system

Abstract

An object of the system according to the embodiment is to automate digitization and analysis of a resume and to efficiently evaluate the aptitude of a candidate.SOLUTION: A system according to an embodiment includes an OCR technology unit, a machine learning algorithm unit, a suitability evaluation unit, and an interface unit. The OCR technology department digitizes the resume using OCR technology. The machine learning algorithm unit analyzes the resume digitized by the OCR technology unit. The aptitude evaluation unit evaluates the aptitude of the candidate on the basis of the result analyzed by the machine learning algorithm unit. The interface unit displays a result evaluated by the aptitude evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, digitizing and analyzing resumes was often done manually, which was time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to automate the digitization and analysis of resumes and efficiently evaluate the suitability of candidates. [Means for solving the problem]

[0006] The system according to the embodiment includes an OCR technology unit, a machine learning algorithm unit, an aptitude evaluation unit, and an interface unit. The OCR technology unit digitizes a resume using OCR technology. The machine learning algorithm unit analyzes the resume digitized by the OCR technology unit. The aptitude evaluation unit evaluates the aptitude of a candidate based on the results of the analysis by the machine learning algorithm unit. The interface unit displays the results of the evaluation by the aptitude evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the digitization and analysis of resumes, enabling efficient evaluation of candidates' suitability. [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 AI-assisted platform according to an embodiment of the present invention is a system that provides an innovative time-saving solution for recruiters and human resources managers. This system utilizes the latest OCR technology and machine learning algorithms to quickly and precisely analyze resumes and efficiently evaluate candidates' aptitude and skills. This allows the AI-assisted platform to significantly streamline the recruitment process and assist in the selection of the most suitable candidates.

[0029] The AI-assisted platform according to the embodiment includes an OCR technology unit, a machine learning algorithm unit, an aptitude assessment unit, and an interface unit. The OCR technology unit digitizes resumes. For example, handwritten resumes are digitized and read using scanning technology. PDF-format resumes can also be directly read. Furthermore, the OCR technology unit can read printed resumes using OCR technology. For example, the OCR technology unit scans handwritten resumes with a high-resolution scanner and converts them into text information using OCR technology. PDF-format resumes submitted in a specific file format can also be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The machine learning algorithm unit analyzes the resumes digitized by the OCR technology unit. For example, the machine learning algorithm unit analyzes the resume content using a neural network. The machine learning algorithm unit can also analyze the resume content using a support vector machine. The machine learning algorithm unit can also analyze the resume content using a decision tree. For example, neural networks learn from large amounts of data and have advanced pattern recognition capabilities. Support vector machines excel at data classification and regression analysis. Decision trees perform classification and prediction based on data features. The aptitude assessment unit evaluates the suitability of candidates based on the results of analysis by the machine learning algorithm unit. For example, the aptitude assessment unit performs skill matching to evaluate the degree of match between the candidate's skills and the job requirements. The aptitude assessment unit can also perform personality assessment to evaluate the candidate's personality traits. The aptitude assessment unit can also perform experience assessment to evaluate the candidate's work experience. For example, skill matching compares the candidate's skill set with the job requirements and scores the degree of match. Personality assessment evaluates the candidate's personality traits based on psychological evaluation criteria. Experience assessment quantitatively evaluates the candidate's work experience. The interface unit displays the results of the evaluation by the aptitude assessment unit. For example, the interface unit allows users to upload resumes using drag-and-drop and provides a dashboard that allows users to visually check the analysis results. The interface unit can also display the analysis results in graphs and charts.The interface unit can also output the analysis results in report format. For example, the dashboard allows for intuitive operation and is easy for recruiters to use. Graphs and charts visually display the analysis results, making them easy to understand. The report format describes the analysis results in detail and allows them to be referenced later. This allows the AI-assisted platform according to the embodiment to automate a series of processes from digitizing resumes to aptitude assessment, thereby streamlining the recruitment process. For example, the output unit displays the recruitment results to recruiters via a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results by email provides quick feedback by sending the results directly to recruiters.

[0030] The OCR technology department analyzes handwritten characters to estimate a candidate's personality and stress level. For example, the OCR technology department scans a handwritten resume and uses a generation AI to analyze the characteristics of the handwriting. For example, the OCR technology department estimates a candidate's personality and stress level based on changes in character size, angle, and writing pressure. The OCR technology department also digitizes the handwritten characters using OCR technology, and a generation AI analyzes the handwriting patterns. For example, it evaluates the consistency and variation of the handwriting to estimate the candidate's psychological state. After digitizing the handwritten characters, the generation AI also performs a detailed analysis of the handwriting to evaluate the candidate's personality traits and stress level. For example, it analyzes changes in writing speed and pressure. This allows for estimation of the candidate's personality and stress level, enabling more appropriate candidate selection.

[0031] The OCR technology department can analyze images and diagrams in resumes to evaluate a candidate's creativity. For example, the OCR technology department digitizes images and diagrams in resumes using OCR technology, and the generation AI analyzes the content. For example, it evaluates the ingenuity of the design and layout to determine the candidate's creativity. The OCR technology department also uses OCR technology to digitize visual elements in resumes, and the generation AI analyzes the content of the images and diagrams. For example, it evaluates the composition and color usage of diagrams to estimate the candidate's creativity. The OCR technology department also digitizes images and diagrams in resumes using OCR technology, and the generation AI analyzes the visual information. For example, it evaluates the uniqueness of the placement and design of diagrams to determine the candidate's creativity. This allows the selection of highly creative candidates by evaluating their creativity.

[0032] In addition to digitizing resumes, the OCR technology department can simultaneously digitize candidates' portfolios and project materials, allowing for comprehensive evaluations. For example, the OCR technology department scans a candidate's portfolio and project materials along with their resume and digitizes them using OCR technology. This allows the generation AI to perform a comprehensive evaluation. The OCR technology department also uses OCR technology to digitize resumes, portfolios, and project materials, and the generation AI analyzes their contents. For example, it evaluates project details and deliverables. The OCR technology department also digitizes a candidate's portfolio and project materials along with their resume, allowing the generation AI to perform a comprehensive evaluation. For example, it analyzes the project's progress and results. This allows for a comprehensive evaluation that includes the candidate's portfolio and project materials.

[0033] The OCR technology department can automatically translate resumes in different languages, enabling the evaluation of international candidates. For example, the OCR technology department inputs resumes digitized with OCR technology into an automatic translation system to translate resumes in different languages. This enables the evaluation of international candidates. The OCR technology department also digitizes resumes with OCR technology and translates them into different languages ​​using an automatic translation system. For example, from English to Japanese, or from French to English. The OCR technology department also inputs resumes digitized with OCR technology into an automatic translation system to translate them into multiple languages. This makes it easier to evaluate international candidates. This enables the evaluation of international candidates by automatically translating resumes in different languages.

[0034] The machine learning algorithm unit can predict a candidate's career path and evaluate their growth potential. For example, the machine learning algorithm unit inputs digital data from a resume into the generation AI to predict the candidate's career path. For example, it evaluates future growth potential based on past work history and skill set. The machine learning algorithm unit also analyzes the contents of the resume using the generation AI to predict the candidate's career path. For example, it evaluates growth potential based on past work experience and skill evolution. The machine learning algorithm unit also inputs digital data from a resume into the generation AI to predict the candidate's career path. For example, it evaluates future growth potential based on past work history and skill set. This makes it possible to hire with a long-term perspective by evaluating a candidate's future growth potential.

[0035] The machine learning algorithm department can develop an algorithm to evaluate a candidate's soft skills. For example, the machine learning algorithm department inputs digital data of a resume into the generation AI and develops an algorithm to evaluate soft skills. For example, by analyzing written expression and project leadership experience. The machine learning algorithm department also develops an algorithm to analyze the content of a resume using the generation AI and evaluate soft skills. For example, by evaluating communication skills and teamwork experience. The machine learning algorithm department also develops an algorithm to input digital data of a resume into the generation AI and evaluate soft skills. For example, by analyzing written expression and project leadership experience. In this way, by evaluating the candidate's soft skills, it is possible to select candidates with communication skills and leadership skills.

[0036] The machine learning algorithm unit can compare the results of the resume analysis with the data of other candidates and perform a relative evaluation. For example, the machine learning algorithm unit inputs the digital data of the resume into the generation AI and compares it with the data of other candidates. For example, a relative evaluation is performed based on work history and skill set. The machine learning algorithm unit also uses the generation AI to analyze the contents of the resume and compares it with the data of other candidates. For example, a relative evaluation is performed based on work experience and educational background. The machine learning algorithm unit also inputs the digital data of the resume into the generation AI and compares it with the data of other candidates. For example, a relative evaluation is performed based on work history and skill set. This makes it possible to compare the candidate with other candidates by performing a relative evaluation.

[0037] The machine learning algorithm unit can compare the results of resume analysis with the company's culture and values ​​to evaluate cultural fit. For example, the machine learning algorithm unit inputs the digital data of a resume into the generation AI and evaluates cultural fit by comparing it with the company's culture and values. For example, it analyzes whether it matches the company's mission and vision. The machine learning algorithm unit can also use the generation AI to analyze the contents of a resume and evaluate cultural fit by comparing it with the company's culture and values. For example, it can evaluate whether it matches the company's core values. The machine learning algorithm unit can also input the digital data of a resume into the generation AI and evaluate cultural fit by comparing it with the company's culture and values. For example, it can analyze whether it matches the company's mission and vision. This makes it possible to select candidates who match the company's culture and values.

[0038] The aptitude assessment unit can analyze a candidate's past performance and project success rate, and conduct an aptitude assessment based on performance. For example, the aptitude assessment unit inputs digital resume data into the generation AI and analyzes past performance and project success rate. For example, it conducts an aptitude assessment based on the project completion rate and the quality of the deliverables. The aptitude assessment unit also uses the generation AI to analyze the contents of the resume and evaluates past performance and project success rate. For example, it determines aptitude based on the project progress and the quality of the deliverables. The aptitude assessment unit also inputs digital resume data into the generation AI and analyzes past performance and project success rate. For example, it conducts an aptitude assessment based on the project completion rate and the quality of the deliverables. In this way, by analyzing a candidate's past performance and project success rate, it becomes possible to conduct an aptitude assessment based on performance.

[0039] The aptitude evaluation unit can analyze a candidate's network and evaluate their reputation and trustworthiness within the industry. For example, the aptitude evaluation unit inputs digital resume data into the generation AI and analyzes the candidate's LinkedIn profile. For example, it determines their reputation and trustworthiness within the industry based on letters of recommendation and the number of skill endorsements. The aptitude evaluation unit also uses the generation AI to analyze a candidate's network and evaluates their reputation and trustworthiness within the industry. For example, it evaluates them based on connections within the industry and the contents of letters of recommendation. The aptitude evaluation unit also inputs digital resume data into the generation AI and analyzes the candidate's LinkedIn profile. For example, it determines their reputation and trustworthiness within the industry based on letters of recommendation and the number of skill endorsements. In this way, by analyzing a candidate's network, it is possible to understand their reputation and trustworthiness within the industry.

[0040] The aptitude evaluation unit can analyze a candidate's hobbies and interests and evaluate their relevance to the job. For example, the aptitude evaluation unit inputs digital data of a resume into the generation AI and analyzes the candidate's hobbies and interests. For example, it evaluates the degree to which the hobbies and interests are related to the job. The aptitude evaluation unit also analyzes the contents of the resume using the generation AI and evaluates the candidate's hobbies and interests. For example, it determines the degree to which the hobbies and interests are related to the job. The aptitude evaluation unit also inputs digital data of the resume into the generation AI and analyzes the candidate's hobbies and interests. For example, it evaluates the degree to which the hobbies and interests are related to the job. In this way, by analyzing a candidate's hobbies and interests, it is possible to evaluate their relevance to the job.

[0041] The aptitude assessment unit can analyze the candidate's social media activity and evaluate their social influence. For example, the aptitude assessment unit inputs digital data of a resume into the generation AI and analyzes the candidate's social media activity. For example, it evaluates social influence based on the number of followers and the content of posts. The aptitude assessment unit also uses the generation AI to analyze the candidate's social media activity and evaluate their social influence. For example, it determines influence based on the number of followers and the content of posts. The aptitude assessment unit also inputs digital data of a resume into the generation AI and analyzes the candidate's social media activity. For example, it evaluates social influence based on the number of followers and the content of posts. In this way, it is possible to evaluate a candidate's social influence by analyzing their social media activity.

[0042] The interface unit can use the generation AI to analyze the user's operation history and provide the optimal operation guide. The interface unit, for example, can use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can prioritize displaying frequently used functions. The interface unit can also use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can display a guide that is suitable for the user based on past operation history. The interface unit can also use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can prioritize displaying frequently used functions. In this way, it is possible to provide the optimal operation guide by analyzing the user's operation history.

[0043] The interface unit can use the generation AI to collect user feedback in real time and reflect it in improving the interface. The interface unit, for example, uses the generation AI to collect user feedback in real time and reflect it in improving the interface. For example, adjusting the design based on the user's opinion. The interface unit can also use the generation AI to collect user feedback in real time and reflect it in improving the interface. For example, adding a function based on the user's opinion. The interface unit can also use the generation AI to collect user feedback in real time and reflect it in improving the interface. For example, adjusting the design based on the user's opinion. This allows user feedback to be collected in real time and reflected in improving the interface.

[0044] The interface unit can use the generation AI to analyze the user's operation pattern and provide an individually customized operation guide. The interface unit, for example, can use the generation AI to analyze the user's operation pattern and provide an individually customized operation guide. For example, it displays an optimal guide based on the user's operation history. The interface unit can also use the generation AI to analyze the user's operation pattern and provide an individually customized operation guide. For example, it displays an optimal guide based on the user's operation history. The interface unit can also use the generation AI to analyze the user's operation pattern and provide an individually customized operation guide. For example, it displays an optimal guide based on the user's operation history. In this way, by analyzing the user's operation pattern, it is possible to provide an individually customized operation guide.

[0045] The interface unit can use the generation AI to analyze the user's operation history and provide the optimal operation guide. The interface unit, for example, can use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can prioritize displaying frequently used functions. The interface unit can also use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can display a guide that is suitable for the user based on past operation history. The interface unit can also use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can prioritize displaying frequently used functions. In this way, it is possible to provide the optimal operation guide by analyzing the user's operation history.

[0046] The aptitude assessment unit uses the generation AI to track the candidate's progress from application to hiring in real time, enabling efficient management. The aptitude assessment unit, for example, uses the generation AI to track the candidate's progress from application to hiring in real time, enabling efficient management. For example, the progress status of each step is displayed on a dashboard. The aptitude assessment unit also uses the generation AI to track the candidate's progress from application to hiring in real time, enabling efficient management. For example, the progress status of each step is displayed on a dashboard. The aptitude assessment unit also uses the generation AI to track the candidate's progress from application to hiring in real time, enabling efficient management. For example, the progress status of each step is displayed on a dashboard. This allows the candidate's progress from application to hiring to be tracked in real time, enabling efficient management.

[0047] The aptitude assessment unit can use the generation AI to automatically adjust the interview schedule of a candidate, thereby realizing an efficient interview process. The aptitude assessment unit can, for example, use the generation AI to automatically adjust the interview schedule of a candidate, thereby realizing an efficient interview process. For example, it can automatically match the available times of the candidate and the interviewer. The aptitude assessment unit can also use the generation AI to automatically adjust the interview schedule of a candidate, thereby realizing an efficient interview process. For example, it can automatically match the available times of the candidate and the interviewer. The aptitude assessment unit can also use the generation AI to automatically adjust the interview schedule of a candidate, thereby realizing an efficient interview process. For example, it can automatically match the available times of the candidate and the interviewer. This allows the candidate's interview schedule to be automatically adjusted, thereby realizing an efficient interview process.

[0048] The aptitude evaluation unit uses the generation AI to automatically compile the evaluation results of candidates, allowing the most suitable candidate to be selected quickly. The aptitude evaluation unit, for example, uses the generation AI to automatically compile the evaluation results of candidates, allowing the most suitable candidate to be selected quickly. For example, the results of interview evaluations and skill tests are automatically compiled. The aptitude evaluation unit also uses the generation AI to automatically compile the evaluation results of candidates, allowing the most suitable candidate to be selected quickly. For example, the results of interview evaluations and skill tests are automatically compiled. The aptitude evaluation unit also uses the generation AI to automatically compile the evaluation results of candidates, allowing the most suitable candidate to be selected quickly. For example, the results of interview evaluations and skill tests are automatically compiled. This allows the most suitable candidate to be selected quickly.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] In addition to digitizing resumes, the OCR technology department can simultaneously digitize candidates' portfolios and project materials, allowing for a comprehensive evaluation. For example, a candidate's portfolio and project materials can be scanned along with the resume and digitized using OCR technology. This allows the generation AI to perform a comprehensive evaluation. The OCR technology department also uses OCR technology to digitize resumes, portfolios, and project materials, and the generation AI analyzes their contents. For example, it evaluates project details and deliverables. The OCR technology department also digitizes a candidate's portfolio and project materials along with the resume, allowing the generation AI to perform a comprehensive evaluation. For example, it analyzes the project's progress and results. This allows for a comprehensive evaluation that also includes the candidate's portfolio and project materials.

[0051] The OCR technology department can automatically translate resumes in different languages, enabling the evaluation of international candidates. For example, resumes digitized with OCR technology are input into an automatic translation system to translate resumes in different languages. This makes it possible to evaluate international candidates. The OCR technology department also digitizes resumes with OCR technology and translates them into different languages ​​using an automatic translation system. For example, from English to Japanese, or from French to English. The OCR technology department also inputs resumes digitized with OCR technology into an automatic translation system to translate them into multiple languages. This makes it easy to evaluate international candidates. This makes it possible to automatically translate resumes in different languages ​​to evaluate international candidates.

[0052] The machine learning algorithm unit can predict a candidate's career path and evaluate their growth potential. For example, digital resume data can be input into the generative AI to predict the candidate's career path. For example, future growth potential can be evaluated based on past work history and skill set. The machine learning algorithm unit can also use the generative AI to analyze the contents of the resume and predict the candidate's career path. For example, growth potential can be evaluated based on past work experience and skill evolution. The machine learning algorithm unit can also input digital resume data into the generative AI to predict the candidate's career path. For example, future growth potential can be evaluated based on past work history and skill set. This makes it possible to hire with a long-term perspective by evaluating a candidate's future growth potential.

[0053] The machine learning algorithm department can develop an algorithm to evaluate a candidate's soft skills. For example, digital data from a resume is input into a generation AI to develop an algorithm to evaluate soft skills. For example, by analyzing written expression and project leadership experience. The machine learning algorithm department can also develop an algorithm to analyze the content of a resume using a generation AI to evaluate soft skills. For example, by evaluating communication skills and teamwork experience. The machine learning algorithm department can also input digital data from a resume into a generation AI to develop an algorithm to evaluate soft skills. For example, by analyzing written expression and project leadership experience. In this way, by evaluating the candidate's soft skills, it is possible to select candidates with good communication skills and leadership skills.

[0054] The machine learning algorithm unit can compare the results of resume analysis with the data of other candidates and perform a relative evaluation. For example, the digital data of the resume is input into the generation AI and compared with the data of other candidates. For example, a relative evaluation is performed based on work history and skill set. The machine learning algorithm unit also uses the generation AI to analyze the contents of the resume and compare it with the data of other candidates. For example, a relative evaluation is performed based on work experience and educational background. The machine learning algorithm unit also inputs the digital data of the resume into the generation AI and compares it with the data of other candidates. For example, a relative evaluation is performed based on work history and skill set. This makes it possible to perform a relative evaluation of the candidate and compare it with other candidates.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The OCR technology department digitizes the resume. For example, a handwritten resume can be digitized and read using scanning technology. It can also directly read PDF-format resumes. The OCR technology department can also read printed resumes using OCR technology. For example, the OCR technology department scans a handwritten resume with a high-resolution scanner and converts it into text information using OCR technology. PDF-format resumes submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: The machine learning algorithm section analyzes the resume digitized by the OCR technology section. For example, the machine learning algorithm section analyzes the contents of the resume using a neural network. It can also analyze the contents of the resume using a support vector machine or decision tree. Neural networks have learned from large amounts of data and have advanced pattern recognition capabilities. Support vector machines excel at data classification and regression analysis. Decision trees perform classification and prediction based on the characteristics of the data. Step 3: The aptitude evaluation unit evaluates the candidate's aptitude based on the results analyzed by the machine learning algorithm unit. For example, the aptitude evaluation unit performs skill matching to evaluate the degree of match between the candidate's skills and the job requirements. It can also perform a personality assessment to evaluate the candidate's personality traits. It can also perform an experience assessment to evaluate the candidate's work experience. Skill matching compares the candidate's skill set with the job requirements and scores the degree of match. Personality assessment evaluates the candidate's personality traits based on psychological evaluation criteria. Experience assessment quantitatively evaluates the candidate's work experience. Step 4: The interface unit displays the results of the evaluation by the aptitude evaluation unit. For example, the interface unit provides a dashboard that allows users to upload resumes using drag-and-drop and visually check the analysis results. The analysis results can also be displayed in graphs and charts. Furthermore, the analysis results can be output in report format. The dashboard is intuitive and easy for recruiters to use. The graphs and charts display the analysis results visually, making them easy to understand. The report format describes the analysis results in detail so that they can be referenced later.

[0057] (Example 2) The AI-assisted platform according to an embodiment of the present invention is a system that provides an innovative time-saving solution for recruiters and human resources managers. This system utilizes the latest OCR technology and machine learning algorithms to quickly and precisely analyze resumes and efficiently evaluate candidates' aptitude and skills. This allows the AI-assisted platform to significantly streamline the recruitment process and assist in the selection of the most suitable candidates.

[0058] The AI-assisted platform according to the embodiment includes an OCR technology unit, a machine learning algorithm unit, an aptitude assessment unit, and an interface unit. The OCR technology unit digitizes resumes. For example, handwritten resumes are digitized and read using scanning technology. PDF-format resumes can also be directly read. Furthermore, the OCR technology unit can read printed resumes using OCR technology. For example, the OCR technology unit scans handwritten resumes with a high-resolution scanner and converts them into text information using OCR technology. PDF-format resumes submitted in a specific file format can also be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The machine learning algorithm unit analyzes the resumes digitized by the OCR technology unit. For example, the machine learning algorithm unit analyzes the resume content using a neural network. The machine learning algorithm unit can also analyze the resume content using a support vector machine. The machine learning algorithm unit can also analyze the resume content using a decision tree. For example, neural networks learn from large amounts of data and have advanced pattern recognition capabilities. Support vector machines excel at data classification and regression analysis. Decision trees perform classification and prediction based on data features. The aptitude assessment unit evaluates the suitability of candidates based on the results of analysis by the machine learning algorithm unit. For example, the aptitude assessment unit performs skill matching to evaluate the degree of match between the candidate's skills and the job requirements. The aptitude assessment unit can also perform personality assessment to evaluate the candidate's personality traits. The aptitude assessment unit can also perform experience assessment to evaluate the candidate's work experience. For example, skill matching compares the candidate's skill set with the job requirements and scores the degree of match. Personality assessment evaluates the candidate's personality traits based on psychological evaluation criteria. Experience assessment quantitatively evaluates the candidate's work experience. The interface unit displays the results of the evaluation by the aptitude assessment unit. For example, the interface unit allows users to upload resumes using drag-and-drop and provides a dashboard that allows users to visually check the analysis results. The interface unit can also display the analysis results in graphs and charts.The interface unit can also output the analysis results in report format. For example, the dashboard allows for intuitive operation and is easy for recruiters to use. Graphs and charts visually display the analysis results, making them easy to understand. The report format describes the analysis results in detail and allows them to be referenced later. This allows the AI-assisted platform according to the embodiment to automate a series of processes from digitizing resumes to aptitude assessment, thereby streamlining the recruitment process. For example, the output unit displays the recruitment results to recruiters via a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results by email provides quick feedback by sending the results directly to recruiters.

[0059] The OCR technology department analyzes handwritten characters to estimate a candidate's personality and stress level. For example, the OCR technology department scans a handwritten resume and uses a generation AI to analyze the characteristics of the handwriting. For example, the OCR technology department estimates a candidate's personality and stress level based on changes in character size, angle, and writing pressure. The OCR technology department also digitizes the handwritten characters using OCR technology, and a generation AI analyzes the handwriting patterns. For example, it evaluates the consistency and variation of the handwriting to estimate the candidate's psychological state. After digitizing the handwritten characters, the generation AI also performs a detailed analysis of the handwriting to evaluate the candidate's personality traits and stress level. For example, it analyzes changes in writing speed and pressure. This allows for estimation of the candidate's personality and stress level, enabling more appropriate candidate selection.

[0060] The OCR technology department can analyze images and diagrams in resumes to evaluate a candidate's creativity. For example, the OCR technology department digitizes images and diagrams in resumes using OCR technology, and the generation AI analyzes the content. For example, it evaluates the ingenuity of the design and layout to determine the candidate's creativity. The OCR technology department also uses OCR technology to digitize visual elements in resumes, and the generation AI analyzes the content of the images and diagrams. For example, it evaluates the composition and color usage of diagrams to estimate the candidate's creativity. The OCR technology department also digitizes images and diagrams in resumes using OCR technology, and the generation AI analyzes the visual information. For example, it evaluates the uniqueness of the placement and design of diagrams to determine the candidate's creativity. This allows the selection of highly creative candidates by evaluating their creativity.

[0061] The OCR technology department uses the emotion estimation function to estimate a candidate's emotional state from the contents of their resume, allowing them to evaluate their level of stress and motivation. For example, the OCR technology department analyzes the text data of a resume and uses the emotion estimation function to estimate the candidate's emotional state. For example, it evaluates the level of stress and motivation based on the tone and expression of the text. The OCR technology department also analyzes the contents of a resume digitized by OCR technology using the emotion estimation function to estimate the candidate's emotional state. For example, it evaluates positive and negative expressions to determine emotional tendencies. The OCR technology department also analyzes the contents of a resume using the emotion estimation function to evaluate the candidate's emotional state. For example, it estimates the level of stress and motivation based on the structure of the sentences and the choice of words. In this way, it is possible to understand the level of stress and motivation by evaluating the candidate's emotional state.

[0062] In addition to digitizing resumes, the OCR technology department can simultaneously digitize candidates' portfolios and project materials, allowing for comprehensive evaluations. For example, the OCR technology department scans a candidate's portfolio and project materials along with their resume and digitizes them using OCR technology. This allows the generation AI to perform a comprehensive evaluation. The OCR technology department also uses OCR technology to digitize resumes, portfolios, and project materials, and the generation AI analyzes their contents. For example, it evaluates project details and deliverables. The OCR technology department also digitizes a candidate's portfolio and project materials along with their resume, allowing the generation AI to perform a comprehensive evaluation. For example, it analyzes the project's progress and results. This allows for a comprehensive evaluation that includes the candidate's portfolio and project materials.

[0063] The OCR technology department can automatically translate resumes in different languages, enabling the evaluation of international candidates. For example, the OCR technology department inputs resumes digitized with OCR technology into an automatic translation system to translate resumes in different languages. This enables the evaluation of international candidates. The OCR technology department also digitizes resumes with OCR technology and translates them into different languages ​​using an automatic translation system. For example, from English to Japanese, or from French to English. The OCR technology department also inputs resumes digitized with OCR technology into an automatic translation system to translate them into multiple languages. This makes it easier to evaluate international candidates. This enables the evaluation of international candidates by automatically translating resumes in different languages.

[0064] The OCR technology department can use the emotion estimation function to identify gaps between a candidate's self-assessment and their actual skills from the contents of their resume. For example, the OCR technology department analyzes the text data of a resume using the emotion estimation function to identify gaps between a candidate's self-assessment and their actual skills. For example, if the candidate has a high self-assessment but few achievements. The OCR technology department also analyzes the contents of a resume digitized using OCR technology using the emotion estimation function to identify gaps between a candidate's self-assessment and their actual skills. For example, if the candidate has a high self-assessment but few achievements. This allows for more accurate evaluations by identifying gaps between a candidate's self-assessment and their actual skills.

[0065] The machine learning algorithm unit can predict a candidate's career path and evaluate their growth potential. For example, the machine learning algorithm unit inputs digital data from a resume into the generation AI to predict the candidate's career path. For example, it evaluates future growth potential based on past work history and skill set. The machine learning algorithm unit also analyzes the contents of the resume using the generation AI to predict the candidate's career path. For example, it evaluates growth potential based on past work experience and skill evolution. The machine learning algorithm unit also inputs digital data from a resume into the generation AI to predict the candidate's career path. For example, it evaluates future growth potential based on past work history and skill set. This makes it possible to hire with a long-term perspective by evaluating a candidate's future growth potential.

[0066] The machine learning algorithm department can develop an algorithm to evaluate a candidate's soft skills. For example, the machine learning algorithm department inputs digital data of a resume into the generation AI and develops an algorithm to evaluate soft skills. For example, by analyzing written expression and project leadership experience. The machine learning algorithm department also develops an algorithm to analyze the content of a resume using the generation AI and evaluate soft skills. For example, by evaluating communication skills and teamwork experience. The machine learning algorithm department also develops an algorithm to input digital data of a resume into the generation AI and evaluate soft skills. For example, by analyzing written expression and project leadership experience. In this way, by evaluating the candidate's soft skills, it is possible to select candidates with communication skills and leadership skills.

[0067] The machine learning algorithm unit can use the emotion estimation function to evaluate a candidate's passion and interest in the job from the contents of their resume. For example, the machine learning algorithm unit analyzes the text data of a resume using the emotion estimation function to evaluate the candidate's passion and interest in the job. For example, the evaluation is based on positive expressions and passionate words. The machine learning algorithm unit also analyzes the contents of a resume digitized using OCR technology using the emotion estimation function to evaluate the candidate's passion and interest in the job. For example, the evaluation is based on positive expressions and passionate words. In this way, the candidate's passion and interest in the job can be evaluated to determine their suitability for the job.

[0068] The machine learning algorithm unit can compare the results of the resume analysis with the data of other candidates and perform a relative evaluation. For example, the machine learning algorithm unit inputs the digital data of the resume into the generation AI and compares it with the data of other candidates. For example, a relative evaluation is performed based on work history and skill set. The machine learning algorithm unit also uses the generation AI to analyze the contents of the resume and compares it with the data of other candidates. For example, a relative evaluation is performed based on work experience and educational background. The machine learning algorithm unit also inputs the digital data of the resume into the generation AI and compares it with the data of other candidates. For example, a relative evaluation is performed based on work history and skill set. This makes it possible to compare the candidate with other candidates by performing a relative evaluation.

[0069] The machine learning algorithm unit can compare the results of resume analysis with the company's culture and values ​​to evaluate cultural fit. For example, the machine learning algorithm unit inputs the digital data of a resume into the generation AI and evaluates cultural fit by comparing it with the company's culture and values. For example, it analyzes whether it matches the company's mission and vision. The machine learning algorithm unit can also use the generation AI to analyze the contents of a resume and evaluate cultural fit by comparing it with the company's culture and values. For example, it can evaluate whether it matches the company's core values. The machine learning algorithm unit can also input the digital data of a resume into the generation AI and evaluate cultural fit by comparing it with the company's culture and values. For example, it can analyze whether it matches the company's mission and vision. This makes it possible to select candidates who match the company's culture and values.

[0070] The machine learning algorithm unit can use the emotion estimation function to evaluate a candidate's stress tolerance and adaptability from the contents of their resume. For example, the machine learning algorithm unit analyzes the text data of a resume using the emotion estimation function to evaluate the candidate's stress tolerance and adaptability. For example, the evaluation is made based on how they respond to difficult situations. The machine learning algorithm unit also analyzes the contents of a resume digitized using OCR technology using the emotion estimation function to evaluate the candidate's stress tolerance and adaptability. For example, the evaluation is made based on how they respond to difficult situations in their past work experience. The machine learning algorithm unit also analyzes the contents of a resume using the emotion estimation function to evaluate the candidate's stress tolerance and adaptability. For example, the evaluation is made based on how they respond to difficult situations. In this way, by evaluating a candidate's stress tolerance and adaptability, it is possible to understand their ability to respond to difficult situations.

[0071] The aptitude assessment unit can analyze a candidate's past performance and project success rate, and conduct an aptitude assessment based on performance. For example, the aptitude assessment unit inputs digital resume data into the generation AI and analyzes past performance and project success rate. For example, it conducts an aptitude assessment based on the project completion rate and the quality of the deliverables. The aptitude assessment unit also uses the generation AI to analyze the contents of the resume and evaluates past performance and project success rate. For example, it determines aptitude based on the project progress and the quality of the deliverables. The aptitude assessment unit also inputs digital resume data into the generation AI and analyzes past performance and project success rate. For example, it conducts an aptitude assessment based on the project completion rate and the quality of the deliverables. In this way, by analyzing a candidate's past performance and project success rate, it becomes possible to conduct an aptitude assessment based on performance.

[0072] The aptitude evaluation unit can analyze a candidate's network and evaluate their reputation and trustworthiness within the industry. For example, the aptitude evaluation unit inputs digital resume data into the generation AI and analyzes the candidate's LinkedIn profile. For example, it determines their reputation and trustworthiness within the industry based on letters of recommendation and the number of skill endorsements. The aptitude evaluation unit also uses the generation AI to analyze a candidate's network and evaluates their reputation and trustworthiness within the industry. For example, it evaluates them based on connections within the industry and the contents of letters of recommendation. The aptitude evaluation unit also inputs digital resume data into the generation AI and analyzes the candidate's LinkedIn profile. For example, it determines their reputation and trustworthiness within the industry based on letters of recommendation and the number of skill endorsements. In this way, by analyzing a candidate's network, it is possible to understand their reputation and trustworthiness within the industry.

[0073] The aptitude evaluation unit can use the emotion estimation function to analyze a candidate's emotional reactions during an interview and evaluate their interview performance. The aptitude evaluation unit, for example, analyzes video data from the interview using the emotion estimation function to evaluate the candidate's emotional reactions. For example, it judges interview performance based on facial expressions and tone of voice. The aptitude evaluation unit also uses the emotion estimation function to analyze a candidate's emotional reactions during an interview and evaluates their interview performance. For example, it evaluates based on the degree of nervousness and confidence. The aptitude evaluation unit also analyzes video data from the interview using the emotion estimation function to evaluate the candidate's emotional reactions. For example, it judges interview performance based on facial expressions and tone of voice. In this way, it is possible to evaluate a candidate's interview performance by analyzing their emotional reactions during the interview.

[0074] The aptitude evaluation unit can analyze a candidate's hobbies and interests and evaluate their relevance to the job. For example, the aptitude evaluation unit inputs digital data of a resume into the generation AI and analyzes the candidate's hobbies and interests. For example, it evaluates the degree to which the hobbies and interests are related to the job. The aptitude evaluation unit also analyzes the contents of the resume using the generation AI and evaluates the candidate's hobbies and interests. For example, it determines the degree to which the hobbies and interests are related to the job. The aptitude evaluation unit also inputs digital data of the resume into the generation AI and analyzes the candidate's hobbies and interests. For example, it evaluates the degree to which the hobbies and interests are related to the job. In this way, by analyzing a candidate's hobbies and interests, it is possible to evaluate their relevance to the job.

[0075] The aptitude assessment unit can analyze the candidate's social media activity and evaluate their social influence. For example, the aptitude assessment unit inputs digital data of a resume into the generation AI and analyzes the candidate's social media activity. For example, it evaluates social influence based on the number of followers and the content of posts. The aptitude assessment unit also uses the generation AI to analyze the candidate's social media activity and evaluate their social influence. For example, it determines influence based on the number of followers and the content of posts. The aptitude assessment unit also inputs digital data of a resume into the generation AI and analyzes the candidate's social media activity. For example, it evaluates social influence based on the number of followers and the content of posts. In this way, it is possible to evaluate a candidate's social influence by analyzing their social media activity.

[0076] The aptitude evaluation unit can use the emotion estimation function to evaluate the emotional satisfaction of a candidate in their past work experience and determine their job aptitude. The aptitude evaluation unit, for example, analyzes text data from a resume using the emotion estimation function to evaluate the emotional satisfaction of their past work experience. For example, it determines job aptitude based on positive expressions and highly satisfying experiences. The aptitude evaluation unit also analyzes the content of a resume digitized using OCR technology using the emotion estimation function to evaluate the emotional satisfaction of their past work experience. For example, it determines job aptitude based on positive expressions and highly satisfying experiences. The aptitude evaluation unit also analyzes the content of a resume using the emotion estimation function to evaluate the emotional satisfaction of their past work experience. For example, it determines job aptitude based on positive expressions and highly satisfying experiences. In this way, it is possible to determine job aptitude by evaluating the emotional satisfaction of a candidate in their past work experience.

[0077] The interface unit can use the generation AI to analyze the user's operation history and provide the optimal operation guide. The interface unit, for example, can use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can prioritize displaying frequently used functions. The interface unit can also use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can display a guide that is suitable for the user based on past operation history. The interface unit can also use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can prioritize displaying frequently used functions. In this way, it is possible to provide the optimal operation guide by analyzing the user's operation history.

[0078] The interface unit can use the generation AI to collect user feedback in real time and reflect it in improving the interface. The interface unit, for example, uses the generation AI to collect user feedback in real time and reflect it in improving the interface. For example, adjusting the design based on the user's opinion. The interface unit can also use the generation AI to collect user feedback in real time and reflect it in improving the interface. For example, adding a function based on the user's opinion. The interface unit can also use the generation AI to collect user feedback in real time and reflect it in improving the interface. For example, adjusting the design based on the user's opinion. This allows user feedback to be collected in real time and reflected in improving the interface.

[0079] The interface unit can use the emotion estimation function to analyze the emotional state of the user during operation and provide an interface design for reducing stress. The interface unit, for example, uses the emotion estimation function to analyze the emotional state of the user during operation and provide a design for reducing stress. For example, it adjusts colors and layout. The interface unit can also use the emotion estimation function to analyze the emotional state of the user during operation and provide an interface design for reducing stress. For example, it can simplify operations or display guides. The interface unit can also use the emotion estimation function to analyze the emotional state of the user during operation and provide a design for reducing stress. For example, it can adjust colors and layout. In this way, it is possible to analyze the emotional state of the user during operation and provide an interface design for reducing stress.

[0080] The interface unit can use the generation AI to analyze the user's operation pattern and provide an individually customized operation guide. The interface unit, for example, can use the generation AI to analyze the user's operation pattern and provide an individually customized operation guide. For example, it displays an optimal guide based on the user's operation history. The interface unit can also use the generation AI to analyze the user's operation pattern and provide an individually customized operation guide. For example, it displays an optimal guide based on the user's operation history. The interface unit can also use the generation AI to analyze the user's operation pattern and provide an individually customized operation guide. For example, it displays an optimal guide based on the user's operation history. In this way, by analyzing the user's operation pattern, it is possible to provide an individually customized operation guide.

[0081] The interface unit can use the generation AI to analyze the user's operation history and provide the optimal operation guide. The interface unit, for example, can use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can prioritize displaying frequently used functions. The interface unit can also use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can display a guide that is suitable for the user based on past operation history. The interface unit can also use the generation AI to analyze the user's operation history and provide the optimal operation guide. For example, it can prioritize displaying frequently used functions. In this way, it is possible to provide the optimal operation guide by analyzing the user's operation history.

[0082] The interface unit can use the emotion estimation function to analyze the emotional state of the user during operation and provide an interface design for reducing stress. The interface unit, for example, uses the emotion estimation function to analyze the emotional state of the user during operation and provide a design for reducing stress. For example, it adjusts colors and layout. The interface unit can also use the emotion estimation function to analyze the emotional state of the user during operation and provide an interface design for reducing stress. For example, it can simplify operations or display guides. The interface unit can also use the emotion estimation function to analyze the emotional state of the user during operation and provide a design for reducing stress. For example, it can adjust colors and layout. In this way, it is possible to analyze the emotional state of the user during operation and provide an interface design for reducing stress.

[0083] The aptitude assessment unit uses the generation AI to track the candidate's progress from application to hiring in real time, enabling efficient management. The aptitude assessment unit, for example, uses the generation AI to track the candidate's progress from application to hiring in real time, enabling efficient management. For example, the progress status of each step is displayed on a dashboard. The aptitude assessment unit also uses the generation AI to track the candidate's progress from application to hiring in real time, enabling efficient management. For example, the progress status of each step is displayed on a dashboard. The aptitude assessment unit also uses the generation AI to track the candidate's progress from application to hiring in real time, enabling efficient management. For example, the progress status of each step is displayed on a dashboard. This allows the candidate's progress from application to hiring to be tracked in real time, enabling efficient management.

[0084] The aptitude assessment unit can use the emotion estimation function to analyze the emotional state of a candidate during the hiring process and propose improvement measures to provide a positive experience. The aptitude assessment unit, for example, analyzes the emotional state of a candidate during the hiring process using the emotion estimation function and proposes improvement measures to provide a positive experience. For example, by improving interview feedback and communication. The aptitude assessment unit can also use the emotion estimation function to analyze the emotional state of a candidate during the hiring process and propose improvement measures to provide a positive experience. For example, by improving interview feedback and communication. The aptitude assessment unit can also use the emotion estimation function to analyze the emotional state of a candidate during the hiring process and propose improvement measures to provide a positive experience. For example, by improving interview feedback and communication. In this way, the aptitude assessment unit can analyze the emotional state of a candidate during the hiring process and propose improvement measures to provide a positive experience.

[0085] The aptitude assessment unit can use the generation AI to automatically adjust the interview schedule of a candidate, thereby realizing an efficient interview process. The aptitude assessment unit can, for example, use the generation AI to automatically adjust the interview schedule of a candidate, thereby realizing an efficient interview process. For example, it can automatically match the available times of the candidate and the interviewer. The aptitude assessment unit can also use the generation AI to automatically adjust the interview schedule of a candidate, thereby realizing an efficient interview process. For example, it can automatically match the available times of the candidate and the interviewer. The aptitude assessment unit can also use the generation AI to automatically adjust the interview schedule of a candidate, thereby realizing an efficient interview process. For example, it can automatically match the available times of the candidate and the interviewer. This allows the candidate's interview schedule to be automatically adjusted, thereby realizing an efficient interview process.

[0086] The aptitude evaluation unit uses the generation AI to automatically compile the evaluation results of candidates, allowing the most suitable candidate to be selected quickly. The aptitude evaluation unit, for example, uses the generation AI to automatically compile the evaluation results of candidates, allowing the most suitable candidate to be selected quickly. For example, the results of interview evaluations and skill tests are automatically compiled. The aptitude evaluation unit also uses the generation AI to automatically compile the evaluation results of candidates, allowing the most suitable candidate to be selected quickly. For example, the results of interview evaluations and skill tests are automatically compiled. The aptitude evaluation unit also uses the generation AI to automatically compile the evaluation results of candidates, allowing the most suitable candidate to be selected quickly. For example, the results of interview evaluations and skill tests are automatically compiled. This allows the most suitable candidate to be selected quickly.

[0087] The aptitude assessment unit can use the emotion estimation function to analyze the emotional state of a candidate during the hiring process and propose improvement measures to provide a positive experience. The aptitude assessment unit, for example, analyzes the emotional state of a candidate during the hiring process using the emotion estimation function and proposes improvement measures to provide a positive experience. For example, by improving interview feedback and communication. The aptitude assessment unit can also use the emotion estimation function to analyze the emotional state of a candidate during the hiring process and propose improvement measures to provide a positive experience. For example, by improving interview feedback and communication. The aptitude assessment unit can also use the emotion estimation function to analyze the emotional state of a candidate during the hiring process and propose improvement measures to provide a positive experience. For example, by improving interview feedback and communication. In this way, the aptitude assessment unit can analyze the emotional state of a candidate during the hiring process and propose improvement measures to provide a positive experience.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] In addition to digitizing resumes, the OCR technology department can simultaneously digitize candidates' portfolios and project materials, allowing for a comprehensive evaluation. For example, a candidate's portfolio and project materials can be scanned along with the resume and digitized using OCR technology. This allows the generation AI to perform a comprehensive evaluation. The OCR technology department also uses OCR technology to digitize resumes, portfolios, and project materials, and the generation AI analyzes their contents. For example, it evaluates project details and deliverables. The OCR technology department also digitizes a candidate's portfolio and project materials along with the resume, allowing the generation AI to perform a comprehensive evaluation. For example, it analyzes the project's progress and results. This allows for a comprehensive evaluation that also includes the candidate's portfolio and project materials.

[0090] The OCR technology department can automatically translate resumes in different languages, enabling the evaluation of international candidates. For example, resumes digitized with OCR technology are input into an automatic translation system to translate resumes in different languages. This makes it possible to evaluate international candidates. The OCR technology department also digitizes resumes with OCR technology and translates them into different languages ​​using an automatic translation system. For example, from English to Japanese, or from French to English. The OCR technology department also inputs resumes digitized with OCR technology into an automatic translation system to translate them into multiple languages. This makes it easy to evaluate international candidates. This makes it possible to automatically translate resumes in different languages ​​to evaluate international candidates.

[0091] The machine learning algorithm unit can predict a candidate's career path and evaluate their growth potential. For example, digital resume data can be input into the generative AI to predict the candidate's career path. For example, future growth potential can be evaluated based on past work history and skill set. The machine learning algorithm unit can also use the generative AI to analyze the contents of the resume and predict the candidate's career path. For example, growth potential can be evaluated based on past work experience and skill evolution. The machine learning algorithm unit can also input digital resume data into the generative AI to predict the candidate's career path. For example, future growth potential can be evaluated based on past work history and skill set. This makes it possible to hire with a long-term perspective by evaluating a candidate's future growth potential.

[0092] The machine learning algorithm department can develop an algorithm to evaluate a candidate's soft skills. For example, digital data from a resume is input into a generation AI to develop an algorithm to evaluate soft skills. For example, by analyzing written expression and project leadership experience. The machine learning algorithm department can also develop an algorithm to analyze the content of a resume using a generation AI to evaluate soft skills. For example, by evaluating communication skills and teamwork experience. The machine learning algorithm department can also input digital data from a resume into a generation AI to develop an algorithm to evaluate soft skills. For example, by analyzing written expression and project leadership experience. In this way, by evaluating the candidate's soft skills, it is possible to select candidates with good communication skills and leadership skills.

[0093] The machine learning algorithm unit can compare the results of resume analysis with the data of other candidates and perform a relative evaluation. For example, the digital data of the resume is input into the generation AI and compared with the data of other candidates. For example, a relative evaluation is performed based on work history and skill set. The machine learning algorithm unit also uses the generation AI to analyze the contents of the resume and compare it with the data of other candidates. For example, a relative evaluation is performed based on work experience and educational background. The machine learning algorithm unit also inputs the digital data of the resume into the generation AI and compares it with the data of other candidates. For example, a relative evaluation is performed based on work history and skill set. This makes it possible to perform a relative evaluation of the candidate and compare it with other candidates.

[0094] The OCR technology department uses the emotion estimation function to estimate a candidate's emotional state from the contents of their resume, allowing them to evaluate their level of stress and motivation. For example, the text data of a resume is analyzed and the emotion estimation function is used to estimate the candidate's emotional state. For example, the level of stress and motivation is evaluated based on the tone and expression of the text. The OCR technology department also uses the emotion estimation function to analyze the contents of a resume digitized using OCR technology to estimate the candidate's emotional state. For example, it evaluates positive and negative expressions to determine emotional tendencies. The OCR technology department also uses the emotion estimation function to analyze the contents of a resume to evaluate the candidate's emotional state. For example, it estimates the level of stress and motivation based on the structure of the sentences and the choice of words. This makes it possible to understand the level of stress and motivation by evaluating the candidate's emotional state.

[0095] The OCR technology department can use the emotion estimation function to identify gaps between a candidate's self-assessment and their actual skills from the contents of their resume. For example, the emotion estimation function can be used to analyze the text data of a resume to identify gaps between a candidate's self-assessment and their actual skills. For example, if a candidate has a high self-assessment but few achievements. The OCR technology department can also use the emotion estimation function to analyze the contents of a resume digitized with OCR technology to identify gaps between a candidate's self-assessment and their actual skills. For example, it can detect overestimation or underestimation. The OCR technology department can also use the emotion estimation function to analyze the contents of a resume to identify gaps between a candidate's self-assessment and their actual skills. For example, it can detect high self-assessment but few achievements. This allows for more accurate evaluation by identifying gaps between a candidate's self-assessment and their actual skills.

[0096] The machine learning algorithm unit can use the emotion estimation function to evaluate a candidate's passion and interest in the job from the contents of their resume. For example, the emotion estimation function can analyze the text data of a resume to evaluate the candidate's passion and interest in the job. For example, the evaluation can be based on positive expressions and passionate words. The machine learning algorithm unit can also analyze the contents of a resume digitized using OCR technology using the emotion estimation function to evaluate the candidate's passion and interest in the job. For example, the evaluation can be based on positive expressions and passionate words. In this way, the candidate's passion and interest in the job can be evaluated to determine their suitability for the job.

[0097] The machine learning algorithm unit can use the emotion estimation function to evaluate a candidate's stress tolerance and adaptability from the contents of their resume. For example, the emotion estimation function can analyze the text data of a resume to evaluate the candidate's stress tolerance and adaptability. For example, the evaluation can be based on how they responded to difficult situations. The machine learning algorithm unit can also analyze the contents of a resume digitized using OCR technology using the emotion estimation function to evaluate the candidate's stress tolerance and adaptability. For example, the evaluation can be based on how they responded to difficult situations in their past work experience. The machine learning algorithm unit can also analyze the contents of a resume using the emotion estimation function to evaluate the candidate's stress tolerance and adaptability. For example, the evaluation can be based on how they responded to difficult situations. In this way, by evaluating a candidate's stress tolerance and adaptability, it is possible to understand their ability to respond to difficult situations.

[0098] The aptitude assessment unit can use the emotion estimation function to analyze the emotional state of a candidate during the hiring process and suggest improvement measures to provide a positive experience. For example, the emotion estimation function can be used to analyze the emotional state of a candidate during the hiring process and suggest improvement measures to provide a positive experience. For example, improving interview feedback and communication. The aptitude assessment unit can also use the emotion estimation function to analyze the emotional state of a candidate during the hiring process and suggest improvement measures to provide a positive experience. For example, improving interview feedback and communication. The aptitude assessment unit can also use the emotion estimation function to analyze the emotional state of a candidate during the hiring process and suggest improvement measures to provide a positive experience. For example, improving interview feedback and communication. This makes it possible to analyze the emotional state of a candidate during the hiring process and suggest improvement measures to provide a positive experience.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The OCR technology department digitizes the resume. For example, a handwritten resume can be digitized and read using scanning technology. It can also directly read PDF-format resumes. The OCR technology department can also read printed resumes using OCR technology. For example, the OCR technology department scans a handwritten resume with a high-resolution scanner and converts it into text information using OCR technology. PDF-format resumes submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: The machine learning algorithm section analyzes the resume digitized by the OCR technology section. For example, the machine learning algorithm section analyzes the contents of the resume using a neural network. It can also analyze the contents of the resume using a support vector machine or decision tree. Neural networks have learned from large amounts of data and have advanced pattern recognition capabilities. Support vector machines excel at data classification and regression analysis. Decision trees perform classification and prediction based on the characteristics of the data. Step 3: The aptitude evaluation unit evaluates the candidate's aptitude based on the results analyzed by the machine learning algorithm unit. For example, the aptitude evaluation unit performs skill matching to evaluate the degree of match between the candidate's skills and the job requirements. It can also perform a personality assessment to evaluate the candidate's personality traits. It can also perform an experience assessment to evaluate the candidate's work experience. Skill matching compares the candidate's skill set with the job requirements and scores the degree of match. Personality assessment evaluates the candidate's personality traits based on psychological evaluation criteria. Experience assessment quantitatively evaluates the candidate's work experience. Step 4: The interface unit displays the results of the evaluation by the aptitude evaluation unit. For example, the interface unit provides a dashboard that allows users to upload resumes using drag-and-drop and visually check the analysis results. The analysis results can also be displayed in graphs and charts. Furthermore, the analysis results can be output in report format. The dashboard is intuitive and easy for recruiters to use. The graphs and charts display the analysis results visually, making them easy to understand. The report format describes the analysis results in detail so that they can be referenced later.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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. The OCR Technology Department uses OCR technology to digitize resumes, and a machine learning algorithm unit that analyzes the resume digitized by the OCR technology unit; an aptitude evaluation unit that evaluates the aptitude of the candidate based on the results of the analysis by the machine learning algorithm unit; an interface unit that displays the results of the evaluation by the aptitude evaluation unit. A system characterized by:

2. The OCR technology department Conducting handwriting analysis to estimate the candidate's personality and stress level 2. The system of claim 1.

3. The OCR technology department Analyze images and diagrams in resumes to assess the candidate's creativity 2. The system of claim 1.

4. The OCR technology department Inferring the candidate's emotional state from the resume and assessing their level of stress and motivation 2. The system of claim 1.

5. The OCR technology department In addition to digitizing resumes, the candidate's portfolio and project materials will also be digitized for comprehensive evaluation.

2. The system of claim 1.

6. The OCR technology department Automatically translate resumes in different languages ​​to enable evaluation of international candidates 2. The system of claim 1.

7. The OCR technology department Identify the candidate's self-assessment and skill gaps based on their resume 2. The system of claim 1.

8. The machine learning algorithm unit Predict the candidate's career path and assess their growth potential 2. The system of claim 1.

Images