system

Abstract

The system according to this embodiment aims to analyze user behavior data and generate and provide customized training scenarios. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a customized training scenario based on the analysis results obtained by the analysis unit. The provision unit provides feedback based on the scenario generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 conventional technology, the generation of customized training scenarios based on user behavior data has not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze user behavior data and generate and provide a customized training scenario.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a customized training scenario based on the analysis results obtained by the analysis unit. The provision unit provides feedback based on the scenario generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze user behavior data and generate and provide customized training scenarios. [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 manages 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 things connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An interactive training platform according to an embodiment of the present invention is a system that conducts vocational training utilizing smart glasses and generative AI. In this system, the user puts on the smart glasses and begins training. The generative AI analyzes the user's actions in real time and provides appropriate advice and feedback. Next, the generative AI generates a customized training scenario according to the user's skill level and learning progress. Furthermore, the generative AI analyzes the user's performance data and generates a detailed report. This allows the user to hone their skills while recreating a realistic work environment within their field of vision, and the real-time advice and feedback from the generative AI enables efficient learning. The system also maximizes the user's learning effectiveness through customized training scenarios and detailed reports. For example, the user puts on the smart glasses and begins training. The generative AI analyzes the user's actions in real time and provides appropriate advice and feedback. Next, the generative AI generates a customized training scenario according to the user's skill level and learning progress. Furthermore, the generative AI analyzes the user's performance data and generates a detailed report. This allows the user to hone their skills while recreating a realistic work environment within their field of vision, and the real-time advice and feedback from the generative AI enables efficient learning. Furthermore, the system maximizes user learning effectiveness through customized training scenarios and detailed reports. This allows the interactive training platform to collect and analyze user behavior data, generate customized training scenarios, and provide feedback, enabling efficient vocational training.

[0029] The interactive training platform according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user behavior data. The collection unit can collect, for example, user click data, movement data, operation logs, etc. The collection unit can also collect, for example, the user's field of view and movements through smart glasses. The collection unit can also, for example, analyze the user's past training history and select the optimal collection method. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data using a generation AI. The analysis unit can also, for example, estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The analysis unit can also, for example, adjust the level of detail of the analysis based on the importance of the data. The generation unit generates customized training scenarios based on the analysis results obtained by the analysis unit. The generation unit can, for example, generate training scenarios that correspond to the user's skill level and learning progress based on the analysis results. The generation unit can also, for example, estimate the user's emotions and adjust the training scenario generation method based on the estimated user emotions. The generation unit can, for example, adjust the level of detail of a scenario based on the user's skill level when generating it. The delivery unit provides feedback based on the scenario generated by the generation unit. The delivery unit can, for example, provide feedback in real time based on the generated scenario. The delivery unit can, for example, analyze user performance data and generate detailed reports. The delivery unit can, for example, estimate the user's emotions and adjust the method of providing feedback based on the estimated user emotions. The delivery unit can, for example, refer to the user's past performance data when providing feedback to provide optimal feedback. As a result, the interactive training platform according to the embodiment enables efficient vocational training by collecting and analyzing user behavior data, generating customized training scenarios, and providing feedback.

[0030] The data collection unit collects user behavior data. For example, it can collect user click data, movement data, and operation logs. Specifically, it meticulously records links and buttons clicked by the user on a webpage, mouse movement trajectories, and the timing and content of keyboard input. This allows for an understanding of the user's operation patterns and preferred content. Furthermore, the data collection unit can also collect the user's field of view and movements through smart glasses. Smart glasses are equipped with cameras and sensors that record the direction the user is looking, the objects they are focusing on, and head movements in real time. This allows for an understanding of the user's visual interests and attentional focus. The data collection unit can also analyze the user's past training history and select the optimal data collection method. For example, based on past training data, it can identify time periods and situations in which specific operations or actions occur frequently and intensify data collection at those times. This allows the data collection unit to efficiently and effectively collect user behavior data and improve the performance of the training platform.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data using generative AI. Specifically, the generative AI analyzes user click data and operation logs to identify user behavior patterns and topics of interest. For example, it can extract pages and content that users frequently access from click data and analyze in detail what operations users are performing from operation logs. Furthermore, the analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, it can estimate whether a user is stressed or interested based on the user's operation speed, frequency of errors, and eye movements, and adjust the level of detail and emphasis of the analysis based on the results. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, if a particular operation or behavior significantly impacts training outcomes, that data will be analyzed in detail, while data with little impact will be analyzed in a simplified manner. In this way, the analysis unit can efficiently and effectively analyze the collected data and accurately understand user behavior and emotions. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns, and identify areas for future training improvements.

[0032] The generation unit generates customized training scenarios based on the analysis results obtained by the analysis unit. For example, the generation unit can generate training scenarios tailored to the user's skill level and learning progress based on the analysis results. Specifically, it creates training scenarios of appropriate difficulty and content based on the user's past training data and current performance. For example, it provides beginners with scenarios focusing on basic operations and knowledge, and advanced users with applied challenges and practical scenarios. Furthermore, the generation unit can estimate the user's emotions and adjust the method of generating training scenarios based on the estimated user emotions. For example, if the user is feeling stressed, it provides relaxing scenarios, and conversely, if the user is interested, it provides challenging scenarios. The generation unit can also adjust the level of detail of the scenarios based on the user's skill level when generating them. For example, it provides beginners with detailed explanations and guides, and advanced users with concise instructions and hints. In this way, the generation unit can generate customized training scenarios that meet the user's needs and circumstances, supporting effective learning. Furthermore, the generation unit can continuously evaluate the generated scenarios and improve them based on user feedback.

[0033] The delivery unit provides feedback based on scenarios generated by the generation unit. For example, the delivery unit can provide real-time feedback based on the generated scenarios. Specifically, it provides advice and corrective instructions at the appropriate time as the user progresses through the training scenario, maximizing the user's learning effectiveness. For example, if the user makes an incorrect operation, it immediately presents the correct operation method, and if successful, it displays a message of praise. The delivery unit can also analyze the user's performance data and generate detailed reports. For example, it can generate and provide the user with a report that includes training progress, achievements, and areas for improvement. This allows the user to understand their learning status and gain guidance for moving on to the next step. Furthermore, the delivery unit can estimate the user's emotions and adjust the way feedback is provided based on the estimated emotions. For example, if the user is feeling stressed, it provides encouraging messages, and conversely, if the user is interested, it presents challenging tasks. When providing feedback, the delivery unit can also refer to the user's past performance data to provide optimal feedback. For example, it provides particularly careful feedback on tasks that the user has struggled with in the past, and encourages further challenges on tasks that the user excels at. This allows the service provider to offer users effective and personalized feedback, maximizing learning effectiveness.

[0034] The data collection unit can collect the user's field of view and movements through smart glasses. For example, the data collection unit can collect the user's field of view using the camera function of the smart glasses. The data collection unit can also collect the user's movements using the sensor function of the smart glasses. The data collection unit can also collect the user's field of view and movements in real time through smart glasses. This allows for the acquisition of more realistic data by collecting the user's field of view and movements through smart glasses. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the field of view data collected using the camera function of the smart glasses into a generating AI and have the generating AI perform analysis of the field of view data.

[0035] The analysis unit can analyze data collected using generative AI. For example, by analyzing data collected using generative AI, the accuracy of data analysis can be improved. The analysis unit can also analyze data collected using deep learning technology. The analysis unit can also analyze data collected using natural language processing technology. This further improves the accuracy of data analysis by using generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input data collected by the collection unit into the generative AI and have the generative AI perform the data analysis.

[0036] The generation unit can generate training scenarios tailored to the user's skill level and learning progress based on the analysis results. For example, the generation unit can generate training scenarios tailored to the user's skill level and learning progress based on the analysis results. The generation unit can also generate training scenarios based on the user's skill level. The generation unit can also generate training scenarios based on the user's learning progress. This enables individually optimized training by generating training scenarios tailored to the user's skill level and learning progress. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input the analysis results obtained by the analysis unit into the generation AI and have the generation AI generate the training scenarios.

[0037] The service provider can provide real-time feedback based on the generated scenario. For example, the service provider can provide real-time feedback based on the generated scenario. The service provider can also provide real-time feedback in response to user actions. The service provider can also provide real-time feedback in response to user learning progress. This enhances the user's learning effectiveness by providing real-time feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated scenario into a generating AI and have the generating AI perform the real-time feedback provision.

[0038] The service provider can analyze user performance data and generate detailed reports. For example, the service provider can analyze user performance data and generate detailed reports. The service provider can also generate detailed reports based on user performance data. For example, the service provider can generate detailed reports based on user learning progress. This makes it easier to understand user learning progress by generating detailed reports. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user performance data into a generating AI and have the generating AI generate a detailed report.

[0039] The data collection unit can analyze the user's past training history and select the optimal data collection method. For example, the data collection unit may prioritize collecting training methods that the user has been successful with in the past. For example, the data collection unit may also avoid collecting training methods that the user has struggled with in the past. For example, the data collection unit may select the most effective data collection method from the user's past training history. In this way, the optimal data collection method can be selected by analyzing the past training history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past training history data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter the collected field of view and movement data based on the user's current job duties and areas of interest. For example, if the user works in the medical field, the data collection unit will prioritize collecting medical-related movement data. For example, if the user is interested in architecture, the data collection unit can also prioritize collecting architecture-related field of view data. The data collection unit can also filter and collect data that is highly relevant according to the user's job duties. This allows for the collection of highly relevant data by filtering the data based on the user's job duties and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's job duties and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information when collecting field of view and movement data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is on the move, the data collection unit can also prioritize the collection of data related to the user's destination. For example, the data collection unit can also collect the most relevant data based on the user's current location. In this way, highly relevant data can be obtained by collecting data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data when collecting field of view and movement data. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activities of the user's social media followers and friends and collect relevant data. For example, the data collection unit can collect data related to the user's interests based on the content of their social media posts. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a basic analysis on data with low importance. The analysis unit can also allocate analysis resources according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a medical-specific analysis algorithm to medical data. For example, the analysis unit can also apply a construction-specific analysis algorithm to construction data. For example, the analysis unit can also apply an engineering-specific analysis algorithm to engineering data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also analyze the most recent data while referring to past data. The analysis unit may also allocate analysis resources according to the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data collection timing into the generative AI and have the generative AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may allocate analysis resources according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0047] The generation unit can adjust the level of detail of a scenario based on the user's skill level when generating a scenario. For example, the generation unit can generate a basic scenario for a beginner user. For example, the generation unit can also generate a detailed scenario for an intermediate user. For example, the generation unit can also generate a complex scenario for an advanced user. This allows for the generation of individually optimized scenarios by adjusting the level of detail of the scenario based on the user's skill level. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user skill level data into a generation AI and have the generation AI perform the adjustment of the level of detail of the scenario.

[0048] The generation unit can apply different scenario generation algorithms depending on the user's learning progress when generating scenarios. For example, the generation unit can apply a basic scenario generation algorithm to users with slow learning progress. For example, the generation unit can apply a detailed scenario generation algorithm to users with average learning progress. For example, the generation unit can apply a complex scenario generation algorithm to users with fast learning progress. This allows for the generation of individually optimized scenarios by applying a scenario generation algorithm according to the user's learning progress. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user learning progress data into a generation AI and have the generation AI execute the application of the scenario generation algorithm.

[0049] The generation unit can determine the priority of scenarios based on the user's past training history when generating scenarios. For example, the generation unit may prioritize training scenarios in which the user has previously succeeded. The generation unit may also postpone training scenarios in which the user has previously struggled. The generation unit may also prioritize the most effective scenario based on the user's past training history. This allows for more effective training by determining the priority of scenarios based on the user's past training history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past training history data into a generation AI and have the generation AI perform the determination of scenario priorities.

[0050] The generation unit can improve the accuracy of scenarios by referring to the user's relevant job data during scenario generation. For example, the generation unit generates highly relevant scenarios based on the user's job data. The generation unit can also adjust the level of detail of scenarios by referring to the user's job data. The generation unit can also analyze the user's job data and generate the optimal scenario. This improves the accuracy of scenarios by referring to the user's relevant job data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's relevant job data into a generation AI and have the generation AI perform the scenario accuracy improvement.

[0051] The service provider can provide optimal feedback by referring to the user's past performance data when providing feedback. For example, the service provider can provide feedback by referring to the user's past successful performances. For example, the service provider can provide feedback while avoiding performances that the user has struggled with in the past. For example, the service provider can provide the most effective feedback from the user's past performance data. In this way, optimal feedback can be provided by referring to the user's past performance data. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past performance data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0052] The feedback provider can adjust the level of detail of the feedback based on the user's current learning progress. For example, it can provide detailed feedback to users with slow learning progress. For example, it can provide basic feedback to users with average learning progress. For example, it can provide concise feedback to users with fast learning progress. By adjusting the level of detail of the feedback based on the user's learning progress, more effective feedback becomes possible. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without AI. For example, the feedback provider can input the user's learning progress data into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0053] The service provider can provide optimal feedback based on the user's geographical location information when providing feedback. For example, if the user is in a specific region, the service provider can provide feedback relevant to that region. For example, if the user is on the move, the service provider can also provide feedback relevant to the user's destination. For example, the service provider can provide the most relevant feedback based on the user's current location. This makes it possible to provide more relevant feedback by providing feedback based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0054] The service provider can analyze the user's social media activity and suggest methods for providing feedback when providing feedback. For example, the service provider can provide relevant feedback based on information shared by the user on social media. The service provider can also analyze the activities of the user's social media followers and friends and provide relevant feedback. The service provider can also provide feedback related to the user's interests based on the content of the user's social media posts. By analyzing the user's social media activity, it is possible to suggest more effective methods for providing feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI suggest methods for providing feedback.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] An interactive training platform can maximize training effectiveness by collecting and analyzing user biometric information. For example, the data collection unit can collect biometric information such as the user's heart rate, blood pressure, and body temperature. The analysis unit can analyze the collected biometric information and evaluate the user's physical condition and stress level. The generation unit can generate training scenarios tailored to the user's physical condition based on the analysis results. The delivery unit can provide feedback that takes the user's physical condition into consideration, based on the generated scenarios. This enables training tailored to the user's physical condition, maximizing the effectiveness of the training.

[0057] An interactive training platform can improve the effectiveness of training by collecting and analyzing user voice data. For example, the collection unit can collect voice data such as the content of the user's speech, tone of voice, and speed. The analysis unit can analyze the collected voice data and evaluate the user's level of understanding and concentration. The generation unit can generate training scenarios tailored to the user's level of understanding based on the analysis results. The delivery unit can provide feedback based on the generated scenarios, tailored to the user's level of understanding. This enables training tailored to the user's level of understanding, thereby improving the effectiveness of the training.

[0058] An interactive training platform can analyze a user's past training data and propose an optimal training plan. For example, the data collection unit can collect the user's past training data. The analysis unit can analyze the collected data and evaluate the user's strengths and weaknesses. Based on the analysis results, the generation unit can generate training scenarios that leverage the user's strengths and overcome their weaknesses. Based on the generated scenarios, the delivery unit can propose an optimal training plan to the user. This enables optimal training based on the user's past training data, maximizing the effectiveness of the training.

[0059] An interactive training platform can leverage users' geographical location information to provide region-specific training scenarios. For example, a data collection unit can collect the user's current location. An analysis unit can analyze the collected geographical location information and evaluate region-specific training needs. A generation unit can generate region-specific training scenarios based on the analysis results. A delivery unit can provide users with region-specific training based on the generated scenarios. This enables training based on the user's geographical location information, allowing them to acquire region-specific skills.

[0060] An interactive training platform can analyze users' social media activity and customize training content. For example, the data collection unit can collect users' social media posts and follower activity. The analysis unit can analyze the collected social media data and evaluate the user's interests. The generation unit can generate training scenarios tailored to the user's interests based on the analysis results. The delivery unit can provide the user with the most suitable training based on the generated scenarios. This enables training based on the user's social media activity, improving the effectiveness of the training.

[0061] An interactive training platform can analyze a user's learning style and suggest the optimal training method. For example, the data collection unit can collect the user's learning history and behavioral data. The analysis unit can analyze the collected data and evaluate the user's learning style. The generation unit can generate training scenarios tailored to the user's learning style based on the analysis results. The delivery unit can then suggest the optimal training method to the user based on the generated scenarios. This enables training based on the user's learning style, maximizing the effectiveness of the training.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects user behavior data. The data collection unit can collect, for example, user click data, movement data, and operation logs. It can also collect the user's field of view and movements through smart glasses. Furthermore, it can analyze the user's past training history and select the optimal data collection method. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data using generative AI. Furthermore, it can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. It can also adjust the level of detail of the analysis based on the importance of the data. Step 3: The generation unit generates a customized training scenario based on the analysis results obtained by the analysis unit. For example, the generation unit can generate a training scenario that is tailored to the user's skill level and learning progress based on the analysis results. Furthermore, it can estimate the user's emotions and adjust the method of generating the training scenario based on the estimated user emotions. It can also adjust the level of detail of the scenario based on the user's skill level during scenario generation. Step 4: The providing unit provides feedback based on the scenarios generated by the generating unit. For example, the providing unit can provide feedback in real time based on the generated scenarios. Furthermore, it can analyze user performance data and generate detailed reports. It can also estimate the user's emotions and adjust the feedback delivery method based on the estimated user emotions. In addition, when providing feedback, it can refer to the user's past performance data to provide optimal feedback.

[0064] (Example of form 2) An interactive training platform according to an embodiment of the present invention is a system that conducts vocational training utilizing smart glasses and generative AI. In this system, the user puts on the smart glasses and begins training. The generative AI analyzes the user's actions in real time and provides appropriate advice and feedback. Next, the generative AI generates a customized training scenario according to the user's skill level and learning progress. Furthermore, the generative AI analyzes the user's performance data and generates a detailed report. This allows the user to hone their skills while recreating a realistic work environment within their field of vision, and the real-time advice and feedback from the generative AI enables efficient learning. The system also maximizes the user's learning effectiveness through customized training scenarios and detailed reports. For example, the user puts on the smart glasses and begins training. The generative AI analyzes the user's actions in real time and provides appropriate advice and feedback. Next, the generative AI generates a customized training scenario according to the user's skill level and learning progress. Furthermore, the generative AI analyzes the user's performance data and generates a detailed report. This allows the user to hone their skills while recreating a realistic work environment within their field of vision, and the real-time advice and feedback from the generative AI enables efficient learning. Furthermore, the system maximizes user learning effectiveness through customized training scenarios and detailed reports. This allows the interactive training platform to collect and analyze user behavior data, generate customized training scenarios, and provide feedback, enabling efficient vocational training.

[0065] The interactive training platform according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user behavior data. The collection unit can collect, for example, user click data, movement data, operation logs, etc. The collection unit can also collect, for example, the user's field of view and movements through smart glasses. The collection unit can also, for example, analyze the user's past training history and select the optimal collection method. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data using a generation AI. The analysis unit can also, for example, estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The analysis unit can also, for example, adjust the level of detail of the analysis based on the importance of the data. The generation unit generates customized training scenarios based on the analysis results obtained by the analysis unit. The generation unit can, for example, generate training scenarios that correspond to the user's skill level and learning progress based on the analysis results. The generation unit can also, for example, estimate the user's emotions and adjust the training scenario generation method based on the estimated user emotions. The generation unit can, for example, adjust the level of detail of a scenario based on the user's skill level when generating it. The delivery unit provides feedback based on the scenario generated by the generation unit. The delivery unit can, for example, provide feedback in real time based on the generated scenario. The delivery unit can, for example, analyze user performance data and generate detailed reports. The delivery unit can, for example, estimate the user's emotions and adjust the method of providing feedback based on the estimated user emotions. The delivery unit can, for example, refer to the user's past performance data when providing feedback to provide optimal feedback. As a result, the interactive training platform according to the embodiment enables efficient vocational training by collecting and analyzing user behavior data, generating customized training scenarios, and providing feedback.

[0066] The data collection unit collects user behavior data. For example, it can collect user click data, movement data, and operation logs. Specifically, it meticulously records links and buttons clicked by the user on a webpage, mouse movement trajectories, and the timing and content of keyboard input. This allows for an understanding of the user's operation patterns and preferred content. Furthermore, the data collection unit can also collect the user's field of view and movements through smart glasses. Smart glasses are equipped with cameras and sensors that record the direction the user is looking, the objects they are focusing on, and head movements in real time. This allows for an understanding of the user's visual interests and attentional focus. The data collection unit can also analyze the user's past training history and select the optimal data collection method. For example, based on past training data, it can identify time periods and situations in which specific operations or actions occur frequently and intensify data collection at those times. This allows the data collection unit to efficiently and effectively collect user behavior data and improve the performance of the training platform.

[0067] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data using generative AI. Specifically, the generative AI analyzes user click data and operation logs to identify user behavior patterns and topics of interest. For example, it can extract pages and content that users frequently access from click data and analyze in detail what operations users are performing from operation logs. Furthermore, the analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, it can estimate whether a user is stressed or interested based on the user's operation speed, frequency of errors, and eye movements, and adjust the level of detail and emphasis of the analysis based on the results. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, if a particular operation or behavior significantly impacts training outcomes, that data will be analyzed in detail, while data with little impact will be analyzed in a simplified manner. In this way, the analysis unit can efficiently and effectively analyze the collected data and accurately understand user behavior and emotions. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns, and identify areas for future training improvements.

[0068] The generation unit generates customized training scenarios based on the analysis results obtained by the analysis unit. For example, the generation unit can generate training scenarios tailored to the user's skill level and learning progress based on the analysis results. Specifically, it creates training scenarios of appropriate difficulty and content based on the user's past training data and current performance. For example, it provides beginners with scenarios focusing on basic operations and knowledge, and advanced users with applied challenges and practical scenarios. Furthermore, the generation unit can estimate the user's emotions and adjust the method of generating training scenarios based on the estimated user emotions. For example, if the user is feeling stressed, it provides relaxing scenarios, and conversely, if the user is interested, it provides challenging scenarios. The generation unit can also adjust the level of detail of the scenarios based on the user's skill level when generating them. For example, it provides beginners with detailed explanations and guides, and advanced users with concise instructions and hints. In this way, the generation unit can generate customized training scenarios that meet the user's needs and circumstances, supporting effective learning. Furthermore, the generation unit can continuously evaluate the generated scenarios and improve them based on user feedback.

[0069] The delivery unit provides feedback based on scenarios generated by the generation unit. For example, the delivery unit can provide real-time feedback based on the generated scenarios. Specifically, it provides advice and corrective instructions at the appropriate time as the user progresses through the training scenario, maximizing the user's learning effectiveness. For example, if the user makes an incorrect operation, it immediately presents the correct operation method, and if successful, it displays a message of praise. The delivery unit can also analyze the user's performance data and generate detailed reports. For example, it can generate and provide the user with a report that includes training progress, achievements, and areas for improvement. This allows the user to understand their learning status and gain guidance for moving on to the next step. Furthermore, the delivery unit can estimate the user's emotions and adjust the way feedback is provided based on the estimated emotions. For example, if the user is feeling stressed, it provides encouraging messages, and conversely, if the user is interested, it presents challenging tasks. When providing feedback, the delivery unit can also refer to the user's past performance data to provide optimal feedback. For example, it provides particularly careful feedback on tasks that the user has struggled with in the past, and encourages further challenges on tasks that the user excels at. This allows the service provider to offer users effective and personalized feedback, maximizing learning effectiveness.

[0070] The data collection unit can collect the user's field of view and movements through smart glasses. For example, the data collection unit can collect the user's field of view using the camera function of the smart glasses. The data collection unit can also collect the user's movements using the sensor function of the smart glasses. The data collection unit can also collect the user's field of view and movements in real time through smart glasses. This allows for the acquisition of more realistic data by collecting the user's field of view and movements through smart glasses. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the field of view data collected using the camera function of the smart glasses into a generating AI and have the generating AI perform analysis of the field of view data.

[0071] The analysis unit can analyze data collected using generative AI. For example, by analyzing data collected using generative AI, the accuracy of data analysis can be improved. The analysis unit can also analyze data collected using deep learning technology. The analysis unit can also analyze data collected using natural language processing technology. This further improves the accuracy of data analysis by using generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input data collected by the collection unit into the generative AI and have the generative AI perform the data analysis.

[0072] The generation unit can generate training scenarios tailored to the user's skill level and learning progress based on the analysis results. For example, the generation unit can generate training scenarios tailored to the user's skill level and learning progress based on the analysis results. The generation unit can also generate training scenarios based on the user's skill level. The generation unit can also generate training scenarios based on the user's learning progress. This enables individually optimized training by generating training scenarios tailored to the user's skill level and learning progress. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input the analysis results obtained by the analysis unit into the generation AI and have the generation AI generate the training scenarios.

[0073] The service provider can provide real-time feedback based on the generated scenario. For example, the service provider can provide real-time feedback based on the generated scenario. The service provider can also provide real-time feedback in response to user actions. The service provider can also provide real-time feedback in response to user learning progress. This enhances the user's learning effectiveness by providing real-time feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated scenario into a generating AI and have the generating AI perform the real-time feedback provision.

[0074] The service provider can analyze user performance data and generate detailed reports. For example, the service provider can analyze user performance data and generate detailed reports. The service provider can also generate detailed reports based on user performance data. For example, the service provider can generate detailed reports based on user learning progress. This makes it easier to understand user learning progress by generating detailed reports. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user performance data into a generating AI and have the generating AI generate a detailed report.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of collecting visual and behavioral data based on the estimated emotions. For example, if the user is concentrating, the data collection unit can increase the collection frequency to obtain more detailed data. For example, if the user is tired, the data collection unit can reduce the collection frequency to alleviate the burden. For example, if the user is stressed, the data collection unit can adjust the collection timing to obtain data in a relaxed state. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The data collection unit can analyze the user's past training history and select the optimal data collection method. For example, the data collection unit may prioritize collecting training methods that the user has been successful with in the past. For example, the data collection unit may also avoid collecting training methods that the user has struggled with in the past. For example, the data collection unit may select the most effective data collection method from the user's past training history. In this way, the optimal data collection method can be selected by analyzing the past training history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past training history data into a generating AI and have the generating AI select the optimal data collection method.

[0077] The data collection unit can filter the collected field of view and movement data based on the user's current job duties and areas of interest. For example, if the user works in the medical field, the data collection unit will prioritize collecting medical-related movement data. For example, if the user is interested in architecture, the data collection unit can also prioritize collecting architecture-related field of view data. The data collection unit can also filter and collect data that is highly relevant according to the user's job duties. This allows for the collection of highly relevant data by filtering the data based on the user's job duties and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's job duties and areas of interest into a generating AI and have the generating AI perform the filtering.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. If the user is tense, for example, the data collection unit may prioritize collecting basic data. If the user is excited, for example, the data collection unit may prioritize collecting behavioral data. This allows for more effective data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data priority.

[0079] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information when collecting field of view and movement data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is on the move, the data collection unit can also prioritize the collection of data related to the user's destination. For example, the data collection unit can also collect the most relevant data based on the user's current location. In this way, highly relevant data can be obtained by collecting data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0080] The data collection unit can analyze the user's social media activity and collect relevant data when collecting field of view and movement data. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activities of the user's social media followers and friends and collect relevant data. For example, the data collection unit can collect data related to the user's interests based on the content of their social media posts. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is tense, the analysis unit can also perform a basic analysis. If the user is excited, the analysis unit can prioritize the analysis of behavioral data. This allows for more appropriate analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a basic analysis on data with low importance. The analysis unit can also allocate analysis resources according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a medical-specific analysis algorithm to medical data. For example, the analysis unit can also apply a construction-specific analysis algorithm to construction data. For example, the analysis unit can also apply an engineering-specific analysis algorithm to engineering data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit may prioritize detailed analysis. If the user is tense, the analysis unit may also prioritize basic analysis. If the user is excited, the analysis unit may also prioritize the analysis of behavioral data. This allows for more effective data analysis by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0085] The analysis unit can determine the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also analyze the most recent data while referring to past data. The analysis unit may also allocate analysis resources according to the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data collection timing into the generative AI and have the generative AI determine the priority of analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may allocate analysis resources according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0087] The generation unit can estimate the user's emotions and adjust the method of generating training scenarios based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a detailed training scenario. For example, if the user is tense, the generation unit can also generate a basic training scenario. For example, if the user is excited, the generation unit can also generate a training scenario that emphasizes actions. This allows for the generation of more appropriate scenarios by adjusting the method of generating training scenarios according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the method of generating training scenarios.

[0088] The generation unit can adjust the level of detail of a scenario based on the user's skill level when generating a scenario. For example, the generation unit can generate a basic scenario for a beginner user. For example, the generation unit can also generate a detailed scenario for an intermediate user. For example, the generation unit can also generate a complex scenario for an advanced user. This allows for the generation of individually optimized scenarios by adjusting the level of detail of the scenario based on the user's skill level. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user skill level data into a generation AI and have the generation AI perform the adjustment of the level of detail of the scenario.

[0089] The generation unit can apply different scenario generation algorithms depending on the user's learning progress when generating scenarios. For example, the generation unit can apply a basic scenario generation algorithm to users with slow learning progress. For example, the generation unit can apply a detailed scenario generation algorithm to users with average learning progress. For example, the generation unit can apply a complex scenario generation algorithm to users with fast learning progress. This allows for the generation of individually optimized scenarios by applying a scenario generation algorithm according to the user's learning progress. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user learning progress data into a generation AI and have the generation AI execute the application of the scenario generation algorithm.

[0090] The generation unit can estimate the user's emotions and determine the priority of scenarios based on the estimated emotions. For example, if the user is relaxed, the generation unit may prioritize detailed scenarios. If the user is tense, the generation unit may also prioritize basic scenarios. If the user is excited, the generation unit may also prioritize action-oriented scenarios. This allows for more effective training by prioritizing scenarios according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of scenarios.

[0091] The generation unit can determine the priority of scenarios based on the user's past training history when generating scenarios. For example, the generation unit may prioritize training scenarios in which the user has previously succeeded. The generation unit may also postpone training scenarios in which the user has previously struggled. The generation unit may also prioritize the most effective scenario based on the user's past training history. This allows for more effective training by determining the priority of scenarios based on the user's past training history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past training history data into a generation AI and have the generation AI perform the determination of scenario priorities.

[0092] The generation unit can improve the accuracy of scenarios by referring to the user's relevant job data during scenario generation. For example, the generation unit generates highly relevant scenarios based on the user's job data. The generation unit can also adjust the level of detail of scenarios by referring to the user's job data. The generation unit can also analyze the user's job data and generate the optimal scenario. This improves the accuracy of scenarios by referring to the user's relevant job data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's relevant job data into a generation AI and have the generation AI perform the scenario accuracy improvement.

[0093] The service provider can estimate the user's emotions and adjust the method of providing feedback based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed feedback. If the user is tense, the service provider can also provide basic feedback. If the user is excited, the service provider can also provide action-oriented feedback. By adjusting the method of providing feedback according to the user's emotions, more effective feedback becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the method of providing feedback.

[0094] The service provider can provide optimal feedback by referring to the user's past performance data when providing feedback. For example, the service provider can provide feedback by referring to the user's past successful performances. For example, the service provider can provide feedback while avoiding performances that the user has struggled with in the past. For example, the service provider can provide the most effective feedback from the user's past performance data. In this way, optimal feedback can be provided by referring to the user's past performance data. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past performance data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0095] The feedback provider can adjust the level of detail of the feedback based on the user's current learning progress. For example, it can provide detailed feedback to users with slow learning progress. For example, it can provide basic feedback to users with average learning progress. For example, it can provide concise feedback to users with fast learning progress. By adjusting the level of detail of the feedback based on the user's learning progress, more effective feedback becomes possible. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without AI. For example, the feedback provider can input the user's learning progress data into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0096] The service provider can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize detailed feedback. If the user is tense, the service provider may also prioritize basic feedback. If the user is excited, the service provider may also prioritize action-oriented feedback. This allows for more effective feedback by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0097] The service provider can provide optimal feedback based on the user's geographical location information when providing feedback. For example, if the user is in a specific region, the service provider can provide feedback relevant to that region. For example, if the user is on the move, the service provider can also provide feedback relevant to the user's destination. For example, the service provider can provide the most relevant feedback based on the user's current location. This makes it possible to provide more relevant feedback by providing feedback based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0098] The service provider can analyze the user's social media activity and suggest methods for providing feedback when providing feedback. For example, the service provider can provide relevant feedback based on information shared by the user on social media. The service provider can also analyze the activities of the user's social media followers and friends and provide relevant feedback. The service provider can also provide feedback related to the user's interests based on the content of the user's social media posts. By analyzing the user's social media activity, it is possible to suggest more effective methods for providing feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI suggest methods for providing feedback.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] An interactive training platform can maximize training effectiveness by collecting and analyzing user biometric information. For example, the data collection unit can collect biometric information such as the user's heart rate, blood pressure, and body temperature. The analysis unit can analyze the collected biometric information and evaluate the user's physical condition and stress level. The generation unit can generate training scenarios tailored to the user's physical condition based on the analysis results. The delivery unit can provide feedback that takes the user's physical condition into consideration, based on the generated scenarios. This enables training tailored to the user's physical condition, maximizing the effectiveness of the training.

[0101] An interactive training platform can improve the effectiveness of training by collecting and analyzing user voice data. For example, the collection unit can collect voice data such as the content of the user's speech, tone of voice, and speed. The analysis unit can analyze the collected voice data and evaluate the user's level of understanding and concentration. The generation unit can generate training scenarios tailored to the user's level of understanding based on the analysis results. The delivery unit can provide feedback based on the generated scenarios, tailored to the user's level of understanding. This enables training tailored to the user's level of understanding, thereby improving the effectiveness of the training.

[0102] An interactive training platform can estimate a user's emotions and adjust the training progress based on those emotions. For example, the analysis unit can analyze the user's facial expressions, voice, and behavioral data to estimate their emotions. The generation unit can generate a more difficult scenario if the user is relaxed, and an easier scenario if the user is stressed, based on the estimated emotions. The delivery unit can provide feedback that takes the user's emotions into consideration based on the generated scenarios. This enables training that is tailored to the user's emotions, thereby improving the effectiveness of the training.

[0103] An interactive training platform can analyze a user's past training data and propose an optimal training plan. For example, the data collection unit can collect the user's past training data. The analysis unit can analyze the collected data and evaluate the user's strengths and weaknesses. Based on the analysis results, the generation unit can generate training scenarios that leverage the user's strengths and overcome their weaknesses. Based on the generated scenarios, the delivery unit can propose an optimal training plan to the user. This enables optimal training based on the user's past training data, maximizing the effectiveness of the training.

[0104] An interactive training platform can estimate a user's emotions and improve their training motivation based on those estimated emotions. For example, the analysis unit can analyze the user's facial expressions, voice, and behavioral data to estimate their emotions. The generation unit can generate training scenarios that help the user maintain motivation based on the estimated emotions. The delivery unit can provide feedback that takes the user's emotions into consideration based on the generated scenarios. This enables training that is tailored to the user's emotions and improves their motivation to train.

[0105] An interactive training platform can leverage users' geographical location information to provide region-specific training scenarios. For example, a data collection unit can collect the user's current location. An analysis unit can analyze the collected geographical location information and evaluate region-specific training needs. A generation unit can generate region-specific training scenarios based on the analysis results. A delivery unit can provide users with region-specific training based on the generated scenarios. This enables training based on the user's geographical location information, allowing them to acquire region-specific skills.

[0106] An interactive training platform can estimate a user's emotions and adjust the training difficulty based on those emotions. For example, the analysis unit can analyze the user's facial expressions, voice, and behavioral data to estimate their emotions. The generation unit can generate a more difficult scenario if the user is relaxed, and a less difficult scenario if the user is stressed, based on the estimated emotions. The delivery unit can provide feedback that takes the user's emotions into consideration based on the generated scenarios. This enables training that is tailored to the user's emotions, thereby improving the effectiveness of the training.

[0107] An interactive training platform can analyze users' social media activity and customize training content. For example, the data collection unit can collect users' social media posts and follower activity. The analysis unit can analyze the collected social media data and evaluate the user's interests. The generation unit can generate training scenarios tailored to the user's interests based on the analysis results. The delivery unit can provide the user with the most suitable training based on the generated scenarios. This enables training based on the user's social media activity, improving the effectiveness of the training.

[0108] An interactive training platform can estimate a user's emotions and adjust training feedback based on those emotions. For example, the analysis unit can analyze the user's facial expressions, voice, and behavioral data to estimate their emotions. Based on the estimated emotions, the feedback unit can provide detailed feedback if the user is relaxed and concise feedback if they are tense. This enables feedback tailored to the user's emotions, thereby improving the effectiveness of the training.

[0109] An interactive training platform can analyze a user's learning style and suggest the optimal training method. For example, the data collection unit can collect the user's learning history and behavioral data. The analysis unit can analyze the collected data and evaluate the user's learning style. The generation unit can generate training scenarios tailored to the user's learning style based on the analysis results. The delivery unit can then suggest the optimal training method to the user based on the generated scenarios. This enables training based on the user's learning style, maximizing the effectiveness of the training.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects user behavior data. The data collection unit can collect, for example, user click data, movement data, and operation logs. It can also collect the user's field of view and movements through smart glasses. Furthermore, it can analyze the user's past training history and select the optimal data collection method. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data using generative AI. Furthermore, it can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. It can also adjust the level of detail of the analysis based on the importance of the data. Step 3: The generation unit generates a customized training scenario based on the analysis results obtained by the analysis unit. For example, the generation unit can generate a training scenario that is tailored to the user's skill level and learning progress based on the analysis results. Furthermore, it can estimate the user's emotions and adjust the method of generating the training scenario based on the estimated user emotions. It can also adjust the level of detail of the scenario based on the user's skill level during scenario generation. Step 4: The providing unit provides feedback based on the scenarios generated by the generating unit. For example, the providing unit can provide feedback in real time based on the generated scenarios. Furthermore, it can analyze user performance data and generate detailed reports. It can also estimate the user's emotions and adjust the feedback delivery method based on the estimated user emotions. In addition, when providing feedback, it can refer to the user's past performance data to provide optimal feedback.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a customized training scenario based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides real-time feedback based on the generated scenario. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and generates a customized training scenario based on the analysis results. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides real-time feedback based on the generated scenario. 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.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a customized training scenario based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides real-time feedback based on the generated scenario. 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.

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

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0162] 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.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a customized training scenario based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides real-time feedback based on the generated scenario. 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (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 customized training scenario based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides feedback based on the scenario generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects the user's field of view and movements through smart glasses. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the analysis results, training scenarios are generated that are tailored to the user's skill level and learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide real-time feedback based on the generated scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Analyze user performance data and generate detailed reports. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting visual and motion data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past training history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting field of view and movement data, filtering is performed based on the user's current job responsibilities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting field of view and movement data, the system prioritizes collecting highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting field of view and movement data, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Apply different analysis algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned analysis unit, Prioritize analysis based on the data collection period. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts how training scenarios are generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating scenarios, adjust the level of detail in the scenarios based on the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating scenarios, different scenario generation algorithms are applied depending on the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates user emotions and prioritizes scenarios based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating scenarios, the system prioritizes scenarios based on the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating scenarios, we improve the accuracy of the scenarios by referencing the user's relevant job data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how feedback is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing feedback, we refer to the user's past performance data to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing feedback, adjust the level of detail in the feedback based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing feedback, we will provide the most appropriate feedback based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing feedback, we analyze the user's social media activity and suggest methods for providing feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A 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 customized training scenario based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides feedback based on the scenario generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects the user's field of view and movements through smart glasses. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the data collected using generative AI. The system according to feature 1.

4. The generating unit is Based on the analysis results, training scenarios are generated that are tailored to the user's skill level and learning progress. The system according to feature 1.

5. The aforementioned supply unit is, Provide real-time feedback based on the generated scenarios. The system according to feature 1.

6. The aforementioned supply unit is, Analyze user performance data and generate detailed reports. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of collecting visual and motion data based on the estimated emotions. The system according to feature 1.

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

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