system

Abstract

The system according to the embodiment aims to suggest a menu of self-study to students and visualize their own growth. [Solution] A system according to an embodiment includes a suggestion unit, a recording unit, and a visualization unit. The suggestion unit suggests a self-practice menu based on the student's level or goal. The recording unit records the practice content performed based on the menu suggested by the suggestion unit. The visualization unit analyzes the data recorded by the recording unit and visualizes the student's progress.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for students to plan their own practice sessions or visualize their own progress, making it difficult to maintain motivation.

[0005] The system according to the embodiment aims to suggest a menu of self-study to students and visualize their own growth. [Means for solving the problem]

[0006] The system according to the embodiment includes a suggestion unit, a recording unit, and a visualization unit. The suggestion unit suggests a self-study menu based on the student's level or goal. The recording unit records the practice content performed based on the menu suggested by the suggestion unit. The visualization unit analyzes the data recorded by the recording unit and visualizes the progress of the student. [Effects of the Invention]

[0007] The system according to the embodiment allows students to visualize their own growth by suggesting a menu of self-study activities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An independent practice support system according to an embodiment of the present invention effectively supports students' independent practice and visualizes their progress. The independent practice support system proposes an independent practice menu based on the student's level and goals, records the content of the practice, and visualizes the student's progress. For example, when a student considers an independent practice menu, the independent practice support system uses AI to suggest an appropriate menu. For example, the AI ​​may propose a menu based on the student's level and goals, such as shooting practice, dribbling practice, or physical training. Next, the independent practice support system records the content of the practice the student performed. For example, the system records the shooting success rate, dribbling speed, and physical training results. This data is automatically collected and analyzed by AI. Furthermore, the independent practice support system visualizes the student's progress based on the data collected by AI. For example, graphs and charts may be used to display the progress of shooting success rate and changes in dribbling speed. This allows students to realize their own progress and maintain their motivation. This allows the independent practice support system to effectively encourage students' independent practice and differentiate themselves outside of group practice. This allows the self-study support system to effectively support students' self-study and increase their motivation by visualizing their progress. For example, improving shooting success rate or dribbling speed will improve performance in a game. Furthermore, by visualizing their progress, students can see that their efforts are paying off, helping them maintain their motivation.

[0029] The independent practice support system according to the embodiment includes a suggestion unit, a recording unit, and a visualization unit. The suggestion unit suggests an independent practice menu based on a student's level or goal. The suggestion unit can suggest a menu based on, for example, the student's self-evaluation, past practice data, or match results. The suggestion unit can also use AI to suggest an optimal menu based on the student's level or goal. For example, the suggestion unit suggests a menu based on the student's level or goal, such as shooting practice, dribbling practice, or physical training. The recording unit records the content of the practice performed based on the menu suggested by the suggestion unit. The recording unit can record, for example, the shooting success rate, dribbling speed, and physical training results. The recording unit can also automatically record the content of the practice using AI. For example, the recording unit can measure the shooting success rate and record the data. The recording unit can also measure the dribbling speed and record the data. The recording unit can also record the results of physical training. The visualization unit analyzes the data recorded by the recording unit and visualizes the student's progress. The visualization unit can display, for example, the progress of a student's shooting success rate or changes in dribbling speed using a graph or chart. The visualization unit can also visualize the student's progress using AI. For example, the visualization unit can display the progress of a student's shooting success rate in a graph, allowing the student's progress to be visually grasped. The visualization unit can also display changes in dribbling speed in a chart, allowing the student's progress to be visually grasped. In this way, the independent practice support system according to the embodiment can effectively support the student's independent practice and visualize the student's progress. For example, the student can realize his or her own progress and maintain motivation. Furthermore, by visualizing the student's progress, the student can realize that his or her efforts are resulting in results, allowing the student to maintain motivation.

[0030] The suggestion unit can suggest a menu based on the student's self-assessment, past practice data, or game results. The suggestion unit, for example, suggests a menu based on the student's self-assessment. For example, the suggestion unit can provide the student with a questionnaire for self-assessment and suggest a menu based on the results. The suggestion unit can also suggest a menu based on past practice data. For example, the suggestion unit can analyze the student's past practice records and suggest a menu based on the results. The suggestion unit can also suggest a menu based on game results. For example, the suggestion unit can analyze the student's game results and suggest a menu based on the results. In this way, the suggestion unit can suggest a practice menu tailored to the student's individual situation, thereby supporting more effective independent practice. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the student's self-assessment data into a generation AI and suggest a menu based on the generation AI.

[0031] The recording unit can record the shooting success rate, dribbling speed, and physical training results. The recording unit, for example, records the shooting success rate. For example, the recording unit can measure the shooting success rate and record the data. The recording unit can also record the dribbling speed. For example, the recording unit can measure the dribbling speed and record the data. The recording unit can also record the results of physical training. For example, the recording unit records the results of a strength test or an endurance test. In this way, the recording unit can accurately grasp the student's growth by recording the details of the practice. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input shooting success rate data into a generation AI and record the data based on the generation AI.

[0032] The visualization unit can display the progress of a shooting success rate or the change in dribbling speed using a graph or chart. The visualization unit, for example, displays the progress of a shooting success rate in a graph. For example, the visualization unit can display the change in the success rate along a time axis based on the shooting success rate data. The visualization unit can also display the change in dribbling speed in a chart. For example, the visualization unit can display the change in speed along a time axis based on the dribbling speed data. This allows the visualization unit to visually grasp the student's progress. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input shooting success rate data into a generation AI and generate a graph based on the generation AI.

[0033] The visualization unit can analyze growth trends by comparing data with past data and provide specific advice. For example, the visualization unit can compare past shooting success rate data with current data to analyze growth trends. For example, the visualization unit can display past data and current data in a graph to visually grasp growth trends. The visualization unit can also provide specific advice based on growth trends. For example, the visualization unit can suggest specific training methods for improving shooting success rate. The visualization unit can also provide specific advice for improving dribbling speed. In this way, the visualization unit can analyze growth trends and provide specific advice to maximize the effectiveness of the student's practice. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input past data and current data into a generation AI, analyze growth trends based on the generation AI, and provide advice.

[0034] The suggestion unit can analyze the student's past practice data and suggest an appropriate practice menu in real time. For example, the suggestion unit can analyze the student's past shooting success rate and adjust the frequency and content of shooting practice. The suggestion unit can also set the intensity of dribbling practice based on the student's dribbling speed data. The suggestion unit can also suggest an appropriate training menu taking into account the results of the student's physical training. In this way, the suggestion unit can maximize the effectiveness of practice by suggesting an optimal menu based on the past practice data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the student's past practice data into a generation AI and suggest a menu in real time based on the generation AI.

[0035] The suggestion unit can dynamically adjust the practice menu based on the student's physical condition data. For example, if the student's heart rate is high, the suggestion unit can suggest a lighter practice menu, taking the student's physical condition into consideration. Furthermore, if the student's fatigue level is high, the suggestion unit can suggest a recovery menu, promoting fatigue recovery. Furthermore, if the student's physical condition is good, the suggestion unit can suggest a regular practice menu, supporting effective practice. In this way, the suggestion unit can improve the effectiveness of practice by suggesting a practice menu that suits the student's physical condition. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the student's physical condition data into the generation AI and dynamically adjust the practice menu based on the generation AI.

[0036] The suggestion unit can gradually evolve the content of the practice menu according to the student's level of goal achievement. For example, when the student achieves a set goal, the suggestion unit uses AI to suggest the next step menu. Furthermore, when the student is approaching the goal, the suggestion unit can also fine-tune the current menu to support goal achievement. Furthermore, when the student is far from the goal, the suggestion unit can also re-suggest a basic menu to improve basic skills. This allows the suggestion unit to maximize the effectiveness of practice by suggesting a menu according to the student's level of goal achievement. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without AI. For example, the suggestion unit can input the student's goal achievement data into a generation AI and gradually evolve the practice menu based on the generation AI.

[0037] The suggestion unit can suggest an appropriate practice location taking into account the student's geographical location information. For example, if the student is at home, the suggestion unit can have the AI ​​suggest a practice menu that can be done at home. Furthermore, if the student is at school, the suggestion unit can have the AI ​​suggest a practice menu that can be done in the school gymnasium. Furthermore, if the student is in a park, the suggestion unit can have the AI ​​suggest a practice menu that can be done in the park. In this way, the suggestion unit can improve the effectiveness of practice by suggesting a practice location based on the student's location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the student's geographical location information into the generation AI and suggest an appropriate practice location based on the generation AI.

[0038] The suggestion unit can analyze the student's social media activity and suggest a related practice menu. For example, the suggestion unit can use AI to suggest a similar practice menu based on a practice video shared by the student on social media. The suggestion unit can also use AI to suggest a practice menu based on the practice menus of basketball players the student follows. The suggestion unit can also use AI to suggest a practice menu based on trends in basketball communities the student participates in on social media. This allows the suggestion unit to attract the student's interest by suggesting a menu based on social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the student's social media activity data into a generation AI and suggest a related practice menu based on the generation AI.

[0039] The suggestion unit can customize the practice menu by reflecting the student's past feedback. For example, the suggestion unit uses AI to suggest a new menu based on practice menus that the student has previously preferred. The suggestion unit can also use AI to adjust the practice menu so that the student avoids practice menus that the student has previously struggled with. The suggestion unit can also optimize the content of the practice menu based on feedback provided by the student in the past. This allows the suggestion unit to improve student satisfaction by suggesting a menu based on past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the student's past feedback data into a generation AI and customize a practice menu based on the generation AI.

[0040] The recording unit can simultaneously record the student's physical condition data when recording the content of the practice. For example, the recording unit can record the student's heart rate during practice and evaluate the intensity of the practice. The recording unit can also record the degree of fatigue after practice and reflect this in the next practice menu. The recording unit can also record physical condition data during practice in real time and analyze the effectiveness of the practice. In this way, the recording unit can accurately grasp the effectiveness of the practice by simultaneously recording physical condition data. Some or all of the above-mentioned processing in the recording unit can be performed, for example, using AI, or can be performed without using AI. For example, the recording unit can input physical condition data during practice into a generation AI and record the data based on the generation AI.

[0041] When recording practice content, the recording unit can select an appropriate recording means depending on the device used by the student. For example, if a smartphone is used, the recording unit records the practice content through an app. Furthermore, if a wearable device is used, the recording unit can also record the practice content through the device's sensors. Furthermore, if a tablet is used, the recording unit can also perform detailed recording on a large screen. This allows the recording unit to efficiently record practice content using a recording means appropriate for the device used. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input data from the device used into a generation AI and select the optimal recording means based on the generation AI.

[0042] When recording practice content, the recording unit can improve the accuracy of the recording by referring to the student's past record data. The recording unit records the current practice content based on, for example, the student's past shooting success rate. The recording unit can also record the current practice content by referring to past dribbling speed data. The recording unit can also record the current practice content based on the results of past physical training. In this way, the recording unit can improve the accuracy of the recording by referring to the past record data. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the past record data into a generation AI and improve the accuracy of the recording based on the generation AI.

[0043] When recording practice content, the recording unit can prioritize recording highly relevant data by taking into account the student's geographical location information. For example, if the student practices at home, the recording unit can prioritize recording practice data from the home. Furthermore, if the student practices at school, the recording unit can prioritize recording practice data from the school. Furthermore, if the student practices in a park, the recording unit can prioritize recording practice data from the park. In this way, the recording unit can improve the effectiveness of practice by recording data based on the geographical location information. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the student's geographical location information into the generation AI and prioritize recording highly relevant data based on the generation AI.

[0044] The recording unit can analyze the student's social media activity and record related data when recording practice content. For example, the recording unit can have AI record related data based on practice videos shared by the student on social media. The recording unit can also have AI record data based on the practice content of basketball players the student follows on social media. The recording unit can also have AI record data based on trends in basketball communities the student participates in on social media. This allows the recording unit to improve the effectiveness of practice by recording data based on social media activity. Some or all of the above-mentioned processing in the recording unit can be performed using AI, for example, or without AI. For example, the recording unit can input the student's social media activity data into a generation AI and record related data based on the generation AI.

[0045] When recording practice content, the recording unit can customize the recording method by reflecting the student's past feedback. For example, the recording unit uses AI to suggest a new recording method based on the student's past preferred recording method. The recording unit can also be adjusted by AI to avoid recording methods that the student has previously struggled with. The recording unit can also optimize the recording method based on feedback provided by the student in the past. This allows the recording unit to improve the effectiveness of practice using a recording method based on past feedback. Some or all of the above-mentioned processing in the recording unit may be performed using AI, for example, or may be performed without using AI. For example, the recording unit can input the student's past feedback data into a generation AI and customize the recording method based on the generation AI.

[0046] During visualization, the visualization unit can analyze growth trends by referring to the student's past performance data. For example, the visualization unit can analyze current performance based on past shooting success rate data. The visualization unit can also analyze current performance based on past dribbling speed data. The visualization unit can also analyze current performance based on the results of past physical training. This allows the visualization unit to improve the effectiveness of practice by analyzing growth trends based on past performance data. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input past performance data into a generation AI and analyze growth trends based on the generation AI.

[0047] The visualization unit can apply different visualization methods depending on the student's level of goal achievement during visualization. For example, when a student achieves a goal, the visualization unit provides a display method in which the AI ​​emphasizes the level of achievement. Furthermore, when a student is approaching a goal, the visualization unit can also display the progress in detail. Furthermore, when a student is far from the goal, the visualization unit can also provide a display method in which the AI ​​emphasizes basic data and indicates areas for improvement. This allows the visualization unit to effectively display the student's progress using a visualization method according to the level of goal achievement. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the student's goal achievement data into a generation AI and apply different visualization methods based on the generation AI.

[0048] The visualization unit can improve the accuracy of the visualization by reflecting student feedback during visualization. For example, the visualization unit uses AI to optimize the display method of the visualization based on feedback previously provided by the student. The visualization unit can also adjust the display method based on the student's preferred visualization technique. The visualization unit can also adjust the display method to avoid visualization techniques that the student finds difficult. This allows the visualization unit to effectively display the degree of growth through feedback-based visualization. Some or all of the above-mentioned processing in the visualization unit may be performed using AI, for example, or may be performed without using AI. For example, the visualization unit can input student feedback data into the generation AI and improve the accuracy of the visualization based on the generation AI.

[0049] When visualizing, the visualization unit can prioritize displaying highly relevant data by taking into account the student's geographical location information. For example, if a student practices at home, the visualization unit can prioritize displaying practice data from the home. Furthermore, if a student practices at school, the visualization unit can prioritize displaying practice data from the school. Furthermore, if a student practices in a park, the visualization unit can prioritize displaying practice data from the park. This allows the visualization unit to effectively display the progress of a student by displaying data based on the geographical location information. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the student's geographical location information into a generation AI and prioritize displaying highly relevant data based on the generation AI.

[0050] During visualization, the visualization unit can analyze the student's social media activity and display related data. For example, the visualization unit can use AI to display related data based on practice videos shared by the student on social media. The visualization unit can also use AI to display data based on the practice content of basketball players the student follows. The visualization unit can also use AI to display data based on trends in basketball communities in which the student participates on social media. This allows the visualization unit to effectively display the student's progress by displaying data based on social media activity. Some or all of the above-described processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the student's social media activity data into a generation AI and display related data based on the generation AI.

[0051] The visualization unit can customize the visualization method by reflecting the student's past feedback during visualization. For example, the visualization unit uses AI to suggest a new visualization method based on the student's past preferred visualization methods. The visualization unit can also use AI to adjust the visualization method so as to avoid visualization methods that the student found difficult in the past. The visualization unit can also optimize the visualization method based on feedback provided by the student in the past. This allows the visualization unit to effectively display the student's progress using a visualization method based on the student's past feedback. Some or all of the above-described processing in the visualization unit may be performed using AI, for example, or may be performed without using AI. For example, the visualization unit can input the student's past feedback data into the generation AI and customize the visualization method based on the generation AI.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The suggestion unit can analyze the student's learning style and propose an individually optimized practice menu. For example, for a student who prefers visual learning, a menu including video tutorials can be proposed. For a student who prefers auditory learning, a practice menu with audio guides can be proposed. Furthermore, for a student who prefers practical learning, a menu that emphasizes actual practice can be proposed. In this way, the suggestion unit can maximize the effectiveness of practice by providing a practice menu that suits the student's learning style.

[0054] The recording unit can record data appropriate to the environment, taking into account the student's practice environment. For example, when practicing outdoors, environmental data such as weather and temperature can be recorded. When practicing indoors, environmental data such as lighting and sound can be recorded. Furthermore, when practicing at a specific facility, data appropriate to the characteristics of the facility can be recorded. In this way, the recording unit can more accurately evaluate the effectiveness of practice by recording data appropriate to the practice environment.

[0055] The proposal department can propose long-term training plans based on students' learning history. For example, it can analyze past training data and propose training menus aimed at goals several months into the future. It can also adjust training menus to match match and tournament schedules so that students can perform at their peak. It can also propose training plans that gradually increase in difficulty according to the student's stage of development. This allows the proposal department to support training from a long-term perspective and promote student growth.

[0056] The suggestion unit can propose a practice menu that takes nutritional balance into consideration based on the student's dietary data. For example, if a student is lacking in a particular nutrient, the AI ​​can propose a practice menu to supplement that nutrient. Also, if a student has an excess of a nutrient, the AI ​​can propose a practice menu to consume that nutrient. Furthermore, the AI ​​can suggest the optimal practice time based on the student's eating patterns. This allows the suggestion unit to support the student's health and performance with a practice menu that takes nutritional balance into consideration.

[0057] The suggestion unit can adjust the practice menu based on the student's sleep data. For example, if the student is sleep-deprived, the AI ​​can suggest a lighter practice menu, taking their physical condition into consideration. If the student has had enough sleep, the AI ​​can also suggest a regular practice menu. Furthermore, if the quality of sleep is good, the AI ​​can suggest a more challenging practice menu. This allows the suggestion unit to maximize the effectiveness of practice with a practice menu based on the student's sleep data.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The proposal unit proposes a self-study menu based on the student's level or goals. The proposal unit can propose menus based on the student's self-evaluation, past practice data, match results, etc. It can also use AI to propose the optimal menu based on the student's level and goals. For example, it proposes a menu based on the student's level and goals, such as shooting practice, dribbling practice, or physical training. Step 2: The recording unit records the training content based on the menu proposed by the proposing unit. The recording unit can record shooting success rate, dribbling speed, physical training results, etc. It can also use AI to automatically record the training content. For example, it can measure shooting success rate and record the data. It can also measure dribbling speed and record the data. Step 3: The visualization unit analyzes the data recorded by the recording unit and visualizes the progress of the student's growth. The visualization unit can use graphs and charts to display the progress of shooting success rates and changes in dribbling speed. It can also use AI to visualize the progress of the student's growth. For example, it can display the progress of shooting success rates in a graph, allowing the student's progress to be visually grasped. It can also display changes in dribbling speed in a chart, allowing the student's progress to be visually grasped.

[0060] (Example 2) An independent practice support system according to an embodiment of the present invention effectively supports students' independent practice and visualizes their progress. The independent practice support system proposes an independent practice menu based on the student's level and goals, records the content of the practice, and visualizes the student's progress. For example, when a student considers an independent practice menu, the independent practice support system uses AI to suggest an appropriate menu. For example, the AI ​​may propose a menu based on the student's level and goals, such as shooting practice, dribbling practice, or physical training. Next, the independent practice support system records the content of the practice the student performed. For example, the system records the shooting success rate, dribbling speed, and physical training results. This data is automatically collected and analyzed by AI. Furthermore, the independent practice support system visualizes the student's progress based on the data collected by AI. For example, graphs and charts may be used to display the progress of shooting success rate and changes in dribbling speed. This allows students to realize their own progress and maintain their motivation. This allows the independent practice support system to effectively encourage students' independent practice and differentiate themselves outside of group practice. This allows the self-study support system to effectively support students' self-study and increase their motivation by visualizing their progress. For example, improving shooting success rate or dribbling speed will improve performance in a game. Furthermore, by visualizing their progress, students can see that their efforts are paying off, helping them maintain their motivation.

[0061] The independent practice support system according to the embodiment includes a suggestion unit, a recording unit, and a visualization unit. The suggestion unit suggests an independent practice menu based on a student's level or goal. The suggestion unit can suggest a menu based on, for example, the student's self-evaluation, past practice data, or match results. The suggestion unit can also use AI to suggest an optimal menu based on the student's level or goal. For example, the suggestion unit suggests a menu based on the student's level or goal, such as shooting practice, dribbling practice, or physical training. The recording unit records the content of the practice performed based on the menu suggested by the suggestion unit. The recording unit can record, for example, the shooting success rate, dribbling speed, and physical training results. The recording unit can also automatically record the content of the practice using AI. For example, the recording unit can measure the shooting success rate and record the data. The recording unit can also measure the dribbling speed and record the data. The recording unit can also record the results of physical training. The visualization unit analyzes the data recorded by the recording unit and visualizes the student's progress. The visualization unit can display, for example, the progress of a student's shooting success rate or changes in dribbling speed using a graph or chart. The visualization unit can also visualize the student's progress using AI. For example, the visualization unit can display the progress of a student's shooting success rate in a graph, allowing the student's progress to be visually grasped. The visualization unit can also display changes in dribbling speed in a chart, allowing the student's progress to be visually grasped. In this way, the independent practice support system according to the embodiment can effectively support the student's independent practice and visualize the student's progress. For example, the student can realize his or her own progress and maintain motivation. Furthermore, by visualizing the student's progress, the student can realize that his or her efforts are resulting in results, allowing the student to maintain motivation.

[0062] The suggestion unit can suggest a menu based on the student's self-assessment, past practice data, or game results. The suggestion unit, for example, suggests a menu based on the student's self-assessment. For example, the suggestion unit can provide the student with a questionnaire for self-assessment and suggest a menu based on the results. The suggestion unit can also suggest a menu based on past practice data. For example, the suggestion unit can analyze the student's past practice records and suggest a menu based on the results. The suggestion unit can also suggest a menu based on game results. For example, the suggestion unit can analyze the student's game results and suggest a menu based on the results. In this way, the suggestion unit can suggest a practice menu tailored to the student's individual situation, thereby supporting more effective independent practice. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the student's self-assessment data into a generation AI and suggest a menu based on the generation AI.

[0063] The recording unit can record the shooting success rate, dribbling speed, and physical training results. The recording unit, for example, records the shooting success rate. For example, the recording unit can measure the shooting success rate and record the data. The recording unit can also record the dribbling speed. For example, the recording unit can measure the dribbling speed and record the data. The recording unit can also record the results of physical training. For example, the recording unit records the results of a strength test or an endurance test. In this way, the recording unit can accurately grasp the student's growth by recording the details of the practice. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input shooting success rate data into a generation AI and record the data based on the generation AI.

[0064] The visualization unit can display the progress of a shooting success rate or the change in dribbling speed using a graph or chart. The visualization unit, for example, displays the progress of a shooting success rate in a graph. For example, the visualization unit can display the change in the success rate along a time axis based on the shooting success rate data. The visualization unit can also display the change in dribbling speed in a chart. For example, the visualization unit can display the change in speed along a time axis based on the dribbling speed data. This allows the visualization unit to visually grasp the student's progress. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input shooting success rate data into a generation AI and generate a graph based on the generation AI.

[0065] The visualization unit can analyze growth trends by comparing data with past data and provide specific advice. For example, the visualization unit can compare past shooting success rate data with current data to analyze growth trends. For example, the visualization unit can display past data and current data in a graph to visually grasp growth trends. The visualization unit can also provide specific advice based on growth trends. For example, the visualization unit can suggest specific training methods for improving shooting success rate. The visualization unit can also provide specific advice for improving dribbling speed. In this way, the visualization unit can analyze growth trends and provide specific advice to maximize the effectiveness of the student's practice. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input past data and current data into a generation AI, analyze growth trends based on the generation AI, and provide advice.

[0066] The self-study support system further includes a suggestion unit that estimates the student's emotions and adjusts the difficulty level of the menu based on the estimated student's emotions. For example, if the student is feeling stressed, the suggestion unit uses AI to suggest an easy menu, thereby reducing the burden of practice. Alternatively, if the student is relaxed, the suggestion unit can also suggest a challenging menu, thereby promoting skill improvement. Alternatively, if the student is feeling motivated, the suggestion unit can also suggest a balanced menu, thereby supporting sustained practice. By suggesting a menu based on the student's emotions, the suggestion unit can reduce the burden of practice and maintain motivation. The estimation of emotions is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the student's emotion data into the generation AI and adjust the difficulty level of the menu based on the generation AI.

[0067] The suggestion unit can analyze the student's past practice data and suggest an appropriate practice menu in real time. For example, the suggestion unit can analyze the student's past shooting success rate and adjust the frequency and content of shooting practice. The suggestion unit can also set the intensity of dribbling practice based on the student's dribbling speed data. The suggestion unit can also suggest an appropriate training menu taking into account the results of the student's physical training. In this way, the suggestion unit can maximize the effectiveness of practice by suggesting an optimal menu based on the past practice data. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the student's past practice data into a generation AI and suggest a menu in real time based on the generation AI.

[0068] The suggestion unit can dynamically adjust the practice menu based on the student's physical condition data. For example, if the student's heart rate is high, the suggestion unit can suggest a lighter practice menu, taking the student's physical condition into consideration. Furthermore, if the student's fatigue level is high, the suggestion unit can suggest a recovery menu, promoting fatigue recovery. Furthermore, if the student's physical condition is good, the suggestion unit can suggest a regular practice menu, supporting effective practice. In this way, the suggestion unit can improve the effectiveness of practice by suggesting a practice menu that suits the student's physical condition. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the student's physical condition data into the generation AI and dynamically adjust the practice menu based on the generation AI.

[0069] The suggestion unit can gradually evolve the content of the practice menu according to the student's level of goal achievement. For example, when the student achieves a set goal, the suggestion unit uses AI to suggest the next step menu. Furthermore, when the student is approaching the goal, the suggestion unit can also fine-tune the current menu to support goal achievement. Furthermore, when the student is far from the goal, the suggestion unit can also re-suggest a basic menu to improve basic skills. This allows the suggestion unit to maximize the effectiveness of practice by suggesting a menu according to the student's level of goal achievement. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without AI. For example, the suggestion unit can input the student's goal achievement data into a generation AI and gradually evolve the practice menu based on the generation AI.

[0070] The suggestion unit can estimate the student's emotions and adjust the order of the practice menu based on the estimated student's emotions. For example, if the student is feeling stressed, the suggestion unit can have the AI ​​first suggest a relaxing menu. Also, if the student is feeling relaxed, the suggestion unit can have the AI ​​first suggest a menu that will improve concentration. Also, if the student is feeling motivated, the suggestion unit can have the AI ​​first suggest a challenging menu. This allows the suggestion unit to improve the effectiveness of practice by suggesting menus in an order that corresponds to the student's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the student's emotion data into the generation AI and adjust the order of the practice menu based on the generation AI.

[0071] The suggestion unit can suggest an appropriate practice location taking into account the student's geographical location information. For example, if the student is at home, the suggestion unit can have the AI ​​suggest a practice menu that can be done at home. Furthermore, if the student is at school, the suggestion unit can have the AI ​​suggest a practice menu that can be done in the school gymnasium. Furthermore, if the student is in a park, the suggestion unit can have the AI ​​suggest a practice menu that can be done in the park. In this way, the suggestion unit can improve the effectiveness of practice by suggesting a practice location based on the student's location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the student's geographical location information into the generation AI and suggest an appropriate practice location based on the generation AI.

[0072] The suggestion unit can analyze the student's social media activity and suggest a related practice menu. For example, the suggestion unit can use AI to suggest a similar practice menu based on a practice video shared by the student on social media. The suggestion unit can also use AI to suggest a practice menu based on the practice menus of basketball players the student follows. The suggestion unit can also use AI to suggest a practice menu based on trends in basketball communities the student participates in on social media. This allows the suggestion unit to attract the student's interest by suggesting a menu based on social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the student's social media activity data into a generation AI and suggest a related practice menu based on the generation AI.

[0073] The suggestion unit can customize the practice menu by reflecting the student's past feedback. For example, the suggestion unit uses AI to suggest a new menu based on practice menus that the student has previously preferred. The suggestion unit can also use AI to adjust the practice menu so that the student avoids practice menus that the student has previously struggled with. The suggestion unit can also optimize the content of the practice menu based on feedback provided by the student in the past. This allows the suggestion unit to improve student satisfaction by suggesting a menu based on past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the student's past feedback data into a generation AI and customize a practice menu based on the generation AI.

[0074] The recording unit can estimate the student's emotions and adjust the recording frequency based on the estimated student's emotions. For example, if the student is feeling stressed, the recording unit can use the AI ​​to reduce the recording frequency, allowing the student to concentrate on practice. Furthermore, if the student is relaxed, the recording unit can also use the AI ​​to increase the recording frequency and collect detailed data. Furthermore, if the student is feeling motivated, the recording unit can also set a balanced recording frequency. This allows the recording unit to improve the effectiveness of practice by using a recording frequency that corresponds to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit can be performed using, for example, an AI, or without an AI. For example, the recording unit can input the student's emotion data into the generation AI and adjust the recording frequency based on the generation AI.

[0075] The recording unit can simultaneously record the student's physical condition data when recording the content of the practice. For example, the recording unit can record the student's heart rate during practice and evaluate the intensity of the practice. The recording unit can also record the degree of fatigue after practice and reflect this in the next practice menu. The recording unit can also record physical condition data during practice in real time and analyze the effectiveness of the practice. In this way, the recording unit can accurately grasp the effectiveness of the practice by simultaneously recording physical condition data. Some or all of the above-mentioned processing in the recording unit can be performed, for example, using AI, or can be performed without using AI. For example, the recording unit can input physical condition data during practice into a generation AI and record the data based on the generation AI.

[0076] When recording practice content, the recording unit can select an appropriate recording means depending on the device used by the student. For example, if a smartphone is used, the recording unit records the practice content through an app. Furthermore, if a wearable device is used, the recording unit can also record the practice content through the device's sensors. Furthermore, if a tablet is used, the recording unit can also perform detailed recording on a large screen. This allows the recording unit to efficiently record practice content using a recording means appropriate for the device used. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input data from the device used into a generation AI and select the optimal recording means based on the generation AI.

[0077] When recording practice content, the recording unit can improve the accuracy of the recording by referring to the student's past record data. The recording unit records the current practice content based on, for example, the student's past shooting success rate. The recording unit can also record the current practice content by referring to past dribbling speed data. The recording unit can also record the current practice content based on the results of past physical training. In this way, the recording unit can improve the accuracy of the recording by referring to the past record data. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the past record data into a generation AI and improve the accuracy of the recording based on the generation AI.

[0078] The recording unit can estimate the student's emotions and determine the priority of data to be recorded based on the estimated student's emotions. For example, if the student is feeling stressed, the recording unit can have the AI ​​prioritize recording only important data. Furthermore, if the student is relaxed, the recording unit can have the AI ​​prioritize recording detailed data. Furthermore, if the student is feeling motivated, the recording unit can have the AI ​​prioritize recording balanced data. This allows the recording unit to improve the effectiveness of practice by prioritizing data according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit can be performed using, for example, an AI, or without an AI. For example, the recording unit can input the student's emotion data into the generation AI and determine the priority of data to be recorded based on the generation AI.

[0079] When recording practice content, the recording unit can prioritize recording highly relevant data by taking into account the student's geographical location information. For example, if the student practices at home, the recording unit can prioritize recording practice data from the home. Furthermore, if the student practices at school, the recording unit can prioritize recording practice data from the school. Furthermore, if the student practices in a park, the recording unit can prioritize recording practice data from the park. In this way, the recording unit can improve the effectiveness of practice by recording data based on the geographical location information. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the student's geographical location information into the generation AI and prioritize recording highly relevant data based on the generation AI.

[0080] The recording unit can analyze the student's social media activity and record related data when recording practice content. For example, the recording unit can have AI record related data based on practice videos shared by the student on social media. The recording unit can also have AI record data based on the practice content of basketball players the student follows on social media. The recording unit can also have AI record data based on trends in basketball communities the student participates in on social media. This allows the recording unit to improve the effectiveness of practice by recording data based on social media activity. Some or all of the above-mentioned processing in the recording unit can be performed using AI, for example, or without AI. For example, the recording unit can input the student's social media activity data into a generation AI and record related data based on the generation AI.

[0081] When recording practice content, the recording unit can customize the recording method by reflecting the student's past feedback. For example, the recording unit uses AI to suggest a new recording method based on the student's past preferred recording method. The recording unit can also be adjusted by AI to avoid recording methods that the student has previously struggled with. The recording unit can also optimize the recording method based on feedback provided by the student in the past. This allows the recording unit to improve the effectiveness of practice using a recording method based on past feedback. Some or all of the above-mentioned processing in the recording unit may be performed using AI, for example, or may be performed without using AI. For example, the recording unit can input the student's past feedback data into a generation AI and customize the recording method based on the generation AI.

[0082] The visualization unit can estimate the student's emotions and adjust the display method of the visualization based on the estimated student's emotions. For example, if the student is feeling stressed, the AI ​​can provide a simple, highly visible display method. Furthermore, if the student is relaxed, the AI ​​can provide a display method including detailed information. Furthermore, if the student is feeling motivated, the AI ​​can provide a balanced display method. This allows the visualization unit to effectively visualize the student's progress using a display method that corresponds to the student's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, AI, or without AI. For example, the visualization unit can input the student's emotion data into the generation AI and adjust the display method of the visualization based on the generation AI.

[0083] During visualization, the visualization unit can analyze growth trends by referring to the student's past performance data. For example, the visualization unit can analyze current performance based on past shooting success rate data. The visualization unit can also analyze current performance based on past dribbling speed data. The visualization unit can also analyze current performance based on the results of past physical training. This allows the visualization unit to improve the effectiveness of practice by analyzing growth trends based on past performance data. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input past performance data into a generation AI and analyze growth trends based on the generation AI.

[0084] The visualization unit can apply different visualization methods depending on the student's level of goal achievement during visualization. For example, when a student achieves a goal, the visualization unit provides a display method in which the AI ​​emphasizes the level of achievement. Furthermore, when a student is approaching a goal, the visualization unit can also display the progress in detail. Furthermore, when a student is far from the goal, the visualization unit can also provide a display method in which the AI ​​emphasizes basic data and indicates areas for improvement. This allows the visualization unit to effectively display the student's progress using a visualization method according to the level of goal achievement. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the student's goal achievement data into a generation AI and apply different visualization methods based on the generation AI.

[0085] The visualization unit can improve the accuracy of the visualization by reflecting student feedback during visualization. For example, the visualization unit uses AI to optimize the display method of the visualization based on feedback previously provided by the student. The visualization unit can also adjust the display method based on the student's preferred visualization technique. The visualization unit can also adjust the display method to avoid visualization techniques that the student finds difficult. This allows the visualization unit to effectively display the degree of growth through feedback-based visualization. Some or all of the above-mentioned processing in the visualization unit may be performed using AI, for example, or may be performed without using AI. For example, the visualization unit can input student feedback data into the generation AI and improve the accuracy of the visualization based on the generation AI.

[0086] The visualization unit can estimate the student's emotions and determine visualization priorities based on the estimated student emotions. For example, if the student is feeling stressed, the AI ​​can prioritize displaying only important data. Furthermore, if the student is relaxed, the AI ​​can prioritize displaying detailed data. Furthermore, if the student is feeling motivated, the AI ​​can prioritize displaying balanced data. This allows the visualization unit to effectively display the student's progress in order of priority according to their emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the visualization unit can be performed using, for example, an AI, or without an AI. For example, the visualization unit can input the student's emotion data into the generative AI and determine visualization priorities based on the generative AI.

[0087] When visualizing, the visualization unit can prioritize displaying highly relevant data by taking into account the student's geographical location information. For example, if a student practices at home, the visualization unit can prioritize displaying practice data from the home. Furthermore, if a student practices at school, the visualization unit can prioritize displaying practice data from the school. Furthermore, if a student practices in a park, the visualization unit can prioritize displaying practice data from the park. This allows the visualization unit to effectively display the progress of a student by displaying data based on the geographical location information. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the student's geographical location information into a generation AI and prioritize displaying highly relevant data based on the generation AI.

[0088] During visualization, the visualization unit can analyze the student's social media activity and display related data. For example, the visualization unit can use AI to display related data based on practice videos shared by the student on social media. The visualization unit can also use AI to display data based on the practice content of basketball players the student follows. The visualization unit can also use AI to display data based on trends in basketball communities in which the student participates on social media. This allows the visualization unit to effectively display the student's progress by displaying data based on social media activity. Some or all of the above-described processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the student's social media activity data into a generation AI and display related data based on the generation AI.

[0089] The visualization unit can customize the visualization method by reflecting the student's past feedback during visualization. For example, the visualization unit uses AI to suggest a new visualization method based on the student's past preferred visualization methods. The visualization unit can also use AI to adjust the visualization method so as to avoid visualization methods that the student found difficult in the past. The visualization unit can also optimize the visualization method based on feedback provided by the student in the past. This allows the visualization unit to effectively display the student's progress using a visualization method based on the student's past feedback. Some or all of the above-described processing in the visualization unit may be performed using AI, for example, or may be performed without using AI. For example, the visualization unit can input the student's past feedback data into the generation AI and customize the visualization method based on the generation AI. === Hard Collateral 1-1 === Each of the above-described elements, including the suggestion unit, recording unit, and visualization unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the suggestion unit is implemented by the control unit 46A of the smart device 14 and proposes a practice menu according to the student's level and goals. The recording unit records the practice content using, for example, the camera 42 or a sensor of the smart device 14, and the recorded content is analyzed by the identification processing unit 290 of the data processing device 12. The visualization unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and displays the progress of the student in graphs or charts based on the recorded data. Furthermore, the suggestion unit has a function of estimating the student's emotions and adjusting the difficulty level of the menu based on the estimated emotions. The emotion estimation is performed, for example, using the emotion identification model 59 of the data processing device 12. === Hard Collateral 1-2 === Each of the above-described elements, including the suggestion unit, recording unit, and visualization unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the suggestion unit is implemented by the control unit 46A of the smart glasses 214 and proposes a practice menu according to the student's level and goals. The recording unit records the practice content using, for example, the camera 42 or a sensor of the smart glasses 214, and the recorded content is analyzed by the identification processing unit 290 of the data processing device 12. The visualization unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and displays the progress of the student in graphs or charts based on the recorded data. Furthermore, the suggestion unit has a function of estimating the student's emotions and adjusting the difficulty level of the menu based on the estimated emotions. The emotion estimation is performed, for example, using the emotion identification model 59 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the suggestion unit, recording unit, and visualization unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the suggestion unit is implemented by the control unit 46A of the headset terminal 314 and proposes a practice menu according to the student's level and goals. The recording unit records the practice content using, for example, the camera 42 or a sensor of the headset terminal 314, and the recorded content is analyzed by the identification processing unit 290 of the data processing device 12. The visualization unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and displays the progress of the student in graphs or charts based on the recorded data. Furthermore, the suggestion unit has a function of estimating the student's emotions and adjusting the difficulty level of the menu based on the estimated emotions. The emotion estimation is performed, for example, using the emotion identification model 59 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the suggestion unit, recording unit, and visualization unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the robot 414 and proposes a practice menu according to the student's level and goals. The recording unit records the practice content using, for example, the camera 42 or a sensor of the robot 414, and the recorded content is analyzed by the identification processing unit 290 of the data processing device 12. The visualization unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and displays the progress of the student in graphs or charts based on the recorded data. Furthermore, the suggestion unit has a function of estimating the student's emotions and adjusting the difficulty level of the menu based on the estimated emotions. The emotion estimation is performed, for example, using the emotion identification model 59 of the data processing device 12.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The suggestion unit can analyze the student's learning style and propose an individually optimized practice menu. For example, for a student who prefers visual learning, a menu including video tutorials can be proposed. For a student who prefers auditory learning, a practice menu with audio guides can be proposed. Furthermore, for a student who prefers practical learning, a menu that emphasizes actual practice can be proposed. In this way, the suggestion unit can maximize the effectiveness of practice by providing a practice menu that suits the student's learning style.

[0092] The recording unit can record data appropriate to the environment, taking into account the student's practice environment. For example, when practicing outdoors, environmental data such as weather and temperature can be recorded. When practicing indoors, environmental data such as lighting and sound can be recorded. Furthermore, when practicing at a specific facility, data appropriate to the characteristics of the facility can be recorded. In this way, the recording unit can more accurately evaluate the effectiveness of practice by recording data appropriate to the practice environment.

[0093] The visualization unit can estimate a student's emotions and adjust the content of the feedback based on the estimated student emotions. For example, if a student is feeling stressed, the AI ​​will prioritize providing positive feedback. Also, if a student is relaxed, the AI ​​can provide detailed feedback. Furthermore, if a student is feeling motivated, the AI ​​can provide challenging feedback. This allows the visualization unit to improve the effectiveness of practice by providing feedback that corresponds to the student's emotions.

[0094] The proposal department can propose long-term training plans based on students' learning history. For example, it can analyze past training data and propose training menus aimed at goals several months into the future. It can also adjust training menus to match match and tournament schedules so that students can perform at their peak. It can also propose training plans that gradually increase in difficulty according to the student's stage of development. This allows the proposal department to support training from a long-term perspective and promote student growth.

[0095] The recording unit can estimate a student's emotions and adjust the way data is displayed based on the estimated student emotions. For example, if a student is feeling stressed, the AI ​​can provide a simple, highly visible display. If a student is feeling relaxed, the AI ​​can also display detailed data. Furthermore, if a student is feeling motivated, the AI ​​can provide a challenging data display. This allows the recording unit to improve the effectiveness of practice by displaying data according to the student's emotions.

[0096] The suggestion unit can propose a practice menu that takes nutritional balance into consideration based on the student's dietary data. For example, if a student is lacking in a particular nutrient, the AI ​​can propose a practice menu to supplement that nutrient. Also, if a student has an excess of a nutrient, the AI ​​can propose a practice menu to consume that nutrient. Furthermore, the AI ​​can suggest the optimal practice time based on the student's eating patterns. This allows the suggestion unit to support the student's health and performance with a practice menu that takes nutritional balance into consideration.

[0097] The visualization unit can estimate the student's emotions and adjust the evaluation criteria for practice results based on the estimated student emotions. For example, if the student is feeling stressed, the AI ​​can apply lenient evaluation criteria. Alternatively, if the student is relaxed, the AI ​​can apply strict evaluation criteria. Furthermore, if the student is feeling motivated, the AI ​​can apply strict evaluation criteria. This allows the visualization unit to improve the effectiveness of practice with evaluation criteria that correspond to the student's emotions.

[0098] The suggestion unit can adjust the practice menu based on the student's sleep data. For example, if the student is sleep-deprived, the AI ​​can suggest a lighter practice menu, taking their physical condition into consideration. If the student has had enough sleep, the AI ​​can also suggest a regular practice menu. Furthermore, if the quality of sleep is good, the AI ​​can suggest a more challenging practice menu. This allows the suggestion unit to maximize the effectiveness of practice with a practice menu based on the student's sleep data.

[0099] The recording unit can estimate the student's emotions and adjust the data collection method based on the estimated student emotions. For example, if the student is feeling stressed, the AI ​​can provide a simple data collection method. If the student is relaxed, the AI ​​can provide a detailed data collection method. Furthermore, if the student is feeling motivated, the AI ​​can provide a balanced data collection method. This allows the recording unit to improve the effectiveness of practice by collecting data in accordance with the student's emotions.

[0100] The visualization unit can estimate the student's emotions and adjust the timing of visualization based on the estimated student emotions. For example, if the student is feeling stressed, the AI ​​can perform visualization immediately after practice. Alternatively, if the student is relaxed, the AI ​​can perform visualization in real time while the student is practicing. Furthermore, if the student is feeling motivated, the AI ​​can visualize past data before practice. This allows the visualization unit to effectively visualize the student's progress at a time that suits the student's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The proposal unit proposes a self-study menu based on the student's level or goals. The proposal unit can propose menus based on the student's self-evaluation, past practice data, match results, etc. It can also use AI to propose the optimal menu based on the student's level and goals. For example, it proposes a menu based on the student's level and goals, such as shooting practice, dribbling practice, or physical training. Step 2: The recording unit records the training content based on the menu proposed by the proposing unit. The recording unit can record shooting success rate, dribbling speed, physical training results, etc. It can also use AI to automatically record the training content. For example, it can measure shooting success rate and record the data. It can also measure dribbling speed and record the data. Step 3: The visualization unit analyzes the data recorded by the recording unit and visualizes the progress of the student's growth. The visualization unit can use graphs and charts to display the progress of shooting success rates and changes in dribbling speed. It can also use AI to visualize the progress of the student's growth. For example, it can display the progress of shooting success rates in a graph, allowing the student's progress to be visually grasped. It can also display changes in dribbling speed in a chart, allowing the student's progress to be visually grasped.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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. The proposal department proposes a self-study menu based on the student's level or goals, and a recording unit that records the practice content performed based on the menu suggested by the suggestion unit; a visualization unit that analyzes the data recorded by the recording unit and visualizes the growth state. A system characterized by:

2. The proposal unit Suggest a menu based on the student's self-evaluation, past practice data, and match results 2. The system of claim 1.

3. The recording unit Track your shooting percentage, dribbling speed, and physical training results 2. The system of claim 1.

4. The visualization unit Use graphs and charts to show changes in shooting percentage or dribbling speed over time 2. The system of claim 1.

5. The visualization unit Analyze growth trends by comparing with historical data and provide specific advice 2. The system of claim 1.

6. The proposal unit Estimate student emotions and adjust menu difficulty based on the estimated student emotions 2. The system of claim 1.

7. The proposal unit Analyze students' past practice data and suggest appropriate practice menus in real time 2. The system of claim 1.

8. The proposal unit Dynamically adjust training menus based on students' physical condition data 2. The system of claim 1.

Images