System

Abstract

An object of the system according to the embodiment is to improve efficiency of information exchange and a selection process between a student and a company in a job hunting activity.SOLUTION: A system includes a common entry sheet reception part, a company information registration part, a question answer part, an entry processing part, and a primary selection part. The common entry sheet receiving unit receives a common entry sheet. The company information registration unit registers information on a company. The question answering unit answers the question from the student using the generated AI. An entry processing part allows a student to enter a company. A primary selection part asks a question to the entered student by using the generated AI and executes primary selection.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] With conventional technology, there was a problem in that the information exchange and selection process between students and companies during job hunting was not carried out efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of the information exchange and selection process between students and companies during job hunting. [Means for solving the problem]

[0006] The system according to the embodiment includes a common entry form acceptance unit, a company information registration unit, a question and answer unit, an entry processing unit, and a first selection unit. The common entry form acceptance unit accepts common entry forms. The company information registration unit registers company information. The question and answer unit answers questions from students using a generation AI. The entry processing unit allows students to apply to companies. The first selection unit uses a generation AI to ask questions to students who have applied, and conducts the first selection process. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of the information exchange and selection process between students and companies during job hunting. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The job hunting platform according to an embodiment of the present invention is a system in which students create a common application form, supporting companies register their company information, the generating AI answers questions from students, students apply to companies, and supporting companies conduct the first selection process through the generating AI. This enables the job hunting platform to improve the efficiency and convenience of job hunting.

[0029] A job hunting platform according to an embodiment includes a common application form receiving unit, a company information registration unit, a question and answering unit, an application processing unit, and an initial selection unit. The common application form receiving unit receives a common application form. For example, a student can apply to multiple companies by creating a common application form once and uploading it to the platform. The company information registration unit registers company information, such as a company overview, recruitment requirements, and employee benefits. The question and answering unit uses a generation AI to answer questions from students. For example, in response to a question such as "Please tell me about your company's employee benefits," the generation AI responds with "Our company offers employee benefits such as health insurance, a pension system, and company trips." The entry processing unit allows students to apply to companies. For example, a student browses company information and submits an application form to companies that interest them. The initial selection unit uses the generation AI to ask questions to the applied students and conducts the initial selection process. For example, the generation AI sends a question such as "Please tell us about yourself," to the student, who then inputs the answer. The generation AI analyzes the answer and provides an evaluation to the company. This allows the job hunting platform to improve the efficiency and convenience of job hunting. For example, students can apply to multiple companies by filling out a single application form, and companies can use generative AI to efficiently answer students' questions and conduct the first round of selection. This makes the job hunting process go more smoothly and improves convenience for both students and companies.

[0030] The common application form reception unit uses a generation AI to automatically analyze the contents of the application form and extract the student's strengths and aptitudes. The common application form reception unit, for example, uses a generation AI to analyze the contents of the application form and automatically extract the student's strengths and aptitudes. For example, it analyzes keywords and phrases in the text and identifies the student's special skills and areas of interest. The generation AI uses natural language generation technology and machine learning models to analyze the contents of the application form and extract the student's strengths and aptitudes. For example, the generation AI analyzes the text of the application form and identifies the student's special skills and areas of interest. This makes it possible to automatically extract the student's strengths and aptitudes.

[0031] The common application form reception unit provides individual feedback to students based on the contents of their application forms and suggests areas for improvement to their application forms. The common application form reception unit, for example, uses a generation AI to analyze the contents of the application forms and provide individual feedback to students. For example, it suggests specific areas for improvement in the structure and expression of sentences. The generation AI uses natural language generation technology and machine learning models to analyze the contents of application forms and provide individual feedback to students. For example, the generation AI analyzes the sentences in application forms and suggests specific areas for improvement in the structure and expression of sentences. This makes it possible to provide individual feedback to students and suggest areas for improvement to their application forms.

[0032] The common application form reception unit links the contents of the application form with other platforms and automatically updates the student's profile. For example, the common application form reception unit links the contents of the application form with LinkedIn and automatically updates the student's profile. For example, it automatically reflects information such as educational background and work history. The common application form reception unit links the contents of the application form with other platforms and automatically updates the student's profile. For example, it links the contents of the application form with LinkedIn or Indeed and automatically updates the student's profile. This makes it possible to automatically update the student's profile.

[0033] The common entry sheet reception unit proposes appropriate internship and part-time job opportunities to students based on the contents of the entry sheet. The common entry sheet reception unit, for example, analyzes the contents of the entry sheet and proposes appropriate internship opportunities to students. For example, it recommends internships based on the student's areas of interest and skills. The common entry sheet reception unit proposes appropriate internship and part-time job opportunities to students based on the contents of the entry sheet. For example, it analyzes the contents of the entry sheet and proposes internship and part-time job opportunities based on the student's areas of interest and skills. This makes it possible to propose appropriate internship and part-time job opportunities to students.

[0034] The company information registration unit uses a generation AI to automatically update company information and always provide the latest information. The company information registration unit, for example, uses a generation AI to automatically update company information and always provide the latest information. For example, company news and press releases are automatically reflected. The generation AI uses natural language generation technology and machine learning models to automatically update company information and always provide the latest information. For example, the generation AI automatically reflects company news and press releases. This makes it possible to automatically update company information and always provide the latest information.

[0035] The company information registration unit collects student feedback on company information and improves the company information based on that feedback. The company information registration unit, for example, collects student feedback on company information and builds a system that improves company information based on that feedback. For example, the company information is updated to reflect student opinions. The company information registration unit collects student feedback on company information and improves company information based on that feedback. For example, the company information registration unit collects student feedback on company information and improves company information based on that feedback. In this way, company information can be improved based on student feedback.

[0036] The company information registration unit links company information with other platforms to maintain consistency of information. For example, the company information registration unit links company information with Glassdoor to build a system to maintain consistency of information. For example, it automatically reflects company ratings and reviews. The company information registration unit links company information with other platforms to maintain consistency of information. For example, it links company information with Glassdoor and the company's official website to maintain consistency of information. This makes it possible to maintain consistency of company information.

[0037] The company information registration unit proposes schedules for company visits and information sessions to students based on company information. The company information registration unit, for example, builds a system that proposes schedules for company visits to students based on company information. For example, it automatically proposes company locations and available dates and times for visits. The company information registration unit proposes schedules for company visits and information sessions to students based on company information. For example, it proposes schedules for company visits and information sessions to students based on company information. This makes it possible to propose schedules for company visits and information sessions to students.

[0038] The entry processing unit uses generative AI to automatically organize the information collected by students and suggest the most suitable application destination. The entry processing unit, for example, uses generative AI to automatically organize the information collected by students and build a system that suggests the most suitable application destination. For example, it recommends application destinations based on the student's areas of interest and skills. The generative AI uses natural language generation technology and machine learning models to automatically organize the information collected by students and suggest the most suitable application destination. For example, the generative AI recommends application destinations based on the student's areas of interest and skills. This makes it possible to automatically organize the information collected by students and suggest the most suitable application destination.

[0039] The entry processing unit proposes an individual entry strategy based on the information collected by the student. The entry processing unit, for example, builds a system that proposes an individual entry strategy based on the information collected by the student. For example, it proposes how to approach a specific company and how to write an application form. The entry processing unit proposes an individual entry strategy based on the information collected by the student. For example, it proposes how to approach a specific company and how to write an application form based on the information collected by the student. This makes it possible to propose an individual entry strategy to the student.

[0040] The entry processing unit shares the information collected by students with other students and promotes information exchange. The entry processing unit, for example, builds a platform for students to share the information they have collected with other students. For example, it provides a bulletin board or chat function for information sharing. The entry processing unit shares the information collected by students with other students and promotes information exchange. For example, it builds a platform for students to share the information they have collected with other students. This can promote information exchange between students.

[0041] The entry processing unit provides appropriate career counseling based on the information collected by the student. The entry processing unit, for example, builds a system that provides appropriate career counseling based on the information collected by the student. For example, it provides career advice based on the student's areas of interest and skills. The entry processing unit provides appropriate career counseling based on the information collected by the student. For example, it provides appropriate career counseling based on the information collected by the student. In this way, appropriate career counseling can be provided to the student.

[0042] The initial selection department uses generative AI to automatically evaluate students' answers and provide detailed feedback. The initial selection department, for example, builds a system that uses generative AI to automatically evaluate students' answers and provide detailed feedback. For example, it suggests specific improvements to the content and expression of the answer. The generative AI uses natural language generation technology and machine learning models to automatically evaluate students' answers and provide detailed feedback. For example, the generative AI analyzes students' answers and suggests specific improvements to the content and expression. This makes it possible to automatically evaluate students' answers and provide detailed feedback.

[0043] The initial selection department automatically optimizes the content of questions asked in the initial selection process to more accurately evaluate students' aptitude. The initial selection department, for example, uses generative AI to automatically optimize the content of questions asked in the initial selection process and builds a system to more accurately evaluate students' aptitude. For example, the question content is adjusted based on students' answers. The generative AI uses natural language generation technology and machine learning models to automatically optimize the content of questions asked in the initial selection process to more accurately evaluate students' aptitude. For example, the generative AI adjusts the question content based on students' answers. This makes it possible to more accurately evaluate students' aptitude.

[0044] The initial selection department links the results of the initial selection with other selection stages to streamline the entire selection process. The initial selection department, for example, links the results of the initial selection with the second interview and final interview, and builds a system to streamline the entire selection process. For example, the content of the questions for the next stage is adjusted based on the evaluation of the initial selection. The initial selection department links the results of the initial selection with other selection stages to streamline the entire selection process. For example, the results of the initial selection with the second interview and final interview, and streamline the entire selection process. This makes it possible to streamline the entire selection process.

[0045] The initial selection department provides appropriate feedback to students based on the results of the initial selection and supports their preparation for the next selection stage. The initial selection department, for example, builds a system that provides appropriate feedback to students based on the results of the initial selection and supports their preparation for the next selection stage. For example, it specifically points out the strengths and weaknesses of students. The initial selection department provides appropriate feedback to students based on the results of the initial selection and supports their preparation for the next selection stage. For example, it provides appropriate feedback to students based on the results of the initial selection and supports their preparation for the next selection stage. This makes it possible to provide appropriate feedback to students and support their preparation for the next selection stage.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] Job-hunting platforms can also be equipped with a skills assessment module that evaluates students' skill sets and suggests appropriate training programs. For example, the module analyzes the skills listed on a student's application form and, if a specific skill is lacking, suggests online courses or workshops to improve that skill. The skills assessment module can also compare a student's skill set with the skills required by companies and evaluate the degree of match. This allows students to receive a specific action plan for improving their skills, and companies can efficiently find students with the skills they are looking for.

[0048] The common application form reception unit can also be equipped with a career path suggestion unit that suggests appropriate career paths to students based on the contents of the application form. For example, it can suggest possible future career paths based on the student's areas of interest and skills. The career path suggestion unit can also analyze past data and refer to the career paths pursued by students with similar skill sets. This allows students to clarify their own career direction and set specific goals.

[0049] The common application form reception department can also have a mentor introduction department that introduces appropriate mentors to students based on the contents of their application forms. For example, it can introduce seniors and professionals active in the same field based on the student's field of interest and career goals. The mentor introduction department can also evaluate the degree of match between students and mentors and recommend the most suitable mentor. This allows students to receive specific advice about their careers and conduct a more effective job search.

[0050] The common application form reception department can also have a volunteer suggestion department that suggests appropriate volunteer activities and social contribution opportunities to students based on the contents of their application forms. For example, it can suggest local volunteer activities and social contribution projects based on students' areas of interest and skills. The volunteer suggestion department can also provide advice on how to reflect students' volunteer experiences in their application forms. This allows students to put their skills to practical use and grow through social contribution.

[0051] The company information registration department can also have a cultural information provision department that provides students with information about corporate culture and working styles based on company information. For example, it can introduce the company's corporate culture, working style, and team atmosphere. The cultural information provision department can also collect interviews and personal stories from company employees and provide students with real information. This allows students to understand corporate culture and working styles and choose a company that suits them.

[0052] The Company Information Registration Department can also have a Growth Strategy Information Providing Department that provides students with information on a company's growth strategy and future vision based on company information. For example, it can introduce a company's medium- to long-term growth strategy and new business development plans. The Growth Strategy Information Providing Department can also collect information on a company's future vision and provide it to students. This allows students to understand the future potential of a company and select a company based on their own career plans.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The common application form reception department accepts common application forms. For example, a student can create a common application form once and upload it to the platform to apply to multiple companies. Step 2: The company information registration unit registers company information, such as company overview, recruitment requirements, employee benefits, etc. Step 3: The question answering section uses the generation AI to answer questions from students. For example, in response to the question, "Please tell me about your company's employee benefits," the generation AI answers, "Our company offers employee benefits such as health insurance, a pension system, and company trips." Step 4: The entry processing unit allows students to apply to companies. For example, students browse company information and submit application forms to companies that interest them. Step 5: The first selection department uses the generation AI to ask questions to the students who have applied, and conducts the first selection process. For example, the generation AI sends the question "Please tell us about yourself," to the student, who then enters their answer. The generation AI analyzes the answer and provides an evaluation to the company.

[0055] (Example 2) The job hunting platform according to an embodiment of the present invention is a system in which students create a common application form, supporting companies register their company information, the generating AI answers questions from students, students apply to companies, and supporting companies conduct the first selection process through the generating AI. This enables the job hunting platform to improve the efficiency and convenience of job hunting.

[0056] A job hunting platform according to an embodiment includes a common application form receiving unit, a company information registration unit, a question and answering unit, an application processing unit, and an initial selection unit. The common application form receiving unit receives a common application form. For example, a student can apply to multiple companies by creating a common application form once and uploading it to the platform. The company information registration unit registers company information, such as a company overview, recruitment requirements, and employee benefits. The question and answering unit uses a generation AI to answer questions from students. For example, in response to a question such as "Please tell me about your company's employee benefits," the generation AI responds with "Our company offers employee benefits such as health insurance, a pension system, and company trips." The entry processing unit allows students to apply to companies. For example, a student browses company information and submits an application form to companies that interest them. The initial selection unit uses the generation AI to ask questions to the applied students and conducts the initial selection process. For example, the generation AI sends a question such as "Please tell us about yourself," to the student, who then inputs the answer. The generation AI analyzes the answer and provides an evaluation to the company. This allows the job hunting platform to improve the efficiency and convenience of job hunting. For example, students can apply to multiple companies by filling out a single application form, and companies can use generative AI to efficiently answer students' questions and conduct the first round of selection. This makes the job hunting process go more smoothly and improves convenience for both students and companies.

[0057] The common application form reception unit uses a generation AI to automatically analyze the contents of the application form and extract the student's strengths and aptitudes. The common application form reception unit, for example, uses a generation AI to analyze the contents of the application form and automatically extract the student's strengths and aptitudes. For example, it analyzes keywords and phrases in the text and identifies the student's special skills and areas of interest. The generation AI uses natural language generation technology and machine learning models to analyze the contents of the application form and extract the student's strengths and aptitudes. For example, the generation AI analyzes the text of the application form and identifies the student's special skills and areas of interest. This makes it possible to automatically extract the student's strengths and aptitudes.

[0058] The common application form reception unit provides individual feedback to students based on the contents of their application forms and suggests areas for improvement to their application forms. The common application form reception unit, for example, uses a generation AI to analyze the contents of the application forms and provide individual feedback to students. For example, it suggests specific areas for improvement in the structure and expression of sentences. The generation AI uses natural language generation technology and machine learning models to analyze the contents of application forms and provide individual feedback to students. For example, the generation AI analyzes the sentences in application forms and suggests specific areas for improvement in the structure and expression of sentences. This makes it possible to provide individual feedback to students and suggest areas for improvement to their application forms.

[0059] The common application form reception unit uses an emotion estimation function to analyze students' emotions when they are creating their application forms and provides advice to draw out positive emotions. The common application form reception unit, for example, uses the emotion estimation function to analyze students' emotions when they are creating their application forms in real time and provides advice to draw out positive emotions. For example, it displays an encouraging message. The emotion estimation function uses an emotion analysis model and natural language processing technology to analyze students' emotions when they are creating their application forms. For example, the emotion estimation function analyzes students' facial expressions and voices when they are creating their application forms and estimates their emotions. This makes it possible to provide advice to draw out positive emotions in students.

[0060] The common application form reception unit links the contents of the application form with other platforms and automatically updates the student's profile. For example, the common application form reception unit links the contents of the application form with LinkedIn and automatically updates the student's profile. For example, it automatically reflects information such as educational background and work history. The common application form reception unit links the contents of the application form with other platforms and automatically updates the student's profile. For example, it links the contents of the application form with LinkedIn or Indeed and automatically updates the student's profile. This makes it possible to automatically update the student's profile.

[0061] The common entry sheet reception unit proposes appropriate internship and part-time job opportunities to students based on the contents of the entry sheet. The common entry sheet reception unit, for example, analyzes the contents of the entry sheet and proposes appropriate internship opportunities to students. For example, it recommends internships based on the student's areas of interest and skills. The common entry sheet reception unit proposes appropriate internship and part-time job opportunities to students based on the contents of the entry sheet. For example, it analyzes the contents of the entry sheet and proposes internship and part-time job opportunities based on the student's areas of interest and skills. This makes it possible to propose appropriate internship and part-time job opportunities to students.

[0062] The common application form reception unit uses an emotion estimation function to analyze companies' emotional reactions to the contents of the application form and suggests the optimal way to write the application form to students. The common application form reception unit, for example, uses the emotion estimation function to analyze companies' emotional reactions to the contents of the application form and suggests the optimal way to write the application form to students. For example, it recommends a way of expressing the application form that will elicit a positive reaction. The emotion estimation function uses an emotion analysis model and natural language processing technology to analyze companies' emotional reactions to the contents of the application form. For example, the emotion estimation function analyzes the emotional tone of companies' responses and recommends a way of expressing the application form that will elicit a positive reaction. This makes it possible to analyze companies' emotional reactions and suggest the optimal way to write the application form to students.

[0063] The company information registration unit uses a generation AI to automatically update company information and always provide the latest information. The company information registration unit, for example, uses a generation AI to automatically update company information and always provide the latest information. For example, company news and press releases are automatically reflected. The generation AI uses natural language generation technology and machine learning models to automatically update company information and always provide the latest information. For example, the generation AI automatically reflects company news and press releases. This makes it possible to automatically update company information and always provide the latest information.

[0064] The company information registration unit collects student feedback on company information and improves the company information based on that feedback. The company information registration unit, for example, collects student feedback on company information and builds a system that improves company information based on that feedback. For example, the company information is updated to reflect student opinions. The company information registration unit collects student feedback on company information and improves company information based on that feedback. For example, the company information registration unit collects student feedback on company information and improves company information based on that feedback. In this way, company information can be improved based on student feedback.

[0065] The company information registration unit uses the emotion estimation function to analyze the emotional tone of the company's response to a student's question and provides advice for generating a positive response. The company information registration unit, for example, uses the emotion estimation function to analyze the emotional tone of the company's response to a student's question and provides advice for generating a positive response. For example, adjusting the tone of the response. The emotion estimation function uses an emotion analysis model or natural language processing technology to analyze the emotional tone of the company's response to a student's question. For example, the emotion estimation function analyzes the emotional tone of the company's response and provides advice for generating a positive response. This makes it possible to analyze the emotional tone of the company's response and provide advice for generating a positive response.

[0066] The company information registration unit links company information with other platforms to maintain consistency of information. For example, the company information registration unit links company information with Glassdoor to build a system to maintain consistency of information. For example, it automatically reflects company ratings and reviews. The company information registration unit links company information with other platforms to maintain consistency of information. For example, it links company information with Glassdoor and the company's official website to maintain consistency of information. This makes it possible to maintain consistency of company information.

[0067] The company information registration unit proposes schedules for company visits and information sessions to students based on company information. The company information registration unit, for example, builds a system that proposes schedules for company visits to students based on company information. For example, it automatically proposes company locations and available dates and times for visits. The company information registration unit proposes schedules for company visits and information sessions to students based on company information. For example, it proposes schedules for company visits and information sessions to students based on company information. This makes it possible to propose schedules for company visits and information sessions to students.

[0068] The company information registration unit uses the emotion estimation function to analyze students' emotional reactions to company information and suggests improvements to the information to the company. The company information registration unit, for example, uses the emotion estimation function to analyze students' emotional reactions to company information and suggests improvements to the information to the company. For example, it suggests specific improvements to elicit a positive reaction. The emotion estimation function uses an emotion analysis model or natural language processing technology to analyze students' emotional reactions to company information. For example, the emotion estimation function analyzes students' emotional reactions to company information and suggests specific improvements to elicit a positive reaction. This makes it possible to analyze students' emotional reactions to company information and suggest improvements to the information to the company.

[0069] The entry processing unit uses generative AI to automatically organize the information collected by students and suggest the most suitable application destination. The entry processing unit, for example, uses generative AI to automatically organize the information collected by students and build a system that suggests the most suitable application destination. For example, it recommends application destinations based on the student's areas of interest and skills. The generative AI uses natural language generation technology and machine learning models to automatically organize the information collected by students and suggest the most suitable application destination. For example, the generative AI recommends application destinations based on the student's areas of interest and skills. This makes it possible to automatically organize the information collected by students and suggest the most suitable application destination.

[0070] The entry processing unit proposes an individual entry strategy based on the information collected by the student. The entry processing unit, for example, builds a system that proposes an individual entry strategy based on the information collected by the student. For example, it proposes how to approach a specific company and how to write an application form. The entry processing unit proposes an individual entry strategy based on the information collected by the student. For example, it proposes how to approach a specific company and how to write an application form based on the information collected by the student. This makes it possible to propose an individual entry strategy to the student.

[0071] The entry processing unit uses an emotion estimation function to analyze the emotions felt by students when collecting information and provides information to elicit positive emotions. The entry processing unit, for example, uses the emotion estimation function to analyze the emotions felt by students when collecting information in real time and provides information to elicit positive emotions. For example, it displays an encouraging message. The emotion estimation function uses an emotion analysis model or natural language processing technology to analyze the emotions felt by students when collecting information. For example, the emotion estimation function analyzes the emotions felt by students when collecting information in real time and provides information to elicit positive emotions. This makes it possible to provide information to elicit positive emotions in students.

[0072] The entry processing unit shares the information collected by students with other students and promotes information exchange. The entry processing unit, for example, builds a platform for students to share the information they have collected with other students. For example, it provides a bulletin board or chat function for information sharing. The entry processing unit shares the information collected by students with other students and promotes information exchange. For example, it builds a platform for students to share the information they have collected with other students. This can promote information exchange between students.

[0073] The entry processing unit provides appropriate career counseling based on the information collected by the student. The entry processing unit, for example, builds a system that provides appropriate career counseling based on the information collected by the student. For example, it provides career advice based on the student's areas of interest and skills. The entry processing unit provides appropriate career counseling based on the information collected by the student. For example, it provides appropriate career counseling based on the information collected by the student. In this way, appropriate career counseling can be provided to the student.

[0074] The entry processing unit uses an emotion estimation function to analyze students' emotional reactions to the collected information and proposes an optimal method of providing the information. The entry processing unit, for example, uses the emotion estimation function to analyze students' emotional reactions to the collected information in real time and proposes an optimal method of providing the information. For example, it recommends a method of expression that will elicit a positive reaction. The emotion estimation function analyzes students' emotional reactions to the collected information using an emotion analysis model or natural language processing technology. For example, the emotion estimation function analyzes students' emotional reactions to the collected information in real time and recommends a method of expression that will elicit a positive reaction. This makes it possible to analyze students' emotional reactions and propose an optimal method of providing the information.

[0075] The initial selection department uses generative AI to automatically evaluate students' answers and provide detailed feedback. The initial selection department, for example, builds a system that uses generative AI to automatically evaluate students' answers and provide detailed feedback. For example, it suggests specific improvements to the content and expression of the answer. The generative AI uses natural language generation technology and machine learning models to automatically evaluate students' answers and provide detailed feedback. For example, the generative AI analyzes students' answers and suggests specific improvements to the content and expression. This makes it possible to automatically evaluate students' answers and provide detailed feedback.

[0076] The initial selection department automatically optimizes the content of questions asked in the initial selection process to more accurately evaluate students' aptitude. The initial selection department, for example, uses generative AI to automatically optimize the content of questions asked in the initial selection process and builds a system to more accurately evaluate students' aptitude. For example, the question content is adjusted based on students' answers. The generative AI uses natural language generation technology and machine learning models to automatically optimize the content of questions asked in the initial selection process to more accurately evaluate students' aptitude. For example, the generative AI adjusts the question content based on students' answers. This makes it possible to more accurately evaluate students' aptitude.

[0077] The initial selection unit uses an emotion estimation function to analyze the company's emotional response to the student's answers and provides advice to elicit a positive evaluation. The initial selection unit, for example, uses the emotion estimation function to analyze the company's emotional response to the student's answers and provides advice to elicit a positive evaluation. For example, adjusting the tone of the answer. The emotion estimation function uses an emotion analysis model or natural language processing technology to analyze the company's emotional response to the student's answers. For example, the emotion estimation function analyzes the company's emotional response to the student's answers and provides advice to elicit a positive evaluation. This makes it possible to analyze the company's emotional response and provide advice to elicit a positive evaluation.

[0078] The initial selection department links the results of the initial selection with other selection stages to streamline the entire selection process. The initial selection department, for example, links the results of the initial selection with the second interview and final interview, and builds a system to streamline the entire selection process. For example, the content of the questions for the next stage is adjusted based on the evaluation of the initial selection. The initial selection department links the results of the initial selection with other selection stages to streamline the entire selection process. For example, the results of the initial selection with the second interview and final interview, and streamline the entire selection process. This makes it possible to streamline the entire selection process.

[0079] The initial selection department provides appropriate feedback to students based on the results of the initial selection and supports their preparation for the next selection stage. The initial selection department, for example, builds a system that provides appropriate feedback to students based on the results of the initial selection and supports their preparation for the next selection stage. For example, it specifically points out the strengths and weaknesses of students. The initial selection department provides appropriate feedback to students based on the results of the initial selection and supports their preparation for the next selection stage. For example, it provides appropriate feedback to students based on the results of the initial selection and supports their preparation for the next selection stage. This makes it possible to provide appropriate feedback to students and support their preparation for the next selection stage.

[0080] The first selection unit uses an emotion estimation function to analyze students' emotional reactions to the questions in the first selection and proposes optimal questions. The first selection unit, for example, uses the emotion estimation function to analyze students' emotional reactions to the questions in the first selection in real time and proposes optimal questions. For example, it generates questions that will elicit a positive response. The emotion estimation function uses an emotion analysis model or natural language processing technology to analyze students' emotional reactions to the questions in the first selection. For example, the emotion estimation function analyzes students' emotional reactions to the questions in the first selection in real time and generates questions that will elicit a positive response. This makes it possible to analyze students' emotional reactions and propose optimal questions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] Job-hunting platforms can also be equipped with a skills assessment module that evaluates students' skill sets and suggests appropriate training programs. For example, the module analyzes the skills listed on a student's application form and, if a specific skill is lacking, suggests online courses or workshops to improve that skill. The skills assessment module can also compare a student's skill set with the skills required by companies and evaluate the degree of match. This allows students to receive a specific action plan for improving their skills, and companies can efficiently find students with the skills they are looking for.

[0083] The common application form reception unit can also be equipped with a career path suggestion unit that suggests appropriate career paths to students based on the contents of the application form. For example, it can suggest possible future career paths based on the student's areas of interest and skills. The career path suggestion unit can also analyze past data and refer to the career paths pursued by students with similar skill sets. This allows students to clarify their own career direction and set specific goals.

[0084] The common application form reception unit can also use the emotion estimation function to analyze students' emotions when they are filling out their application forms and suggest relaxation methods to reduce stress. For example, if a student feels stressed while filling out their application form, a message recommending deep breathing or taking a short break will be displayed. The emotion estimation function can also suggest music or videos that will help students relax. This allows students to fill out their application forms in a relaxed state, resulting in better content.

[0085] The common application form reception unit can also use the emotion estimation function to analyze students' emotions when they are filling out their application forms and provide advice to improve their motivation. For example, if a student's motivation drops while they are filling out their application form, it can display success stories or encouraging messages. The emotion estimation function can also display messages that remind students of past achievements and positive experiences. This allows students to maintain their motivation while filling out their application forms.

[0086] The common application form reception department can also have a mentor introduction department that introduces appropriate mentors to students based on the contents of their application forms. For example, it can introduce seniors and professionals active in the same field based on the student's field of interest and career goals. The mentor introduction department can also evaluate the degree of match between students and mentors and recommend the most suitable mentor. This allows students to receive specific advice about their careers and conduct a more effective job search.

[0087] The common application form reception unit can also use the emotion estimation function to analyze students' emotions when they are filling out their application forms and provide advice to reduce negative emotions. For example, if a student feels anxious or nervous while filling out their application form, it can suggest simple exercises or meditation to help them relax. The emotion estimation function can also display messages that remind students of past successful experiences. This helps students reduce negative emotions and fill out their application forms with more confidence.

[0088] The common application form reception department can also have a volunteer suggestion department that suggests appropriate volunteer activities and social contribution opportunities to students based on the contents of their application forms. For example, it can suggest local volunteer activities and social contribution projects based on students' areas of interest and skills. The volunteer suggestion department can also provide advice on how to reflect students' volunteer experiences in their application forms. This allows students to put their skills to practical use and grow through social contribution.

[0089] The company information registration department can also have a cultural information provision department that provides students with information about corporate culture and working styles based on company information. For example, it can introduce the company's corporate culture, working style, and team atmosphere. The cultural information provision department can also collect interviews and personal stories from company employees and provide students with real information. This allows students to understand corporate culture and working styles and choose a company that suits them.

[0090] The company information registration department can use the emotion estimation function to analyze students' emotional reactions to company information and suggest ways to improve the information to the company. For example, if a company's information evokes negative emotions in students, the department can identify the cause and suggest ways to improve the information. The emotion estimation function can also provide specific advice on how to make the company's information evoke positive emotions in students. This allows companies to provide more attractive information to students.

[0091] The Company Information Registration Department can also have a Growth Strategy Information Providing Department that provides students with information on a company's growth strategy and future vision based on company information. For example, it can introduce a company's medium- to long-term growth strategy and new business development plans. The Growth Strategy Information Providing Department can also collect information on a company's future vision and provide it to students. This allows students to understand the future potential of a company and select a company based on their own career plans.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The common application form reception department accepts common application forms. For example, a student can create a common application form once and upload it to the platform to apply to multiple companies. Step 2: The company information registration unit registers company information, such as company overview, recruitment requirements, employee benefits, etc. Step 3: The question answering section uses the generation AI to answer questions from students. For example, in response to the question, "Please tell me about your company's employee benefits," the generation AI answers, "Our company offers employee benefits such as health insurance, a pension system, and company trips." Step 4: The entry processing unit allows students to apply to companies. For example, students browse company information and submit application forms to companies that interest them. Step 5: The first selection department uses the generation AI to ask questions to the students who have applied, and conducts the first selection process. For example, the generation AI sends the question "Please tell us about yourself," to the student, who then enters their answer. The generation AI analyzes the answer and provides an evaluation to the company.

[0094] 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.

[0095] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0100] 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0115] 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0135] 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.

[0136] 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.

[0137] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a common entry sheet reception unit that receives common entry sheets; a company information registration department that registers company information; A question answering section that uses generative AI to answer questions from students; an entry processing section where students apply to companies; The first selection department uses a generation AI to ask questions to students who have applied, and conducts the first selection process. A system characterized by:

2. The common entry sheet reception unit Using emotion estimation function, the company analyzes the student's emotions when filling out the application form and provides advice on how to bring out positive emotions.

2. The system of claim 1.

3. The company information registration unit Using generative AI, the company's information is automatically updated, and the latest information is always provided.

2. The system of claim 1.

4. The entry processing unit Using generative AI, the information collected by the student is automatically organized and the most suitable application destination is suggested.

2. The system of claim 1.

5. The first selection section is Using generative AI to automatically evaluate the student's answers and provide detailed feedback 2. The system of claim 1.

6. The common entry sheet reception unit Using an emotion estimation function, the company's emotional response to the contents of the application form is analyzed, and the optimal way to write the application form is suggested to the student.

2. The system of claim 1.

7. The company information registration unit Using a sentiment estimation function to analyze the emotional tone of the company's response to the student's question and provide advice on how to create a positive response.

2. The system of claim 1.

8. The entry processing unit Using an emotion estimation function, the emotions felt by the student when collecting information are analyzed, and information is provided to elicit positive emotions.

2. The system of claim 1.

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