system

Abstract

The system according to this embodiment aims to automate scheduling, Q&A, and position recommendations in recruitment activities. [Solution] The system according to the embodiment comprises a collection unit, an adjustment unit, a distribution unit, a question and answer unit, and a recommendation unit. The collection unit collects schedules from candidates, HR, or the business side. The adjustment unit automatically adjusts the schedule based on the schedule collected by the collection unit. The distribution unit automatically distributes the schedule adjusted by the adjustment unit via email. The question and answer unit receives questions from candidates and provides answers by referring to the company's internal database. The recommendation unit recommends other suitable positions if a candidate applies for a specific position.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, processes such as schedule adjustment, question-and-answer, and position recommendation in the recruitment activity are performed manually, so there is a problem that the human labor cost is extremely high.

[0005] The system according to the embodiment aims to automate schedule adjustment, question-and-answer, and position recommendation in the recruitment activity.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a coordination unit, a distribution unit, a question and answer unit, and a recommendation unit. The collection unit collects schedules from candidates, HR, or the business side. The coordination unit automatically adjusts the schedule based on the schedule collected by the collection unit. The distribution unit automatically distributes the schedule adjusted by the coordination unit via email. The question and answer unit receives questions from candidates and provides answers by referring to the company's internal database. The recommendation unit recommends other suitable positions if a candidate applies for a specific position. [Effects of the Invention]

[0007] The system according to this embodiment can automate scheduling, Q&A, and position recommendations in recruitment activities. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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).

[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 recruitment support system according to an embodiment of the present invention is a system in which a generating AI acts as a coordinator connecting three parties: candidates, human resources, and the business side. This recruitment support system collects the schedules of candidates, human resources, and the business side, automatically adjusts the optimal interview schedule, and automatically distributes the confirmed schedule to the candidates, human resources, and the business side. It also receives questions from candidates, provides appropriate answers by referring to the company's internal database, automatically recommends other suitable positions if a candidate applies for a specific position, and automatically sends emails to those who have declined, encouraging them to reapply. For example, the scheduling unit collects the schedules of candidates, human resources, and the business side and automatically adjusts the optimal interview schedule. Next, the email distribution unit automatically distributes the confirmed schedule to the candidates, human resources, and the business side. The question and answer unit receives questions from candidates, provides appropriate answers by referring to the company's internal database, and also provides detailed information about the open positions. The recommendation unit automatically recommends other suitable positions if a candidate applies for a specific position. It also automatically sends emails to those who have declined, encouraging them to reapply. As a result, the recruitment process becomes more efficient and human resources are significantly reduced. This allows the recruitment support system to automate scheduling, email distribution, Q&A, and recommendations for candidates, HR, and business teams, thereby streamlining the recruitment process.

[0029] The recruitment support system according to this embodiment comprises a collection unit, an adjustment unit, a distribution unit, a question and answer unit, and a recommendation unit. The collection unit collects the schedules of candidates, HR, and business. The schedules of candidates, HR, and business include, but are not limited to, interview dates, meeting dates, and task deadlines. The collection unit automatically collects schedules, for example, in conjunction with a calendar service. The collection unit can also estimate the candidate's emotions and adjust the timing of schedule collection based on the estimated emotions. For example, if a candidate is feeling stressed, schedule collection can be performed during a time when they are relaxed. The adjustment unit automatically adjusts the schedules based on the schedules collected by the collection unit. The adjustment unit adjusts the schedule using, for example, a generating AI to automatically determine the optimal interview date. The adjustment unit can also estimate the candidate's emotions and adjust the scheduling method based on the estimated emotions. For example, if a candidate is feeling stressed, flexible scheduling can be performed. The distribution unit automatically distributes the schedules adjusted by the adjustment unit via email. The distribution department, for example, uses generative AI to automatically distribute confirmed schedules to candidates, HR, and business teams. The distribution department can also estimate a candidate's emotions and adjust the timing of email delivery based on the estimated emotions. For example, if a candidate is feeling stressed, emails can be sent during a time when they are relaxed. The Q&A department receives questions from candidates and provides answers by referring to the company's internal database. The Q&A department provides appropriate answers to candidate questions, for example, using generative AI. The Q&A department can also estimate a candidate's emotions and adjust the way the answers are expressed based on the estimated emotions. For example, if a candidate is feeling stressed, concise and clear answers can be provided. The recommendation department recommends other suitable positions when a candidate applies for a specific position. The recommendation department automatically recommends other suitable positions to candidates, for example, using generative AI. The recommendation department can also estimate a candidate's emotions and adjust the recommendation method based on the estimated emotions. For example, if a candidate is feeling stressed, concise and clear recommendations can be provided.As a result, the recruitment support system according to this embodiment can automate scheduling, email distribution, Q&A, and recommendations for candidates, HR, and business teams, thereby streamlining the recruitment process.

[0030] The data collection unit can analyze a candidate's past application history and select an appropriate schedule collection method. For example, the data collection unit can analyze a candidate's past application history and select the optimal schedule collection method. For example, the data collection unit can perform immediate collection for candidates who have responded quickly in the past. The data collection unit can analyze a candidate's past application history and perform immediate collection for candidates who have responded quickly. The data collection unit can also perform schedule collection with ample time for candidates who have frequently been late in the past. For example, the data collection unit can analyze a candidate's past application history and perform schedule collection with ample time for candidates who have frequently been late. The data collection unit can also collect data at specific time periods based on past application history. For example, the data collection unit can analyze a candidate's past application history and perform schedule collection at specific time periods. This allows the data collection unit to select the optimal schedule collection method for each candidate by analyzing their past application history. 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 past application history data into a generating AI and have the generating AI select the optimal schedule collection method.

[0031] The data collection unit can filter schedules based on the candidate's current job status or areas of interest. For example, the data collection unit can analyze the candidate's current job status and collect schedules at appropriate times. The data collection unit can also prioritize the collection of relevant schedules based on the candidate's areas of interest. For example, the data collection unit can analyze the candidate's areas of interest and prioritize the collection of relevant schedules. The data collection unit can also perform flexible schedule collection depending on the candidate's job status. For example, the data collection unit can analyze the candidate's job status and perform flexible schedule collection. This allows for the collection of more relevant schedules by filtering them based on the candidate's job status and areas of interest. 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 job status data and areas of interest data into a generating AI and have the generating AI perform schedule filtering.

[0032] The data collection unit can prioritize the collection of highly relevant schedules by considering the candidate's geographical location information when collecting schedules. For example, if a candidate is nearby, the data collection unit can prioritize the collection of nearby schedules. For example, the data collection unit can analyze the candidate's geographical location information and prioritize the collection of nearby schedules. The data collection unit can also prioritize online schedules if the candidate is far away. For example, the data collection unit can analyze the candidate's geographical location information and prioritize online schedules if the candidate is far away. The data collection unit can also suggest the most suitable schedule based on the candidate's geographical location information. For example, the data collection unit can analyze the candidate's geographical location information and suggest the most suitable schedule. This allows for the collection of more relevant schedules by considering the candidate's geographical location information. 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 geographical location information data into a generating AI and have the generating AI determine the schedule priorities.

[0033] The data collection unit can analyze a candidate's social media activity and collect relevant schedules when collecting schedules. For example, the data collection unit can collect events of interest from the candidate's social media activity. The data collection unit can also suggest relevant schedules based on the candidate's social media activity. The data collection unit can also analyze the candidate's social media activity and suggest relevant schedules. The data collection unit can also analyze the candidate's social media activity and collect the most suitable schedule. By analyzing the candidate's social media activity, more relevant schedules can be collected. 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 social media activity data into a generating AI and have the generating AI collect schedules.

[0034] The scheduling unit can select the optimal scheduling method by referring to the candidate's past interview history when scheduling. For example, the scheduling unit can select the optimal scheduling method by referring to the candidate's past interview history. For example, the scheduling unit can immediately schedule interviews for candidates who have responded quickly in the past. The scheduling unit can refer to the candidate's past interview history and immediately schedule interviews for candidates who have responded quickly. The scheduling unit can also schedule interviews with more leeway for candidates who have frequently been late in the past. For example, the scheduling unit can refer to the candidate's past interview history and schedule interviews with more leeway for candidates who have frequently been late. The scheduling unit can also schedule interviews for specific time slots based on past interview history. For example, the scheduling unit can refer to the candidate's past interview history and schedule interviews for specific time slots. This allows the scheduling unit to select the optimal scheduling method for each candidate by referring to their past interview history. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the candidate's past interview history data into a generating AI and have the generating AI select the optimal scheduling method.

[0035] The scheduling unit can customize the scheduling method based on the candidate's current job situation when scheduling. For example, the scheduling unit can consider the candidate's current job situation and schedule the meeting at an appropriate time. For example, the scheduling unit can analyze the candidate's current job situation and schedule the meeting at an appropriate time. The scheduling unit can also perform flexible scheduling according to the candidate's job situation. For example, the scheduling unit can analyze the candidate's job situation and perform flexible scheduling. The scheduling unit can also propose the optimal scheduling method based on the candidate's job situation. For example, the scheduling unit can analyze the candidate's job situation and propose the optimal scheduling method. By customizing the scheduling method based on the candidate's job situation, more appropriate scheduling becomes possible. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input candidate job situation data into a generating AI and have the generating AI perform the customization of the scheduling method.

[0036] The scheduling unit can select the optimal scheduling method by considering the candidate's geographical location information when scheduling. For example, if the candidate is nearby, the scheduling unit can prioritize scheduling nearby interview dates. For example, the scheduling unit can analyze the candidate's geographical location information and prioritize scheduling nearby interview dates. The scheduling unit can also prioritize online interview dates if the candidate is far away. For example, the scheduling unit can analyze the candidate's geographical location information and prioritize online interview dates if the candidate is far away. The scheduling unit can also suggest the optimal date based on the candidate's geographical location information. For example, the scheduling unit can analyze the candidate's geographical location information and suggest the optimal date. This makes it possible to schedule more appropriately by considering the candidate's geographical location information. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can input the candidate's geographical location information data into a generating AI and have the generating AI select the scheduling method.

[0037] The scheduling unit can analyze a candidate's social media activity and propose scheduling methods when scheduling. For example, the scheduling unit can schedule events considering the candidate's interests based on their social media activity. The scheduling unit can also propose the optimal schedule based on the candidate's social media activity. For example, the scheduling unit can analyze the candidate's social media activity and propose the optimal schedule. The scheduling unit can also analyze the candidate's social media activity and propose the optimal scheduling method. For example, the scheduling unit can analyze the candidate's social media activity and propose the optimal scheduling method. This makes it possible to schedule more appropriately by analyzing the candidate's social media activity. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input candidate's social media activity data into a generating AI and have the generating AI propose scheduling methods.

[0038] The distribution unit can select the optimal distribution method by referring to the candidate's past email open history when sending emails. For example, the distribution unit can select the optimal distribution method by referring to the candidate's past email open history. For example, the distribution unit can immediately send emails to candidates who have opened emails quickly in the past. The distribution unit can refer to the candidate's past email open history and immediately send emails to candidates who have opened emails quickly. The distribution unit can also send emails with more time to spare to candidates who have opened emails slowly in the past. For example, the distribution unit can refer to the candidate's past email open history and send emails with more time to spare to candidates who have opened emails slowly. The distribution unit can also send emails at specific time periods based on past email open history. For example, the distribution unit can refer to the candidate's past email open history and send emails at specific time periods. This allows the distribution unit to select the optimal distribution method for each candidate by referring to their past email open history. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution department can input candidate data on their past email open history into a generating AI and have the AI ​​select the optimal distribution method.

[0039] The distribution unit can customize the content of emails based on the candidate's current job situation when sending them. For example, the distribution unit can consider the candidate's current job situation and send emails with appropriate content. For example, the distribution unit can analyze the candidate's current job situation and send emails with appropriate content. The distribution unit can also provide flexible content depending on the candidate's job situation. For example, the distribution unit can analyze the candidate's job situation and provide flexible content. The distribution unit can also suggest optimal content based on the candidate's job situation. For example, the distribution unit can analyze the candidate's job situation and suggest optimal content. By customizing the content based on the candidate's job situation, more appropriate emails can be sent. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input candidate job situation data into a generating AI and have the generating AI perform the customization of the content.

[0040] The distribution unit can select the optimal distribution method when sending emails, taking into account the candidate's geographical location. For example, if the candidate is nearby, the distribution unit can send an email containing information about the surrounding area. For example, the distribution unit can analyze the candidate's geographical location and send an email containing information about the surrounding area. The distribution unit can also send an email containing online information if the candidate is far away. For example, the distribution unit can analyze the candidate's geographical location and send an email containing online information if the candidate is far away. The distribution unit can also suggest the optimal distribution method based on the candidate's geographical location. For example, the distribution unit can analyze the candidate's geographical location and suggest the optimal distribution method. This allows for the selection of a more appropriate distribution method by considering the candidate's geographical location. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the candidate's geographical location data into a generating AI and have the generating AI select the distribution method.

[0041] The distribution department can analyze a candidate's social media activity and suggest content for email distribution. For example, the distribution department can distribute emails containing information of interest based on the candidate's social media activity. For example, the distribution department can analyze a candidate's social media activity and distribute emails containing information of interest. The distribution department can also suggest optimal content based on the candidate's social media activity. For example, the distribution department can analyze a candidate's social media activity and suggest optimal content. The distribution department can also analyze a candidate's social media activity and provide optimal content. For example, the distribution department can analyze a candidate's social media activity and provide optimal content. By analyzing a candidate's social media activity, it is possible to suggest more appropriate content. Some or all of the above processing in the distribution department may be performed using AI, for example, or without AI. For example, the distribution department can input candidate social media activity data into a generating AI and have the generating AI suggest content.

[0042] The question-and-answer unit can select the most appropriate response method by referring to the candidate's past question history during the question-and-answer session. For example, the question-and-answer unit can select the most appropriate response method by referring to the candidate's past question history. For example, the question-and-answer unit can provide immediate answers to candidates who have responded quickly in the past. The question-and-answer unit can refer to the candidate's past question history and provide immediate answers to candidates who have responded quickly. The question-and-answer unit can also provide more timely answers to candidates who have frequently delayed responses in the past. For example, the question-and-answer unit can refer to the candidate's past question history and provide more timely answers to candidates who have frequently delayed responses. The question-and-answer unit can also provide answers at specific time periods based on past question history. For example, the question-and-answer unit can refer to the candidate's past question history and provide answers at specific time periods. This allows the system to select the most appropriate response method for each candidate by referring to their past question history. Some or all of the above processing in the question-and-answer unit may be performed using AI, for example, or without AI. For example, the question-and-answer department can input data on a candidate's past question history into a generating AI and have the AI ​​select the most appropriate answer.

[0043] The question-and-answer unit can customize the content of answers based on the candidate's current job situation during the question-and-answer session. For example, the question-and-answer unit can consider the candidate's current job situation and provide an appropriate answer. For example, the question-and-answer unit can analyze the candidate's current job situation and provide an appropriate answer. The question-and-answer unit can also provide flexible answers depending on the candidate's job situation. For example, the question-and-answer unit can analyze the candidate's job situation and provide flexible answers. The question-and-answer unit can also suggest the optimal answer based on the candidate's job situation. For example, the question-and-answer unit can analyze the candidate's job situation and suggest the optimal answer. This allows for the provision of more appropriate answers by customizing the content of answers based on the candidate's job situation. Some or all of the above processing in the question-and-answer unit may be performed using AI, for example, or not. For example, the question-and-answer unit can input candidate job situation data into a generating AI and have the generating AI perform the customization of the answer content.

[0044] The question-and-answer unit can select the most appropriate response method during the question-and-answer session, taking into account the candidate's geographical location. For example, if the candidate is nearby, the question-and-answer unit can provide an answer that includes information about the surrounding area. For example, the question-and-answer unit can analyze the candidate's geographical location and provide an answer that includes information about the surrounding area. The question-and-answer unit can also provide an answer that includes information about online resources if the candidate is far away. For example, the question-and-answer unit can analyze the candidate's geographical location and provide an answer that includes information about online resources if the candidate is far away. The question-and-answer unit can also suggest the most appropriate response method based on the candidate's geographical location. For example, the question-and-answer unit can analyze the candidate's geographical location and suggest the most appropriate response method. This allows for the selection of a more appropriate response method by considering the candidate's geographical location. Some or all of the above processing in the question-and-answer unit may be performed using AI, for example, or without AI. For example, the question-and-answer unit can input the candidate's geographical location data into a generating AI and have the generating AI select the response method.

[0045] The question-and-answer unit can analyze a candidate's social media activity during the question-and-answer session and propose answers. For example, the question-and-answer unit can provide answers that include information of interest based on the candidate's social media activity. For example, the question-and-answer unit can analyze a candidate's social media activity and provide answers that include information of interest. The question-and-answer unit can also propose the most appropriate answers based on the candidate's social media activity. For example, the question-and-answer unit can analyze a candidate's social media activity and propose the most appropriate answers. The question-and-answer unit can also analyze a candidate's social media activity and provide the most appropriate answers. For example, the question-and-answer unit can analyze a candidate's social media activity and provide the most appropriate answers. This allows for the proposal of more appropriate answers by analyzing the candidate's social media activity. Some or all of the above processing in the question-and-answer unit may be performed using AI, for example, or without AI. For example, the question-and-answer unit can input the candidate's social media activity data into a generating AI and have the generating AI propose answers.

[0046] The recommendation system can select the most suitable recommendation method by referring to a candidate's past application history. For example, the recommendation system can immediately recommend candidates who have responded quickly in the past. The recommendation system can refer to a candidate's past application history and immediately recommend candidates who have responded quickly. The recommendation system can also provide recommendations with ample time for candidates who have frequently delayed applications in the past. For example, the recommendation system can refer to a candidate's past application history and provide recommendations with ample time for candidates who have frequently delayed applications. The recommendation system can also provide recommendations for specific time periods based on past application history. For example, the recommendation system can refer to a candidate's past application history and provide recommendations for specific time periods. This allows the system to select the most suitable recommendation method for each candidate by referring to their past application history. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input a candidate's past application history data into a generating AI and have the generating AI select the optimal recommendation method.

[0047] The recommendation unit can customize its recommendations based on the candidate's current job situation. For example, the recommendation unit can consider the candidate's current job situation and provide appropriate recommendations. For instance, it can analyze the candidate's current job situation and provide appropriate recommendations. Furthermore, the recommendation unit can provide flexible recommendations depending on the candidate's job situation. For example, it can analyze the candidate's job situation and provide flexible recommendations. The recommendation unit can also propose optimal recommendations based on the candidate's job situation. For example, it can analyze the candidate's job situation and propose optimal recommendations. This allows for more appropriate recommendations by customizing recommendations based on the candidate's job situation. Some or all of the above processing in the recommendation unit may be performed using AI, or without AI. For example, the recommendation unit can input candidate job status data into a generating AI and have the AI ​​customize the recommendation content.

[0048] The recommendation unit can select the optimal recommendation method by considering the candidate's geographical location information when making recommendations. For example, if the candidate is nearby, the recommendation unit can provide recommendations that include information about the surrounding area. For example, the recommendation unit can analyze the candidate's geographical location information and provide recommendations that include information about the surrounding area. Also, if the candidate is far away, the recommendation unit can provide recommendations that include information about online resources. For example, the recommendation unit can analyze the candidate's geographical location information and provide recommendations that include information about online resources if the candidate is far away. Furthermore, the recommendation unit can propose the optimal recommendation method based on the candidate's geographical location information. For example, the recommendation unit can analyze the candidate's geographical location information and propose the optimal recommendation method. This makes it possible to provide more appropriate recommendations by considering the candidate's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI. For example, the recommendation unit can input candidate geographic location data into a generating AI and have the AI ​​select a recommendation method.

[0049] The recommendation unit can analyze a candidate's social media activity and propose recommendations. For example, the recommendation unit can provide recommendations that include information of interest based on the candidate's social media activity. For example, the recommendation unit can analyze a candidate's social media activity and provide recommendations that include information of interest. The recommendation unit can also propose optimal recommendations based on the candidate's social media activity. For example, the recommendation unit can analyze a candidate's social media activity and propose optimal recommendations. The recommendation unit can also analyze a candidate's social media activity and provide optimal recommendations. For example, the recommendation unit can analyze a candidate's social media activity and provide optimal recommendations. By analyzing a candidate's social media activity, it is possible to propose more appropriate recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation department can input candidate social media activity data into a generating AI and have the AI ​​generate recommendations.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The recruitment support system can also include a candidate skills matching section. This section analyzes candidates' resumes and work histories to evaluate their skill suitability for the applied position. For example, it can analyze a candidate's technical and soft skills and calculate a suitability score. It can also evaluate suitability based on a candidate's past project experience. Furthermore, it can analyze a candidate's educational background and qualifications to evaluate suitability. This allows for the selection of more suitable candidates by evaluating the match between a candidate's skills and the applied position.

[0052] The data collection unit can analyze candidates' hobbies and interests and prioritize collecting relevant schedules. For example, it can collect schedules for relevant events and seminars based on a candidate's hobbies and interests. It can also prioritize collecting schedules for relevant industries based on a candidate's hobbies and interests. Furthermore, it can collect schedules for relevant companies based on a candidate's hobbies and interests. This allows for the collection of more relevant schedules by filtering them based on the candidate's hobbies and interests.

[0053] The data collection unit can analyze candidates' past interview results and optimize the schedule collection method. For example, it can collect schedules during times when candidates received high ratings based on past interview results. It can also avoid collecting schedules during times when candidates received low ratings based on past interview results. Furthermore, it can collect schedules during times when candidates were most relaxed based on past interview results. In this way, by analyzing past interview results, the optimal schedule collection method can be selected for each candidate.

[0054] The scheduling unit can analyze a candidate's past interview results and optimize the scheduling process. For example, it can schedule interviews during times when the candidate received high ratings based on past interview results. It can also avoid scheduling interviews during times when the candidate received low ratings based on past interview results. Furthermore, it can schedule interviews during times when the candidate appeared most relaxed based on past interview results. In this way, by analyzing past interview results, the optimal scheduling method can be selected for each candidate.

[0055] The scheduling unit can select the most suitable scheduling method by considering the candidate's geographical location. For example, if the candidate is nearby, the scheduling unit can prioritize scheduling an in-person interview nearby. If the candidate is far away, the scheduling unit can prioritize scheduling an online interview. Furthermore, the scheduling unit can suggest the most suitable dates based on the candidate's geographical location. This allows for more appropriate scheduling by considering the candidate's geographical location.

[0056] The scheduling department can analyze candidates' social media activity and propose scheduling methods. For example, it can schedule events considering the candidates' interests based on their social media activity. It can also propose the optimal schedule based on the candidates' social media activity. Furthermore, the scheduling department can analyze candidates' social media activity and propose the most suitable scheduling methods. This allows for more appropriate scheduling by analyzing candidates' social media activity.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection unit collects schedules from candidates, HR, and the business side. The collection unit automatically collects schedules, for example, by linking with a calendar service. The collection unit can also estimate the candidate's mood and adjust the timing of schedule collection based on the estimated mood. For example, if a candidate is feeling stressed, schedule collection can be conducted during a time when they are relaxed. Step 2: The scheduling unit automatically adjusts the schedule based on the schedule collected by the collection unit. The scheduling unit adjusts the schedule using, for example, a generating AI to automatically determine the optimal interview date. The scheduling unit can also estimate the candidate's emotions and adjust the scheduling method based on the estimated emotions. For example, if the candidate is feeling stressed, flexible scheduling can be performed. Step 3: The distribution department automatically sends out the schedule adjusted by the coordination department via email. The distribution department can, for example, use a generation AI to automatically send the confirmed schedule to candidates, HR, and the business side. The distribution department can also estimate the candidate's emotions and adjust the timing of email delivery based on those emotions. For example, if a candidate is feeling stressed, the email can be sent during a time when they are likely to be relaxed. Step 4: The Q&A department receives questions from candidates and provides answers by referring to the company's internal database. The Q&A department can, for example, use generative AI to provide appropriate answers to candidates' questions. The Q&A department can also estimate the candidate's emotions and adjust the way the answer is phrased based on the estimated emotions. For example, if the candidate is feeling stressed, it can provide a concise and clear answer. Step 5: The recommendation unit recommends other suitable positions if a candidate applies for a specific position. The recommendation unit can, for example, use generative AI to automatically recommend other suitable positions to a candidate. The recommendation unit can also estimate the candidate's emotions and adjust its recommendation method based on those emotions. For example, if a candidate is feeling stressed, it can provide concise and clear recommendations.

[0059] (Example of form 2) The recruitment support system according to an embodiment of the present invention is a system in which a generating AI acts as a coordinator connecting three parties: candidates, human resources, and the business side. This recruitment support system collects the schedules of candidates, human resources, and the business side, automatically adjusts the optimal interview schedule, and automatically distributes the confirmed schedule to the candidates, human resources, and the business side. It also receives questions from candidates, provides appropriate answers by referring to the company's internal database, automatically recommends other suitable positions if a candidate applies for a specific position, and automatically sends emails to those who have declined, encouraging them to reapply. For example, the scheduling unit collects the schedules of candidates, human resources, and the business side and automatically adjusts the optimal interview schedule. Next, the email distribution unit automatically distributes the confirmed schedule to the candidates, human resources, and the business side. The question and answer unit receives questions from candidates, provides appropriate answers by referring to the company's internal database, and also provides detailed information about the open positions. The recommendation unit automatically recommends other suitable positions if a candidate applies for a specific position. It also automatically sends emails to those who have declined, encouraging them to reapply. As a result, the recruitment process becomes more efficient and human resources are significantly reduced. This allows the recruitment support system to automate scheduling, email distribution, Q&A, and recommendations for candidates, HR, and business teams, thereby streamlining the recruitment process.

[0060] The recruitment support system according to this embodiment comprises a collection unit, an adjustment unit, a distribution unit, a question and answer unit, and a recommendation unit. The collection unit collects the schedules of candidates, HR, and business. The schedules of candidates, HR, and business include, but are not limited to, interview dates, meeting dates, and task deadlines. The collection unit automatically collects schedules, for example, in conjunction with a calendar service. The collection unit can also estimate the candidate's emotions and adjust the timing of schedule collection based on the estimated emotions. For example, if a candidate is feeling stressed, schedule collection can be performed during a time when they are relaxed. The adjustment unit automatically adjusts the schedules based on the schedules collected by the collection unit. The adjustment unit adjusts the schedule using, for example, a generating AI to automatically determine the optimal interview date. The adjustment unit can also estimate the candidate's emotions and adjust the scheduling method based on the estimated emotions. For example, if a candidate is feeling stressed, flexible scheduling can be performed. The distribution unit automatically distributes the schedules adjusted by the adjustment unit via email. The distribution department, for example, uses generative AI to automatically distribute confirmed schedules to candidates, HR, and business teams. The distribution department can also estimate a candidate's emotions and adjust the timing of email delivery based on the estimated emotions. For example, if a candidate is feeling stressed, emails can be sent during a time when they are relaxed. The Q&A department receives questions from candidates and provides answers by referring to the company's internal database. The Q&A department provides appropriate answers to candidate questions, for example, using generative AI. The Q&A department can also estimate a candidate's emotions and adjust the way the answers are expressed based on the estimated emotions. For example, if a candidate is feeling stressed, concise and clear answers can be provided. The recommendation department recommends other suitable positions when a candidate applies for a specific position. The recommendation department automatically recommends other suitable positions to candidates, for example, using generative AI. The recommendation department can also estimate a candidate's emotions and adjust the recommendation method based on the estimated emotions. For example, if a candidate is feeling stressed, concise and clear recommendations can be provided.As a result, the recruitment support system according to this embodiment can automate scheduling, email distribution, Q&A, and recommendations for candidates, HR, and business teams, thereby streamlining the recruitment process.

[0061] The data collection unit can estimate a candidate's emotions and adjust the timing of schedule collection based on the estimated emotions. For example, if a candidate is stressed, the data collection unit will collect schedule information during a time when the candidate is relaxed. For example, to estimate a candidate's emotions, the data collection unit can capture the candidate's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can calculate an emotion score based on changes in facial expressions and collect schedule information during a time when the candidate is relaxed. The data collection unit can also collect schedule information immediately if the candidate is relaxed. For example, the data collection unit can record the candidate's voice and estimate their emotions using voice analysis technology. The data collection unit can analyze the tone and speed of the voice, calculate an emotion score, and collect schedule information immediately. The data collection unit can also find a time when a candidate is free and collect schedule information if they are busy. For example, the data collection unit can collect the candidate's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The data collection unit can calculate an emotion score based on fluctuations in heart rate and find a time when the candidate is free to collect schedule information. This allows schedule collection to be adjusted according to the candidate's emotions, enabling schedule collection at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 collection unit may be performed using AI or not. For example, the collection unit can input candidate image data captured by a camera into the generative AI and have the generative AI perform the candidate's emotion estimation.

[0062] The data collection unit can analyze a candidate's past application history and select an appropriate schedule collection method. For example, the data collection unit can analyze a candidate's past application history and select the optimal schedule collection method. For example, the data collection unit can perform immediate collection for candidates who have responded quickly in the past. The data collection unit can analyze a candidate's past application history and perform immediate collection for candidates who have responded quickly. The data collection unit can also perform schedule collection with ample time for candidates who have frequently been late in the past. For example, the data collection unit can analyze a candidate's past application history and perform schedule collection with ample time for candidates who have frequently been late. The data collection unit can also collect data at specific time periods based on past application history. For example, the data collection unit can analyze a candidate's past application history and perform schedule collection at specific time periods. This allows the data collection unit to select the optimal schedule collection method for each candidate by analyzing their past application history. 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 past application history data into a generating AI and have the generating AI select the optimal schedule collection method.

[0063] The data collection unit can filter schedules based on the candidate's current job status or areas of interest. For example, the data collection unit can analyze the candidate's current job status and collect schedules at appropriate times. The data collection unit can also prioritize the collection of relevant schedules based on the candidate's areas of interest. For example, the data collection unit can analyze the candidate's areas of interest and prioritize the collection of relevant schedules. The data collection unit can also perform flexible schedule collection depending on the candidate's job status. For example, the data collection unit can analyze the candidate's job status and perform flexible schedule collection. This allows for the collection of more relevant schedules by filtering them based on the candidate's job status and areas of interest. 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 job status data and areas of interest data into a generating AI and have the generating AI perform schedule filtering.

[0064] The data collection unit can estimate a candidate's emotions and prioritize the schedules to collect based on those emotions. For example, if a candidate is stressed, the data collection unit can postpone less important schedules. For example, if a candidate is relaxed, the data collection unit can prioritize more important schedules. For example, if a candidate is relaxed, the data collection unit can prioritize more important schedules. For example, if a candidate is busy, the data collection unit can prioritize the most important schedules. For example, if a candidate is busy, the data collection unit can prioritize the most important schedules. This allows for the collection of more appropriate schedules by prioritizing schedules according to the candidate's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input candidate sentiment data into a generating AI and have the generating AI determine schedule priorities.

[0065] The data collection unit can prioritize the collection of highly relevant schedules by considering the candidate's geographical location information when collecting schedules. For example, if a candidate is nearby, the data collection unit can prioritize the collection of nearby schedules. For example, the data collection unit can analyze the candidate's geographical location information and prioritize the collection of nearby schedules. The data collection unit can also prioritize online schedules if the candidate is far away. For example, the data collection unit can analyze the candidate's geographical location information and prioritize online schedules if the candidate is far away. The data collection unit can also suggest the most suitable schedule based on the candidate's geographical location information. For example, the data collection unit can analyze the candidate's geographical location information and suggest the most suitable schedule. This allows for the collection of more relevant schedules by considering the candidate's geographical location information. 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 geographical location information data into a generating AI and have the generating AI determine the schedule priorities.

[0066] The data collection unit can analyze a candidate's social media activity and collect relevant schedules when collecting schedules. For example, the data collection unit can collect events of interest from the candidate's social media activity. The data collection unit can also suggest relevant schedules based on the candidate's social media activity. The data collection unit can also analyze the candidate's social media activity and suggest relevant schedules. The data collection unit can also analyze the candidate's social media activity and collect the most suitable schedule. By analyzing the candidate's social media activity, more relevant schedules can be collected. 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 social media activity data into a generating AI and have the generating AI collect schedules.

[0067] The scheduling unit can estimate the candidate's emotions and adjust the scheduling method based on the estimated emotions. For example, if the candidate is feeling stressed, the scheduling unit can perform flexible scheduling. For example, if the scheduling unit estimates the candidate's emotions and is feeling stressed, it can perform flexible scheduling. The scheduling unit can also perform rapid scheduling if the candidate is relaxed. For example, if the scheduling unit estimates the candidate's emotions and is relaxed, it can perform rapid scheduling. The scheduling unit can also find the candidate's free time and schedule if the candidate is busy. For example, if the scheduling unit estimates the candidate's emotions and is busy, it can find the candidate's free time and schedule. This allows for more appropriate scheduling by adjusting the scheduling method according to the candidate's emotions. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input candidate emotional data into a generating AI and have the generating AI perform adjustments to the scheduling method.

[0068] The scheduling unit can select the optimal scheduling method by referring to the candidate's past interview history when scheduling. For example, the scheduling unit can select the optimal scheduling method by referring to the candidate's past interview history. For example, the scheduling unit can immediately schedule interviews for candidates who have responded quickly in the past. The scheduling unit can refer to the candidate's past interview history and immediately schedule interviews for candidates who have responded quickly. The scheduling unit can also schedule interviews with more leeway for candidates who have frequently been late in the past. For example, the scheduling unit can refer to the candidate's past interview history and schedule interviews with more leeway for candidates who have frequently been late. The scheduling unit can also schedule interviews for specific time slots based on past interview history. For example, the scheduling unit can refer to the candidate's past interview history and schedule interviews for specific time slots. This allows the scheduling unit to select the optimal scheduling method for each candidate by referring to their past interview history. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the candidate's past interview history data into a generating AI and have the generating AI select the optimal scheduling method.

[0069] The scheduling unit can customize the scheduling method based on the candidate's current job situation when scheduling. For example, the scheduling unit can consider the candidate's current job situation and schedule the meeting at an appropriate time. For example, the scheduling unit can analyze the candidate's current job situation and schedule the meeting at an appropriate time. The scheduling unit can also perform flexible scheduling according to the candidate's job situation. For example, the scheduling unit can analyze the candidate's job situation and perform flexible scheduling. The scheduling unit can also propose the optimal scheduling method based on the candidate's job situation. For example, the scheduling unit can analyze the candidate's job situation and propose the optimal scheduling method. By customizing the scheduling method based on the candidate's job situation, more appropriate scheduling becomes possible. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input candidate job situation data into a generating AI and have the generating AI perform the customization of the scheduling method.

[0070] The scheduling unit can estimate a candidate's emotions and determine scheduling priorities based on those emotions. For example, if a candidate is feeling stressed, the scheduling unit can postpone less important dates. For example, if a candidate is feeling stressed, the scheduling unit can postpone less important dates. For example, if a candidate is feeling relaxed, the scheduling unit can prioritize more important dates. For example, if a candidate is feeling relaxed, the scheduling unit can prioritize more important dates. For example, if a candidate is feeling busy, the scheduling unit can prioritize the most important dates. For example, if a candidate is feeling busy, the scheduling unit can prioritize the most important dates. This allows for more appropriate scheduling by determining scheduling priorities according to the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can input candidate sentiment data into a generating AI and have the generating AI determine the priority of scheduling.

[0071] The scheduling unit can select the optimal scheduling method by considering the candidate's geographical location information when scheduling. For example, if the candidate is nearby, the scheduling unit can prioritize scheduling nearby interview dates. For example, the scheduling unit can analyze the candidate's geographical location information and prioritize scheduling nearby interview dates. The scheduling unit can also prioritize online interview dates if the candidate is far away. For example, the scheduling unit can analyze the candidate's geographical location information and prioritize online interview dates if the candidate is far away. The scheduling unit can also suggest the optimal date based on the candidate's geographical location information. For example, the scheduling unit can analyze the candidate's geographical location information and suggest the optimal date. This makes it possible to schedule more appropriately by considering the candidate's geographical location information. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can input the candidate's geographical location information data into a generating AI and have the generating AI select the scheduling method.

[0072] The scheduling unit can analyze a candidate's social media activity and propose scheduling methods when scheduling. For example, the scheduling unit can schedule events considering the candidate's interests based on their social media activity. The scheduling unit can also propose the optimal schedule based on the candidate's social media activity. For example, the scheduling unit can analyze the candidate's social media activity and propose the optimal schedule. The scheduling unit can also analyze the candidate's social media activity and propose the optimal scheduling method. For example, the scheduling unit can analyze the candidate's social media activity and propose the optimal scheduling method. This makes it possible to schedule more appropriately by analyzing the candidate's social media activity. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input candidate's social media activity data into a generating AI and have the generating AI propose scheduling methods.

[0073] The distribution unit can estimate a candidate's emotions and adjust the timing of email delivery based on the estimated emotions. For example, if a candidate is feeling stressed, the distribution unit can send emails during a time when the candidate is relaxed. For example, if a candidate is feeling stressed, the distribution unit can estimate the candidate's emotions and send emails during a time when the candidate is relaxed. For example, if a candidate is feeling relaxed, the distribution unit can estimate the candidate's emotions and send emails immediately. For example, if a candidate is busy, the distribution unit can find a time when the candidate is free to send emails. For example, if a candidate is feeling busy, the distribution unit can find a time when the candidate is free to send emails. This allows for more appropriate timing of email delivery by adjusting the timing of email delivery according to the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input candidate sentiment data into a generating AI and have the generating AI adjust the timing of email delivery.

[0074] The distribution unit can select the optimal distribution method by referring to the candidate's past email open history when sending emails. For example, the distribution unit can select the optimal distribution method by referring to the candidate's past email open history. For example, the distribution unit can immediately send emails to candidates who have opened emails quickly in the past. The distribution unit can refer to the candidate's past email open history and immediately send emails to candidates who have opened emails quickly. The distribution unit can also send emails with more time to spare to candidates who have opened emails slowly in the past. For example, the distribution unit can refer to the candidate's past email open history and send emails with more time to spare to candidates who have opened emails slowly. The distribution unit can also send emails at specific time periods based on past email open history. For example, the distribution unit can refer to the candidate's past email open history and send emails at specific time periods. This allows the distribution unit to select the optimal distribution method for each candidate by referring to their past email open history. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution department can input candidate data on their past email open history into a generating AI and have the AI ​​select the optimal distribution method.

[0075] The distribution unit can customize the content of emails based on the candidate's current job situation when sending them. For example, the distribution unit can consider the candidate's current job situation and send emails with appropriate content. For example, the distribution unit can analyze the candidate's current job situation and send emails with appropriate content. The distribution unit can also provide flexible content depending on the candidate's job situation. For example, the distribution unit can analyze the candidate's job situation and provide flexible content. The distribution unit can also suggest optimal content based on the candidate's job situation. For example, the distribution unit can analyze the candidate's job situation and suggest optimal content. By customizing the content based on the candidate's job situation, more appropriate emails can be sent. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input candidate job situation data into a generating AI and have the generating AI perform the customization of the content.

[0076] The email distribution system can estimate a candidate's emotions and prioritize email delivery based on those emotions. For example, if a candidate is stressed, the distribution system can postpone less important emails. Similarly, if a candidate is relaxed, the distribution system can prioritize more important emails. Furthermore, if a candidate is busy, the distribution system can prioritize the most important emails. This allows for more appropriate email delivery by prioritizing email delivery according to the candidate's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input candidate sentiment data into a generating AI and have the generating AI determine the priority of email distribution.

[0077] The distribution unit can select the optimal distribution method when sending emails, taking into account the candidate's geographical location. For example, if the candidate is nearby, the distribution unit can send an email containing information about the surrounding area. For example, the distribution unit can analyze the candidate's geographical location and send an email containing information about the surrounding area. The distribution unit can also send an email containing online information if the candidate is far away. For example, the distribution unit can analyze the candidate's geographical location and send an email containing online information if the candidate is far away. The distribution unit can also suggest the optimal distribution method based on the candidate's geographical location. For example, the distribution unit can analyze the candidate's geographical location and suggest the optimal distribution method. This allows for the selection of a more appropriate distribution method by considering the candidate's geographical location. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the candidate's geographical location data into a generating AI and have the generating AI select the distribution method.

[0078] The distribution department can analyze a candidate's social media activity and suggest content for email distribution. For example, the distribution department can distribute emails containing information of interest based on the candidate's social media activity. For example, the distribution department can analyze a candidate's social media activity and distribute emails containing information of interest. The distribution department can also suggest optimal content based on the candidate's social media activity. For example, the distribution department can analyze a candidate's social media activity and suggest optimal content. The distribution department can also analyze a candidate's social media activity and provide optimal content. For example, the distribution department can analyze a candidate's social media activity and provide optimal content. By analyzing a candidate's social media activity, it is possible to suggest more appropriate content. Some or all of the above processing in the distribution department may be performed using AI, for example, or without AI. For example, the distribution department can input candidate social media activity data into a generating AI and have the generating AI suggest content.

[0079] The question-and-answer section can estimate the candidate's emotions and adjust the way it expresses its answers based on those emotions. For example, if the candidate is stressed, the question-and-answer section can provide concise and clear answers. For example, if the candidate is stressed, the question-and-answer section can provide concise and clear answers. For example, if the candidate is relaxed, the question-and-answer section can provide detailed answers. For example, if the candidate is relaxed, the question-and-answer section can provide detailed answers. For example, if the candidate is excited, the question-and-answer section can provide visually appealing answers. For example, if the candidate is excited, the question-and-answer section can provide visually appealing answers. This allows for more appropriate answers to be provided by adjusting the way the answers are expressed according to the candidate's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the question-and-answer section may be performed using AI, for example, or without AI. For example, the question-and-answer section may input candidate emotional data into a generating AI and have the generating AI adjust the way the answers are expressed.

[0080] The question-and-answer unit can select the most appropriate response method by referring to the candidate's past question history during the question-and-answer session. For example, the question-and-answer unit can select the most appropriate response method by referring to the candidate's past question history. For example, the question-and-answer unit can provide immediate answers to candidates who have responded quickly in the past. The question-and-answer unit can refer to the candidate's past question history and provide immediate answers to candidates who have responded quickly. The question-and-answer unit can also provide more timely answers to candidates who have frequently delayed responses in the past. For example, the question-and-answer unit can refer to the candidate's past question history and provide more timely answers to candidates who have frequently delayed responses. The question-and-answer unit can also provide answers at specific time periods based on past question history. For example, the question-and-answer unit can refer to the candidate's past question history and provide answers at specific time periods. This allows the system to select the most appropriate response method for each candidate by referring to their past question history. Some or all of the above processing in the question-and-answer unit may be performed using AI, for example, or without AI. For example, the question-and-answer department can input data on a candidate's past question history into a generating AI and have the AI ​​select the most appropriate answer.

[0081] The question-and-answer unit can customize the content of answers based on the candidate's current job situation during the question-and-answer session. For example, the question-and-answer unit can consider the candidate's current job situation and provide an appropriate answer. For example, the question-and-answer unit can analyze the candidate's current job situation and provide an appropriate answer. The question-and-answer unit can also provide flexible answers depending on the candidate's job situation. For example, the question-and-answer unit can analyze the candidate's job situation and provide flexible answers. The question-and-answer unit can also suggest the optimal answer based on the candidate's job situation. For example, the question-and-answer unit can analyze the candidate's job situation and suggest the optimal answer. This allows for the provision of more appropriate answers by customizing the content of answers based on the candidate's job situation. Some or all of the above processing in the question-and-answer unit may be performed using AI, for example, or not. For example, the question-and-answer unit can input candidate job situation data into a generating AI and have the generating AI perform the customization of the answer content.

[0082] The question-and-answer (Q&A) system can estimate a candidate's emotions and prioritize answers based on those emotions. For example, if a candidate is stressed, the Q&A system can postpone less important questions. It can also prioritize more important questions if the candidate is relaxed. Furthermore, if a candidate is busy, the Q&A system can prioritize the most important questions. This allows for more appropriate answers by prioritizing responses according to the candidate's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the question-and-answer section may be performed using AI, for example, or without AI. For example, the question-and-answer section may input candidate sentiment data into a generating AI and have the generating AI determine the priority of answers.

[0083] The question-and-answer unit can select the most appropriate response method during the question-and-answer session, taking into account the candidate's geographical location. For example, if the candidate is nearby, the question-and-answer unit can provide an answer that includes information about the surrounding area. For example, the question-and-answer unit can analyze the candidate's geographical location and provide an answer that includes information about the surrounding area. The question-and-answer unit can also provide an answer that includes information about online resources if the candidate is far away. For example, the question-and-answer unit can analyze the candidate's geographical location and provide an answer that includes information about online resources if the candidate is far away. The question-and-answer unit can also suggest the most appropriate response method based on the candidate's geographical location. For example, the question-and-answer unit can analyze the candidate's geographical location and suggest the most appropriate response method. This allows for the selection of a more appropriate response method by considering the candidate's geographical location. Some or all of the above processing in the question-and-answer unit may be performed using AI, for example, or without AI. For example, the question-and-answer unit can input the candidate's geographical location data into a generating AI and have the generating AI select the response method.

[0084] The question-and-answer unit can analyze a candidate's social media activity during the question-and-answer session and propose answers. For example, the question-and-answer unit can provide answers that include information of interest based on the candidate's social media activity. For example, the question-and-answer unit can analyze a candidate's social media activity and provide answers that include information of interest. The question-and-answer unit can also propose the most appropriate answers based on the candidate's social media activity. For example, the question-and-answer unit can analyze a candidate's social media activity and propose the most appropriate answers. The question-and-answer unit can also analyze a candidate's social media activity and provide the most appropriate answers. For example, the question-and-answer unit can analyze a candidate's social media activity and provide the most appropriate answers. This allows for the proposal of more appropriate answers by analyzing the candidate's social media activity. Some or all of the above processing in the question-and-answer unit may be performed using AI, for example, or without AI. For example, the question-and-answer unit can input the candidate's social media activity data into a generating AI and have the generating AI propose answers.

[0085] The recommendation system can estimate a candidate's emotions and adjust its recommendation methods based on those emotions. For example, if a candidate is feeling stressed, the recommendation system can provide concise and clear recommendations. It can also provide detailed recommendations if a candidate is relaxed. Furthermore, if a candidate is excited, the recommendation system can provide visually appealing recommendations. This allows for more appropriate recommendations by adjusting the recommendation method according to the candidate's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit may input candidate sentiment data into the generative AI and have the generative AI adjust the recommendation method.

[0086] The recommendation system can select the most suitable recommendation method by referring to a candidate's past application history. For example, the recommendation system can immediately recommend candidates who have responded quickly in the past. The recommendation system can refer to a candidate's past application history and immediately recommend candidates who have responded quickly. The recommendation system can also provide recommendations with ample time for candidates who have frequently delayed applications in the past. For example, the recommendation system can refer to a candidate's past application history and provide recommendations with ample time for candidates who have frequently delayed applications. The recommendation system can also provide recommendations for specific time periods based on past application history. For example, the recommendation system can refer to a candidate's past application history and provide recommendations for specific time periods. This allows the system to select the most suitable recommendation method for each candidate by referring to their past application history. Some or all of the above-described processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input a candidate's past application history data into a generating AI and have the generating AI select the optimal recommendation method.

[0087] The recommendation unit can customize its recommendations based on the candidate's current job situation. For example, the recommendation unit can consider the candidate's current job situation and provide appropriate recommendations. For instance, it can analyze the candidate's current job situation and provide appropriate recommendations. Furthermore, the recommendation unit can provide flexible recommendations depending on the candidate's job situation. For example, it can analyze the candidate's job situation and provide flexible recommendations. The recommendation unit can also propose optimal recommendations based on the candidate's job situation. For example, it can analyze the candidate's job situation and propose optimal recommendations. This allows for more appropriate recommendations by customizing recommendations based on the candidate's job situation. Some or all of the above processing in the recommendation unit may be performed using AI, or without AI. For example, the recommendation unit can input candidate job status data into a generating AI and have the AI ​​customize the recommendation content.

[0088] The recommendation system can estimate a candidate's emotions and prioritize recommendations based on those emotions. For example, if a candidate is stressed, the recommendation system can postpone less important recommendations. It can also prioritize more important recommendations if the candidate is relaxed. Furthermore, if the candidate is busy, the recommendation system can prioritize the most important recommendations. This allows for more appropriate recommendations by prioritizing recommendations according to the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the processing described above in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit may input candidate sentiment data into the generative AI and have the generative AI determine the recommendation priority.

[0089] The recommendation unit can select the optimal recommendation method by considering the candidate's geographical location information when making recommendations. For example, if the candidate is nearby, the recommendation unit can provide recommendations that include information about the surrounding area. For example, the recommendation unit can analyze the candidate's geographical location information and provide recommendations that include information about the surrounding area. Also, if the candidate is far away, the recommendation unit can provide recommendations that include information about online resources. For example, the recommendation unit can analyze the candidate's geographical location information and provide recommendations that include information about online resources if the candidate is far away. Furthermore, the recommendation unit can propose the optimal recommendation method based on the candidate's geographical location information. For example, the recommendation unit can analyze the candidate's geographical location information and propose the optimal recommendation method. This makes it possible to provide more appropriate recommendations by considering the candidate's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without using AI. For example, the recommendation unit can input candidate geographic location data into a generating AI and have the AI ​​select a recommendation method.

[0090] The recommendation unit can analyze a candidate's social media activity and propose recommendations. For example, the recommendation unit can provide recommendations that include information of interest based on the candidate's social media activity. For example, the recommendation unit can analyze a candidate's social media activity and provide recommendations that include information of interest. The recommendation unit can also propose optimal recommendations based on the candidate's social media activity. For example, the recommendation unit can analyze a candidate's social media activity and propose optimal recommendations. The recommendation unit can also analyze a candidate's social media activity and provide optimal recommendations. For example, the recommendation unit can analyze a candidate's social media activity and provide optimal recommendations. By analyzing a candidate's social media activity, it is possible to propose more appropriate recommendations. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation department can input candidate social media activity data into a generating AI and have the AI ​​generate recommendations. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, adjustment unit, distribution unit, question and answer unit, and recommendation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the schedules of candidates, HR, and business. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automatically adjusts the schedule based on the collected schedule. The distribution unit is implemented, for example, by the control unit 46A of the smart device 14 and automatically distributes the adjusted schedule to candidates, HR, and business. The question and answer unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides appropriate answers to questions from candidates. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and recommends other suitable positions to candidates. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, adjustment unit, distribution unit, question and answer unit, and recommendation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the schedules of candidates, HR, and business. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automatically adjusts the schedule based on the collected schedule. The distribution unit is implemented, for example, by the control unit 46A of the smart glasses 214 and automatically distributes the adjusted schedule to candidates, HR, and business. The question and answer unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides appropriate answers to questions from candidates. The recommendation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and recommends other suitable positions to candidates. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, adjustment unit, distribution unit, question and answer unit, and recommendation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the schedules of candidates, HR, and business. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically adjusts the schedule based on the collected schedule. The distribution unit is implemented by, for example, the control unit 46A of the headset terminal 314 and automatically distributes the adjusted schedule to candidates, HR, and business. The question and answer unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides appropriate answers to questions from candidates. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends other suitable positions to candidates. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, adjustment unit, distribution unit, question-and-answer unit, and recommendation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects the schedules of candidates, HR, and business. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically adjusts the schedule based on the collected schedules. The distribution unit is implemented by, for example, the control unit 46A of the robot 414 and automatically distributes the adjusted schedule to candidates, HR, and business. The question-and-answer unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides appropriate answers to questions from candidates. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends other suitable positions to candidates.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The recruitment support system can also include a candidate skills matching section. This section analyzes candidates' resumes and work histories to evaluate their skill suitability for the applied position. For example, it can analyze a candidate's technical and soft skills and calculate a suitability score. It can also evaluate suitability based on a candidate's past project experience. Furthermore, it can analyze a candidate's educational background and qualifications to evaluate suitability. This allows for the selection of more suitable candidates by evaluating the match between a candidate's skills and the applied position.

[0093] The data collection unit can monitor the candidate's health status and adjust the timing of schedule collection based on that status. For example, the unit can monitor the candidate's heart rate and sleep patterns and collect schedule information when the candidate is in good health. It can also collect schedule information after the candidate has rested if they are fatigued. Furthermore, the unit can adjust the frequency of schedule collection based on the candidate's health status. This allows for more appropriate schedule collection by adjusting the timing according to the candidate's health condition.

[0094] The data collection unit can analyze candidates' hobbies and interests and prioritize collecting relevant schedules. For example, it can collect schedules for relevant events and seminars based on a candidate's hobbies and interests. It can also prioritize collecting schedules for relevant industries based on a candidate's hobbies and interests. Furthermore, it can collect schedules for relevant companies based on a candidate's hobbies and interests. This allows for the collection of more relevant schedules by filtering them based on the candidate's hobbies and interests.

[0095] The data collection unit can analyze candidates' past interview results and optimize the schedule collection method. For example, it can collect schedules during times when candidates received high ratings based on past interview results. It can also avoid collecting schedules during times when candidates received low ratings based on past interview results. Furthermore, it can collect schedules during times when candidates were most relaxed based on past interview results. In this way, by analyzing past interview results, the optimal schedule collection method can be selected for each candidate.

[0096] The collection unit can estimate a candidate's emotions and prioritize the schedules to collect based on those estimates. For example, if a candidate is stressed, the collection unit can postpone less important schedules. Conversely, if a candidate is relaxed, the collection unit can prioritize more important schedules. Furthermore, if a candidate is busy, the collection unit can prioritize the most important schedules. This allows for the collection of more appropriate schedules by prioritizing them according to the candidate's emotions.

[0097] The scheduling unit can analyze a candidate's past interview results and optimize the scheduling process. For example, it can schedule interviews during times when the candidate received high ratings based on past interview results. It can also avoid scheduling interviews during times when the candidate received low ratings based on past interview results. Furthermore, it can schedule interviews during times when the candidate appeared most relaxed based on past interview results. In this way, by analyzing past interview results, the optimal scheduling method can be selected for each candidate.

[0098] The scheduling system can estimate a candidate's emotions and prioritize scheduling based on those emotions. For example, if a candidate is stressed, the system can postpone less important dates. Conversely, if a candidate is relaxed, it can prioritize more important dates. Furthermore, if a candidate is busy, it can prioritize the most important dates. This allows for more appropriate scheduling by prioritizing appointments according to the candidate's emotions.

[0099] The scheduling unit can select the most suitable scheduling method by considering the candidate's geographical location. For example, if the candidate is nearby, the scheduling unit can prioritize scheduling an in-person interview nearby. If the candidate is far away, the scheduling unit can prioritize scheduling an online interview. Furthermore, the scheduling unit can suggest the most suitable dates based on the candidate's geographical location. This allows for more appropriate scheduling by considering the candidate's geographical location.

[0100] The scheduling department can analyze candidates' social media activity and propose scheduling methods. For example, it can schedule events considering the candidates' interests based on their social media activity. It can also propose the optimal schedule based on the candidates' social media activity. Furthermore, the scheduling department can analyze candidates' social media activity and propose the most suitable scheduling methods. This allows for more appropriate scheduling by analyzing candidates' social media activity.

[0101] The distribution team can estimate a candidate's emotions and adjust the timing of email delivery based on those estimates. For example, if a candidate is feeling stressed, the distribution team can send an email during a time when the candidate is relaxed. Alternatively, if the candidate is relaxed, the distribution team can send an email immediately. Furthermore, if a candidate is busy, the distribution team can find a time when the candidate is free to send an email. This allows for more appropriate timing of email delivery by adjusting the delivery schedule according to the candidate's emotions.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The collection unit collects schedules from candidates, HR, and the business side. The collection unit automatically collects schedules, for example, by linking with a calendar service. The collection unit can also estimate the candidate's mood and adjust the timing of schedule collection based on the estimated mood. For example, if a candidate is feeling stressed, schedule collection can be conducted during a time when they are relaxed. Step 2: The scheduling unit automatically adjusts the schedule based on the schedule collected by the collection unit. The scheduling unit adjusts the schedule using, for example, a generating AI to automatically determine the optimal interview date. The scheduling unit can also estimate the candidate's emotions and adjust the scheduling method based on the estimated emotions. For example, if the candidate is feeling stressed, flexible scheduling can be performed. Step 3: The distribution department automatically sends out the schedule adjusted by the coordination department via email. The distribution department can, for example, use a generation AI to automatically send the confirmed schedule to candidates, HR, and the business side. The distribution department can also estimate the candidate's emotions and adjust the timing of email delivery based on those emotions. For example, if a candidate is feeling stressed, the email can be sent during a time when they are likely to be relaxed. Step 4: The Q&A department receives questions from candidates and provides answers by referring to the company's internal database. The Q&A department can, for example, use generative AI to provide appropriate answers to candidates' questions. The Q&A department can also estimate the candidate's emotions and adjust the way the answer is phrased based on the estimated emotions. For example, if the candidate is feeling stressed, it can provide a concise and clear answer. Step 5: The recommendation unit recommends other suitable positions if a candidate applies for a specific position. The recommendation unit can, for example, use generative AI to automatically recommend other suitable positions to a candidate. The recommendation unit can also estimate the candidate's emotions and adjust its recommendation method based on those emotions. For example, if a candidate is feeling stressed, it can provide concise and clear recommendations.

[0104] 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.

[0105] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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. 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.

[0122] 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.

[0123] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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. 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.

[0138] 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.

[0139] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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. 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.

[0155] 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.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 collection department that gathers schedules from candidates, HR, or business teams, An adjustment unit that automatically adjusts the schedule based on the schedule collected by the aforementioned collection unit, A distribution unit that automatically sends out the schedule adjusted by the aforementioned adjustment unit via email, The Q&A department receives questions from candidates and provides answers by referring to the company's internal database, It includes a recommendation unit that recommends other suitable positions when a candidate applies for a specific position. A system characterized by the following features.

2. The aforementioned collection unit is Estimate the candidate's emotions and adjust the timing of schedule collection based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the candidate's past application history and select the appropriate method for collecting schedules. The system according to feature 1.

4. The aforementioned collection unit is When collecting schedules, filter candidates based on their current job status or areas of interest. The system according to feature 1.

5. The aforementioned collection unit is Estimate the candidates' sentiments and prioritize the schedule for collecting information based on those estimated sentiments. The system according to feature 1.

6. The aforementioned collection unit is When collecting schedules, prioritize collecting highly relevant schedules by considering the candidates' geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting schedules, analyze candidates' social media activity and collect relevant schedules. The system according to feature 1.

8. The adjustment unit is, Estimate the candidates' sentiments and adjust the scheduling method based on those estimated sentiments. The system according to feature 1.

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