System

Abstract

An object of the system according to the embodiment is to analyze a user's action and provide specific feedback.SOLUTION: A system includes an action analysis part, a scoring part, an evaluation part, and a feedback part. The action analysis unit analyzes an action of a user. The scoring unit scores the user's action analyzed by the action analysis unit. The evaluation unit evaluates the improvement with respect to the action of the user scored by the scoring unit. The feedback unit provides specific feedback to the action of the user evaluated by the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately evaluate or provide feedback on user actions, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze the user's actions and provide specific feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes an action analysis unit, a scoring unit, an evaluation unit, and a feedback unit. The action analysis unit analyzes a user's actions. The scoring unit scores the user's actions analyzed by the action analysis unit. The evaluation unit evaluates areas for improvement for the user's actions scored by the scoring unit. The feedback unit provides specific feedback for the user's actions evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's actions and provide specific feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The training system according to the embodiment of the present invention uses generative AI to score user actions and evaluate areas for improvement, thereby providing users with concrete experience in solving IT problems.

[0029] A training system according to an embodiment includes an action analysis unit, a scoring unit, an evaluation unit, and a feedback unit. The action analysis unit analyzes user actions. For example, it collects and analyzes data on actions such as clicks, inputs, and movements performed by the user. The action analysis unit can also analyze user actions in real time and provide immediate feedback. For example, while the user is solving a programming problem, a generation AI analyzes the code in real time and immediately points out errors and areas for improvement. The scoring unit scores the user actions analyzed by the action analysis unit. For example, it evaluates and scores the accuracy and efficiency of the code written by the user. The scoring unit can also accumulate the user's action history and compare it with past data to evaluate the user's level of growth. For example, it accumulates data on programming problems solved in the past and compares it with current problems to evaluate the user's level of growth. The evaluation unit evaluates areas for improvement for the user actions scored by the scoring unit. For example, it suggests specific areas for improvement for the code written by the user, such as, "Simplifying the logic in this part will improve processing speed." The evaluation unit can also score the user's performance by taking into account the user's emotional state and providing feedback that reduces stress. For example, while the user is working on a programming assignment, the generation AI can analyze the user's facial expressions and voice and provide gentle feedback if the user is feeling stressed. The feedback unit provides specific feedback for the user's actions evaluated by the evaluation unit. For example, it can visualize errors in the code written by the user and display the scoring results in a visually easy-to-understand format. The feedback unit can also visualize the user's actions and display the scoring results in a visually easy-to-understand format. For example, it can visualize the code structure and error locations and display the scoring results in a visually easy-to-understand format. This allows the training system according to the embodiment to provide the user with concrete experience in IT assignments. For example, the user can learn how to write efficient code and fix bugs through programming assignments.Generative AI helps users improve their practical skills by evaluating their actions.

[0030] The action analysis unit analyzes user actions in real time and can provide instant feedback. For example, while a user is solving a programming problem, the generative AI analyzes the code in real time and immediately points out errors and areas for improvement. For example, if part of the code is inefficient, it will immediately suggest an efficient way to write it. This provides real-time feedback, improving the user's learning effect.

[0031] The scoring unit can accumulate the user's action history and compare it with past data to evaluate the user's growth. For example, the scoring unit accumulates data on programming problems the user has solved in the past and compares it with current problems to evaluate the user's growth. For example, the scoring unit calculates the growth level based on the efficiency of the code or the reduction in errors. In this way, the evaluation of the user's growth level visualizes the user's progress and maintains their motivation.

[0032] The feedback unit visualizes the user's actions and displays the scoring results in a visually easy-to-understand format. For example, after a user solves a programming problem, the generative AI visualizes the code structure and error locations, and displays the scoring results in a visually easy-to-understand format. For example, it highlights error locations in red and indicates areas for improvement with arrows. This allows the user to deepen their understanding by displaying the scoring results in a visually easy-to-understand format.

[0033] The action analysis unit can expand the scoring algorithm of the generative AI so that it can handle tasks in different fields. For example, the action analysis unit expands the scoring algorithm of the generative AI so that it can handle not only programming tasks but also design tasks. For example, it adds an algorithm to evaluate the balance and color scheme of the design. This makes it possible to handle tasks in different fields, thereby catering to a wide range of users.

[0034] The evaluation unit can refer to the user's past improvement history and provide individually optimized feedback. For example, the evaluation unit refers to the history of feedback the user has received in the past and provides individually optimized feedback for the current task. For example, the evaluation unit avoids pointing out errors that have been pointed out in the past. In this way, providing individually optimized feedback improves the user's learning effect.

[0035] The evaluation unit can include specific advice based on success stories of other users. For example, when a user is working on a programming assignment, the evaluation unit provides specific advice based on success stories of other users. For example, the evaluation unit may provide advice such as, "Another user successfully wrote this part of code like this." This allows the user to learn specific ways to improve by referring to success stories of other users.

[0036] The feedback unit can visually explain feedback on areas for improvement using videos and animations. For example, when a user is working on a programming task, the generative AI can visually explain areas for improvement using videos and animations. For example, it can use animations to show how to correct code. This visual explanation deepens the user's understanding.

[0037] The feedback unit can provide feedback that corresponds to different languages ​​and cultural spheres. For example, when a user is working on a programming task, the generation AI provides feedback that corresponds to different languages ​​and cultural spheres. For example, feedback is provided in English and Chinese. This allows the system to accommodate different languages ​​and cultural spheres and thus accommodate global users.

[0038] The action analysis unit can automatically adjust the difficulty of the task according to the user's skill level. For example, when a user is working on a programming task, the action analysis unit uses a generation AI to analyze the user's skill level and automatically provide a task of an appropriate difficulty level. For example, basic tasks are provided to beginners, and applied tasks are provided to intermediate users. This provides an appropriate challenge by providing tasks according to the user's skill level.

[0039] The action analysis unit can provide tasks that mimic actual business scenarios. For example, when a user works on a programming task, the generation AI provides tasks that mimic actual business scenarios. For example, it provides tasks that involve fixing bugs or adding features that arise in actual projects. In this way, by providing tasks that mimic actual business scenarios, the user can acquire practical skills.

[0040] In addition to IT tasks, the action analysis unit can also provide business and soft skill tasks. For example, when a user works on a programming task, the generation AI can also provide business and soft skill tasks. For example, it can add tasks on project management and team communication. This allows for the provision of business and soft skill tasks, thereby improving overall skills.

[0041] The action analysis unit can introduce team tasks to promote cooperation between users. For example, when a user is working on a programming task, the action analysis unit has the generation AI provide a team task to foster collaboration skills. For example, the action analysis unit provides a task in which multiple users work together to complete a project. In this way, introducing a team task fosters collaboration skills.

[0042] The action analysis unit can customize the format of the training program according to the user's learning style. For example, when a user is working on a programming assignment, the action analysis unit uses the generation AI to analyze the user's learning style and provide the training program in the most appropriate format. For example, videos can be provided to users who prefer explanations in video format. This maximizes the learning effect by providing a training program that suits the user's learning style.

[0043] The action analysis unit can collect user feedback in real time and dynamically adjust the training program. For example, when a user works on a programming task, the generative AI collects feedback in real time and dynamically adjusts the training program. For example, if the user feels the task is difficult, the difficulty of the task can be lowered. In this way, by collecting feedback in real time and dynamically adjusting the training program, the user's learning effectiveness can be improved.

[0044] The action analysis unit can provide training programs tailored to different industries and occupations. For example, when a user works on a programming task, the generation AI provides a training program tailored to the different industry or occupation. For example, it provides programming tasks for the financial industry and programming tasks for the medical industry. This allows it to cater to a wide range of users by catering to different industries and occupations.

[0045] The action analysis unit visualizes the progress of the training program, allowing the user to realize their own growth. For example, when a user works on a programming assignment, the action analysis unit uses the generation AI to visualize the progress of the training program, allowing the user to realize their own growth. For example, the number of assignments completed and the number of areas for improvement are displayed in a graph. In this way, by visualizing the progress of the training program, the user can realize their own growth and maintain their motivation.

[0046] The Action Analysis Unit provides case studies on how to utilize generative AI, allowing users to learn specific application examples. For example, when users are working on a programming task, the Action Analysis Unit provides case studies on how to utilize generative AI, allowing users to learn specific application examples. For example, it introduces examples of automatic code generation and bug fixing using generative AI. By learning specific application examples of generative AI, users can acquire the skills to effectively utilize generative AI in their work.

[0047] The action analysis unit can provide a platform for sharing experiences using generative AI with other users. For example, when a user is working on a programming task, the action analysis unit provides a platform for sharing experiences using generative AI with other users. For example, a forum can be set up for sharing success stories and failure stories. In this way, sharing experiences using generative AI promotes the sharing of knowledge and improves overall skills.

[0048] The action analysis unit can introduce a certification program to evaluate the user's experience using the generative AI. For example, the action analysis unit introduces a certification program to evaluate the user's experience using the generative AI when working on a programming task. For example, a certificate is issued to a user who has completed a certain task. In this way, introducing the certification program proves the user's skills and increases their motivation.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The training system can further include a health management unit that monitors the user's health condition. For example, when a user is engaged in long training sessions, the health management unit monitors the user's heart rate and posture and encourages appropriate breaks. This makes it possible to support effective learning while maintaining the user's health. The health management unit can also suggest individually optimized break times and exercises based on the user's health data. For example, it can suggest stretching or light exercise to a user who has been sitting for long periods of time. Furthermore, the health management unit can adjust the difficulty of the training program taking into account the user's health condition. For example, it can provide less strenuous tasks to a user who is not feeling well.

[0051] The training system can further include a learning style analysis unit that analyzes the user's learning style. For example, if the user prefers visual information, the learning style analysis unit provides visually-oriented feedback based on that information. This makes it possible to provide optimal feedback according to the user's learning style. The learning style analysis unit can also suggest the most effective learning method based on the user's learning history. For example, it can identify the most effective learning method for the user from past data and recommend that method. Furthermore, the learning style analysis unit can customize the format of the training program according to the user's learning style. For example, a training program that makes heavy use of videos can be provided to a user who prefers explanations in video format.

[0052] The training system can further include a progress visualization unit that visualizes the user's learning progress. For example, the number of tasks the user has completed and the number of areas for improvement can be displayed in a graph, allowing the user to see their own growth. This can maintain the user's motivation and improve the effectiveness of their learning. The progress visualization unit can also suggest the next task to tackle based on the user's progress data. For example, it can present an appropriate task based on the user's current skill level. Furthermore, the progress visualization unit can also include a function to compare the user's progress with that of other users. For example, it can compare the progress of the user with that of other users taking the same training program, stimulating a competitive spirit.

[0053] The training system may further include a history analysis unit that analyzes a user's learning history and provides individually optimized feedback. For example, the system may refer to the history of feedback the user has received in the past and provide individually optimized feedback for the current assignment. This allows the system to provide optimal feedback that takes the user's learning history into consideration. The history analysis unit may also suggest the next assignment to tackle based on the user's learning history. For example, it may identify areas in which the user is weak from past data and provide assignments specific to those areas. Furthermore, the history analysis unit may also have a function to compare the user's learning history with other users. For example, the system may compare the user's learning history with the history of other users taking the same training program to stimulate a competitive spirit.

[0054] The training system may further include an environment analysis unit that analyzes the user's learning environment and provides the optimal learning environment. For example, when the user is working on a programming assignment, the environment analysis unit analyzes environmental data surrounding the user and suggests the optimal learning environment. This optimizes the user's learning environment, thereby improving learning effectiveness. The environment analysis unit may also adjust the content of the training program according to the user's learning environment. For example, a user studying in a quiet environment may be provided with an assignment that requires concentration. Furthermore, the environment analysis unit may also have a function to compare the user's learning environment with that of other users. For example, the environment analysis unit may compare the user's learning environment with that of other users taking the same training program and suggest the optimal environment.

[0055] The training system can also have a sharing section that allows users to share their learning results with other users. For example, users can share the tasks and areas for improvement they have completed with other users and provide each other with feedback. This promotes knowledge sharing among users and improves overall skills. The sharing section can also provide specific advice based on the user's learning results, taking into account the success stories of other users. For example, it could provide advice such as, "Another user successfully wrote this part of the code like this." Furthermore, the sharing section can also have a function that allows users to compare their learning results with those of other users. For example, comparing the results with those of other users taking the same training program can stimulate a competitive spirit.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The action analysis unit analyzes the user's actions. For example, it collects and analyzes data on actions such as clicks, inputs, and movements made by the user. The action analysis unit can also analyze the user's actions in real time and provide immediate feedback. For example, while the user is solving a programming problem, the generative AI analyzes the code in real time and immediately points out errors and areas for improvement. Step 2: The scoring unit scores the user's actions analyzed by the action analysis unit. For example, it evaluates the accuracy and efficiency of the code written by the user and assigns a score. The scoring unit can also accumulate the user's action history and compare it with past data to evaluate the user's level of growth. For example, it can accumulate data on programming problems solved in the past and compare it with the current problem to evaluate the user's level of growth. Step 3: The evaluator evaluates the user's actions, which have been scored by the scorer, and suggests improvements. For example, it may suggest specific improvements to the code written by the user, such as "Simplifying the logic in this part will improve processing speed." The evaluator can also score the user's actions taking into account their emotional state and provide feedback that reduces stress. For example, while the user is working on a programming task, the generator AI can analyze the user's facial expressions and voice and provide gentle feedback if the user is feeling stressed. Step 4: The feedback unit provides specific feedback to the user's actions evaluated by the evaluation unit. For example, it visualizes errors in the code written by the user and displays the scoring results in a visually easy-to-understand format. The feedback unit can also visualize the user's actions and display the scoring results in a visually easy-to-understand format. For example, it visualizes the code structure and error locations and displays the scoring results in a visually easy-to-understand format.

[0058] (Example 2) The training system according to the embodiment of the present invention uses generative AI to score user actions and evaluate areas for improvement, thereby providing users with concrete experience in solving IT problems.

[0059] A training system according to an embodiment includes an action analysis unit, a scoring unit, an evaluation unit, and a feedback unit. The action analysis unit analyzes user actions. For example, it collects and analyzes data on actions such as clicks, inputs, and movements performed by the user. The action analysis unit can also analyze user actions in real time and provide immediate feedback. For example, while the user is solving a programming problem, a generation AI analyzes the code in real time and immediately points out errors and areas for improvement. The scoring unit scores the user actions analyzed by the action analysis unit. For example, it evaluates and scores the accuracy and efficiency of the code written by the user. The scoring unit can also accumulate the user's action history and compare it with past data to evaluate the user's level of growth. For example, it accumulates data on programming problems solved in the past and compares it with current problems to evaluate the user's level of growth. The evaluation unit evaluates areas for improvement for the user actions scored by the scoring unit. For example, it suggests specific areas for improvement for the code written by the user, such as, "Simplifying the logic in this part will improve processing speed." The evaluation unit can also score the user's performance by taking into account the user's emotional state and providing feedback that reduces stress. For example, while the user is working on a programming assignment, the generation AI can analyze the user's facial expressions and voice and provide gentle feedback if the user is feeling stressed. The feedback unit provides specific feedback for the user's actions evaluated by the evaluation unit. For example, it can visualize errors in the code written by the user and display the scoring results in a visually easy-to-understand format. The feedback unit can also visualize the user's actions and display the scoring results in a visually easy-to-understand format. For example, it can visualize the code structure and error locations and display the scoring results in a visually easy-to-understand format. This allows the training system according to the embodiment to provide the user with concrete experience in IT assignments. For example, the user can learn how to write efficient code and fix bugs through programming assignments.Generative AI helps users improve their practical skills by evaluating their actions.

[0060] The action analysis unit analyzes user actions in real time and can provide instant feedback. For example, while a user is solving a programming problem, the generative AI analyzes the code in real time and immediately points out errors and areas for improvement. For example, if part of the code is inefficient, it will immediately suggest an efficient way to write it. This provides real-time feedback, improving the user's learning effect.

[0061] The scoring unit can accumulate the user's action history and compare it with past data to evaluate the user's growth. For example, the scoring unit accumulates data on programming problems the user has solved in the past and compares it with current problems to evaluate the user's growth. For example, the scoring unit calculates the growth level based on the efficiency of the code or the reduction in errors. In this way, the evaluation of the user's growth level visualizes the user's progress and maintains their motivation.

[0062] The evaluation unit can score the user based on their emotional state and provide feedback that reduces stress. For example, when a user is working on a programming task, the generation AI analyzes the user's facial expressions and voice, and provides gentle feedback if the user is feeling stressed. For example, it can display a message such as "Take a short break and try again." By taking the user's emotional state into consideration, this reduces stress and improves learning effectiveness.

[0063] The feedback unit visualizes the user's actions and displays the scoring results in a visually easy-to-understand format. For example, after a user solves a programming problem, the generative AI visualizes the code structure and error locations, and displays the scoring results in a visually easy-to-understand format. For example, it highlights error locations in red and indicates areas for improvement with arrows. This allows the user to deepen their understanding by displaying the scoring results in a visually easy-to-understand format.

[0064] The action analysis unit can expand the scoring algorithm of the generative AI so that it can handle tasks in different fields. For example, the action analysis unit expands the scoring algorithm of the generative AI so that it can handle not only programming tasks but also design tasks. For example, it adds an algorithm to evaluate the balance and color scheme of the design. This makes it possible to handle tasks in different fields, thereby catering to a wide range of users.

[0065] The evaluation unit can provide feedback at the moment when the user feels most motivated. For example, when a user is working on a programming assignment, the generation AI analyzes the user's emotional state and provides feedback at the moment when motivation is highest. For example, the evaluation unit displays a message such as, "This code is very good. Keep it up." This maintains the user's motivation and improves learning effectiveness.

[0066] The evaluation unit can refer to the user's past improvement history and provide individually optimized feedback. For example, the evaluation unit refers to the history of feedback the user has received in the past and provides individually optimized feedback for the current task. For example, the evaluation unit avoids pointing out errors that have been pointed out in the past. In this way, providing individually optimized feedback improves the user's learning effect.

[0067] The evaluation unit can include specific advice based on success stories of other users. For example, when a user is working on a programming assignment, the evaluation unit provides specific advice based on success stories of other users. For example, the evaluation unit may provide advice such as, "Another user successfully wrote this part of code like this." This allows the user to learn specific ways to improve by referring to success stories of other users.

[0068] The evaluation unit presents improvements in a way that is easy for the user to accept, eliciting positive emotions. For example, when a user is working on a programming assignment, the generation AI analyzes the user's emotional state and presents improvements in a way that elicits positive emotions. For example, it displays a message such as, "If you improve this part like this, it will be even better." This elicits positive emotions and increases the user's motivation to learn.

[0069] The feedback unit can visually explain feedback on areas for improvement using videos and animations. For example, when a user is working on a programming task, the generative AI can visually explain areas for improvement using videos and animations. For example, it can use animations to show how to correct code. This visual explanation deepens the user's understanding.

[0070] The feedback unit can provide feedback that corresponds to different languages ​​and cultural spheres. For example, when a user is working on a programming task, the generation AI provides feedback that corresponds to different languages ​​and cultural spheres. For example, feedback is provided in English and Chinese. This allows the system to accommodate different languages ​​and cultural spheres and thus accommodate global users.

[0071] The evaluation unit can present improvements in a format that is easiest for the user to understand. For example, when the user is working on a programming task, the generation AI analyzes the user's emotional state and presents improvements in a format that is easiest for the user to understand. For example, if the user prefers visual information, the evaluation unit can explain improvements using diagrams and graphs. This enhances learning effectiveness by presenting improvements in a format that is easiest for the user to understand.

[0072] The action analysis unit can automatically adjust the difficulty of the task according to the user's skill level. For example, when a user is working on a programming task, the action analysis unit uses a generation AI to analyze the user's skill level and automatically provide a task of an appropriate difficulty level. For example, basic tasks are provided to beginners, and applied tasks are provided to intermediate users. This provides an appropriate challenge by providing tasks according to the user's skill level.

[0073] The action analysis unit can provide tasks that mimic actual business scenarios. For example, when a user works on a programming task, the generation AI provides tasks that mimic actual business scenarios. For example, it provides tasks that involve fixing bugs or adding features that arise in actual projects. In this way, by providing tasks that mimic actual business scenarios, the user can acquire practical skills.

[0074] In addition to IT tasks, the action analysis unit can also provide business and soft skill tasks. For example, when a user works on a programming task, the generation AI can also provide business and soft skill tasks. For example, it can add tasks on project management and team communication. This allows for the provision of business and soft skill tasks, thereby improving overall skills.

[0075] The action analysis unit can introduce team tasks to promote cooperation between users. For example, when a user is working on a programming task, the action analysis unit has the generation AI provide a team task to foster collaboration skills. For example, the action analysis unit provides a task in which multiple users work together to complete a project. In this way, introducing a team task fosters collaboration skills.

[0076] The action analysis unit can provide tasks at a time when the user is most relaxed. For example, when a user is working on a programming task, the generation AI analyzes the user's emotional state and provides the task at a time when the user is most relaxed. For example, the task is presented at a time when the user is not feeling stressed. This improves learning effectiveness by providing tasks at a time when the user is most relaxed.

[0077] The action analysis unit can customize the format of the training program according to the user's learning style. For example, when a user is working on a programming assignment, the action analysis unit uses the generation AI to analyze the user's learning style and provide the training program in the most appropriate format. For example, videos can be provided to users who prefer explanations in video format. This maximizes the learning effect by providing a training program that suits the user's learning style.

[0078] The action analysis unit can collect user feedback in real time and dynamically adjust the training program. For example, when a user works on a programming task, the generative AI collects feedback in real time and dynamically adjusts the training program. For example, if the user feels the task is difficult, the difficulty of the task can be lowered. In this way, by collecting feedback in real time and dynamically adjusting the training program, the user's learning effectiveness can be improved.

[0079] The action analysis unit can provide a training program based on the user's emotional state. For example, when a user is working on a programming task, the generation AI analyzes the user's emotional state and provides a training program at the optimal time. For example, the task can be presented when the user is relaxed. This maximizes the learning effect by providing a training program that suits the user's emotional state.

[0080] The action analysis unit can provide training programs tailored to different industries and occupations. For example, when a user works on a programming task, the generation AI provides a training program tailored to the different industry or occupation. For example, it provides programming tasks for the financial industry and programming tasks for the medical industry. This allows it to cater to a wide range of users by catering to different industries and occupations.

[0081] The action analysis unit visualizes the progress of the training program, allowing the user to realize their own growth. For example, when a user works on a programming assignment, the action analysis unit uses the generation AI to visualize the progress of the training program, allowing the user to realize their own growth. For example, the number of assignments completed and the number of areas for improvement are displayed in a graph. In this way, by visualizing the progress of the training program, the user can realize their own growth and maintain their motivation.

[0082] The action analysis unit can provide a training program at a time when the user can concentrate best. For example, when a user is working on a programming assignment, the generation AI analyzes the user's emotional state and provides the training program at a time when the user can concentrate best. For example, the task is presented at a time when the user is most focused. This maximizes the learning effect by providing the training program at a time when the user can concentrate best.

[0083] The Action Analysis Unit provides case studies on how to utilize generative AI, allowing users to learn specific application examples. For example, when users are working on a programming task, the Action Analysis Unit provides case studies on how to utilize generative AI, allowing users to learn specific application examples. For example, it introduces examples of automatic code generation and bug fixing using generative AI. By learning specific application examples of generative AI, users can acquire the skills to effectively utilize generative AI in their work.

[0084] The action analysis unit can identify the application field of generative AI that the user is most interested in and provide training specialized in that field. For example, when the user is working on a programming task, the action analysis unit allows the generative AI to analyze the user's emotional state and identify the application field of generative AI that the user is most interested in. For example, the action analysis unit provides training specialized in automatic code generation that the user is interested in. This improves learning effectiveness by providing training specialized in the application field that the user is most interested in.

[0085] The action analysis unit can provide a platform for sharing experiences using generative AI with other users. For example, when a user is working on a programming task, the action analysis unit provides a platform for sharing experiences using generative AI with other users. For example, a forum can be set up for sharing success stories and failure stories. In this way, sharing experiences using generative AI promotes the sharing of knowledge and improves overall skills.

[0086] The action analysis unit can introduce a certification program to evaluate the user's experience using the generative AI. For example, the action analysis unit introduces a certification program to evaluate the user's experience using the generative AI when working on a programming task. For example, a certificate is issued to a user who has completed a certain task. In this way, introducing the certification program proves the user's skills and increases their motivation.

[0087] The action analysis unit can identify the method of using the generative AI in which the user is most confident and provide training to strengthen that method. For example, when the user is working on a programming task, the action analysis unit can have the generative AI analyze the user's emotional state and identify the method of using the generative AI in which the user is most confident. For example, the action analysis unit can provide training specialized in automatic code generation in which the user is most confident. This helps improve skills by strengthening the method of use in which the user is most confident.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The training system can further include a health management unit that monitors the user's health condition. For example, when a user is engaged in long training sessions, the health management unit monitors the user's heart rate and posture and encourages appropriate breaks. This makes it possible to support effective learning while maintaining the user's health. The health management unit can also suggest individually optimized break times and exercises based on the user's health data. For example, it can suggest stretching or light exercise to a user who has been sitting for long periods of time. Furthermore, the health management unit can adjust the difficulty of the training program taking into account the user's health condition. For example, it can provide less strenuous tasks to a user who is not feeling well.

[0090] The training system can further include a learning style analysis unit that analyzes the user's learning style. For example, if the user prefers visual information, the learning style analysis unit provides visually-oriented feedback based on that information. This makes it possible to provide optimal feedback according to the user's learning style. The learning style analysis unit can also suggest the most effective learning method based on the user's learning history. For example, it can identify the most effective learning method for the user from past data and recommend that method. Furthermore, the learning style analysis unit can customize the format of the training program according to the user's learning style. For example, a training program that makes heavy use of videos can be provided to a user who prefers explanations in video format.

[0091] The training system can further include a progress visualization unit that visualizes the user's learning progress. For example, the number of tasks the user has completed and the number of areas for improvement can be displayed in a graph, allowing the user to see their own growth. This can maintain the user's motivation and improve the effectiveness of their learning. The progress visualization unit can also suggest the next task to tackle based on the user's progress data. For example, it can present an appropriate task based on the user's current skill level. Furthermore, the progress visualization unit can also include a function to compare the user's progress with that of other users. For example, it can compare the progress of the user with that of other users taking the same training program, stimulating a competitive spirit.

[0092] The training system can further include an emotion analysis unit that analyzes the user's emotional state and provides feedback based on the emotion. For example, when a user is working on a programming assignment, the emotion analysis unit analyzes the user's facial expressions and voice, and provides gentle feedback if the user is feeling stressed. This provides feedback that takes the user's emotional state into consideration, thereby reducing stress and improving learning effectiveness. The emotion analysis unit can also adjust the difficulty of the training program according to the user's emotional state. For example, a user who is feeling stressed can be provided with a less burdensome assignment. Furthermore, the emotion analysis unit can suggest appropriate times to take a break based on the user's emotional state. For example, if the user is tired, a message urging them to take a break is displayed.

[0093] The training system may further include a motivation analysis unit that analyzes a user's motivation and provides feedback based on that motivation. For example, while a user is working on a programming assignment, the motivation analysis unit analyzes the user's behavioral data and provides an encouraging message if the user's motivation is declining. This helps maintain the user's motivation and improve learning effectiveness. The motivation analysis unit may also adjust the content of the training program according to the user's motivation. For example, a user with high motivation may be provided with more challenging assignments. Furthermore, the motivation analysis unit may introduce a reward system to increase user motivation. For example, badges or points may be awarded to users who complete certain assignments.

[0094] The training system may further include a history analysis unit that analyzes a user's learning history and provides individually optimized feedback. For example, the system may refer to the history of feedback the user has received in the past and provide individually optimized feedback for the current assignment. This allows the system to provide optimal feedback that takes the user's learning history into consideration. The history analysis unit may also suggest the next assignment to tackle based on the user's learning history. For example, it may identify areas in which the user is weak from past data and provide assignments specific to those areas. Furthermore, the history analysis unit may also have a function to compare the user's learning history with other users. For example, the system may compare the user's learning history with the history of other users taking the same training program to stimulate a competitive spirit.

[0095] The training system may further include an environment analysis unit that analyzes the user's learning environment and provides the optimal learning environment. For example, when the user is working on a programming assignment, the environment analysis unit analyzes environmental data surrounding the user and suggests the optimal learning environment. This optimizes the user's learning environment, thereby improving learning effectiveness. The environment analysis unit may also adjust the content of the training program according to the user's learning environment. For example, a user studying in a quiet environment may be provided with an assignment that requires concentration. Furthermore, the environment analysis unit may also have a function to compare the user's learning environment with that of other users. For example, the environment analysis unit may compare the user's learning environment with that of other users taking the same training program and suggest the optimal environment.

[0096] The training system can further include an emotion analysis unit that analyzes the user's emotional state and provides a training program based on the emotion. For example, when a user is working on a programming task, the emotion analysis unit analyzes the user's facial expressions and voice, and if the user is relaxed, provides a more difficult task. This makes it possible to provide an optimal training program that takes the user's emotional state into consideration. The emotion analysis unit can also adjust the format of the training program according to the user's emotional state. For example, a user who is feeling stressed can be provided with a training program in a relaxing format. Furthermore, the emotion analysis unit can suggest appropriate times to take a break based on the user's emotional state. For example, if the user is tired, a message urging them to take a break is displayed.

[0097] The training system can also have a sharing section that allows users to share their learning results with other users. For example, users can share the tasks and areas for improvement they have completed with other users and provide each other with feedback. This promotes knowledge sharing among users and improves overall skills. The sharing section can also provide specific advice based on the user's learning results, taking into account the success stories of other users. For example, it could provide advice such as, "Another user successfully wrote this part of the code like this." Furthermore, the sharing section can also have a function that allows users to compare their learning results with those of other users. For example, comparing the results with those of other users taking the same training program can stimulate a competitive spirit.

[0098] The training system can further analyze the user's emotional state and introduce an emotion-based reward system. For example, when a user is working on a programming task, the emotion analysis unit analyzes the user's facial expressions and voice and provides rewards in a way that elicits positive emotions. This can increase motivation by introducing a reward system that takes the user's emotional state into consideration. The emotion analysis unit can also adjust the content of the reward according to the user's emotional state. For example, a user who is feeling stressed can be provided with a reward that helps them relax. Furthermore, the emotion analysis unit can provide rewards at appropriate times based on the user's emotional state. For example, if the user feels a sense of accomplishment, a reward can be provided immediately.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The action analysis unit analyzes the user's actions. For example, it collects and analyzes data on actions such as clicks, inputs, and movements made by the user. The action analysis unit can also analyze the user's actions in real time and provide immediate feedback. For example, while the user is solving a programming problem, the generative AI analyzes the code in real time and immediately points out errors and areas for improvement. Step 2: The scoring unit scores the user's actions analyzed by the action analysis unit. For example, it evaluates the accuracy and efficiency of the code written by the user and assigns a score. The scoring unit can also accumulate the user's action history and compare it with past data to evaluate the user's level of growth. For example, it can accumulate data on programming problems solved in the past and compare it with the current problem to evaluate the user's level of growth. Step 3: The evaluator evaluates the user's actions, which have been scored by the scorer, and suggests improvements. For example, it may suggest specific improvements to the code written by the user, such as "Simplifying the logic in this part will improve processing speed." The evaluator can also score the user's actions taking into account their emotional state and provide feedback that reduces stress. For example, while the user is working on a programming task, the generator AI can analyze the user's facial expressions and voice and provide gentle feedback if the user is feeling stressed. Step 4: The feedback unit provides specific feedback to the user's actions evaluated by the evaluation unit. For example, it visualizes errors in the code written by the user and displays the scoring results in a visually easy-to-understand format. The feedback unit can also visualize the user's actions and display the scoring results in a visually easy-to-understand format. For example, it visualizes the code structure and error locations and displays the scoring results in a visually easy-to-understand format.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] 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.

[0160] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an action analysis unit that analyzes user actions; a scoring unit that scores the user's actions analyzed by the action analysis unit; an evaluation unit that evaluates points for improvement in the user's actions scored by the scoring unit; a feedback unit that provides specific feedback for the user's action evaluated by the evaluation unit. A system characterized by:

2. The action analysis unit Analyze the user's actions in real time and provide immediate feedback 2. The system of claim 1.

3. The scoring unit Accumulating the user's action history and comparing it with past data to evaluate the user's growth 2. The system of claim 1.

4. The feedback unit Visualize the user's actions and display the scoring results in a visually easy-to-understand format.

2. The system of claim 1.

5. The evaluation unit Present improvements in a way that is easy for the user to accept, eliciting positive emotions 2. The system of claim 1.

6. The action analysis unit Automatically adjust the difficulty of the task according to the skill level of the user.

2. The system of claim 1.

7. The action analysis unit Providing a training program based on the emotional state of the user 2. The system of claim 1.

8. The action analysis unit Identify the application areas of generative AI that interest the user most and provide specialized training in those areas.

2. The system of claim 1.

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