System

Abstract

An object of a system according to an embodiment is to analyze behavior data of a crew and propose an individual training course.SOLUTION: A system according to an embodiment includes a behavior analysis unit, a skill evaluation unit, and a training suggestion unit. The behavior analysis unit analyzes behavior data of the crew. The skill evaluator evaluates the skill level based on the crew behavior data analyzed by the behavior analyzer. The training suggestion unit suggests an individual training course based on the skill level of the crew evaluated by the skill evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately propose individual training courses based on crew behavior data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze crew behavior data and propose individual training courses. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior analysis unit, a skill evaluation unit, and a training suggestion unit. The behavior analysis unit analyzes behavioral data of the crew. The skill evaluation unit evaluates the skill level of the crew based on the behavioral data analyzed by the behavior analysis unit. The training suggestion unit proposes an individual training course based on the skill level of the crew evaluated by the skill evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze crew behavior data and suggest individual training courses. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The training system according to an embodiment of the present invention analyzes the behavior and skill level of crew members and automatically provides optimal training courses for each individual. This allows the training system to provide optimal training tailored to the characteristics and circumstances of the crew members, thereby improving their skills and streamlining their work.

[0029] The training system according to the embodiment includes a behavior analysis unit, a skill evaluation unit, and a training suggestion unit. The behavior analysis unit analyzes behavioral data of the crew. For example, the behavior analysis unit analyzes the crew's work log to evaluate the efficiency of the work. The behavior analysis unit can analyze the crew's movement history to optimize the work. The behavior analysis unit analyzes the crew's operation history to evaluate the accuracy of the operation. The skill evaluation unit evaluates the skill level based on the behavioral data of the crew analyzed by the behavior analysis unit. For example, the skill evaluation unit evaluates the accuracy of the work. The skill evaluation unit can also evaluate the efficiency of the work. The skill evaluation unit can also evaluate the speed of the work. The training suggestion unit proposes an individual training course based on the crew's skill level evaluated by the skill evaluation unit. For example, the training suggestion unit proposes an online course. The training suggestion unit can also propose on-the-job training. The training suggestion unit can also propose a simulation. As a result, the training system according to the embodiment can improve the crew's skills by analyzing the crew's behavioral data and skill level and proposing an individual training course.

[0030] The behavior analysis unit can analyze subtle behavioral patterns based on the behavioral data of the crew and evaluate the quality of their behavior. For example, the behavior analysis unit analyzes audio data of the crew when they are interacting with customers to extract conversational tone and speaking patterns. For example, it analyzes the tone of voice and speaking speed when talking to customers and evaluates the correlation with customer satisfaction. The behavior analysis unit also uses video analysis technology to analyze the gestures and body language of the crew when interacting with customers and evaluates the quality of their communication with customers. For example, it measures the frequency of smiling and eye contact. The behavior analysis unit also analyzes emotional expressions during conversations with customers based on the behavioral data of the crew and evaluates the correlation with customer reactions. For example, it measures customer satisfaction for crew members who express a lot of positive emotional expressions. This enables more detailed evaluation of the quality of the crew's behavior to make more precise training suggestions.

[0031] The skill evaluation unit can analyze the correlation between a crew member's past training history and their actual work performance. For example, the skill evaluation unit integrates the crew member's past training history with work performance data and analyzes the correlation between skill levels. For example, it evaluates the degree of improvement in work performance after training. The skill evaluation unit also analyzes the impact of a specific skill set on work performance based on the crew member's training history. For example, it evaluates the impact of customer service skill training on sales performance. The skill evaluation unit also uses the crew member's training history and work performance data to draw a skill level growth curve and evaluate individual growth patterns. For example, it measures the rate at which skills improve after training. This makes it possible to propose more effective training by analyzing the correlation between past training history and work performance.

[0032] The behavior analysis unit can compare and analyze the behavioral data of a crew member with other crew members to evaluate their relative skill levels. For example, the behavior analysis unit compares the behavioral data of a crew member with other crew members to build a system for evaluating their relative skill levels. For example, it analyzes performance differences between crew members who are in charge of the same work. The behavior analysis unit also visualizes the differences in skill levels with other crew members based on the behavioral data of the crew member. For example, it performs a relative evaluation of customer service skills and sales skills. The behavior analysis unit also compares the behavioral data of a crew member with other crew members to set a skill level benchmark. For example, it evaluates the skill levels of other crew members based on the behavior patterns of top performers. This makes it possible to compare the skill levels of a crew member with other crew members and perform a relative evaluation.

[0033] The behavior analysis unit can use the crew's behavioral data to predict their performance in different work scenarios and suggest suitable tasks. The behavior analysis unit, for example, builds a system that predicts their performance in different work scenarios based on the crew's behavioral data. For example, it evaluates their aptitude for customer service work and back office work. The behavior analysis unit also analyzes the crew's behavioral data and predicts their performance in different work scenarios. For example, it evaluates their aptitude for sales work and customer support work. The behavior analysis unit also predicts their performance in different work scenarios based on the crew's behavioral data and suggests suitable tasks. For example, it identifies tasks that can make use of the crew's strengths. This makes it possible to maximize the crew's performance by suggesting suitable tasks based on the crew's behavioral data.

[0034] The training proposal unit can analyze the effects of a crew member's past training and identify and propose the most effective training method. The training proposal unit, for example, analyzes a crew member's past training history and work performance data to identify the most effective training method. For example, it evaluates the impact of specific training on work performance. The training proposal unit also identifies effective training methods based on the crew member's training history and proposes individual training courses. For example, it selects optimal training based on the effects of past training. The training proposal unit also builds a system that analyzes the effects of a crew member's training and identifies the most effective training method. For example, it evaluates the degree of performance improvement after taking training. In this way, by analyzing the effects of past training, the most effective training method can be identified and proposed.

[0035] The training proposal unit can analyze the learning style of the crew and customize the training course accordingly. The training proposal unit, for example, analyzes the learning style of the crew and builds a system that customizes the training course accordingly. For example, it provides visual learning materials to visual learners. The training proposal unit also proposes the optimal training course based on the crew's learning style. For example, it provides audio learning materials to auditory learners. The training proposal unit also analyzes the learning style of the crew and customizes the training course accordingly. For example, it provides practical training to experiential learners. In this way, the learning effect can be maximized by customizing the training course according to the crew's learning style.

[0036] The training proposal unit can provide training courses tailored to the characteristics of a crew member as generic training modules that can be applied to other crew members. The training proposal unit, for example, builds a system that provides training courses tailored to the characteristics of a crew member as generic training modules. For example, it creates training modules that correspond to specific skill sets. The training proposal unit also provides training courses customized based on the characteristics of a crew member as generic training modules that can be applied to other crew members. For example, it provides training that aims to improve common skills. The training proposal unit also provides training courses tailored to the characteristics of a crew member as generic training modules and applies them to other crew members. For example, it provides training that strengthens specific work skills. In this way, by providing training courses tailored to the characteristics of a crew member as generic training modules, training that can be applied to other crew members can be realized.

[0037] The training suggestion unit can incorporate gamification elements into the training course suggestions to increase the crew's motivation to learn. The training suggestion unit, for example, incorporates gamification elements into the training course to build a system to increase the crew's motivation to learn. For example, it introduces a point system or badges. The training suggestion unit also proposes training courses that incorporate gamification elements to increase the crew's motivation to learn. For example, it provides rewards according to the progress of the training. The training suggestion unit also incorporates gamification elements into the training course to increase the crew's motivation to learn. For example, it provides training that incorporates a competitive element. In this way, by incorporating gamification elements, the crew's motivation to learn can be increased.

[0038] The behavioral analysis unit can analyze the sales data of crew members and identify factors that contribute to the success of sales of specific products and services. The behavioral analysis unit, for example, analyzes the sales data of crew members and builds a system that identifies factors that contribute to the success of sales of specific products and services. For example, it analyzes customer responses and proposal methods during sales. The behavioral analysis unit also identifies factors that contribute to the success of sales of specific products and services based on the sales data of crew members. For example, it evaluates the timing of sales and the impact of promotions. The behavioral analysis unit also analyzes the sales data of crew members and identifies factors that contribute to the success of sales of specific products and services. For example, it evaluates factors that contribute to the success of sales based on customer purchasing history and preferences. In this way, by analyzing the sales data, it is possible to identify factors that contribute to the success of sales of specific products and services and optimize sales strategies.

[0039] The behavior analysis unit analyzes the crew's sales data in chronological order to understand fluctuations in sales trends due to seasons and events. The behavior analysis unit, for example, analyzes the crew's sales data in chronological order to build a system that understands fluctuations in sales trends due to seasons and events. For example, it analyzes seasonal sales patterns. The behavior analysis unit also understands fluctuations in sales trends due to seasons and events based on the crew's sales data. For example, it analyzes sales data during a specific event period. The behavior analysis unit also analyzes the crew's sales data in chronological order to understand fluctuations in sales trends due to seasons and events. For example, it evaluates sales trends throughout the year. In this way, by analyzing the sales data in chronological order, it is possible to understand fluctuations in sales trends due to seasons and events and optimize sales strategies.

[0040] The behavior analysis unit can compare the crew's sales data with other stores or regions to evaluate relative sales performance. The behavior analysis unit, for example, compares the crew's sales data with other stores or regions to build a system for evaluating relative sales performance. For example, it makes a comparison with other stores that sell the same product. The behavior analysis unit also evaluates sales performance relative to other stores or regions based on the crew's sales data. For example, it compares sales performance by region. The behavior analysis unit also compares the crew's sales data with other stores or regions to evaluate relative sales performance. For example, it makes a comparison with stores with high sales records. In this way, by comparing sales data with other stores or regions, it is possible to evaluate relative sales performance and identify areas for improvement.

[0041] The behavior analysis unit can use the crew's sales data to predict performance in different sales channels. The behavior analysis unit, for example, builds a system that predicts performance in different sales channels based on the crew's sales data. For example, it compares the performance of online sales and offline sales. The behavior analysis unit also analyzes the crew's sales data to predict performance in different sales channels. For example, it predicts offline sales based on online sales results. The behavior analysis unit also predicts performance in different sales channels based on the crew's sales data. For example, it integrates online and offline sales data to make a prediction. In this way, by using the sales data to predict performance in different sales channels, an optimal sales strategy can be developed.

[0042] The behavior analysis unit analyzes customer demographic data for the area where the crew works, and is able to understand area-specific customer needs. The behavior analysis unit, for example, analyzes customer demographic data for the area where the crew works, and builds a system that understands area-specific customer needs. For example, it analyzes age groups, gender, and purchasing history. The behavior analysis unit also understands specific customer needs based on the area's customer demographic data. For example, it identifies products that are popular among specific age groups. The behavior analysis unit also analyzes customer demographic data for the area where the crew works, and is able to understand area-specific customer needs. For example, it evaluates purchasing patterns by region. In this way, by analyzing customer demographic data, it is possible to understand area-specific customer needs and provide appropriate training.

[0043] The behavior analysis unit can analyze sales data for each area and identify sales trends specific to that area. The behavior analysis unit, for example, builds a system that analyzes sales data for each area and identifies sales trends specific to that area. For example, it identifies products and services that are popular in a specific area. The behavior analysis unit also identifies sales trends specific to that area based on sales data for that area. For example, it analyzes sales patterns by season. The behavior analysis unit also analyzes sales data for each area and identifies sales trends specific to that area. For example, it analyzes sales data during a specific event period. In this way, by analyzing sales data for each area, it is possible to identify sales trends specific to that area and optimize sales strategies.

[0044] The behavior analysis unit can provide training based on area characteristics as a general-purpose training module that can be applied to other areas. The behavior analysis unit, for example, builds a system that provides training based on area characteristics as a general-purpose training module. For example, it creates training that can be applied to both urban and rural areas. The behavior analysis unit also provides training customized based on area characteristics as a general-purpose training module that can be applied to other areas. For example, it provides training that meets common customer needs. The behavior analysis unit also provides training based on area characteristics as a general-purpose training module that can be applied to other areas. For example, it provides training that meets the unique needs of a specific region. In this way, by providing training based on area characteristics as a general-purpose training module, training that can be applied to other areas can be realized.

[0045] The behavior analysis unit can apply training courses that take into account the characteristics of each area to different industries and business types. For example, the behavior analysis unit builds a system that applies training courses that take into account the characteristics of each area to different industries and business types. For example, it creates training that can be applied to both customer service and manufacturing work. The behavior analysis unit also applies training courses customized based on the characteristics of each area to different industries and business types. For example, it provides training that meets common customer needs. The behavior analysis unit also applies training courses that take into account the characteristics of each area to different industries and business types. For example, it provides training that meets the needs unique to a specific region. This allows for a wide range of applications by applying training courses that take into account the characteristics of each area to different industries and business types.

[0046] The behavior analysis unit can analyze the work history of the crew in detail and identify strengths and weaknesses in specific tasks. The behavior analysis unit, for example, analyzes the work history of the crew in detail and builds a system to identify strengths and weaknesses in specific tasks. For example, it evaluates strengths and weaknesses based on past work performance data. The behavior analysis unit also identifies strengths and weaknesses in specific tasks based on the work history of the crew. For example, it analyzes success stories and failure stories in specific tasks. The behavior analysis unit also analyzes the work history of the crew in detail and identifies strengths and weaknesses in specific tasks. For example, it evaluates performance for each type of task. In this way, by analyzing the work history in detail, it is possible to identify strengths and weaknesses in specific tasks and provide appropriate training.

[0047] The behavior analysis unit can predict the future career path of the crew based on their work history and suggest training accordingly. The behavior analysis unit, for example, builds a system that predicts the future career path of the crew based on their work history and suggests training accordingly. For example, it provides training to strengthen the skills necessary for the career path. The behavior analysis unit also analyzes the work history of the crew and predicts the future career path. For example, it evaluates the career direction based on past work experience. The behavior analysis unit also predicts the future career path of the crew based on their work history and suggests training accordingly. For example, it provides training to strengthen the skills necessary for career advancement. In this way, it is possible to support the crew's career advancement by predicting the future career path based on their work history and suggesting training accordingly.

[0048] The behavior analysis unit can compare and analyze the work history of a crew member with other crew members to evaluate their relative work skills. The behavior analysis unit, for example, compares the work history of a crew member with other crew members to build a system for evaluating their relative work skills. For example, it analyzes performance differences between crew members who are in charge of the same work. The behavior analysis unit also visualizes the difference in skill level with other crew members based on the crew member's work history. For example, it performs a relative evaluation of specific work skills. The behavior analysis unit also compares the work history of a crew member with other crew members to evaluate their relative work skills. For example, it evaluates the skill level of other crew members based on the work history of a top performer. In this way, by comparing and analyzing work history with other crew members, it is possible to evaluate their relative work skills and provide appropriate training.

[0049] The behavior analysis unit can use the crew's work history to predict their performance in different work scenarios and suggest suitable tasks. The behavior analysis unit, for example, builds a system that predicts their performance in different work scenarios based on the crew's work history. For example, it evaluates their aptitude for customer service work and back office work. The behavior analysis unit also analyzes the crew's work history and predicts their performance in different work scenarios. For example, it evaluates their aptitude for sales work and customer support work. The behavior analysis unit also predicts their performance in different work scenarios based on the crew's work history. For example, it identifies tasks that can make use of the crew's strengths. This makes it possible to maximize the crew's performance by predicting their performance in different work scenarios based on their work history and suggesting suitable tasks.

[0050] The behavior analysis unit can analyze crew behavior data and develop algorithms for early detection of signs of fraudulent activity. The behavior analysis unit, for example, analyzes crew behavior data and builds a system that develops algorithms for early detection of signs of fraudulent activity. For example, it detects abnormal sales patterns. The behavior analysis unit also develops algorithms for early detection of signs of fraudulent activity based on crew behavior data. For example, it analyzes trends in overselling. The behavior analysis unit also analyzes crew behavior data and develops algorithms for early detection of signs of fraudulent activity. For example, it detects abnormal customer response patterns. In this way, by analyzing behavior data and developing algorithms for early detection of signs of fraudulent activity, it becomes possible to prevent fraudulent activity from occurring.

[0051] The behavior analysis unit can assess the risk of fraudulent activity based on the behavioral data of crew members and propose special training for high-risk crew members. The behavior analysis unit, for example, builds a system that assesses the risk of fraudulent activity based on the behavioral data of crew members and proposes special training for high-risk crew members. For example, training is provided for crew members who are at high risk of overselling. The behavior analysis unit also analyzes the behavioral data of crew members and assesses the risk of fraudulent activity. For example, the risk is assessed based on abnormal sales patterns. The behavior analysis unit also assesses the risk of fraudulent activity based on the behavioral data of crew members and proposes special training for high-risk crew members. For example, training is provided for crew members who are at high risk of overselling. In this way, by assessing the risk of fraudulent activity based on behavioral data and proposing special training for high-risk crew members, it is possible to prevent fraudulent activity from occurring.

[0052] The behavior analysis unit can apply the algorithm for detecting signs of fraud to other businesses and industries. For example, the behavior analysis unit builds a system that applies the algorithm for detecting signs of fraud to other businesses and industries. For example, it applies it to detecting fraud in the financial or medical industry. The behavior analysis unit also applies the algorithm for detecting signs of fraud to different businesses and industries. For example, it applies it to detecting fraud in the manufacturing or service industry. The behavior analysis unit also applies the algorithm for detecting signs of fraud to other businesses and industries. For example, it analyzes fraud patterns in different industries and reflects this in the algorithm. This allows the algorithm for detecting signs of fraud to be applied to other businesses and industries, enabling a wide range of applications.

[0053] The behavior analysis unit can use the fraud risk assessment to compare and analyze different stores and regions, and identify high-risk areas. The behavior analysis unit, for example, uses the fraud risk assessment to compare and analyze different stores and regions, and builds a system to identify high-risk areas. For example, it identifies stores with a high risk of overselling. The behavior analysis unit also performs comparative analyses of different stores and regions based on the fraud risk assessment. For example, it performs risk assessment based on abnormal sales patterns. The behavior analysis unit also uses the fraud risk assessment to compare and analyze different stores and regions, and identify high-risk areas. For example, it identifies areas with a high risk of overselling. In this way, by using the fraud risk assessment to compare and analyze different stores and regions, it is possible to identify high-risk areas and take measures.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The behavioral analysis unit can analyze the behavioral data of crew members and evaluate their energy consumption during work. For example, it calculates energy consumption based on the distance traveled and the working time of the crew members. The behavioral analysis unit can also evaluate energy consumption according to the type of work the crew members do. For example, it can compare the energy consumption of work such as carrying heavy objects with that of desk work. The behavioral analysis unit can also suggest appropriate break times based on the crew members' energy consumption. This makes it possible to understand the crew members' energy consumption and suggest appropriate breaks, thereby improving work efficiency.

[0056] The Skill Evaluation Department can incorporate crew self-assessment data when assessing crew skill levels. For example, crew members fill out self-assessment sheets and reflect that data in skill evaluations. The Skill Evaluation Department can also compare crew self-assessments with actual work performance to evaluate the accuracy of self-assessments. For example, it can verify the actual performance of crew members with high self-assessments. The Skill Evaluation Department can also identify gaps between self-assessments and actual skill levels based on the crew self-assessment data and reflect these in training proposals. In this way, incorporating crew self-assessments enables more accurate skill evaluations.

[0057] The training suggestion unit can analyze the crew's health condition and suggest training content to maintain their health. For example, it can suggest an exercise program to maintain their health based on the crew's health check data. The training suggestion unit can also monitor the crew's health condition and suggest training content to reduce health risks. For example, it can provide a stretching program to reduce health risks caused by long hours of desk work. The training suggestion unit can also suggest individual health maintenance plans based on the crew's health condition. This makes it possible to suggest training that takes the crew's health condition into consideration.

[0058] The behavior analysis unit can evaluate safety during work based on the behavioral data of the crew. For example, it can analyze the crew's work environment and work procedures to identify safety risks. The behavior analysis unit can also make suggestions for improving safety based on the behavioral data of the crew. For example, it can make suggestions for improving work procedures that pose a high safety risk. The behavior analysis unit can also suggest training content for improving safety based on the behavioral data of the crew. This makes it possible to evaluate safety during work based on the behavioral data of the crew and make suggestions for improving safety.

[0059] The behavior analysis unit can analyze communication patterns during work based on the behavioral data of crew members. For example, it can analyze the frequency and content of communication between crew members and evaluate the quality of communication. The behavior analysis unit can also make suggestions for improving communication based on the crew's communication patterns. For example, it can suggest training to improve communication skills for crew members who lack communication skills. The behavior analysis unit can also make suggestions for improving teamwork based on the crew's communication patterns. This makes it possible to analyze the crew's communication patterns and make suggestions for improving communication.

[0060] The skill evaluation unit can incorporate the crew's learning history when evaluating the crew's skill level. For example, it can evaluate skills based on the training courses and learning content that the crew has taken in the past. The skill evaluation unit can also compare the crew's learning history with their actual work performance to evaluate the learning effectiveness. For example, it can verify the work performance of crew members who have taken a specific training course. The skill evaluation unit can also make training suggestions to maximize the learning effectiveness based on the crew's learning history. Incorporating the crew's learning history enables more accurate skill evaluation and training suggestions.

[0061] The behavior analysis unit can evaluate the fatigue level during work based on the behavioral data of the crew. For example, it calculates the fatigue level based on the crew's working time and work content. The behavior analysis unit can also suggest appropriate break times based on the crew's fatigue level. For example, it can suggest a break after working for a long period of time. The behavior analysis unit can also suggest training content to reduce fatigue based on the crew's fatigue level. This makes it possible to understand the crew's fatigue level and suggest appropriate breaks and training, thereby improving work efficiency.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The behavior analysis unit analyzes the behavioral data of the crew. For example, the behavior analysis unit analyzes the crew's work log and evaluates the efficiency of the work. The behavior analysis unit can also analyze the crew's movement history and optimize operations. The behavior analysis unit can also analyze the crew's operation history and evaluate the accuracy of the operations. Step 2: The skill evaluation unit evaluates the skill level based on the crew's behavioral data analyzed by the behavior analysis unit. For example, the skill evaluation unit evaluates the accuracy of the work. The skill evaluation unit can also evaluate the efficiency of the work. The skill evaluation unit can also evaluate the speed of the work. Step 3: The training suggestion unit suggests individual training courses based on the crew's skill level assessed by the skill assessment unit. For example, the training suggestion unit suggests online courses. The training suggestion unit can also suggest on-the-job training. The training suggestion unit can also suggest simulations.

[0064] (Example 2) The training system according to an embodiment of the present invention analyzes the behavior and skill level of crew members and automatically provides optimal training courses for each individual. This allows the training system to provide optimal training tailored to the characteristics and circumstances of the crew members, thereby improving their skills and streamlining their work.

[0065] The training system according to the embodiment includes a behavior analysis unit, a skill evaluation unit, and a training suggestion unit. The behavior analysis unit analyzes behavioral data of the crew. For example, the behavior analysis unit analyzes the crew's work log to evaluate the efficiency of the work. The behavior analysis unit can analyze the crew's movement history to optimize the work. The behavior analysis unit analyzes the crew's operation history to evaluate the accuracy of the operation. The skill evaluation unit evaluates the skill level based on the behavioral data of the crew analyzed by the behavior analysis unit. For example, the skill evaluation unit evaluates the accuracy of the work. The skill evaluation unit can also evaluate the efficiency of the work. The skill evaluation unit can also evaluate the speed of the work. The training suggestion unit proposes an individual training course based on the crew's skill level evaluated by the skill evaluation unit. For example, the training suggestion unit proposes an online course. The training suggestion unit can also propose on-the-job training. The training suggestion unit can also propose a simulation. As a result, the training system according to the embodiment can improve the crew's skills by analyzing the crew's behavioral data and skill level and proposing an individual training course.

[0066] The behavior analysis unit can analyze subtle behavioral patterns based on the behavioral data of the crew and evaluate the quality of their behavior. For example, the behavior analysis unit analyzes audio data of the crew when they are interacting with customers to extract conversational tone and speaking patterns. For example, it analyzes the tone of voice and speaking speed when talking to customers and evaluates the correlation with customer satisfaction. The behavior analysis unit also uses video analysis technology to analyze the gestures and body language of the crew when interacting with customers and evaluates the quality of their communication with customers. For example, it measures the frequency of smiling and eye contact. The behavior analysis unit also analyzes emotional expressions during conversations with customers based on the behavioral data of the crew and evaluates the correlation with customer reactions. For example, it measures customer satisfaction for crew members who express a lot of positive emotional expressions. This enables more detailed evaluation of the quality of the crew's behavior to make more precise training suggestions.

[0067] The skill evaluation unit can analyze the correlation between a crew member's past training history and their actual work performance. For example, the skill evaluation unit integrates the crew member's past training history with work performance data and analyzes the correlation between skill levels. For example, it evaluates the degree of improvement in work performance after training. The skill evaluation unit also analyzes the impact of a specific skill set on work performance based on the crew member's training history. For example, it evaluates the impact of customer service skill training on sales performance. The skill evaluation unit also uses the crew member's training history and work performance data to draw a skill level growth curve and evaluate individual growth patterns. For example, it measures the rate at which skills improve after training. This makes it possible to propose more effective training by analyzing the correlation between past training history and work performance.

[0068] The training suggestion unit can analyze the emotional state of the crew and grasp fluctuations in stress and motivation. For example, the training suggestion unit analyzes facial expression data of the crew while they are working and evaluates their stress level using an emotion estimation function. For example, it measures increases or decreases in stress based on changes in facial expressions. The training suggestion unit also analyzes the crew's voice data and grasps fluctuations in motivation using an emotion estimation function. For example, it evaluates the level of motivation based on changes in voice tone and speaking style. The training suggestion unit also integrates the crew's behavioral data and emotion estimation data to analyze fluctuations in their emotional state while they are working. For example, it evaluates the difference in emotional state between peak and off-peak work times. In this way, by understanding the crew's emotional state, it becomes possible to propose training that helps reduce stress and improve motivation.

[0069] The behavior analysis unit can compare and analyze the behavioral data of a crew member with other crew members to evaluate their relative skill levels. For example, the behavior analysis unit compares the behavioral data of a crew member with other crew members to build a system for evaluating their relative skill levels. For example, it analyzes performance differences between crew members who are in charge of the same work. The behavior analysis unit also visualizes the differences in skill levels with other crew members based on the behavioral data of the crew member. For example, it performs a relative evaluation of customer service skills and sales skills. The behavior analysis unit also compares the behavioral data of a crew member with other crew members to set a skill level benchmark. For example, it evaluates the skill levels of other crew members based on the behavior patterns of top performers. This makes it possible to compare the skill levels of a crew member with other crew members and perform a relative evaluation.

[0070] The behavior analysis unit can use the crew's behavioral data to predict their performance in different work scenarios and suggest suitable tasks. The behavior analysis unit, for example, builds a system that predicts their performance in different work scenarios based on the crew's behavioral data. For example, it evaluates their aptitude for customer service work and back office work. The behavior analysis unit also analyzes the crew's behavioral data and predicts their performance in different work scenarios. For example, it evaluates their aptitude for sales work and customer support work. The behavior analysis unit also predicts their performance in different work scenarios based on the crew's behavioral data and suggests suitable tasks. For example, it identifies tasks that can make use of the crew's strengths. This makes it possible to maximize the crew's performance by suggesting suitable tasks based on the crew's behavioral data.

[0071] The training suggestion unit can provide feedback in real time according to the emotional state of the crew, thereby reducing stress during work. The training suggestion unit, for example, uses an emotion estimation function to analyze the emotional state of the crew in real time and provide feedback to reduce stress. For example, it can suggest relaxation methods when stress increases. The training suggestion unit can also monitor the emotional state of the crew in real time and provide feedback to reduce stress during work. For example, it can send positive feedback or encouraging messages. The training suggestion unit can also build a system that provides feedback in real time according to the emotional state of the crew based on the emotion estimation data. For example, it can suggest taking a break when stress increases. In this way, by providing feedback in real time according to the emotional state of the crew, it is possible to reduce stress during work.

[0072] The training proposal unit can analyze the effects of a crew member's past training and identify and propose the most effective training method. The training proposal unit, for example, analyzes a crew member's past training history and work performance data to identify the most effective training method. For example, it evaluates the impact of specific training on work performance. The training proposal unit also identifies effective training methods based on the crew member's training history and proposes individual training courses. For example, it selects optimal training based on the effects of past training. The training proposal unit also builds a system that analyzes the effects of a crew member's training and identifies the most effective training method. For example, it evaluates the degree of performance improvement after taking training. In this way, by analyzing the effects of past training, the most effective training method can be identified and proposed.

[0073] The training proposal unit can analyze the learning style of the crew and customize the training course accordingly. The training proposal unit, for example, analyzes the learning style of the crew and builds a system that customizes the training course accordingly. For example, it provides visual learning materials to visual learners. The training proposal unit also proposes the optimal training course based on the crew's learning style. For example, it provides audio learning materials to auditory learners. The training proposal unit also analyzes the learning style of the crew and customizes the training course accordingly. For example, it provides practical training to experiential learners. In this way, the learning effect can be maximized by customizing the training course according to the crew's learning style.

[0074] The training suggestion unit can use the emotion estimation function to suggest training content to increase the crew's motivation. The training suggestion unit, for example, uses the emotion estimation function to build a system that suggests training content to increase the crew's motivation. For example, it provides training that elicits positive emotions. The training suggestion unit also analyzes the crew's emotional state and suggests training content to increase their motivation. For example, it sends encouraging messages when their motivation drops. The training suggestion unit also suggests training content to increase the crew's motivation based on the emotion estimation data. For example, it provides training that includes positive feedback. In this way, the crew's performance can be improved by using the emotion estimation function to suggest training content that increases the crew's motivation.

[0075] The training proposal unit can provide training courses tailored to the characteristics of a crew member as generic training modules that can be applied to other crew members. The training proposal unit, for example, builds a system that provides training courses tailored to the characteristics of a crew member as generic training modules. For example, it creates training modules that correspond to specific skill sets. The training proposal unit also provides training courses customized based on the characteristics of a crew member as generic training modules that can be applied to other crew members. For example, it provides training that aims to improve common skills. The training proposal unit also provides training courses tailored to the characteristics of a crew member as generic training modules and applies them to other crew members. For example, it provides training that strengthens specific work skills. In this way, by providing training courses tailored to the characteristics of a crew member as generic training modules, training that can be applied to other crew members can be realized.

[0076] The training suggestion unit can incorporate gamification elements into the training course suggestions to increase the crew's motivation to learn. The training suggestion unit, for example, incorporates gamification elements into the training course to build a system to increase the crew's motivation to learn. For example, it introduces a point system or badges. The training suggestion unit also proposes training courses that incorporate gamification elements to increase the crew's motivation to learn. For example, it provides rewards according to the progress of the training. The training suggestion unit also incorporates gamification elements into the training course to increase the crew's motivation to learn. For example, it provides training that incorporates a competitive element. In this way, by incorporating gamification elements, the crew's motivation to learn can be increased.

[0077] The training suggestion unit can use the emotion estimation function to monitor the emotional reactions of the crew during training and adjust the training content in real time. The training suggestion unit, for example, uses the emotion estimation function to build a system that monitors the emotional reactions of the crew during training in real time and adjusts the training content. For example, the training content is changed when stress increases. The training suggestion unit also monitors the emotional reactions of the crew in real time and adjusts the training content. For example, training content that evokes strong positive emotions is provided preferentially. The training suggestion unit also monitors the emotional reactions of the crew during training based on the emotion estimation data and adjusts the training content in real time. For example, an encouraging message is sent when motivation drops. In this way, effective training can be achieved by monitoring the emotional reactions of the crew during training in real time and adjusting the training content.

[0078] The behavioral analysis unit can analyze the sales data of crew members and identify factors that contribute to the success of sales of specific products and services. The behavioral analysis unit, for example, analyzes the sales data of crew members and builds a system that identifies factors that contribute to the success of sales of specific products and services. For example, it analyzes customer responses and proposal methods during sales. The behavioral analysis unit also identifies factors that contribute to the success of sales of specific products and services based on the sales data of crew members. For example, it evaluates the timing of sales and the impact of promotions. The behavioral analysis unit also analyzes the sales data of crew members and identifies factors that contribute to the success of sales of specific products and services. For example, it evaluates factors that contribute to the success of sales based on customer purchasing history and preferences. In this way, by analyzing the sales data, it is possible to identify factors that contribute to the success of sales of specific products and services and optimize sales strategies.

[0079] The behavior analysis unit analyzes the crew's sales data in chronological order to understand fluctuations in sales trends due to seasons and events. The behavior analysis unit, for example, analyzes the crew's sales data in chronological order to build a system that understands fluctuations in sales trends due to seasons and events. For example, it analyzes seasonal sales patterns. The behavior analysis unit also understands fluctuations in sales trends due to seasons and events based on the crew's sales data. For example, it analyzes sales data during a specific event period. The behavior analysis unit also analyzes the crew's sales data in chronological order to understand fluctuations in sales trends due to seasons and events. For example, it evaluates sales trends throughout the year. In this way, by analyzing the sales data in chronological order, it is possible to understand fluctuations in sales trends due to seasons and events and optimize sales strategies.

[0080] The behavior analysis unit can use the emotion estimation function to analyze the relationship between the crew's emotional state and sales performance and suggest emotion-based training. The behavior analysis unit, for example, uses the emotion estimation function to build a system that analyzes the relationship between the crew's emotional state and sales performance. For example, it evaluates the impact of positive emotions on sales performance. The behavior analysis unit also analyzes the crew's emotional state and evaluates the relationship with sales performance. For example, it analyzes the impact of stress on sales performance. The behavior analysis unit also analyzes the relationship between the crew's emotional state and sales performance based on the emotion estimation data and suggests emotion-based training. For example, it provides training to increase motivation. In this way, sales performance can be improved by using the emotion estimation function to analyze the relationship between the emotional state and sales performance and suggesting emotion-based training.

[0081] The behavior analysis unit can compare the crew's sales data with other stores or regions to evaluate relative sales performance. The behavior analysis unit, for example, compares the crew's sales data with other stores or regions to build a system for evaluating relative sales performance. For example, it makes a comparison with other stores that sell the same product. The behavior analysis unit also evaluates sales performance relative to other stores or regions based on the crew's sales data. For example, it compares sales performance by region. The behavior analysis unit also compares the crew's sales data with other stores or regions to evaluate relative sales performance. For example, it makes a comparison with stores with high sales records. In this way, by comparing sales data with other stores or regions, it is possible to evaluate relative sales performance and identify areas for improvement.

[0082] The behavior analysis unit can use the crew's sales data to predict performance in different sales channels. The behavior analysis unit, for example, builds a system that predicts performance in different sales channels based on the crew's sales data. For example, it compares the performance of online sales and offline sales. The behavior analysis unit also analyzes the crew's sales data to predict performance in different sales channels. For example, it predicts offline sales based on online sales results. The behavior analysis unit also predicts performance in different sales channels based on the crew's sales data. For example, it integrates online and offline sales data to make a prediction. In this way, by using the sales data to predict performance in different sales channels, an optimal sales strategy can be developed.

[0083] The behavior analysis unit uses the emotion estimation function to propose a sales strategy according to the emotional state of the crew, thereby improving sales performance. The behavior analysis unit, for example, uses the emotion estimation function to build a system that proposes a sales strategy according to the emotional state of the crew. For example, it proposes an aggressive sales strategy to a crew member with strong positive emotions. The behavior analysis unit also analyzes the emotional state of the crew and proposes a sales strategy based on the emotions. For example, it proposes a relaxation method when stress increases. The behavior analysis unit also proposes a sales strategy according to the emotional state of the crew based on the emotion estimation data, thereby improving sales performance. For example, it provides a sales strategy that increases motivation. As a result, sales performance can be improved by using the emotion estimation function to propose a sales strategy according to the emotional state.

[0084] The behavior analysis unit analyzes customer demographic data for the area where the crew works, and is able to understand area-specific customer needs. The behavior analysis unit, for example, analyzes customer demographic data for the area where the crew works, and builds a system that understands area-specific customer needs. For example, it analyzes age groups, gender, and purchasing history. The behavior analysis unit also understands specific customer needs based on the area's customer demographic data. For example, it identifies products that are popular among specific age groups. The behavior analysis unit also analyzes customer demographic data for the area where the crew works, and is able to understand area-specific customer needs. For example, it evaluates purchasing patterns by region. In this way, by analyzing customer demographic data, it is possible to understand area-specific customer needs and provide appropriate training.

[0085] The behavior analysis unit can analyze sales data for each area and identify sales trends specific to that area. The behavior analysis unit, for example, builds a system that analyzes sales data for each area and identifies sales trends specific to that area. For example, it identifies products and services that are popular in a specific area. The behavior analysis unit also identifies sales trends specific to that area based on sales data for that area. For example, it analyzes sales patterns by season. The behavior analysis unit also analyzes sales data for each area and identifies sales trends specific to that area. For example, it analyzes sales data during a specific event period. In this way, by analyzing sales data for each area, it is possible to identify sales trends specific to that area and optimize sales strategies.

[0086] The behavior analysis unit can use the emotion estimation function to analyze the emotional reactions of customers in each area and suggest training based on that. The behavior analysis unit, for example, uses the emotion estimation function to build a system that analyzes the emotional reactions of customers in each area. For example, it evaluates customer satisfaction in a specific area. The behavior analysis unit also analyzes the emotional reactions of customers in each area and suggests training based on that. For example, it strengthens customer service skills in areas where positive emotions are strong. The behavior analysis unit also analyzes the emotional reactions of customers in each area based on the emotion estimation data and suggests training based on that. For example, it suggests how to respond in areas where stress is high. In this way, customer satisfaction can be improved by using the emotion estimation function to analyze the emotional reactions of customers in each area and suggesting training based on that.

[0087] The behavior analysis unit can provide training based on area characteristics as a general-purpose training module that can be applied to other areas. The behavior analysis unit, for example, builds a system that provides training based on area characteristics as a general-purpose training module. For example, it creates training that can be applied to both urban and rural areas. The behavior analysis unit also provides training customized based on area characteristics as a general-purpose training module that can be applied to other areas. For example, it provides training that meets common customer needs. The behavior analysis unit also provides training based on area characteristics as a general-purpose training module that can be applied to other areas. For example, it provides training that meets the unique needs of a specific region. In this way, by providing training based on area characteristics as a general-purpose training module, training that can be applied to other areas can be realized.

[0088] The behavior analysis unit can apply training courses that take into account the characteristics of each area to different industries and business types. For example, the behavior analysis unit builds a system that applies training courses that take into account the characteristics of each area to different industries and business types. For example, it creates training that can be applied to both customer service and manufacturing work. The behavior analysis unit also applies training courses customized based on the characteristics of each area to different industries and business types. For example, it provides training that meets common customer needs. The behavior analysis unit also applies training courses that take into account the characteristics of each area to different industries and business types. For example, it provides training that meets the needs unique to a specific region. This allows for a wide range of applications by applying training courses that take into account the characteristics of each area to different industries and business types.

[0089] The behavior analysis unit can use the emotion estimation function to monitor the emotional reactions of customers in each area in real time and adjust the training content. The behavior analysis unit, for example, uses the emotion estimation function to build a system that monitors the emotional reactions of customers in each area in real time and adjusts the training content. For example, it proposes ways to respond in areas where stress is high. The behavior analysis unit also monitors the emotional reactions of customers in each area in real time and adjusts the training content. For example, it strengthens customer service skills in areas where positive emotions are strong. The behavior analysis unit also monitors the emotional reactions of customers in each area in real time based on the emotion estimation data and adjusts the training content. For example, it proposes ways to respond in areas where stress is high. In this way, effective training can be achieved by using the emotion estimation function to monitor the emotional reactions of customers in each area in real time and adjusting the training content.

[0090] The behavior analysis unit can analyze the work history of the crew in detail and identify strengths and weaknesses in specific tasks. The behavior analysis unit, for example, analyzes the work history of the crew in detail and builds a system to identify strengths and weaknesses in specific tasks. For example, it evaluates strengths and weaknesses based on past work performance data. The behavior analysis unit also identifies strengths and weaknesses in specific tasks based on the work history of the crew. For example, it analyzes success stories and failure stories in specific tasks. The behavior analysis unit also analyzes the work history of the crew in detail and identifies strengths and weaknesses in specific tasks. For example, it evaluates performance for each type of task. In this way, by analyzing the work history in detail, it is possible to identify strengths and weaknesses in specific tasks and provide appropriate training.

[0091] The behavior analysis unit can predict the future career path of the crew based on their work history and suggest training accordingly. The behavior analysis unit, for example, builds a system that predicts the future career path of the crew based on their work history and suggests training accordingly. For example, it provides training to strengthen the skills necessary for the career path. The behavior analysis unit also analyzes the work history of the crew and predicts the future career path. For example, it evaluates the career direction based on past work experience. The behavior analysis unit also predicts the future career path of the crew based on their work history and suggests training accordingly. For example, it provides training to strengthen the skills necessary for career advancement. In this way, it is possible to support the crew's career advancement by predicting the future career path based on their work history and suggesting training accordingly.

[0092] The behavior analysis unit can use the emotion estimation function to analyze the emotional state of the crew based on their work history and suggest training to reduce stress. The behavior analysis unit, for example, uses the emotion estimation function to analyze the emotional state of the crew based on their work history and build a system that suggests training to reduce stress. For example, it suggests relaxation methods when stress increases. The behavior analysis unit also analyzes the emotional state based on the crew's work history and suggests training to reduce stress. For example, it identifies the cause of stress based on past work experience. The behavior analysis unit also analyzes the emotional state of the crew based on their work history based on the emotion estimation data and suggests training to reduce stress. For example, it suggests relaxation methods when stress increases. In this way, the crew's stress can be reduced by using the emotion estimation function to analyze the emotional state based on their work history and suggesting training to reduce stress.

[0093] The behavior analysis unit can compare and analyze the work history of a crew member with other crew members to evaluate their relative work skills. The behavior analysis unit, for example, compares the work history of a crew member with other crew members to build a system for evaluating their relative work skills. For example, it analyzes performance differences between crew members who are in charge of the same work. The behavior analysis unit also visualizes the difference in skill level with other crew members based on the crew member's work history. For example, it performs a relative evaluation of specific work skills. The behavior analysis unit also compares the work history of a crew member with other crew members to evaluate their relative work skills. For example, it evaluates the skill level of other crew members based on the work history of a top performer. In this way, by comparing and analyzing work history with other crew members, it is possible to evaluate their relative work skills and provide appropriate training.

[0094] The behavior analysis unit can use the crew's work history to predict their performance in different work scenarios and suggest suitable tasks. The behavior analysis unit, for example, builds a system that predicts their performance in different work scenarios based on the crew's work history. For example, it evaluates their aptitude for customer service work and back office work. The behavior analysis unit also analyzes the crew's work history and predicts their performance in different work scenarios. For example, it evaluates their aptitude for sales work and customer support work. The behavior analysis unit also predicts their performance in different work scenarios based on the crew's work history. For example, it identifies tasks that can make use of the crew's strengths. This makes it possible to maximize the crew's performance by predicting their performance in different work scenarios based on their work history and suggesting suitable tasks.

[0095] The behavior analysis unit uses the emotion estimation function to monitor emotional responses based on the crew's work history, thereby reducing stress during work. The behavior analysis unit, for example, uses the emotion estimation function to monitor emotional responses based on the crew's work history in real time, thereby building a system to reduce stress during work. For example, when stress increases, the behavior analysis unit suggests relaxation methods. The behavior analysis unit also monitors the crew's emotional responses in real time, thereby reducing stress during work. For example, it sends positive feedback or encouraging messages. The behavior analysis unit also monitors the crew's emotional responses based on the work history based on the emotion estimation data, thereby reducing stress during work. For example, it suggests taking a break when stress increases. In this way, the emotion estimation function can be used to monitor emotional responses based on the work history and reduce stress during work, thereby reducing the crew's stress.

[0096] The behavior analysis unit can analyze crew behavior data and develop algorithms for early detection of signs of fraudulent activity. The behavior analysis unit, for example, analyzes crew behavior data and builds a system that develops algorithms for early detection of signs of fraudulent activity. For example, it detects abnormal sales patterns. The behavior analysis unit also develops algorithms for early detection of signs of fraudulent activity based on crew behavior data. For example, it analyzes trends in overselling. The behavior analysis unit also analyzes crew behavior data and develops algorithms for early detection of signs of fraudulent activity. For example, it detects abnormal customer response patterns. In this way, by analyzing behavior data and developing algorithms for early detection of signs of fraudulent activity, it becomes possible to prevent fraudulent activity from occurring.

[0097] The behavior analysis unit can assess the risk of fraudulent activity based on the behavioral data of crew members and propose special training for high-risk crew members. The behavior analysis unit, for example, builds a system that assesses the risk of fraudulent activity based on the behavioral data of crew members and proposes special training for high-risk crew members. For example, training is provided for crew members who are at high risk of overselling. The behavior analysis unit also analyzes the behavioral data of crew members and assesses the risk of fraudulent activity. For example, the risk is assessed based on abnormal sales patterns. The behavior analysis unit also assesses the risk of fraudulent activity based on the behavioral data of crew members and proposes special training for high-risk crew members. For example, training is provided for crew members who are at high risk of overselling. In this way, by assessing the risk of fraudulent activity based on behavioral data and proposing special training for high-risk crew members, it is possible to prevent fraudulent activity from occurring.

[0098] The behavior analysis unit can use the emotion estimation function to analyze the emotional state of the crew and assess the risk that stress or dissatisfaction will lead to misconduct. The behavior analysis unit, for example, uses the emotion estimation function to analyze the emotional state of the crew and build a system that assesses the risk that stress or dissatisfaction will lead to misconduct. For example, the behavior analysis unit performs a risk assessment when stress increases. The behavior analysis unit also analyzes the emotional state of the crew and assesses the risk that stress or dissatisfaction will lead to misconduct. For example, the behavior analysis unit performs a risk assessment based on emotion data. The behavior analysis unit also analyzes the emotional state of the crew based on emotion estimation data and assesses the risk that stress or dissatisfaction will lead to misconduct. For example, the behavior analysis unit performs a risk assessment when stress increases. In this way, by using the emotion estimation function to analyze the emotional state and assessing the risk that stress or dissatisfaction will lead to misconduct, it is possible to prevent misconduct from occurring.

[0099] The behavior analysis unit can apply the algorithm for detecting signs of fraud to other businesses and industries. For example, the behavior analysis unit builds a system that applies the algorithm for detecting signs of fraud to other businesses and industries. For example, it applies it to detecting fraud in the financial or medical industry. The behavior analysis unit also applies the algorithm for detecting signs of fraud to different businesses and industries. For example, it applies it to detecting fraud in the manufacturing or service industry. The behavior analysis unit also applies the algorithm for detecting signs of fraud to other businesses and industries. For example, it analyzes fraud patterns in different industries and reflects this in the algorithm. This allows the algorithm for detecting signs of fraud to be applied to other businesses and industries, enabling a wide range of applications.

[0100] The behavior analysis unit can use the fraud risk assessment to compare and analyze different stores and regions, and identify high-risk areas. The behavior analysis unit, for example, uses the fraud risk assessment to compare and analyze different stores and regions, and builds a system to identify high-risk areas. For example, it identifies stores with a high risk of overselling. The behavior analysis unit also performs comparative analyses of different stores and regions based on the fraud risk assessment. For example, it performs risk assessment based on abnormal sales patterns. The behavior analysis unit also uses the fraud risk assessment to compare and analyze different stores and regions, and identify high-risk areas. For example, it identifies areas with a high risk of overselling. In this way, by using the fraud risk assessment to compare and analyze different stores and regions, it is possible to identify high-risk areas and take measures.

[0101] The behavior analysis unit can use the emotion estimation function to monitor the emotional state of the crew in real time and continuously evaluate the risk of misconduct. The behavior analysis unit, for example, uses the emotion estimation function to monitor the emotional state of the crew in real time and build a system that continuously evaluates the risk of misconduct. For example, a risk assessment is performed when stress increases. The behavior analysis unit also monitors the emotional state of the crew in real time and continuously evaluates the risk of misconduct. For example, a risk assessment is performed based on emotion data. The behavior analysis unit also monitors the emotional state of the crew in real time based on emotion estimation data and continuously evaluates the risk of misconduct. For example, a risk assessment is performed when stress increases. In this way, by using the emotion estimation function to monitor the emotional state in real time and continuously evaluate the risk of misconduct, it is possible to prevent misconduct before it occurs.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The behavioral analysis unit can analyze the behavioral data of crew members and evaluate their energy consumption during work. For example, it calculates energy consumption based on the distance traveled and the working time of the crew members. The behavioral analysis unit can also evaluate energy consumption according to the type of work the crew members do. For example, it can compare the energy consumption of work such as carrying heavy objects with that of desk work. The behavioral analysis unit can also suggest appropriate break times based on the crew members' energy consumption. This makes it possible to understand the crew members' energy consumption and suggest appropriate breaks, thereby improving work efficiency.

[0104] The Skill Evaluation Department can incorporate crew self-assessment data when assessing crew skill levels. For example, crew members fill out self-assessment sheets and reflect that data in skill evaluations. The Skill Evaluation Department can also compare crew self-assessments with actual work performance to evaluate the accuracy of self-assessments. For example, it can verify the actual performance of crew members with high self-assessments. The Skill Evaluation Department can also identify gaps between self-assessments and actual skill levels based on the crew self-assessment data and reflect these in training proposals. In this way, incorporating crew self-assessments enables more accurate skill evaluations.

[0105] The training suggestion unit can analyze the crew's health condition and suggest training content to maintain their health. For example, it can suggest an exercise program to maintain their health based on the crew's health check data. The training suggestion unit can also monitor the crew's health condition and suggest training content to reduce health risks. For example, it can provide a stretching program to reduce health risks caused by long hours of desk work. The training suggestion unit can also suggest individual health maintenance plans based on the crew's health condition. This makes it possible to suggest training that takes the crew's health condition into consideration.

[0106] The behavior analysis unit can evaluate safety during work based on the behavioral data of the crew. For example, it can analyze the crew's work environment and work procedures to identify safety risks. The behavior analysis unit can also make suggestions for improving safety based on the behavioral data of the crew. For example, it can make suggestions for improving work procedures that pose a high safety risk. The behavior analysis unit can also suggest training content for improving safety based on the behavioral data of the crew. This makes it possible to evaluate safety during work based on the behavioral data of the crew and make suggestions for improving safety.

[0107] The training suggestion unit can analyze the emotional state of the crew and provide feedback based on the emotions. For example, it can analyze the emotional state of the crew and provide feedback to elicit positive emotions. The training suggestion unit can also suggest training content that corresponds to the emotions based on the emotional state of the crew. For example, it can suggest relaxation methods when stress levels rise. The training suggestion unit can also analyze the emotional state of the crew and provide feedback to improve motivation based on emotions. This makes it possible to provide feedback and training suggestions that take the emotional state of the crew into consideration.

[0108] The behavior analysis unit can analyze communication patterns during work based on the behavioral data of crew members. For example, it can analyze the frequency and content of communication between crew members and evaluate the quality of communication. The behavior analysis unit can also make suggestions for improving communication based on the crew's communication patterns. For example, it can suggest training to improve communication skills for crew members who lack communication skills. The behavior analysis unit can also make suggestions for improving teamwork based on the crew's communication patterns. This makes it possible to analyze the crew's communication patterns and make suggestions for improving communication.

[0109] The skill evaluation unit can incorporate the crew's learning history when evaluating the crew's skill level. For example, it can evaluate skills based on the training courses and learning content that the crew has taken in the past. The skill evaluation unit can also compare the crew's learning history with their actual work performance to evaluate the learning effectiveness. For example, it can verify the work performance of crew members who have taken a specific training course. The skill evaluation unit can also make training suggestions to maximize the learning effectiveness based on the crew's learning history. Incorporating the crew's learning history enables more accurate skill evaluation and training suggestions.

[0110] The training suggestion unit can analyze the emotional state of the crew and suggest training content based on the emotions. For example, it can analyze the emotional state of the crew and suggest training content for stress reduction. The training suggestion unit can also suggest training content for improving motivation based on the emotional state of the crew. For example, it can provide training to elicit positive emotions. The training suggestion unit can also analyze the emotional state of the crew and provide feedback based on the emotions. This makes it possible to provide training content and feedback that takes the emotional state of the crew into consideration.

[0111] The behavior analysis unit can evaluate the fatigue level during work based on the behavioral data of the crew. For example, it calculates the fatigue level based on the crew's working time and work content. The behavior analysis unit can also suggest appropriate break times based on the crew's fatigue level. For example, it can suggest a break after working for a long period of time. The behavior analysis unit can also suggest training content to reduce fatigue based on the crew's fatigue level. This makes it possible to understand the crew's fatigue level and suggest appropriate breaks and training, thereby improving work efficiency.

[0112] The training suggestion unit can analyze the emotional state of the crew and suggest training content based on the emotions. For example, it can analyze the emotional state of the crew and suggest training content for stress reduction. The training suggestion unit can also suggest training content for improving motivation based on the emotional state of the crew. For example, it can provide training to elicit positive emotions. The training suggestion unit can also analyze the emotional state of the crew and provide feedback based on the emotions. This makes it possible to provide training content and feedback that takes the emotional state of the crew into consideration.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The behavior analysis unit analyzes the behavioral data of the crew. For example, the behavior analysis unit analyzes the crew's work log and evaluates the efficiency of the work. The behavior analysis unit can also analyze the crew's movement history and optimize operations. The behavior analysis unit can also analyze the crew's operation history and evaluate the accuracy of the operations. Step 2: The skill evaluation unit evaluates the skill level based on the crew's behavioral data analyzed by the behavior analysis unit. For example, the skill evaluation unit evaluates the accuracy of the work. The skill evaluation unit can also evaluate the efficiency of the work. The skill evaluation unit can also evaluate the speed of the work. Step 3: The training suggestion unit suggests individual training courses based on the crew's skill level assessed by the skill assessment unit. For example, the training suggestion unit suggests online courses. The training suggestion unit can also suggest on-the-job training. The training suggestion unit can also suggest simulations.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

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

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

[0135] The data processing device 12 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

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

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0182] 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 behavior analysis unit that analyzes the behavior data of the crew; a skill evaluation unit that evaluates a skill level of the crew member based on the behavioral data analyzed by the behavior analysis unit; a training suggestion unit that suggests an individual training course based on the skill level of the crew evaluated by the skill evaluation unit. A system characterized by:

2. The behavior analysis unit Analyzing minute behavioral patterns based on the crew's behavioral data and evaluating the quality of their behavior 2. The system of claim 1.

3. The skill evaluation unit Analyze the correlation between the crew's past training history and their actual work performance 2. The system of claim 1.

4. The training suggestion unit Analyzing the crew's emotional state to understand fluctuations in stress or motivation 2. The system of claim 1.

5. The behavior analysis unit The crew's behavioral data is compared with that of other crews to assess their relative skill levels.

2. The system of claim 1.

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