System

Abstract

An object of a system according to an embodiment is to reduce the rate of employee turnover by identifying a cause of dissatisfaction or trouble of a crew and providing an appropriate training recommendation.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a bias reduction unit. The collection unit collects data related to employment and resignation of the crew. The analysis unit analyzes the data collected by the collection unit and identifies a cause of dissatisfaction or trouble of the crew. The recommendation unit provides a training recommendation to the crew based on the cause identified by the analysis unit. The bias reduction unit evaluates the crew based on the training recommendation provided by the recommendation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to identify the causes of crew dissatisfaction and concerns and take appropriate measures, leaving room for improvement in reducing turnover rates.

[0005] The system according to the embodiment aims to reduce turnover by identifying the causes of crew dissatisfaction and concerns and providing appropriate training recommendations. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, and a bias mitigation unit. The collection unit collects data related to crew recruitment and turnover. The analysis unit analyzes the data collected by the collection unit and identifies causes of crew dissatisfaction and concerns. The recommendation unit provides training recommendations for the crew based on the causes identified by the analysis unit. The bias mitigation unit evaluates the crew based on the training recommendations provided by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce turnover by identifying the causes of crew dissatisfaction and concerns and providing appropriate training recommendations. [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) An AI system according to an embodiment of the present invention analyzes various data related to crew recruitment and turnover and provides training recommendations that lead to the next step. This system prevents crew dissatisfaction and distress, avoiding a chain of resignations and negative loops. The system uses AI to mitigate bias, fairly evaluate crew members, and provide managers with insights. For example, the system collects data such as crew members' joining dates, departure dates, reasons for leaving, job duties, and evaluation results. Next, the AI ​​analyzes the collected data to identify the causes of crew dissatisfaction and distress. For example, if a specific job or evaluation result is the cause of turnover, the AI ​​identifies that cause. Next, the AI ​​provides training recommendations for the crew based on the identified causes. For example, if the cause is dissatisfaction with a specific job, the AI ​​recommends training to improve skills related to that job. Furthermore, if the cause is dissatisfaction with the evaluation results, the AI ​​recommends training to ensure a fair evaluation. Furthermore, the AI ​​is equipped with a bias reduction function to ensure fair crew evaluations. For example, the AI ​​performs unbiased evaluations based on the crew's job duties and evaluation results. This allows crew members to be evaluated fairly and gives managers an awareness of the crew's situation. This allows the AI ​​system to prevent crew dissatisfaction and worries before they arise, avoiding a chain of resignations and negative feedback loops.

[0029] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, and a bias reduction unit. The collection unit collects data related to crew hiring and turnover. The data related to crew hiring and turnover includes, but is not limited to, the hire date, the leave date, the reason for leaving, the job description, and the evaluation results. For example, the collection unit acquires the crew's hire date and leave date from a database. The collection unit can also collect the crew's reason for leaving from questionnaires and interview records. The collection unit can also acquire the crew's job description and evaluation results from a business system. For example, the collection unit acquires the crew's hire date and leave date from a database and collects the reason for leaving from questionnaires and interview records. The analysis unit uses AI to analyze the data collected by the collection unit and identify the cause of the crew's dissatisfaction or concerns. For example, the analysis unit uses a machine learning algorithm to analyze the crew's reason for leaving and the job description, and identify the cause of the leaving. The analysis unit can also use natural language processing technology to analyze crew interview records and survey results to identify the causes of dissatisfaction or concerns. For example, the analysis unit can use a machine learning algorithm to analyze the crew's reasons for leaving and their work content to identify the causes of leaving. The analysis unit can also use natural language processing technology to analyze crew interview records and survey results to identify the causes of dissatisfaction or concerns. The recommendation unit provides training recommendations to the crew based on the causes identified by the analysis unit. For example, if the cause is dissatisfaction with a specific work content, the recommendation unit can recommend training to improve skills related to that work. If the cause is dissatisfaction with evaluation results, the recommendation unit can also recommend training to ensure fair evaluation. For example, if the cause is dissatisfaction with a specific work content, the recommendation unit can recommend training to improve skills related to that work. If the cause is dissatisfaction with evaluation results, the recommendation unit can also recommend training to ensure fair evaluation. The bias mitigation unit performs a fair evaluation of the crew members based on the training recommendations provided by the recommendation unit. For example, the bias mitigation unit performs an unbiased evaluation based on the crew members' work content and evaluation results.The bias mitigation unit can also mitigate bias by using methods such as standardizing evaluation criteria and introducing third-party evaluation. For example, the bias mitigation unit performs unbiased evaluations based on the crew's work content and evaluation results. The bias mitigation unit can also mitigate bias by using methods such as standardizing evaluation criteria and introducing third-party evaluation. As a result, the AI ​​system according to the embodiment can prevent crew dissatisfaction and worries and avoid a chain of resignations and negative loops.

[0030] The collection unit can collect data on crew members' employment dates, departure dates, reasons for leaving, job duties, and evaluation results. For example, the collection unit acquires the crew members' employment dates and departure dates from a database. The collection unit can also collect the crew members' reasons for leaving from questionnaires and interview records. The collection unit can also acquire the crew members' job duties and evaluation results from a business system. For example, the collection unit acquires the crew members' employment dates and departure dates from a database and collects the reasons for leaving from questionnaires and interview records. This allows for more accurate analysis by collecting detailed crew data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can acquire the crew members' employment dates and departure dates from a database and collect the reasons for leaving from questionnaires and interview records using AI.

[0031] The analysis unit can analyze the data collected by the collection unit using artificial intelligence to identify the causes of the crew's dissatisfaction and concerns. The analysis unit can, for example, use a machine learning algorithm to analyze the crew's reasons for leaving and their work content to identify the causes of their leaving. The analysis unit can also use natural language processing technology to analyze the crew's interview records and survey results to identify the causes of their dissatisfaction and concerns. For example, the analysis unit can use a machine learning algorithm to analyze the crew's reasons for leaving and their work content to identify the causes of their leaving. The analysis unit can also use natural language processing technology to analyze the crew's interview records and survey results to identify the causes of their dissatisfaction and concerns. In this way, by using AI, the causes of the crew's dissatisfaction and concerns can be quickly and accurately identified. Some or all of the above-described 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 cause AI to perform processing to analyze the crew's reasons for leaving and their work content to identify the causes of their leaving.

[0032] The recommendation unit can provide training suggestions to the crew based on the identified cause. For example, if the cause is dissatisfaction with a specific task content, the recommendation unit can recommend training to improve skills related to that task. Furthermore, if the cause is dissatisfaction with the evaluation results, the recommendation unit can also recommend training to perform a fair evaluation. For example, if the cause is dissatisfaction with a specific task content, the recommendation unit can recommend training to improve skills related to that task. Furthermore, if the cause is dissatisfaction with the evaluation results, the recommendation unit can also recommend training to perform a fair evaluation. This makes it possible to provide appropriate training to resolve the crew's dissatisfaction and concerns. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, if the cause is dissatisfaction with a specific task content, the recommendation unit can cause AI to execute a process to recommend training to improve skills related to that task.

[0033] The bias mitigation unit can perform evaluations based on the crew's work content and evaluation results. The bias mitigation unit can perform unbiased evaluations based on, for example, the crew's work content and evaluation results. The bias mitigation unit can also reduce bias by using methods such as standardizing evaluation criteria and introducing third-party evaluations. For example, the bias mitigation unit can perform unbiased evaluations based on the crew's work content and evaluation results. The bias mitigation unit can also reduce bias by using methods such as standardizing evaluation criteria and introducing third-party evaluations. This can improve crew motivation by evaluating them fairly. Some or all of the above-described processing in the bias mitigation unit can be performed using, for example, AI, or can be performed without using AI. For example, the bias mitigation unit can cause AI to perform processing to perform unbiased evaluations based on the crew's work content and evaluation results.

[0034] The collection unit can analyze the crew's past work history and select an appropriate data collection method. For example, the collection unit prioritizes selecting a data collection method (such as a questionnaire or interview) that the crew has used in the past. The collection unit can also focus on collecting data related to specific work content from the crew's work history. Furthermore, the collection unit can select the most efficient data collection method based on the crew's work history. For example, the collection unit prioritizes selecting a data collection method (such as a questionnaire or interview) that the crew has used in the past. The collection unit can also focus on collecting data related to specific work content from the crew's work history. This enables efficient data collection by selecting the optimal data collection method based on the crew's past work history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the crew's past work history and cause AI to execute a process of selecting the optimal data collection method.

[0035] When collecting data, the collection unit can filter the data based on the crew's current project or area of ​​interest. For example, the collection unit prioritizes collecting data related to the project the crew is currently working on. The collection unit can also filter and collect relevant data based on the crew's area of ​​interest. Furthermore, the collection unit can selectively collect necessary data according to the crew's current work content. For example, the collection unit prioritizes collecting data related to the project the crew is currently working on. The collection unit can also filter and collect relevant data based on the crew's area of ​​interest. In this way, by filtering data based on the crew's current project or area of ​​interest, highly relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to perform a process of filtering data based on the crew's current project or area of ​​interest.

[0036] When collecting data, the collection unit can select an appropriate collection means depending on the crew's input method. For example, if the crew prefers voice input, the collection unit can collect voice data. Furthermore, if the crew prefers text input, the collection unit can also collect text-based data. Furthermore, if the crew prefers image input, the collection unit can collect image-based data. For example, if the crew prefers voice input, the collection unit can collect voice data. Furthermore, if the crew prefers text input, the collection unit can also collect text-based data. This enables efficient data collection by selecting the optimal collection means depending on the crew's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to execute processing to select the optimal collection means depending on the crew's input method.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data based on the crew's geographical location information. For example, if the crew works in a specific area, the collection unit prioritizes collecting data related to that area. Also, if the crew is on a business trip, the collection unit can collect data related to the business trip destination. Furthermore, the collection unit can selectively collect necessary data based on the crew's work location. For example, if the crew works in a specific area, the collection unit prioritizes collecting data related to that area. Also, if the crew is on a business trip, the collection unit can collect data related to the business trip destination. In this way, by taking the crew's geographical location information into consideration, highly relevant data can be efficiently collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to execute processing to prioritize collecting highly relevant data based on the crew's geographical location information.

[0038] When collecting data, the collection unit can collect relevant data based on the crew's social media activities. For example, the collection unit collects relevant data based on information shared by the crew on social media. The collection unit can also analyze the crew's social media activities and collect necessary data. Furthermore, the collection unit can collect relevant data by referring to the crew's friendships on social media. For example, the collection unit collects relevant data based on information shared by the crew on social media. The collection unit can also analyze the crew's social media activities and collect necessary data. In this way, highly relevant data can be collected by analyzing the crew's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the crew's social media activities and cause AI to execute processing to collect relevant data.

[0039] The collection unit can adjust the collection method based on the crew's past feedback when collecting data. For example, the collection unit selects the optimal data collection method based on feedback provided by the crew in the past. The collection unit can also customize the data collection means by reflecting the crew's past feedback. The collection unit can also adjust the timing and method of data collection by referring to the crew's feedback. For example, the collection unit selects the optimal data collection method based on feedback provided by the crew in the past. The collection unit can also customize the data collection means by reflecting the crew's past feedback. In this way, the optimal data collection method can be selected by reflecting the crew's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to execute processing to adjust the collection method based on the crew's past feedback.

[0040] During the analysis, the analysis unit can adjust the level of detail of the analysis according to the importance of the data. The analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. 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 may cause AI to perform a process of adjusting the level of detail of the analysis based on the importance of the data.

[0041] During analysis, the analysis unit can apply different analysis algorithms according to the data category. For example, the analysis unit can apply an algorithm that evaluates work efficiency to data related to work content. The analysis unit can also apply an algorithm that evaluates employee turnover risk to data related to reasons for turnover. The analysis unit can also apply an algorithm that evaluates performance to data related to evaluation results. For example, the analysis unit can apply an algorithm that evaluates work efficiency to data related to work content. The analysis unit can also apply an algorithm that evaluates employee turnover risk to data related to reasons for turnover. This enables highly accurate analysis by applying an appropriate analysis algorithm according to the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can cause AI to execute a process that applies different analysis algorithms depending on the data category.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on the crew's past analysis results. The analysis unit, for example, corrects the current analysis result based on the crew's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the crew's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the crew's past analysis results. For example, the analysis unit corrects the current analysis result based on the crew's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the crew's past analysis results. In this way, the accuracy of the current analysis result can be improved by referring to the crew's past analysis results. Some or all of the above-described 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 cause AI to execute processing to improve the accuracy of the analysis based on the crew's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis according to the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone analysis of data that has been submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone analysis of data that has been submitted earlier. In this way, determining the priority of analysis based on the time of data submission enables efficient analysis. 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 cause AI to execute processing to determine the priority of analysis based on the time of data submission.

[0044] During analysis, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. In this way, adjusting the order of analysis based on the relevance of the data enables efficient analysis. Some or all of the above-described 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 cause AI to execute processing to adjust the order of analysis based on the relevance of the data.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the crew's level of expertise. For example, if the crew's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the crew's level of expertise is low, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the crew's level of expertise. For example, if the crew's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the crew's level of expertise is low, the analysis unit can provide analysis results that avoid technical terminology. In this way, by adjusting the use of technical terminology according to the crew's level of expertise, it is possible to provide analysis results that are easy to understand. 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 cause AI to perform processing to adjust the use of technical terminology according to the crew's level of expertise.

[0046] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation according to the importance of the training. For example, the recommendation unit makes a detailed recommendation for a highly important training. The recommendation unit can also make a simplified recommendation for a less important training. Furthermore, the recommendation unit can adjust the depth and scope of the recommendation according to the importance of the training. For example, the recommendation unit makes a detailed recommendation for a highly important training. The recommendation unit can also make a simplified recommendation for a less important training. This enables efficient training recommendations by adjusting the level of detail of the recommendation according to the importance of the training. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause AI to execute processing to adjust the level of detail of the recommendation based on the importance of the training.

[0047] The recommendation unit can apply different recommendation algorithms according to the category of training when making recommendations. For example, the recommendation unit applies a recommendation algorithm for improving technical skills to technical training. A recommendation algorithm for improving leadership can be applied to management training. Furthermore, the recommendation unit can apply a recommendation algorithm for improving interpersonal skills to communication training. For example, the recommendation unit applies a recommendation algorithm for improving technical skills to technical training. Furthermore, the recommendation unit can apply a recommendation algorithm for improving leadership to management training. This enables highly accurate training recommendations by applying an appropriate recommendation algorithm depending on the training category. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause AI to execute processing to apply different recommendation algorithms depending on the training category.

[0048] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation based on the crew's past recommendation results. The recommendation unit, for example, corrects the current recommendation based on the crew's past recommendation results. The recommendation unit can also adjust the recommendation algorithm by referring to the crew's past recommendation results. The recommendation unit can also improve the accuracy of the recommendation by using the crew's past recommendation results. For example, the recommendation unit corrects the current recommendation based on the crew's past recommendation results. The recommendation unit can also adjust the recommendation algorithm by referring to the crew's past recommendation results. In this way, the accuracy of the current recommendation can be improved by referring to the crew's past recommendation results. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can have the AI ​​perform a process to improve the accuracy of recommendations based on the crew's past recommendation results.

[0049] When making a recommendation, the recommendation unit can determine the priority of the recommendations according to the time of submission of the training. For example, the recommendation unit prioritizes the most recently submitted training. The recommendation unit can also recommend older training at a later date. Furthermore, the recommendation unit can adjust the order of recommendations based on the time of submission. For example, the recommendation unit prioritizes the most recently submitted training. The recommendation unit can also recommend older training at a later date. This enables efficient training recommendation by determining the priority of recommendations based on the time of submission of the training. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause AI to execute a process of determining the priority of recommendations based on the time of submission of the training.

[0050] When making a recommendation, the recommendation unit can adjust the order of recommendations according to the relevance of the training. For example, the recommendation unit prioritizes recommending highly relevant training. The recommendation unit can also recommend less relevant training at a later date. Furthermore, the recommendation unit can adjust the order of recommendations based on the relevance of the training. For example, the recommendation unit prioritizes recommending highly relevant training. The recommendation unit can also recommend less relevant training at a later date. This enables efficient training recommendation by adjusting the order of recommendations based on the relevance of the training. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause AI to execute processing to adjust the order of recommendations based on the relevance of the training.

[0051] When making a recommendation, the recommendation unit can adjust the use of technical terminology in the recommendation according to the crew's level of expertise. For example, if the crew's level of expertise is high, the recommendation unit can provide a recommendation that uses a lot of technical terminology. Also, if the crew's level of expertise is low, the recommendation unit can provide a recommendation that avoids technical terminology. Furthermore, the recommendation unit can adjust the way the recommendation is expressed according to the crew's level of expertise. For example, if the crew's level of expertise is high, the recommendation unit can provide a recommendation that uses a lot of technical terminology. Also, if the crew's level of expertise is low, the recommendation unit can provide a recommendation that avoids technical terminology. In this way, by adjusting the use of technical terminology according to the crew's level of expertise, it is possible to provide training recommendations that are easy to understand. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause AI to perform a process of adjusting the use of technical terminology according to the crew's level of expertise.

[0052] During bias mitigation, the bias mitigation unit can analyze the crew's past evaluation results and select an appropriate evaluation method. For example, the bias mitigation unit corrects the current evaluation method based on the crew's past evaluation results. The bias mitigation unit can also adjust the evaluation algorithm by referring to the crew's past evaluation results. Furthermore, the bias mitigation unit can improve the accuracy of the evaluation by using the crew's past evaluation results. For example, the bias mitigation unit corrects the current evaluation method based on the crew's past evaluation results. The bias mitigation unit can also adjust the evaluation algorithm by referring to the crew's past evaluation results. This enables efficient evaluation by selecting an optimal evaluation method based on the crew's past evaluation results. Some or all of the above-described processing in the bias mitigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the bias mitigation unit can cause AI to execute processing to select an appropriate evaluation method based on the crew's past evaluation results.

[0053] During bias mitigation, the bias mitigation department can adjust the evaluation method according to the crew's current work situation. For example, the bias mitigation department prioritizes evaluations related to the crew's current project. The bias mitigation unit can also customize the evaluation means according to the crew's work situation. Furthermore, the bias mitigation unit can select and perform necessary evaluations based on the crew's current work content. For example, the bias mitigation unit prioritizes evaluations related to the project the crew is currently working on. The bias mitigation unit can also customize the evaluation means according to the crew's work situation. This enables more appropriate evaluations by customizing the evaluation means based on the crew's current work situation. Some or all of the above-mentioned processing in the bias mitigation unit may be performed using AI, for example, or may be performed without using AI. For example, the bias mitigation unit can cause AI to perform processing to customize the evaluation means based on the crew's current work situation.

[0054] During bias mitigation, the bias mitigation unit can improve the evaluation method based on crew feedback. For example, the bias mitigation unit selects the optimal evaluation method based on feedback provided by the crew in the past. The bias mitigation unit can also customize the evaluation means by reflecting the crew's past feedback. The bias mitigation unit can also adjust the timing and method of evaluation by referring to the crew's feedback. For example, the bias mitigation unit selects the optimal evaluation method based on feedback provided by the crew in the past. The bias mitigation unit can also customize the evaluation means by reflecting the crew's past feedback. In this way, a more appropriate evaluation method can be selected by reflecting the crew's feedback. Some or all of the above-described processing in the bias mitigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the bias mitigation unit can cause AI to execute processing to improve the evaluation method based on the crew's feedback.

[0055] During bias mitigation, the bias mitigation unit can select an appropriate evaluation method based on the crew's geographic location information. For example, if the crew works in a specific region, the bias mitigation unit prioritizes evaluations related to that region. Also, if the crew is on a business trip, the bias mitigation unit can also perform evaluations related to the business trip destination. Furthermore, the bias mitigation unit can select and perform necessary evaluations based on the crew's work location. For example, if the crew works in a specific region, the bias mitigation unit prioritizes evaluations related to that region. Also, if the crew is on a business trip, the bias mitigation unit can also perform evaluations related to the business trip destination. In this way, by taking the crew's geographic location information into consideration, highly relevant evaluations can be performed efficiently. Some or all of the above-described processing in the bias mitigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the bias mitigation unit can cause AI to perform a process of selecting an appropriate evaluation method based on the crew's geographic location information.

[0056] During bias mitigation, the bias mitigation unit can suggest evaluation measures based on the crew's social media activity. For example, the bias mitigation unit makes relevant evaluations based on information shared by the crew on social media. The bias mitigation unit can also analyze the crew's social media activities and make necessary evaluations. Furthermore, the bias mitigation unit can make relevant evaluations based on the crew's friendships on social media. For example, the bias mitigation unit makes relevant evaluations based on information shared by the crew on social media. The bias mitigation unit can also analyze the crew's social media activities and make necessary evaluations. In this way, by analyzing the crew's social media activities, highly relevant evaluations can be made. Some or all of the above-described processing in the bias mitigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the bias mitigation unit can cause AI to execute processing to suggest evaluation measures based on the crew's social media activities.

[0057] During bias mitigation, the bias mitigation unit can adjust the evaluation method based on the crew's past feedback. For example, the bias mitigation unit selects the optimal evaluation method based on feedback provided by the crew in the past. The bias mitigation unit can also customize the evaluation means by reflecting the crew's past feedback. The bias mitigation unit can also adjust the timing and method of evaluation by referring to the crew's feedback. For example, the bias mitigation unit selects the optimal evaluation method based on feedback provided by the crew in the past. The bias mitigation unit can also customize the evaluation means by reflecting the crew's past feedback. In this way, the optimal evaluation method can be selected by reflecting the crew's past feedback. Some or all of the above-described processing in the bias mitigation unit may be performed using AI, for example, or may be performed without using AI. For example, the bias mitigation unit can cause AI to execute processing to adjust the evaluation method based on the crew's past feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The AI ​​system can also be equipped with a health management module that collects crew health data and provides training recommendations based on their health status. For example, the health management module can monitor crew stress levels and sleep patterns and recommend relaxation training if stress levels are high. The health management module can also analyze crew exercise habits and recommend fitness training if lack of exercise is the cause. Furthermore, the health management module can evaluate crew diets and recommend nutritional management training if nutritional imbalance is detected. This can improve the crew's overall performance by providing training recommendations based on their health status.

[0060] The collection unit can also analyze the crew's social media activities and collect data based on the crew's interests. For example, the collection unit can analyze the content of posts shared by the crew on social media and prioritize collecting data related to the crew's areas of interest. The collection unit can also analyze the crew's friendships on social media and collect data related to topics that the friends are interested in. Furthermore, the collection unit can adjust the timing of data collection based on the frequency of the crew's social media activity. This makes it possible to efficiently collect more relevant data by utilizing the crew's social media activities.

[0061] The recommendation department can also analyze the crew's past training history and recommend new training based on the results of past training. For example, if a previous training session was highly effective, it can recommend the same training again. It can also recommend training to improve the skills needed as the next step based on the skills acquired in past training. Furthermore, it can refer to feedback from past training sessions and recommend training related to areas in which the crew is particularly interested. This makes it possible to utilize the crew's past training history to provide more effective training recommendations.

[0062] The collection unit can also prioritize collection of highly relevant data based on the crew's geographical location information. For example, if the crew works in a specific area, data related to that area can be collected preferentially. Also, if the crew is on a business trip, data related to the business trip destination can be collected. Furthermore, necessary data can be selectively collected based on the crew's work location. In this way, highly relevant data can be collected efficiently by taking the crew's geographical location information into consideration.

[0063] The analysis unit can also improve the accuracy of the analysis based on the crew's past evaluation results. For example, the current analysis result is corrected based on the crew's past evaluation results. The analysis unit can also adjust the analysis algorithm by referring to the crew's past evaluation results. Furthermore, the analysis unit can also improve the accuracy of the analysis by using the crew's past evaluation results. In this way, the accuracy of the current analysis result can be improved by referring to the crew's past evaluation results.

[0064] The bias reduction unit can also improve the evaluation method based on the crew's past feedback. For example, it can select the optimal evaluation method based on the crew's past feedback. It can also customize the evaluation method by reflecting the crew's past feedback. It can also adjust the timing and method of evaluation by referring to the crew's feedback. In this way, it is possible to select a more appropriate evaluation method by reflecting the crew's feedback.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection department collects data on crew hiring and turnover. Specifically, data such as start date, departure date, reason for leaving, job duties, and evaluation results are obtained from databases, questionnaires, interview records, and business systems. Step 2: The analysis department uses AI to analyze the data collected by the collection department and identify the causes of crew dissatisfaction and concerns. Specifically, machine learning algorithms and natural language processing technology are used to analyze reasons for leaving, work content, interview records, and survey results. Step 3: The Recommendation Department provides training recommendations to crew members based on the causes identified by the Analysis Department. Specifically, if the cause is dissatisfaction with a specific task, the Department will recommend training to improve skills related to that task or training to conduct fair evaluations. Step 4: The bias mitigation department will fairly evaluate the crew based on the training recommendations provided by the recommendation department. Specifically, the department will conduct unbiased evaluations based on the crew's work content and evaluation results, and will mitigate bias by using methods such as standardizing evaluation criteria and introducing third-party evaluations.

[0067] (Example 2) An AI system according to an embodiment of the present invention analyzes various data related to crew recruitment and turnover and provides training recommendations that lead to the next step. This system prevents crew dissatisfaction and distress, avoiding a chain of resignations and negative loops. The system uses AI to mitigate bias, fairly evaluate crew members, and provide managers with insights. For example, the system collects data such as crew members' joining dates, departure dates, reasons for leaving, job duties, and evaluation results. Next, the AI ​​analyzes the collected data to identify the causes of crew dissatisfaction and distress. For example, if a specific job or evaluation result is the cause of turnover, the AI ​​identifies that cause. Next, the AI ​​provides training recommendations for the crew based on the identified causes. For example, if the cause is dissatisfaction with a specific job, the AI ​​recommends training to improve skills related to that job. Furthermore, if the cause is dissatisfaction with the evaluation results, the AI ​​recommends training to ensure a fair evaluation. Furthermore, the AI ​​is equipped with a bias reduction function to ensure fair crew evaluations. For example, the AI ​​performs unbiased evaluations based on the crew's job duties and evaluation results. This allows crew members to be evaluated fairly and gives managers an awareness of the crew's situation. This allows the AI ​​system to prevent crew dissatisfaction and worries before they arise, avoiding a chain of resignations and negative feedback loops.

[0068] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, and a bias reduction unit. The collection unit collects data related to crew hiring and turnover. The data related to crew hiring and turnover includes, but is not limited to, the hire date, the leave date, the reason for leaving, the job description, and the evaluation results. For example, the collection unit acquires the crew's hire date and leave date from a database. The collection unit can also collect the crew's reason for leaving from questionnaires and interview records. The collection unit can also acquire the crew's job description and evaluation results from a business system. For example, the collection unit acquires the crew's hire date and leave date from a database and collects the reason for leaving from questionnaires and interview records. The analysis unit uses AI to analyze the data collected by the collection unit and identify the cause of the crew's dissatisfaction or concerns. For example, the analysis unit uses a machine learning algorithm to analyze the crew's reason for leaving and the job description, and identify the cause of the leaving. The analysis unit can also use natural language processing technology to analyze crew interview records and survey results to identify the causes of dissatisfaction or concerns. For example, the analysis unit can use a machine learning algorithm to analyze the crew's reasons for leaving and their work content to identify the causes of leaving. The analysis unit can also use natural language processing technology to analyze crew interview records and survey results to identify the causes of dissatisfaction or concerns. The recommendation unit provides training recommendations to the crew based on the causes identified by the analysis unit. For example, if the cause is dissatisfaction with a specific work content, the recommendation unit can recommend training to improve skills related to that work. If the cause is dissatisfaction with evaluation results, the recommendation unit can also recommend training to ensure fair evaluation. For example, if the cause is dissatisfaction with a specific work content, the recommendation unit can recommend training to improve skills related to that work. If the cause is dissatisfaction with evaluation results, the recommendation unit can also recommend training to ensure fair evaluation. The bias mitigation unit performs a fair evaluation of the crew members based on the training recommendations provided by the recommendation unit. For example, the bias mitigation unit performs an unbiased evaluation based on the crew members' work content and evaluation results.The bias mitigation unit can also mitigate bias by using methods such as standardizing evaluation criteria and introducing third-party evaluation. For example, the bias mitigation unit performs unbiased evaluations based on the crew's work content and evaluation results. The bias mitigation unit can also mitigate bias by using methods such as standardizing evaluation criteria and introducing third-party evaluation. As a result, the AI ​​system according to the embodiment can prevent crew dissatisfaction and worries and avoid a chain of resignations and negative loops.

[0069] The collection unit can collect data on crew members' employment dates, departure dates, reasons for leaving, job duties, and evaluation results. For example, the collection unit acquires the crew members' employment dates and departure dates from a database. The collection unit can also collect the crew members' reasons for leaving from questionnaires and interview records. The collection unit can also acquire the crew members' job duties and evaluation results from a business system. For example, the collection unit acquires the crew members' employment dates and departure dates from a database and collects the reasons for leaving from questionnaires and interview records. This allows for more accurate analysis by collecting detailed crew data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can acquire the crew members' employment dates and departure dates from a database and collect the reasons for leaving from questionnaires and interview records using AI.

[0070] The analysis unit can analyze the data collected by the collection unit using artificial intelligence to identify the causes of the crew's dissatisfaction and concerns. The analysis unit can, for example, use a machine learning algorithm to analyze the crew's reasons for leaving and their work content to identify the causes of their leaving. The analysis unit can also use natural language processing technology to analyze the crew's interview records and survey results to identify the causes of their dissatisfaction and concerns. For example, the analysis unit can use a machine learning algorithm to analyze the crew's reasons for leaving and their work content to identify the causes of their leaving. The analysis unit can also use natural language processing technology to analyze the crew's interview records and survey results to identify the causes of their dissatisfaction and concerns. In this way, by using AI, the causes of the crew's dissatisfaction and concerns can be quickly and accurately identified. Some or all of the above-described 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 cause AI to perform processing to analyze the crew's reasons for leaving and their work content to identify the causes of their leaving.

[0071] The recommendation unit can provide training suggestions to the crew based on the identified cause. For example, if the cause is dissatisfaction with a specific task content, the recommendation unit can recommend training to improve skills related to that task. Furthermore, if the cause is dissatisfaction with the evaluation results, the recommendation unit can also recommend training to perform a fair evaluation. For example, if the cause is dissatisfaction with a specific task content, the recommendation unit can recommend training to improve skills related to that task. Furthermore, if the cause is dissatisfaction with the evaluation results, the recommendation unit can also recommend training to perform a fair evaluation. This makes it possible to provide appropriate training to resolve the crew's dissatisfaction and concerns. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, if the cause is dissatisfaction with a specific task content, the recommendation unit can cause AI to execute a process to recommend training to improve skills related to that task.

[0072] The bias mitigation unit can perform evaluations based on the crew's work content and evaluation results. The bias mitigation unit can perform unbiased evaluations based on, for example, the crew's work content and evaluation results. The bias mitigation unit can also reduce bias by using methods such as standardizing evaluation criteria and introducing third-party evaluations. For example, the bias mitigation unit can perform unbiased evaluations based on the crew's work content and evaluation results. The bias mitigation unit can also reduce bias by using methods such as standardizing evaluation criteria and introducing third-party evaluations. This can improve crew motivation by evaluating them fairly. Some or all of the above-described processing in the bias mitigation unit can be performed using, for example, AI, or can be performed without using AI. For example, the bias mitigation unit can cause AI to perform processing to perform unbiased evaluations based on the crew's work content and evaluation results.

[0073] The AI ​​system further includes a collection unit that estimates the crew's emotions and determines the timing of data collection according to the estimated crew's emotions. For example, if the crew is feeling stressed, the collection unit reduces the frequency of data collection and collects data when the crew is relaxed. The collection unit can also refrain from collecting data when the crew is busy and collect data when the crew has more time. The collection unit can also collect detailed data when the crew is feeling positive emotions. For example, if the crew is feeling stressed, the collection unit reduces the frequency of data collection and collects data when the crew is relaxed. The collection unit can also refrain from collecting data when the crew is busy and collect data when the crew has more time. This allows for more appropriate data to be collected by adjusting the timing of data collection according to the crew's emotions. 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 these examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the AI ​​to execute a process of estimating the emotions of the crew members and determining the timing of data collection based on the estimated emotions of the crew members.

[0074] The collection unit can analyze the crew's past work history and select an appropriate data collection method. For example, the collection unit prioritizes selecting a data collection method (such as a questionnaire or interview) that the crew has used in the past. The collection unit can also focus on collecting data related to specific work content from the crew's work history. Furthermore, the collection unit can select the most efficient data collection method based on the crew's work history. For example, the collection unit prioritizes selecting a data collection method (such as a questionnaire or interview) that the crew has used in the past. The collection unit can also focus on collecting data related to specific work content from the crew's work history. This enables efficient data collection by selecting the optimal data collection method based on the crew's past work history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the crew's past work history and cause AI to execute a process of selecting the optimal data collection method.

[0075] When collecting data, the collection unit can filter the data based on the crew's current project or area of ​​interest. For example, the collection unit prioritizes collecting data related to the project the crew is currently working on. The collection unit can also filter and collect relevant data based on the crew's area of ​​interest. Furthermore, the collection unit can selectively collect necessary data according to the crew's current work content. For example, the collection unit prioritizes collecting data related to the project the crew is currently working on. The collection unit can also filter and collect relevant data based on the crew's area of ​​interest. In this way, by filtering data based on the crew's current project or area of ​​interest, highly relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to perform a process of filtering data based on the crew's current project or area of ​​interest.

[0076] When collecting data, the collection unit can select an appropriate collection means depending on the crew's input method. For example, if the crew prefers voice input, the collection unit can collect voice data. Furthermore, if the crew prefers text input, the collection unit can also collect text-based data. Furthermore, if the crew prefers image input, the collection unit can collect image-based data. For example, if the crew prefers voice input, the collection unit can collect voice data. Furthermore, if the crew prefers text input, the collection unit can also collect text-based data. This enables efficient data collection by selecting the optimal collection means depending on the crew's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to execute processing to select the optimal collection means depending on the crew's input method.

[0077] The collection unit can estimate the crew member's emotions and determine the priority of data to be collected according to the estimated crew member's emotions. For example, if the crew member is feeling stressed, the collection unit prioritizes collecting data related to the cause of stress. Furthermore, if the crew member is relaxed, the collection unit can also collect long-term data. Furthermore, if the crew member is feeling positive emotions, the collection unit can collect detailed data. For example, if the crew member is feeling stressed, the collection unit prioritizes collecting data related to the cause of stress. Furthermore, if the crew member is relaxed, the collection unit can also collect long-term data. Thus, by determining the priority of data to be collected according to the crew member's emotions, important data can be collected preferentially. Emotion estimation is realized 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-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can have the AI ​​execute a process of estimating the crew's emotions and determining the priority of data to be collected based on the estimated crew's emotions.

[0078] When collecting data, the collection unit can prioritize collecting highly relevant data based on the crew's geographical location information. For example, if the crew works in a specific area, the collection unit prioritizes collecting data related to that area. Also, if the crew is on a business trip, the collection unit can collect data related to the business trip destination. Furthermore, the collection unit can selectively collect necessary data based on the crew's work location. For example, if the crew works in a specific area, the collection unit prioritizes collecting data related to that area. Also, if the crew is on a business trip, the collection unit can collect data related to the business trip destination. In this way, by taking the crew's geographical location information into consideration, highly relevant data can be efficiently collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to execute processing to prioritize collecting highly relevant data based on the crew's geographical location information.

[0079] When collecting data, the collection unit can collect relevant data based on the crew's social media activities. For example, the collection unit collects relevant data based on information shared by the crew on social media. The collection unit can also analyze the crew's social media activities and collect necessary data. Furthermore, the collection unit can collect relevant data by referring to the crew's friendships on social media. For example, the collection unit collects relevant data based on information shared by the crew on social media. The collection unit can also analyze the crew's social media activities and collect necessary data. In this way, highly relevant data can be collected by analyzing the crew's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the crew's social media activities and cause AI to execute processing to collect relevant data.

[0080] The collection unit can adjust the collection method based on the crew's past feedback when collecting data. For example, the collection unit selects the optimal data collection method based on feedback provided by the crew in the past. The collection unit can also customize the data collection means by reflecting the crew's past feedback. The collection unit can also adjust the timing and method of data collection by referring to the crew's feedback. For example, the collection unit selects the optimal data collection method based on feedback provided by the crew in the past. The collection unit can also customize the data collection means by reflecting the crew's past feedback. In this way, the optimal data collection method can be selected by reflecting the crew's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to execute processing to adjust the collection method based on the crew's past feedback.

[0081] The analysis unit can estimate the crew member's emotions and adjust the presentation method of the analysis according to the estimated crew member's emotions. For example, if the crew member is feeling stressed, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the crew member is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the crew member has positive emotions, the analysis unit can provide a visually appealing analysis result. For example, if the crew member is feeling stressed, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the crew member is relaxed, the analysis unit can provide a detailed analysis result. By adjusting the presentation method of the analysis according to the crew member's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can infer the crew's emotions and have the AI ​​perform a process to adjust the way the analysis is presented based on the inferred crew's emotions.

[0082] During the analysis, the analysis unit can adjust the level of detail of the analysis according to the importance of the data. The analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. 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 may cause AI to perform a process of adjusting the level of detail of the analysis based on the importance of the data.

[0083] During analysis, the analysis unit can apply different analysis algorithms according to the data category. For example, the analysis unit can apply an algorithm that evaluates work efficiency to data related to work content. The analysis unit can also apply an algorithm that evaluates employee turnover risk to data related to reasons for turnover. The analysis unit can also apply an algorithm that evaluates performance to data related to evaluation results. For example, the analysis unit can apply an algorithm that evaluates work efficiency to data related to work content. The analysis unit can also apply an algorithm that evaluates employee turnover risk to data related to reasons for turnover. This enables highly accurate analysis by applying an appropriate analysis algorithm according to the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can cause AI to execute a process that applies different analysis algorithms depending on the data category.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis based on the crew's past analysis results. The analysis unit, for example, corrects the current analysis result based on the crew's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the crew's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the crew's past analysis results. For example, the analysis unit corrects the current analysis result based on the crew's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the crew's past analysis results. In this way, the accuracy of the current analysis result can be improved by referring to the crew's past analysis results. Some or all of the above-described 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 cause AI to execute processing to improve the accuracy of the analysis based on the crew's past analysis results.

[0085] The analysis unit can estimate the crew member's emotions and adjust the length of the analysis according to the estimated crew member's emotions. For example, if the crew member is stressed, the analysis unit can provide a short and concise analysis result. Furthermore, if the crew member is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the crew member has positive emotions, the analysis unit can provide a visually appealing analysis result. For example, if the crew member is stressed, the analysis unit can provide a short and concise analysis result. Furthermore, if the crew member is relaxed, the analysis unit can provide a detailed analysis result. By adjusting the length of the analysis according to the crew member's emotions, more appropriate analysis results can be provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can cause the AI ​​to estimate the crew's emotions and adjust the length of the analysis based on the estimated crew's emotions.

[0086] During analysis, the analysis unit can determine the priority of analysis according to the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone analysis of data that has been submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also postpone analysis of data that has been submitted earlier. In this way, determining the priority of analysis based on the time of data submission enables efficient analysis. 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 cause AI to execute processing to determine the priority of analysis based on the time of data submission.

[0087] During analysis, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. In this way, adjusting the order of analysis based on the relevance of the data enables efficient analysis. Some or all of the above-described 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 cause AI to execute processing to adjust the order of analysis based on the relevance of the data.

[0088] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the crew's level of expertise. For example, if the crew's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the crew's level of expertise is low, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the crew's level of expertise. For example, if the crew's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the crew's level of expertise is low, the analysis unit can provide analysis results that avoid technical terminology. In this way, by adjusting the use of technical terminology according to the crew's level of expertise, it is possible to provide analysis results that are easy to understand. 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 cause AI to perform processing to adjust the use of technical terminology according to the crew's level of expertise.

[0089] The recommendation unit can estimate the crew member's emotions and adjust the way recommendations are expressed according to the estimated crew member's emotions. For example, if the crew member is feeling stressed, the recommendation unit can provide a simple, highly visible recommendation. Also, if the crew member is relaxed, the recommendation unit can provide a detailed recommendation. Furthermore, if the crew member has positive emotions, the recommendation unit can provide a visually appealing recommendation. For example, if the crew member is feeling stressed, the recommendation unit can provide a simple, highly visible recommendation. Also, if the crew member is relaxed, the recommendation unit can provide a detailed recommendation. In this way, by adjusting the way recommendations are expressed according to the crew member's emotions, more appropriate training recommendations can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may estimate the crew member's emotions and cause AI to execute processing to adjust the way recommendations are presented based on the estimated crew member's emotions.

[0090] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation according to the importance of the training. For example, the recommendation unit makes a detailed recommendation for a highly important training. The recommendation unit can also make a simplified recommendation for a less important training. Furthermore, the recommendation unit can adjust the depth and scope of the recommendation according to the importance of the training. For example, the recommendation unit makes a detailed recommendation for a highly important training. The recommendation unit can also make a simplified recommendation for a less important training. This enables efficient training recommendations by adjusting the level of detail of the recommendation according to the importance of the training. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause AI to execute processing to adjust the level of detail of the recommendation based on the importance of the training.

[0091] The recommendation unit can apply different recommendation algorithms according to the category of training when making recommendations. For example, the recommendation unit applies a recommendation algorithm for improving technical skills to technical training. A recommendation algorithm for improving leadership can be applied to management training. Furthermore, the recommendation unit can apply a recommendation algorithm for improving interpersonal skills to communication training. For example, the recommendation unit applies a recommendation algorithm for improving technical skills to technical training. Furthermore, the recommendation unit can apply a recommendation algorithm for improving leadership to management training. This enables highly accurate training recommendations by applying an appropriate recommendation algorithm depending on the training category. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause AI to execute processing to apply different recommendation algorithms depending on the training category.

[0092] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation based on the crew's past recommendation results. The recommendation unit, for example, corrects the current recommendation based on the crew's past recommendation results. The recommendation unit can also adjust the recommendation algorithm by referring to the crew's past recommendation results. The recommendation unit can also improve the accuracy of the recommendation by using the crew's past recommendation results. For example, the recommendation unit corrects the current recommendation based on the crew's past recommendation results. The recommendation unit can also adjust the recommendation algorithm by referring to the crew's past recommendation results. In this way, the accuracy of the current recommendation can be improved by referring to the crew's past recommendation results. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can have the AI ​​perform a process to improve the accuracy of recommendations based on the crew's past recommendation results.

[0093] The recommendation unit can estimate the crew member's emotions and adjust the length of the recommendation according to the estimated crew member's emotions. For example, if the crew member is feeling stressed, the recommendation unit can provide a short and to-the-point recommendation. Furthermore, if the crew member is feeling relaxed, the recommendation unit can also provide a detailed recommendation. Furthermore, if the crew member is feeling positive emotions, the recommendation unit can also provide a visually appealing recommendation. For example, if the crew member is feeling stressed, the recommendation unit can provide a short and to-the-point recommendation. Furthermore, if the crew member is feeling relaxed, the recommendation unit can also provide a detailed recommendation. By adjusting the length of the recommendation according to the crew member's emotions, more appropriate training recommendations can be provided. The emotion estimation is realized using an emotion estimation function, for example, using 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-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may estimate the crew member's emotions and cause AI to execute processing to adjust the length of the recommendation based on the estimated crew member's emotions.

[0094] When making a recommendation, the recommendation unit can determine the priority of the recommendations according to the time of submission of the training. For example, the recommendation unit prioritizes the most recently submitted training. The recommendation unit can also recommend older training at a later date. Furthermore, the recommendation unit can adjust the order of recommendations based on the time of submission. For example, the recommendation unit prioritizes the most recently submitted training. The recommendation unit can also recommend older training at a later date. This enables efficient training recommendation by determining the priority of recommendations based on the time of submission of the training. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause AI to execute a process of determining the priority of recommendations based on the time of submission of the training.

[0095] When making a recommendation, the recommendation unit can adjust the order of recommendations according to the relevance of the training. For example, the recommendation unit prioritizes recommending highly relevant training. The recommendation unit can also recommend less relevant training at a later date. Furthermore, the recommendation unit can adjust the order of recommendations based on the relevance of the training. For example, the recommendation unit prioritizes recommending highly relevant training. The recommendation unit can also recommend less relevant training at a later date. This enables efficient training recommendation by adjusting the order of recommendations based on the relevance of the training. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause AI to execute processing to adjust the order of recommendations based on the relevance of the training.

[0096] When making a recommendation, the recommendation unit can adjust the use of technical terminology in the recommendation according to the crew's level of expertise. For example, if the crew's level of expertise is high, the recommendation unit can provide a recommendation that uses a lot of technical terminology. Also, if the crew's level of expertise is low, the recommendation unit can provide a recommendation that avoids technical terminology. Furthermore, the recommendation unit can adjust the way the recommendation is expressed according to the crew's level of expertise. For example, if the crew's level of expertise is high, the recommendation unit can provide a recommendation that uses a lot of technical terminology. Also, if the crew's level of expertise is low, the recommendation unit can provide a recommendation that avoids technical terminology. In this way, by adjusting the use of technical terminology according to the crew's level of expertise, it is possible to provide training recommendations that are easy to understand. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause AI to perform a process of adjusting the use of technical terminology according to the crew's level of expertise.

[0097] The bias mitigation unit can estimate the crew member's emotions and adjust the evaluation method according to the estimated crew member's emotions. For example, if the crew member is feeling stressed, the bias mitigation unit can reduce the frequency of evaluations and perform evaluations when the crew member is relaxed. The bias mitigation unit can also refrain from evaluations when the crew member is busy and perform evaluations when the crew member has more time. The bias mitigation unit can also perform detailed evaluations when the crew member has positive emotions. For example, if the crew member is feeling stressed, the bias mitigation unit can reduce the frequency of evaluations and perform evaluations when the crew member is relaxed. The bias mitigation unit can also refrain from evaluations when the crew member is busy and perform evaluations when the crew member has more time. This allows for more appropriate evaluations by adjusting the evaluation method according to the crew member's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the bias mitigation unit can be performed using, for example, AI, or without AI. For example, the bias reduction unit can estimate the emotions of the crew and have the AI ​​perform a process to adjust the evaluation method based on the estimated emotions of the crew.

[0098] During bias mitigation, the bias mitigation unit can analyze the crew's past evaluation results and select an appropriate evaluation method. For example, the bias mitigation unit corrects the current evaluation method based on the crew's past evaluation results. The bias mitigation unit can also adjust the evaluation algorithm by referring to the crew's past evaluation results. Furthermore, the bias mitigation unit can improve the accuracy of the evaluation by using the crew's past evaluation results. For example, the bias mitigation unit corrects the current evaluation method based on the crew's past evaluation results. The bias mitigation unit can also adjust the evaluation algorithm by referring to the crew's past evaluation results. This enables efficient evaluation by selecting an optimal evaluation method based on the crew's past evaluation results. Some or all of the above-described processing in the bias mitigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the bias mitigation unit can cause AI to execute processing to select an appropriate evaluation method based on the crew's past evaluation results.

[0099] During bias mitigation, the bias mitigation department can adjust the evaluation method according to the crew's current work situation. For example, the bias mitigation department prioritizes evaluations related to the crew's current project. The bias mitigation unit can also customize the evaluation means according to the crew's work situation. Furthermore, the bias mitigation unit can select and perform necessary evaluations based on the crew's current work content. For example, the bias mitigation unit prioritizes evaluations related to the project the crew is currently working on. The bias mitigation unit can also customize the evaluation means according to the crew's work situation. This enables more appropriate evaluations by customizing the evaluation means based on the crew's current work situation. Some or all of the above-mentioned processing in the bias mitigation unit may be performed using AI, for example, or may be performed without using AI. For example, the bias mitigation unit can cause AI to perform processing to customize the evaluation means based on the crew's current work situation.

[0100] During bias mitigation, the bias mitigation unit can improve the evaluation method based on crew feedback. For example, the bias mitigation unit selects the optimal evaluation method based on feedback provided by the crew in the past. The bias mitigation unit can also customize the evaluation means by reflecting the crew's past feedback. The bias mitigation unit can also adjust the timing and method of evaluation by referring to the crew's feedback. For example, the bias mitigation unit selects the optimal evaluation method based on feedback provided by the crew in the past. The bias mitigation unit can also customize the evaluation means by reflecting the crew's past feedback. In this way, a more appropriate evaluation method can be selected by reflecting the crew's feedback. Some or all of the above-described processing in the bias mitigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the bias mitigation unit can cause AI to execute processing to improve the evaluation method based on the crew's feedback.

[0101] The bias mitigation unit can estimate the crew member's emotions and determine the priority of evaluations according to the estimated crew member's emotions. For example, if the crew member is feeling stressed, the bias mitigation unit can prioritize evaluations related to the cause of stress. Furthermore, if the crew member is relaxed, the bias mitigation unit can also perform a long-term evaluation. Furthermore, if the crew member has positive emotions, the bias mitigation unit can perform a detailed evaluation. For example, if the crew member is feeling stressed, the bias mitigation unit can prioritize evaluations related to the cause of stress. Furthermore, if the crew member is relaxed, the bias mitigation unit can also perform a long-term evaluation. Thus, by determining the priority of evaluations according to the crew member's emotions, important evaluations can be prioritized. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the bias mitigation unit may be performed using, for example, AI, or without AI. For example, the bias reduction unit can cause the AI ​​to perform a process of estimating the crew's emotions and determining the priority of evaluations based on the estimated crew's emotions.

[0102] During bias mitigation, the bias mitigation unit can select an appropriate evaluation method based on the crew's geographic location information. For example, if the crew works in a specific region, the bias mitigation unit prioritizes evaluations related to that region. Also, if the crew is on a business trip, the bias mitigation unit can also perform evaluations related to the business trip destination. Furthermore, the bias mitigation unit can select and perform necessary evaluations based on the crew's work location. For example, if the crew works in a specific region, the bias mitigation unit prioritizes evaluations related to that region. Also, if the crew is on a business trip, the bias mitigation unit can also perform evaluations related to the business trip destination. In this way, by taking the crew's geographic location information into consideration, highly relevant evaluations can be performed efficiently. Some or all of the above-described processing in the bias mitigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the bias mitigation unit can cause AI to perform a process of selecting an appropriate evaluation method based on the crew's geographic location information.

[0103] During bias mitigation, the bias mitigation unit can suggest evaluation measures based on the crew's social media activity. For example, the bias mitigation unit makes relevant evaluations based on information shared by the crew on social media. The bias mitigation unit can also analyze the crew's social media activities and make necessary evaluations. Furthermore, the bias mitigation unit can make relevant evaluations based on the crew's friendships on social media. For example, the bias mitigation unit makes relevant evaluations based on information shared by the crew on social media. The bias mitigation unit can also analyze the crew's social media activities and make necessary evaluations. In this way, by analyzing the crew's social media activities, highly relevant evaluations can be made. Some or all of the above-described processing in the bias mitigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the bias mitigation unit can cause AI to execute processing to suggest evaluation measures based on the crew's social media activities.

[0104] During bias mitigation, the bias mitigation unit can adjust the evaluation method based on the crew's past feedback. For example, the bias mitigation unit selects the optimal evaluation method based on feedback provided by the crew in the past. The bias mitigation unit can also customize the evaluation means by reflecting the crew's past feedback. The bias mitigation unit can also adjust the timing and method of evaluation by referring to the crew's feedback. For example, the bias mitigation unit selects the optimal evaluation method based on feedback provided by the crew in the past. The bias mitigation unit can also customize the evaluation means by reflecting the crew's past feedback. In this way, the optimal evaluation method can be selected by reflecting the crew's past feedback. Some or all of the above-described processing in the bias mitigation unit may be performed using AI, for example, or may be performed without using AI. For example, the bias mitigation unit can cause AI to execute processing to adjust the evaluation method based on the crew's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, recommendation unit, and bias mitigation unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects crew data using the camera 42 and microphone 38B of the smart device 14 and analyzes the data using the specific processing unit 290 of the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI. The recommendation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides training recommendations based on the analysis results. The bias mitigation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, evaluates the crew fairly. Furthermore, the collection unit estimates the crew's emotions and determines the timing of data collection based on the estimated emotions, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, recommendation unit, and bias mitigation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects crew data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the data using the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI. The recommendation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides training recommendations based on the analysis results. The bias mitigation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, evaluates the crew fairly. Furthermore, the collection unit estimates the crew's emotions and determines the timing of data collection based on the estimated emotions, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, recommendation unit, and bias mitigation unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects crew data using the camera 42 and microphone 238 of the headset-type terminal 314 and analyzes the data using the specific processing unit 290 of the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI. The recommendation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides training recommendations based on the analysis results. The bias mitigation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, evaluates the crew fairly. Furthermore, the collection unit estimates the crew's emotions and determines the timing of data collection based on the estimated emotions, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, recommendation unit, and bias mitigation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects crew data using the camera 42 and microphone 238 of the robot 414 and analyzes the data using the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI. The recommendation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides training recommendations based on the analysis results. The bias mitigation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, evaluates the crew fairly. Furthermore, the collection unit estimates the crew's emotions and determines the timing of data collection based on the estimated emotions, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The AI ​​system can also be equipped with a health management module that collects crew health data and provides training recommendations based on their health status. For example, the health management module can monitor crew stress levels and sleep patterns and recommend relaxation training if stress levels are high. The health management module can also analyze crew exercise habits and recommend fitness training if lack of exercise is the cause. Furthermore, the health management module can evaluate crew diets and recommend nutritional management training if nutritional imbalance is detected. This can improve the crew's overall performance by providing training recommendations based on their health status.

[0107] The collection unit can also analyze the crew's social media activities and collect data based on the crew's interests. For example, the collection unit can analyze the content of posts shared by the crew on social media and prioritize collecting data related to the crew's areas of interest. The collection unit can also analyze the crew's friendships on social media and collect data related to topics that the friends are interested in. Furthermore, the collection unit can adjust the timing of data collection based on the frequency of the crew's social media activity. This makes it possible to efficiently collect more relevant data by utilizing the crew's social media activities.

[0108] The analysis unit can also estimate the crew's emotions and determine the priorities of analysis based on the estimated crew's emotions. For example, if the crew is feeling stressed, it can prioritize analysis of data related to the cause of stress. If the crew is relaxed, it can perform long-term data analysis. Furthermore, if the crew is feeling positive, it can perform detailed data analysis. This allows the analysis unit to adjust the priorities of analysis according to the crew's emotions and provide more appropriate analysis results.

[0109] The recommendation department can also analyze the crew's past training history and recommend new training based on the results of past training. For example, if a previous training session was highly effective, it can recommend the same training again. It can also recommend training to improve the skills needed as the next step based on the skills acquired in past training. Furthermore, it can refer to feedback from past training sessions and recommend training related to areas in which the crew is particularly interested. This makes it possible to utilize the crew's past training history to provide more effective training recommendations.

[0110] The bias reduction unit can also estimate the crew's emotions and adjust the evaluation method based on the estimated crew's emotions. For example, if a crew member is feeling stressed, the evaluation frequency can be reduced and evaluations can be conducted when the crew member is relaxed. It can also refrain from evaluations when the crew member is busy and conduct evaluations when they have more time. Furthermore, it can conduct detailed evaluations when the crew member is feeling positive. This allows for more appropriate evaluations by adjusting the evaluation method according to the crew member's emotions.

[0111] The collection unit can also prioritize collection of highly relevant data based on the crew's geographical location information. For example, if the crew works in a specific area, data related to that area can be collected preferentially. Also, if the crew is on a business trip, data related to the business trip destination can be collected. Furthermore, necessary data can be selectively collected based on the crew's work location. In this way, highly relevant data can be collected efficiently by taking the crew's geographical location information into consideration.

[0112] The collection unit can also estimate the crew's emotions and determine the timing of data collection based on the estimated crew's emotions. For example, if the crew is feeling stressed, the frequency of data collection can be reduced and data can be collected when the crew is relaxed. Data collection can also be refrained from when the crew is busy and collected when the crew has more time. Furthermore, detailed data collection can be performed when the crew is feeling positive. This allows more appropriate data to be collected by adjusting the timing of data collection according to the crew's emotions.

[0113] The analysis unit can also improve the accuracy of the analysis based on the crew's past evaluation results. For example, the current analysis result is corrected based on the crew's past evaluation results. The analysis unit can also adjust the analysis algorithm by referring to the crew's past evaluation results. Furthermore, the analysis unit can also improve the accuracy of the analysis by using the crew's past evaluation results. In this way, the accuracy of the current analysis result can be improved by referring to the crew's past evaluation results.

[0114] The recommendation unit can also estimate the crew's emotions and adjust the way recommendations are presented based on the estimated crew's emotions. For example, if the crew is feeling stressed, it can provide simple, highly visible recommendations. If the crew is relaxed, it can also provide detailed recommendations. Furthermore, if the crew is feeling positive, it can also provide visually appealing recommendations. This makes it possible to provide more appropriate training recommendations by adjusting the way recommendations are presented according to the crew's emotions.

[0115] The bias reduction unit can also improve the evaluation method based on the crew's past feedback. For example, it can select the optimal evaluation method based on the crew's past feedback. It can also customize the evaluation method by reflecting the crew's past feedback. It can also adjust the timing and method of evaluation by referring to the crew's feedback. In this way, it is possible to select a more appropriate evaluation method by reflecting the crew's feedback.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection department collects data on crew hiring and turnover. Specifically, data such as start date, departure date, reason for leaving, job duties, and evaluation results are obtained from databases, questionnaires, interview records, and business systems. Step 2: The analysis department uses AI to analyze the data collected by the collection department and identify the causes of crew dissatisfaction and concerns. Specifically, machine learning algorithms and natural language processing technology are used to analyze reasons for leaving, work content, interview records, and survey results. Step 3: The Recommendation Department provides training recommendations to crew members based on the causes identified by the Analysis Department. Specifically, if the cause is dissatisfaction with a specific task, the Department will recommend training to improve skills related to that task or training to conduct fair evaluations. Step 4: The bias mitigation department will fairly evaluate the crew based on the training recommendations provided by the recommendation department. Specifically, the department will conduct unbiased evaluations based on the crew's work content and evaluation results, and will mitigate bias by using methods such as standardizing evaluation criteria and introducing third-party evaluations.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 collection department that collects data on crew recruitment and turnover; an analysis unit that analyzes the data collected by the collection unit and identifies causes of dissatisfaction and worries of the crew; a recommendation unit that provides training recommendations to crew members based on the causes identified by the analysis unit; a bias reduction unit that evaluates the crew based on the training recommendations provided by the recommendation unit. A system characterized by:

2. The collecting unit Collect data on crew joining date, leaving date, reason for leaving, job description, and evaluation results 2. The system of claim 1.

3. The analysis unit The data collected by the collection unit is analyzed using artificial intelligence to identify the causes of crew dissatisfaction and worries.

2. The system of claim 1.

4. The recommendation unit Provide training recommendations for crew based on identified causes 2. The system of claim 1.

5. The bias reduction unit Evaluation is based on the crew's work and evaluation results.

2. The system of claim 1.

6. The collecting unit Estimate the crew's emotions and decide the timing of data collection according to the estimated crew's emotions.

2. The system of claim 1.

7. The collecting unit Analyze crew members' past work history and select appropriate data collection methods 2. The system of claim 1.

8. The collecting unit As data is collected, filtering is performed based on the crew's current projects or areas of interest.

2. The system of claim 1.

Images