system

Abstract

The system according to the embodiment aims to automate the process of extracting important information from documents submitted by applicants and interview records, and organizing the interview content. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an interview content selection unit, and a result summarization unit. The data collection unit collects documents submitted by applicants or interview records. The analysis unit analyzes the data collected by the data collection unit and extracts information such as the applicant's motivation for applying, their strengths and weaknesses, and other information important to selection decisions. The interview content selection unit selects information to be asked during the interview based on the information extracted by the analysis unit. The result summarization unit automatically summarizes the interview results based on the information selected by the interview content selection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem that extracting important information from applicants' submitted documents and interview records and organizing the interview content was time-consuming and laborious.

[0005] The system according to the embodiment aims to automate the process of extracting important information from documents submitted by applicants and interview records, and organizing the interview content. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, an interview content selection unit, and a result summarization unit. The data collection unit collects documents submitted by applicants or interview records. The analysis unit analyzes the data collected by the data collection unit and extracts information such as the applicant's motivation for applying, their strengths and weaknesses, and other information important to selection decisions. The interview content selection unit selects information to be asked during the interview based on the information extracted by the analysis unit. The result summarization unit automatically summarizes the interview results based on the information selected by the interview content selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the process of extracting important information from documents submitted by applicants and interview records, and organizing the interview content. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention automates the process of automatically extracting motivations for applying, the applicant's strengths and weaknesses, and other important information for selection decisions, collecting interview details during interviews, and summarizing interview results. This system automates the process of automatically extracting motivations for applying, the applicant's strengths and weaknesses, and other important information for selection decisions, collecting interview details during interviews, and summarizing interview results. For example, a data collection unit collects documents submitted by applicants and interview records. Next, an analysis unit analyzes the collected data and extracts motivations for applying, the applicant's strengths and weaknesses, and other important information for selection decisions. Furthermore, an interview content selection unit selects information to be asked during interviews. Finally, a result summary unit automatically summarizes the interview results. This system improves the efficiency of interviews and the accuracy of selection. For example, the data collection unit collects documents submitted by applicants and interview records. These include resumes, job histories, and audio recordings of interviews. This data is then input into a generation AI. Next, the analysis unit analyzes the input data. The generation AI automatically extracts the applicant's motivation for applying, their strengths and weaknesses, and other information important to the selection process. For example, it extracts information such as "I want to contribute to the company's growth" as a motivation for applying, "I'm good at teamwork" as a strength, and "I'm not good at detailed work" as a weakness. The interview content selection unit then selects the information to be asked during the interview. This information includes specific anecdotes related to the applicant's motivation for applying, experiences that capitalize on their strengths, and efforts to overcome their weaknesses. Finally, the results summary unit automatically summarizes the interview results. For example, it organizes the interview evaluation points and information important to the selection process, and outputs the results in report format. This report can be used as reference material by interviewers when making selections. This system improves the efficiency of interviews and the accuracy of selections. Interviewers can conduct interviews more efficiently based on the information extracted by the generation AI, thereby improving the accuracy of selections. For example, by accurately understanding the applicant's motivations for applying and their strengths and weaknesses and asking appropriate questions, the system can more accurately evaluate the applicant's suitability. This allows the system to improve the efficiency of interviews and the accuracy of selection.

[0029] The system according to the embodiment includes a data collection unit, an analysis unit, an interview content selection unit, and a result summary unit. The data collection unit collects documents submitted by applicants or interview records. Documents submitted by applicants include, but are not limited to, resumes, curriculum vitae, and interview audio recordings. The data collection unit, for example, scans resumes submitted by applicants and saves them as digital data. The data collection unit can also record interview audio recordings and convert them into text data. For example, speech recognition technology can be used to automatically convert the interview audio into text. The analysis unit analyzes the collected data and extracts the applicant's motivation for applying, the applicant's strengths and weaknesses, and other important information for selection decisions. The analysis unit, for example, uses a generation AI to analyze the applicant's documents submitted and interview records. The generation AI automatically extracts the applicant's motivation for applying and the applicant's strengths and weaknesses using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI extracts the applicant's motivation for applying, such as "I want to contribute to the growth of the company," from the applicant's resume. The generation AI can also extract a strength such as "good at teamwork" from an applicant's resume. Furthermore, the generation AI can extract a weakness such as "not good at detailed work" from an interview audio recording. The interview content selection unit selects content to be asked during the interview based on the information extracted by the analysis unit. For example, the interview content selection unit selects specific episodes related to the applicant's motivation for applying. The interview content selection unit can also select experiences that make use of the applicant's strengths. The interview content selection unit can also select efforts to overcome the applicant's weaknesses. The result summary unit automatically summarizes the interview results based on the content selected by the interview content selection unit. For example, the result summary unit organizes the interview evaluation points and content important for selection decisions and outputs them in report format. For example, the result summary unit evaluates the applicant's aptitude based on the interview evaluation points. The result summary unit can also determine the applicant's selection results based on content important for selection decisions.As a result, the system according to the embodiment can efficiently collect and analyze documents submitted by applicants and interview records, select interview content, and summarize interview results.

[0030] The data collection unit can analyze the applicant's past history of submitted documents and select the optimal collection method. The data collection unit analyzes the applicant's past history of submitted documents and selects the optimal collection method. For example, if the applicant previously submitted documents by email, the data collection unit collects them in the same way. Also, if the applicant previously used an online form, the data collection unit can collect them in the same way. Furthermore, if the applicant previously submitted documents by mail, the data collection unit can collect them in the same way. In this way, the optimal collection method can be selected by analyzing the past history of submitted documents. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the applicant's past history of submitted documents into the generation AI and have the generation AI select the optimal collection method.

[0031] When collecting documents to be submitted, the data collection unit can filter them based on the applicant's current job status or areas of interest. When collecting documents to be submitted, the data collection unit can filter them based on the applicant's current job status or areas of interest. For example, the data collection unit can prioritize collecting documents related to the applicant's current job status. It can also prioritize collecting documents related to the applicant's areas of interest. It can also filter out unnecessary documents based on the applicant's job status or areas of interest. This makes it possible to filter out unnecessary documents based on the applicant's job status or areas of interest. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, for example, or may be performed without using AI. For example, the data collection unit can input the applicant's job status and area of ​​interest data into the generation AI and have the generation AI perform the filtering.

[0032] When collecting documents to be submitted, the data collection unit can prioritize collecting relevant documents by taking into account the applicant's geographic location information. When collecting documents to be submitted, the data collection unit prioritizes collecting relevant documents by taking into account the applicant's geographic location information. For example, if the applicant lives in a particular area, the data collection unit can prioritize collecting documents related to that area. Also, if the applicant works in a particular area, the data collection unit can prioritize collecting documents related to that area. Furthermore, if the applicant is interested in a particular area, the data collection unit can prioritize collecting documents related to that area. This allows for prioritized collection of relevant documents by taking into account the applicant's geographic location information. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or may be performed without using AI. For example, the data collection unit can input the applicant's geographic location information data into the generation AI and cause the generation AI to select relevant documents.

[0033] The data collection unit can analyze the applicant's social media activity and collect relevant documents when collecting the documents to be submitted. The data collection unit can analyze the applicant's social media activity and collect relevant documents when collecting the documents to be submitted. For example, the data collection unit can collect relevant documents based on information shared by the applicant on social media. The data collection unit can also collect relevant documents based on accounts the applicant follows on social media. The data collection unit can also collect relevant documents based on groups the applicant participates in on social media. In this way, the data collection unit can collect relevant documents by analyzing the applicant's social media activity. Some or all of the above-mentioned processing in the data collection unit can be performed, for example, using AI or without AI. For example, the data collection unit can input the applicant's social media activity data into the generation AI and have the generation AI select relevant documents.

[0034] The analysis unit can adjust the level of detail of the analysis based on the importance of the motivation for applying and the advantages and disadvantages. The analysis unit adjusts the level of detail of the analysis based on the importance of the motivation for applying and the advantages and disadvantages. For example, if the motivation for applying is important, the analysis unit performs a detailed analysis. Also, if the advantages and disadvantages are important, the analysis unit can perform a detailed analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the importance of the motivation for applying and the advantages and disadvantages. This makes it possible to adjust the level of detail of the analysis depending on the importance of the motivation for applying and the advantages and disadvantages. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input importance data of the motivation for applying and the advantages and disadvantages into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the applicant's work history. The analysis unit applies different analysis algorithms depending on the applicant's work history. For example, if the applicant's work history is in a technical position, the analysis unit applies an analysis algorithm that focuses on technical skills. Furthermore, if the applicant's work history is in a managerial position, the analysis unit can also apply an analysis algorithm that focuses on leadership. Furthermore, if the applicant's work history is in a sales position, the analysis unit can also apply an analysis algorithm that focuses on communication skills. This allows an appropriate analysis algorithm to be applied depending on the applicant's work history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the applicant's work history data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0036] The analysis unit can determine the priority of analysis based on the submission date of the submitted document. The analysis unit determines the priority of analysis based on the submission date of the submitted document. For example, if the submitted document is recently submitted, the analysis unit prioritizes analysis. Also, if the submitted document is old, the analysis unit can lower the priority of analysis. Furthermore, the analysis unit can adjust the priority of analysis based on the submission date of the submitted document. In this way, the priority of analysis can be determined based on the submission date of the submitted document. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input submission date data of the submitted document to the generation AI and have the generation AI determine the priority of analysis.

[0037] The analysis unit can adjust the order of analysis based on the relevance of the submitted documents. The analysis unit adjusts the order of analysis based on the relevance of the submitted documents. For example, if the submitted documents are related to motivation for applying, the analysis unit prioritizes analysis. Also, if the submitted documents are related to strengths and weaknesses, the analysis unit can also prioritize analysis. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the submitted documents. This makes it possible to adjust the order of analysis based on the relevance of the submitted documents. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the submitted documents into the generation AI and have the generation AI adjust the order of analysis.

[0038] The hearing content picking unit can adjust the level of detail of the content based on the importance of the motivation for applying and the advantages and disadvantages. The hearing content picking unit adjusts the level of detail of the content based on the importance of the motivation for applying and the advantages and disadvantages. For example, if the motivation for applying is important, the hearing content picking unit provides detailed hearing content. Also, if the advantages and disadvantages are important, the hearing content picking unit can also provide detailed hearing content. Furthermore, the hearing content picking unit can adjust the level of detail of the content depending on the importance of the motivation for applying and the advantages and disadvantages. This makes it possible to adjust the level of detail of the content depending on the importance of the motivation for applying and the advantages and disadvantages. Some or all of the above-mentioned processing in the hearing content picking unit may be performed using AI, for example, or may be performed without using AI. For example, the hearing content picking unit can input importance data of the motivation for applying and the advantages and disadvantages into the generation AI and cause the generation AI to adjust the level of detail of the content.

[0039] The interview content pick-up unit can apply different pick-up algorithms depending on the applicant's work history. The interview content pick-up unit applies different pick-up algorithms depending on the applicant's work history. For example, if the applicant's work history is in a technical position, the interview content pick-up unit can apply a pick-up algorithm that focuses on technical skills. Also, if the applicant's work history is in a managerial position, the interview content pick-up unit can apply a pick-up algorithm that focuses on leadership. Furthermore, if the applicant's work history is in a sales position, the interview content pick-up unit can apply a pick-up algorithm that focuses on communication skills. This allows an appropriate pick-up algorithm to be applied depending on the applicant's work history. Some or all of the above-mentioned processing in the interview content pick-up unit may be performed using AI, for example, or may be performed without using AI. For example, the interview content pick-up unit can input the applicant's work history data into a generation AI and cause the generation AI to apply an appropriate pick-up algorithm.

[0040] The hearing content pick-up unit can determine the priority of the content based on the submission time of the submitted document. The hearing content pick-up unit determines the priority of the content based on the submission time of the submitted document. For example, if the submitted document is recently submitted, the hearing content pick-up unit preferentially picks up the hearing content. Also, if the submitted document is old, the hearing content pick-up unit can lower the priority and pick up the hearing content. Furthermore, the hearing content pick-up unit can adjust the priority of the content based on the submission time of the submitted document. In this way, the priority of the content can be determined based on the submission time of the submitted document. Some or all of the above-mentioned processing in the hearing content pick-up unit may be performed using AI, for example, or may be performed without using AI. For example, the hearing content pick-up unit can input submission time data of the submitted document to the generation AI and cause the generation AI to determine the priority of the content.

[0041] The hearing content picking unit can adjust the order of the contents based on the relevance of the submitted documents. The hearing content picking unit adjusts the order of the contents based on the relevance of the submitted documents. For example, if the submitted documents are related to the motivation for applying, the hearing content picking unit can prioritize picking up hearing contents. Also, if the submitted documents are related to strengths and weaknesses, the hearing content picking unit can also prioritize picking up hearing contents. Furthermore, the hearing content picking unit can adjust the order of the contents based on the relevance of the submitted documents. This allows the order of the contents to be adjusted based on the relevance of the submitted documents. Some or all of the above-mentioned processing in the hearing content picking unit may be performed using AI, for example, or may be performed without using AI. For example, the hearing content picking unit can input relevance data of the submitted documents to the generation AI and cause the generation AI to adjust the order of the contents.

[0042] The result summarizing unit can adjust the level of detail of the results based on the importance of the interview evaluation points. The result summarizing unit adjusts the level of detail of the results based on the importance of the interview evaluation points. For example, if the interview evaluation points are important, the result summarizing unit provides detailed results. On the other hand, if the interview evaluation points are not important, the result summarizing unit can also provide concise results. Furthermore, the result summarizing unit can adjust the level of detail of the results according to the importance of the interview evaluation points. This makes it possible to adjust the level of detail of the results according to the importance of the interview evaluation points. Some or all of the above-mentioned processing in the result summarizing unit may be performed using AI, for example, or may be performed without using AI. For example, the result summarizing unit can input interview evaluation point data to a generation AI and have the generation AI adjust the level of detail of the results.

[0043] The result summarizing unit can apply different summarizing algorithms depending on the applicant's work history. The result summarizing unit applies different summarizing algorithms depending on the applicant's work history. For example, if the applicant's work history is in a technical position, the result summarizing unit can apply a summarizing algorithm that focuses on technical skills. Also, if the applicant's work history is in a managerial position, the result summarizing unit can apply a summarizing algorithm that focuses on leadership. Furthermore, if the applicant's work history is in a sales position, the result summarizing unit can apply a summarizing algorithm that focuses on communication skills. This allows an appropriate summarizing algorithm to be applied depending on the applicant's work history. Some or all of the above-mentioned processing in the result summarizing unit may be performed using AI, for example, or may be performed without using AI. For example, the result summarizing unit can input the applicant's work history data into the generation AI and cause the generation AI to apply an appropriate summarizing algorithm.

[0044] The result summarizing unit can determine the priority of results based on when the interview was conducted. The result summarizing unit determines the priority of results based on when the interview was conducted. For example, if the interview was conducted recently, the result summarizing unit prioritizes summarizing the results. Also, if the interview is old, the result summarizing unit can lower the priority of the results when summarizing. Furthermore, the result summarizing unit can adjust the priority of results based on when the interview was conducted. In this way, the priority of results can be determined based on when the interview was conducted. Some or all of the above-mentioned processing in the result summarizing unit may be performed using AI, for example, or may be performed without using AI. For example, the result summarizing unit can input interview time data into the generation AI and have the generation AI determine the priority of the results.

[0045] The result summarizing unit can adjust the order of the results based on the relevance of the interview. The result summarizing unit adjusts the order of the results based on the relevance of the interview. For example, if the interview is related to motivation for applying, the result summarizing unit prioritizes summarizing the results. Also, if the interview is related to strengths and weaknesses, the result summarizing unit can also prioritize summarizing the results. Furthermore, the result summarizing unit can adjust the order of the results based on the relevance of the interview. In this way, the order of the results can be adjusted based on the relevance of the interview. Some or all of the above-mentioned processing in the result summarizing unit may be performed using AI, for example, or may be performed without using AI. For example, the result summarizing unit can input interview relevance data to the generation AI and have the generation AI adjust the order of the results.

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

[0047] The data collection department can analyze the results of an applicant's past interviews and optimize the way the interview proceeds. For example, if the applicant was nervous in a past interview, the data collection department can prioritize collecting questions that will help the applicant feel relaxed. Also, if the applicant gave detailed answers to specific questions in a past interview, the data collection department can collect similar questions. Furthermore, if the applicant showed interest in a specific topic in a past interview, the data collection department can collect questions related to that topic. In this way, the results of past interviews can be used to provide the applicant with the best way to proceed with the interview.

[0048] The interview content selection unit can analyze an applicant's past interview history and select the most appropriate interview content. For example, if an applicant gave detailed answers to specific questions in past interviews, similar questions can be selected. Also, if an applicant showed interest in a specific topic in past interviews, questions related to that topic can be selected. Furthermore, if an applicant seemed nervous in past interviews, questions that will help them relax can be selected. In this way, past interview history can be utilized to provide the most appropriate interview content for the applicant.

[0049] The data collection department can analyze the applicant's social media activity and collect relevant documents based on the applicant's interests. For example, relevant documents can be collected based on information shared by the applicant on social media. Related documents can also be collected based on the accounts the applicant follows on social media. Furthermore, related documents can be collected based on groups the applicant participates in on social media. In this way, the data collection department can collect relevant documents by analyzing the applicant's social media activity.

[0050] The interview content pick-up unit can apply different pick-up algorithms depending on the applicant's work history. For example, if the applicant's work history is in a technical position, the interview content pick-up unit can apply a pick-up algorithm that focuses on technical skills. Also, if the applicant's work history is in a managerial position, the interview content pick-up unit can apply a pick-up algorithm that focuses on leadership. Furthermore, if the applicant's work history is in a sales position, the interview content pick-up unit can apply a pick-up algorithm that focuses on communication skills. This makes it possible to apply an appropriate pick-up algorithm depending on the applicant's work history.

[0051] The result summarizing unit can determine the priority of the results based on when the interview was conducted. For example, if the interview was conducted recently, the result summarizing unit can prioritize summarizing the results. Also, if the interview is old, the result summarizing unit can also summarize the results with a lower priority. Furthermore, the result summarizing unit can adjust the priority of the results based on when the interview was conducted. In this way, the priority of the results can be determined based on when the interview was conducted.

[0052] The analysis unit can determine the priority of analysis based on the time of submission of the submitted document. For example, if the submitted document is recently submitted, the analysis unit can prioritize the analysis. Also, if the submitted document is old, the analysis unit can lower the priority of analysis. Furthermore, the analysis unit can adjust the priority of analysis based on the time of submission of the submitted document. This makes it possible to determine the priority of analysis based on the time of submission of the submitted document.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The data collection department collects documents or interview records submitted by applicants. These may include resumes, curriculum vitae, and audio recordings of interviews. The data collection department scans the resumes submitted by applicants and saves them as digital data. It may also record audio recordings of interviews and convert them into text data using speech recognition technology. Step 2: The analysis unit analyzes the collected data and extracts the applicant's motivation, strengths and weaknesses, and other information important to the selection process. For example, using generative AI, the application documents and interview records of the applicant can be analyzed to automatically extract the applicant's motivation, strengths, and weaknesses. Step 3: The Interview Content Selection Department selects the content to be asked during the interview based on the information extracted by the Analysis Department. For example, specific anecdotes related to the applicant's motivation for applying, experiences that make use of their strengths, and efforts to overcome their weaknesses are selected. Step 4: The results summary section automatically summarizes the interview results based on the content picked up by the interview content selection section. For example, it organizes the evaluation points of the interview and the content important for selection decisions, and outputs it in report format.

[0055] (Example 2) A system according to an embodiment of the present invention automates the process of automatically extracting motivations for applying, the applicant's strengths and weaknesses, and other important information for selection decisions, collecting interview details during interviews, and summarizing interview results. This system automates the process of automatically extracting motivations for applying, the applicant's strengths and weaknesses, and other important information for selection decisions, collecting interview details during interviews, and summarizing interview results. For example, a data collection unit collects documents submitted by applicants and interview records. Next, an analysis unit analyzes the collected data and extracts motivations for applying, the applicant's strengths and weaknesses, and other important information for selection decisions. Furthermore, an interview content selection unit selects information to be asked during interviews. Finally, a result summary unit automatically summarizes the interview results. This system improves the efficiency of interviews and the accuracy of selection. For example, the data collection unit collects documents submitted by applicants and interview records. These include resumes, job histories, and audio recordings of interviews. This data is then input into a generation AI. Next, the analysis unit analyzes the input data. The generation AI automatically extracts the applicant's motivation for applying, their strengths and weaknesses, and other information important to the selection process. For example, it extracts information such as "I want to contribute to the company's growth" as a motivation for applying, "I'm good at teamwork" as a strength, and "I'm not good at detailed work" as a weakness. The interview content selection unit then selects the information to be asked during the interview. This information includes specific anecdotes related to the applicant's motivation for applying, experiences that capitalize on their strengths, and efforts to overcome their weaknesses. Finally, the results summary unit automatically summarizes the interview results. For example, it organizes the interview evaluation points and information important to the selection process, and outputs the results in report format. This report can be used as reference material by interviewers when making selections. This system improves the efficiency of interviews and the accuracy of selections. Interviewers can conduct interviews more efficiently based on the information extracted by the generation AI, thereby improving the accuracy of selections. For example, by accurately understanding the applicant's motivations for applying and their strengths and weaknesses and asking appropriate questions, the system can more accurately evaluate the applicant's suitability. This allows the system to improve the efficiency of interviews and the accuracy of selection.

[0056] The system according to the embodiment includes a data collection unit, an analysis unit, an interview content selection unit, and a result summary unit. The data collection unit collects documents submitted by applicants or interview records. Documents submitted by applicants include, but are not limited to, resumes, curriculum vitae, and interview audio recordings. The data collection unit, for example, scans resumes submitted by applicants and saves them as digital data. The data collection unit can also record interview audio recordings and convert them into text data. For example, speech recognition technology can be used to automatically convert the interview audio into text. The analysis unit analyzes the collected data and extracts the applicant's motivation for applying, the applicant's strengths and weaknesses, and other information important to the selection process. The analysis unit, for example, uses a generation AI to analyze the applicant's documents submitted by applicants and interview records. The generation AI automatically extracts the applicant's motivation for applying and the applicant's strengths and weaknesses using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI extracts the applicant's motivation for applying, such as "I want to contribute to the growth of the company," from the applicant's resume. The generation AI can also extract a strength such as "good at teamwork" from an applicant's resume. Furthermore, the generation AI can extract a weakness such as "not good at detailed work" from an interview audio recording. The interview content selection unit selects content to be asked during the interview based on the information extracted by the analysis unit. For example, the interview content selection unit selects specific episodes related to the applicant's motivation for applying. The interview content selection unit can also select experiences that make use of the applicant's strengths. The interview content selection unit can also select efforts to overcome the applicant's weaknesses. The result summary unit automatically summarizes the interview results based on the content selected by the interview content selection unit. For example, the result summary unit organizes the interview evaluation points and content important for selection decisions and outputs them in report format. For example, the result summary unit evaluates the applicant's aptitude based on the interview evaluation points. The result summary unit can also determine the applicant's selection results based on content important for selection decisions.As a result, the system according to the embodiment can efficiently collect and analyze documents submitted by applicants and interview records, select interview content, and summarize interview results.

[0057] The system includes a data collection unit that estimates an applicant's emotions and adjusts the timing of collecting submitted documents based on the estimated emotions. The data collection unit estimates the applicant's emotions and adjusts the timing of collecting submitted documents based on the estimated emotions. For example, if an applicant is feeling stressed, the data collection unit adjusts the collection timing so that the applicant can submit the documents in a relaxed state. Furthermore, if the applicant is relaxed, the data collection unit can also collect the submitted documents immediately. Furthermore, if the applicant is busy, the data collection unit can collect the submitted documents at an appropriate time. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit may input facial expression data of the applicant into the generative AI and have the generative AI estimate the applicant's emotions. This allows the timing of collecting submitted documents to be adjusted according to the applicant's emotions, thereby collecting documents at a more appropriate time.

[0058] The data collection unit can analyze the applicant's past history of submitted documents and select the optimal collection method. The data collection unit analyzes the applicant's past history of submitted documents and selects the optimal collection method. For example, if the applicant previously submitted documents by email, the data collection unit collects them in the same way. Also, if the applicant previously used an online form, the data collection unit can collect them in the same way. Furthermore, if the applicant previously submitted documents by mail, the data collection unit can collect them in the same way. In this way, the optimal collection method can be selected by analyzing the past history of submitted documents. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the applicant's past history of submitted documents into the generation AI and have the generation AI select the optimal collection method.

[0059] When collecting documents to be submitted, the data collection unit can filter them based on the applicant's current job status or areas of interest. When collecting documents to be submitted, the data collection unit can filter them based on the applicant's current job status or areas of interest. For example, the data collection unit can prioritize collecting documents related to the applicant's current job status. It can also prioritize collecting documents related to the applicant's areas of interest. It can also filter out unnecessary documents based on the applicant's job status or areas of interest. This makes it possible to filter out unnecessary documents based on the applicant's job status or areas of interest. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, for example, or may be performed without using AI. For example, the data collection unit can input the applicant's job status and area of ​​interest data into the generation AI and have the generation AI perform the filtering.

[0060] The data collection unit can estimate the applicant's emotions and prioritize the documents to be collected based on the estimated emotions. The data collection unit can estimate the applicant's emotions and prioritize the documents to be collected based on the estimated emotions. For example, if the applicant is stressed, the data collection unit can prioritize collecting important documents. Alternatively, if the applicant is relaxed, the data collection unit can collect all documents equally. Furthermore, if the applicant is busy, the data collection unit can prioritize collecting the most important documents. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the data collection unit can be performed using AI, for example, or without AI. For example, the data collection unit can input the applicant's facial expression data into the generative AI and have the generative AI perform emotion estimation. This allows the priority of documents to be collected to be determined based on the applicant's emotions.

[0061] When collecting documents to be submitted, the data collection unit can prioritize collecting relevant documents by taking into account the applicant's geographic location information. When collecting documents to be submitted, the data collection unit prioritizes collecting relevant documents by taking into account the applicant's geographic location information. For example, if the applicant lives in a particular area, the data collection unit can prioritize collecting documents related to that area. Also, if the applicant works in a particular area, the data collection unit can prioritize collecting documents related to that area. Furthermore, if the applicant is interested in a particular area, the data collection unit can prioritize collecting documents related to that area. This allows for prioritized collection of relevant documents by taking into account the applicant's geographic location information. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or may be performed without using AI. For example, the data collection unit can input the applicant's geographic location information data into the generation AI and cause the generation AI to select relevant documents.

[0062] The data collection unit can analyze the applicant's social media activity and collect relevant documents when collecting the documents to be submitted. The data collection unit can analyze the applicant's social media activity and collect relevant documents when collecting the documents to be submitted. For example, the data collection unit can collect relevant documents based on information shared by the applicant on social media. The data collection unit can also collect relevant documents based on accounts the applicant follows on social media. The data collection unit can also collect relevant documents based on groups the applicant participates in on social media. In this way, the data collection unit can collect relevant documents by analyzing the applicant's social media activity. Some or all of the above-mentioned processing in the data collection unit can be performed, for example, using AI or without AI. For example, the data collection unit can input the applicant's social media activity data into the generation AI and have the generation AI select relevant documents.

[0063] The analysis unit can estimate the applicant's emotions and adjust the presentation method of the analysis based on the estimated emotions of the applicant. The analysis unit can estimate the applicant's emotions and adjust the presentation method of the analysis based on the estimated emotions of the applicant. For example, if the applicant is relaxed, the analysis unit can provide detailed analysis results. If the applicant is nervous, the analysis unit can provide concise analysis results. Furthermore, if the applicant is excited, the analysis unit can provide visually appealing analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the applicant's facial expression data into the generation AI and have the generation AI perform emotion estimation. This allows the presentation method of the analysis to be adjusted according to the applicant's emotions.

[0064] The analysis unit can adjust the level of detail of the analysis based on the importance of the motivation for applying and the advantages and disadvantages. The analysis unit adjusts the level of detail of the analysis based on the importance of the motivation for applying and the advantages and disadvantages. For example, if the motivation for applying is important, the analysis unit performs a detailed analysis. Also, if the advantages and disadvantages are important, the analysis unit can perform a detailed analysis. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the importance of the motivation for applying and the advantages and disadvantages. This makes it possible to adjust the level of detail of the analysis depending on the importance of the motivation for applying and the advantages and disadvantages. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input importance data of the motivation for applying and the advantages and disadvantages into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0065] The analysis unit can apply different analysis algorithms depending on the applicant's work history. The analysis unit applies different analysis algorithms depending on the applicant's work history. For example, if the applicant's work history is in a technical position, the analysis unit applies an analysis algorithm that focuses on technical skills. Furthermore, if the applicant's work history is in a managerial position, the analysis unit can also apply an analysis algorithm that focuses on leadership. Furthermore, if the applicant's work history is in a sales position, the analysis unit can also apply an analysis algorithm that focuses on communication skills. This allows an appropriate analysis algorithm to be applied depending on the applicant's work history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the applicant's work history data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0066] The analysis unit can estimate the applicant's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit can estimate the applicant's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the applicant is relaxed, the analysis unit can perform a detailed analysis. If the applicant is nervous, the analysis unit can perform a concise analysis. Furthermore, if the applicant is excited, the analysis unit can perform a visually appealing analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the applicant's facial expression data into the generation AI and have the generation AI perform emotion estimation. This allows the length of the analysis to be adjusted according to the applicant's emotions.

[0067] The analysis unit can determine the priority of analysis based on the submission date of the submitted document. The analysis unit determines the priority of analysis based on the submission date of the submitted document. For example, if the submitted document is recently submitted, the analysis unit prioritizes analysis. Also, if the submitted document is old, the analysis unit can lower the priority of analysis. Furthermore, the analysis unit can adjust the priority of analysis based on the submission date of the submitted document. In this way, the priority of analysis can be determined based on the submission date of the submitted document. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input submission date data of the submitted document to the generation AI and have the generation AI determine the priority of analysis.

[0068] The analysis unit can adjust the order of analysis based on the relevance of the submitted documents. The analysis unit adjusts the order of analysis based on the relevance of the submitted documents. For example, if the submitted documents are related to motivation for applying, the analysis unit prioritizes analysis. Also, if the submitted documents are related to strengths and weaknesses, the analysis unit can also prioritize analysis. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the submitted documents. This makes it possible to adjust the order of analysis based on the relevance of the submitted documents. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input relevance data of the submitted documents into the generation AI and have the generation AI adjust the order of analysis.

[0069] The interview content pick-up unit can estimate the applicant's emotions and adjust the way the interview content is presented based on the estimated emotions. The interview content pick-up unit can estimate the applicant's emotions and adjust the way the interview content is presented based on the estimated emotions. For example, if the applicant is relaxed, the interview content pick-up unit can provide detailed interview content. Also, if the applicant is nervous, the interview content pick-up unit can provide concise interview content. Furthermore, if the applicant is excited, the interview content pick-up unit can provide visually appealing interview content. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the interview content pick-up unit can be performed using AI, for example, or without AI. For example, the interview content pick-up unit can input facial expression data of the applicant into the generation AI and cause the generation AI to estimate emotions. This allows you to adjust the way the interview content is presented depending on the applicant's emotions.

[0070] The hearing content picking unit can adjust the level of detail of the content based on the importance of the motivation for applying and the advantages and disadvantages. The hearing content picking unit adjusts the level of detail of the content based on the importance of the motivation for applying and the advantages and disadvantages. For example, if the motivation for applying is important, the hearing content picking unit provides detailed hearing content. Also, if the advantages and disadvantages are important, the hearing content picking unit can also provide detailed hearing content. Furthermore, the hearing content picking unit can adjust the level of detail of the content depending on the importance of the motivation for applying and the advantages and disadvantages. This makes it possible to adjust the level of detail of the content depending on the importance of the motivation for applying and the advantages and disadvantages. Some or all of the above-mentioned processing in the hearing content picking unit may be performed using AI, for example, or may be performed without using AI. For example, the hearing content picking unit can input importance data of the motivation for applying and the advantages and disadvantages into the generation AI and cause the generation AI to adjust the level of detail of the content.

[0071] The interview content pick-up unit can apply different pick-up algorithms depending on the applicant's work history. The interview content pick-up unit applies different pick-up algorithms depending on the applicant's work history. For example, if the applicant's work history is in a technical position, the interview content pick-up unit can apply a pick-up algorithm that focuses on technical skills. Also, if the applicant's work history is in a managerial position, the interview content pick-up unit can apply a pick-up algorithm that focuses on leadership. Furthermore, if the applicant's work history is in a sales position, the interview content pick-up unit can apply a pick-up algorithm that focuses on communication skills. This allows an appropriate pick-up algorithm to be applied depending on the applicant's work history. Some or all of the above-mentioned processing in the interview content pick-up unit may be performed using AI, for example, or may be performed without using AI. For example, the interview content pick-up unit can input the applicant's work history data into a generation AI and cause the generation AI to apply an appropriate pick-up algorithm.

[0072] The interview content pick-up unit can estimate the applicant's emotions and adjust the length of the interview content based on the estimated emotions. The interview content pick-up unit can estimate the applicant's emotions and adjust the length of the interview content based on the estimated emotions. For example, if the applicant is relaxed, the interview content pick-up unit can provide detailed interview content. Also, if the applicant is nervous, the interview content pick-up unit can provide concise interview content. Furthermore, if the applicant is excited, the interview content pick-up unit can provide visually appealing interview content. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the interview content pick-up unit can be performed using AI, for example, or without AI. For example, the interview content pick-up unit can input facial expression data of the applicant into the generation AI and cause the generation AI to estimate emotions. This allows the length of the interview to be adjusted depending on the applicant's emotions.

[0073] The hearing content pick-up unit can determine the priority of the content based on the submission time of the submitted document. The hearing content pick-up unit determines the priority of the content based on the submission time of the submitted document. For example, if the submitted document is recently submitted, the hearing content pick-up unit preferentially picks up the hearing content. Also, if the submitted document is old, the hearing content pick-up unit can lower the priority and pick up the hearing content. Furthermore, the hearing content pick-up unit can adjust the priority of the content based on the submission time of the submitted document. In this way, the priority of the content can be determined based on the submission time of the submitted document. Some or all of the above-mentioned processing in the hearing content pick-up unit may be performed using AI, for example, or may be performed without using AI. For example, the hearing content pick-up unit can input submission time data of the submitted document to the generation AI and cause the generation AI to determine the priority of the content.

[0074] The hearing content picking unit can adjust the order of the contents based on the relevance of the submitted documents. The hearing content picking unit adjusts the order of the contents based on the relevance of the submitted documents. For example, if the submitted documents are related to the motivation for applying, the hearing content picking unit can prioritize picking up hearing contents. Also, if the submitted documents are related to strengths and weaknesses, the hearing content picking unit can also prioritize picking up hearing contents. Furthermore, the hearing content picking unit can adjust the order of the contents based on the relevance of the submitted documents. This allows the order of the contents to be adjusted based on the relevance of the submitted documents. Some or all of the above-mentioned processing in the hearing content picking unit may be performed using AI, for example, or may be performed without using AI. For example, the hearing content picking unit can input relevance data of the submitted documents to the generation AI and cause the generation AI to adjust the order of the contents.

[0075] The result summarizing unit can estimate the applicant's emotions and adjust the presentation method of the results based on the estimated emotions of the applicant. The result summarizing unit can estimate the applicant's emotions and adjust the presentation method of the results based on the estimated emotions of the applicant. For example, if the applicant is relaxed, the result summarizing unit can provide detailed results. Also, if the applicant is nervous, the result summarizing unit can provide concise results. Furthermore, if the applicant is excited, the result summarizing unit can provide visually appealing results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the result summarizing unit can be performed using AI, for example, or without AI. For example, the result summarizing unit can input facial expression data of the applicant into the generation AI and have the generation AI perform emotion estimation. This allows the presentation method of the results to be adjusted according to the applicant's emotions.

[0076] The result summarizing unit can adjust the level of detail of the results based on the importance of the interview evaluation points. The result summarizing unit adjusts the level of detail of the results based on the importance of the interview evaluation points. For example, if the interview evaluation points are important, the result summarizing unit provides detailed results. On the other hand, if the interview evaluation points are not important, the result summarizing unit can also provide concise results. Furthermore, the result summarizing unit can adjust the level of detail of the results according to the importance of the interview evaluation points. This makes it possible to adjust the level of detail of the results according to the importance of the interview evaluation points. Some or all of the above-mentioned processing in the result summarizing unit may be performed using AI, for example, or may be performed without using AI. For example, the result summarizing unit can input interview evaluation point data to a generation AI and have the generation AI adjust the level of detail of the results.

[0077] The result summarizing unit can apply different summarizing algorithms depending on the applicant's work history. The result summarizing unit applies different summarizing algorithms depending on the applicant's work history. For example, if the applicant's work history is in a technical position, the result summarizing unit can apply a summarizing algorithm that focuses on technical skills. Also, if the applicant's work history is in a managerial position, the result summarizing unit can apply a summarizing algorithm that focuses on leadership. Furthermore, if the applicant's work history is in a sales position, the result summarizing unit can apply a summarizing algorithm that focuses on communication skills. This allows an appropriate summarizing algorithm to be applied depending on the applicant's work history. Some or all of the above-mentioned processing in the result summarizing unit may be performed using AI, for example, or may be performed without using AI. For example, the result summarizing unit can input the applicant's work history data into the generation AI and cause the generation AI to apply an appropriate summarizing algorithm.

[0078] The result summarizing unit can estimate the applicant's emotions and adjust the length of the results based on the estimated emotions. The result summarizing unit can estimate the applicant's emotions and adjust the length of the results based on the estimated emotions. For example, if the applicant is relaxed, the result summarizing unit can provide detailed results. Also, if the applicant is nervous, the result summarizing unit can provide concise results. Furthermore, if the applicant is excited, the result summarizing unit can provide visually appealing results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the result summarizing unit can be performed using AI, for example, or without AI. For example, the result summarizing unit can input the applicant's facial expression data into the generation AI and have the generation AI perform emotion estimation. This allows the length of the results to be adjusted according to the applicant's emotions.

[0079] The result summarizing unit can determine the priority of results based on when the interview was conducted. The result summarizing unit determines the priority of results based on when the interview was conducted. For example, if the interview was conducted recently, the result summarizing unit prioritizes summarizing the results. Also, if the interview is old, the result summarizing unit can lower the priority of the results when summarizing. Furthermore, the result summarizing unit can adjust the priority of results based on when the interview was conducted. In this way, the priority of results can be determined based on when the interview was conducted. Some or all of the above-mentioned processing in the result summarizing unit may be performed using AI, for example, or may be performed without using AI. For example, the result summarizing unit can input interview time data into the generation AI and have the generation AI determine the priority of the results.

[0080] The result summarizing unit can adjust the order of the results based on the relevance of the interview. The result summarizing unit adjusts the order of the results based on the relevance of the interview. For example, if the interview is related to motivation for applying, the result summarizing unit prioritizes summarizing the results. Also, if the interview is related to strengths and weaknesses, the result summarizing unit can also prioritize summarizing the results. Furthermore, the result summarizing unit can adjust the order of the results based on the relevance of the interview. In this way, the order of the results can be adjusted based on the relevance of the interview. Some or all of the above-mentioned processing in the result summarizing unit may be performed using AI, for example, or may be performed without using AI. For example, the result summarizing unit can input interview relevance data to the generation AI and have the generation AI adjust the order of the results. === Hard Collateral 1-1 === Each of the multiple elements, including the data collection unit, analysis unit, interview content selection unit, and result summary unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects applicants' submitted documents and interview records using the camera 42 and microphone 38B of the smart device 14 and stores them as digital data using the control unit 46A. The analysis unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI to extract the applicant's motivation for applying and their strengths and weaknesses. The interview content selection unit, for example, is implemented by the control unit 46A of the smart device 14 and selects interview content to be asked during the interview based on the information extracted by the analysis unit. The result summary unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and automatically summarizes the interview results and outputs them in report format. The data collection unit, for example, uses an emotion engine to estimate the applicant's emotions and adjust the timing of collecting submitted documents. === Hard Collateral 1-2 === Each of the multiple elements, including the data collection unit, analysis unit, interview content selection unit, and result summary unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects applicants' submitted documents and interview records using the camera 42 and microphone 238 of the smart glasses 214 and stores them as digital data using the control unit 46A. The analysis unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI to extract the applicant's motivation for applying and their strengths and weaknesses. The interview content selection unit, for example, is implemented by the control unit 46A of the smart glasses 214 and selects interview content to be asked during the interview based on the information extracted by the analysis unit. The result summary unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and automatically summarizes the interview results and outputs them in report format. The data collection unit, for example, uses an emotion engine to estimate the applicant's emotions and adjust the timing of collecting submitted documents. === Hard Collateral 1-3 === Each of the multiple elements, including the data collection unit, analysis unit, interview content selection unit, and result summary unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the data collection unit collects applicants' submitted documents and interview records using the camera 42 and microphone 238 of the headset terminal 314 and stores them as digital data using the control unit 46A. The analysis unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI to extract the applicant's motivation for applying and their strengths and weaknesses. The interview content selection unit, for example, is implemented by the control unit 46A of the headset terminal 314 and selects interview content to be asked during the interview based on the information extracted by the analysis unit. The result summary unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and automatically summarizes the interview results and outputs them in report format. The data collection unit, for example, uses an emotion engine to estimate the applicant's emotions and adjust the timing of collecting submitted documents. === Hard Collateral 1-4 === Each of the multiple elements, including the data collection unit, analysis unit, interview content selection unit, and result summary unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit collects applicants' submitted documents and interview records using the camera 42 and microphone 238 of the robot 414 and stores them as digital data using the control unit 46A. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using a generative AI and extracts the applicant's motivation for applying and their strengths and weaknesses. The interview content selection unit, implemented, for example, by the control unit 46A of the robot 414, selects interview content to be asked during the interview based on the information extracted by the analysis unit. The result summary unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, automatically summarizes the interview results and outputs them in report format. The data collection unit, for example, uses an emotion engine to estimate the applicant's emotions and adjust the timing of collecting submitted documents.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The data collection department can analyze the results of an applicant's past interviews and optimize the way the interview proceeds. For example, if the applicant was nervous in a past interview, the data collection department can prioritize collecting questions that will help the applicant feel relaxed. Also, if the applicant gave detailed answers to specific questions in a past interview, the data collection department can collect similar questions. Furthermore, if the applicant showed interest in a specific topic in a past interview, the data collection department can collect questions related to that topic. In this way, the results of past interviews can be used to provide the applicant with the best way to proceed with the interview.

[0083] The analysis unit can estimate the applicant's emotions and adjust the timing of the analysis based on the estimated emotions of the applicant. For example, if the applicant is feeling stressed, the analysis unit adjusts the timing to perform the analysis when the applicant is relaxed. Also, if the applicant is relaxed, the analysis can be performed immediately. Furthermore, if the applicant is busy, the analysis can be performed at an appropriate time. In this way, by adjusting the timing of the analysis according to the applicant's emotions, more accurate analysis results can be obtained.

[0084] The interview content selection unit can analyze an applicant's past interview history and select the most appropriate interview content. For example, if an applicant gave detailed answers to specific questions in past interviews, similar questions can be selected. Also, if an applicant showed interest in a specific topic in past interviews, questions related to that topic can be selected. Furthermore, if an applicant seemed nervous in past interviews, questions that will help them relax can be selected. In this way, past interview history can be utilized to provide the most appropriate interview content for the applicant.

[0085] The result summarizing unit can estimate the emotions of the applicant and determine the priority of the results based on the estimated emotions of the applicant. For example, if the applicant is feeling stressed, the result summarizing unit can provide important results with priority. Also, if the applicant is relaxed, the result summarizing unit can provide all results equally. Furthermore, if the applicant is busy, the most important results can be provided with priority. In this way, by determining the priority of the results according to the emotions of the applicant, more appropriate results can be provided.

[0086] The data collection department can analyze the applicant's social media activity and collect relevant documents based on the applicant's interests. For example, relevant documents can be collected based on information shared by the applicant on social media. Related documents can also be collected based on the accounts the applicant follows on social media. Furthermore, related documents can be collected based on groups the applicant participates in on social media. In this way, the data collection department can collect relevant documents by analyzing the applicant's social media activity.

[0087] The analysis unit can estimate the emotions of the applicant and adjust the way the analysis is presented based on the estimated emotions of the applicant. For example, if the applicant is relaxed, the analysis unit can provide detailed analysis results. If the applicant is nervous, the analysis unit can also provide concise analysis results. Furthermore, if the applicant is excited, the analysis unit can also provide visually appealing analysis results. This allows the way the analysis is presented to be adjusted according to the emotions of the applicant.

[0088] The interview content pick-up unit can apply different pick-up algorithms depending on the applicant's work history. For example, if the applicant's work history is in a technical position, the interview content pick-up unit can apply a pick-up algorithm that focuses on technical skills. Also, if the applicant's work history is in a managerial position, the interview content pick-up unit can apply a pick-up algorithm that focuses on leadership. Furthermore, if the applicant's work history is in a sales position, the interview content pick-up unit can apply a pick-up algorithm that focuses on communication skills. This makes it possible to apply an appropriate pick-up algorithm depending on the applicant's work history.

[0089] The result summarizing unit can determine the priority of the results based on when the interview was conducted. For example, if the interview was conducted recently, the result summarizing unit can prioritize summarizing the results. Also, if the interview is old, the result summarizing unit can also summarize the results with a lower priority. Furthermore, the result summarizing unit can adjust the priority of the results based on when the interview was conducted. In this way, the priority of the results can be determined based on when the interview was conducted.

[0090] The analysis unit can determine the priority of analysis based on the time of submission of the submitted document. For example, if the submitted document is recently submitted, the analysis unit can prioritize the analysis. Also, if the submitted document is old, the analysis unit can lower the priority of analysis. Furthermore, the analysis unit can adjust the priority of analysis based on the time of submission of the submitted document. This makes it possible to determine the priority of analysis based on the time of submission of the submitted document.

[0091] The result summary unit can estimate the emotion of the applicant and adjust the length of the result based on the estimated emotion of the applicant. For example, if the applicant is relaxed, the result summary unit can provide detailed results. If the applicant is nervous, the result summary unit can also provide concise results. Furthermore, if the applicant is excited, the result summary unit can also provide visually appealing results. In this way, the length of the result can be adjusted according to the emotion of the applicant.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The data collection department collects documents or interview records submitted by applicants. These may include resumes, curriculum vitae, and audio recordings of interviews. The data collection department scans the resumes submitted by applicants and saves them as digital data. It may also record audio recordings of interviews and convert them into text data using speech recognition technology. Step 2: The analysis unit analyzes the collected data and extracts the applicant's motivation, strengths and weaknesses, and other information important to the selection process. For example, using generative AI, the application documents and interview records of the applicant can be analyzed to automatically extract the applicant's motivation, strengths, and weaknesses. Step 3: The Interview Content Selection Department selects the content to be asked during the interview based on the information extracted by the Analysis Department. For example, specific anecdotes related to the applicant's motivation for applying, experiences that make use of their strengths, and efforts to overcome their weaknesses are selected. Step 4: The results summary section automatically summarizes the interview results based on the content picked up by the interview content selection section. For example, it organizes the evaluation points of the interview and the content important for selection decisions, and outputs it in report format.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection department that collects documents submitted by applicants or interview records; an analysis unit that analyzes the data collected by the data collection unit and extracts information about the applicant's motivation for applying, strengths and weaknesses, and other information that is important for selection decisions; a hearing content selection unit that selects content to be heard during an interview based on the information extracted by the analysis unit; and a result summarizing unit that automatically summarizes the interview results based on the contents picked up by the interview content picking unit. A system characterized by:

2. The data collection unit Estimate applicants' emotions and adjust the timing of collecting submitted documents based on the estimated emotions of the applicants.

2. The system of claim 1.

3. The data collection unit Analyze applicants' past document submission history and select the appropriate collection method 2. The system of claim 1.

4. The data collection unit When collecting submissions, filter them based on the applicant's current job status or areas of interest 2. The system of claim 1.

5. The data collection unit Estimate the applicant's feelings and prioritize the documents to be collected based on the estimated feelings of the applicant 2. The system of claim 1.

6. The data collection unit When collecting documents to be submitted, we prioritize the collection of documents that are most relevant by taking into account the applicant's geographic location.

2. The system of claim 1.

7. The data collection unit When collecting documents for submission, analyze applicants' social media activity and collect relevant documents.

2. The system of claim 1.

8. The analysis unit Estimate the applicant's emotions and adjust the analysis presentation based on the estimated emotions of the applicant.

2. The system of claim 1.

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