System

Abstract

The system according to the embodiment aims to eliminate bias and achieve an objective and fair adoption process.SOLUTION: A system includes a document examination part, an interview support part, and an informal decision part. The document examination unit analyzes the applicant's resume or curriculum vitae. The interview support unit generates a list of questions to be used in the interview based on the curriculum vitae or curriculum vitae analyzed by the document examination unit. The informal decision section comprehensively evaluates the results of the document examination section and the interview support section to decide the informal decision.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 not being able to eliminate bias in the hiring process, making it difficult to make objective and fair evaluations.

[0005] The system according to the embodiment aims to eliminate bias and realize an objective and fair hiring process. [Means for solving the problem]

[0006] The system according to the embodiment includes a document screening unit, an interview support unit, and a job offer determination unit. The document screening unit analyzes the applicant's resume or curriculum vitae. The interview support unit generates a list of questions to be used during the interview based on the resume or curriculum vitae analyzed by the document screening unit. The job offer determination unit comprehensively evaluates the results of the document screening unit and the interview support unit to determine the job offer. [Effects of the Invention]

[0007] The system according to the embodiment can eliminate bias and realize an objective and fair hiring process. [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 recruitment process system according to an embodiment of the present invention utilizes generative AI to eliminate bias, comprehensively supports all processes from document screening, interviews, and job offers, and realizes objective and fair recruitment based on the candidate's skills and aptitude. This allows companies to respect diversity and hire excellent talent, thereby building a more innovative and sustainable business environment.

[0029] The hiring process system according to the embodiment includes a document screening unit, an interview support unit, and a job offer decision unit. The document screening unit analyzes an applicant's resume or work history. For example, the document screening unit uses a generation AI to analyze the applicant's resume or work history and extract specific skill sets and years of experience. The document screening unit can also analyze the applicant's past projects and achievements to quantify specific achievements and contributions. The document screening unit can also use an emotion estimation function to evaluate the applicant's emotional strengths and motivations from their self-promotional statements. For example, the generation AI analyzes the applicant's resume or work history and extracts specific skill sets. The applicant's skill level is then evaluated by comparing them with industry-standard skill sets. The generation AI evaluates the skill sets extracted from the applicant's work history against the latest industry trends and technological developments. The generation AI analyzes the applicant's skill sets and compares them with an industry-standard skill matrix. The interview support unit generates a list of questions to be used during interviews based on the resume or work history analyzed by the document screening unit. For example, the generation AI generates a list of questions to be used during interviews and adjusts the difficulty of the questions in real time based on the candidate's answers. During the interview, the generation AI analyzes the candidate's answers and adjusts the difficulty of the questions according to the content of the answers. The generation AI supports the progress of the interview and adjusts the difficulty of the questions in real time based on the candidate's answers. The job offer decision unit comprehensively evaluates the results of the document screening unit and the interview support unit to decide on a job offer. For example, the generation AI comprehensively evaluates the results of the document screening and interview, and analyzes the candidate's past work history and performance in detail. In the job offer decision process, the generation AI analyzes the candidate's past work history and performance in detail and reflects this in the evaluation. The generation AI comprehensively evaluates the results of the document screening and interview, and analyzes the candidate's past work history and performance in detail. As a result, the hiring process system according to the embodiment realizes an objective and fair hiring process that eliminates bias. For example, the generation AI comprehensively evaluates the results of the document screening and interview, and when comprehensively evaluating the candidate's skills, experience, and performance in the interview, it also takes into account the latest industry trends and the company's strategic goals.In the job offer decision process, generative AI comprehensively evaluates the candidate's skills, experience, and interview performance, and takes into account the company's strategic goals. When generative AI comprehensively evaluates the candidate's skills, experience, and interview performance, it takes into account the latest industry trends and the company's strategic goals.

[0030] The document screening department can analyze resumes or work histories and extract specific skill sets or years of experience. In the document screening department, for example, the generation AI analyzes an applicant's resume or work history and extracts specific skill sets. It then compares these with industry-standard skill sets to evaluate the applicant's skill level. The generation AI evaluates the skill sets extracted from the applicant's work history in light of the latest industry trends and technological developments. The generation AI analyzes the applicant's skill sets and compares them with an industry-standard skill matrix. This allows for an accurate evaluation of the applicant's skills and experience.

[0031] The document screening department can analyze resumes or work histories and quantify projects or achievements. In the document screening department, for example, the generation AI analyzes the applicant's work history and extracts past projects and achievements. It then quantifies specific achievements and contributions and reflects them in the evaluation. The generation AI analyzes the applicant's past project data and quantifies the project's scale, difficulty, and achievements. The generation AI analyzes the applicant's achievement data and quantifies specific achievements and contributions. This allows the applicant's specific achievements and contributions to be objectively evaluated.

[0032] The document review department can analyze external information such as social media activity or published papers to conduct a comprehensive evaluation. For example, the document review department uses a generation AI to analyze an applicant's social media activity and reflect job-related posts and activities in the evaluation. The generation AI analyzes the applicant's published papers and research results and reflects specialized knowledge and research ability in the evaluation. The generation AI comprehensively analyzes the applicant's external information and reflects job-related skills and experience in the evaluation. This makes it possible to conduct a comprehensive evaluation that includes the applicant's external information.

[0033] The document screening department anonymously shares the results of the document screening with other companies' recruitment data, allowing it to understand recruitment trends across the industry. For example, the document screening department uses generation AI to anonymously share the results of the document screening with other companies and analyze recruitment trends across the industry. By anonymously sharing the results of the document screening and comparing them with the recruitment data of other companies, it is possible to understand the recruitment standards across the industry. A system is built in which generation AI anonymously shares the results of the document screening and understands recruitment trends across the industry. This makes it possible to understand recruitment trends across the industry.

[0034] The interview support unit can generate a list of questions and adjust the difficulty of the questions in real time based on the candidate's answers. For example, the interview support unit generates a list of questions to be used by the generation AI during interviews and adjusts the difficulty of the questions in real time based on the candidate's answers. During the interview, the generation AI analyzes the candidate's answers and adjusts the difficulty of the questions according to the content of the answers. The generation AI supports the progress of the interview and adjusts the difficulty of the questions in real time based on the candidate's answers. This improves the quality of the interview and enables a more accurate evaluation of the candidate's skills and aptitude.

[0035] The interview support unit can analyze the candidate's non-verbal communication and make a comprehensive evaluation. In the interview support unit, for example, the generation AI analyzes the candidate's non-verbal communication during the interview and makes a comprehensive evaluation. During the interview, the generation AI analyzes the candidate's non-verbal communication in real time and reflects this in the evaluation. The generation AI supports the progress of the interview, analyzes the candidate's non-verbal communication, and makes a comprehensive evaluation. This makes it possible to make a comprehensive evaluation that includes the candidate's non-verbal communication.

[0036] The interview support unit can customize the question list according to different industries or job types. For example, the interview support unit customizes the question list used by the generation AI during interviews according to different industries or job types. The generation AI customizes the interview question list and provides questions that are suitable for a specific industry or job type. The generation AI customizes the interview question list according to different industries or job types and provides appropriate questions. In this way, it is possible to provide an appropriate question list according to the industry or job type.

[0037] The interview support unit can analyze a candidate's past interview data and propose the optimal question pattern. In the interview support unit, for example, the generation AI analyzes a candidate's past interview data and proposes the optimal question pattern. The generation AI analyzes a candidate's past interview data and optimizes the order and content of questions. The generation AI analyzes a candidate's past interview data and proposes the optimal question pattern. This makes it possible to provide the optimal question pattern based on the past interview data.

[0038] The job offer decision department can comprehensively evaluate the results of the document review or interview, and analyze the candidate's work history or achievements in detail, and reflect this in the decision to offer the candidate. In the job offer decision department, for example, the generation AI can comprehensively evaluate the results of the document review or interview, and analyze the candidate's past work history and achievements in detail. In the job offer decision process, the generation AI can analyze the candidate's past work history and achievements in detail, and reflect this in the evaluation. The generation AI can comprehensively evaluate the results of the document review or interview, and analyze the candidate's past work history and achievements in detail. This allows the candidate's past work history and achievements to be analyzed in detail and reflected in the decision to offer the candidate.

[0039] The job offer decision department can also take into account the latest industry trends or the company's strategic goals when comprehensively evaluating a candidate's skills or experience, and performance in an interview. For example, the job offer decision department takes into account the latest industry trends when the generation AI comprehensively evaluates a candidate's skills, experience, and performance in an interview. In the job offer decision process, the generation AI comprehensively evaluates a candidate's skills, experience, and performance in an interview, and takes into account the company's strategic goals. When the generation AI comprehensively evaluates a candidate's skills, experience, and performance in an interview, it takes into account the latest industry trends and the company's strategic goals. This makes it possible to make a comprehensive evaluation that takes into account the latest industry trends and the company's strategic goals.

[0040] The job offer decision unit can customize the job offer decision process according to different industries or job types. In the job offer decision unit, for example, the generation AI customizes the job offer decision process according to different industries or job types. The generation AI customizes the job offer decision process and provides evaluation criteria that are suitable for specific industries or job types. The generation AI customizes the job offer decision process according to different industries or job types and provides appropriate evaluation criteria. This makes it possible to provide an appropriate job offer decision process according to industry or job type.

[0041] The job offer decision unit can convert the job offer decision process into a visual note or mind map to make it easier to understand visually. For example, the job offer decision unit converts the job offer decision process into a visual note using a generation AI to make it easier to understand visually. The job offer decision unit converts the job offer decision process into a mind map format to visually organize related keywords and concepts. A tool that automatically generates visual notes and mind maps is developed to enable users to easily visually display the job offer decision process. This makes it easier to understand visually the job offer decision process.

[0042] When comprehensively evaluating the results of document screening or interviews, a candidate's work history or achievements can be analyzed in detail and reflected in the evaluation. When comprehensively evaluating the results of document screening or interviews, for example, generation AI analyzes a candidate's past work history and achievements in detail. When comprehensively evaluating the results of document screening or interviews, generation AI analyzes a candidate's past work history and achievements in detail and reflects this in the evaluation. Generation AI comprehensively evaluates the results of document screening and interviews and analyzes a candidate's past work history and achievements in detail. This allows a candidate's past work history and achievements to be analyzed in detail and reflected in the evaluation.

[0043] When comprehensively evaluating the results of a document review or an interview, the latest industry trends or the strategic goals of the company can also be taken into consideration. When comprehensively evaluating the results of a document review or an interview, for example, the generation AI takes into consideration the latest industry trends. When comprehensively evaluating the results of a document review or an interview, the generation AI takes into consideration the latest industry trends and the strategic goals of the company. When comprehensively evaluating the results of a document review or an interview, the generation AI takes into consideration the latest industry trends and the strategic goals of the company. This makes it possible to make a comprehensive evaluation that takes into consideration the latest industry trends and the strategic goals of the company.

[0044] The results of document screening or interviews can be customized for different industries or job types. The results of document screening or interviews are customized for different industries or job types. For example, a generation AI customizes the results of document screening or interviews for different industries or job types. A generation AI customizes the results of document screening or interviews to provide evaluation criteria suitable for specific industries or job types. A generation AI customizes the results of document screening or interviews for different industries or job types to provide appropriate evaluation criteria. This makes it possible to provide appropriate evaluation criteria for different industries and job types.

[0045] The results of document screening or interviews can be converted into visual notes or mind maps to make them easier to understand visually. The results of document screening or interviews can be converted into visual notes to make them easier to understand visually. For example, generative AI can convert the results of document screening or interviews into visual notes to make them easier to understand visually. The results of document screening or interviews can be converted into mind map format to visually organize related keywords and concepts. A tool that automatically generates visual notes and mind maps can be developed to enable users to easily visually display the results of document screening or interviews. This makes the results of document screening or interviews easier to understand visually.

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

[0047] The recruitment process system can further include a health assessment unit that evaluates the candidate's health condition. The health assessment unit, for example, analyzes the candidate's health check results and self-reported health information to assess whether the candidate's health condition is suitable for the job. The generative AI analyzes the candidate's health check results and evaluates specific health risks. The health assessment unit analyzes the candidate's self-reported health information to assess whether the candidate's health condition is suitable for the job. This enables companies to make hiring decisions that take the candidate's health condition into consideration.

[0048] The recruitment process system can further include a cultural fit assessment unit that evaluates the cultural fit of candidates. The cultural fit assessment unit, for example, analyzes the candidate's past work history and self-promotional statement to assess whether they fit into the corporate culture. The generation AI analyzes the candidate's past work history and assesses whether they fit into the corporate culture. The cultural fit assessment unit analyzes the candidate's self-promotional statement to assess whether they fit into the corporate culture. This enables companies to make hiring decisions that take into account the candidate's cultural fit.

[0049] The hiring process system can further include a learning ability assessment unit that evaluates the candidate's learning ability. The learning ability assessment unit, for example, analyzes the candidate's past learning history and self-learning efforts to assess their learning ability. The generative AI analyzes the candidate's past learning history and assesses their learning ability. The learning ability assessment unit analyzes the candidate's self-learning efforts and assesses their learning ability. This enables companies to make hiring decisions that take the candidate's learning ability into consideration.

[0050] The recruitment process system can further include a leadership evaluation unit that evaluates the leadership ability of candidates. The leadership evaluation unit, for example, analyzes the candidate's past project leadership experience and self-promotional statement to evaluate their leadership ability. The generation AI analyzes the candidate's past project leadership experience and evaluates their leadership ability. The leadership evaluation unit analyzes the candidate's self-promotional statement to evaluate their leadership ability. This enables companies to make hiring decisions that take into account the candidate's leadership ability.

[0051] The recruitment process system can further include a creativity evaluation unit that evaluates the creativity of candidates. The creativity evaluation unit, for example, analyzes the candidate's past projects and self-promotional statements to evaluate their creativity. The generative AI analyzes the candidate's past projects and evaluates their creativity. The creativity evaluation unit analyzes the candidate's self-promotional statements to evaluate their creativity. This enables companies to make hiring decisions that take into account the candidate's creativity.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The document screening department analyzes the applicant's resume or work history. For example, generative AI can be used to extract specific skill sets and years of experience, and past projects and achievements can be analyzed to quantify specific achievements and contributions. Emotion estimation can also be used to assess emotional strengths and motivations from the applicant's self-promotional statement. Step 2: The interview support department generates a list of questions to be used during interviews based on the resume or work history analyzed by the document screening department. For example, the generation AI generates a list of questions to be used during interviews and adjusts the difficulty of the questions in real time based on the candidate's answers. Step 3: The job offer decision department comprehensively evaluates the results of the document screening department and the interview support department to make a job offer. For example, the generative AI comprehensively evaluates the results of the document screening and interview, and conducts a detailed analysis of the candidate's past work history and performance. This ensures an objective and fair hiring process that eliminates bias.

[0054] (Example 2) The recruitment process system according to an embodiment of the present invention utilizes generative AI to eliminate bias, comprehensively supports all processes from document screening, interviews, and job offers, and realizes objective and fair recruitment based on the candidate's skills and aptitude. This allows companies to respect diversity and hire excellent talent, thereby building a more innovative and sustainable business environment.

[0055] The hiring process system according to the embodiment includes a document screening unit, an interview support unit, and a job offer decision unit. The document screening unit analyzes an applicant's resume or work history. For example, the document screening unit uses a generation AI to analyze the applicant's resume or work history and extract specific skill sets and years of experience. The document screening unit can also analyze the applicant's past projects and achievements to quantify specific achievements and contributions. The document screening unit can also use an emotion estimation function to evaluate the applicant's emotional strengths and motivations from their self-promotional statements. For example, the generation AI analyzes the applicant's resume or work history and extracts specific skill sets. The applicant's skill level is then evaluated by comparing them with industry-standard skill sets. The generation AI evaluates the skill sets extracted from the applicant's work history against the latest industry trends and technological developments. The generation AI analyzes the applicant's skill sets and compares them with an industry-standard skill matrix. The interview support unit generates a list of questions to be used during interviews based on the resume or work history analyzed by the document screening unit. For example, the generation AI generates a list of questions to be used during interviews and adjusts the difficulty of the questions in real time based on the candidate's answers. During the interview, the generation AI analyzes the candidate's answers and adjusts the difficulty of the questions according to the content of the answers. The generation AI supports the progress of the interview and adjusts the difficulty of the questions in real time based on the candidate's answers. The job offer decision unit comprehensively evaluates the results of the document screening unit and the interview support unit to decide on a job offer. For example, the generation AI comprehensively evaluates the results of the document screening and interview, and analyzes the candidate's past work history and performance in detail. In the job offer decision process, the generation AI analyzes the candidate's past work history and performance in detail and reflects this in the evaluation. The generation AI comprehensively evaluates the results of the document screening and interview, and analyzes the candidate's past work history and performance in detail. As a result, the hiring process system according to the embodiment realizes an objective and fair hiring process that eliminates bias. For example, the generation AI comprehensively evaluates the results of the document screening and interview, and when comprehensively evaluating the candidate's skills, experience, and performance in the interview, it also takes into account the latest industry trends and the company's strategic goals.In the job offer decision process, generative AI comprehensively evaluates the candidate's skills, experience, and interview performance, and takes into account the company's strategic goals. When generative AI comprehensively evaluates the candidate's skills, experience, and interview performance, it takes into account the latest industry trends and the company's strategic goals.

[0056] The document screening department can analyze resumes or work histories and extract specific skill sets or years of experience. In the document screening department, for example, the generation AI analyzes an applicant's resume or work history and extracts specific skill sets. It then compares these with industry-standard skill sets to evaluate the applicant's skill level. The generation AI evaluates the skill sets extracted from the applicant's work history in light of the latest industry trends and technological developments. The generation AI analyzes the applicant's skill sets and compares them with an industry-standard skill matrix. This allows for an accurate evaluation of the applicant's skills and experience.

[0057] The document screening department can analyze resumes or work histories and quantify projects or achievements. In the document screening department, for example, the generation AI analyzes the applicant's work history and extracts past projects and achievements. It then quantifies specific achievements and contributions and reflects them in the evaluation. The generation AI analyzes the applicant's past project data and quantifies the project's scale, difficulty, and achievements. The generation AI analyzes the applicant's achievement data and quantifies specific achievements and contributions. This allows the applicant's specific achievements and contributions to be objectively evaluated.

[0058] The document screening department can use the emotion estimation function to evaluate emotional strengths or motivation from the self-promotional statement. For example, the document screening department uses the emotion estimation function to analyze the applicant's self-promotional statement and evaluate the emotional strengths and motivation. The emotion estimation function is used to identify emotional strengths from the applicant's self-promotional statement. The generation AI analyzes the applicant's self-promotional statement and evaluates motivation and passion using the emotion estimation function. This makes it possible to evaluate the applicant's emotional strengths and motivation.

[0059] The document review department can analyze external information such as social media activity or published papers to conduct a comprehensive evaluation. For example, the document review department uses a generation AI to analyze an applicant's social media activity and reflect job-related posts and activities in the evaluation. The generation AI analyzes the applicant's published papers and research results and reflects specialized knowledge and research ability in the evaluation. The generation AI comprehensively analyzes the applicant's external information and reflects job-related skills and experience in the evaluation. This makes it possible to conduct a comprehensive evaluation that includes the applicant's external information.

[0060] The document screening department anonymously shares the results of the document screening with other companies' recruitment data, allowing it to understand recruitment trends across the industry. For example, the document screening department uses generation AI to anonymously share the results of the document screening with other companies and analyze recruitment trends across the industry. By anonymously sharing the results of the document screening and comparing them with the recruitment data of other companies, it is possible to understand the recruitment standards across the industry. A system is built in which generation AI anonymously shares the results of the document screening and understands recruitment trends across the industry. This makes it possible to understand recruitment trends across the industry.

[0061] The document screening department can use the emotion estimation function to evaluate stress tolerance or teamwork ability from descriptions in a resume or work history. In the document screening department, for example, the generation AI analyzes the applicant's resume or work history and uses the emotion estimation function to evaluate stress tolerance. The emotion estimation function is used to evaluate teamwork ability from the applicant's resume or work history. The generation AI analyzes the applicant's resume or work history and uses the emotion estimation function to evaluate stress tolerance or teamwork ability. In this way, the applicant's stress tolerance and teamwork ability can be evaluated.

[0062] The interview support unit can generate a list of questions and adjust the difficulty of the questions in real time based on the candidate's answers. For example, the interview support unit generates a list of questions to be used by the generation AI during interviews and adjusts the difficulty of the questions in real time based on the candidate's answers. During the interview, the generation AI analyzes the candidate's answers and adjusts the difficulty of the questions according to the content of the answers. The generation AI supports the progress of the interview and adjusts the difficulty of the questions in real time based on the candidate's answers. This improves the quality of the interview and enables a more accurate evaluation of the candidate's skills and aptitude.

[0063] The interview support unit can analyze the candidate's non-verbal communication and make a comprehensive evaluation. In the interview support unit, for example, the generation AI analyzes the candidate's non-verbal communication during the interview and makes a comprehensive evaluation. During the interview, the generation AI analyzes the candidate's non-verbal communication in real time and reflects this in the evaluation. The generation AI supports the progress of the interview, analyzes the candidate's non-verbal communication, and makes a comprehensive evaluation. This makes it possible to make a comprehensive evaluation that includes the candidate's non-verbal communication.

[0064] The interview support unit can use the emotion estimation function to analyze the emotional state of a candidate during an interview in real time and provide appropriate feedback. The interview support unit, for example, uses a generation AI to analyze the emotional state of a candidate during an interview in real time and provide appropriate feedback. The emotion estimation function can be used to analyze the emotional state of a candidate during an interview and provide appropriate feedback to the interviewer. The generation AI can analyze the emotional state of a candidate during an interview in real time and provide appropriate feedback. This makes it possible to provide appropriate feedback according to the emotional state of the candidate during the interview.

[0065] The interview support unit can customize the question list according to different industries or job types. For example, the interview support unit customizes the question list used by the generation AI during interviews according to different industries or job types. The generation AI customizes the interview question list and provides questions that are suitable for a specific industry or job type. The generation AI customizes the interview question list according to different industries or job types and provides appropriate questions. In this way, it is possible to provide an appropriate question list according to the industry or job type.

[0066] The interview support unit can analyze a candidate's past interview data and propose the optimal question pattern. In the interview support unit, for example, the generation AI analyzes a candidate's past interview data and proposes the optimal question pattern. The generation AI analyzes a candidate's past interview data and optimizes the order and content of questions. The generation AI analyzes a candidate's past interview data and proposes the optimal question pattern. This makes it possible to provide the optimal question pattern based on the past interview data.

[0067] The interview support unit uses the emotion estimation function to analyze the emotional state of the candidate during the interview and propose an appropriate response method to the interviewer. For example, the interview support unit uses the emotion estimation function to analyze the emotional state of the candidate during the interview and propose an appropriate response method to the interviewer. The generation AI analyzes the emotional state of the candidate during the interview and proposes an appropriate response method to the interviewer. This makes it possible to propose an appropriate response method according to the emotional state of the candidate during the interview.

[0068] The job offer decision department can comprehensively evaluate the results of the document review or interview, and analyze the candidate's work history or achievements in detail, and reflect this in the decision to offer the candidate. In the job offer decision department, for example, the generation AI can comprehensively evaluate the results of the document review or interview, and analyze the candidate's past work history and achievements in detail. In the job offer decision process, the generation AI can analyze the candidate's past work history and achievements in detail, and reflect this in the evaluation. The generation AI can comprehensively evaluate the results of the document review or interview, and analyze the candidate's past work history and achievements in detail. This allows the candidate's past work history and achievements to be analyzed in detail and reflected in the decision to offer the candidate.

[0069] The job offer decision department can also take into account the latest industry trends or the company's strategic goals when comprehensively evaluating a candidate's skills or experience, and performance in an interview. For example, the job offer decision department takes into account the latest industry trends when the generation AI comprehensively evaluates a candidate's skills, experience, and performance in an interview. In the job offer decision process, the generation AI comprehensively evaluates a candidate's skills, experience, and performance in an interview, and takes into account the company's strategic goals. When the generation AI comprehensively evaluates a candidate's skills, experience, and performance in an interview, it takes into account the latest industry trends and the company's strategic goals. This makes it possible to make a comprehensive evaluation that takes into account the latest industry trends and the company's strategic goals.

[0070] The job offer decision unit can use the emotion estimation function to evaluate the emotional state or motivation of a candidate during an interview and reflect this in the decision to offer the candidate. For example, the job offer decision unit uses the generation AI to analyze the emotional state of a candidate during an interview in real time and reflect this in the decision to offer the candidate. The emotion estimation function can be used to evaluate the emotional state of a candidate during an interview and reflect this in the decision to offer the candidate. The generation AI can analyze the emotional state of a candidate during an interview in real time and reflect this in the decision to offer the candidate. This makes it possible to make job offers that take into account the emotional state and motivation of the candidate.

[0071] The job offer decision unit can customize the job offer decision process according to different industries or job types. In the job offer decision unit, for example, the generation AI customizes the job offer decision process according to different industries or job types. The generation AI customizes the job offer decision process and provides evaluation criteria that are suitable for specific industries or job types. The generation AI customizes the job offer decision process according to different industries or job types and provides appropriate evaluation criteria. This makes it possible to provide an appropriate job offer decision process according to industry or job type.

[0072] The job offer decision unit can convert the job offer decision process into a visual note or mind map to make it easier to understand visually. For example, the job offer decision unit converts the job offer decision process into a visual note using a generation AI to make it easier to understand visually. The job offer decision unit converts the job offer decision process into a mind map format to visually organize related keywords and concepts. A tool that automatically generates visual notes and mind maps is developed to enable users to easily visually display the job offer decision process. This makes it easier to understand visually the job offer decision process.

[0073] The job offer decision unit can use the emotion estimation function to analyze the candidate's emotional response to the job offer decision process and propose the optimal job offer notification method. In the job offer decision unit, for example, the generation AI analyzes the candidate's emotional response to the job offer decision process and proposes the optimal job offer notification method. The emotion estimation function analyzes the candidate's emotional response to the job offer decision process and optimizes the job offer notification method. The generation AI analyzes the candidate's emotional response to the job offer decision process and proposes the optimal job offer notification method. This makes it possible to propose the optimal job offer notification method based on the candidate's emotional response.

[0074] When comprehensively evaluating the results of document screening or interviews, a candidate's work history or achievements can be analyzed in detail and reflected in the evaluation. When comprehensively evaluating the results of document screening or interviews, for example, generation AI analyzes a candidate's past work history and achievements in detail. When comprehensively evaluating the results of document screening or interviews, generation AI analyzes a candidate's past work history and achievements in detail and reflects this in the evaluation. Generation AI comprehensively evaluates the results of document screening and interviews and analyzes a candidate's past work history and achievements in detail. This allows a candidate's past work history and achievements to be analyzed in detail and reflected in the evaluation.

[0075] When comprehensively evaluating the results of a document review or an interview, the latest industry trends or the strategic goals of the company can also be taken into consideration. When comprehensively evaluating the results of a document review or an interview, for example, the generation AI takes into consideration the latest industry trends. When comprehensively evaluating the results of a document review or an interview, the generation AI takes into consideration the latest industry trends and the strategic goals of the company. When comprehensively evaluating the results of a document review or an interview, the generation AI takes into consideration the latest industry trends and the strategic goals of the company. This makes it possible to make a comprehensive evaluation that takes into consideration the latest industry trends and the strategic goals of the company.

[0076] When comprehensively evaluating the results of a document review or interview, the emotion estimation function can be used to evaluate the candidate's emotional state or motivation during the interview and reflect this in the overall evaluation. When comprehensively evaluating the results of a document review or interview, for example, the generation AI can analyze the candidate's emotional state during the interview in real time and reflect this in the overall evaluation. The emotion estimation function can be used to evaluate the candidate's emotional state during the interview and reflect this in the overall evaluation. The generation AI can analyze the candidate's emotional state during the interview in real time and reflect this in the overall evaluation. This makes it possible to perform an overall evaluation that takes into account the candidate's emotional state and motivation.

[0077] The results of document screening or interviews can be customized for different industries or job types. The results of document screening or interviews are customized for different industries or job types. For example, a generation AI customizes the results of document screening or interviews for different industries or job types. A generation AI customizes the results of document screening or interviews to provide evaluation criteria suitable for specific industries or job types. A generation AI customizes the results of document screening or interviews for different industries or job types to provide appropriate evaluation criteria. This makes it possible to provide appropriate evaluation criteria for different industries and job types.

[0078] The results of document screening or interviews can be converted into visual notes or mind maps to make them easier to understand visually. The results of document screening or interviews can be converted into visual notes to make them easier to understand visually. For example, generative AI can convert the results of document screening or interviews into visual notes to make them easier to understand visually. The results of document screening or interviews can be converted into mind map format to visually organize related keywords and concepts. A tool that automatically generates visual notes and mind maps can be developed to enable users to easily visually display the results of document screening or interviews. This makes the results of document screening or interviews easier to understand visually.

[0079] It is possible to analyze a candidate's emotional response to the results of a document screening or interview and propose the optimal evaluation method. It is possible to analyze a candidate's emotional response to the results of a document screening or interview and propose the optimal evaluation method. For example, the generative AI analyzes a candidate's emotional response to the results of a document screening or interview and propose the optimal evaluation method. Using the emotion estimation function, the candidate's emotional response to the results of a document screening or interview is analyzed and the evaluation method is optimized. The generative AI analyzes a candidate's emotional response to the results of a document screening or interview and propose the optimal evaluation method. This makes it possible to propose the optimal evaluation method based on the candidate's emotional response.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The recruitment process system can further include a health assessment unit that evaluates the candidate's health condition. The health assessment unit, for example, analyzes the candidate's health check results and self-reported health information to assess whether the candidate's health condition is suitable for the job. The generative AI analyzes the candidate's health check results and evaluates specific health risks. The health assessment unit analyzes the candidate's self-reported health information to assess whether the candidate's health condition is suitable for the job. This enables companies to make hiring decisions that take the candidate's health condition into consideration.

[0082] The recruitment process system can further include a cultural fit assessment unit that evaluates the cultural fit of candidates. The cultural fit assessment unit, for example, analyzes the candidate's past work history and self-promotional statement to assess whether they fit into the corporate culture. The generation AI analyzes the candidate's past work history and assesses whether they fit into the corporate culture. The cultural fit assessment unit analyzes the candidate's self-promotional statement to assess whether they fit into the corporate culture. This enables companies to make hiring decisions that take into account the candidate's cultural fit.

[0083] The hiring process system can further include a learning ability assessment unit that evaluates the candidate's learning ability. The learning ability assessment unit, for example, analyzes the candidate's past learning history and self-learning efforts to assess their learning ability. The generative AI analyzes the candidate's past learning history and assesses their learning ability. The learning ability assessment unit analyzes the candidate's self-learning efforts and assesses their learning ability. This enables companies to make hiring decisions that take the candidate's learning ability into consideration.

[0084] The recruitment process system can further include a leadership evaluation unit that evaluates the leadership ability of candidates. The leadership evaluation unit, for example, analyzes the candidate's past project leadership experience and self-promotional statement to evaluate their leadership ability. The generation AI analyzes the candidate's past project leadership experience and evaluates their leadership ability. The leadership evaluation unit analyzes the candidate's self-promotional statement to evaluate their leadership ability. This enables companies to make hiring decisions that take into account the candidate's leadership ability.

[0085] The recruitment process system can further include a creativity evaluation unit that evaluates the creativity of candidates. The creativity evaluation unit, for example, analyzes the candidate's past projects and self-promotional statements to evaluate their creativity. The generative AI analyzes the candidate's past projects and evaluates their creativity. The creativity evaluation unit analyzes the candidate's self-promotional statements to evaluate their creativity. This enables companies to make hiring decisions that take into account the candidate's creativity.

[0086] The recruitment process system can also use the candidate's emotion estimation function to assess the candidate's stress level during the interview. For example, the generative AI analyzes the candidate's facial expressions and tone of voice during the interview to assess their stress level. Using the emotion estimation function, the system can analyze the candidate's stress level during the interview in real time and suggest appropriate ways to respond to the interviewer. This makes it possible to respond appropriately, taking into account the candidate's stress level during the interview.

[0087] The recruitment process system can also use the candidate's emotion estimation function to evaluate the candidate's motivation during the interview. For example, the generative AI analyzes the candidate's facial expressions and tone of voice during the interview to evaluate their motivation. Using the emotion estimation function, the system can analyze the candidate's motivation during the interview in real time and suggest appropriate ways to respond to the interviewer. This makes it possible to respond appropriately, taking into account the candidate's motivation during the interview.

[0088] The recruitment process system can also use the candidate's emotion estimation function to evaluate the candidate's confidence level during the interview. For example, the generative AI analyzes the candidate's facial expressions and tone of voice during the interview to evaluate their confidence level. Using the emotion estimation function, the system can analyze the candidate's confidence level during the interview in real time and suggest appropriate responses to the interviewer. This enables appropriate responses that take into account the candidate's confidence level during the interview.

[0089] The recruitment process system can also use the candidate's emotion estimation function to evaluate the candidate's sincerity during the interview. For example, the generative AI analyzes the candidate's facial expressions and tone of voice during the interview to evaluate sincerity. Using the emotion estimation function, the candidate's sincerity during the interview can be analyzed in real time and an appropriate response can be suggested to the interviewer. This makes it possible to respond appropriately, taking into account the candidate's sincerity during the interview.

[0090] The recruitment process system can also use the candidate's emotion estimation function to assess the candidate's level of nervousness during the interview. For example, the generative AI analyzes the candidate's facial expressions and tone of voice during the interview to assess the candidate's level of nervousness. Using the emotion estimation function, the system can analyze the candidate's level of nervousness during the interview in real time and suggest appropriate ways to respond to the interviewer. This makes it possible to respond appropriately, taking into account the candidate's level of nervousness during the interview.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The document screening department analyzes the applicant's resume or work history. For example, generative AI can be used to extract specific skill sets and years of experience, and past projects and achievements can be analyzed to quantify specific achievements and contributions. Emotion estimation can also be used to assess emotional strengths and motivations from the applicant's self-promotional statement. Step 2: The interview support department generates a list of questions to be used during interviews based on the resume or work history analyzed by the document screening department. For example, the generation AI generates a list of questions to be used during interviews and adjusts the difficulty of the questions in real time based on the candidate's answers. Step 3: The job offer decision department comprehensively evaluates the results of the document screening department and the interview support department to make a job offer. For example, the generative AI comprehensively evaluates the results of the document screening and interview, and conducts a detailed analysis of the candidate's past work history and performance. This ensures an objective and fair hiring process that eliminates bias.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A document screening department that analyzes applicants' resumes or work histories; an interview support unit that generates a list of questions to be used during an interview based on the resume or the curriculum vitae analyzed by the document screening unit; a job offer decision unit that comprehensively evaluates the results of the document screening unit and the interview support unit and decides on a job offer; A system characterized by:

2. The document examination department Parsing the resume or CV to extract specific skill sets or years of experience 2. The system of claim 1.

3. The document examination department Analyze the resume or CV and quantify projects or achievements 2. The system of claim 1.

4. The document examination department Evaluate emotional strengths or motivations from personal statements 2. The system of claim 1.

5. The document examination department Analyze external information from social media activities or published papers to make the comprehensive evaluation.

2. The system of claim 1.

6. The document examination department The results of the document screening will be anonymously shared with the recruitment data of the other companies to understand the recruitment trends of the entire industry.

2. The system of claim 1.

7. The document examination department Evaluate stress tolerance or teamwork ability from the resume or curriculum vitae 2. The system of claim 1.

8. The interview support department: Generate the list of questions and adjust the difficulty of the questions in real time based on the candidate's answers 2. The system of claim 1.

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