System

Abstract

An object of a system according to an embodiment is to enable a person in charge of personnel affairs of a company to efficiently generate job offer information.SOLUTION: A system includes an information acquisition unit, an analysis unit, and a generation unit. The information acquisition unit acquires information provided by a personnel manager of a company. The analysis unit analyzes the information acquired by the information acquisition unit. The generation unit generates job offer information based on the information analyzed by the analysis 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 drawback of requiring time and effort for company human resources personnel to manually create job information.

[0005] The system according to the embodiment aims to enable human resources personnel in a company to efficiently generate job information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information acquisition unit, an analysis unit, and a generation unit. The information acquisition unit acquires information provided by a company's human resources officer. The analysis unit analyzes the information acquired by the information acquisition unit. The generation unit generates job information based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a company's human resources personnel to efficiently generate 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 that uses a generation AI to automatically generate job information based on information provided by a company's human resources personnel, thereby supporting the company's recruitment activities. As a result, the automatic job information generation system allows a company's human resources personnel to efficiently create job information and effectively approach suitable job seekers.

[0029] The automatic job information generation system according to the embodiment includes an information acquisition unit, an analysis unit, and a generation unit. The information acquisition unit acquires information provided by a company's human resources personnel. For example, the information acquisition unit acquires information such as job requirements, the company's vision, and salary information. The information acquisition unit can also collect information from the company's official website and public relations materials. The analysis unit analyzes the information acquired by the information acquisition unit. For example, the analysis unit may use text analysis technology to analyze the provided information. The analysis unit may also use data mining technology to extract useful patterns from the provided information. The analysis unit may also use natural language processing technology to analyze the provided information. The generation unit generates job information based on the information analyzed by the analysis unit. For example, the generation AI may generate job information using a text generation AI (e.g., LLM). The generation AI may also generate job information using a multimodal generation AI. The generation AI may also customize job information according to the specific needs and requirements of the company. For example, the generation AI may generate job information that emphasizes specific skill sets or years of experience. This allows the automatic job information generation system to enable a company's human resources personnel to efficiently create job information and effectively reach suitable job seekers.

[0030] The generation unit learns from past job postings and their application results, and can generate more effective job postings. The generation unit, for example, stores past job postings and their application results in a database, and generates new job postings based on these. For example, it extracts characteristics of job postings that have had many applicants in the past and reflects these in new job postings. The generation unit also analyzes past job postings and their application results, and identifies expressions and keywords that have received a good response from applicants. This makes it possible to generate effective job postings. The generation unit also learns from past job postings and their application results, and analyzes applicants' attributes and motivations for applying. Based on this, it can generate job postings that resonate with the target demographic. This makes it possible to learn from past data and generate effective job postings.

[0031] The generation unit can analyze the job information of competitors of the company and generate differentiated job information. The generation unit, for example, collects and analyzes the job information of competitors. This identifies points of differentiation from competitors and generates job information that reflects these. The generation unit also analyzes the job information of competitors and extracts common keywords and expressions. Based on this, unique job information can be generated. The generation unit can also analyze the job information of competitors and generate job information that avoids points emphasized by competitors and highlights the company's unique strengths. This makes it possible to generate job information that differentiates the company from competitors.

[0032] The generation unit can generate job information that reflects the company's vision and mission and emphasizes the company culture. The generation unit, for example, analyzes the company's vision and mission and generates job information that reflects them. For example, the generation unit can emphasize the company's long-term goals and values. The generation unit also generates job information that appeals to job seekers about the company culture based on the company's vision and mission. For example, the generation unit can emphasize the company's social responsibility and environmental efforts. The generation unit also generates job information that reflects the company's vision and mission, and conveys to job seekers the appeal of being a part of the company. For example, the generation unit can introduce the company's growth story and success stories. In this way, job information that reflects the company's vision and mission can be generated.

[0033] The generation unit can generate personalized job information by taking into account the job seeker's past application history and skill set. The generation unit, for example, analyzes the job seeker's past application history and skill set and generates personalized job information based on the analysis. For example, it suggests job types that match the job seeker's skills. The generation unit also generates job information that the job seeker is likely to be interested in based on the job seeker's past application history. For example, it can provide job information related to job types or industries that the job seeker has previously applied for. The generation unit also analyzes the job seeker's skill set and generates job information that matches the job seeker's career path. For example, it can provide job information that supports skill development and career changes. This makes it possible to generate job information that takes into account the job seeker's past application history and skill set.

[0034] The information acquisition unit can automatically incorporate industry-specific terminology and trends when analyzing information provided by a company's human resources personnel. For example, the information acquisition unit analyzes information provided by a company's human resources personnel and automatically incorporates industry-specific terminology and trends. For example, the latest technical terminology and industry trends can be reflected. The information acquisition unit also collects industry-specific terminology and trends from a database and reflects them in job information. For example, it can incorporate industry terminology and the latest technological trends. The information acquisition unit also analyzes information provided by a company's human resources personnel and generates job information that reflects the latest industry trends. For example, it can highlight growth areas in the industry and skills that are in demand. This makes it possible to generate job information that incorporates industry-specific terminology and trends.

[0035] When analyzing information provided by a company's human resources personnel, the information acquisition unit can refer to past recruitment data and generate optimal job information. The information acquisition unit, for example, analyzes a company's past recruitment data and generates optimal job information. For example, job information is created based on past successful recruitment cases. The information acquisition unit also refers to a company's past recruitment data and extracts characteristics of job information that have received a good response from applicants. Based on this, effective job information can be generated. The information acquisition unit also analyzes a company's past recruitment data and analyzes applicant attributes and motivations for applying. Based on this, job information that resonates with the target demographic can be generated. In this way, optimal job information can be generated by referring to past recruitment data.

[0036] The generation unit can match the skills of job seekers based on information provided by a company's human resources personnel and generate optimal job information. The generation unit, for example, analyzes information provided by a company's human resources personnel and matches the skills of job seekers. For example, it generates optimal job information based on the skill set of the job seeker. The generation unit also matches the skills of job seekers based on information provided by a company's human resources personnel and customizes job information. For example, it can provide attractive job information to job seekers with specific skills. The generation unit also analyzes information provided by a company's human resources personnel and builds a system that matches the skills of job seekers. For example, it can generate job information that matches the skills of job seekers with the needs of the company. This makes it possible to match the skills of job seekers and generate optimal job information.

[0037] When analyzing the specific needs and requirements of a company, the generation unit can incorporate region-specific labor market data to generate job information optimized for the region. For example, the generation unit analyzes the specific needs and requirements of a company and incorporates region-specific labor market data. For example, job information that reflects the average salary in the region and the skill sets of job seekers is generated. The generation unit also collects region-specific labor market data and generates job information tailored to the specific needs of a company. For example, job information that reflects regional labor market trends can be provided. The generation unit also analyzes region-specific labor market data to generate job information optimized for the region. For example, job information that reflects the characteristics and needs of job seekers in the region can be created. This makes it possible to generate job information that incorporates region-specific labor market data.

[0038] When analyzing the specific needs and requirements of a company, the generation unit can generate job information that reflects the company's growth strategy and future vision. For example, the generation unit analyzes the company's growth strategy and future vision and generates job information that reflects the analysis. For example, the generation unit can emphasize the company's long-term goals and vision. The generation unit also generates job information that appeals to job seekers about the company's future potential based on the company's growth strategy. For example, the generation unit can introduce the company's growth plans and prospects for new businesses. The generation unit also generates job information that reflects the company's future vision and conveys to job seekers the appeal of being a member of the company. For example, the generation unit can emphasize the company's vision and mission. This makes it possible to generate job information that reflects the company's growth strategy and future vision.

[0039] The generation unit can generate customized job information for different job types and job positions based on the specific needs and requirements of the company. For example, the generation unit analyzes the specific needs and requirements of the company and generates customized job information for different job types and job positions. For example, job information specialized for job types such as engineers and marketing personnel is created. The generation unit also generates different job information for each job position based on the specific needs of the company. For example, job information for managerial positions and entry-level positions can be provided. The generation unit also analyzes the specific needs and requirements of the company and generates customized job information for each job type and job position. For example, job information that emphasizes specific skill sets or years of experience can be created. This allows customized job information to be generated for different job types and job positions.

[0040] The generation unit can generate long-term job information that takes into account the career path of a job seeker based on the specific needs and requirements of a company. The generation unit, for example, analyzes the specific needs and requirements of a company and generates long-term job information that takes into account the career path of a job seeker. For example, it emphasizes opportunities for career advancement and skill development. The generation unit also generates job information from a long-term perspective based on the career path of a job seeker. For example, it can introduce prospects for future promotion and career paths. The generation unit also analyzes the specific needs and requirements of a company and generates job information that takes into account the career path of a job seeker. For example, it can provide job information that supports long-term career growth. In this way, long-term job information that takes into account the career path of a job seeker can be generated.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The automatic job information generation system can further include a reliability evaluation unit that evaluates the reliability of information provided by a company's human resources personnel. The reliability evaluation unit evaluates reliability based on, for example, the source of the provided information and past performance. This makes it possible to generate job information based on highly reliable information. The reliability evaluation unit can also check the consistency and accuracy of the provided information and eliminate information with low reliability. Furthermore, the reliability evaluation unit can supplement the reliability of the provided information by referring to information from external databases or third-party organizations. This makes it possible to generate highly reliable job information.

[0043] The automatic job information generation system can further include a skill evaluation unit that evaluates the skills and experience of job seekers. The skill evaluation unit, for example, analyzes the resume and curriculum vitae of the job seeker and evaluates the skills and experience. This makes it possible to generate job information that matches the skills of the job seeker. The skill evaluation unit can also suggest appropriate job types and positions based on the skill set of the job seeker. Furthermore, the skill evaluation unit can generate job information that supports the job seeker in improving their skills or changing their career. This makes it possible to generate job information that takes into account the skills and experience of the job seeker.

[0044] The automatic job information generation system can further include an effectiveness evaluation unit that evaluates the effectiveness of a company's recruitment activities. The effectiveness evaluation unit, for example, analyzes data on past recruitment activities and evaluates the effectiveness of the recruitment activities. This makes it possible to generate effective job information. The effectiveness evaluation unit can also identify areas for improvement in the job information based on the results of the recruitment activities. Furthermore, the effectiveness evaluation unit can monitor the effectiveness of the recruitment activities in real time and continuously improve the quality of the job information. This makes it possible to evaluate the effectiveness of the recruitment activities and generate optimal job information.

[0045] The job information automatic generation system can further include an incentive provision unit for increasing job seekers' motivation to apply. The incentive provision unit can, for example, provide job seekers with benefits or rewards at the time of application. This can increase job seekers' motivation to apply. The incentive provision unit can also dynamically adjust incentives according to the job seeker's application status. Furthermore, the incentive provision unit can generate job information that emphasizes company benefits and employee benefits. This can increase job seekers' motivation to apply.

[0046] The job information automatic generation system may further include a feedback collection unit that collects feedback from job seekers. The feedback collection unit may, for example, collect impressions and opinions from job seekers after viewing job information. This may identify areas for improvement in the job information. The feedback collection unit may also dynamically adjust the job information based on the job seeker feedback. Furthermore, the feedback collection unit may analyze the job seeker feedback and continuously improve the quality of the job information. This may allow job information that reflects the job seeker feedback to be generated.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The information acquisition department acquires information provided by the company's human resources personnel, such as job requirements, the company's vision, salary information, etc. The information acquisition department can also collect information from the company's official website and public relations materials. Step 2: The analysis unit analyzes the information acquired by the information acquisition unit. For example, the analysis unit analyzes the provided information using text analysis technology. The analysis unit can also extract useful patterns from the provided information using data mining technology. Furthermore, the analysis unit can also analyze the provided information using natural language processing technology. Step 3: The generation unit generates job postings based on the information analyzed by the analysis unit. For example, the generation AI may generate job postings using text generation AI (e.g., LLM). The generation AI may also generate job postings using multimodal generation AI. The generation AI may also customize job postings according to the specific needs and requirements of the company. For example, the generation AI may generate job postings that emphasize specific skill sets or years of experience.

[0049] (Example 2) The automatic job information generation system according to an embodiment of the present invention is a system that uses a generation AI to automatically generate job information based on information provided by a company's human resources personnel, thereby supporting the company's recruitment activities. As a result, the automatic job information generation system allows a company's human resources personnel to efficiently create job information and effectively approach suitable job seekers.

[0050] The automatic job information generation system according to the embodiment includes an information acquisition unit, an analysis unit, and a generation unit. The information acquisition unit acquires information provided by a company's human resources personnel. For example, the information acquisition unit acquires information such as job requirements, the company's vision, and salary information. The information acquisition unit can also collect information from the company's official website and public relations materials. The analysis unit analyzes the information acquired by the information acquisition unit. For example, the analysis unit may use text analysis technology to analyze the provided information. The analysis unit may also use data mining technology to extract useful patterns from the provided information. The analysis unit may also use natural language processing technology to analyze the provided information. The generation unit generates job information based on the information analyzed by the analysis unit. For example, the generation AI may generate job information using a text generation AI (e.g., LLM). The generation AI may also generate job information using a multimodal generation AI. The generation AI may also customize job information according to the specific needs and requirements of the company. For example, the generation AI may generate job information that emphasizes specific skill sets or years of experience. This allows the automatic job information generation system to enable a company's human resources personnel to efficiently create job information and effectively reach suitable job seekers.

[0051] The generation unit learns from past job postings and their application results, and can generate more effective job postings. The generation unit, for example, stores past job postings and their application results in a database, and generates new job postings based on these. For example, it extracts characteristics of job postings that have had many applicants in the past and reflects these in new job postings. The generation unit also analyzes past job postings and their application results, and identifies expressions and keywords that have received a good response from applicants. This makes it possible to generate effective job postings. The generation unit also learns from past job postings and their application results, and analyzes applicants' attributes and motivations for applying. Based on this, it can generate job postings that resonate with the target demographic. This makes it possible to learn from past data and generate effective job postings.

[0052] The generation unit can analyze the job information of competitors of the company and generate differentiated job information. The generation unit, for example, collects and analyzes the job information of competitors. This identifies points of differentiation from competitors and generates job information that reflects these. The generation unit also analyzes the job information of competitors and extracts common keywords and expressions. Based on this, unique job information can be generated. The generation unit can also analyze the job information of competitors and generate job information that avoids points emphasized by competitors and highlights the company's unique strengths. This makes it possible to generate job information that differentiates the company from competitors.

[0053] The generation unit uses the emotion estimation function to analyze the emotions of job seekers who have viewed the job information and can automatically incorporate expressions that elicit a positive response. For example, the generation unit uses the emotion estimation function to analyze the facial expressions and voice of job seekers who have viewed the job information and identify expressions that elicit a positive response. Job information is generated based on this. The generation unit also collects the emotional responses of job seekers in real time and incorporates expressions and keywords that elicit many positive responses into the job information. This makes it possible to generate job information that attracts the interest of job seekers. The generation unit also dynamically adjusts the expressions in the job information based on the emotion estimation data to optimize the emotional responses of job seekers. For example, expressions that elicit positive emotions are emphasized. This makes it possible to analyze the emotions of job seekers and generate job information that elicits a positive response.

[0054] The generation unit can generate job information that reflects the company's vision and mission and emphasizes the company culture. The generation unit, for example, analyzes the company's vision and mission and generates job information that reflects them. For example, the generation unit can emphasize the company's long-term goals and values. The generation unit also generates job information that appeals to job seekers about the company culture based on the company's vision and mission. For example, the generation unit can emphasize the company's social responsibility and environmental efforts. The generation unit also generates job information that reflects the company's vision and mission, and conveys to job seekers the appeal of being a part of the company. For example, the generation unit can introduce the company's growth story and success stories. In this way, job information that reflects the company's vision and mission can be generated.

[0055] The generation unit can generate personalized job information by taking into account the job seeker's past application history and skill set. The generation unit, for example, analyzes the job seeker's past application history and skill set and generates personalized job information based on the analysis. For example, it suggests job types that match the job seeker's skills. The generation unit also generates job information that the job seeker is likely to be interested in based on the job seeker's past application history. For example, it can provide job information related to job types or industries that the job seeker has previously applied for. The generation unit also analyzes the job seeker's skill set and generates job information that matches the job seeker's career path. For example, it can provide job information that supports skill development and career changes. This makes it possible to generate job information that takes into account the job seeker's past application history and skill set.

[0056] The generation unit uses the emotion estimation function to analyze the emotions of job seekers in real time when generating job information, and can deliver the job information at optimal timing. For example, the generation unit uses the emotion estimation function to analyze the emotions of job seekers in real time and deliver the job information at a time when positive emotions are high. For example, the generation unit identifies time periods when job seekers are likely to be interested. The generation unit also builds a system that optimizes the timing of delivery of job information based on the emotion data of job seekers. For example, the job information can be delivered during time periods when job seekers are relaxed. The generation unit also collects emotion estimation data in real time and adjusts the timing of delivery of the job information according to changes in the emotion of the job seeker. For example, the job information is delivered when the job seeker is feeling positive. This makes it possible to analyze the emotions of job seekers in real time and deliver the job information at optimal timing.

[0057] The information acquisition unit can automatically incorporate industry-specific terminology and trends when analyzing information provided by a company's human resources personnel. For example, the information acquisition unit analyzes information provided by a company's human resources personnel and automatically incorporates industry-specific terminology and trends. For example, the latest technical terminology and industry trends can be reflected. The information acquisition unit also collects industry-specific terminology and trends from a database and reflects them in job information. For example, it can incorporate industry terminology and the latest technological trends. The information acquisition unit also analyzes information provided by a company's human resources personnel and generates job information that reflects the latest industry trends. For example, it can highlight growth areas in the industry and skills that are in demand. This makes it possible to generate job information that incorporates industry-specific terminology and trends.

[0058] When analyzing information provided by a company's human resources personnel, the information acquisition unit can refer to past recruitment data and generate optimal job information. The information acquisition unit, for example, analyzes a company's past recruitment data and generates optimal job information. For example, job information is created based on past successful recruitment cases. The information acquisition unit also refers to a company's past recruitment data and extracts characteristics of job information that have received a good response from applicants. Based on this, effective job information can be generated. The information acquisition unit also analyzes a company's past recruitment data and analyzes applicant attributes and motivations for applying. Based on this, job information that resonates with the target demographic can be generated. In this way, optimal job information can be generated by referring to past recruitment data.

[0059] The information acquisition unit can use the emotion estimation function to analyze the emotional nuances of information provided by a human resources personnel and reflect them in job information. For example, the information acquisition unit can use the emotion estimation function to analyze the emotional nuances of information provided by a human resources personnel and reflect them in job information. For example, expressions with positive emotions can be emphasized. The information acquisition unit also builds a system that adjusts the expressions in job information based on the emotion data of the human resources personnel. For example, expressions that are easily emotionally relatable can be incorporated. The information acquisition unit also generates job information that reflects the emotional nuances of the human resources personnel based on the emotion estimation data. For example, expressions that convey the enthusiasm and vision of the company can be emphasized. This makes it possible to generate job information that reflects the emotional nuances of the human resources personnel.

[0060] The generation unit can match the skills of job seekers based on information provided by a company's human resources personnel and generate optimal job information. The generation unit, for example, analyzes information provided by a company's human resources personnel and matches the skills of job seekers. For example, it generates optimal job information based on the skill set of the job seeker. The generation unit also matches the skills of job seekers based on information provided by a company's human resources personnel and customizes job information. For example, it can provide attractive job information to job seekers with specific skills. The generation unit also analyzes information provided by a company's human resources personnel and builds a system that matches the skills of job seekers. For example, it can generate job information that matches the skills of job seekers with the needs of the company. This makes it possible to match the skills of job seekers and generate optimal job information.

[0061] The information acquisition unit uses the emotion estimation function to provide real-time feedback on the emotional reactions of human resources personnel to information provided by them, thereby improving the accuracy of job information. For example, the information acquisition unit uses the emotion estimation function to build a system that provides real-time feedback on the emotional reactions of human resources personnel to information provided by them. For example, information with strong positive emotions is preferentially reflected. The information acquisition unit also analyzes the emotion data of human resources personnel in real time to improve the accuracy of job information. For example, expressions that are easily emotionally relatable can be incorporated. The information acquisition unit also generates job information that reflects the emotional reactions of human resources personnel based on the emotion estimation data. For example, expressions that convey the enthusiasm and vision of the company can be emphasized. This allows for real-time feedback on the emotional reactions of human resources personnel to improve the accuracy of job information.

[0062] When analyzing the specific needs and requirements of a company, the generation unit can incorporate region-specific labor market data to generate job information optimized for the region. For example, the generation unit analyzes the specific needs and requirements of a company and incorporates region-specific labor market data. For example, job information that reflects the average salary in the region and the skill sets of job seekers is generated. The generation unit also collects region-specific labor market data and generates job information tailored to the specific needs of a company. For example, job information that reflects regional labor market trends can be provided. The generation unit also analyzes region-specific labor market data to generate job information optimized for the region. For example, job information that reflects the characteristics and needs of job seekers in the region can be created. This makes it possible to generate job information that incorporates region-specific labor market data.

[0063] When analyzing the specific needs and requirements of a company, the generation unit can generate job information that reflects the company's growth strategy and future vision. For example, the generation unit analyzes the company's growth strategy and future vision and generates job information that reflects the analysis. For example, the generation unit can emphasize the company's long-term goals and vision. The generation unit also generates job information that appeals to job seekers about the company's future potential based on the company's growth strategy. For example, the generation unit can introduce the company's growth plans and prospects for new businesses. The generation unit also generates job information that reflects the company's future vision and conveys to job seekers the appeal of being a member of the company. For example, the generation unit can emphasize the company's vision and mission. This makes it possible to generate job information that reflects the company's growth strategy and future vision.

[0064] The generation unit can use the emotion estimation function to analyze the emotional reactions of job seekers to the specific needs and requirements of a company, and generate job listings that elicit positive reactions. For example, the generation unit can use the emotion estimation function to analyze the emotional reactions of job seekers to the specific needs and requirements of a company, and generate job listings that elicit positive reactions. For example, the generation unit incorporates expressions that are likely to interest job seekers. The generation unit also adjusts the expression of the job listings to address the specific needs and requirements of a company based on the emotion data of job seekers. For example, it can emphasize keywords that elicit positive emotions. The generation unit also collects emotion estimation data in real time, and dynamically adjusts the expression of the job listings according to the emotional reactions of job seekers. For example, it can emphasize expressions that evoke positive emotions in job seekers. This makes it possible to analyze the emotional reactions of job seekers and generate job listings that elicit positive reactions.

[0065] The generation unit can generate customized job information for different job types and job positions based on the specific needs and requirements of the company. For example, the generation unit analyzes the specific needs and requirements of the company and generates customized job information for different job types and job positions. For example, job information specialized for job types such as engineers and marketing personnel is created. The generation unit also generates different job information for each job position based on the specific needs of the company. For example, job information for managerial positions and entry-level positions can be provided. The generation unit also analyzes the specific needs and requirements of the company and generates customized job information for each job type and job position. For example, job information that emphasizes specific skill sets or years of experience can be created. This allows customized job information to be generated for different job types and job positions.

[0066] The generation unit can generate long-term job information that takes into account the career path of a job seeker based on the specific needs and requirements of a company. The generation unit, for example, analyzes the specific needs and requirements of a company and generates long-term job information that takes into account the career path of a job seeker. For example, it emphasizes opportunities for career advancement and skill development. The generation unit also generates job information from a long-term perspective based on the career path of a job seeker. For example, it can introduce prospects for future promotion and career paths. The generation unit also analyzes the specific needs and requirements of a company and generates job information that takes into account the career path of a job seeker. For example, it can provide job information that supports long-term career growth. In this way, long-term job information that takes into account the career path of a job seeker can be generated.

[0067] The generation unit can use the emotion estimation function to monitor job seekers' emotions regarding specific needs and requirements of companies in real time, and continuously generate optimal job information. For example, the generation unit can use the emotion estimation function to monitor job seekers' emotions regarding specific needs and requirements of companies in real time, and continuously generate optimal job information. For example, the generation unit can adjust the job information according to changes in job seekers' emotions. The generation unit can also collect job seekers' emotion data in real time and dynamically adjust the expression of the job information regarding specific needs and requirements of companies. For example, it can emphasize expressions that elicit positive emotions. The generation unit can also monitor job seekers' emotions regarding specific needs and requirements of companies based on the emotion estimation data, and continuously improve the quality of the job information. For example, it can optimize the job information according to the job seekers' emotional reactions. This makes it possible to monitor job seekers' emotions in real time and continuously generate optimal job information.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The automatic job information generation system can further include a reliability evaluation unit that evaluates the reliability of information provided by a company's human resources personnel. The reliability evaluation unit evaluates reliability based on, for example, the source of the provided information and past performance. This makes it possible to generate job information based on highly reliable information. The reliability evaluation unit can also check the consistency and accuracy of the provided information and eliminate information with low reliability. Furthermore, the reliability evaluation unit can supplement the reliability of the provided information by referring to information from external databases or third-party organizations. This makes it possible to generate highly reliable job information.

[0070] The automatic job information generation system can further include a skill evaluation unit that evaluates the skills and experience of job seekers. The skill evaluation unit, for example, analyzes the resume and curriculum vitae of the job seeker and evaluates the skills and experience. This makes it possible to generate job information that matches the skills of the job seeker. The skill evaluation unit can also suggest appropriate job types and positions based on the skill set of the job seeker. Furthermore, the skill evaluation unit can generate job information that supports the job seeker in improving their skills or changing their career. This makes it possible to generate job information that takes into account the skills and experience of the job seeker.

[0071] The automatic job information generation system can further include an effectiveness evaluation unit that evaluates the effectiveness of a company's recruitment activities. The effectiveness evaluation unit, for example, analyzes data on past recruitment activities and evaluates the effectiveness of the recruitment activities. This makes it possible to generate effective job information. The effectiveness evaluation unit can also identify areas for improvement in the job information based on the results of the recruitment activities. Furthermore, the effectiveness evaluation unit can monitor the effectiveness of the recruitment activities in real time and continuously improve the quality of the job information. This makes it possible to evaluate the effectiveness of the recruitment activities and generate optimal job information.

[0072] The job information automatic generation system can further include an incentive provision unit for increasing job seekers' motivation to apply. The incentive provision unit can, for example, provide job seekers with benefits or rewards at the time of application. This can increase job seekers' motivation to apply. The incentive provision unit can also dynamically adjust incentives according to the job seeker's application status. Furthermore, the incentive provision unit can generate job information that emphasizes company benefits and employee benefits. This can increase job seekers' motivation to apply.

[0073] The job information automatic generation system may further include a feedback collection unit that collects feedback from job seekers. The feedback collection unit may, for example, collect impressions and opinions from job seekers after viewing job information. This may identify areas for improvement in the job information. The feedback collection unit may also dynamically adjust the job information based on the job seeker feedback. Furthermore, the feedback collection unit may analyze the job seeker feedback and continuously improve the quality of the job information. This may allow job information that reflects the job seeker feedback to be generated.

[0074] The automatic job information generation system can further use an emotion estimation function to analyze the emotions of job seekers and generate job information that elicits positive emotions. For example, it can analyze the facial expressions and voice of job seekers when they view job information and identify expressions that elicit positive emotions. It can also dynamically adjust the expressions in the job information based on the job seeker's emotion data. It can also collect the job seeker's emotional responses in real time and generate job information that elicits positive emotions. This makes it possible to analyze the emotions of job seekers and generate job information that elicits positive responses.

[0075] The automatic job information generation system can also use an emotion estimation function to monitor the emotions of job seekers in real time and deliver job information at the optimal timing. For example, it can identify the times when job seekers are feeling positive emotions and deliver job information at those times. It can also dynamically adjust the timing of delivering job information based on the job seeker's emotion data. Furthermore, it can collect the job seeker's emotional responses in real time and deliver job information at the optimal timing. This makes it possible to monitor the emotions of job seekers in real time and deliver job information at the optimal timing.

[0076] The automatic job information generation system can further use an emotion estimation function to analyze the emotional nuances of information provided by a company's human resources personnel and reflect them in the job information. For example, it can emphasize the positive emotions of the information provided by the human resources personnel. It can also adjust the expression of the job information based on the emotional data of the human resources personnel. Furthermore, it can generate job information that reflects the emotional nuances of the human resources personnel. This makes it possible to generate job information that reflects the emotional nuances of the human resources personnel.

[0077] The automatic job information generation system can further use an emotion estimation function to analyze job seekers' emotional responses to a company's specific needs and requirements, and generate job information that elicits a positive response. For example, it can incorporate expressions that are likely to interest job seekers. It can also adjust the expression of job information to reflect a company's specific needs and requirements based on the job seeker's emotional data. It can also collect job seekers' emotional responses in real time and generate job information that elicits a positive response. This makes it possible to analyze job seekers' emotional responses and generate job information that elicits a positive response.

[0078] The automatic job information generation system can further use the emotion estimation function to monitor job seekers' emotions regarding the specific needs and requirements of companies in real time and continuously generate optimal job information. For example, the system can adjust job information according to changes in job seekers' emotions. It can also collect job seekers' emotion data in real time and dynamically adjust the expression of job information regarding the specific needs and requirements of companies. Furthermore, it can continuously improve the quality of job information based on the job seekers' emotional responses. This makes it possible to monitor job seekers' emotions in real time and continuously generate optimal job information.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The information acquisition department acquires information provided by the company's human resources personnel, such as job requirements, the company's vision, salary information, etc. The information acquisition department can also collect information from the company's official website and public relations materials. Step 2: The analysis unit analyzes the information acquired by the information acquisition unit. For example, the analysis unit analyzes the provided information using text analysis technology. The analysis unit can also extract useful patterns from the provided information using data mining technology. Furthermore, the analysis unit can also analyze the provided information using natural language processing technology. Step 3: The generation unit generates job postings based on the information analyzed by the analysis unit. For example, the generation AI may generate job postings using text generation AI (e.g., LLM). The generation AI may also generate job postings using multimodal generation AI. The generation AI may also customize job postings according to the specific needs and requirements of the company. For example, the generation AI may generate job postings that emphasize specific skill sets or years of experience.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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. an information acquisition unit that acquires information provided by a human resources person in a company; an analysis unit that analyzes the information acquired by the information acquisition unit; a generation unit that generates job information based on the information analyzed by the analysis unit. A system characterized by:

2. The generation unit Learn from past job postings and application results to generate more effective job postings 2. The system of claim 1.

3. The generation unit Analyze the job listings of your company's competitors and generate differentiated job listings 2. The system of claim 1.

4. The generation unit Analyzes the emotions of job seekers who view job listings and automatically incorporates expressions that elicit positive responses 2. The system of claim 1.

5. The generation unit Generate job listings that reflect your company's vision and mission and highlight your company culture 2. The system of claim 1.

6. The generation unit Generate personalized job listings based on job seekers' past application history and skill sets 2. The system of claim 1.

7. The generation unit When generating job information, job seeker sentiment is analyzed in real time to deliver job information at the optimal time.

2. The system of claim 1.

8. The information acquisition unit Automatically incorporates industry-specific terminology and trends when parsing information provided by corporate HR professionals 2. The system of claim 1.

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