a user (job seeker) uploads a resume or work history through a web portal. The server that receives the application documents first stores the documents in a database, and then uses an emotion engine to analyze the user's facial expressions and voice tone to extract emotion data. Specific software that can be used includes a "face analysis API" and a "voice analysis API."

[1861] For example, when a user logs in to a job-seeking website and uploads a PDF resume, the server saves the document in cloud storage. The server then uses face analysis APIs and voice analysis APIs to extract emotional data from the uploaded data.

[1862] Next, generative AI is used to analyze the text data and extracted emotion data from the submitted application documents. Natural language processing AI can be used as generative AI. As a result of the analysis, the server scores the suitability of the candidates and generates a list of optimal candidates based on this. For scoring, a machine learning library is used to quantify the suitability of each applicant.

[1863] Finally, the server notifies the HR department of the generated candidate list using a messaging API, automating the process of efficiently selecting and notifying highly suitable candidates.

[1864] Career Services Prompt Examples

[1865] "Analyze the facial expressions and tone of voice of job applicants and evaluate them along with the content of their application documents. For example, if a job applicant shows signs of stress, reflect that emotion in your evaluation."

[1866] By inputting this prompt into the generative AI model, the emotion engine and generative AI work together to process the prompt. This enables highly accurate analysis and evaluation using emotion data, improving the efficiency and accuracy of the hiring process.

[1867] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1868] Specific processing steps of the program

[1869] Combining automated application screening with an emotion engine

[1870] Step 1:

[1871] User: A job seeker uploads their resume or CV through a web portal.

[1872] Input: A user-uploaded PDF resume or CV.

[1873] Output: The application data sent to the server.

[1874] Specific operation: Click the "Upload Documents" button on the web portal, select your resume from the file selection dialog, and upload it.

[1875] Step 2:

[1876] Server: Receives uploaded documents and stores them in a database.

[1877] Input: Application data submitted by the user.

[1878] Output: File path of application data saved in the database.

[1879] Specific operation: The server uploads the received file to "cloud storage" and writes the file metadata and storage location to the database.

[1880] Step 3:

[1881] Server: The emotion engine analyzes the user's facial expressions, tone of voice, etc. to extract emotional data.

[1882] Input: Application document data stored on the server and user's voice and facial image data.

[1883] Output: Emotion data obtained from the emotion engine.

[1884] Specific operation: Using the "face analysis API" and "voice analysis API," facial images and voice data are analyzed, and the emotional state is quantified and stored in a database.

[1885] Step 4:

[1886] Server: Uses a combination of generative AI and an emotion engine to analyze text data and emotion data from application documents.

[1887] Input: Text data and sentiment data from job applications.

[1888] Output: Analysis result data.

[1889] Specific operation: The content of the resume is analyzed using generative AI (natural language processing AI), emotional data obtained from the emotion engine is integrated, and the analysis results are stored in a database.

[1890] Step 5:

[1891] Server: Scores the analysis results and generates a list of optimal candidates, including emotional data.

[1892] Input: Analysis result data.

[1893] Output: A list of the best candidates.

[1894] Specific operation: Using a "machine learning library," it calculates the score for each applicant, and based on the results, generates a list of optimal candidates and stores them in a database.

[1895] Step 6:

[1896] Server: Notifies the HR department of the generated candidate list.

[1897] Input: A list of best candidates.

[1898] Output: Notification message to HR department.

[1899] Specific operation: Using the "Messaging API," a notification message is sent to a specific channel in the HR department, providing a link to the candidate list.

[1900] (Application example 2)

[1901] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1902] Conventional application screening, customer service, and automated interview systems are unable to take into account the user's emotional state, making it difficult to provide personalized services. Furthermore, evaluations and responses without emotion analysis have limited the potential for improving the user experience. Therefore, there is a need for technology that can recognize user emotions in real time and respond and evaluate accordingly.

[1903] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1904] In this invention, the server includes: a means for a job seeker to submit an application; a means for a generative AI to analyze the submitted application and select candidates; a means for notifying a human resources department of information about the selected candidates; a means including an emotion engine for analyzing the job seeker's emotions at the time of submission; a means for re-evaluating and scoring based on the emotion data; a means for accepting customer inquiries; a means for generating responses to customer inquiries using a generative AI; a means for providing the generated responses to the customers; a means including an emotion engine for analyzing customer emotions in real time; a means for adjusting responses based on the emotion data; a means for scheduling interviews with the job seeker; a means for generating interview questions using a generative AI and presenting them to the job seeker; a means for analyzing and evaluating the job seeker's answers; a means including an emotion engine for analyzing the job seeker's emotions during the interview; and a means for adjusting the evaluation results based on the emotion data. This enables more precise and personalized responses and evaluations while taking into account the user's emotional state.

[1905] "Means for submitting applications" refers to the interface that job seekers use to upload application documents, such as resumes and curriculum vitae, to the system.

[1906] "Generative AI" is artificial intelligence that uses natural language processing and machine learning techniques to analyze and generate responses to application documents and customer inquiries.

[1907] The "means of candidate selection" refers to the method for selecting suitable candidates based on the information in the application documents analyzed by the generative AI.

[1908] "Means of notifying the human resources department" refers to the means by which information about the selected candidate is communicated to the human resources department via email or internal systems.

[1909] The "emotion engine that analyzes job seekers' emotions at the time of submission" is a technology that recognizes emotions from facial expressions and voice when job seekers submit their application documents and extracts them as data.

[1910] "Means for re-evaluating and scoring based on emotional data" refers to a method for adjusting the evaluation of application documents and recalculating scores using recognized emotional data.

[1911] "Means for accepting customer inquiries" are interfaces such as chat widgets or forms that allow customers to submit questions or requests.

[1912] The "means of generating a response" is how the generative AI creates an appropriate response to a customer inquiry.

[1913] "Means of providing the generated response to the customer" refers to the method of communicating the answer created by the generative AI to the customer via a chat window, email, etc.

[1914] The "emotion engine that analyzes customer emotions in real time" is a technology that instantly recognizes emotions from customer text input, voice data, and even facial expressions.

[1915] "Means for adjusting responses based on emotional data" refers to a method in which generative AI changes the content and tone of responses based on recognized emotional data.

[1916] "Means for scheduling interviews" refers to a system that allows job seekers to schedule interview dates and times online.

[1917] "Means for generating interview questions and presenting them to job seekers" refers to a method in which generative AI generates specific interview questions and presents them to job seekers in text or audio.

[1918] "Means for analyzing and evaluating job seekers' responses" refers to the method by which generative AI analyzes the content of job seekers' responses and assigns an evaluation score.

[1919] The "emotion engine that analyzes the emotions of job seekers during interviews" is a technology that recognizes emotions in real time from the facial expressions and voice of job seekers during interviews.

[1920] "Means for adjusting evaluation results based on emotional data" refers to a method in which generative AI recalculates evaluation results by taking into account emotional data recognized during the interview.

[1921] This invention implements the following steps: We present specific procedures for combining generative AI and an emotion engine to screen job applicants' applications, conduct online interviews, provide customer support, and provide a recommendation system.

[1922] Screening job applicant applications

[1923] Hardware:

[1924] Users use a computer or smartphone to upload their application documents to a web portal.

[1925] software:

[1926] The server uses generative AI (e.g., OpenAI's GPT-3 model) and emotion engine (e.g., Microsoft's Azure Cognitive Services' Face API).

[1927] Data processing:

[1928] When an application is uploaded, a generative AI analyzes it and evaluates the job seeker's skills and experience. In parallel, an emotion engine recognizes emotions from video and audio data and adds that data to the analysis.

[1929] As a specific example, if a user is feeling angry or impatient, the generative AI will use the recognized emotional data to perform more flexible scoring.

[1930] Interview automation

[1931] Hardware:

[1932] A webcam and microphone are used when the interview is conducted between the job seeker and the server.

[1933] software:

[1934] The server conducts interviews using generative AI and an emotion engine. The generative AI generates interview questions, and the emotion engine analyzes the job seeker's emotions in real time as they answer.

[1935] Data processing:

[1936] The responses are re-evaluated based on the job seeker's emotional state to generate a final rating. For example, if the job seeker is nervous, the rating will adjust to reflect this.

[1937] An example prompt might be, "The user is nervous, so please keep your questions brief."

[1938] Customer Service

[1939] Hardware:

[1940] Customers use a chat widget or inquiry form on their computer or smartphone.

[1941] software:

[1942] Virtual assistants use generative AI to generate responses to customer queries, and here too, an emotion engine recognizes customer emotions in real time.

[1943] Data processing:

[1944] It generates and quickly delivers responses that reflect the customer's emotions. For example, if a customer expresses dissatisfaction, generative AI will generate an appropriate and comforting response.

[1945] An example would be, "The customer is unhappy, so please carefully explain the specific steps you will take to resolve the issue."

[1946] Product Recommendations

[1947] Hardware:

[1948] Users of the online shopping site access it from their smartphones or computers.

[1949] software:

[1950] The server uses generative AI and an emotion engine to analyze the product pages the user views and their emotions at the time.

[1951] Data processing:

[1952] The recommendation algorithm reflects the user's emotional data and recommends appropriate products. For example, if a user feels happy when looking at a product, it will suggest more products from that category.

[1953] An example prompt might be, "Since this user is happy, please recommend products in the same category."

[1954] These embodiments allow for application analysis, interviews, customer service, and product recommendations to take into account the user's emotional state, improving overall service quality.

[1955] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1956] Step 1:

[1957] A job seeker uploads their application to a web portal.

[1958] Operation:

[1959] Users (job seekers) upload their resumes and work histories along with the necessary documents to the system.

[1960] input:

[1961] Resume, work history

[1962] output:

[1963] Saved application data

[1964] Step 2:

[1965] The server receives the uploaded application documents and stores them in a database.

[1966] Operation:

[1967] The server receives the applications and stores the data in a database for further analysis.

[1968] input:

[1969] Application document data

[1970] output:

[1971] Document data stored in the database

[1972] Step 3:

[1973] The server uses a sentiment engine to analyze the job seeker's sentiment at the time of submission.

[1974] Operation:

[1975] The server activates an emotion engine and extracts emotional data by analyzing the job seeker's facial expressions and tone of voice.

[1976] input:

[1977] Video or audio data when submitting documents

[1978] output:

[1979] Emotional Data

[1980] Step 4:

[1981] Generative AI analyzes the text data of submitted application documents.

[1982] Operation:

[1983] Generative AI (such as the GPT-3 model) analyzes the content of application documents and evaluates skills and experience.

[1984] input:

[1985] Text data of application documents

[1986] output:

[1987] Initial evaluation data

[1988] Step 5:

[1989] Generative AI will readjust the evaluation of your application based on emotional data.

[1990] Operation:

[1991] The server recalculates the evaluation results based on the emotional data and adjusts the scoring. For example, if a job applicant is nervous, the server will take that into account and adjust the scoring accordingly.

[1992] input:

[1993] Initial evaluation data, emotion data

[1994] output:

[1995] Adjusted evaluation data

[1996] Step 6:

[1997] Based on the evaluation results, the server generates a list of optimal candidates.

[1998] Operation:

[1999] The server creates a list of appropriate candidates based on the evaluation data adjusted by the generative AI.

[2000] input:

[2001] Adjusted evaluation data

[2002] output:

[2003] Candidate List

[2004] Step 7:

[2005] The server notifies the HR department of the best candidates.

[2006] Operation:

[2007] The server notifies the human resources department of the generated candidate list via email or an internal company system.

[2008] input:

[2009] Candidate List

[2010] output:

[2011] Candidate list notified to HR department

[2012] Step 8:

[2013] Schedule candidate interviews.

[2014] Operation:

[2015] The user (job seeker) sets the date and time of the interview on the system.

[2016] input:

[2017] Interview reservation information

[2018] output:

[2019] Scheduled Interview Sessions

[2020] Step 9:

[2021] Generative AI generates interview questions and presents them to job seekers.

[2022] Operation:

[2023] The server uses generative AI to generate and present appropriate interview questions to job seekers.

[2024] input:

[2025] Interview Question Generation Prompts

[2026] output:

[2027] Generated interview questions

[2028] Step 10:

[2029] Generative AI analyzes job seekers' responses, and an emotion engine recognizes their emotions in real time.

[2030] Operation:

[2031] The server uses generative AI to analyze job seekers' responses, and an emotion engine analyzes emotions during the interview in real time.

[2032] input:

[2033] Job seeker response data, emotion data

[2034] output:

[2035] Analyzed response data, real-time sentiment data

[2036] Step 11:

[2037] Evaluation results are generated based on emotion data.

[2038] Operation:

[2039] The server evaluates the job seeker's responses taking into account the emotional data and synthesizes the results.

[2040] input:

[2041] Analyzed response data and emotion data

[2042] output:

[2043] Final evaluation results

[2044] Step 12:

[2045] The server determines whether the job seeker passes or fails based on the final evaluation results and notifies the job seeker of the result.

[2046] Operation:

[2047] The server determines whether the job seeker has passed or failed based on the generated final evaluation results and notifies the job seeker of the result via email or system notification.

[2048] input:

[2049] Final evaluation results

[2050] output:

[2051] The result of the job applicant's acceptance was notified

[2052] Example prompts:

[2053] "Please keep your questions brief as users are nervous."

[2054] "The customer is unhappy, so please carefully explain the specific steps you will take to resolve the issue."

[2055] "Recommend products in the same category because they bring joy to the user."

[2056] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2057] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2058] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2059] [Fourth embodiment]

[2060] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2061] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[2062] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2063] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2064] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2065] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2066] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2067] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2068] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2069] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[2070] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2071] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2072] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2073] Automated Application Screening

[2074] One form of this system is automated application screening.

[2075] System Overview

[2076] It provides a web portal for users to submit their applications, and the server analyzes the applications using generative AI. Based on the analysis results, it creates a list of the most suitable candidates and notifies the HR department of this information.

[2077] Program processing

[2078] 1. User: Job seekers upload their resumes and CVs through a web portal.

[2079] 2. Server: Receives uploaded documents and stores them in a database. It then launches generative AI to analyze these documents. For example, if a job candidate applies for an IT engineer position, it evaluates their skill set, years of experience, and specific project experience.

[2080] 3. Server: Scores the analysis results and generates a list of optimal candidates.

[2081] 4. Server: Finally, notify the HR department of the generated list and move on to the next step in the hiring process.

[2082] Improved customer service

[2083] Next, there is the use of generative AI to improve customer service.

[2084] System Overview

[2085] When a customer makes an inquiry, generative AI generates an appropriate response and provides it through a virtual assistant.

[2086] Program processing

[2087] 1. User: A customer submits a question through a chat widget or contact form.

[2088] 2. Terminal: The virtual assistant receives the query and uses generative AI to analyze the required information.

[2089] 3. On the device: Generative AI generates appropriate answers and provides them to users. For example, if a customer asks about how to use a product, a virtual assistant will guide them to the manual or related videos.

[2090] 4. Server: Logs all queries and responses for further analysis.

[2091] Interview automation

[2092] Additionally, the automation of interviews using generative AI is also included in the embodiments.

[2093] System Overview

[2094] An interview session is held between job seekers and generative AI, and the responses are analyzed and evaluated in real time.

[2095] Program processing

[2096] 1. User: A job seeker books an interview through an online platform.

[2097] 2. Server: When the scheduled time arrives, the generative AI generates interview questions and presents them to the job seeker.

[2098] 3. User: The job seeker answers the questions by typing or speaking.

[2099] 4. Server: Generative AI analyzes and scores answers in real time, for example, case study questions to assess suitability for a consulting position.

[2100] 5. Server: Determines whether the application is successful and notifies the result.

[2101] Enhanced employee training

[2102] In some embodiments, employee training is enhanced.

[2103] System Overview

[2104] Employees access a training platform and receive assistance with answers from generative AI.

[2105] Program processing

[2106] 1. User: An employee logs into the training platform and types in a question.

[2107] 2. Terminal: Sends the question to the generative AI and requests the required answer.

[2108] 3. Server: The generative AI generates the optimal answer to the question and returns it to the device.

[2109] 4. Terminal: Generate answers and display them on the employee's screen, for example, step-by-step instructions for a question about how to use new software.

[2110] Providing career advice

[2111] Finally, there are embodiments that provide career advice.

[2112] System Overview

[2113] Generative AI analyzes users' profile information and suggests career paths and skill development.

[2114] Program processing

[2115] 1. User: An employee or job seeker applies for career counseling.

[2116] 2. Server: Collects user profile information and analyzes it using generative AI.

[2117] 3. Server: Generates optimal career paths and creates reports. For example, it suggests the skills and experience required for a user who wants to become a project manager within five years.

[2118] 4. User: Receives the generated report and checks its contents.

[2119] The above-described embodiment enables efficient business operations in the human resources industry, reduces the burden on the human resources department and related departments, and improves the quality of work.

[2120] The processing flow will be explained below.

[2121] Automated Application Screening

[2122] Program processing

[2123] Step 1:

[2124] A user uploads their application documents (resume, curriculum vitae, etc.) through a web portal.

[2125] Step 2:

[2126] The server receives the uploaded application documents and stores them in a database.

[2127] Step 3:

[2128] The server launches a generative AI and extracts the contents of the application documents stored in the database as text.

[2129] Step 4:

[2130] The server analyzes the text data and evaluates skill sets, experience, qualifications, etc.

[2131] Step 5:

[2132] The server scores the candidates based on the analysis results and generates a list of optimal candidates.

[2133] Step 6:

[2134] The server notifies the human resources personnel of the generated candidate list.

[2135] Improved customer service

[2136] Program processing

[2137] Step 1:

[2138] A user (customer) submits a question via a chat widget or contact form.

[2139] Step 2:

[2140] The on-device virtual assistant receives inquiries in real time.

[2141] Step 3:

[2142] The device analyzes the inquiry and sends the data to the generative AI.

[2143] Step 4:

[2144] The server uses generative AI to generate the best answer to the query.

[2145] Step 5:

[2146] The server generates a response and sends it back to the terminal.

[2147] Step 6:

[2148] The device displays the answer to the user and provides it in a chat window or via email.

[2149] Step 7:

[2150] The server logs all queries and responses.

[2151] Interview automation

[2152] Program processing

[2153] Step 1:

[2154] A user schedules an interview through an online platform.

[2155] Step 2:

[2156] The server starts the scheduled interview session.

[2157] Step 3:

[2158] The server runs a generative AI to generate interview questions based on the user's profile.

[2159] Step 4:

[2160] The server presents the generated question to the user.

[2161] Step 5:

[2162] The user answers the questions by typing or speaking.

[2163] Step 6:

[2164] The server uses generative AI to analyze user responses in real time.

[2165] Step 7:

[2166] The server scores the analysis results and generates an evaluation result.

[2167] Step 8:

[2168] The server determines whether the application passes or fails based on the evaluation results and notifies the user of the result.

[2169] Enhanced employee training

[2170] Program processing

[2171] Step 1:

[2172] A user (employee) logs into the training platform.

[2173] Step 2:

[2174] A user enters a question into a question form within the training platform.

[2175] Step 3:

[2176] The device sends the entered question to the generative AI.

[2177] Step 4:

[2178] The server uses generative AI to generate the best answer to the question.

[2179] Step 5:

[2180] The server sends the generated response to the terminal.

[2181] Step 6:

[2182] The terminal displays the answer to the user.

[2183] Providing career advice

[2184] Program processing

[2185] Step 1:

[2186] A user (employee or job seeker) applies for career counseling through an online platform.

[2187] Step 2:

[2188] The server collects user profile information.

[2189] Step 3:

[2190] The profile information collected by the server is sent to the generative AI for analysis.

[2191] Step 4:

[2192] The server uses generative AI to generate the optimal career path based on the user's goals.

[2193] Step 5:

[2194] The server documents the generated career path and creates a career advice report.

[2195] Step 6:

[2196] The server provides the reports to the user and makes them available for download from an online platform.

[2197] In this way, by performing specific actions at each processing step, efficient and consistent business operations can be achieved using generative AI.

[2198] Example 1

[2199] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2200] Traditional recruitment and customer service processes require a lot of manual work and lack advanced analysis and automation. This has led to problems that reduce recruitment efficiency and the quality of customer service. In particular, processing large volumes of application documents and customer inquiries takes time and effort, increasing the risk of human error. Furthermore, interview automation and the provision of career advice are not fully implemented, creating challenges for improving job seeker and employee satisfaction. A new system is needed to solve these problems and provide efficient, high-quality services.

[2201] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2202] In this invention, the server includes a means for job seekers to submit application documents, a means for a generative AI to analyze the submitted application documents and select candidates, a means for notifying the human resources department of information on the selected candidates, a means for saving the submitted application documents in a database and analyzing them, a means for generating a candidate list based on the analysis results, and a means for notifying the human resources department of the generated list and advancing the hiring process. This reduces manual work in hiring activities and makes it more efficient through advanced analysis.

[2203] "Means of submitting application documents" refers to the means by which job seekers submit application documents, such as resumes and curriculum vitae, through an online platform.

[2204] "Generative AI" is a type of artificial intelligence model that generates and analyzes advanced information through natural language processing and data analysis.

[2205] "Means for analyzing application documents" refers to using generative AI to analyze the contents of submitted application documents and extract information such as skills and experience.

[2206] The "means of selecting candidates" refers to the means of evaluating and selecting appropriate candidates based on the information in the application documents analyzed by the generative AI.

[2207] "Means for notifying the human resources department of the candidate's information" refers to the means for notifying the human resources department of the information about the selected candidate, including sending a notification email and displaying the information on the management screen.

[2208] "Means of storing in a database" refers to the means of storing submitted application documents in a digital format so that they can be accessed and analyzed at a later date.

[2209] "Means for generating a candidate list" refers to the means for creating a list of appropriate candidates based on the results of analysis and scoring by the generative AI.

[2210] The "means for notifying the human resources department of the generated list" refers to a means for notifying the human resources department of the generated candidate list and for proceeding to the next step in the recruitment process.

[2211] "Means for accepting customer inquiries" refers to the means by which customers can submit questions, such as through a chat widget or a contact form.

[2212] "Means for generating responses to customer inquiries" refers to means for using generative AI to analyze the content of customer inquiries and generate appropriate responses.

[2213] "Means for providing the generated response to the customer" refers to means for providing the response generated by the generative AI to the customer, including via a chat widget or email.

[2214] "Means for scheduling an interview" means the means by which a job seeker schedules an interview on the online platform.

[2215] "Means for generating and presenting interview questions" refers to the means by which generative AI generates interview questions and presents them to job seekers.

[2216] "Means for analyzing and evaluating job seeker responses in real time" refers to a means for using generative AI to analyze responses provided by job seekers in interviews in real time and evaluate their content.

[2217] "Means for scoring the analysis results and determining pass / fail" refers to means for scoring the analyzed answers and determining pass / fail.

[2218] "Means of notifying results" refers to the means used to notify job seekers of the results of their application, including sending emails and displaying the results on the platform.

[2219] This invention is a system that uses generative AI models to automatically screen job applications, improve customer service, automate interviews, and streamline employee training and career advice. To implement this system, a server, terminals, and users must all work together.

[2220] Automated application screening

[2221] The system begins with a user submitting an application through a web portal. Job seekers upload their resumes and CVs in PDF format. The server stores the uploaded documents in a database and runs a generative AI model (e.g., OpenAI GPT-4) to analyze them. Based on the analysis results, the server scores candidates and generates a list of the best candidates. This list is automatically notified to the HR department.

[2222] As a concrete example, taking the position of an IT engineer, the server inputs the following prompt sentence into the generative AI: "From this application document, please generate a list of technical skills and experience that are suitable for the position of IT engineer."

[2223] Improved customer service

[2224] When a customer submits a question using a website chat widget or contact form, the device receives the inquiry. The generative AI model analyzes the information and generates an appropriate response. The generated response is provided to the user from the device, and all inquiries and responses are logged on the server.

[2225] For example, if a customer inquires, "I don't know how to use the new product," the device will send the following prompt to the generation AI: "Please generate a sentence that provides detailed instructions on how to use the new product."

[2226] Interview automation

[2227] When a job seeker makes an interview reservation through the online platform, the server uses generative AI to generate appropriate questions based on the reservation time. The job seeker answers the questions via text or voice, and the answers are analyzed in real time. The server scores the analysis results and notifies the job seeker of their success or failure.

[2228] For example, send the following prompt to the generation AI: "Generate case study questions to assess suitability as a consultant."

[2229] Employee training and career advice

[2230] When an employee accesses the training platform and enters a question, the device sends the question to the generative AI, which generates the best answer, and the device displays the answer on the employee's screen.

[2231] For example, if asked how to use new project management software, the device would send the following prompt to the generating AI: "Please provide a step-by-step explanation of the basics of how to use the new project management software."

[2232] In the career advice service, employees and job seekers apply for career counseling, and the server analyzes the user's profile information using AI to generate an optimal career path and prepares a report to provide to the user.

[2233] For example, send the following prompt to the generation AI: "Generate a skills and experience development plan for a user who wants to become a project manager within five years."

[2234] As described above, by having the server, terminals, and users all work together, the system can efficiently and effectively perform automated screening of application documents, improve customer service, automate interviews, train employees, and provide career advice.

[2235] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2236] Automated Application Screening

[2237] Step 1:

[2238] User: Job seekers upload their resumes and CVs through a web portal.

[2239] Input: PDF resume or curriculum vitae selected from local disk

[2240] Specific operation: The user clicks the "Upload documents" button on the web portal, selects application documents in the file selection dialog, and then presses the "Upload" button, which sends the selected documents to the server.

[2241] Output: The web portal sends the file to the server.

[2242] Step 2:

[2243] Server: Receives uploaded documents, stores them in a database, and runs a generative AI to analyze these documents.

[2244] Input: Application file submitted by the user

[2245] What happens: The server receives the HTTP POST request and saves the application in a specific folder. It then records the file path and associated metadata in a database. It then generates and sends a prompt to a generative AI model (e.g., OpenAI GPT-4) to analyze the application.

[2246] Output: Prompt for analysis and analysis results

[2247] Step 3:

[2248] Server: Scores the analysis results and generates a list of optimal candidates.

[2249] Input: Analysis results obtained from generative AI

[2250] Specific operation: The server scores the analysis results (skills, years of experience, project details, etc.) obtained from the generative AI model based on evaluation criteria. For example, it assigns points to "years of Java experience" and "project leadership experience" and calculates an overall score.

[2251] Output: A list of the best candidates

[2252] Step 4:

[2253] Server: Notifies the HR department of the generated list and moves on to the next step in the hiring process.

[2254] Input: Scored candidate list

[2255] What it does: The server generates a report containing the best candidates and automatically sends it to the HR department's email address. It also displays the list in the HR admin panel of the web portal so that HR personnel can log in and view it.

[2256] Output: Notify the HR department and display the data on the management screen

[2257] Improved customer service

[2258] Step 1:

[2259] User: A customer submits a question through a chat widget or contact form.

[2260] Input: Customer inquiry

[2261] What it does: A user accesses a chat widget on a website, enters a question in the text field, and clicks the "Send" button to send the question to their device.

[2262] Output: Send query to device

[2263] Step 2:

[2264] On the device: The virtual assistant receives the query and uses generative AI to parse the required information.

[2265] Input: Received customer inquiry

[2266] How it works: The device analyzes the query and generates and sends an appropriate prompt to a generative AI model (e.g., GPT-4), such as "Please generate an answer on how to use product X."

[2267] Output: The prompt sent to the AI ​​model and the answer it gives

[2268] Step 3:

[2269] Terminal: Generative AI generates appropriate answers and provides them to the user.

[2270] Input: Answer obtained from generative AI

[2271] What it does: The device receives a response from the generative AI model and displays the text response in the chat widget, such as a snippet from the product manual or a related link.

[2272] Output: The answer displayed to the user

[2273] Step 4:

[2274] Server: Logs all queries and responses for further analysis.

[2275] Input: History of inquiries received and responses generated

[2276] What it does: The server stores the query and its response in a database, allowing for later log analysis and retrieval of statistics.

[2277] Output: Saving and managing log data

[2278] Interview automation

[2279] Step 1:

[2280] User: A job seeker schedules an interview through an online platform.

[2281] Input: Reservation information (date and time, job seeker information, etc.)

[2282] Specific operation: The user accesses the platform's reservation page, selects the desired date and time from the calendar, and presses the "Confirm reservation" button, which sends the reservation information to the server.

[2283] Output: Send reservation information to the server

[2284] Step 2:

[2285] Server: When the scheduled time arrives, the generative AI generates interview questions and presents them to the job seeker.

[2286] Input: Booking information and job seeker profile

[2287] How it works: When the appointment time arrives, the server sends a prompt to the generative AI model to generate appropriate interview questions, which are then displayed on the job seeker's device.

[2288] Output: Generated interview questions

[2289] Step 3:

[2290] User: The job seeker answers questions by typing or speaking.

[2291] Input: Job seeker's response (text or voice)

[2292] Specific behavior: The user answers the questions using the text box or voice input function. Once the answer is complete, the user presses the "Submit" button to send the answer to the server.

[2293] Output: Send response to server

[2294] Step 4:

[2295] Server: Generative AI analyzes and scores answers in real time.

[2296] Input: Job seeker's answers

[2297] Specific operation: After receiving the answer, the server analyzes the answer using the generative AI model. The analysis results are scored based on evaluation criteria, such as logic, the presence or absence of specific examples, and the level of expertise.

[2298] Output: Scoring results

[2299] Step 5:

[2300] Server: Determines whether the application is successful and notifies the result.

[2301] Input: Scoring results

[2302] Specific operation: The server determines whether the job seeker has passed or failed based on the scoring results, automatically generates a notification email and sends it to the job seeker. The result is also displayed on the management screen.

[2303] Output: Notification of pass / fail result

[2304] Employee training and career advice

[2305] Step 1:

[2306] User: An employee logs into the training platform and types in a question.

[2307] Input: Employee question

[2308] What happens: A user accesses the training platform through a login form, selects the "Ask a Question" option from the dashboard, enters a question in the text field, and presses the "Submit" button to send a request to the server.

[2309] Output: Send the question

[2310] Step 2:

[2311] Terminal: Sends the question to the generative AI and requests the required answer.

[2312] Input: Question submitted by employee

[2313] What it does: The device converts the input question into a prompt and sends it to the generative AI model, for example, "Please tell me how to use my new accounting software."

[2314] Output: The prompt sent to the AI ​​model and the answer from the AI

[2315] Step 3:

[2316] Server: The generative AI generates the optimal answer to the question and returns it to the device.

[2317] Input: Answer obtained from generative AI

[2318] How it works: The server formats the answer received from the generative AI and sends it to the device. The answer may be in text format, but it may also include related materials and links.

[2319] Output: Sending formatted answers

[2320] Step 4:

[2321] Terminal: Displays the generated answers on the employee's screen.

[2322] Input: Answer sent from the server

[2323] What happens: The device displays the received answer in a user interface, for example, a step-by-step guide or a related video tutorial.

[2324] Output: The answer presented to the user

[2325] Providing career advice

[2326] Step 1:

[2327] Users: Employees and job seekers request career counseling.

[2328] Input: Career counseling application information

[2329] Specific operation: The user accesses the career counseling application form and enters the required information (current position, goals, skills, etc.). Presses the "Apply" button to send the information to the server.

[2330] Output: Send application details

[2331] Step 2:

[2332] Server: Collects user profile information and analyzes it using generative AI.

[2333] Input: Career counseling application information and profile information

[2334] What it does: The server stores the input information in a database and sends it to a generative AI model for analysis, which generates prompts that assess gaps in your current skill set, experience, and goals.

[2335] Output: Profile analysis results

[2336] Step 3:

[2337] Server: Generates optimal career paths and creates reports.

[2338] Input: Analysis results from generative AI

[2339] How it works: Based on the analysis results from the generative AI model, the server generates a report proposing the optimal career path, including required skills, recommended training courses, and specific steps to gain experience.

[2340] Output: Career Path Report

[2341] Step 4:

[2342] User: Receives the generated report and reviews its contents.

[2343] Input: Career path report sent from the server

[2344] What happens next: Users log in and download the report or receive it via email. They review the report and consider the suggested career paths.

[2345] Output: Review the report and review the contents

[2346] (Application example 1)

[2347] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2348] In physical stores, in order to improve the speed and accuracy of customer service and response to inquiries, a system that allows store staff to instantly provide appropriate information is required. There is also a need to reduce the workload of store staff while maintaining the quality of communication with customers.

[2349] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2350] In this invention, the server includes a means for customers to input and send questions by hand or voice, a means for the generative AI to analyze the received questions and generate appropriate answers, a means for providing the generated answers to customers or employees, and a means for recording the transmitted questions and the generated answers for further analysis, thereby improving the speed and accuracy of customer service in physical stores and reducing the workload of store employees.

[2351] "Customer" means any individual or entity that purchases goods or services at a physical store.

[2352] A "question" refers to information that a customer inputs to a store clerk or system in the form of a request for an answer.

[2353] "Input" refers to the act of a customer providing a question to the system via text or voice.

[2354] "Submit" refers to the act of sending the entered question to the system.

[2355] "Generative AI" refers to a system that uses artificial intelligence technology to analyze questions and generate appropriate answers.

[2356] "Answer" refers to the answer information provided by generative AI in response to a question.

[2357] "Providing" refers to the act of displaying the answer generated by generative AI to a customer or employee.

[2358] "Employee" refers to staff who provide customer service in physical stores.

[2359] "Devices" refers to electronic devices used by employees, such as smartphones, smart glasses, etc.

[2360] "Recording" refers to the act of storing submitted questions and generated answers in a database.

[2361] "Analysis" refers to the processing of recorded data for later review and analysis.

[2362] "Real-time" means that information is processed and provided immediately, without delay.

[2363] A system for realizing this invention is intended to improve the efficiency of customer service in brick-and-mortar stores, enabling employees to respond to customer questions quickly and accurately. Specific embodiments of the system are described below.

[2364] When a customer types or speaks a question in a physical store using a smartphone or smart glasses, the question is sent to a server via the device. The server analyzes the received question data and activates a generative AI (specifically, OpenAI's GPT-3 model). The generative AI understands the content of the question and generates an appropriate answer. This process uses software such as Google's TensorFlow and Flask.

[2365] The server then provides the generated answers in real time to the customer or employee's device, which could be a smartphone, smart glasses, or other electronic device. This allows employees to respond to customer questions quickly and accurately. The server also records the submitted questions and the generated answers for future analysis.

[2366] Examples:

[2367] 1. A customer asks a question about a specific product: "How many calories are in this cream puff?"

[2368] 2. The customer or employee enters the question into the application using the smart glasses.

[2369] 3. The application receives the query and sends it to the server.

[2370] 4. The server passes the question to OpenAI's GPT-3 model, and the generative AI generates an answer: "A cream puff has about 200 calories."

[2371] 5. The server immediately sends the generated response to the customer or employee device.

[2372] 6. The employee communicates the answer to the customer.

[2373] Example prompt sentence:

[2374] Q: How many calories are in this cream puff?

[2375] answer:

[2376] In this way, it is possible to improve the efficiency of customer service in physical stores and reduce the workload of employees. The above is a detailed description of the embodiment of the present invention.

[2377] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2378] Step 1:

[2379] A user (customer or employee) uses a smartphone or smart glasses to type or speak a question and submit it. The input is recorded as text or voice data and sent from the device to the server. The input includes specific questions such as "How many calories are in this cream puff?"

[2380] Step 2:

[2381] The server analyzes the received question data and converts it into text data if it is voice data. Specifically, it uses voice recognition technology (e.g., Google Speech-to-Text API) to convert the voice data into text data. The output is text-formatted question data.

[2382] Step 3:

[2383] The server sends text-based question data to the generative AI (OpenAI's GPT-3 model). The server passes the question as a prompt to the generative AI and waits for a response. The input is text-based question data, and an example prompt includes "Question: How many calories are in this cream puff?"

[2384] Step 4:

[2385] The generative AI generates an appropriate answer based on the prompt it receives. For data processing, the generative AI uses natural language processing technology to analyze the question and generate the optimal answer based on known information. The output is answer data in text format. As a specific example, the generated answer would be "Answer: A cream puff has approximately 200 calories."

[2386] Step 5:

[2387] The server receives the generated response data and sends it to the customer or employee's device. The input is the text-formatted response data output from the generative AI, and the output is the response information displayed on the device. The server performs the data transfer process.

[2388] Step 6:

[2389] The device then displays the received answer data to the user. Specifically, the screen of the smartphone or smart glasses displays "A cream puff has approximately 200 calories." The input is the answer data sent from the server, and the output is the answer information that the user can visually confirm.

[2390] Step 7:

[2391] The server records the submitted questions and generated answers in a database, which can then be used for future analysis and improvement. The input is the question data and the answer data, and the output is the records stored in the database. The recording process must be consistent and reliable.

[2392] The above is the specific processing flow of the program of this system.

[2393] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2394] Combining automated application screening with an emotion engine

[2395] One form of this system combines an emotion engine with automated application screening.

[2396] System Overview

[2397] When a user submits their application documents, the generative AI analyzes them. At the same time, the emotion engine also analyzes the user's emotions at the time of submission and reflects them in the analysis results.

[2398] Program processing

[2399] 1. User: Job seekers upload their resumes and CVs through a web portal.

[2400] 2. Server: Receives the uploaded documents and stores them in a database. The emotion engine analyzes the user's facial expressions, tone of voice, etc. to extract emotional data.

[2401] 3. Server: Uses generative AI and an emotion engine to analyze the text and emotion data of documents.

[2402] 4. Server: Scores the analysis results and generates a list of optimal candidates, including emotional data.

[2403] 5. Server: Finally, the generated candidate list is notified to the HR personnel.

[2404] Combining customer service improvement with emotion engines

[2405] There are also embodiments in which emotion engines are used to improve customer service.

[2406] System Overview

[2407] When a customer makes an inquiry, the emotion engine recognizes their emotion, and the generative AI generates an appropriate response based on that emotion.

[2408] Program processing

[2409] 1. User: A customer submits a question via the chat widget or contact form.

[2410] 2. Terminal: The virtual assistant receives the inquiry in real time, and the emotion engine analyzes the customer's input and voice data to determine their emotions.

[2411] 3. Terminal: Sends the analysis results of the emotion engine to the generative AI.

[2412] 4. Server: Generative AI generates responses that take emotional data into account, for example, if a customer is stressed, it will generate a more polite and reassuring response.

[2413] 5. Terminal: The generated response is displayed to the user and provided in a chat window or email.

[2414] 6. Server: Logs all queries and responses.

[2415] Combining automated interviews with an emotion engine

[2416] There are also embodiments in which interviews are automated using emotion engines.

[2417] System Overview

[2418] Generative AI is used to generate interview questions, and an emotion engine recognizes the job seeker's emotions and reflects them in the results.

[2419] Program processing

[2420] 1. User: A job seeker books an interview on an online platform.

[2421] 2. Server: Starts scheduled interview sessions. Generative AI generates interview questions and emotion engine analyzes job seeker reactions.

[2422] 3. User: The job seeker answers the questions by typing or speaking.

[2423] 4. Server: The emotion engine analyzes the job seeker's facial expressions and vocal changes, which the generative AI takes into account when analyzing the answers.

[2424] 5. Server: The generative AI combines the answers and emotional data to generate an evaluation result. For example, it can reflect whether the job seeker is feeling stressed.

[2425] 6. Server: Determines whether the application passes or fails based on the evaluation results and notifies the user of the result.

[2426] Combining enhanced employee training with the Emotion Engine

[2427] In some embodiments, employee training is enhanced with an emotion engine.

[2428] System Overview

[2429] The emotion engine recognizes employees' emotions, and generative AI provides appropriate training content based on those emotions.

[2430] Program processing

[2431] 1. User: An employee logs into the training platform and begins training.

[2432] 2. On the device: The emotion engine analyzes the employee's facial expressions and tone of voice to recognize their emotions.

[2433] 3. Terminal: Sends the recognized emotion data to the generative AI.

[2434] 4. Server: Generative AI customizes training content based on emotional data, for example, offering lighter training if an employee is tired.

[2435] 5. Terminal: Presents customized training content to employees and facilitates training.

[2436] Combining career advice and emotion engines

[2437] In some embodiments, career advice is combined with an emotion engine.

[2438] System Overview

[2439] The emotion engine recognizes the user's emotions, and the generative AI provides career paths and advice that take their emotional state into account.

[2440] Program processing

[2441] 1. User: An employee or job seeker applies for career counseling.

[2442] 2. Server: Collects the emotional state along with the user's profile information.

[2443] 3. Server: Analyzes the profile information and emotional data collected by the generative AI.

[2444] 4. Server: Generates career paths that take into account sentiment data and creates reports, for example adding specific advice to alleviate user anxiety.

[2445] 5. Server: Provides the generated reports to users and makes them available for download from the online platform.

[2446] In this way, combining emotion engines enables more precise data analysis and service provision that takes into account the user's emotional state, significantly improving overall efficiency and user satisfaction.

[2447] The processing flow will be explained below.

[2448] Combining automated application screening with an emotion engine

[2449] Program processing

[2450] Step 1:

[2451] A user uploads their application documents (resume, curriculum vitae, etc.) through a web portal.

[2452] Step 2:

[2453] The server receives the uploaded application documents and stores them in a database.

[2454] Step 3:

[2455] The device's built-in emotion engine extracts emotion data from the user's facial expressions and voice and sends it to the server.

[2456] Step 4:

[2457] The server launches a generative AI and analyzes the text data in the application documents.

[2458] Step 5:

[2459] The server integrates the emotional data into the analysis results and performs scoring. For example, positive emotional expressions receive a high score.

[2460] Step 6:

[2461] The server generates a list of the best candidates and notifies the HR personnel.

[2462] Combining customer service improvement with emotion engines

[2463] Program processing

[2464] Step 1:

[2465] A user (customer) submits a question via a chat widget or contact form.

[2466] Step 2:

[2467] The on-device virtual assistant receives inquiries in real time.

[2468] Step 3:

[2469] The emotion engine built into the device recognizes the customer's emotions from the input content and voice data and sends this to the server.

[2470] Step 4:

[2471] The server sends the emotion data and the query content to the generative AI.

[2472] Step 5:

[2473] The server takes emotional data into account and the generative AI generates the optimal response to the inquiry. For example, if the customer is feeling stressed, it will generate a more polite response.

[2474] Step 6:

[2475] The device displays the generated answer to the user and provides it in a chat window or via email.

[2476] Step 7:

[2477] The server logs all queries and responses.

[2478] Combining automated interviews with an emotion engine

[2479] Program processing

[2480] Step 1:

[2481] A user (job seeker) schedules an interview on an online platform.

[2482] Step 2:

[2483] The server starts the scheduled interview session.

[2484] Step 3:

[2485] The device's built-in emotion engine detects the job seeker's emotions from their facial expressions and voice and sends this information to the server.

[2486] Step 4:

[2487] The server uses generative AI to generate interview questions and present them to job seekers.

[2488] Step 5:

[2489] The user answers the questions by typing or speaking.

[2490] Step 6:

[2491] The server receives the emotion data and uses generative AI to comprehensively analyze the responses and emotion data. For example, if a job seeker is nervous, that will be reflected in the evaluation.

[2492] Step 7:

[2493] The server scores the evaluation results and determines whether the application passes or fails.

[2494] Step 8:

[2495] The server notifies the result and provides it to the user.

[2496] Combining enhanced employee training with the Emotion Engine

[2497] Program processing

[2498] Step 1:

[2499] A user (employee) logs into the training platform.

[2500] Step 2:

[2501] Employees enter their questions into a question form within the training platform.

[2502] Step 3:

[2503] The device's built-in emotion engine recognizes emotions from the employee's facial expressions and tone of voice and transmits this information to a server.

[2504] Step 4:

[2505] The server sends the emotional data to a generative AI, which generates the optimal answer to the question.

[2506] Step 5:

[2507] The server sends the generated response to the terminal.

[2508] Step 6:

[2509] The device displays customized responses to the user, for example, suggesting a lighter workout if the user is tired.

[2510] Combining career advice and emotion engines

[2511] Program processing

[2512] Step 1:

[2513] A user (employee or job seeker) applies for career counseling through an online platform.

[2514] Step 2:

[2515] The device's built-in emotion engine recognizes the user's emotions from their facial expressions and voice and transmits them to the server.

[2516] Step 3:

[2517] The server collects user profile information and emotional data and analyzes it using generative AI.

[2518] Step 4:

[2519] The server generates a report based on the emotional data, generating an optimal career path, adding specific advice to alleviate any anxiety the user may have, for example.

[2520] Step 5:

[2521] The server provides the generated reports to the user and makes them available for download from an online platform.

[2522] In this way, combining emotion engines enables more precise data analysis and service provision that takes into account the user's emotional state, significantly improving overall efficiency and user satisfaction.

[2523] Example 2

[2524] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2525] Traditional application screening cannot take into account the emotional state of job seekers, which can result in the overlooking of highly suitable candidates. Furthermore, in customer service and interview evaluations, responses and evaluations that ignore the emotional state of employees can be inaccurate. This can lead to issues such as a decline in the quality of a company's recruitment process and customer service.

[2526] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2527] In this invention, the server includes a means for job seekers to submit application documents, a means for analyzing facial expressions and tone of voice to extract emotional data, a means for a generative AI to analyze the submitted application documents and the extracted emotional data to select candidates, and a means for notifying the human resources department of the information on the selected candidates. This enables screening that takes into account the emotional state of applicants, allowing for the selection of more suitable candidates. Similarly, utilizing emotional data in customer service and interview evaluations allows for more accurate and effective responses.

[2528] "Application documents" refers to documents such as resumes and work histories that job seekers submit to companies seeking employment.

[2529] "Generative AI" refers to artificial intelligence that can analyze text and voice data using natural language processing technology and create new products.

[2530] "Emotional data" refers to data analyzed from facial expressions, vocal tone, etc., and is information that expresses an individual's emotional state in numerical or categorical terms.

[2531] "Means" refers to a device, system, method, etc. used to achieve a particular purpose.

[2532] "Candidate" refers to an applicant who has been assessed as suitable for a particular job or role.

[2533] "Notification" refers to the act of conveying specific information to a recipient.

[2534] "Analysis" refers to the process of examining data or information in detail to clarify its content and structure.

[2535] An "interview" refers to the process by which a job seeker and an interviewer evaluate the job seeker's aptitude and abilities through dialogue.

[2536] "Inquiry" means a question or request made by a Customer seeking information or support regarding a product or service.

[2537] Combining automated application screening with an emotion engine

[2538] In this embodiment of the invention, a user (job seeker) uploads a resume or work history through a web portal. The server that receives the application documents first stores the documents in a database, and then uses an emotion engine to analyze the user's facial expressions and voice tone to extract emotion data. Specific software that can be used includes a "face analysis API" and a "voice analysis API."

[2539] For example, when a user logs in to a job-seeking website and uploads a PDF resume, the server saves the document in cloud storage. The server then uses face analysis APIs and voice analysis APIs to extract emotional data from the uploaded data.

[2540] Next, generative AI is used to analyze the text data and extracted emotion data from the submitted application documents. Natural language processing AI can be used as generative AI. As a result of the analysis, the server scores the suitability of the candidates and generates a list of optimal candidates based on this. For scoring, a machine learning library is used to quantify the suitability of each applicant.

[2541] Finally, the server notifies the HR department of the generated candidate list using a messaging API, automating the process of efficiently selecting and notifying highly suitable candidates.

[2542] Career Services Prompt Examples

[2543] "Analyze the facial expressions and tone of voice of job applicants and evaluate them along with the content of their application documents. For example, if a job applicant shows signs of stress, reflect that emotion in your evaluation."

[2544] By inputting this prompt into the generative AI model, the emotion engine and generative AI work together to process the prompt. This enables highly accurate analysis and evaluation using emotion data, improving the efficiency and accuracy of the hiring process.

[2545] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2546] Specific processing steps of the program

[2547] Combining automated application screening with an emotion engine

[2548] Step 1:

[2549] User: A job seeker uploads their resume or CV through a web portal.

[2550] Input: A user-uploaded PDF resume or CV.

[2551] Output: The application data sent to the server.

[2552] Specific operation: Click the "Upload Documents" button on the web portal, select your resume from the file selection dialog, and upload it.

[2553] Step 2:

[2554] Server: Receives uploaded documents and stores them in a database.

[2555] Input: Application data submitted by the user.

[2556] Output: File path of application data saved in the database.

[2557] Specific operation: The server uploads the received file to "cloud storage" and writes the file metadata and storage location to the database.

[2558] Step 3:

[2559] Server: The emotion engine analyzes the user's facial expressions, tone of voice, etc. to extract emotional data.

[2560] Input: Application document data stored on the server and user's voice and facial image data.

[2561] Output: Emotion data obtained from the emotion engine.

[2562] Specific operation: Using the "face analysis API" and "voice analysis API," facial images and voice data are analyzed, and the emotional state is quantified and stored in a database.

[2563] Step 4:

[2564] Server: Uses a combination of generative AI and an emotion engine to analyze text data and emotion data from application documents.

[2565] Input: Text data and sentiment data from job applications.

[2566] Output: Analysis result data.

[2567] Specific operation: The content of the resume is analyzed using generative AI (natural language processing AI), emotional data obtained from the emotion engine is integrated, and the analysis results are stored in a database.

[2568] Step 5:

[2569] Server: Scores the analysis results and generates a list of optimal candidates, including emotional data.

[2570] Input: Analysis result data.

[2571] Output: A list of the best candidates.

[2572] Specific operation: Using a "machine learning library," it calculates the score for each applicant, and based on the results, generates a list of optimal candidates and stores them in a database.

[2573] Step 6:

[2574] Server: Notifies the HR department of the generated candidate list.

[2575] Input: A list of best candidates.

[2576] Output: Notification message to HR department.

[2577] Specific operation: Using the "Messaging API," a notification message is sent to a specific channel in the HR department, providing a link to the candidate list.

[2578] (Application example 2)

[2579] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2580] Conventional application screening, customer service, and automated interview systems are unable to take into account the user's emotional state, making it difficult to provide personalized services. Furthermore, evaluations and responses without emotion analysis have limited the potential for improving the user experience. Therefore, there is a need for technology that can recognize user emotions in real time and respond and evaluate accordingly.

[2581] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2582] In this invention, the server includes: a means for a job seeker to submit an application; a means for a generative AI to analyze the submitted application and select candidates; a means for notifying a human resources department of information about the selected candidates; a means including an emotion engine for analyzing the job seeker's emotions at the time of submission; a means for re-evaluating and scoring based on the emotion data; a means for accepting customer inquiries; a means for generating responses to customer inquiries using a generative AI; a means for providing the generated responses to the customers; a means including an emotion engine for analyzing customer emotions in real time; a means for adjusting responses based on the emotion data; a means for scheduling interviews with the job seeker; a means for generating interview questions using a generative AI and presenting them to the job seeker; a means for analyzing and evaluating the job seeker's answers; a means including an emotion engine for analyzing the job seeker's emotions during the interview; and a means for adjusting the evaluation results based on the emotion data. This enables more precise and personalized responses and evaluations while taking into account the user's emotional state.

[2583] "Means for submitting applications" refers to the interface that job seekers use to upload application documents, such as resumes and curriculum vitae, to the system.

[2584] "Generative AI" is artificial intelligence that uses natural language processing and machine learning techniques to analyze and generate responses to application documents and customer inquiries.

[2585] The "means of candidate selection" refers to the method for selecting suitable candidates based on the information in the application documents analyzed by the generative AI.

[2586] "Means of notifying the human resources department" refers to the means by which information about the selected candidate is communicated to the human resources department via email or internal systems.

[2587] The "emotion engine that analyzes job seekers' emotions at the time of submission" is a technology that recognizes emotions from facial expressions and voice when job seekers submit their application documents and extracts them as data.

[2588] "Means for re-evaluating and scoring based on emotional data" refers to a method for adjusting the evaluation of application documents and recalculating scores using recognized emotional data.

[2589] "Means for accepting customer inquiries" are interfaces such as chat widgets or forms that allow customers to submit questions or requests.

[2590] The "means of generating a response" is how the generative AI creates an appropriate response to a customer inquiry.

[2591] "Means of providing the generated response to the customer" refers to the method of communicating the answer created by the generative AI to the customer via a chat window, email, etc.

[2592] The "emotion engine that analyzes customer emotions in real time" is a technology that instantly recognizes emotions from customer text input, voice data, and even facial expressions.

[2593] "Means for adjusting responses based on emotional data" refers to a method in which generative AI changes the content and tone of responses based on recognized emotional data.

[2594] "Means for scheduling interviews" refers to a system that allows job seekers to schedule interview dates and times online.

[2595] "Means for generating interview questions and presenting them to job seekers" refers to a method in which generative AI generates specific interview questions and presents them to job seekers in text or audio.

[2596] "Means for analyzing and evaluating job seekers' responses" refers to the method by which generative AI analyzes the content of job seekers' responses and assigns an evaluation score.

[2597] The "emotion engine that analyzes the emotions of job seekers during interviews" is a technology that recognizes emotions in real time from the facial expressions and voice of job seekers during interviews.

[2598] "Means for adjusting evaluation results based on emotional data" refers to a method in which generative AI recalculates evaluation results by taking into account emotional data recognized during the interview.

[2599] This invention implements the following steps: We present specific procedures for combining generative AI and an emotion engine to screen job applicants' applications, conduct online interviews, provide customer support, and provide a recommendation system.

[2600] Screening job applicant applications

[2601] Hardware:

[2602] Users use a computer or smartphone to upload their application documents to a web portal.

[2603] software:

[2604] The server uses generative AI (e.g., OpenAI's GPT-3 model) and emotion engine (e.g., Microsoft's Azure Cognitive Services' Face API).

[2605] Data processing:

[2606] When an application is uploaded, a generative AI analyzes it and evaluates the job seeker's skills and experience. In parallel, an emotion engine recognizes emotions from video and audio data and adds that data to the analysis.

[2607] As a specific example, if a user is feeling angry or impatient, the generative AI will use the recognized emotional data to perform more flexible scoring.

[2608] Interview automation

[2609] Hardware:

[2610] A webcam and microphone are used when the interview is conducted between the job seeker and the server.

[2611] software:

[2612] The server conducts interviews using generative AI and an emotion engine. The generative AI generates interview questions, and the emotion engine analyzes the job seeker's emotions in real time as they answer.

[2613] Data processing:

[2614] The responses are re-evaluated based on the job seeker's emotional state to generate a final rating. For example, if the job seeker is nervous, the rating will adjust to reflect this.

[2615] An example prompt might be, "The user is nervous, so please keep your questions brief."

[2616] Customer Service

[2617] Hardware:

[2618] Customers use a chat widget or inquiry form on their computer or smartphone.

[2619] software:

[2620] Virtual assistants use generative AI to generate responses to customer queries, and here too, an emotion engine recognizes customer emotions in real time.

[2621] Data processing:

[2622] It generates and quickly delivers responses that reflect the customer's emotions. For example, if a customer expresses dissatisfaction, generative AI will generate an appropriate and comforting response.

[2623] An example would be, "The customer is unhappy, so please carefully explain the specific steps you will take to resolve the issue."

[2624] Product Recommendations

[2625] Hardware:

[2626] Users of the online shopping site access it from their smartphones or computers.

[2627] software:

[2628] The server uses generative AI and an emotion engine to analyze the product pages the user views and their emotions at the time.

[2629] Data processing:

[2630] The recommendation algorithm reflects the user's emotional data and recommends appropriate products. For example, if a user feels happy when looking at a product, it will suggest more products from that category.

[2631] An example prompt might be, "Since this user is happy, please recommend products in the same category."

[2632] These embodiments allow for application analysis, interviews, customer service, and product recommendations to take into account the user's emotional state, improving overall service quality.

[2633] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2634] Step 1:

[2635] A job seeker uploads their application to a web portal.

[2636] Operation:

[2637] Users (job seekers) upload their resumes and work histories along with the necessary documents to the system.

[2638] input:

[2639] Resume, work history

[2640] output:

[2641] Saved application data

[2642] Step 2:

[2643] The server receives the uploaded application documents and stores them in a database.

[2644] Operation:

[2645] The server receives the applications and stores the data in a database for further analysis.

[2646] input:

[2647] Application document data

[2648] output:

[2649] Document data stored in the database

[2650] Step 3:

[2651] The server uses a sentiment engine to analyze the job seeker's sentiment at the time of submission.

[2652] Operation:

[2653] The server activates an emotion engine and extracts emotional data by analyzing the job seeker's facial expressions and tone of voice.

[2654] input:

[2655] Video or audio data when submitting documents

[2656] output:

[2657] Emotional Data

[2658] Step 4:

[2659] Generative AI analyzes the text data of submitted application documents.

[2660] Operation:

[2661] Generative AI (such as the GPT-3 model) analyzes the content of application documents and evaluates skills and experience.

[2662] input:

[2663] Text data of application documents

[2664] output:

[2665] Initial evaluation data

[2666] Step 5:

[2667] Generative AI will readjust the evaluation of your application based on emotional data.

[2668] Operation:

[2669] The server recalculates the evaluation results based on the emotional data and adjusts the scoring. For example, if a job applicant is nervous, the server will take that into account and adjust the scoring accordingly.

[2670] input:

[2671] Initial evaluation data, emotion data

[2672] output:

[2673] Adjusted evaluation data

[2674] Step 6:

[2675] Based on the evaluation results...

Claims

1. a means by which job seekers can submit their applications; A generative AI analyzes submitted applications and selects candidates; A means of notifying the Human Resources department of the information of the selected candidate; A system including:

2. a means for receiving customer enquiries; a means for generating responses to customer inquiries using generative AI; a means for providing the generated response to the customer; The system of claim 1 , comprising:

3. A means to schedule interviews for job seekers; A method for generating interview questions using generative AI and presenting them to job seekers; a means of analyzing and evaluating job seeker responses; The system of claim 1 , comprising:

4. A means for employees to access the training platform and enter questions; A means for generative AI to generate answers to questions; a means for displaying the generated answers to the employee; The system of claim 1 , comprising:

5. means for collecting user profile information; A means of generating and proposing career paths for users using generative AI; A means for creating a career advice report and providing it to users; The system of claim 1 , comprising:

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