System

Abstract

An object of a system according to an embodiment is to efficiently create job offer information.SOLUTION: A system includes a job offer information input unit, a generation unit, and an output unit. The job offer information input part inputs the outline of the job offer information. The generation part generates the job offer information on the basis of the outline of the job offer information input by the job offer information input part. The output unit outputs the job offer information generated by the generation unit.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] Conventional technology has the problem that creating job information is time-consuming and labor-intensive, making it difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently create job information. [Means for solving the problem]

[0006] The system according to the embodiment includes a job information input unit, a generation unit, and an output unit. The job information input unit inputs an outline of the job information. The generation unit generates job information based on the outline of the job information input by the job information input unit. The output unit outputs the job information generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create job information. [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 touch of 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 automatic job information generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates attractive job information based on the outline of the job information entered by a human resources personnel. As a result, the automatic job information generation system can significantly reduce the time required to create job information and attract more applicants.

[0029] The job information automatic generation system according to the embodiment includes a job information input unit, a generation unit, and an output unit. The job information input unit inputs an outline of the job information. For example, information such as job type, required skills, work location, salary, working hours, and employee benefits is input. The generation unit generates job information based on the outline of the job information input by the job information input unit. For example, the generation AI learns from past job information and industry trends and generates optimal expressions and content based on the input information. The output unit outputs the job information generated by the generation unit. For example, the generated job information is automatically published on a company's website or a job portal site. This allows the job information automatic generation system to efficiently automatically generate job information.

[0030] The job information input unit can suggest related skills and qualifications based on the input information and complement the input content. For example, when a job title is entered into a web form, the generation AI automatically suggests skills and qualifications related to that job title. For example, if "software engineer" is entered, skills such as "Python," "Java," and "database management" are suggested. Furthermore, when a work location is entered into the web form, the generation AI suggests skills and qualifications commonly required in that area. For example, if "Tokyo" is entered, suggestions such as "Japanese language proficiency" and "project management experience in urban areas" are suggested. Furthermore, when a salary is entered into the web form, the generation AI suggests skills and qualifications that match that salary range. For example, if "annual salary of 6 million yen" is entered, suggestions such as "intermediate or higher programming skills" and "project leadership experience" are suggested. This streamlines the entry of job information and provides more accurate information.

[0031] The job information input unit is equipped with a voice input function, allowing human resources personnel to input job information by voice. For example, the job information input unit adds a voice input function to a web form, allowing human resources personnel to input job information by speaking into a microphone. For example, when a person speaks, "Software engineer wanted, required skills: Python and Java," the speech is automatically converted to text. The job information input unit also uses the voice input function to allow multiple fields to be entered at once. For example, when a person speaks, "Work location: Tokyo, annual salary: 6 million yen, flexible working hours," each field is automatically filled in. The job information input unit also uses voice recognition technology to convert the input speech into text in real time and reflect it in the web form. For example, when a person speaks, "Health insurance provided," the corresponding field is immediately filled in. This allows for quick and efficient input of job information using voice input.

[0032] The job information input unit can be equipped with an auto-completion function that references past job postings and industry trends. For example, the job information input unit adds a function to a web form that references past job postings and performs auto-completion based on the input content. For example, if "software engineer" is entered, skills such as "Python" and "Java" are automatically completed from past job postings. The job information input unit also adds a function that references industry trends and automatically completes the latest skills and qualifications. For example, if "data scientist" is entered, trending skills such as "machine learning" and "data analysis" are automatically completed. The job information input unit also uses the auto-completion function to suggest related information based on the input content. For example, if "flexible working hours" is entered, related information such as "remote work available" and "flexible working hours" is automatically completed. This allows for more accurate job information to be provided by utilizing past data and trends.

[0033] The job information input unit has a function for comparing with the job information of other companies, making it possible to clarify points of differentiation from competitors. For example, the job information input unit adds a function to a web form that references the job information of other companies and compares the input content. For example, if "software engineer" is input, the input content will be compared with the job information of other companies and points of differentiation will be displayed. The job information input unit also adds a function that makes suggestions to improve the input content based on the job information of competitors. For example, if "annual salary of 6 million yen" is input, appropriate suggestions will be made by comparing with the salary ranges of other companies. The job information input unit also adds a function that clarifies the strengths and weaknesses of the input content by comparing with the job information of other companies. For example, if "flexible working hours" is input, points of differentiation will be displayed in comparison with the working style of other companies. This allows the company to differentiate from competitors and provide more attractive job information.

[0034] The generation unit can generate job listings that reflect a company's vision and mission. For example, the generation AI learns a company's vision and mission and reflects this in the job listings it generates. For example, it incorporates phrases such as "Our vision is to transform society through technology" into the job listings. The generation unit also customizes the job listings based on the company's mission statement. For example, it reflects the company's mission as "a company that promotes sustainable development." The generation AI also adjusts the tone and style of the job listings based on the company's vision and mission. For example, it emphasizes the company's image as "a company that pursues innovative technology." This makes it possible to provide job listings that reflect the company's vision and mission.

[0035] The generation unit can generate job postings that incorporate storytelling elements to attract applicants' interest. For example, the generation AI uses storytelling technology to incorporate elements that attract applicants' interest into the job posting. For example, it adds a story such as, "Our team works at the forefront of technological innovation." The generation unit also incorporates storytelling elements into the job posting based on the company's history and success stories. For example, it adds a phrase such as, "We have successfully completed many innovative projects over the past 10 years." The generation unit also incorporates storytelling elements into the job posting that elicit applicants' empathy. For example, it adds a phrase such as, "Our projects are having a major impact on society." This makes it possible to provide job postings that attract applicants' interest.

[0036] The generation unit has a function for incorporating videos and images into job information, making it possible to provide visually appealing job information. For example, the generation unit adds a function in which the generation AI automatically selects and incorporates videos and images related to the job information. For example, the generation unit includes a company introduction video and office photos in the job information. The generation unit also attracts applicants' attention by having the generation AI add visual elements to the job information. For example, the generation unit incorporates visual presentations of projects and team photos. The generation unit also provides visually appealing job information by having the generation AI incorporate videos and images into the job information. For example, the generation unit includes videos of company events and employee interviews. This makes it possible to provide visually appealing job information.

[0037] The generation unit can add interactive elements to job information to attract applicants' interest. For example, the generation AI adds an interactive quiz to the job information to attract applicants' interest. For example, it includes a quiz such as, "Is your skill set suitable for this position?" The generation unit also adds a questionnaire to the job information to attract applicants' interest. For example, it includes a questionnaire such as, "What kind of work environment are you looking for?" The generation unit also attracts applicants' interest by adding interactive elements to the job information. For example, it includes an interactive question such as, "Please tell us why you are applying for this position." This makes it possible to provide job information that attracts applicants' interest through interactive elements.

[0038] The generation unit can generate job information that reflects a company's past recruitment data and applicant feedback. For example, the generation unit reflects a company's past recruitment data in the input data to the generation AI. For example, it generates similar job information based on past successful recruitment cases. The generation unit also collects applicant feedback and reflects it in the input data to the generation AI. For example, it generates job information based on feedback such as "this job information was attractive." The generation unit also integrates a company's past recruitment data with applicant feedback and uses this as input data to the generation AI. For example, it generates job information that reflects past success cases and applicant preferences. This makes it possible to provide more accurate job information by utilizing past data and feedback.

[0039] The generation unit can generate job information that reflects the latest industry trends and market trends. For example, the generation unit reflects the latest industry trends in the input data to the generation AI. For example, the generation unit generates job information based on information such as, "With advances in AI technology, demand for machine learning engineers is increasing." The generation unit also provides input data that reflects market trends to the generation AI to generate competitive job information. For example, the generation unit reflects content such as, "With the spread of remote work, we will generate job information that is remote-compatible." The generation unit also collects the latest industry trends and market trends in real time and uses them as input data for the generation AI. For example, the generation unit provides content such as, "Generate job information that reflects the latest technological trends." This makes it possible to provide job information that reflects the latest trends and market trends.

[0040] The generation unit can generate job information that reflects job information from other companies and industry benchmark data. For example, the generation unit reflects job information from other companies in the input data to the generation AI. For example, more attractive job information is generated based on the job information of competitors. The generation unit also collects industry benchmark data and uses it as input data to the generation AI. For example, it reflects content such as "generating job information based on the industry average salary range." The generation unit also integrates job information from other companies and industry benchmark data and uses it as input data to the generation AI. For example, it generates job information that incorporates the strengths of competitors. This makes it possible to provide competitive job information by utilizing data and benchmarks from other companies.

[0041] The generation unit can generate job information that reflects a company's brand image and corporate culture. For example, the generation unit reflects a company's brand image in the input data to the generation AI. For example, job information is generated based on an image as a "company that pursues innovative technology." The generation unit also provides input data that reflects the company's culture to the generation AI to increase its appeal to applicants. For example, job information is generated based on a "corporate culture that emphasizes teamwork." The generation unit also collects a company's brand image and corporate culture in real time and uses this as input data for the generation AI. For example, job information is generated based on an image as a "company that promotes sustainable development." This makes it possible to provide job information that reflects a company's brand image and culture.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The job information input unit can display related industry news and trends based on the input information. For example, when a job type is input, the latest industry news and trends related to that job type are displayed. For example, when "software engineer" is input, news such as "latest advances in AI technology" and "the spread of remote work" are displayed. In addition, when a work location is input, the job information input unit displays the latest economic trends and employment situation in that area. For example, when "Tokyo" is input, information such as "the growth rate of the IT industry in Tokyo" and "the opening of a new technology park" is displayed. Furthermore, when a salary is input, the job information input unit displays market trends related to that salary range. For example, when "annual salary of 6 million yen" is input, information such as "competitive skill sets in this salary range" and "job trends in the same salary range" is displayed. This allows users to refer to the latest industry information when inputting job information.

[0044] The job information input unit can suggest related educational institutions and training programs based on the input information. For example, when a job type is input, the unit suggests educational institutions and training programs related to that job type. For example, when "data scientist" is input, suggestions such as "online data science courses" and "machine learning vocational schools" are made. Furthermore, when required skills are input, the job information input unit suggests training programs to acquire those skills. For example, when "Python" is input, suggestions such as "online Python programming courses" and "Python practical workshops" are made. Furthermore, when a work location is input, the job information input unit suggests educational institutions and training programs offered in that area. For example, when "Osaka" is input, suggestions such as "IT training centers in Osaka" and "data science schools in Osaka" are made. This allows users to refer to related educational institutions and training programs when inputting job information.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The job information input unit inputs an outline of the job information, such as the job type, required skills, work location, salary, working hours, and employee benefits. Step 2: The generation unit generates job information based on the job information summary entered by the job information input unit. For example, the generation AI learns from past job information and industry trends, and generates optimal expressions and content based on the entered information. Step 3: The output unit outputs the job information generated by the generation unit. For example, the generated job information is automatically published on a company website or a job portal site.

[0047] (Example 2) The automatic job information generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates attractive job information based on the outline of the job information entered by a human resources personnel. As a result, the automatic job information generation system can significantly reduce the time required to create job information and attract more applicants.

[0048] The job information automatic generation system according to the embodiment includes a job information input unit, a generation unit, and an output unit. The job information input unit inputs an outline of the job information. For example, information such as job type, required skills, work location, salary, working hours, and employee benefits is input. The generation unit generates job information based on the outline of the job information input by the job information input unit. For example, the generation AI learns from past job information and industry trends and generates optimal expressions and content based on the input information. The output unit outputs the job information generated by the generation unit. For example, the generated job information is automatically published on a company's website or a job portal site. This allows the job information automatic generation system to efficiently automatically generate job information.

[0049] The job information input unit can suggest related skills and qualifications based on the input information and complement the input content. For example, when a job title is entered into a web form, the generation AI automatically suggests skills and qualifications related to that job title. For example, if "software engineer" is entered, skills such as "Python," "Java," and "database management" are suggested. Furthermore, when a work location is entered into the web form, the generation AI suggests skills and qualifications commonly required in that area. For example, if "Tokyo" is entered, suggestions such as "Japanese language proficiency" and "project management experience in urban areas" are suggested. Furthermore, when a salary is entered into the web form, the generation AI suggests skills and qualifications that match that salary range. For example, if "annual salary of 6 million yen" is entered, suggestions such as "intermediate or higher programming skills" and "project leadership experience" are suggested. This streamlines the entry of job information and provides more accurate information.

[0050] The job information input unit is equipped with a voice input function, allowing human resources personnel to input job information by voice. For example, the job information input unit adds a voice input function to a web form, allowing human resources personnel to input job information by speaking into a microphone. For example, when a person speaks, "Software engineer wanted, required skills: Python and Java," the speech is automatically converted to text. The job information input unit also uses the voice input function to allow multiple fields to be entered at once. For example, when a person speaks, "Work location: Tokyo, annual salary: 6 million yen, flexible working hours," each field is automatically filled in. The job information input unit also uses voice recognition technology to convert the input speech into text in real time and reflect it in the web form. For example, when a person speaks, "Health insurance provided," the corresponding field is immediately filled in. This allows for quick and efficient input of job information using voice input.

[0051] The job information input unit can use the emotion estimation function to analyze the emotions of the human resources personnel when they are entering information and provide an interface that draws out positive emotions. For example, the job information input unit can use the emotion estimation function to analyze the facial expressions and tone of voice of the human resources personnel when they are entering information and provide an interface that draws out positive emotions. For example, an encouraging message can be displayed if a smile is detected. The job information input unit can also use the emotion estimation function to detect stress or fatigue during entry and provide an interface that helps the user relax. For example, relaxing background music can be played. The job information input unit can also use the emotion estimation function to provide positive feedback on the entered content in real time. For example, a message such as "That's an excellent skill set!" can be displayed. This takes the emotions of the human resources personnel into consideration and provides a better entry environment.

[0052] The job information input unit can be equipped with an auto-completion function that references past job postings and industry trends. For example, the job information input unit adds a function to a web form that references past job postings and performs auto-completion based on the input content. For example, if "software engineer" is entered, skills such as "Python" and "Java" are automatically completed from past job postings. The job information input unit also adds a function that references industry trends and automatically completes the latest skills and qualifications. For example, if "data scientist" is entered, trending skills such as "machine learning" and "data analysis" are automatically completed. The job information input unit also uses the auto-completion function to suggest related information based on the input content. For example, if "flexible working hours" is entered, related information such as "remote work available" and "flexible working hours" is automatically completed. This allows for more accurate job information to be provided by utilizing past data and trends.

[0053] The job information input unit has a function for comparing with the job information of other companies, making it possible to clarify points of differentiation from competitors. For example, the job information input unit adds a function to a web form that references the job information of other companies and compares the input content. For example, if "software engineer" is input, the input content will be compared with the job information of other companies and points of differentiation will be displayed. The job information input unit also adds a function that makes suggestions to improve the input content based on the job information of competitors. For example, if "annual salary of 6 million yen" is input, appropriate suggestions will be made by comparing with the salary ranges of other companies. The job information input unit also adds a function that clarifies the strengths and weaknesses of the input content by comparing with the job information of other companies. For example, if "flexible working hours" is input, points of differentiation will be displayed in comparison with the working style of other companies. This allows the company to differentiate from competitors and provide more attractive job information.

[0054] The job information input unit can use the emotion estimation function to provide real-time feedback on the input content. The job information input unit, for example, uses the emotion estimation function to provide real-time feedback on the input content. For example, if "annual salary of 6 million yen" is input, feedback such as "This salary range is higher than the market average" is displayed. The job information input unit also uses the emotion estimation function to provide positive feedback on the input content. For example, if "Python and Java skills are required," a message such as "That's an excellent skill set!" is displayed. The job information input unit also uses the emotion estimation function to make improvement suggestions for the input content in real time. For example, if "Work location is within Tokyo," a suggestion such as "Adding a remote work option would be even more attractive" is displayed. This allows for real-time feedback on the input content and generates better job information.

[0055] The generation unit can generate job listings that reflect a company's vision and mission. For example, the generation AI learns a company's vision and mission and reflects this in the job listings it generates. For example, it incorporates phrases such as "Our vision is to transform society through technology" into the job listings. The generation unit also customizes the job listings based on the company's mission statement. For example, it reflects the company's mission as "a company that promotes sustainable development." The generation AI also adjusts the tone and style of the job listings based on the company's vision and mission. For example, it emphasizes the company's image as "a company that pursues innovative technology." This makes it possible to provide job listings that reflect the company's vision and mission.

[0056] The generation unit can generate job postings that incorporate storytelling elements to attract applicants' interest. For example, the generation AI uses storytelling technology to incorporate elements that attract applicants' interest into the job posting. For example, it adds a story such as, "Our team works at the forefront of technological innovation." The generation unit also incorporates storytelling elements into the job posting based on the company's history and success stories. For example, it adds a phrase such as, "We have successfully completed many innovative projects over the past 10 years." The generation unit also incorporates storytelling elements into the job posting that elicit applicants' empathy. For example, it adds a phrase such as, "Our projects are having a major impact on society." This makes it possible to provide job postings that attract applicants' interest.

[0057] The generation unit can use the emotion estimation function to evaluate the emotional impact that the generated job information has on applicants and select optimal expressions. The generation unit, for example, uses the emotion estimation function to evaluate the emotional impact that the generated job information has on applicants. For example, it makes an evaluation such as, "This expression evokes positive emotions in applicants." The generation unit also uses the emotion estimation function to optimize the expression of the generated job information. For example, it adjusts the expression based on feedback such as, "This expression is attractive to applicants." The generation unit also uses the emotion estimation function to evaluate the emotional impact of the generated job information in real time and select optimal expressions. For example, it selects expressions based on an evaluation such as, "This expression has a positive impact on applicants." This makes it possible to provide job information using optimal expressions to applicants.

[0058] The generation unit has a function for incorporating videos and images into job information, making it possible to provide visually appealing job information. For example, the generation unit adds a function in which the generation AI automatically selects and incorporates videos and images related to the job information. For example, the generation unit includes a company introduction video and office photos in the job information. The generation unit also attracts applicants' attention by having the generation AI add visual elements to the job information. For example, the generation unit incorporates visual presentations of projects and team photos. The generation unit also provides visually appealing job information by having the generation AI incorporate videos and images into the job information. For example, the generation unit includes videos of company events and employee interviews. This makes it possible to provide visually appealing job information.

[0059] The generation unit can add interactive elements to job information to attract applicants' interest. For example, the generation AI adds an interactive quiz to the job information to attract applicants' interest. For example, it includes a quiz such as, "Is your skill set suitable for this position?" The generation unit also adds a questionnaire to the job information to attract applicants' interest. For example, it includes a questionnaire such as, "What kind of work environment are you looking for?" The generation unit also attracts applicants' interest by adding interactive elements to the job information. For example, it includes an interactive question such as, "Please tell us why you are applying for this position." This makes it possible to provide job information that attracts applicants' interest through interactive elements.

[0060] The generation unit uses the emotion estimation function to monitor the emotional reactions of applicants to the generated job information in real time, and is able to provide optimal job information. The generation unit, for example, uses the emotion estimation function to monitor the emotional reactions of applicants to the generated job information in real time. For example, it analyzes the applicant's facial expressions and tone of voice and calculates an emotion score. The generation unit also optimizes the generated job information based on the applicant's emotional reaction data. For example, it prioritizes the use of expressions that have a high number of positive emotional reactions. The generation unit also uses the emotion estimation function to monitor the emotional reactions of applicants in real time, and provides optimal job information. For example, it prioritizes the display of job information with a high emotion score. This makes it possible to monitor the emotional reactions of applicants in real time and provide optimal job information.

[0061] The generation unit can generate job information that reflects a company's past recruitment data and applicant feedback. For example, the generation unit reflects a company's past recruitment data in the input data to the generation AI. For example, it generates similar job information based on past successful recruitment cases. The generation unit also collects applicant feedback and reflects it in the input data to the generation AI. For example, it generates job information based on feedback such as "this job information was attractive." The generation unit also integrates a company's past recruitment data with applicant feedback and uses this as input data to the generation AI. For example, it generates job information that reflects past success cases and applicant preferences. This makes it possible to provide more accurate job information by utilizing past data and feedback.

[0062] The generation unit can generate job information that reflects the latest industry trends and market trends. For example, the generation unit reflects the latest industry trends in the input data to the generation AI. For example, the generation unit generates job information based on information such as, "With advances in AI technology, demand for machine learning engineers is increasing." The generation unit also provides input data that reflects market trends to the generation AI to generate competitive job information. For example, the generation unit reflects content such as, "With the spread of remote work, we will generate job information that is remote-compatible." The generation unit also collects the latest industry trends and market trends in real time and uses them as input data for the generation AI. For example, the generation unit provides content such as, "Generate job information that reflects the latest technological trends." This makes it possible to provide job information that reflects the latest trends and market trends.

[0063] The generation unit can use the emotion estimation function to evaluate the emotional impact that input data to the generation AI has on applicants and select optimal input data. For example, the generation unit uses the emotion estimation function to evaluate the emotional impact that input data to the generation AI has on applicants. For example, it makes an evaluation such as, "This skill set evokes positive emotions in applicants." The generation unit also uses the emotion estimation function to evaluate the emotional impact of the input data in real time and select optimal input data. For example, it adjusts the input data based on feedback such as, "This work location is attractive to applicants." The generation unit also uses the emotion estimation function to evaluate the emotional impact of input data to the generation AI and select optimal input data. For example, it selects input data based on an evaluation such as, "This salary range has a positive impact on applicants." This makes it possible to provide job information using optimal input data to applicants.

[0064] The generation unit can generate job information that reflects job information from other companies and industry benchmark data. For example, the generation unit reflects job information from other companies in the input data to the generation AI. For example, more attractive job information is generated based on the job information of competitors. The generation unit also collects industry benchmark data and uses it as input data to the generation AI. For example, it reflects content such as "generating job information based on the industry average salary range." The generation unit also integrates job information from other companies and industry benchmark data and uses it as input data to the generation AI. For example, it generates job information that incorporates the strengths of competitors. This makes it possible to provide competitive job information by utilizing data and benchmarks from other companies.

[0065] The generation unit can generate job information that reflects a company's brand image and corporate culture. For example, the generation unit reflects a company's brand image in the input data to the generation AI. For example, job information is generated based on an image as a "company that pursues innovative technology." The generation unit also provides input data that reflects the company's culture to the generation AI to increase its appeal to applicants. For example, job information is generated based on a "corporate culture that emphasizes teamwork." The generation unit also collects a company's brand image and corporate culture in real time and uses this as input data for the generation AI. For example, job information is generated based on an image as a "company that promotes sustainable development." This makes it possible to provide job information that reflects a company's brand image and culture.

[0066] The generation unit uses the emotion estimation function to monitor the emotional reactions of applicants to the input data to the generation AI in real time and provide optimal input data. The generation unit, for example, uses the emotion estimation function to monitor the emotional reactions of applicants to the input data to the generation AI in real time. For example, it analyzes the applicant's facial expressions and tone of voice and calculates an emotion score. The generation unit also optimizes the input data to the generation AI based on the applicant's emotional reaction data. For example, it prioritizes the use of input data with a high number of positive emotional reactions. The generation unit also uses the emotion estimation function to monitor the emotional reactions of applicants in real time and provide optimal input data. For example, it prioritizes the use of input data with a high emotion score. This makes it possible to monitor the emotional reactions of applicants in real time and provide job information using optimal input data.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The job information input unit can display related industry news and trends based on the input information. For example, when a job type is input, the latest industry news and trends related to that job type are displayed. For example, when "software engineer" is input, news such as "latest advances in AI technology" and "the spread of remote work" are displayed. In addition, when a work location is input, the job information input unit displays the latest economic trends and employment situation in that area. For example, when "Tokyo" is input, information such as "the growth rate of the IT industry in Tokyo" and "the opening of a new technology park" is displayed. Furthermore, when a salary is input, the job information input unit displays market trends related to that salary range. For example, when "annual salary of 6 million yen" is input, information such as "competitive skill sets in this salary range" and "job trends in the same salary range" is displayed. This allows users to refer to the latest industry information when inputting job information.

[0069] The job information input unit can suggest related educational institutions and training programs based on the input information. For example, when a job type is input, the unit suggests educational institutions and training programs related to that job type. For example, when "data scientist" is input, suggestions such as "online data science courses" and "machine learning vocational schools" are made. Furthermore, when required skills are input, the job information input unit suggests training programs to acquire those skills. For example, when "Python" is input, suggestions such as "online Python programming courses" and "Python practical workshops" are made. Furthermore, when a work location is input, the job information input unit suggests educational institutions and training programs offered in that area. For example, when "Osaka" is input, suggestions such as "IT training centers in Osaka" and "data science schools in Osaka" are made. This allows users to refer to related educational institutions and training programs when inputting job information.

[0070] The job information input unit can use the emotion estimation function to analyze the user's emotions regarding the input content and make suggestions to improve the input content. For example, if the user is dissatisfied with the input content, the unit identifies the cause and makes suggestions for improvement. For example, if dissatisfaction is detected when the user enters "annual salary 6 million yen," the unit makes a suggestion such as "consider raising your annual salary to 7 million yen." Furthermore, if the user is satisfied with the input content, the unit makes a suggestion to emphasize that content. For example, if satisfaction is detected when the user enters "flexible working hours," the unit makes a suggestion such as "please emphasize the details of the flexible working hours more." Furthermore, if the user shows interest in the input content, the unit makes a suggestion to further enhance the content. For example, if interest is detected when the user enters "remote work available," the unit makes a suggestion such as "please add specific conditions for remote work." This makes it possible to input job information that takes the user's emotions into consideration.

[0071] The job information input unit can use the emotion estimation function to analyze the user's emotions regarding the input content and make suggestions to improve the input content. For example, if the user is dissatisfied with the input content, the unit identifies the cause and makes suggestions for improvement. For example, if dissatisfaction is detected when the user enters "annual salary 6 million yen," the unit makes a suggestion such as "consider raising your annual salary to 7 million yen." Furthermore, if the user is satisfied with the input content, the unit makes a suggestion to emphasize that content. For example, if satisfaction is detected when the user enters "flexible working hours," the unit makes a suggestion such as "please emphasize the details of the flexible working hours more." Furthermore, if the user shows interest in the input content, the unit makes a suggestion to further enhance the content. For example, if interest is detected when the user enters "remote work available," the unit makes a suggestion such as "please add specific conditions for remote work." This makes it possible to input job information that takes the user's emotions into consideration.

[0072] The job information input unit can use the emotion estimation function to analyze the user's emotions regarding the input content and make suggestions to improve the input content. For example, if the user is dissatisfied with the input content, the unit identifies the cause and makes suggestions for improvement. For example, if dissatisfaction is detected when the user enters "annual salary 6 million yen," the unit makes a suggestion such as "consider raising your annual salary to 7 million yen." Furthermore, if the user is satisfied with the input content, the unit makes a suggestion to emphasize that content. For example, if satisfaction is detected when the user enters "flexible working hours," the unit makes a suggestion such as "please emphasize the details of the flexible working hours more." Furthermore, if the user shows interest in the input content, the unit makes a suggestion to further enhance the content. For example, if interest is detected when the user enters "remote work available," the unit makes a suggestion such as "please add specific conditions for remote work." This makes it possible to input job information that takes the user's emotions into consideration.

[0073] The job information input unit can use the emotion estimation function to analyze the user's emotions regarding the input content and make suggestions to improve the input content. For example, if the user is dissatisfied with the input content, the unit identifies the cause and makes suggestions for improvement. For example, if dissatisfaction is detected when the user enters "annual salary 6 million yen," the unit makes a suggestion such as "consider raising your annual salary to 7 million yen." Furthermore, if the user is satisfied with the input content, the unit makes a suggestion to emphasize that content. For example, if satisfaction is detected when the user enters "flexible working hours," the unit makes a suggestion such as "please emphasize the details of the flexible working hours more." Furthermore, if the user shows interest in the input content, the unit makes a suggestion to further enhance the content. For example, if interest is detected when the user enters "remote work available," the unit makes a suggestion such as "please add specific conditions for remote work." This makes it possible to input job information that takes the user's emotions into consideration.

[0074] The job information input unit can use the emotion estimation function to analyze the user's emotions regarding the input content and make suggestions to improve the input content. For example, if the user is dissatisfied with the input content, the unit identifies the cause and makes suggestions for improvement. For example, if dissatisfaction is detected when the user enters "annual salary 6 million yen," the unit makes a suggestion such as "consider raising your annual salary to 7 million yen." Furthermore, if the user is satisfied with the input content, the unit makes a suggestion to emphasize that content. For example, if satisfaction is detected when the user enters "flexible working hours," the unit makes a suggestion such as "please emphasize the details of the flexible working hours more." Furthermore, if the user shows interest in the input content, the unit makes a suggestion to further enhance the content. For example, if interest is detected when the user enters "remote work available," the unit makes a suggestion such as "please add specific conditions for remote work." This makes it possible to input job information that takes the user's emotions into consideration.

[0075] The job information input unit can use the emotion estimation function to analyze the user's emotions regarding the input content and make suggestions to improve the input content. For example, if the user is dissatisfied with the input content, the unit identifies the cause and makes suggestions for improvement. For example, if dissatisfaction is detected when the user enters "annual salary 6 million yen," the unit makes a suggestion such as "consider raising your annual salary to 7 million yen." Furthermore, if the user is satisfied with the input content, the unit makes a suggestion to emphasize that content. For example, if satisfaction is detected when the user enters "flexible working hours," the unit makes a suggestion such as "please emphasize the details of the flexible working hours more." Furthermore, if the user shows interest in the input content, the unit makes a suggestion to further enhance the content. For example, if interest is detected when the user enters "remote work available," the unit makes a suggestion such as "please add specific conditions for remote work." This makes it possible to input job information that takes the user's emotions into consideration.

[0076] The job information input unit can use the emotion estimation function to analyze the user's emotions regarding the input content and make suggestions to improve the input content. For example, if the user is dissatisfied with the input content, the unit identifies the cause and makes suggestions for improvement. For example, if dissatisfaction is detected when the user enters "annual salary 6 million yen," the unit makes a suggestion such as "consider raising your annual salary to 7 million yen." Furthermore, if the user is satisfied with the input content, the unit makes a suggestion to emphasize that content. For example, if satisfaction is detected when the user enters "flexible working hours," the unit makes a suggestion such as "please emphasize the details of the flexible working hours more." Furthermore, if the user shows interest in the input content, the unit makes a suggestion to further enhance the content. For example, if interest is detected when the user enters "remote work available," the unit makes a suggestion such as "please add specific conditions for remote work." This makes it possible to input job information that takes the user's emotions into consideration.

[0077] The job information input unit can use the emotion estimation function to analyze the user's emotions regarding the input content and make suggestions to improve the input content. For example, if the user is dissatisfied with the input content, the unit identifies the cause and makes suggestions for improvement. For example, if dissatisfaction is detected when the user enters "annual salary 6 million yen," the unit makes a suggestion such as "consider raising your annual salary to 7 million yen." Furthermore, if the user is satisfied with the input content, the unit makes a suggestion to emphasize that content. For example, if satisfaction is detected when the user enters "flexible working hours," the unit makes a suggestion such as "please emphasize the details of the flexible working hours more." Furthermore, if the user shows interest in the input content, the unit makes a suggestion to further enhance the content. For example, if interest is detected when the user enters "remote work available," the unit makes a suggestion such as "please add specific conditions for remote work." This makes it possible to input job information that takes the user's emotions into consideration.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The job information input unit inputs an outline of the job information, such as the job type, required skills, work location, salary, working hours, and employee benefits. Step 2: The generation unit generates job information based on the job information summary entered by the job information input unit. For example, the generation AI learns from past job information and industry trends, and generates optimal expressions and content based on the entered information. Step 3: The output unit outputs the job information generated by the generation unit. For example, the generated job information is automatically published on a company website or a job portal site.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0093] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 job information input section for inputting a summary of the job information; a generation unit that generates job information based on an outline of the job information input by the job information input unit; an output unit that outputs the job information generated by the generation unit; A system characterized by:

2. The job information input unit Complement your input by suggesting relevant skills and qualifications based on the information you provide 2. The system of claim 1.

3. The job information input unit Equipped with a voice input function, human resources personnel can input job information by voice 2. The system of claim 1.

4. The job information input unit Analyzes the emotions of HR personnel when they enter data and provides an interface to elicit positive emotions 2. The system of claim 1.

5. The job information input unit It has an auto-complete function that references past job postings and industry trends.

2. The system of claim 1.

Images