system

Abstract

The system according to this embodiment aims to eliminate bias and find the most suitable personnel for the company's needs. [Solution] The system according to the embodiment comprises a data collection unit, an evaluation unit, a matching unit, and a future prediction unit. The data collection unit collects biometric data of candidates. The evaluation unit evaluates candidates based on the data collected by the data collection unit. The matching unit matches candidates with the needs of companies based on the evaluation results obtained by the evaluation unit. The future prediction unit makes future predictions for candidates based on the results obtained by the matching unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that bias exists in the evaluation of candidates, and it is difficult to find the most suitable personnel for the needs of the enterprise.

[0005] The system according to the embodiment aims to eliminate bias and find the most suitable personnel for the needs of the enterprise.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an evaluation unit, a matching unit, and a future prediction unit. The data collection unit collects biometric data of candidates. The evaluation unit evaluates candidates based on the data collected by the data collection unit. The matching unit matches candidates with the needs of companies based on the evaluation results obtained by the evaluation unit. The future prediction unit makes future predictions for candidates based on the results obtained by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can eliminate bias and find the most suitable personnel for the company's needs. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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). <关于专利文本的翻译,需要注意的是,专利文本通常具有专业性和准确性要求,因此在翻译过程中需要严格遵循相关的术语和规范。对于一些特定的技术术语和行业词汇,应尽量使用准确、规范的译文,以确保翻译的质量和专业性。同时,要注意保持原文的逻辑结构和格式,使译文能够准确传达原文的含义。]]

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The talent acquisition platform according to an embodiment of the present invention is a system that combines generative AI and biometrics. This system works in collaboration with a candidate-interactive AI to conduct skill tests and problem-solving exercises. Simultaneously, it collects biometric data to eliminate evaluation bias. The AI ​​matches candidate evaluations with the company's needs and proposes the optimal placement. It also predicts the candidate's future and achieves appropriate recruitment based on long-term history. For example, the candidate-interactive AI interacts with the candidate and presents skill tests and problem-solving tasks. For example, to evaluate programming skills, it may present a task to implement a specific algorithm. In this case, the AI ​​evaluates the candidate's answers and solution process in real time. Next, it collects biometric data. This biometric data includes the candidate's heart rate, facial expressions, and voice tone. This allows the AI ​​to understand the candidate's stress level and emotional state and eliminate evaluation bias. For example, if a candidate is nervous, the AI ​​takes that into account when evaluating them. Based on the collected data, the AI ​​evaluates the candidate and matches them with the company's needs. For example, it selects the most suitable candidate based on the skill set and experience required by the company. Furthermore, the platform also forecasts candidates' future potential and proposes appropriate placements from a long-term perspective. For example, it evaluates whether a candidate can contribute to the company in the future based on their past work experience and projected skill growth. This mechanism eliminates evaluation bias and allows for accurate assessment of candidates' skills and aptitudes. It also selects the candidates best suited to the company's needs, achieving appropriate recruitment from a long-term perspective. For instance, by predicting whether a candidate can demonstrate leadership in the future and proposing placements based on the results, the platform can contribute to the company's growth. In this way, the talent acquisition platform can accurately assess candidates' skills and aptitudes and select the candidates best suited to the company's needs.

[0029] The talent acquisition platform according to this embodiment comprises a data collection unit, an evaluation unit, a matching unit, and a future prediction unit. The data collection unit collects biometric data of candidates. For example, the data collection unit collects biometric data such as the candidate's heart rate, facial expression, and voice tone. For example, the data collection unit can have the candidate wear a wearable device to measure their heart rate. The data collection unit can also use facial recognition technology to analyze the candidate's facial expression by taking a picture of their face with a camera. The data collection unit can also use voice analysis technology to analyze the candidate's voice tone by recording their voice. The evaluation unit evaluates the candidate based on the data collected by the data collection unit. For example, the evaluation unit evaluates the candidate's stress level and emotional state based on the collected biometric data. For example, the evaluation unit can analyze fluctuations in heart rate to evaluate the candidate's stress level. The evaluation unit can also analyze changes in facial expression to evaluate the candidate's emotional state. The evaluation unit can also analyze changes in voice tone to evaluate the candidate's emotional state. The Matching Department matches company needs with candidates based on evaluation results obtained by the Evaluation Department. For example, the Matching Department selects the most suitable candidates based on the skill sets and experience required by the company. The Matching Department can, for example, compare the skill sets required by the company with those of the candidates to select the most suitable candidates. It can also, compare the experience required by the company with that of the candidates to select the most suitable candidates. Furthermore, the Matching Department can compare the attribute information required by the company with that of the candidates to select the most suitable candidates. The Future Forecasting Department makes future predictions about candidates based on the results obtained by the Matching Department. For example, the Future Forecasting Department evaluates whether a candidate can contribute to the company in the future based on their past work experience and predicted skill growth. For example, the Future Forecasting Department can predict what skills a candidate will acquire in the future based on their past work experience. Furthermore, the Future Forecasting Department can predict what roles a candidate will play in the future based on their predicted skill growth. Furthermore, the Future Forecasting Department can predict what projects a candidate will participate in in the future based on their past work experience and predicted skill growth.As a result, the talent acquisition platform according to this embodiment collects, evaluates, matches, and predicts the future of candidates' biometric data, thereby enabling appropriate recruitment.

[0030] The data collection unit collects biometric data from candidates. For example, it collects biometric data such as the candidate's heart rate, facial expressions, and voice tone. Specifically, to measure heart rate, candidates can be fitted with a wearable device. This wearable device can collect not only heart rate but also physiological data such as blood pressure and skin temperature. This allows for a more detailed understanding of the candidate's stress level and tension. In addition, to analyze facial expressions, the data collection unit can photograph the candidate's face with a camera and use facial recognition technology. The camera is high resolution and can capture even subtle changes in facial expressions. Facial recognition technology analyzes facial feature points to identify emotions such as joy, anger, sadness, and surprise. Furthermore, to analyze voice tone, the data collection unit can record the candidate's voice and use voice analysis technology. Voice analysis technology analyzes the pitch, tempo, and volume of the voice to evaluate the candidate's emotional state and stress level. This allows the data collection unit to gain a multifaceted understanding of the candidate's physiological and psychological state. The collected data is transmitted in real time to a central database, making it accessible to the evaluation and matching units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, the frequency of data collection can be increased to conduct a detailed analysis of responses to specific interview questions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The evaluation unit evaluates candidates based on data collected by the data collection unit. For example, the evaluation unit assesses candidates' stress levels and emotional states based on collected biometric data. Specifically, it can analyze heart rate variability to assess a candidate's stress level. Heart rate variability is widely used as an indicator of stress and tension, and the evaluation unit uses this to assess the candidate's psychological state. The evaluation unit can also analyze changes in facial expressions to assess a candidate's emotional state. Using facial recognition technology, it analyzes the feature points of the candidate's face to identify emotions such as joy, anger, sadness, and surprise. Furthermore, the evaluation unit can analyze changes in voice tone to assess a candidate's emotional state. Using voice analysis technology, it analyzes voice pitch, tempo, and volume to assess the candidate's emotional state and stress level. This allows the evaluation unit to comprehensively evaluate the candidate's physiological and psychological state. In addition, the evaluation unit can use AI to analyze collected data and evaluate candidates' performance and suitability. For example, machine learning algorithms can be used to predict a candidate's performance by comparing it with past data. Furthermore, the evaluation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the evaluation unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The matching department matches company needs with candidates based on evaluation results obtained by the evaluation department. Specifically, it selects the most suitable candidates based on the skill sets and experience required by the company. For example, it can compare the skill sets required by the company with those of the candidates to select the most suitable candidates. To compare skill sets, it uses natural language processing technology to analyze the candidate's resume and work history to identify skills that match the company's requirements. The matching department can also compare the experience required by the company with that of the candidates to select the most suitable candidates. To compare experience, it analyzes past projects and job descriptions to identify candidates with experience that matches the company's needs. Furthermore, the matching department can compare the attribute information required by the company with that of the candidates to select the most suitable candidates. Attribute information includes age, gender, education level, and work location, and based on this information, it selects candidates that match the diversity and culture of the company. The matching department uses AI to analyze this data and simulate multiple scenarios to identify the most suitable candidates. As a result, the matching department can select the most suitable candidates for the company's needs with high accuracy and support appropriate recruitment. Furthermore, the matching unit can continuously revise matching results based on real-time updated data, enabling it to adapt to the latest situations. For example, if a company's needs or a candidate's circumstances change, the matching unit immediately incorporates the new data and updates the matching results. This allows the matching unit to always provide highly accurate matching based on the latest information, supporting quick and appropriate responses.

[0033] The Future Forecasting Department makes future predictions about candidates based on the results obtained by the Matching Department. Specifically, it evaluates whether a candidate can contribute to the company in the future based on their past work experience and predicted skill growth. For example, it can predict what skills a candidate will acquire in the future based on their past work experience. By analyzing past work experience data, it understands what skills a candidate acquired and over what period of time, and predicts future skill growth. Furthermore, based on the predicted skill growth of a candidate, the Future Forecasting Department can predict what role they will play in the future. For example, it evaluates whether a candidate has the potential to demonstrate leadership in the future by acquiring specific skills. In addition, based on the candidate's past work experience and predicted skill growth, the Future Forecasting Department can predict what projects a candidate will participate in in the future. This allows companies to understand in advance what contributions candidates will make in the future and make appropriate hiring decisions. The Future Forecasting Department uses AI to analyze this data and simulate multiple scenarios to identify the most likely future prediction. As a result, the Future Forecasting Department can predict candidates' future performance with high accuracy and support the long-term growth of companies. Furthermore, the future forecasting unit can continuously revise its prediction results based on real-time updated data, enabling it to adapt to the latest situations. For example, if a candidate's skills or experience change, the future forecasting unit immediately incorporates the new data and updates the prediction results. This allows the future forecasting unit to always provide highly accurate future forecasts based on the latest information, supporting quick and appropriate responses.

[0034] The data collection unit collects biometric data such as the candidate's heart rate, facial expressions, and voice tone. For example, the data collection unit can have the candidate wear a wearable device to measure their heart rate. The data collection unit can also capture the candidate's face with a camera and use facial recognition technology to analyze their facial expressions. Furthermore, the data collection unit can record the candidate's voice and use voice analysis technology to analyze their voice tone. By collecting biometric data such as the candidate's heart rate, facial expressions, and voice tone, evaluation bias can be eliminated. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the candidate's heart rate data into a generating AI and have the generating AI perform heart rate analysis.

[0035] The evaluation unit assesses the candidate's stress level and emotional state based on the collected biometric data. For example, the evaluation unit can assess the candidate's stress level based on the collected heart rate data. For example, the evaluation unit can assess the candidate's stress level by analyzing the fluctuations in heart rate. The evaluation unit can also assess the candidate's emotional state based on the collected facial expression data. For example, the evaluation unit can assess the candidate's emotional state by analyzing changes in facial expression. The evaluation unit can also assess the candidate's emotional state based on the collected voice tone data. For example, the evaluation unit can assess the candidate's emotional state by analyzing changes in voice tone. This improves the accuracy of the evaluation by assessing the candidate's stress level and emotional state based on the collected biometric data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the collected heart rate data into a generating AI and have the generating AI perform the stress level assessment.

[0036] The matching unit selects the most suitable candidates based on the skill sets and experience required by the company. For example, the matching unit can compare the skill sets required by the company with those of the candidates to select the most suitable candidates. Furthermore, the matching unit can compare the experience required by the company with that of the candidates to select the most suitable candidates. Additionally, the matching unit can compare the attribute information required by the company with that of the candidates to select the most suitable candidates. This allows for recruitment that meets the company's needs by selecting the most suitable candidates based on the required skill sets and experience. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input the skill sets required by the company and those of the candidates into a generating AI and have the generating AI select the most suitable candidates.

[0037] The Future Forecasting Unit evaluates whether a candidate can contribute to the company in the future based on their past work experience and predicted skill growth. For example, the Future Forecasting Unit can predict what skills a candidate will acquire in the future based on their past work experience. The Future Forecasting Unit can also predict what role a candidate will play in the future based on predicted skill growth. Furthermore, the Future Forecasting Unit can predict what projects a candidate will participate in in the future based on their past work experience and predicted skill growth. This enables appropriate recruitment from a long-term perspective by evaluating whether a candidate can contribute to the company in the future based on their past work experience and predicted skill growth. Some or all of the above processing in the Future Forecasting Unit may be performed using AI, or not. For example, the Future Forecasting Unit can input a candidate's past work experience data into a generating AI and have the generating AI perform skill growth predictions.

[0038] The data collection unit analyzes the candidate's past biometric data and selects the optimal collection method. For example, the data collection unit can identify the time of day when the candidate is most relaxed based on past data and collect data during that time. Furthermore, based on past data, the data collection unit can analyze what biometric data the candidate exhibits in specific situations and adjust the collection method accordingly. The data collection unit can also refer to past data to avoid collecting data in situations where the candidate is likely to experience stress. This improves the efficiency of data collection by analyzing the candidate's past biometric data and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past biometric data into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit filters the biometric data based on the candidate's current health status and lifestyle. For example, if a candidate provides the results of a health checkup, the data collection unit can filter the data to be collected based on that information. The data collection unit can also select the types of data to be collected by considering the candidate's lifestyle (e.g., smoking and drinking habits). Furthermore, if a candidate is taking a specific medication, the data collection unit can filter the data considering its effects. This improves the accuracy of the collected data by filtering it based on the candidate's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the candidate's health checkup data into a generating AI and have the generating AI perform the data filtering.

[0040] The data collection unit prioritizes the collection of highly relevant data, taking into account the candidate's geographical location when collecting biometric data. For example, if the candidate is in an office, the data collection unit can prioritize the collection of heart rate and facial expression data. If the candidate is at home, the data collection unit can prioritize the collection of voice tone and data related to lifestyle habits. If the candidate is in a public place, the data collection unit can prioritize the collection of data related to stress levels. This improves the relevance of the data by prioritizing the collection of highly relevant data, taking into account the candidate's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the candidate's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit analyzes the candidate's social media activity and collects relevant data when collecting biometric data. For example, if a candidate posts on social media indicating they are feeling stressed, the data collection unit can collect biometric data at that time. The data collection unit can also collect heart rate and facial expression data at the time a candidate posts indicating they are relaxed. Furthermore, if a candidate posts indicating they are excited, the data collection unit can collect voice tone data at that time. By analyzing the candidate's social media activity and collecting relevant data, the accuracy of the data is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the candidate's social media post data into a generating AI and have the generating AI collect the relevant biometric data.

[0042] The evaluation unit adjusts the level of detail of the evaluation based on the importance of the biometric data during the evaluation. For example, if the heart rate is high, the evaluation unit can perform a detailed evaluation of the stress level. The evaluation unit can also perform a detailed evaluation of the emotional state if the facial expression is tense. Furthermore, if the tone of voice changes, the evaluation unit can perform a detailed evaluation of emotional fluctuations. By adjusting the level of detail of the evaluation based on the importance of the biometric data, the accuracy of the evaluation is improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the collected heart rate data into a generating AI and have the generating AI perform a stress level evaluation.

[0043] The evaluation unit applies different evaluation algorithms depending on the category of biometric data during evaluation. For example, the evaluation unit can apply an algorithm to evaluate stress levels to heart rate data. The evaluation unit can also apply an algorithm to evaluate emotional states to facial expression data. Furthermore, the evaluation unit can apply an algorithm to evaluate emotional fluctuations to voice tone data. By applying different evaluation algorithms depending on the category of biometric data, the accuracy of the evaluation is improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input collected heart rate data into a generating AI and have the generating AI perform a stress level evaluation.

[0044] The evaluation unit determines the evaluation priority based on the timing of biometric data collection during the evaluation process. For example, the evaluation unit can prioritize the evaluation of recently collected data. The evaluation unit can also prioritize the evaluation of data collected during a specific event. Furthermore, the evaluation unit can comprehensively evaluate data collected over a long period of time. This improves the efficiency of the evaluation by determining the evaluation priority based on the timing of biometric data collection. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the collected biometric data into a generating AI and have the generating AI determine the evaluation priority.

[0045] The evaluation unit adjusts the order of evaluation based on the relevance of the biometric data during the evaluation process. For example, the evaluation unit may evaluate data related to stress levels first. It may also evaluate data related to emotional state next, and finally evaluate data related to voice tone. This improves the efficiency of the evaluation by adjusting the order of evaluation based on the relevance of the biometric data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may input the collected biometric data into a generating AI and have the generating AI execute the evaluation order.

[0046] The matching unit improves the accuracy of matching by considering the interrelationship between the candidate's skill set and the company's needs during the matching process. For example, the matching unit can compare the candidate's skill set with the skill set required by the company to achieve the optimal match. The matching unit can also improve the accuracy of matching by considering the candidate's experience and the company's needs. Furthermore, the matching unit can compare the candidate's past projects with the company's current projects to achieve the optimal match. This improves the accuracy of matching by considering the interrelationship between the candidate's skill set and the company's needs. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the candidate's skill set and the company's needs into a generating AI and have the generating AI perform the optimal matching.

[0047] The matching unit performs matching while considering the candidate's attribute information. For example, the matching unit can perform matching while considering the candidate's age and gender. The matching unit can also perform matching while considering the candidate's educational background and work history. Furthermore, the matching unit can perform matching while considering the candidate's hobbies and interests. This makes it possible to perform more appropriate matching by considering the candidate's attribute information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the candidate's attribute information into a generating AI and have the generating AI perform the optimal matching.

[0048] The matching unit performs matching while considering the geographical distribution of candidates. For example, the matching unit can perform matching while considering the candidate's place of residence and the location of the company. The matching unit can also perform matching while considering the candidate's commute time. Furthermore, the matching unit can perform matching while considering the candidate's geographical mobility. This makes it possible to perform more appropriate matching by considering the geographical distribution of candidates. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input candidate geographical distribution data into a generating AI and have the generating AI perform the optimal matching.

[0049] The matching unit improves the accuracy of matching by referring to the candidate's relevant literature during the matching process. For example, the matching unit can perform matching by referring to papers written by the candidate. The matching unit can also perform matching by referring to reports from projects in which the candidate participated. Furthermore, the matching unit can perform matching by referring to presentations given by the candidate. This improves the accuracy of matching by referring to the candidate's relevant literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the candidate's relevant literature data into a generating AI and have the generating AI perform the optimal matching.

[0050] The future prediction unit predicts current skill growth by referring to past work history data during future prediction. For example, the future prediction unit can analyze a candidate's skill growth patterns based on past work history data and predict future skill growth. The future prediction unit can also analyze what skills a candidate has acquired by referring to past work history data and predict future skill growth. Furthermore, the future prediction unit can analyze what projects a candidate has participated in based on past work history data and predict future skill growth. By predicting current skill growth by referring to past work history data, more accurate future predictions become possible. Some or all of the above processing in the future prediction unit may be performed using AI, for example, or without AI. For example, the future prediction unit can input a candidate's past work history data into a generating AI and have the generating AI perform the skill growth prediction.

[0051] The future prediction unit applies different predictive analysis methods to each candidate category when making future predictions. For example, the future prediction unit can apply a method to predict the growth of technical skills to candidates for technical positions. Furthermore, the future prediction unit can apply a method to predict the growth of leadership skills to candidates for management positions. Furthermore, the future prediction unit can apply a method to predict the growth of creativity to candidates for creative positions. By applying different predictive analysis methods to each candidate category, more accurate future predictions become possible. Some or all of the above processing in the future prediction unit may be performed using AI, for example, or without AI. For example, the future prediction unit can input candidate category data into a generating AI and have the generating AI perform the application of predictive analysis methods.

[0052] The future prediction unit analyzes changes in predictions based on the timing of candidate work history submissions. For example, the future prediction unit can predict future skill growth based on recent work history data. The future prediction unit can also analyze skill growth patterns based on past work history data and predict future skill growth. Furthermore, the future prediction unit can analyze changes in skill growth based on the timing of work history data submissions and predict future skill growth. This allows for more accurate future predictions by analyzing changes in predictions based on the timing of candidate work history submissions. Some or all of the above processing in the future prediction unit may be performed using AI, for example, or without AI. For example, the future prediction unit can input candidate work history data into a generating AI and have the generating AI execute changes in predictions.

[0053] The future forecasting unit analyzes predictions by referring to the candidate's relevant market data when making future predictions. For example, the future forecasting unit can predict future skill growth based on market data where the candidate's skills are in high demand. The future forecasting unit can also predict future skill growth based on market data where the candidate's work history is relevant. Furthermore, the future forecasting unit can predict future skill growth based on market data where the candidate's skills are likely to grow. By analyzing predictions by referring to the candidate's relevant market data, more accurate future predictions become possible. Some or all of the above processing in the future forecasting unit may be performed using AI, for example, or without AI. For example, the future forecasting unit can input the candidate's relevant market data into a generating AI and have the generating AI perform the prediction analysis.

[0054] The future prediction unit analyzes changes in predictions based on the timing of candidate work history submissions. For example, the future prediction unit can predict future skill growth based on recent work history data. The future prediction unit can also analyze skill growth patterns based on past work history data and predict future skill growth. Furthermore, the future prediction unit can analyze changes in skill growth based on the timing of work history data submissions and predict future skill growth. This allows for more accurate future predictions by analyzing changes in predictions based on the timing of candidate work history submissions. Some or all of the above processing in the future prediction unit may be performed using AI, for example, or without AI. For example, the future prediction unit can input candidate work history data into a generating AI and have the generating AI execute changes in predictions.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can collect not only biometric data from candidates but also lifestyle data. For example, it can collect sleep patterns, eating habits, and exercise frequency, and use this data to assess the candidate's health status. The data collection unit can, for instance, acquire sleep data from wearable devices if the candidate is wearing one. It can also collect data from food logging apps if the candidate is using one. Furthermore, if the candidate is using a fitness tracker, it can collect data to assess exercise frequency and intensity. This allows for a more comprehensive assessment by collecting lifestyle data from candidates.

[0057] The matching department can analyze candidates' biometric data as well as their past project data to match them with the needs of companies. For example, it can analyze the content and results of projects a candidate has participated in in the past and use that data to match them with the company's current projects. If a candidate has participated in a project in the past where they demonstrated leadership, the matching department can use that experience to match them with a position that requires leadership. Similarly, if a candidate has participated in a project that utilized specific technical skills, the matching department can match them with a position that requires those skills. Furthermore, based on data from successful past projects, the department can assess the candidate's potential to contribute to the company's success. In this way, analyzing past project data enables more appropriate matching.

[0058] The Future Prediction Department can predict future skill growth by analyzing candidates' learning history data in addition to their biometric data. For example, it can analyze data on online courses a candidate has taken in the past and qualifications they have obtained, and use that data to predict future skill growth. For instance, if a candidate has taken many online courses in a particular field, the Future Prediction Department can predict that they are likely to develop skills in that field. Similarly, if a candidate has obtained multiple qualifications, it can predict that they are likely to develop skills related to those qualifications. Furthermore, if a candidate continues to learn consistently, the Future Prediction Department can predict future skill growth based on their motivation to learn. This allows for more accurate future predictions by analyzing learning history data.

[0059] The data collection unit can collect and utilize candidate geographical location data in addition to biometric data. For example, understanding where a candidate lives and the environment in which they live can improve the accuracy of the evaluation. If a candidate lives in an urban area, the data collection unit can consider the characteristics of that area when conducting the evaluation. If a candidate lives in an area with a rich natural environment, the unit can consider the impact of that environment on the candidate's stress level when conducting the evaluation. Furthermore, if a candidate moves frequently, the unit can analyze their movement patterns and evaluate the impact on their lifestyle and stress level. In this way, collecting geographical location data enables a more comprehensive evaluation.

[0060] The matching department can analyze candidates' biometric data, as well as their hobbies and interests, to match them with the company's culture. For example, it can analyze a candidate's hobbies and interests and use that data to match them with the company's culture. If a candidate enjoys outdoor activities, the matching department can evaluate whether that hobby aligns with the company's culture. If a candidate has technical hobbies, it can evaluate whether that hobby aligns with the company's technology-oriented culture. Furthermore, if a candidate has creative hobbies, it can evaluate whether that hobby aligns with the company's creative culture. By analyzing hobbies and interests, a more appropriate cultural matching becomes possible.

[0061] The Future Forecasting Department can predict future career paths by analyzing candidates' biometric data as well as their career goal data. For example, it can analyze the career goals and desired job types set by candidates and use that data to predict their future career paths. If a candidate is aiming for a leadership position, the Future Forecasting Department can predict the skill development required to achieve that goal. If a candidate desires a career in a specific technology field, it can predict the skill development required in that field. Furthermore, if a candidate is aiming for an international career, it can predict the skill development required to achieve that goal. By analyzing career goal data, more accurate future predictions become possible.

[0062] The data collection unit can collect not only candidate biometric data but also past interview data and use it for evaluation. For example, it can collect the content and evaluation results of past interviews a candidate has had and use that data to make a current evaluation. The data collection unit can, for example, analyze the content of answers a candidate has given in past interviews and evaluate the candidate's skills and aptitude from that content. It can also make a current evaluation based on the evaluation results of past interviews a candidate has had. Furthermore, it can analyze the candidate's performance in past interviews and use that data to make a current evaluation. In this way, collecting past interview data enables a more comprehensive evaluation.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects biometric data from the candidate. For example, the data collection unit collects biometric data such as the candidate's heart rate, facial expressions, and voice tone. The data collection unit can have the candidate wear a wearable device to measure their heart rate. The data collection unit can also use facial recognition technology to photograph the candidate's face with a camera to analyze their facial expressions. Furthermore, the data collection unit can use voice analysis technology to record the candidate's voice to analyze their voice tone. Step 2: The evaluation unit evaluates the candidates based on the data collected by the data collection unit. The evaluation unit assesses the candidates' stress levels and emotional states based on the collected biometric data. For example, it can analyze heart rate variability to assess the candidate's stress level. It can also analyze changes in facial expressions to assess the candidate's emotional state. Furthermore, it can analyze changes in voice tone to assess the candidate's emotional state. Step 3: The Matching Department matches the company's needs with candidates based on the evaluation results obtained by the Evaluation Department. The Matching Department selects the most suitable candidates based on the skill sets and experience required by the company. For example, it can compare the skill sets required by the company with those of the candidates to select the most suitable candidates. It can also compare the experience required by the company with that of the candidates to select the most suitable candidates. Furthermore, it can compare the attribute information required by the company with that of the candidates to select the most suitable candidates. Step 4: The Future Forecasting Department makes future predictions about candidates based on the results obtained by the Matching Department. The Future Forecasting Department evaluates whether candidates can contribute to the company in the future based on their past work experience and predicted skill growth. For example, it can predict what skills a candidate will acquire in the future based on their past work experience. It can also predict what role a candidate will play in the future based on their predicted skill growth. Furthermore, it can predict what projects a candidate will participate in in the future based on their past work experience and predicted skill growth.

[0065] (Example of form 2) The talent acquisition platform according to an embodiment of the present invention is a system that combines generative AI and biometrics. This system works in collaboration with a candidate-interactive AI to conduct skill tests and problem-solving exercises. Simultaneously, it collects biometric data to eliminate evaluation bias. The AI ​​matches candidate evaluations with the company's needs and proposes the optimal placement. It also predicts the candidate's future and achieves appropriate recruitment based on long-term history. For example, the candidate-interactive AI interacts with the candidate and presents skill tests and problem-solving tasks. For example, to evaluate programming skills, it may present a task to implement a specific algorithm. In this case, the AI ​​evaluates the candidate's answers and solution process in real time. Next, it collects biometric data. This biometric data includes the candidate's heart rate, facial expressions, and voice tone. This allows the AI ​​to understand the candidate's stress level and emotional state and eliminate evaluation bias. For example, if a candidate is nervous, the AI ​​takes that into account when evaluating them. Based on the collected data, the AI ​​evaluates the candidate and matches them with the company's needs. For example, it selects the most suitable candidate based on the skill set and experience required by the company. Furthermore, the platform also forecasts candidates' future potential and proposes appropriate placements from a long-term perspective. For example, it evaluates whether a candidate can contribute to the company in the future based on their past work experience and projected skill growth. This mechanism eliminates evaluation bias and allows for accurate assessment of candidates' skills and aptitudes. It also selects the candidates best suited to the company's needs, achieving appropriate recruitment from a long-term perspective. For instance, by predicting whether a candidate can demonstrate leadership in the future and proposing placements based on the results, the platform can contribute to the company's growth. In this way, the talent acquisition platform can accurately assess candidates' skills and aptitudes and select the candidates best suited to the company's needs.

[0066] The talent acquisition platform according to this embodiment comprises a data collection unit, an evaluation unit, a matching unit, and a future prediction unit. The data collection unit collects biometric data of candidates. For example, the data collection unit collects biometric data such as the candidate's heart rate, facial expression, and voice tone. For example, the data collection unit can have the candidate wear a wearable device to measure their heart rate. The data collection unit can also use facial recognition technology to analyze the candidate's facial expression by taking a picture of their face with a camera. The data collection unit can also use voice analysis technology to analyze the candidate's voice tone by recording their voice. The evaluation unit evaluates the candidate based on the data collected by the data collection unit. For example, the evaluation unit evaluates the candidate's stress level and emotional state based on the collected biometric data. For example, the evaluation unit can analyze fluctuations in heart rate to evaluate the candidate's stress level. The evaluation unit can also analyze changes in facial expression to evaluate the candidate's emotional state. The evaluation unit can also analyze changes in voice tone to evaluate the candidate's emotional state. The Matching Department matches company needs with candidates based on evaluation results obtained by the Evaluation Department. For example, the Matching Department selects the most suitable candidates based on the skill sets and experience required by the company. The Matching Department can, for example, compare the skill sets required by the company with those of the candidates to select the most suitable candidates. It can also, compare the experience required by the company with that of the candidates to select the most suitable candidates. Furthermore, the Matching Department can compare the attribute information required by the company with that of the candidates to select the most suitable candidates. The Future Forecasting Department makes future predictions about candidates based on the results obtained by the Matching Department. For example, the Future Forecasting Department evaluates whether a candidate can contribute to the company in the future based on their past work experience and predicted skill growth. For example, the Future Forecasting Department can predict what skills a candidate will acquire in the future based on their past work experience. Furthermore, the Future Forecasting Department can predict what roles a candidate will play in the future based on their predicted skill growth. Furthermore, the Future Forecasting Department can predict what projects a candidate will participate in in the future based on their past work experience and predicted skill growth.As a result, the talent acquisition platform according to this embodiment collects, evaluates, matches, and predicts the future of candidates' biometric data, thereby enabling appropriate recruitment.

[0067] The data collection unit collects biometric data from candidates. For example, it collects biometric data such as the candidate's heart rate, facial expressions, and voice tone. Specifically, to measure heart rate, candidates can be fitted with a wearable device. This wearable device can collect not only heart rate but also physiological data such as blood pressure and skin temperature. This allows for a more detailed understanding of the candidate's stress level and tension. In addition, to analyze facial expressions, the data collection unit can photograph the candidate's face with a camera and use facial recognition technology. The camera is high resolution and can capture even subtle changes in facial expressions. Facial recognition technology analyzes facial feature points to identify emotions such as joy, anger, sadness, and surprise. Furthermore, to analyze voice tone, the data collection unit can record the candidate's voice and use voice analysis technology. Voice analysis technology analyzes the pitch, tempo, and volume of the voice to evaluate the candidate's emotional state and stress level. This allows the data collection unit to gain a multifaceted understanding of the candidate's physiological and psychological state. The collected data is transmitted in real time to a central database, making it accessible to the evaluation and matching units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, the frequency of data collection can be increased to conduct a detailed analysis of responses to specific interview questions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The evaluation unit evaluates candidates based on data collected by the data collection unit. For example, the evaluation unit assesses candidates' stress levels and emotional states based on collected biometric data. Specifically, it can analyze heart rate variability to assess a candidate's stress level. Heart rate variability is widely used as an indicator of stress and tension, and the evaluation unit uses this to assess the candidate's psychological state. The evaluation unit can also analyze changes in facial expressions to assess a candidate's emotional state. Using facial recognition technology, it analyzes the feature points of the candidate's face to identify emotions such as joy, anger, sadness, and surprise. Furthermore, the evaluation unit can analyze changes in voice tone to assess a candidate's emotional state. Using voice analysis technology, it analyzes voice pitch, tempo, and volume to assess the candidate's emotional state and stress level. This allows the evaluation unit to comprehensively evaluate the candidate's physiological and psychological state. In addition, the evaluation unit can use AI to analyze collected data and evaluate candidates' performance and suitability. For example, machine learning algorithms can be used to predict a candidate's performance by comparing it with past data. Furthermore, the evaluation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the evaluation unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0069] The matching department matches company needs with candidates based on evaluation results obtained by the evaluation department. Specifically, it selects the most suitable candidates based on the skill sets and experience required by the company. For example, it can compare the skill sets required by the company with those of the candidates to select the most suitable candidates. To compare skill sets, it uses natural language processing technology to analyze the candidate's resume and work history to identify skills that match the company's requirements. The matching department can also compare the experience required by the company with that of the candidates to select the most suitable candidates. To compare experience, it analyzes past projects and job descriptions to identify candidates with experience that matches the company's needs. Furthermore, the matching department can compare the attribute information required by the company with that of the candidates to select the most suitable candidates. Attribute information includes age, gender, education level, and work location, and based on this information, it selects candidates that match the diversity and culture of the company. The matching department uses AI to analyze this data and simulate multiple scenarios to identify the most suitable candidates. As a result, the matching department can select the most suitable candidates for the company's needs with high accuracy and support appropriate recruitment. Furthermore, the matching unit can continuously revise matching results based on real-time updated data, enabling it to adapt to the latest situations. For example, if a company's needs or a candidate's circumstances change, the matching unit immediately incorporates the new data and updates the matching results. This allows the matching unit to always provide highly accurate matching based on the latest information, supporting quick and appropriate responses.

[0070] The Future Forecasting Department makes future predictions about candidates based on the results obtained by the Matching Department. Specifically, it evaluates whether a candidate can contribute to the company in the future based on their past work experience and predicted skill growth. For example, it can predict what skills a candidate will acquire in the future based on their past work experience. By analyzing past work experience data, it understands what skills a candidate acquired and over what period of time, and predicts future skill growth. Furthermore, based on the predicted skill growth of a candidate, the Future Forecasting Department can predict what role they will play in the future. For example, it evaluates whether a candidate has the potential to demonstrate leadership in the future by acquiring specific skills. In addition, based on the candidate's past work experience and predicted skill growth, the Future Forecasting Department can predict what projects a candidate will participate in in the future. This allows companies to understand in advance what contributions candidates will make in the future and make appropriate hiring decisions. The Future Forecasting Department uses AI to analyze this data and simulate multiple scenarios to identify the most likely future prediction. As a result, the Future Forecasting Department can predict candidates' future performance with high accuracy and support the long-term growth of companies. Furthermore, the future forecasting unit can continuously revise its prediction results based on real-time updated data, enabling it to adapt to the latest situations. For example, if a candidate's skills or experience change, the future forecasting unit immediately incorporates the new data and updates the prediction results. This allows the future forecasting unit to always provide highly accurate future forecasts based on the latest information, supporting quick and appropriate responses.

[0071] The data collection unit collects biometric data such as the candidate's heart rate, facial expressions, and voice tone. For example, the data collection unit can have the candidate wear a wearable device to measure their heart rate. The data collection unit can also capture the candidate's face with a camera and use facial recognition technology to analyze their facial expressions. Furthermore, the data collection unit can record the candidate's voice and use voice analysis technology to analyze their voice tone. By collecting biometric data such as the candidate's heart rate, facial expressions, and voice tone, evaluation bias can be eliminated. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the candidate's heart rate data into a generating AI and have the generating AI perform heart rate analysis.

[0072] The evaluation unit assesses the candidate's stress level and emotional state based on the collected biometric data. For example, the evaluation unit can assess the candidate's stress level based on the collected heart rate data. For example, the evaluation unit can assess the candidate's stress level by analyzing the fluctuations in heart rate. The evaluation unit can also assess the candidate's emotional state based on the collected facial expression data. For example, the evaluation unit can assess the candidate's emotional state by analyzing changes in facial expression. The evaluation unit can also assess the candidate's emotional state based on the collected voice tone data. For example, the evaluation unit can assess the candidate's emotional state by analyzing changes in voice tone. This improves the accuracy of the evaluation by assessing the candidate's stress level and emotional state based on the collected biometric data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the collected heart rate data into a generating AI and have the generating AI perform the stress level assessment.

[0073] The matching unit selects the most suitable candidates based on the skill sets and experience required by the company. For example, the matching unit can compare the skill sets required by the company with those of the candidates to select the most suitable candidates. Furthermore, the matching unit can compare the experience required by the company with that of the candidates to select the most suitable candidates. Additionally, the matching unit can compare the attribute information required by the company with that of the candidates to select the most suitable candidates. This allows for recruitment that meets the company's needs by selecting the most suitable candidates based on the required skill sets and experience. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input the skill sets required by the company and those of the candidates into a generating AI and have the generating AI select the most suitable candidates.

[0074] The Future Forecasting Unit evaluates whether a candidate can contribute to the company in the future based on their past work experience and predicted skill growth. For example, the Future Forecasting Unit can predict what skills a candidate will acquire in the future based on their past work experience. The Future Forecasting Unit can also predict what role a candidate will play in the future based on predicted skill growth. Furthermore, the Future Forecasting Unit can predict what projects a candidate will participate in in the future based on their past work experience and predicted skill growth. This enables appropriate recruitment from a long-term perspective by evaluating whether a candidate can contribute to the company in the future based on their past work experience and predicted skill growth. Some or all of the above processing in the Future Forecasting Unit may be performed using AI, or not. For example, the Future Forecasting Unit can input a candidate's past work experience data into a generating AI and have the generating AI perform skill growth predictions.

[0075] The data collection unit estimates the candidate's emotions and adjusts the timing of biometric data collection based on the estimated emotions. For example, if the candidate is relaxed, the data collection unit can delay the collection of heart rate and facial expression data. If the candidate is tense, the data collection unit can prioritize the collection of voice tone data. If the candidate is stressed, the data collection unit can shorten the collection interval and collect data more frequently. By adjusting the timing of biometric data collection based on the candidate's emotions, more accurate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input candidate facial expression data into a generating AI, which can then perform emotion estimation.

[0076] The data collection unit analyzes the candidate's past biometric data and selects the optimal collection method. For example, the data collection unit can identify the time of day when the candidate is most relaxed based on past data and collect data during that time. Furthermore, based on past data, the data collection unit can analyze what biometric data the candidate exhibits in specific situations and adjust the collection method accordingly. The data collection unit can also refer to past data to avoid collecting data in situations where the candidate is likely to experience stress. This improves the efficiency of data collection by analyzing the candidate's past biometric data and selecting the optimal collection method. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past biometric data into a generating AI and have the generating AI select the optimal collection method.

[0077] The data collection unit filters the biometric data based on the candidate's current health status and lifestyle. For example, if a candidate provides the results of a health checkup, the data collection unit can filter the data to be collected based on that information. The data collection unit can also select the types of data to be collected by considering the candidate's lifestyle (e.g., smoking and drinking habits). Furthermore, if a candidate is taking a specific medication, the data collection unit can filter the data considering its effects. This improves the accuracy of the collected data by filtering it based on the candidate's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the candidate's health checkup data into a generating AI and have the generating AI perform the data filtering.

[0078] The data collection unit estimates the candidate's emotions and determines the priority of biometric data to collect based on the estimated candidate's emotions. For example, if the candidate is relaxed, the data collection unit can prioritize collecting heart rate data. If the candidate is tense, the data collection unit can prioritize collecting facial expression data. If the candidate is stressed, the data collection unit can prioritize collecting voice tone data. This allows for the priority collection of important data by determining the priority of biometric data to collect based on the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the candidate's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The data collection unit prioritizes the collection of highly relevant data, taking into account the candidate's geographical location when collecting biometric data. For example, if the candidate is in an office, the data collection unit can prioritize the collection of heart rate and facial expression data. If the candidate is at home, the data collection unit can prioritize the collection of voice tone and data related to lifestyle habits. If the candidate is in a public place, the data collection unit can prioritize the collection of data related to stress levels. This improves the relevance of the data by prioritizing the collection of highly relevant data, taking into account the candidate's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the candidate's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0080] The data collection unit analyzes the candidate's social media activity and collects relevant data when collecting biometric data. For example, if a candidate posts on social media indicating they are feeling stressed, the data collection unit can collect biometric data at that time. The data collection unit can also collect heart rate and facial expression data at the time a candidate posts indicating they are relaxed. Furthermore, if a candidate posts indicating they are excited, the data collection unit can collect voice tone data at that time. By analyzing the candidate's social media activity and collecting relevant data, the accuracy of the data is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the candidate's social media post data into a generating AI and have the generating AI collect the relevant biometric data.

[0081] The evaluation unit estimates the candidate's emotions and adjusts the way the evaluation is presented based on the estimated emotions. For example, if the candidate is relaxed, the evaluation unit can provide detailed feedback. If the candidate is tense, the evaluation unit can provide concise, positive feedback. If the candidate is stressed, the evaluation unit can provide feedback that includes advice on how to reduce stress. By adjusting the way the evaluation is presented based on the candidate's emotions, a more appropriate evaluation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the candidate's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The evaluation unit adjusts the level of detail of the evaluation based on the importance of the biometric data during the evaluation. For example, if the heart rate is high, the evaluation unit can perform a detailed evaluation of the stress level. The evaluation unit can also perform a detailed evaluation of the emotional state if the facial expression is tense. Furthermore, if the tone of voice changes, the evaluation unit can perform a detailed evaluation of emotional fluctuations. By adjusting the level of detail of the evaluation based on the importance of the biometric data, the accuracy of the evaluation is improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the collected heart rate data into a generating AI and have the generating AI perform a stress level evaluation.

[0083] The evaluation unit applies different evaluation algorithms depending on the category of biometric data during evaluation. For example, the evaluation unit can apply an algorithm to evaluate stress levels to heart rate data. The evaluation unit can also apply an algorithm to evaluate emotional states to facial expression data. Furthermore, the evaluation unit can apply an algorithm to evaluate emotional fluctuations to voice tone data. By applying different evaluation algorithms depending on the category of biometric data, the accuracy of the evaluation is improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input collected heart rate data into a generating AI and have the generating AI perform a stress level evaluation.

[0084] The evaluation unit estimates the candidate's emotions and adjusts the length of the evaluation based on the estimated emotions. For example, if the candidate is relaxed, the evaluation unit can provide a detailed evaluation. If the candidate is tense, the evaluation unit can provide a concise evaluation. If the candidate is stressed, the evaluation unit can provide a short evaluation to alleviate stress. By adjusting the length of the evaluation based on the candidate's emotions, a more appropriate evaluation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input the candidate's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The evaluation unit determines the evaluation priority based on the timing of biometric data collection during the evaluation process. For example, the evaluation unit can prioritize the evaluation of recently collected data. The evaluation unit can also prioritize the evaluation of data collected during a specific event. Furthermore, the evaluation unit can comprehensively evaluate data collected over a long period of time. This improves the efficiency of the evaluation by determining the evaluation priority based on the timing of biometric data collection. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the collected biometric data into a generating AI and have the generating AI determine the evaluation priority.

[0086] The evaluation unit adjusts the order of evaluation based on the relevance of the biometric data during the evaluation process. For example, the evaluation unit may evaluate data related to stress levels first. It may also evaluate data related to emotional state next, and finally evaluate data related to voice tone. This improves the efficiency of the evaluation by adjusting the order of evaluation based on the relevance of the biometric data. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may input the collected biometric data into a generating AI and have the generating AI execute the evaluation order.

[0087] The matching unit estimates the candidate's emotions and adjusts the matching criteria based on the estimated emotions. For example, if the candidate is relaxed, the matching unit can perform matching based on their skill set. If the candidate is tense, the matching unit can perform matching based on their experience. If the candidate is stressed, the matching unit can perform matching based on their emotional state. By adjusting the matching criteria based on the candidate's emotions, more appropriate matching becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the candidate's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The matching unit improves the accuracy of matching by considering the interrelationship between the candidate's skill set and the company's needs during the matching process. For example, the matching unit can compare the candidate's skill set with the skill set required by the company to achieve the optimal match. The matching unit can also improve the accuracy of matching by considering the candidate's experience and the company's needs. Furthermore, the matching unit can compare the candidate's past projects with the company's current projects to achieve the optimal match. This improves the accuracy of matching by considering the interrelationship between the candidate's skill set and the company's needs. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the candidate's skill set and the company's needs into a generating AI and have the generating AI perform the optimal matching.

[0089] The matching unit performs matching while considering the candidate's attribute information. For example, the matching unit can perform matching while considering the candidate's age and gender. The matching unit can also perform matching while considering the candidate's educational background and work history. Furthermore, the matching unit can perform matching while considering the candidate's hobbies and interests. This makes it possible to perform more appropriate matching by considering the candidate's attribute information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the candidate's attribute information into a generating AI and have the generating AI perform the optimal matching.

[0090] The matching unit estimates the candidate's emotions and adjusts the order in which matching results are displayed based on the estimated candidate's emotions. For example, if the candidate is relaxed, the matching unit can display results based on their skill set. If the candidate is tense, the matching unit can display results based on their experience. If the candidate is stressed, the matching unit can display results based on their emotional state. By adjusting the order in which matching results are displayed based on the candidate's emotions, more appropriate results can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the candidate's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0091] The matching unit performs matching while considering the geographical distribution of candidates. For example, the matching unit can perform matching while considering the candidate's place of residence and the location of the company. The matching unit can also perform matching while considering the candidate's commute time. Furthermore, the matching unit can perform matching while considering the candidate's geographical mobility. This makes it possible to perform more appropriate matching by considering the geographical distribution of candidates. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input candidate geographical distribution data into a generating AI and have the generating AI perform the optimal matching.

[0092] The matching unit improves the accuracy of matching by referring to the candidate's relevant literature during the matching process. For example, the matching unit can perform matching by referring to papers written by the candidate. The matching unit can also perform matching by referring to reports from projects in which the candidate participated. Furthermore, the matching unit can perform matching by referring to presentations given by the candidate. This improves the accuracy of matching by referring to the candidate's relevant literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the candidate's relevant literature data into a generating AI and have the generating AI perform the optimal matching.

[0093] The future prediction unit estimates the candidate's emotions and adjusts the display method of the future prediction based on the estimated candidate's emotions. For example, if the candidate is relaxed, the future prediction unit can display a detailed future prediction. If the candidate is tense, the future prediction unit can display a concise future prediction. If the candidate is stressed, the future prediction unit can display future predictions to alleviate stress. By adjusting the display method of the future prediction based on the candidate's emotions, more appropriate future predictions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the future prediction unit may be performed using AI, for example, or not using AI. For example, the future prediction unit can input the candidate's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0094] The future prediction unit predicts current skill growth by referring to past work history data during future prediction. For example, the future prediction unit can analyze a candidate's skill growth patterns based on past work history data and predict future skill growth. The future prediction unit can also analyze what skills a candidate has acquired by referring to past work history data and predict future skill growth. Furthermore, the future prediction unit can analyze what projects a candidate has participated in based on past work history data and predict future skill growth. By predicting current skill growth by referring to past work history data, more accurate future predictions become possible. Some or all of the above processing in the future prediction unit may be performed using AI, for example, or without AI. For example, the future prediction unit can input a candidate's past work history data into a generating AI and have the generating AI perform the skill growth prediction.

[0095] The future prediction unit applies different predictive analysis methods to each candidate category when making future predictions. For example, the future prediction unit can apply a method to predict the growth of technical skills to candidates for technical positions. Furthermore, the future prediction unit can apply a method to predict the growth of leadership skills to candidates for management positions. Furthermore, the future prediction unit can apply a method to predict the growth of creativity to candidates for creative positions. By applying different predictive analysis methods to each candidate category, more accurate future predictions become possible. Some or all of the above processing in the future prediction unit may be performed using AI, for example, or without AI. For example, the future prediction unit can input candidate category data into a generating AI and have the generating AI perform the application of predictive analysis methods.

[0096] The future prediction unit estimates the candidate's emotions and adjusts the importance of future predictions based on the estimated candidate's emotions. For example, if the candidate is relaxed, the future prediction unit can provide a detailed future prediction. If the candidate is tense, the future prediction unit can provide a concise future prediction. If the candidate is stressed, the future prediction unit can provide future predictions to alleviate stress. By adjusting the importance of future predictions based on the candidate's emotions, more appropriate future predictions become possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the future prediction unit may be performed using AI, for example, or without AI. For example, the future prediction unit can input the candidate's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0097] The future prediction unit analyzes changes in predictions based on the timing of candidate work history submissions. For example, the future prediction unit can predict future skill growth based on recent work history data. The future prediction unit can also analyze skill growth patterns based on past work history data and predict future skill growth. Furthermore, the future prediction unit can analyze changes in skill growth based on the timing of work history data submissions and predict future skill growth. This allows for more accurate future predictions by analyzing changes in predictions based on the timing of candidate work history submissions. Some or all of the above processing in the future prediction unit may be performed using AI, for example, or without AI. For example, the future prediction unit can input candidate work history data into a generating AI and have the generating AI execute changes in predictions.

[0098] The future forecasting unit analyzes predictions by referring to the candidate's relevant market data when making future predictions. For example, the future forecasting unit can predict future skill growth based on market data where the candidate's skills are in high demand. The future forecasting unit can also predict future skill growth based on market data where the candidate's work history is relevant. Furthermore, the future forecasting unit can predict future skill growth based on market data where the candidate's skills are likely to grow. By analyzing predictions by referring to the candidate's relevant market data, more accurate future predictions become possible. Some or all of the above processing in the future forecasting unit may be performed using AI, for example, or without AI. For example, the future forecasting unit can input the candidate's relevant market data into a generating AI and have the generating AI perform the prediction analysis.

[0099] The future prediction unit analyzes changes in predictions based on the timing of candidate work history submissions. For example, the future prediction unit can predict future skill growth based on recent work history data. The future prediction unit can also analyze skill growth patterns based on past work history data and predict future skill growth. Furthermore, the future prediction unit can analyze changes in skill growth based on the timing of work history data submissions and predict future skill growth. This allows for more accurate future predictions by analyzing changes in predictions based on the timing of candidate work history submissions. Some or all of the above processing in the future prediction unit may be performed using AI, for example, or without AI. For example, the future prediction unit can input candidate work history data into a generating AI and have the generating AI execute changes in predictions.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can collect not only biometric data from candidates but also lifestyle data. For example, it can collect sleep patterns, eating habits, and exercise frequency, and use this data to assess the candidate's health status. The data collection unit can, for instance, acquire sleep data from wearable devices if the candidate is wearing one. It can also collect data from food logging apps if the candidate is using one. Furthermore, if the candidate is using a fitness tracker, it can collect data to assess exercise frequency and intensity. This allows for a more comprehensive assessment by collecting lifestyle data from candidates.

[0102] The evaluation unit can assess a candidate's emotional state by analyzing their social media activity in addition to their biometric data. For example, it can analyze the content and comments a candidate posts on social media and estimate their emotional state from that content. For instance, if a candidate posts positive content, the evaluation unit can assess their emotional state at that time as relaxed. Conversely, if a candidate posts negative content, it can assess their emotional state at that time as stressed. Furthermore, if a candidate frequently uses social media, their emotional state can also be estimated from the frequency of their use. This allows for a more accurate emotional assessment by analyzing social media activity.

[0103] The matching department can analyze candidates' biometric data as well as their past project data to match them with the needs of companies. For example, it can analyze the content and results of projects a candidate has participated in in the past and use that data to match them with the company's current projects. If a candidate has participated in a project in the past where they demonstrated leadership, the matching department can use that experience to match them with a position that requires leadership. Similarly, if a candidate has participated in a project that utilized specific technical skills, the matching department can match them with a position that requires those skills. Furthermore, based on data from successful past projects, the department can assess the candidate's potential to contribute to the company's success. In this way, analyzing past project data enables more appropriate matching.

[0104] The Future Prediction Department can predict future skill growth by analyzing candidates' learning history data in addition to their biometric data. For example, it can analyze data on online courses a candidate has taken in the past and qualifications they have obtained, and use that data to predict future skill growth. For instance, if a candidate has taken many online courses in a particular field, the Future Prediction Department can predict that they are likely to develop skills in that field. Similarly, if a candidate has obtained multiple qualifications, it can predict that they are likely to develop skills related to those qualifications. Furthermore, if a candidate continues to learn consistently, the Future Prediction Department can predict future skill growth based on their motivation to learn. This allows for more accurate future predictions by analyzing learning history data.

[0105] The data collection unit can collect and utilize candidate geographical location data in addition to biometric data. For example, understanding where a candidate lives and the environment in which they live can improve the accuracy of the evaluation. If a candidate lives in an urban area, the data collection unit can consider the characteristics of that area when conducting the evaluation. If a candidate lives in an area with a rich natural environment, the unit can consider the impact of that environment on the candidate's stress level when conducting the evaluation. Furthermore, if a candidate moves frequently, the unit can analyze their movement patterns and evaluate the impact on their lifestyle and stress level. In this way, collecting geographical location data enables a more comprehensive evaluation.

[0106] The evaluation department can assess a candidate's emotional state by analyzing not only their biometric data but also their work environment data. For example, it can analyze the environment in which a candidate currently works (e.g., office layout, relationships with colleagues, workload, etc.) and assess the impact of that environment on the candidate's emotional state. For instance, if a candidate works in an open office, the evaluation department can assess the impact of that environment on the candidate's stress level. Similarly, if a candidate works remotely, the evaluation department can assess the impact of that environment on the candidate's emotional state. Furthermore, if a candidate has a high workload, the evaluation department can assess the impact of that workload on the candidate's emotional state. This allows for a more accurate emotional assessment by analyzing work environment data.

[0107] The matching department can analyze candidates' biometric data, as well as their hobbies and interests, to match them with the company's culture. For example, it can analyze a candidate's hobbies and interests and use that data to match them with the company's culture. If a candidate enjoys outdoor activities, the matching department can evaluate whether that hobby aligns with the company's culture. If a candidate has technical hobbies, it can evaluate whether that hobby aligns with the company's technology-oriented culture. Furthermore, if a candidate has creative hobbies, it can evaluate whether that hobby aligns with the company's creative culture. By analyzing hobbies and interests, a more appropriate cultural matching becomes possible.

[0108] The Future Forecasting Department can predict future career paths by analyzing candidates' biometric data as well as their career goal data. For example, it can analyze the career goals and desired job types set by candidates and use that data to predict their future career paths. If a candidate is aiming for a leadership position, the Future Forecasting Department can predict the skill development required to achieve that goal. If a candidate desires a career in a specific technology field, it can predict the skill development required in that field. Furthermore, if a candidate is aiming for an international career, it can predict the skill development required to achieve that goal. By analyzing career goal data, more accurate future predictions become possible.

[0109] The data collection unit can collect not only candidate biometric data but also past interview data and use it for evaluation. For example, it can collect the content and evaluation results of past interviews a candidate has had and use that data to make a current evaluation. The data collection unit can, for example, analyze the content of answers a candidate has given in past interviews and evaluate the candidate's skills and aptitude from that content. It can also make a current evaluation based on the evaluation results of past interviews a candidate has had. Furthermore, it can analyze the candidate's performance in past interviews and use that data to make a current evaluation. In this way, collecting past interview data enables a more comprehensive evaluation.

[0110] The evaluation unit can estimate the candidate's emotions in addition to their biometric data and adjust the evaluation feedback based on the estimated emotions. For example, if the candidate is relaxed, detailed feedback can be provided. If the candidate is tense, concise and positive feedback can be provided. Furthermore, if the candidate is stressed, feedback including advice to reduce stress can be provided. This allows for a more appropriate evaluation by adjusting the evaluation feedback based on the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input the candidate's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects biometric data from the candidate. For example, the data collection unit collects biometric data such as the candidate's heart rate, facial expressions, and voice tone. The data collection unit can have the candidate wear a wearable device to measure their heart rate. The data collection unit can also use facial recognition technology to photograph the candidate's face with a camera to analyze their facial expressions. Furthermore, the data collection unit can use voice analysis technology to record the candidate's voice to analyze their voice tone. Step 2: The evaluation unit evaluates the candidates based on the data collected by the data collection unit. The evaluation unit assesses the candidates' stress levels and emotional states based on the collected biometric data. For example, it can analyze heart rate variability to assess the candidate's stress level. It can also analyze changes in facial expressions to assess the candidate's emotional state. Furthermore, it can analyze changes in voice tone to assess the candidate's emotional state. Step 3: The Matching Department matches the company's needs with candidates based on the evaluation results obtained by the Evaluation Department. The Matching Department selects the most suitable candidates based on the skill sets and experience required by the company. For example, it can compare the skill sets required by the company with those of the candidates to select the most suitable candidates. It can also compare the experience required by the company with that of the candidates to select the most suitable candidates. Furthermore, it can compare the attribute information required by the company with that of the candidates to select the most suitable candidates. Step 4: The Future Forecasting Department makes future predictions about candidates based on the results obtained by the Matching Department. The Future Forecasting Department evaluates whether candidates can contribute to the company in the future based on their past work experience and predicted skill growth. For example, it can predict what skills a candidate will acquire in the future based on their past work experience. It can also predict what role a candidate will play in the future based on their predicted skill growth. Furthermore, it can predict what projects a candidate will participate in in the future based on their past work experience and predicted skill growth.

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0116] Each of the multiple elements described above, including the data collection unit, evaluation unit, matching unit, and future prediction unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects candidate biometric data using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The evaluation unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 to evaluate the candidate's stress level and emotional state. The matching unit matches the company's needs with the candidate's skill set with the specific processing unit 290 of the data processing unit 12. The future prediction unit predicts the candidate's future with the specific processing unit 290 of the data processing unit 12 and proposes appropriate placement from a long-term perspective. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, evaluation unit, matching unit, and future prediction unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects candidate biometric data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The evaluation unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 to evaluate the candidate's stress level and emotional state. The matching unit matches the company's needs with the candidate's skill set with the specific processing unit 290 of the data processing unit 12. The future prediction unit predicts the candidate's future with the specific processing unit 290 of the data processing unit 12 and proposes appropriate placement from a long-term perspective. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the data collection unit, evaluation unit, matching unit, and future prediction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects candidate biometric data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The evaluation unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 to evaluate the candidate's stress level and emotional state. The matching unit matches the company's needs with the candidate's skill set with the specific processing unit 290 of the data processing unit 12. The future prediction unit predicts the candidate's future with the specific processing unit 290 of the data processing unit 12 and proposes appropriate placement from a long-term perspective. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the data collection unit, evaluation unit, matching unit, and future prediction unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects candidate biometric data using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The evaluation unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 to evaluate the candidate's stress level and emotional state. The matching unit matches the company's needs with the candidate's skill set with the specific processing unit 290 of the data processing unit 12. The future prediction unit predicts the candidate's future with the specific processing unit 290 of the data processing unit 12 and proposes appropriate placement from a long-term perspective. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A data collection unit that collects candidate biometric data, An evaluation unit that evaluates candidates based on the data collected by the aforementioned collection unit, Based on the evaluation results obtained by the aforementioned evaluation unit, a matching unit matches the needs of companies with candidates. The system includes a future prediction unit that predicts the future of candidates based on the results obtained by the matching unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect biometric data such as the candidate's heart rate, facial expressions, and voice tone. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, Based on the collected biometric data, we evaluate the candidates' stress levels and emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is We select the most suitable candidates based on the skill sets and experience required by the company. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned future prediction unit, We assess whether candidates can contribute to the company in the future based on their past work experience and projected skill growth. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, The candidate interacts with an AI to present skill tests and problem-solving tasks, and evaluates the candidate's responses and solution process in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the candidate's sentiment and adjusts the timing of biometric data collection based on the estimated candidate sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the candidate's past biometric data and select the optimal data collection method. The system described in Appendix 2, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting biometric data, filtering is performed based on the candidate's current health status and lifestyle. The system described in Appendix 2, characterized by the features described herein. (Note 10) The aforementioned collection unit is Estimate candidates' sentiments and prioritize the biometric data to collect based on those estimated sentiments. The system described in Appendix 2, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting biometric data, the system prioritizes the collection of highly relevant data, taking into account the candidate's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting biometric data, analyze candidates' social media activity and collect relevant data. The system described in Appendix 2, characterized by the features described herein. (Note 13) The evaluation unit, The system estimates the candidate's emotions and adjusts the way evaluations are expressed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 14) The evaluation unit, During evaluation, adjust the level of detail based on the importance of the biometric data. The system described in Appendix 3, characterized by the features described herein. (Note 15) The evaluation unit, During evaluation, different evaluation algorithms are applied depending on the category of biometric data. The system described in Appendix 3, characterized by the features described herein. (Note 16) The evaluation unit, Estimate the candidate's sentiment and adjust the length of the evaluation based on the estimated candidate's sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 17) The evaluation unit, During the evaluation, the evaluation priorities are determined based on when the biometric data was collected. The system described in Appendix 3, characterized by the features described herein. (Note 18) The evaluation unit, During evaluation, the order of evaluations will be adjusted based on the relevance of the biometric data. The system described in Appendix 3, characterized by the features described herein. (Note 19) The matching unit is The system estimates the candidates' emotions and adjusts the matching criteria based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 20) The matching unit is During the matching process, we improve the accuracy of the matching by considering the interrelationship between the candidate's skill set and the company's needs. The system described in Appendix 4, characterized by the features described herein. (Note 21) The matching unit is During the matching process, candidate attribute information is taken into consideration. The system described in Appendix 4, characterized by the features described herein. (Note 22) The matching unit is It estimates the candidate's sentiment and adjusts the order in which matching results are displayed based on the estimated candidate's sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 23) The matching unit is During the matching process, the geographical distribution of candidates is taken into consideration. The system described in Appendix 4, characterized by the features described herein. (Note 24) The matching unit is During the matching process, we improve the accuracy of the matching by referring to the candidate's relevant literature. The system described in Appendix 4, characterized by the features described herein. (Note 25) The aforementioned future prediction unit, The system estimates the candidates' sentiments and adjusts how future predictions are displayed based on those estimated sentiments. The system described in Appendix 5, characterized by the features described herein. (Note 26) The aforementioned future prediction unit, When making future predictions, past work experience data is used to predict current skill growth. The system described in Appendix 5, characterized by the features described herein. (Note 27) The aforementioned future prediction unit, When making future predictions, different predictive analytics methods are applied to each candidate category. The system described in Appendix 5, characterized by the features described herein. (Note 28) The aforementioned future prediction unit, We estimate the candidates' sentiments and adjust the importance of future predictions based on those estimated sentiments. The system described in Appendix 5, characterized by the features described herein. (Note 29) The aforementioned future prediction unit, When making future predictions, we analyze how predictions change based on when candidates submit their work history. The system described in Appendix 5, characterized by the features described herein. (Note 30) The aforementioned future prediction unit, When making future predictions, we analyze forecasts by referring to relevant market data for the candidates. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects candidate biometric data, An evaluation unit that evaluates candidates based on the data collected by the aforementioned collection unit, Based on the evaluation results obtained by the aforementioned evaluation unit, a matching unit matches the needs of companies with candidates. The system includes a future prediction unit that predicts the future of candidates based on the results obtained by the matching unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect biometric data such as the candidate's heart rate, facial expressions, and voice tone. The system according to feature 1.

3. The evaluation unit, Based on the collected biometric data, we evaluate the candidates' stress levels and emotional state. The system according to feature 1.

4. The matching unit is We select the most suitable candidates based on the skill sets and experience required by the company. The system according to feature 1.

5. The aforementioned future prediction unit, We assess whether candidates can contribute to the company in the future based on their past work experience and projected skill growth. The system according to feature 1.

6. The evaluation unit, The system collaborates with a candidate-interactive AI to present skill tests and problem-solving tasks, and evaluates candidates' responses and solution processes in real time. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the candidate's sentiment and adjusts the timing of biometric data collection based on the estimated candidate sentiment. The system according to feature 2.

8. The aforementioned collection unit is Analyze the candidate's past biometric data and select the optimal data collection method. The system according to feature 2.

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