system

Abstract

The system according to this embodiment aims to provide efficient and systematic soft skills training. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a monitoring unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a training program based on the data analyzed by the analysis unit. The provision unit provides training based on the training program generated by the generation unit. The monitoring unit monitors the effectiveness of the training provided by the provision unit in real time.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that soft skill training is costly and time-consuming, and it is difficult to effectively evaluate and maintain.

[0005] The system according to the embodiment aims to provide efficient and systematic soft skill training.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, a provision unit, and a monitoring unit. The data collection unit collects user behavior data. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates a training program based on the data analyzed by the analysis unit. The provision unit provides training based on the training program generated by the generation unit. The monitoring unit monitors the effectiveness of the training provided by the provision unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can provide efficient and systematic soft skills training. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An interactive game-based training platform according to an embodiment of the present invention is a system for efficiently and systematically improving employees' soft skills. This system improves employees' interpersonal and behavioral abilities, contributing to professional success and the long-term prosperity of the company. Companies currently face significant challenges in soft skill development. These challenges include talent shortages, poor decision-making, difficulty adapting to change, communication gaps, inadequate stress management, and declining social skills (especially among Generation Z). Furthermore, current soft skills training methods and services are costly, time-consuming, and often disrupt normal operations. They also provide subjective skill assessments, raising concerns about cost-effectiveness uncertainty and skill retention. To address these issues, the interactive game-based training platform provides soft skills training that combines neuroscience, biofeedback, gamification, and generative AI. This system aims to develop a "booster," a gamified neuroscience experiment designed to provide efficient and systematic soft skills training to companies undergoing transformation. Generative AI supports the gamification process, defining game goals, desirable behaviors, interactions, rewards, and feedback mechanisms. An interactive, game-based training platform provides evaluation and metrics for users to track their performance. Specifically, once a user begins training, a generative AI collects and analyzes the user's behavioral data. Next, based on the analysis results, it generates an optimal training program for the user. This training program is presented in a game format, allowing users to improve their soft skills while having fun. Furthermore, biofeedback is used to monitor the user's stress levels and concentration in real time, maximizing the effectiveness of the training. In this way, the interactive, game-based training platform solves the soft skills training challenges faced by companies, supporting employee skill development and the long-term success of the company.This allows interactive, game-based training platforms to efficiently and systematically improve employees' soft skills.

[0029] The interactive game-based training platform according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a monitoring unit. The collection unit collects user behavior data. User behavior data includes, but is not limited to, website browsing history, app usage, and fitness data. For example, the collection unit collects the user's website browsing history to understand the user's interests and preferences. The collection unit can also collect app usage and analyze the user's behavior patterns. Furthermore, the collection unit can collect fitness data to understand the user's health status. For example, the collection unit collects data from the user's smartphone or wearable device and centrally manages the user's behavior data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and understand the user's behavior patterns and trends. For example, the analysis unit uses AI to cluster the user's behavior data and classify the user's interests and preferences. The analysis unit can also use AI to predict user behavior data and predict future behavior. Furthermore, the analysis unit can use AI to visualize user behavior data and understand data trends. For example, the analysis unit can display user behavior data as graphs and charts to visually understand data trends. The generation unit generates training programs based on the data analyzed by the analysis unit. For example, the generation unit generates training programs using generation AI and provides the user with the optimal training program. For example, the generation unit customizes training programs based on user behavior data using generation AI. The generation unit can also adjust the difficulty level of training programs based on user behavior data using generation AI. Furthermore, the generation unit can optimize the content of training programs based on user behavior data using generation AI. For example, the generation unit automatically adjusts the content of training programs based on user behavior data and provides the user with the optimal training program. The provision unit provides training based on the training programs generated by the generation unit.The service provider, for example, uses AI to provide training programs, enabling users to receive training. The service provider, for example, uses AI to provide training programs in real time. Furthermore, the service provider can use AI to monitor the progress of training programs and provide feedback to users. In addition, the service provider can use AI to evaluate the effectiveness of training programs and provide users with the most suitable training programs. For example, the service provider monitors the progress of users' training programs in real time and provides feedback to users. The monitoring unit monitors the effectiveness of the training provided by the service provider in real time. The monitoring unit, for example, uses AI to monitor the effectiveness of training and understand the progress of users' training. The monitoring unit, for example, uses AI to evaluate the effectiveness of users' training and maximize its effectiveness. Furthermore, the monitoring unit can use AI to monitor the progress of users' training in real time and provide feedback to users. In addition, the monitoring unit can use AI to analyze the effectiveness of users' training and optimize its effectiveness. For example, the monitoring unit monitors the progress of users' training in real time and provides feedback to users. This enables the interactive game-based training platform according to the embodiment to efficiently collect, analyze, generate, provide, and monitor user behavior data, as well as training programs.

[0030] The data collection unit collects user behavior data. This data includes, but is not limited to, website browsing history, app usage, and fitness data. For example, the unit collects users' website browsing history to understand their interests. It can also collect app usage data to analyze user behavior patterns. Furthermore, it can collect fitness data to understand users' health status. For instance, the unit collects data from users' smartphones and wearable devices, centrally managing user behavior data. The unit collects this data in real time and transmits it to a central database. Website browsing history includes detailed information such as pages visited, time spent on each page, and links clicked. App usage includes the number of times an app is launched, usage time, and frequency of use of specific features. Fitness data includes steps, heart rate, calories burned, type and duration of exercise, etc. This data is crucial for gaining a detailed understanding of user behavior and health. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and understand user behavior patterns and trends. For example, the analysis unit uses AI to cluster user behavior data and classify user interests and concerns. The analysis unit can also use AI to predict user behavior data and forecast future behavior. Furthermore, the analysis unit can use AI to visualize user behavior data and understand data trends. For example, the analysis unit displays user behavior data as graphs and charts to visually understand data trends. The analysis unit uses AI machine learning algorithms to analyze user behavior data. For example, it uses clustering algorithms to group user behavior data and identify users with common interests and concerns. It uses prediction algorithms to predict future user behavior and provide information to maximize the effectiveness of training programs. It uses visualization algorithms to visually display user behavior data and understand data trends and patterns. As a result, the analysis unit can quickly and accurately analyze the collected data and understand user behavior patterns and trends. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict changes in behavior for specific user groups or time periods based on past behavioral data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The generation unit generates a training program based on the data analyzed by the analysis unit. The generation unit, for example, generates a training program using a generation AI and provides the user with the optimal training program. The generation unit, for example, customizes the training program based on user behavior data using a generation AI. Furthermore, the generation unit can adjust the difficulty level of the training program based on user behavior data using a generation AI. In addition, the generation unit can optimize the content of the training program based on user behavior data using a generation AI. For example, the generation unit automatically adjusts the content of the training program based on user behavior data and provides the user with the optimal training program. The generation unit analyzes user behavior data using the natural language processing technology of the generation AI and generates the content of the training program. For example, it customizes the theme and content of the training program based on the user's interests. It adjusts the difficulty level and pace of the training program based on the user's behavior patterns. It optimizes the content of the training program based on the user's health status. Based on this information, the generation unit provides the user with the optimal training program. The generation unit analyzes user behavior data and generates the content of the training program using the machine learning algorithm of the generation AI. For example, the system automatically adjusts the training program content based on user behavior data to provide the user with the optimal training program. The generation unit uses the natural language processing technology of the generation AI to analyze user behavior data and generate the training program content. For example, it customizes the theme and content of the training program based on the user's interests and preferences. It adjusts the difficulty level and pace of the training program based on the user's behavior patterns. It optimizes the training program content based on the user's health status. Based on this information, the generation unit provides the user with the optimal training program.

[0033] The service provider provides training based on the training program generated by the generation unit. The service provider, for example, uses AI to provide the training program, enabling the user to receive training. The service provider, for example, uses AI to provide the training program in real time. The service provider can also use AI to monitor the progress of the training program and provide feedback to the user. Furthermore, the service provider can use AI to evaluate the effectiveness of the training program and provide the user with the optimal training program. For example, the service provider monitors the user's training program progress in real time and provides feedback to the user. The service provider uses AI natural language processing technology to provide the user with the training program. For example, it automatically adjusts the content of the training program based on the user's behavioral data to provide the user with the optimal training program. The service provider uses AI machine learning algorithms to monitor the user's training program progress and provide feedback to the user. For example, it monitors the user's training program progress in real time and provides feedback to the user. The service provider uses AI natural language processing technology to provide the user with the training program. For example, it automatically adjusts the content of the training program based on the user's behavioral data to provide the user with the optimal training program. The service provider uses AI machine learning algorithms to monitor the progress of the user's training program and provide feedback to the user. For example, it monitors the progress of the user's training program in real time and provides feedback to the user.

[0034] The monitoring unit monitors the effectiveness of the training provided by the service provider in real time. The monitoring unit, for example, uses AI to monitor the effectiveness of the training and understand the user's training progress. The monitoring unit, for example, uses AI to evaluate the effectiveness of the user's training and maximize its effectiveness. Furthermore, the monitoring unit can also use AI to monitor the user's training progress in real time and provide feedback to the user. In addition, the monitoring unit can use AI to analyze the effectiveness of the user's training and optimize its effectiveness. For example, the monitoring unit monitors the user's training progress in real time and provides feedback to the user. The monitoring unit uses AI machine learning algorithms to monitor the user's training progress in real time and provide feedback to the user. For example, it monitors the progress of the user's training program in real time and provides feedback to the user. The monitoring unit uses AI natural language processing technology to evaluate the effectiveness of the user's training and maximize its effectiveness. For example, it monitors the progress of the user's training program in real time and provides feedback to the user. The monitoring unit uses AI machine learning algorithms to monitor the user's training progress in real time and provide feedback to the user. For example, it can monitor the progress of a user's training program in real time and provide feedback to the user.

[0035] The generation unit includes a definition unit that defines the game's goals, desired behaviors, interactions, rewards, and feedback mechanisms. For example, the generation unit can use generative AI to define the game's goals and provide users with objectives to achieve. It can also use generative AI to set game objectives and provide users with scores to achieve and stages to clear. Furthermore, the generation unit can use generative AI to define desired behaviors and provide users with specific operations and behavioral patterns. Additionally, the generation unit can use generative AI to define interactions and provide users with opportunities for cooperation or competition with other users. For example, it can use generative AI to encourage cooperation or competition with other users. The generation unit can also use generative AI to define rewards and provide users with points, badges, or perks. For example, it can use generative AI to provide users with points, badges, or perks to increase their motivation. Finally, the generation unit can use generative AI to define feedback mechanisms and provide users with real-time feedback and periodic evaluations. For example, it can use generative AI to provide users with real-time feedback and periodic evaluations to maximize the user's training effectiveness. This allows the generator to maximize the effectiveness of the training program by defining the game's goals, desired behaviors, interactions, rewards, and feedback mechanisms.

[0036] The evaluation unit assesses user performance. For example, the evaluation unit uses AI to evaluate user performance and measure the effectiveness of training. For example, the evaluation unit uses AI to evaluate user scores, achievement levels, and efficiency. Furthermore, the evaluation unit can use AI to assess user performance in real time and understand training progress. In addition, the evaluation unit can use AI to periodically evaluate user performance and maximize training effectiveness. For example, the evaluation unit evaluates training effectiveness based on user scores, achievement levels, and efficiency. This allows the evaluation unit to measure training effectiveness by evaluating user performance.

[0037] The adjustment unit makes adjustments to maximize the effectiveness of training based on the user's condition monitored by the monitoring unit. For example, the adjustment unit uses AI to analyze the user's condition and make adjustments to maximize the effectiveness of training. For example, the adjustment unit uses AI to analyze the user's heart rate, stress level, and concentration level and make adjustments to maximize the effectiveness of training. The adjustment unit can also use AI to adjust the content and difficulty of training based on the user's condition. Furthermore, the adjustment unit can use AI to adjust the progress of training based on the user's condition. For example, the adjustment unit adjusts the content and difficulty of training based on the user's heart rate, stress level, and concentration level. In this way, the adjustment unit improves the effectiveness of training by making adjustments to maximize the effectiveness of training based on the user's condition.

[0038] The data collection unit analyzes the user's past behavior history and selects the optimal data collection method. For example, the data collection unit uses AI to analyze the user's past behavior history and selects the optimal data collection method. For example, the data collection unit optimizes the timing of data collection based on actions the user frequently performed in the past. For example, the data collection unit analyzes the user's past behavior patterns and selects the most effective data collection method. For example, the data collection unit selects a method for collecting data at specific time periods based on the user's past behavior history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past behavior history.

[0039] The data collection unit filters the data based on the user's current work situation and areas of interest when collecting behavioral data. For example, the data collection unit uses AI to understand the user's current work situation and areas of interest and filters the data. For example, if a user is focused on a specific project, the data collection unit prioritizes collecting data related to that project. For example, the data collection unit filters and collects relevant data based on the user's areas of interest. For example, the data collection unit collects only the necessary data according to the user's work situation. In this way, the data collection unit can collect only the necessary data by filtering the data based on the user's current work situation and areas of interest.

[0040] The data collection unit prioritizes collecting highly relevant data by considering the user's geographical location when collecting behavioral data. For example, the data collection unit uses AI to consider the user's geographical location and prioritizes collecting highly relevant data. For example, if the user is in a specific location, the data collection unit prioritizes collecting data related to that location. For example, the data collection unit selects the optimal data collection method based on the user's geographical location. For example, if the user is on the move, the data collection unit prioritizes collecting data related to their destination. In this way, the data collection unit can prioritize collecting highly relevant data by considering the user's geographical location.

[0041] The data collection unit analyzes users' social media activity and collects relevant data when collecting behavioral data. For example, the data collection unit uses AI to analyze users' social media activity and collect relevant data. For example, the data collection unit analyzes users' social media activity and collects data related to their interests. For example, the data collection unit selects the optimal data collection method based on users' statements and actions on social media. For example, the data collection unit prioritizes collecting data related to specific topics from users' social media activity. This allows the data collection unit to collect relevant data by analyzing users' social media activity.

[0042] The analysis unit adjusts the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit uses AI to evaluate the importance of the behavioral data and adjusts the level of detail of the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. In this way, the analysis unit can perform efficient data analysis by adjusting the level of detail of the analysis based on the importance of the behavioral data.

[0043] The analysis unit applies different analysis algorithms depending on the category of the behavioral data during analysis. For example, the analysis unit uses AI to classify the categories of behavioral data and applies different analysis algorithms. For example, the analysis unit applies a stress analysis algorithm to data related to stress management. For example, the analysis unit applies a learning analysis algorithm to data related to learning and growth. For example, the analysis unit applies a work efficiency analysis algorithm to data related to work efficiency. In this way, the accuracy of data analysis is improved by the analysis unit applying different analysis algorithms depending on the category of the behavioral data.

[0044] The analysis unit determines the priority of analysis based on the timing of behavioral data collection. For example, the analysis unit uses AI to evaluate the timing of behavioral data collection and determines the priority of analysis. For example, the analysis unit prioritizes the analysis of recently collected data. For example, the analysis unit analyzes current data while referring to past data. For example, the analysis unit determines the priority of analysis according to the timing of data collection. In this way, the analysis unit can prioritize the analysis of the latest data by determining the priority of analysis based on the timing of behavioral data collection.

[0045] The analysis unit adjusts the order of analysis based on the relevance of the behavioral data during the analysis process. For example, the analysis unit uses AI to evaluate the relevance of the behavioral data and adjusts the order of analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. For example, the analysis unit postpones the analysis of less relevant data. For example, the analysis unit adjusts the order of analysis according to the relevance of the data. This enables efficient data analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the behavioral data.

[0046] The generation unit generates the optimal training program by referring to the user's past training history. For example, the generation unit uses AI to analyze the user's past training history and generate the optimal program. For example, the generation unit generates the optimal program based on the user's past training. For example, the generation unit selects an effective program from the user's past training history. For example, the generation unit analyzes the user's past training history and generates the most effective program. In this way, the generation unit can generate the optimal training program by referring to the user's past training history.

[0047] The generation unit adjusts the difficulty of the training program based on the user's current skill level when generating the training program. For example, the generation unit uses AI to evaluate the user's current skill level and adjusts the program difficulty. For example, the generation unit adjusts the program difficulty according to the user's skill level. For example, the generation unit generates a program with the optimal difficulty level based on the user's skill level. For example, the generation unit generates a program that gradually increases in difficulty according to the user's skill level. In this way, the generation unit can provide a training program of appropriate difficulty by adjusting the program difficulty based on the user's current skill level.

[0048] The generation unit generates the optimal training program by considering the user's geographical location information. For example, the generation unit uses AI to consider the user's geographical location information and generate the optimal program. For example, if the user is in a specific location, the generation unit provides a program relevant to that location. For example, the generation unit generates the optimal program based on the user's geographical location information. For example, if the user is on the move, the generation unit provides a program relevant to their destination. In this way, the generation unit can provide the optimal training program by considering the user's geographical location information.

[0049] The generation unit analyzes the user's social media activity and generates relevant programs when generating training programs. For example, the generation unit uses AI to analyze the user's social media activity and generates relevant programs. For example, the generation unit analyzes the user's social media activity and generates programs related to their interests. For example, the generation unit generates the optimal program based on the user's social media posts and actions. For example, the generation unit provides programs related to specific topics based on the user's social media activity. In this way, the generation unit can provide relevant training programs by analyzing the user's social media activity.

[0050] The service provider selects the optimal delivery method by referring to the user's past training history when providing training. For example, the service provider may use AI to analyze the user's past training history and select the optimal delivery method. For example, the service provider may select the optimal delivery method based on the user's past training. For example, the service provider may select an effective delivery method from the user's past training history. For example, the service provider may analyze the user's past training history and select the most effective delivery method. This allows the service provider to select the optimal training delivery method by referring to the user's past training history.

[0051] The service provider adjusts the timing of training delivery based on the user's current work situation. For example, the service provider uses AI to evaluate the user's current work situation and adjust the timing accordingly. For example, if the user is busy, the service provider will provide training during breaks in their work. For example, the service provider will provide training at the optimal time according to the user's work situation. For example, the service provider will adjust the timing of training delivery considering the user's work situation. This allows the service provider to deliver training at the appropriate time by adjusting the timing based on the user's current work situation.

[0052] The service provider selects the optimal delivery method when providing training, taking into account the user's geographical location. For example, the service provider may use AI to consider the user's geographical location and select the optimal delivery method. For example, if the user is in a specific location, the service provider will provide training relevant to that location. For example, the service provider will select the optimal delivery method based on the user's geographical location. For example, if the user is on the move, the service provider will provide training relevant to their destination. In this way, the service provider can select the optimal training delivery method by taking into account the user's geographical location.

[0053] The service provider analyzes the user's social media activity and provides relevant training when delivering training. For example, the service provider uses AI to analyze the user's social media activity and provides relevant training. For example, the service provider analyzes the user's social media activity and provides training related to their interests. For example, the service provider provides optimal training based on the user's statements and actions on social media. For example, the service provider provides training related to specific topics based on the user's social media activity. In this way, the service provider can provide relevant training by analyzing the user's social media activity.

[0054] The monitoring unit selects the optimal monitoring method by referring to the user's past training history during monitoring. For example, the monitoring unit uses AI to analyze the user's past training history and selects the optimal monitoring method. For example, the monitoring unit selects the optimal monitoring method based on the user's past training. For example, the monitoring unit selects an effective monitoring method from the user's past training history. For example, the monitoring unit analyzes the user's past training history and selects the most effective monitoring method. This allows the monitoring unit to select the optimal monitoring method by referring to the user's past training history.

[0055] The monitoring unit adjusts the timing of monitoring based on the user's current work status. For example, the monitoring unit uses AI to evaluate the user's current work status and adjusts the timing of monitoring. For example, if the user is busy, the monitoring unit performs monitoring in between tasks. For example, the monitoring unit performs monitoring at the optimal time according to the user's work status. For example, the monitoring unit adjusts the timing of monitoring considering the user's work status. As a result, the monitoring unit can perform monitoring at the appropriate time by adjusting the timing based on the user's current work status.

[0056] The monitoring unit selects the optimal monitoring method by considering the user's geographical location information during monitoring. For example, the monitoring unit may use AI to consider the user's geographical location information and select the optimal monitoring method. For example, if the user is in a specific location, the monitoring unit will prioritize monitoring data related to that location. For example, the monitoring unit will select the optimal monitoring method based on the user's geographical location information. For example, if the user is on the move, the monitoring unit will prioritize monitoring data related to the destination. In this way, the monitoring unit can select the optimal monitoring method by considering the user's geographical location information.

[0057] The monitoring unit analyzes users' social media activity and performs relevant monitoring during the monitoring process. For example, the monitoring unit uses AI to analyze users' social media activity and performs relevant monitoring. For example, the monitoring unit analyzes users' social media activity and monitors data related to their interests. For example, the monitoring unit selects the optimal monitoring method based on users' statements and actions on social media. For example, the monitoring unit prioritizes monitoring data related to specific topics from users' social media activity. In this way, the monitoring unit can monitor relevant data by analyzing users' social media activity.

[0058] The definition unit makes optimal definitions of game goals and desired actions by referring to the user's past game history. For example, the definition unit uses AI to analyze the user's past game history and make optimal definitions. For example, the definition unit defines optimal goals and actions based on the games the user has played in the past. For example, the definition unit defines effective goals and actions from the user's past game history. For example, the definition unit analyzes the user's past game history and defines the most effective goals and actions. In this way, the definition unit can define optimal goals and actions by referring to the user's past game history.

[0059] The definition unit adjusts the difficulty of definitions based on the user's current skill level when defining game goals and desired actions. For example, the definition unit uses AI to evaluate the user's current skill level and adjusts the difficulty of definitions. For example, the definition unit adjusts the difficulty of goals and actions according to the user's skill level. For example, the definition unit defines goals and actions of optimal difficulty based on the user's skill level. For example, the definition unit defines goals and actions that gradually increase in difficulty according to the user's skill level. In this way, the definition unit can define goals and actions of appropriate difficulty by adjusting the difficulty of definitions based on the user's current skill level.

[0060] The definition unit considers the user's geographical location when defining game objectives and desired actions to make the optimal definition. For example, the definition unit uses AI to consider the user's geographical location to make the optimal definition. For example, if the user is in a specific location, the definition unit defines objectives and actions related to that location. For example, the definition unit defines optimal objectives and actions based on the user's geographical location. For example, if the user is on the move, the definition unit defines objectives and actions related to the destination. In this way, the definition unit can define optimal objectives and actions by considering the user's geographical location.

[0061] The definition unit analyzes users' social media activity to define game goals and desired behaviors. For example, the definition unit uses AI to analyze users' social media activity and make relevant definitions. For example, the definition unit analyzes users' social media activity and defines goals and behaviors related to their interests. For example, the definition unit defines optimal goals and behaviors based on users' social media posts and actions. For example, the definition unit defines goals and behaviors related to specific topics from users' social media activity. In this way, the definition unit can define relevant goals and behaviors by analyzing users' social media activity.

[0062] The evaluation unit selects the optimal evaluation method by referring to the user's past evaluation history during performance evaluation. For example, the evaluation unit uses AI to analyze the user's past evaluation history and selects the optimal evaluation method. For example, the evaluation unit selects the optimal evaluation method based on the user's past evaluations. For example, the evaluation unit selects an effective evaluation method from the user's past evaluation history. For example, the evaluation unit analyzes the user's past evaluation history and selects the most effective evaluation method. This allows the evaluation unit to select the optimal evaluation method by referring to the user's past evaluation history.

[0063] The evaluation unit adjusts the timing of performance evaluations based on the user's current work situation. For example, the evaluation unit uses AI to assess the user's current work situation and adjusts the evaluation timing accordingly. For example, if the user is busy, the evaluation unit conducts evaluations during breaks in their work. For example, the evaluation unit conducts evaluations at the optimal time according to the user's work situation. For example, the evaluation unit adjusts the timing of evaluations considering the user's work situation. This allows the evaluation unit to conduct evaluations at the appropriate time by adjusting the timing based on the user's current work situation.

[0064] The evaluation unit selects the optimal evaluation method when evaluating performance, taking into account the user's geographical location. For example, the evaluation unit uses AI to consider the user's geographical location and select the optimal evaluation method. For example, if the user is in a specific location, the evaluation unit performs evaluations related to that location. For example, the evaluation unit selects the optimal evaluation method based on the user's geographical location. For example, if the user is on the move, the evaluation unit performs evaluations related to their destination. In this way, the evaluation unit can select the optimal evaluation method by considering the user's geographical location.

[0065] The evaluation department analyzes users' social media activity and performs relevant evaluations during performance assessments. For example, the evaluation department uses AI to analyze users' social media activity and perform relevant evaluations. For example, the evaluation department analyzes users' social media activity and performs evaluations related to their interests and concerns. For example, the evaluation department selects the optimal evaluation method based on users' statements and actions on social media. For example, the evaluation department performs evaluations related to specific topics from users' social media activity. In this way, the evaluation department can perform relevant evaluations by analyzing users' social media activity.

[0066] The adjustment unit selects the optimal adjustment method by referring to the user's past training history during training adjustment. For example, the adjustment unit may use AI to analyze the user's past training history and select the optimal adjustment method. For example, the adjustment unit may select the optimal adjustment method based on the user's past training. For example, the adjustment unit may select an effective adjustment method from the user's past training history. For example, the adjustment unit may analyze the user's past training history and select the most effective adjustment method. In this way, the adjustment unit can select the optimal adjustment method by referring to the user's past training history.

[0067] The adjustment unit adjusts the timing of training adjustments based on the user's current work situation. The adjustment unit, for example, uses AI to evaluate the user's current work situation and adjusts the timing of adjustments. The adjustment unit, for example, adjusts training during breaks in the user's work if the user is busy. The adjustment unit, for example, adjusts training at the optimal timing according to the user's work situation. The adjustment unit, for example, adjusts the timing of training adjustments considering the user's work situation. In this way, the adjustment unit can adjust training at the appropriate time by adjusting the timing of adjustments based on the user's current work situation.

[0068] The adjustment unit selects the optimal adjustment method when adjusting training, taking into account the user's geographical location information. For example, the adjustment unit uses AI to consider the user's geographical location information and select the optimal adjustment method. For example, if the user is in a specific location, the adjustment unit adjusts the training related to that location. For example, the adjustment unit selects the optimal adjustment method based on the user's geographical location information. For example, if the user is on the move, the adjustment unit adjusts the training related to the destination. In this way, the adjustment unit can select the optimal adjustment method by taking into account the user's geographical location information.

[0069] The adjustment unit analyzes the user's social media activity and makes relevant adjustments during training adjustment. For example, the adjustment unit uses AI to analyze the user's social media activity and makes relevant adjustments. For example, the adjustment unit analyzes the user's social media activity and adjusts training related to their interests. For example, the adjustment unit provides optimally high-level training based on the user's social media posts and actions. For example, the adjustment unit analyzes the user's social media activity and adjusts training related to their interests. For example, the adjustment unit adjusts optimal training based on the user's social media posts and actions. For example, the adjustment unit adjusts training related to specific topics based on the user's social media activity. This allows the adjustment unit to adjust relevant training by analyzing the user's social media activity.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The generation unit can also generate the optimal training program by referring to the user's past training history. For example, it can generate the optimal program based on the user's past training. It can also select an effective program from the user's past training history. Furthermore, it can analyze the user's past training history and generate the most effective program. In this way, the generation unit can generate the optimal training program by referring to the user's past training history.

[0072] The analysis unit can also adjust the level of detail of the analysis based on the importance of the behavioral data. For example, it can perform detailed analysis on high-importance data and simplified analysis on low-importance data. Furthermore, it can determine the priority of the analysis according to the importance of the data. In this way, the analysis unit can perform efficient data analysis by adjusting the level of detail of the analysis based on the importance of the behavioral data.

[0073] The service provider can also adjust the timing of training delivery based on the user's current work situation. For example, if a user is busy, training can be provided during breaks in their work. Furthermore, training can be provided at the optimal time depending on the user's work situation. In addition, the service provider can adjust the timing of training delivery considering the user's work situation. This allows the service provider to deliver training at the appropriate time by adjusting the timing based on the user's current work situation.

[0074] The evaluation unit can also select the optimal evaluation method by referring to the user's past evaluation history. For example, it can select the optimal evaluation method based on the evaluations the user has received in the past. It can also select an effective evaluation method from the user's past evaluation history. Furthermore, it can analyze the user's past evaluation history and select the most effective evaluation method. In this way, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history.

[0075] The data collection unit can also analyze users' social media activity and collect relevant data. For example, it can analyze users' social media activity and collect data related to their interests. It can also select the optimal data collection method based on users' statements and actions on social media. Furthermore, it can prioritize the collection of data related to specific topics from users' social media activity. In this way, the data collection unit can collect relevant data by analyzing users' social media activity.

[0076] The following briefly describes the processing flow for example form 1.

[0077] Step 1: The data collection unit collects user behavior data. This data includes website browsing history, app usage, and fitness data. The data collection unit collects data from users' smartphones and wearable devices and centrally manages user behavior data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and understand user behavior patterns and trends. For example, it clusters user behavior data and classifies interests and concerns. It also predicts future behavior and visualizes data trends. Step 3: The generation unit generates a training program based on the data analyzed by the analysis unit. The generation unit uses a generation AI to generate a training program and provides the user with the optimal training program. For example, it automatically adjusts the content and difficulty level of the training program. Step 4: The delivery unit provides training based on the training program generated by the generation unit. The delivery unit uses AI to provide the training program in real time, enabling the user to receive training. It also monitors the progress of the training program and provides feedback to the user. Step 5: The monitoring unit monitors the effectiveness of the training provided by the delivery unit in real time. The monitoring unit uses AI to evaluate the effectiveness of the training and understands the user's training progress. For example, it provides feedback to maximize the effectiveness of the training.

[0078] (Example of form 2) An interactive game-based training platform according to an embodiment of the present invention is a system for efficiently and systematically improving employees' soft skills. This system improves employees' interpersonal and behavioral abilities, contributing to professional success and the long-term prosperity of the company. Companies currently face significant challenges in soft skill development. These challenges include talent shortages, poor decision-making, difficulty adapting to change, communication gaps, inadequate stress management, and declining social skills (especially among Generation Z). Furthermore, current soft skills training methods and services are costly, time-consuming, and often disrupt normal operations. They also provide subjective skill assessments, raising concerns about cost-effectiveness uncertainty and skill retention. To address these issues, the interactive game-based training platform provides soft skills training that combines neuroscience, biofeedback, gamification, and generative AI. This system aims to develop a "booster," a gamified neuroscience experiment designed to provide efficient and systematic soft skills training to companies undergoing transformation. Generative AI supports the gamification process, defining game goals, desirable behaviors, interactions, rewards, and feedback mechanisms. An interactive, game-based training platform provides evaluation and metrics for users to track their performance. Specifically, once a user begins training, a generative AI collects and analyzes the user's behavioral data. Next, based on the analysis results, it generates an optimal training program for the user. This training program is presented in a game format, allowing users to improve their soft skills while having fun. Furthermore, biofeedback is used to monitor the user's stress levels and concentration in real time, maximizing the effectiveness of the training. In this way, the interactive, game-based training platform solves the soft skills training challenges faced by companies, supporting employee skill development and the long-term success of the company.This allows interactive, game-based training platforms to efficiently and systematically improve employees' soft skills.

[0079] The interactive game-based training platform according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a monitoring unit. The collection unit collects user behavior data. User behavior data includes, but is not limited to, website browsing history, app usage, and fitness data. For example, the collection unit collects the user's website browsing history to understand the user's interests and preferences. The collection unit can also collect app usage and analyze the user's behavior patterns. Furthermore, the collection unit can collect fitness data to understand the user's health status. For example, the collection unit collects data from the user's smartphone or wearable device and centrally manages the user's behavior data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and understand the user's behavior patterns and trends. For example, the analysis unit uses AI to cluster the user's behavior data and classify the user's interests and preferences. The analysis unit can also use AI to predict user behavior data and predict future behavior. Furthermore, the analysis unit can use AI to visualize user behavior data and understand data trends. For example, the analysis unit can display user behavior data as graphs and charts to visually understand data trends. The generation unit generates training programs based on the data analyzed by the analysis unit. For example, the generation unit generates training programs using generation AI and provides the user with the optimal training program. For example, the generation unit customizes training programs based on user behavior data using generation AI. The generation unit can also adjust the difficulty level of training programs based on user behavior data using generation AI. Furthermore, the generation unit can optimize the content of training programs based on user behavior data using generation AI. For example, the generation unit automatically adjusts the content of training programs based on user behavior data and provides the user with the optimal training program. The provision unit provides training based on the training programs generated by the generation unit.The service provider, for example, uses AI to provide training programs, enabling users to receive training. The service provider, for example, uses AI to provide training programs in real time. Furthermore, the service provider can use AI to monitor the progress of training programs and provide feedback to users. In addition, the service provider can use AI to evaluate the effectiveness of training programs and provide users with the most suitable training programs. For example, the service provider monitors the progress of users' training programs in real time and provides feedback to users. The monitoring unit monitors the effectiveness of the training provided by the service provider in real time. The monitoring unit, for example, uses AI to monitor the effectiveness of training and understand the progress of users' training. The monitoring unit, for example, uses AI to evaluate the effectiveness of users' training and maximize its effectiveness. Furthermore, the monitoring unit can use AI to monitor the progress of users' training in real time and provide feedback to users. In addition, the monitoring unit can use AI to analyze the effectiveness of users' training and optimize its effectiveness. For example, the monitoring unit monitors the progress of users' training in real time and provides feedback to users. This enables the interactive game-based training platform according to the embodiment to efficiently collect, analyze, generate, provide, and monitor user behavior data, as well as training programs.

[0080] The data collection unit collects user behavior data. This data includes, but is not limited to, website browsing history, app usage, and fitness data. For example, the unit collects users' website browsing history to understand their interests. It can also collect app usage data to analyze user behavior patterns. Furthermore, it can collect fitness data to understand users' health status. For instance, the unit collects data from users' smartphones and wearable devices, centrally managing user behavior data. The unit collects this data in real time and transmits it to a central database. Website browsing history includes detailed information such as pages visited, time spent on each page, and links clicked. App usage includes the number of times an app is launched, usage time, and frequency of use of specific features. Fitness data includes steps, heart rate, calories burned, type and duration of exercise, etc. This data is crucial for gaining a detailed understanding of user behavior and health. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0081] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and understand user behavior patterns and trends. For example, the analysis unit uses AI to cluster user behavior data and classify user interests and concerns. The analysis unit can also use AI to predict user behavior data and forecast future behavior. Furthermore, the analysis unit can use AI to visualize user behavior data and understand data trends. For example, the analysis unit displays user behavior data as graphs and charts to visually understand data trends. The analysis unit uses AI machine learning algorithms to analyze user behavior data. For example, it uses clustering algorithms to group user behavior data and identify users with common interests and concerns. It uses prediction algorithms to predict future user behavior and provide information to maximize the effectiveness of training programs. It uses visualization algorithms to visually display user behavior data and understand data trends and patterns. As a result, the analysis unit can quickly and accurately analyze the collected data and understand user behavior patterns and trends. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict changes in behavior for specific user groups or time periods based on past behavioral data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0082] The generation unit generates a training program based on the data analyzed by the analysis unit. The generation unit, for example, generates a training program using a generation AI and provides the user with the optimal training program. The generation unit, for example, customizes the training program based on user behavior data using a generation AI. Furthermore, the generation unit can adjust the difficulty level of the training program based on user behavior data using a generation AI. In addition, the generation unit can optimize the content of the training program based on user behavior data using a generation AI. For example, the generation unit automatically adjusts the content of the training program based on user behavior data and provides the user with the optimal training program. The generation unit analyzes user behavior data using the natural language processing technology of the generation AI and generates the content of the training program. For example, it customizes the theme and content of the training program based on the user's interests. It adjusts the difficulty level and pace of the training program based on the user's behavior patterns. It optimizes the content of the training program based on the user's health status. Based on this information, the generation unit provides the user with the optimal training program. The generation unit analyzes user behavior data and generates the content of the training program using the machine learning algorithm of the generation AI. For example, the system automatically adjusts the training program content based on user behavior data to provide the user with the optimal training program. The generation unit uses the natural language processing technology of the generation AI to analyze user behavior data and generate the training program content. For example, it customizes the theme and content of the training program based on the user's interests and preferences. It adjusts the difficulty level and pace of the training program based on the user's behavior patterns. It optimizes the training program content based on the user's health status. Based on this information, the generation unit provides the user with the optimal training program.

[0083] The service provider provides training based on the training program generated by the generation unit. The service provider, for example, uses AI to provide the training program, enabling the user to receive training. The service provider, for example, uses AI to provide the training program in real time. The service provider can also use AI to monitor the progress of the training program and provide feedback to the user. Furthermore, the service provider can use AI to evaluate the effectiveness of the training program and provide the user with the optimal training program. For example, the service provider monitors the user's training program progress in real time and provides feedback to the user. The service provider uses AI natural language processing technology to provide the user with the training program. For example, it automatically adjusts the content of the training program based on the user's behavioral data to provide the user with the optimal training program. The service provider uses AI machine learning algorithms to monitor the user's training program progress and provide feedback to the user. For example, it monitors the user's training program progress in real time and provides feedback to the user. The service provider uses AI natural language processing technology to provide the user with the training program. For example, it automatically adjusts the content of the training program based on the user's behavioral data to provide the user with the optimal training program. The service provider uses AI machine learning algorithms to monitor the progress of the user's training program and provide feedback to the user. For example, it monitors the progress of the user's training program in real time and provides feedback to the user.

[0084] The monitoring unit monitors the effectiveness of the training provided by the service provider in real time. The monitoring unit, for example, uses AI to monitor the effectiveness of the training and understand the user's training progress. The monitoring unit, for example, uses AI to evaluate the effectiveness of the user's training and maximize its effectiveness. Furthermore, the monitoring unit can also use AI to monitor the user's training progress in real time and provide feedback to the user. In addition, the monitoring unit can use AI to analyze the effectiveness of the user's training and optimize its effectiveness. For example, the monitoring unit monitors the user's training progress in real time and provides feedback to the user. The monitoring unit uses AI machine learning algorithms to monitor the user's training progress in real time and provide feedback to the user. For example, it monitors the progress of the user's training program in real time and provides feedback to the user. The monitoring unit uses AI natural language processing technology to evaluate the effectiveness of the user's training and maximize its effectiveness. For example, it monitors the progress of the user's training program in real time and provides feedback to the user. The monitoring unit uses AI machine learning algorithms to monitor the user's training progress in real time and provide feedback to the user. For example, it can monitor the progress of a user's training program in real time and provide feedback to the user.

[0085] The generation unit includes a definition unit that defines the game's goals, desired behaviors, interactions, rewards, and feedback mechanisms. For example, the generation unit can use generative AI to define the game's goals and provide users with objectives to achieve. It can also use generative AI to set game objectives and provide users with scores to achieve and stages to clear. Furthermore, the generation unit can use generative AI to define desired behaviors and provide users with specific operations and behavioral patterns. Additionally, the generation unit can use generative AI to define interactions and provide users with opportunities for cooperation or competition with other users. For example, it can use generative AI to encourage cooperation or competition with other users. The generation unit can also use generative AI to define rewards and provide users with points, badges, or perks. For example, it can use generative AI to provide users with points, badges, or perks to increase their motivation. Finally, the generation unit can use generative AI to define feedback mechanisms and provide users with real-time feedback and periodic evaluations. For example, it can use generative AI to provide users with real-time feedback and periodic evaluations to maximize the user's training effectiveness. This allows the generator to maximize the effectiveness of the training program by defining the game's goals, desired behaviors, interactions, rewards, and feedback mechanisms.

[0086] The evaluation unit assesses user performance. For example, the evaluation unit uses AI to evaluate user performance and measure the effectiveness of training. For example, the evaluation unit uses AI to evaluate user scores, achievement levels, and efficiency. Furthermore, the evaluation unit can use AI to assess user performance in real time and understand training progress. In addition, the evaluation unit can use AI to periodically evaluate user performance and maximize training effectiveness. For example, the evaluation unit evaluates training effectiveness based on user scores, achievement levels, and efficiency. This allows the evaluation unit to measure training effectiveness by evaluating user performance.

[0087] The adjustment unit makes adjustments to maximize the effectiveness of training based on the user's condition monitored by the monitoring unit. For example, the adjustment unit uses AI to analyze the user's condition and make adjustments to maximize the effectiveness of training. For example, the adjustment unit uses AI to analyze the user's heart rate, stress level, and concentration level and make adjustments to maximize the effectiveness of training. The adjustment unit can also use AI to adjust the content and difficulty of training based on the user's condition. Furthermore, the adjustment unit can use AI to adjust the progress of training based on the user's condition. For example, the adjustment unit adjusts the content and difficulty of training based on the user's heart rate, stress level, and concentration level. In this way, the adjustment unit improves the effectiveness of training by making adjustments to maximize the effectiveness of training based on the user's condition.

[0088] The data collection unit estimates the user's emotions and adjusts the timing of behavioral data collection based on the estimated emotions. For example, the data collection unit estimates the user's emotions using an emotion engine or generative AI and adjusts the collection timing accordingly. For example, if the user is stressed, the data collection unit delays the collection timing to collect data when the user is relaxed. For example, if the user is focused, the data collection unit collects behavioral data at that time to obtain accurate data. For example, if the user is tired, the data collection unit adjusts the collection timing to collect data after rest. This allows the data collection unit to collect more accurate data by adjusting the timing of behavioral data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The data collection unit analyzes the user's past behavior history and selects the optimal data collection method. For example, the data collection unit uses AI to analyze the user's past behavior history and selects the optimal data collection method. For example, the data collection unit optimizes the timing of data collection based on actions the user frequently performed in the past. For example, the data collection unit analyzes the user's past behavior patterns and selects the most effective data collection method. For example, the data collection unit selects a method for collecting data at specific time periods based on the user's past behavior history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past behavior history.

[0090] The data collection unit filters the data based on the user's current work situation and areas of interest when collecting behavioral data. For example, the data collection unit uses AI to understand the user's current work situation and areas of interest and filters the data. For example, if a user is focused on a specific project, the data collection unit prioritizes collecting data related to that project. For example, the data collection unit filters and collects relevant data based on the user's areas of interest. For example, the data collection unit collects only the necessary data according to the user's work situation. In this way, the data collection unit can collect only the necessary data by filtering the data based on the user's current work situation and areas of interest.

[0091] The data collection unit estimates the user's emotions and determines the priority of behavioral data to collect based on the estimated emotions. For example, the data collection unit uses an emotion engine or generative AI to estimate the user's emotions and determine the priority of behavioral data to collect. For example, if the user is stressed, the data collection unit prioritizes collecting data related to stress management. For example, if the user is relaxed, the data collection unit prioritizes collecting data related to learning and growth. For example, if the user is focused, the data collection unit prioritizes collecting data related to work efficiency. This allows the data collection unit to prioritize important data by prioritizing behavioral data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The data collection unit prioritizes collecting highly relevant data by considering the user's geographical location when collecting behavioral data. For example, the data collection unit uses AI to consider the user's geographical location and prioritizes collecting highly relevant data. For example, if the user is in a specific location, the data collection unit prioritizes collecting data related to that location. For example, the data collection unit selects the optimal data collection method based on the user's geographical location. For example, if the user is on the move, the data collection unit prioritizes collecting data related to their destination. In this way, the data collection unit can prioritize collecting highly relevant data by considering the user's geographical location.

[0093] The data collection unit analyzes users' social media activity and collects relevant data when collecting behavioral data. For example, the data collection unit uses AI to analyze users' social media activity and collect relevant data. For example, the data collection unit analyzes users' social media activity and collects data related to their interests. For example, the data collection unit selects the optimal data collection method based on users' statements and actions on social media. For example, the data collection unit prioritizes collecting data related to specific topics from users' social media activity. This allows the data collection unit to collect relevant data by analyzing users' social media activity.

[0094] The analysis unit estimates the user's emotions and adjusts the data analysis method based on the estimated user emotions. For example, the analysis unit estimates the user's emotions using an emotion engine or generative AI and adjusts the data analysis method accordingly. For example, if the user is stressed, the analysis unit prioritizes analyzing data related to stress management. For example, if the user is relaxed, the analysis unit prioritizes analyzing data related to learning and growth. For example, if the user is focused, the analysis unit prioritizes analyzing data related to work efficiency. This allows the analysis unit to perform more effective analysis by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The analysis unit adjusts the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit uses AI to evaluate the importance of the behavioral data and adjusts the level of detail of the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. In this way, the analysis unit can perform efficient data analysis by adjusting the level of detail of the analysis based on the importance of the behavioral data.

[0096] The analysis unit applies different analysis algorithms depending on the category of the behavioral data during analysis. For example, the analysis unit uses AI to classify the categories of behavioral data and applies different analysis algorithms. For example, the analysis unit applies a stress analysis algorithm to data related to stress management. For example, the analysis unit applies a learning analysis algorithm to data related to learning and growth. For example, the analysis unit applies a work efficiency analysis algorithm to data related to work efficiency. In this way, the accuracy of data analysis is improved by the analysis unit applying different analysis algorithms depending on the category of the behavioral data.

[0097] The analysis unit estimates the user's emotions and determines the analysis priority based on the estimated emotions. For example, the analysis unit estimates the user's emotions using an emotion engine or generative AI and determines the analysis priority. For example, if the user is stressed, the analysis unit prioritizes analyzing data related to stress management. For example, if the user is relaxed, the analysis unit prioritizes analyzing data related to learning and growth. For example, if the user is focused, the analysis unit prioritizes analyzing data related to work efficiency. This allows the analysis unit to prioritize important data by determining the analysis priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The analysis unit determines the priority of analysis based on the timing of behavioral data collection. For example, the analysis unit uses AI to evaluate the timing of behavioral data collection and determines the priority of analysis. For example, the analysis unit prioritizes the analysis of recently collected data. For example, the analysis unit analyzes current data while referring to past data. For example, the analysis unit determines the priority of analysis according to the timing of data collection. In this way, the analysis unit can prioritize the analysis of the latest data by determining the priority of analysis based on the timing of behavioral data collection.

[0099] The analysis unit adjusts the order of analysis based on the relevance of the behavioral data during the analysis process. For example, the analysis unit uses AI to evaluate the relevance of the behavioral data and adjusts the order of analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. For example, the analysis unit postpones the analysis of less relevant data. For example, the analysis unit adjusts the order of analysis according to the relevance of the data. This enables efficient data analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the behavioral data.

[0100] The generation unit estimates the user's emotions and adjusts the training program generation method based on the estimated user emotions. For example, the generation unit estimates the user's emotions using an emotion engine or generative AI and adjusts the training program generation method accordingly. For example, if the user is stressed, the generation unit generates a training program with a relaxing effect. For example, if the user is relaxed, the generation unit generates a training program with a high learning effect. For example, if the user is focused, the generation unit generates a training program that improves work efficiency. In this way, the generation unit can provide more effective training programs by adjusting the training program generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The generation unit generates the optimal training program by referring to the user's past training history. For example, the generation unit uses AI to analyze the user's past training history and generate the optimal program. For example, the generation unit generates the optimal program based on the user's past training. For example, the generation unit selects an effective program from the user's past training history. For example, the generation unit analyzes the user's past training history and generates the most effective program. In this way, the generation unit can generate the optimal training program by referring to the user's past training history.

[0102] The generation unit adjusts the difficulty of the training program based on the user's current skill level when generating the training program. For example, the generation unit uses AI to evaluate the user's current skill level and adjusts the program difficulty. For example, the generation unit adjusts the program difficulty according to the user's skill level. For example, the generation unit generates a program with the optimal difficulty level based on the user's skill level. For example, the generation unit generates a program that gradually increases in difficulty according to the user's skill level. In this way, the generation unit can provide a training program of appropriate difficulty by adjusting the program difficulty based on the user's current skill level.

[0103] The generation unit estimates the user's emotions and prioritizes training programs based on the estimated emotions. For example, the generation unit estimates the user's emotions using an emotion engine or generative AI and determines the priority of training programs. For example, if the user is stressed, the generation unit prioritizes programs related to stress management. For example, if the user is relaxed, the generation unit prioritizes programs related to learning and growth. For example, if the user is focused, the generation unit prioritizes programs related to work efficiency. This allows the generation unit to prioritize important programs by determining training program priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The generation unit generates the optimal training program by considering the user's geographical location information. For example, the generation unit uses AI to consider the user's geographical location information and generate the optimal program. For example, if the user is in a specific location, the generation unit provides a program relevant to that location. For example, the generation unit generates the optimal program based on the user's geographical location information. For example, if the user is on the move, the generation unit provides a program relevant to their destination. In this way, the generation unit can provide the optimal training program by considering the user's geographical location information.

[0105] The generation unit analyzes the user's social media activity and generates relevant programs when generating training programs. For example, the generation unit uses AI to analyze the user's social media activity and generates relevant programs. For example, the generation unit analyzes the user's social media activity and generates programs related to their interests. For example, the generation unit generates the optimal program based on the user's social media posts and actions. For example, the generation unit provides programs related to specific topics based on the user's social media activity. In this way, the generation unit can provide relevant training programs by analyzing the user's social media activity.

[0106] The service provider estimates the user's emotions and adjusts the training delivery method based on the estimated user emotions. The service provider estimates the user's emotions using, for example, an emotion engine or generative AI, and adjusts the training delivery method. For example, if the user is stressed, the service provider provides a training method with a relaxing effect. For example, if the user is relaxed, the service provider provides a training method with a high learning effect. For example, if the user is focused, the service provider provides a training method that improves work efficiency. In this way, the service provider can provide more effective training by adjusting the training delivery method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The service provider selects the optimal delivery method by referring to the user's past training history when providing training. For example, the service provider may use AI to analyze the user's past training history and select the optimal delivery method. For example, the service provider may select the optimal delivery method based on the user's past training. For example, the service provider may select an effective delivery method from the user's past training history. For example, the service provider may analyze the user's past training history and select the most effective delivery method. This allows the service provider to select the optimal training delivery method by referring to the user's past training history.

[0108] The service provider adjusts the timing of training delivery based on the user's current work situation. For example, the service provider uses AI to evaluate the user's current work situation and adjust the timing accordingly. For example, if the user is busy, the service provider will provide training during breaks in their work. For example, the service provider will provide training at the optimal time according to the user's work situation. For example, the service provider will adjust the timing of training delivery considering the user's work situation. This allows the service provider to deliver training at the appropriate time by adjusting the timing based on the user's current work situation.

[0109] The service provider estimates the user's emotions and determines the order in which training is delivered based on the estimated emotions. The service provider estimates the user's emotions using, for example, an emotion engine or generative AI, and determines the order in which training is delivered. For example, if the user is feeling stressed, the service provider will prioritize providing training related to stress management. For example, if the user is relaxed, the service provider will prioritize providing training related to learning and growth. For example, if the user is focused, the service provider will prioritize providing training related to work efficiency. In this way, the service provider can prioritize providing important training by determining the order in which training is delivered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The service provider selects the optimal delivery method when providing training, taking into account the user's geographical location. For example, the service provider may use AI to consider the user's geographical location and select the optimal delivery method. For example, if the user is in a specific location, the service provider will provide training relevant to that location. For example, the service provider will select the optimal delivery method based on the user's geographical location. For example, if the user is on the move, the service provider will provide training relevant to their destination. In this way, the service provider can select the optimal training delivery method by taking into account the user's geographical location.

[0111] The service provider analyzes the user's social media activity and provides relevant training when delivering training. For example, the service provider uses AI to analyze the user's social media activity and provides relevant training. For example, the service provider analyzes the user's social media activity and provides training related to their interests. For example, the service provider provides optimal training based on the user's statements and actions on social media. For example, the service provider provides training related to specific topics based on the user's social media activity. In this way, the service provider can provide relevant training by analyzing the user's social media activity.

[0112] The monitoring unit estimates the user's emotions and adjusts the monitoring method based on the estimated emotions. The monitoring unit estimates the user's emotions using, for example, an emotion engine or generative AI, and adjusts the monitoring method. For example, if the user is stressed, the monitoring unit prioritizes monitoring data related to stress management. For example, if the user is relaxed, the monitoring unit prioritizes monitoring data related to learning and growth. For example, if the user is focused, the monitoring unit prioritizes monitoring data related to work efficiency. This allows the monitoring unit to perform more effective monitoring by adjusting the monitoring method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The monitoring unit selects the optimal monitoring method by referring to the user's past training history during monitoring. For example, the monitoring unit uses AI to analyze the user's past training history and selects the optimal monitoring method. For example, the monitoring unit selects the optimal monitoring method based on the user's past training. For example, the monitoring unit selects an effective monitoring method from the user's past training history. For example, the monitoring unit analyzes the user's past training history and selects the most effective monitoring method. This allows the monitoring unit to select the optimal monitoring method by referring to the user's past training history.

[0114] The monitoring unit adjusts the timing of monitoring based on the user's current work status. For example, the monitoring unit uses AI to evaluate the user's current work status and adjusts the timing of monitoring. For example, if the user is busy, the monitoring unit performs monitoring in between tasks. For example, the monitoring unit performs monitoring at the optimal time according to the user's work status. For example, the monitoring unit adjusts the timing of monitoring considering the user's work status. As a result, the monitoring unit can perform monitoring at the appropriate time by adjusting the timing based on the user's current work status.

[0115] The monitoring unit estimates the user's emotions and determines monitoring priorities based on the estimated emotions. For example, the monitoring unit estimates the user's emotions using an emotion engine or generative AI and determines monitoring priorities. For example, if the user is stressed, the monitoring unit prioritizes monitoring data related to stress management. For example, if the user is relaxed, the monitoring unit prioritizes monitoring data related to learning and growth. For example, if the user is focused, the monitoring unit prioritizes monitoring data related to work efficiency. This allows the monitoring unit to prioritize monitoring important data by determining monitoring priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The monitoring unit selects the optimal monitoring method by considering the user's geographical location information during monitoring. For example, the monitoring unit may use AI to consider the user's geographical location information and select the optimal monitoring method. For example, if the user is in a specific location, the monitoring unit will prioritize monitoring data related to that location. For example, the monitoring unit will select the optimal monitoring method based on the user's geographical location information. For example, if the user is on the move, the monitoring unit will prioritize monitoring data related to the destination. In this way, the monitoring unit can select the optimal monitoring method by considering the user's geographical location information.

[0117] The monitoring unit analyzes users' social media activity and performs relevant monitoring during the monitoring process. For example, the monitoring unit uses AI to analyze users' social media activity and performs relevant monitoring. For example, the monitoring unit analyzes users' social media activity and monitors data related to their interests. For example, the monitoring unit selects the optimal monitoring method based on users' statements and actions on social media. For example, the monitoring unit prioritizes monitoring data related to specific topics from users' social media activity. In this way, the monitoring unit can monitor relevant data by analyzing users' social media activity.

[0118] The definition unit estimates the user's emotions and adjusts how game goals and desired actions are defined based on the estimated user emotions. For example, the definition unit estimates the user's emotions using an emotion engine or generative AI and adjusts how game goals and desired actions are defined. For example, if the user is stressed, the definition unit defines goals and actions that promote relaxation. For example, if the user is relaxed, the definition unit defines goals and actions that promote learning. For example, if the user is focused, the definition unit defines goals and actions that improve work efficiency. This allows the definition unit to define more effective game goals and actions by adjusting how game goals and desired actions are defined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The definition unit makes optimal definitions of game goals and desired actions by referring to the user's past game history. For example, the definition unit uses AI to analyze the user's past game history and make optimal definitions. For example, the definition unit defines optimal goals and actions based on the games the user has played in the past. For example, the definition unit defines effective goals and actions from the user's past game history. For example, the definition unit analyzes the user's past game history and defines the most effective goals and actions. In this way, the definition unit can define optimal goals and actions by referring to the user's past game history.

[0120] The definition unit adjusts the difficulty of definitions based on the user's current skill level when defining game goals and desired actions. For example, the definition unit uses AI to evaluate the user's current skill level and adjusts the difficulty of definitions. For example, the definition unit adjusts the difficulty of goals and actions according to the user's skill level. For example, the definition unit defines goals and actions of optimal difficulty based on the user's skill level. For example, the definition unit defines goals and actions that gradually increase in difficulty according to the user's skill level. In this way, the definition unit can define goals and actions of appropriate difficulty by adjusting the difficulty of definitions based on the user's current skill level.

[0121] The definition unit estimates the user's emotions and determines the priority of game goals and desired actions based on the estimated user emotions. For example, the definition unit estimates the user's emotions using an emotion engine or generative AI and determines the priority of game goals and desired actions. For example, if the user is stressed, the definition unit prioritizes goals and actions related to stress management. For example, if the user is relaxed, the definition unit prioritizes goals and actions related to learning and growth. For example, if the user is focused, the definition unit prioritizes goals and actions related to work efficiency. This allows the definition unit to prioritize important goals and actions by determining the priority of game goals and desired actions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0122] The definition unit considers the user's geographical location when defining game objectives and desired actions to make the optimal definition. For example, the definition unit uses AI to consider the user's geographical location to make the optimal definition. For example, if the user is in a specific location, the definition unit defines objectives and actions related to that location. For example, the definition unit defines optimal objectives and actions based on the user's geographical location. For example, if the user is on the move, the definition unit defines objectives and actions related to the destination. In this way, the definition unit can define optimal objectives and actions by considering the user's geographical location.

[0123] The definition unit analyzes users' social media activity to define game goals and desired behaviors. For example, the definition unit uses AI to analyze users' social media activity and make relevant definitions. For example, the definition unit analyzes users' social media activity and defines goals and behaviors related to their interests. For example, the definition unit defines optimal goals and behaviors based on users' social media posts and actions. For example, the definition unit defines goals and behaviors related to specific topics from users' social media activity. In this way, the definition unit can define relevant goals and behaviors by analyzing users' social media activity.

[0124] The evaluation unit estimates the user's emotions and adjusts the performance evaluation method based on the estimated user emotions. The evaluation unit estimates the user's emotions using, for example, an emotion engine or generative AI and adjusts the performance evaluation method. For example, if the user is stressed, the evaluation unit provides evaluation methods related to stress management. For example, if the user is relaxed, the evaluation unit provides evaluation methods related to learning and growth. For example, if the user is focused, the evaluation unit provides evaluation methods related to work efficiency. This allows the evaluation unit to perform more effective evaluations by adjusting the performance evaluation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The evaluation unit selects the optimal evaluation method by referring to the user's past evaluation history during performance evaluation. For example, the evaluation unit uses AI to analyze the user's past evaluation history and selects the optimal evaluation method. For example, the evaluation unit selects the optimal evaluation method based on the user's past evaluations. For example, the evaluation unit selects an effective evaluation method from the user's past evaluation history. For example, the evaluation unit analyzes the user's past evaluation history and selects the most effective evaluation method. This allows the evaluation unit to select the optimal evaluation method by referring to the user's past evaluation history.

[0126] The evaluation unit adjusts the timing of performance evaluations based on the user's current work situation. For example, the evaluation unit uses AI to assess the user's current work situation and adjusts the evaluation timing accordingly. For example, if the user is busy, the evaluation unit conducts evaluations during breaks in their work. For example, the evaluation unit conducts evaluations at the optimal time according to the user's work situation. For example, the evaluation unit adjusts the timing of evaluations considering the user's work situation. This allows the evaluation unit to conduct evaluations at the appropriate time by adjusting the timing based on the user's current work situation.

[0127] The evaluation unit estimates the user's emotions and determines the priority of performance evaluations based on the estimated emotions. The evaluation unit estimates the user's emotions using, for example, an emotion engine or generative AI, and determines the priority of performance evaluations. For example, if the user is stressed, the evaluation unit prioritizes evaluations related to stress management. For example, if the user is relaxed, the evaluation unit prioritizes evaluations related to learning and growth. For example, if the user is focused, the evaluation unit prioritizes evaluations related to work efficiency. In this way, the evaluation unit can prioritize important evaluations by determining the priority of performance evaluations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0128] The evaluation unit selects the optimal evaluation method when evaluating performance, taking into account the user's geographical location. For example, the evaluation unit uses AI to consider the user's geographical location and select the optimal evaluation method. For example, if the user is in a specific location, the evaluation unit performs evaluations related to that location. For example, the evaluation unit selects the optimal evaluation method based on the user's geographical location. For example, if the user is on the move, the evaluation unit performs evaluations related to their destination. In this way, the evaluation unit can select the optimal evaluation method by considering the user's geographical location.

[0129] The evaluation department analyzes users' social media activity and performs relevant evaluations during performance assessments. For example, the evaluation department uses AI to analyze users' social media activity and perform relevant evaluations. For example, the evaluation department analyzes users' social media activity and performs evaluations related to their interests and concerns. For example, the evaluation department selects the optimal evaluation method based on users' statements and actions on social media. For example, the evaluation department performs evaluations related to specific topics from users' social media activity. In this way, the evaluation department can perform relevant evaluations by analyzing users' social media activity.

[0130] The adjustment unit estimates the user's emotions and adjusts the training adjustment method based on the estimated user emotions. The adjustment unit estimates the user's emotions using, for example, an emotion engine or generative AI, and adjusts the training adjustment method. For example, if the user is stressed, the adjustment unit provides a training method with a relaxing effect. For example, if the user is relaxed, the adjustment unit provides a training method with a high learning effect. For example, if the user is focused, the adjustment unit provides a training method that improves work efficiency. In this way, the adjustment unit can provide more effective training by adjusting the training adjustment method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0131] The adjustment unit selects the optimal adjustment method by referring to the user's past training history during training adjustment. For example, the adjustment unit may use AI to analyze the user's past training history and select the optimal adjustment method. For example, the adjustment unit may select the optimal adjustment method based on the user's past training. For example, the adjustment unit may select an effective adjustment method from the user's past training history. For example, the adjustment unit may analyze the user's past training history and select the most effective adjustment method. In this way, the adjustment unit can select the optimal adjustment method by referring to the user's past training history.

[0132] The adjustment unit adjusts the timing of training adjustments based on the user's current work situation. The adjustment unit, for example, uses AI to evaluate the user's current work situation and adjusts the timing of adjustments. The adjustment unit, for example, adjusts training during breaks in the user's work if the user is busy. The adjustment unit, for example, adjusts training at the optimal timing according to the user's work situation. The adjustment unit, for example, adjusts the timing of training adjustments considering the user's work situation. In this way, the adjustment unit can adjust training at the appropriate time by adjusting the timing of adjustments based on the user's current work situation.

[0133] The adjustment unit estimates the user's emotions and determines the training adjustment priority based on the estimated user emotions. The adjustment unit estimates the user's emotions using, for example, an emotion engine or generative AI and determines the training adjustment priority. For example, if the user is stressed, the adjustment unit prioritizes training related to stress management. For example, if the user is relaxed, the adjustment unit prioritizes training related to learning and growth. For example, if the user is focused, the adjustment unit prioritizes training related to work efficiency. In this way, the adjustment unit can prioritize important training by determining the training adjustment priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0134] The adjustment unit selects the optimal adjustment method when adjusting training, taking into account the user's geographical location information. For example, the adjustment unit uses AI to consider the user's geographical location information and select the optimal adjustment method. For example, if the user is in a specific location, the adjustment unit adjusts the training related to that location. For example, the adjustment unit selects the optimal adjustment method based on the user's geographical location information. For example, if the user is on the move, the adjustment unit adjusts the training related to the destination. In this way, the adjustment unit can select the optimal adjustment method by taking into account the user's geographical location information.

[0135] The adjustment unit analyzes the user's social media activity and makes relevant adjustments during training adjustment. For example, the adjustment unit uses AI to analyze the user's social media activity and makes relevant adjustments. For example, the adjustment unit analyzes the user's social media activity and adjusts training related to their interests. For example, the adjustment unit provides optimally high-level training based on the user's social media posts and actions. For example, the adjustment unit analyzes the user's social media activity and adjusts training related to their interests. For example, the adjustment unit adjusts optimal training based on the user's social media posts and actions. For example, the adjustment unit adjusts training related to specific topics based on the user's social media activity. This allows the adjustment unit to adjust relevant training by analyzing the user's social media activity.

[0136] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0137] The service provider can also estimate the user's emotions and adjust the training delivery method based on those emotions. For example, if the user is stressed, they can be offered a training method that promotes relaxation. If the user is relaxed, they can be offered a training method that enhances learning effectiveness. Furthermore, if the user is focused, they can be offered a training method that improves work efficiency. In this way, the service provider can deliver more effective training by adjusting the training delivery method based on the user's emotions.

[0138] The generation unit can also generate the optimal training program by referring to the user's past training history. For example, it can generate the optimal program based on the user's past training. It can also select an effective program from the user's past training history. Furthermore, it can analyze the user's past training history and generate the most effective program. In this way, the generation unit can generate the optimal training program by referring to the user's past training history.

[0139] The data collection unit can also estimate the user's emotions and adjust the timing of behavioral data collection based on those emotions. For example, if the user is stressed, the data collection timing can be delayed to collect data when the user is relaxed. Conversely, if the user is focused, behavioral data can be collected at that time to obtain accurate data. Furthermore, if the user is tired, the data collection timing can be adjusted to collect data after they have rested. In this way, the data collection unit can collect more accurate data by adjusting the timing of behavioral data collection based on the user's emotions.

[0140] The analysis unit can also adjust the level of detail of the analysis based on the importance of the behavioral data. For example, it can perform detailed analysis on high-importance data and simplified analysis on low-importance data. Furthermore, it can determine the priority of the analysis according to the importance of the data. In this way, the analysis unit can perform efficient data analysis by adjusting the level of detail of the analysis based on the importance of the behavioral data.

[0141] The service provider can also adjust the timing of training delivery based on the user's current work situation. For example, if a user is busy, training can be provided during breaks in their work. Furthermore, training can be provided at the optimal time depending on the user's work situation. In addition, the service provider can adjust the timing of training delivery considering the user's work situation. This allows the service provider to deliver training at the appropriate time by adjusting the timing based on the user's current work situation.

[0142] The generation unit can also estimate the user's emotions and adjust the training program generation method based on the estimated user emotions. For example, if the user is stressed, it can generate a training program with a relaxing effect. If the user is relaxed, it can generate a training program with a high learning effect. Furthermore, if the user is focused, it can generate a training program that improves work efficiency. In this way, the generation unit can provide more effective training programs by adjusting the training program generation method based on the user's emotions.

[0143] The monitoring unit can also estimate the user's emotions and adjust the monitoring method based on those emotions. For example, if the user is stressed, it can prioritize monitoring data related to stress management. If the user is relaxed, it can prioritize monitoring data related to learning and growth. Furthermore, if the user is focused, it can prioritize monitoring data related to work efficiency. In this way, the monitoring unit can perform more effective monitoring by adjusting the monitoring method based on the user's emotions.

[0144] The evaluation unit can also select the optimal evaluation method by referring to the user's past evaluation history. For example, it can select the optimal evaluation method based on the evaluations the user has received in the past. It can also select an effective evaluation method from the user's past evaluation history. Furthermore, it can analyze the user's past evaluation history and select the most effective evaluation method. In this way, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history.

[0145] The adjustment unit can also estimate the user's emotions and adjust the training method based on those emotions. For example, if the user is stressed, it can provide a training method that promotes relaxation. If the user is relaxed, it can provide a training method that enhances learning effectiveness. Furthermore, if the user is focused, it can provide a training method that improves work efficiency. In this way, the adjustment unit can provide more effective training by adjusting the training method based on the user's emotions.

[0146] The data collection unit can also analyze users' social media activity and collect relevant data. For example, it can analyze users' social media activity and collect data related to their interests. It can also select the optimal data collection method based on users' statements and actions on social media. Furthermore, it can prioritize the collection of data related to specific topics from users' social media activity. In this way, the data collection unit can collect relevant data by analyzing users' social media activity.

[0147] The following briefly describes the processing flow for example form 2.

[0148] Step 1: The data collection unit collects user behavior data. This data includes website browsing history, app usage, and fitness data. The data collection unit collects data from users' smartphones and wearable devices and centrally manages user behavior data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and understand user behavior patterns and trends. For example, it clusters user behavior data and classifies interests and concerns. It also predicts future behavior and visualizes data trends. Step 3: The generation unit generates a training program based on the data analyzed by the analysis unit. The generation unit uses a generation AI to generate a training program and provides the user with the optimal training program. For example, it automatically adjusts the content and difficulty level of the training program. Step 4: The delivery unit provides training based on the training program generated by the generation unit. The delivery unit uses AI to provide the training program in real time, enabling the user to receive training. It also monitors the progress of the training program and provides feedback to the user. Step 5: The monitoring unit monitors the effectiveness of the training provided by the delivery unit in real time. The monitoring unit uses AI to evaluate the effectiveness of the training and understands the user's training progress. For example, it provides feedback to maximize the effectiveness of the training.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0151] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects user behavior data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a training program based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated training program. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the effectiveness of the training in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0154] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects user behavior data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a training program based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated training program. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the effectiveness of the training in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0169] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0170] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0177] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0178] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0179] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0180] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0181] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0183] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0184] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects user behavior data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a training program based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated training program. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the effectiveness of the training in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0185] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0186] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0187] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0189] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0191] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0192] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0193] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0194] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0195] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0196] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0197] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0198] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0199] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0200] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0201] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and monitoring unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects user behavior data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a training program based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated training program. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the effectiveness of the training in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0202] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0203] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0204] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0205] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0206] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0207] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0208] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0209] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0210] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0211] 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.

[0212] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0213] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0214] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0215] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0216] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0217] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0218] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0219] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0220] (Note 1) A data collection unit that collects user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a training program based on the data analyzed by the analysis unit, A providing unit that provides training based on the training program generated by the generation unit, The system includes a monitoring unit that monitors the effectiveness of the training provided by the aforementioned supply unit in real time. A system characterized by the following features. (Note 2) The generating unit is It includes a definition section that defines the game's goals, desired behaviors, interactions, rewards, and feedback mechanisms. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an evaluation unit that assesses user performance. The system described in Appendix 1, characterized by the features described herein. (Note 4) The system includes an adjustment unit that performs adjustments to maximize the effectiveness of training based on the user's condition monitored by the monitoring unit. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and determines the priority of behavioral data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting behavioral data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the behavioral data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts how the training program is generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a training program, the system references the user's past training history to generate the optimal program. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a training program, adjust the program's difficulty level based on the user's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and prioritizes training programs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a training program, the system takes the user's geographical location into consideration to generate the optimal program. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating training programs, the system analyzes the user's social media activity to generate relevant programs. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the training delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing training, the system selects the optimal delivery method by referring to the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing training, the timing of delivery will be adjusted based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which training is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing training, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing training, we analyze users' social media activity and deliver relevant training. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, We estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The monitoring unit, During monitoring, the optimal monitoring method is selected by referring to the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The monitoring unit, During monitoring, the timing of monitoring is adjusted based on the user's current work status. The system described in Appendix 1, characterized by the features described herein. (Note 32) The monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The monitoring unit, During monitoring, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The monitoring unit, During monitoring, analyze users' social media activity and perform relevant monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 35) The definition section is, The system estimates user emotions and adjusts how game goals and desired actions are defined based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The definition section is, When defining game objectives and desired actions, refer to the user's past game history to make the optimal definition. The system described in Appendix 2, characterized by the features described herein. (Note 37) The definition section is, When defining game objectives and desired actions, adjust the difficulty of the definitions based on the user's current skill level. The system described in Appendix 2, characterized by the features described herein. (Note 38) The definition section is, The system estimates the user's emotions and determines the priority of game goals and desired actions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 39) The definition section is, When defining the game goals and desirable actions, perform an optimal definition by considering the user's geographical location information. The system according to appendix 2, characterized in that. (Appendix 40) The definition unit When defining the game goals and desirable actions, analyze the user's social media activities and perform relevant definitions. The system according to appendix 2, characterized in that. (Appendix 41) The evaluation unit Estimate the user's emotion and adjust the method of performance evaluation based on the estimated user emotion. The system according to appendix 3, characterized in that. (Appendix 42) The evaluation unit When performing performance evaluation, select an optimal evaluation method by referring to the user's past evaluation history. The system according to appendix 3, characterized in that. (Appendix 43) The evaluation unit When performing performance evaluation, adjust the timing of evaluation based on the user's current business situation. The system according to appendix 3, characterized in that. (Appendix 44) [[ID=*37]] The evaluation unit Estimate the user's emotion and determine the priority of performance evaluation based on the estimated user emotion. The system according to appendix 3, characterized in that. (Appendix 45) The evaluation unit When performing performance evaluation, select an optimal evaluation method by considering the user's geographical location information. The system according to appendix 3, characterized in that. (Appendix 46) The evaluation unit [[ID=*55]] (There seems to be a duplicate tag here. Keeping it as per the instruction.) When performing performance evaluation, analyze the user's social media activities and perform relevant evaluations. The system according to appendix 3, characterized in that. (Appendix 47) It should be noted that there are two tags and which seem to be duplicates in the original text. They are kept as per the requirement of preserving all 7 - digit tags exactly as - is.The adjustment unit estimates the user's emotion and adjusts the training adjustment method based on the estimated user emotion The system according to supplementary note 4, characterized by the above. (Supplementary note 48) The adjustment unit selects an optimal adjustment method by referring to the user's past training history during training adjustment The system according to supplementary note 4, characterized by the above. (Supplementary note 49) The adjustment unit adjusts the timing of adjustment based on the user's current business situation during training adjustment The system according to supplementary note 4, characterized by the above. (Supplementary note 50) The adjustment unit estimates the user's emotion and determines the adjustment priority of training based on the estimated user emotion The system according to supplementary note 4, characterized by the above. (Supplementary note 51) The adjustment unit selects an optimal adjustment method by considering the user's geographical location information during training adjustment The system according to supplementary note 4, characterized by the above. (Supplementary note 52) The adjustment unit analyzes the user's social media activities and makes related adjustments during training adjustment The system according to supplementary note 4, characterized by the above.

Explanation of symbols

[0221] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection unit that collects user behavior data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a training program based on the data analyzed by the analysis unit, A providing unit that provides training based on the training program generated by the generation unit, The system includes a monitoring unit that monitors the effectiveness of the training provided by the aforementioned supply unit in real time. A system characterized by the following features.

2. The generating unit is It includes a definition section that defines the game's goals, desired behaviors, interactions, rewards, and feedback mechanisms. The system according to feature 1.

3. It includes an evaluation unit that assesses user performance. The system according to feature 1.

4. The system includes an adjustment unit that performs adjustments to maximize the effectiveness of training based on the user's condition monitored by the monitoring unit. The system according to feature 1.

5. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and determines the priority of behavioral data to collect based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

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