System

Abstract

An object of the system according to the embodiment is to reproduce a situation of a game and to enable even an individual to perform effective practice.SOLUTION: A system includes a reception unit, a generation unit, a recording unit, and a provision unit. The reception unit inputs contents that the user wants to practice. The generation unit reproduces a game situation based on the information input by the reception unit. The recording unit records a practice result of the user based on the game situation reproduced by the generation unit. The providing unit provides the training result recorded by the recording unit as feedback.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for individuals to practice in a way that simulated match situations.

[0005] The system according to the embodiment aims to reproduce the situation of a match and enable effective practice even for individuals. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a recording unit, and a providing unit. The receiving unit inputs the content of the practice that the user wants to do. The generating unit recreates the match situation based on the information input by the receiving unit. The recording unit records the user's practice results based on the match situation recreated by the generating unit. The providing unit provides the practice results recorded by the recording unit as feedback. [Effects of the Invention]

[0007] The system according to the embodiment reproduces the situation of a match, allowing even individuals to practice effectively. [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) A practice support system according to an embodiment of the present invention allows a user to input what they want to practice, and an AI analyzes game data to recreate the game situation, records the user's practice results, and provides feedback. The practice support system allows a user to input what they want to practice, and an AI analyzes game data to recreate the game situation. The user can then practice based on the recreated game situation and receive feedback on the results. For example, in the practice support system, a user inputs "I want to receive a volleyball serve." This information is input to a reception unit of the system. The AI ​​then analyzes the input information and recreates the game situation based on the game data. The generation unit analyzes the game data and recreates the game situation based on the user's practice. For example, it analyzes volleyball serve data or baseball pitch data to recreate the game situation. The user then practices based on the recreated game situation. For example, the user can practice receiving a volleyball serve or hitting a baseball pitch. Furthermore, the user's practice results are recorded and provided as feedback. The provision unit records the user's practice results and provides feedback. This allows the user to review their practice results and identify areas for improvement. This allows the practice support system to improve the user's performance in a match and eliminate frustration. This allows the practice support system to improve the user's performance in a match and eliminate frustration. For example, by repeatedly practicing receiving serves in volleyball, the success rate of serve reception in a match can be improved. Similarly, by repeatedly practicing hitting pitched baseballs, the hitting power in a match can be improved.

[0029] A practice support system according to an embodiment includes a reception unit, a generation unit, a recording unit, and a providing unit. The reception unit inputs the content the user wants to practice. The content the user wants to practice includes, but is not limited to, receiving a volleyball serve or hitting a baseball pitch. For example, the reception unit can receive input such as "I want to receive a volleyball serve." The reception unit can also support multiple input methods, such as voice input and text input. The generation unit uses AI to recreate a game situation based on the information input by the reception unit. For example, the generation unit analyzes game data and recreates the game situation based on the content the user wants to practice. For example, the generation unit analyzes volleyball serve data or baseball pitch data to recreate the game situation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. The recording unit records the user's practice results based on the game situation recreated by the generation unit. For example, the recording unit records the user's practice results as digital data. The recording unit can also record the user's practice results in chronological order and track the progress of practice. Some or all of the above-described processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. The providing unit provides the practice results recorded by the recording unit as feedback. The providing unit, for example, analyzes the user's practice results and identifies areas for improvement. The providing unit can also visually display the user's practice results and provide feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. As a result, the practice support system according to the embodiment can improve the user's performance in a match.

[0030] The generation unit can analyze the match data and recreate the match situation. The match data includes, for example, video data of the match, statistical data, sensor data, etc., but is not limited to these examples. The generation unit, for example, analyzes the video data of the match and recreates the match situation. The generation unit can also analyze statistical data and recreate the match situation. The generation unit can also analyze sensor data and recreate the match situation. For example, the generation unit can input the video data of the match and recreate the match situation using an AI model that recreates the match situation. This allows for the reproduction of a more realistic match situation by analyzing the match data. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0031] The generation unit can analyze volleyball serve data or baseball pitch data to recreate a game situation. Volleyball serve data or baseball pitch data may include, but is not limited to, the type of sensor and the format of data. The generation unit can analyze, for example, volleyball serve data to recreate a game situation. The generation unit can also analyze baseball pitch data to recreate a game situation. The generation unit can also combine volleyball serve data and baseball pitch data to recreate a game situation. For example, the generation unit can input volleyball serve data and recreate a game situation using an AI model that recreates a game situation. This allows for more specific practice by recreating the game situation of a specific sport. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0032] The providing unit can analyze the user's practice results and identify areas for improvement. The providing unit, for example, analyzes the user's practice results and identifies areas for improvement. For example, the providing unit analyzes the user's practice results and identifies areas for improvement based on performance evaluation criteria. The providing unit can also analyze the user's practice results and identify areas for improvement using an analysis algorithm. The providing unit can also visually display the user's practice results and identify areas for improvement. For example, the providing unit displays the user's practice results as a graph or chart and identifies areas for improvement. In this way, specific areas for improvement can be identified by analyzing the user's practice results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI.

[0033] The reception unit can analyze the user's past practice history and suggest appropriate practice content. The reception unit, for example, analyzes the user's past practice history and suggests appropriate practice content. For example, the reception unit automatically displays practice content that the user has frequently performed in the past as candidates. The reception unit can also predict and suggest practice content to be performed at a specific time period based on the user's past practice history. The reception unit can also analyze the user's past practice history and suggest the most effective practice content. For example, the reception unit can analyze the user's past practice data and suggest practice content using an AI model that suggests optimal practice content. In this way, the optimal practice content can be suggested to the user by analyzing the past practice history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0034] The reception unit can filter the practice content based on the user's current physical condition and fatigue level when inputting the practice content. For example, the reception unit can filter the practice content based on the user's current physical condition and fatigue level when inputting the practice content. For example, if the user is tired, the reception unit can suggest lighter practice content. Furthermore, if the user is feeling unwell, the reception unit can also suggest that the user prioritize rest. Furthermore, if the user is feeling well, the reception unit can suggest harder practice content. For example, the reception unit can analyze the user's vital signs data and evaluate the user's physical condition and fatigue level. This makes it possible to suggest more appropriate practice content by taking the user's physical condition and fatigue level into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0035] The reception unit can select the optimal input means depending on the user's input method when inputting practice content. For example, the reception unit can select the optimal input means depending on the user's input method when inputting practice content. For example, the reception unit can automatically set the practice content when the user simply inputs, by voice, "I want to receive a volleyball serve." The reception unit can also allow the user to easily set the practice content by performing a specific gesture on the smartphone screen. The reception unit can also allow the user to set the practice content more intuitively by combining voice input and gesture input. For example, the reception unit can use voice recognition technology to analyze the user's voice input and set the practice content. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI or without AI.

[0036] When inputting practice content, the reception unit can prioritize suggesting highly relevant practice content by taking into account the user's geographical location information. For example, when inputting practice content, the reception unit prioritizes suggesting highly relevant practice content by taking into account the user's geographical location information. For example, when the user is at a specific sports facility, the reception unit can suggest practice content that can be done at the facility. Furthermore, when the user is at home, the reception unit can suggest practice content that can be done at home. Furthermore, when the user is in a park, the reception unit can suggest practice content that can be done in the park. For example, the reception unit can obtain the user's geographical location information using GPS data and suggest practice content. In this way, more appropriate practice content can be suggested by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without using AI.

[0037] The reception unit can analyze the user's social media activity when inputting practice content and suggest related practice content. For example, when inputting practice content, the reception unit can analyze the user's social media activity and suggest related practice content. For example, the reception unit can suggest practice content based on sports activities shared by the user on social media. The reception unit can also analyze the user's social media posts and suggest related practice content. The reception unit can also suggest related practice content by referring to the activities of the user's friends on social media. For example, the reception unit can analyze social media posting data and suggest practice content based on the user's interests. In this way, by analyzing the user's social media activity, more relevant practice content can be suggested. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI.

[0038] The reception unit can customize the input method by reflecting the user's past feedback when inputting practice content. The reception unit customizes the input method by reflecting the user's past feedback when inputting practice content, for example. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also suggest the optimal input method based on the user's past feedback. The reception unit can also customize the input method to avoid input methods that the user has previously been dissatisfied with. For example, the reception unit can analyze the user's past feedback data and suggest the optimal input method. In this way, a more appropriate input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0039] The generation unit can adjust the level of detail of the reproduction based on the importance of the match data when reproducing the match situation. For example, the generation unit adjusts the level of detail of the reproduction based on the importance of the match data when reproducing the match situation. For example, the generation unit performs detailed reproduction based on important match data. The generation unit can also perform simplified reproduction based on less important match data. The generation unit can also dynamically adjust the level of detail of the reproduction based on the importance of the match data. For example, the generation unit can adjust the level of detail of the reproduction based on the reliability and relevance of the match data. This allows for more effective practice by adjusting the level of detail of the reproduction based on the importance of the match data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The generation unit can apply different reproduction algorithms depending on the type of sport when reproducing a game situation. For example, the generation unit applies different reproduction algorithms depending on the type of sport when reproducing a game situation. For example, the generation unit applies a reproduction algorithm dedicated to volleyball to reproduce a volleyball game situation. The generation unit can also apply a reproduction algorithm dedicated to baseball to reproduce a baseball game situation. The generation unit can also apply a reproduction algorithm dedicated to tennis to reproduce a tennis game situation. For example, the generation unit can apply a reproduction algorithm depending on the type of sport using a physical simulation or a machine learning algorithm. In this way, by applying a reproduction algorithm depending on the type of sport, more realistic game situations can be reproduced. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0041] The generation unit can improve the accuracy of the reproduction by referring to the user's past practice results when reproducing a match situation. For example, the generation unit can improve the accuracy of the reproduction by referring to the user's past practice results when reproducing a match situation. For example, the generation unit improves the accuracy of the reproduction based on the user's past practice results. The generation unit can also analyze the user's past practice data and propose an optimal reproduction method. The generation unit can also adjust the level of detail of the reproduction by referring to the user's past practice results. For example, the generation unit can analyze the user's past practice data and improve the accuracy of the reproduction using an AI model that improves the accuracy of the reproduction. As a result, the accuracy of the reproduction is improved by referring to the user's past practice results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without AI.

[0042] The generation unit can determine the priority of replay based on the time of the game when replaying the game situation. For example, the generation unit determines the priority of replay based on the time of the game when replaying the game situation. For example, the generation unit prioritizes replaying the most recent game data. The generation unit can also prioritize replaying important game data. The generation unit can also dynamically adjust the priority of replay based on the time of the game. For example, the generation unit can determine the priority of replay based on the season or the importance of the game. As a result, determining the priority of replay based on the time of the game enables more effective practice. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0043] The generation unit can adjust the order of reproduction based on the relevance of the match when reproducing the match situation. For example, the generation unit adjusts the order of reproduction based on the relevance of the match when reproducing the match situation. For example, the generation unit prioritizes reproducing important match data. The generation unit can also prioritize reproducing highly relevant match data. The generation unit can also dynamically adjust the order of reproduction based on the relevance of the match. For example, the generation unit can adjust the order of reproduction based on past match results and team tactics. This enables more effective practice by adjusting the order of reproduction based on the relevance of the match. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The generation unit can adjust the use of technical terminology in the reproduction according to the user's level of expertise when reproducing a match situation. For example, the generation unit can adjust the use of technical terminology in the reproduction according to the user's level of expertise when reproducing a match situation. For example, if the user is a beginner, the generation unit can reproduce the situation using simple terminology. If the user is an intermediate player, the generation unit can also reproduce the situation using appropriate technical terminology. If the user is an advanced player, the generation unit can also reproduce the situation using detailed technical terminology. For example, the generation unit can adjust the use of technical terminology in the reproduction based on the user's level of knowledge. This allows for a more easily understandable reproduction by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI.

[0045] The recording unit can adjust the level of detail of the recording based on the importance of the practice data when recording practice results. For example, the recording unit can adjust the level of detail of the recording based on the importance of the practice data when recording practice results. For example, the recording unit can record important practice data in detail. The recording unit can also simplify and record less important practice data. The recording unit can also dynamically adjust the level of detail of the recording based on the importance of the practice data. For example, the recording unit can adjust the level of detail of the recording based on the reliability and relevance of the practice data. This allows for more effective recording by adjusting the level of detail of the recording based on the importance of the practice data. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI.

[0046] The recording unit can apply different recording algorithms depending on the type of sport when recording practice results. For example, the recording unit applies different recording algorithms depending on the type of sport when recording practice results. For example, the recording unit can apply a recording algorithm dedicated to volleyball to record volleyball practice results. The recording unit can also apply a recording algorithm dedicated to baseball to record baseball practice results. The recording unit can also apply a recording algorithm dedicated to tennis to record tennis practice results. For example, the recording unit can apply a recording algorithm depending on the type of sport based on the design of the database and the selection of the algorithm. This enables more appropriate recording by applying a recording algorithm depending on the type of sport. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or without using AI.

[0047] The recording unit can improve the accuracy of the recording by referring to the user's past practice results when recording practice results. For example, the recording unit can improve the accuracy of the recording by referring to the user's past practice results when recording practice results. For example, the recording unit improves the accuracy of the recording based on the user's past practice results. The recording unit can also analyze the user's past practice data and suggest an optimal recording method. The recording unit can also adjust the level of detail of the recording by referring to the user's past practice results. For example, the recording unit can analyze the user's past practice data and improve the accuracy of the recording using an AI model that improves the accuracy of the recording. As a result, the accuracy of the recording is improved by referring to the user's past practice results. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The recording unit can determine the priority of recording when recording practice results based on the time when the practice was performed. For example, the recording unit determines the priority of recording when recording practice results based on the time when the practice was performed. For example, the recording unit prioritizes recording of the most recent practice results. The recording unit can also prioritize recording of important practice results. The recording unit can also dynamically adjust the priority of recording based on the time when the practice was performed. For example, the recording unit can determine the priority of recording based on the season or the importance of the practice. This enables more effective recording by determining the priority of recording based on the time when the practice was performed. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The recording unit can adjust the order of recording based on the relevance of the practice when recording the practice results. For example, the recording unit can adjust the order of recording based on the relevance of the practice when recording the practice results. For example, the recording unit can prioritize recording important practice results. The recording unit can also prioritize recording highly relevant practice results. The recording unit can also dynamically adjust the order of recording based on the relevance of the practice. For example, the recording unit can adjust the order of recording based on the content of past practice or the purpose of practice. This enables more effective recording by adjusting the order of recording based on the relevance of the practice. Some or all of the above-mentioned processing in the recording unit can be performed, for example, using AI or without using AI.

[0050] The recording unit can adjust the use of technical terms in the recording according to the user's level of expertise when recording the practice results. For example, the recording unit can adjust the use of technical terms in the recording according to the user's level of expertise when recording the practice results. For example, if the user is a beginner, the recording unit can record using simple terms. If the user is an intermediate user, the recording unit can record using appropriate technical terms. If the user is an advanced user, the recording unit can record using detailed technical terms. For example, the recording unit can adjust the use of technical terms in the recording based on the user's level of knowledge. This allows for easier-to-understand recording by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI.

[0051] The providing unit can adjust the level of detail of the feedback based on the importance of the practice result when providing the feedback. For example, the providing unit adjusts the level of detail of the feedback based on the importance of the practice result when providing the feedback. For example, the providing unit provides detailed feedback based on an important practice result. The providing unit can also provide simplified feedback based on a less important practice result. The providing unit can also dynamically adjust the level of detail of the feedback according to the importance of the practice result. For example, the providing unit can adjust the level of detail of the feedback based on the reliability and relevance of the practice result. This enables more effective feedback by adjusting the level of detail of the feedback according to the importance of the practice result. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0052] The providing unit can apply different feedback algorithms depending on the type of sport when providing feedback. For example, the providing unit applies different feedback algorithms depending on the type of sport when providing feedback. For example, the providing unit applies a feedback algorithm dedicated to volleyball based on the results of volleyball practice. The providing unit can also apply a feedback algorithm dedicated to baseball based on the results of baseball practice. The providing unit can also apply a feedback algorithm dedicated to tennis based on the results of tennis practice. For example, the providing unit can apply a feedback algorithm depending on the type of sport based on the design of the database and the selection of the algorithm. This enables more appropriate feedback by applying a feedback algorithm depending on the type of sport. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0053] The providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit improves the accuracy of the feedback based on the user's past feedback results. The providing unit can also analyze the user's past feedback data and suggest an optimal feedback method. The providing unit can also adjust the level of detail of the feedback by referring to the user's past feedback results. For example, the providing unit can analyze the user's past feedback data and improve the accuracy of the feedback using an AI model that improves the accuracy of the feedback. As a result, the accuracy of the feedback is improved by referring to the user's past feedback results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0054] The providing unit, when providing feedback, can determine the priority of feedback based on the time of practice. For example, when providing feedback, the providing unit determines the priority of feedback based on the time of practice. For example, the providing unit provides feedback preferentially based on the results of the most recent practice. The providing unit can also provide feedback preferentially based on important practice results. The providing unit can also dynamically adjust the priority of feedback based on the time of practice. For example, the providing unit can determine the priority of feedback based on the season or the importance of practice. This enables more effective feedback by determining the priority of feedback based on the time of practice. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0055] The providing unit can adjust the order of feedback based on the relevance of practice when providing feedback. For example, the providing unit adjusts the order of feedback based on the relevance of practice when providing feedback. For example, the providing unit prioritizes providing feedback based on important practice results. The providing unit can also prioritize providing feedback based on highly relevant practice results. The providing unit can also dynamically adjust the order of feedback based on the relevance of practice. For example, the providing unit can adjust the order of feedback based on past practice content or the purpose of practice. This enables more effective feedback by adjusting the order of feedback based on the relevance of practice. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0056] The providing unit can adjust the use of technical terminology in the feedback according to the user's level of expertise when providing feedback. For example, the providing unit can adjust the use of technical terminology in the feedback according to the user's level of expertise when providing feedback. For example, if the user is a beginner, the providing unit can provide feedback using simple terminology. If the user is an intermediate user, the providing unit can provide feedback using appropriate technical terminology. If the user is an advanced user, the providing unit can provide feedback using detailed technical terminology. For example, the providing unit can adjust the use of technical terminology in the feedback based on the user's level of knowledge. This allows for feedback that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can analyze the user's past practice history and suggest appropriate practice content. For example, the reception unit can automatically display practice content that the user has frequently performed in the past as candidates. The reception unit can also predict and suggest practice content to be performed at a specific time period based on the user's past practice history. The reception unit can also analyze the user's past practice history and suggest the most effective practice content. For example, the reception unit can analyze the user's past practice data and suggest practice content using an AI model that suggests optimal practice content. In this way, the optimal practice content can be suggested to the user by analyzing the past practice history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0059] The generation unit can analyze the match data and recreate the match situation. The match data includes, for example, video data of the match, statistical data, sensor data, etc., but is not limited to these examples. The generation unit, for example, analyzes the video data of the match and recreates the match situation. The generation unit can also analyze statistical data and recreate the match situation. The generation unit can also analyze sensor data and recreate the match situation. For example, the generation unit can input the video data of the match and recreate the match situation using an AI model that recreates the match situation. This allows for the reproduction of a more realistic match situation by analyzing the match data. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0060] The generation unit can analyze volleyball serve data or baseball pitch data to recreate a game situation. Volleyball serve data or baseball pitch data may include, but is not limited to, the type of sensor and the format of data. The generation unit can analyze, for example, volleyball serve data to recreate a game situation. The generation unit can also analyze baseball pitch data to recreate a game situation. The generation unit can also combine volleyball serve data and baseball pitch data to recreate a game situation. For example, the generation unit can input volleyball serve data and recreate a game situation using an AI model that recreates a game situation. This allows for more specific practice by recreating the game situation of a specific sport. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0061] The providing unit can analyze the user's practice results and identify areas for improvement. The providing unit, for example, analyzes the user's practice results and identifies areas for improvement. For example, the providing unit analyzes the user's practice results and identifies areas for improvement based on performance evaluation criteria. The providing unit can also analyze the user's practice results and identify areas for improvement using an analysis algorithm. The providing unit can also visually display the user's practice results and identify areas for improvement. For example, the providing unit displays the user's practice results as a graph or chart and identifies areas for improvement. In this way, specific areas for improvement can be identified by analyzing the user's practice results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI.

[0062] The reception unit can filter the practice content based on the user's current physical condition and fatigue level when inputting the practice content. For example, the reception unit can filter the practice content based on the user's current physical condition and fatigue level when inputting the practice content. For example, if the user is tired, the reception unit can suggest lighter practice content. Furthermore, if the user is feeling unwell, the reception unit can also suggest that the user prioritize rest. Furthermore, if the user is feeling well, the reception unit can suggest harder practice content. For example, the reception unit can analyze the user's vital signs data and evaluate the user's physical condition and fatigue level. This makes it possible to suggest more appropriate practice content by taking the user's physical condition and fatigue level into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0063] The reception unit can select the optimal input means depending on the user's input method when inputting practice content. For example, the reception unit can select the optimal input means depending on the user's input method when inputting practice content. For example, the reception unit can automatically set the practice content when the user simply inputs, by voice, "I want to receive a volleyball serve." The reception unit can also allow the user to easily set the practice content by performing a specific gesture on the smartphone screen. The reception unit can also allow the user to set the practice content more intuitively by combining voice input and gesture input. For example, the reception unit can use voice recognition technology to analyze the user's voice input and set the practice content. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI or without AI.

[0064] The generation unit can adjust the level of detail of the reproduction based on the importance of the match data when reproducing the match situation. For example, the generation unit adjusts the level of detail of the reproduction based on the importance of the match data when reproducing the match situation. For example, the generation unit performs detailed reproduction based on important match data. The generation unit can also perform simplified reproduction based on less important match data. The generation unit can also dynamically adjust the level of detail of the reproduction based on the importance of the match data. For example, the generation unit can adjust the level of detail of the reproduction based on the reliability and relevance of the match data. This allows for more effective practice by adjusting the level of detail of the reproduction based on the importance of the match data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0065] The recording unit can adjust the level of detail of the recording based on the importance of the practice data when recording practice results. For example, the recording unit can adjust the level of detail of the recording based on the importance of the practice data when recording practice results. For example, the recording unit can record important practice data in detail. The recording unit can also simplify and record less important practice data. The recording unit can also dynamically adjust the level of detail of the recording based on the importance of the practice data. For example, the recording unit can adjust the level of detail of the recording based on the reliability and relevance of the practice data. This allows for more effective recording by adjusting the level of detail of the recording based on the importance of the practice data. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI.

[0066] The recording unit can apply different recording algorithms depending on the type of sport when recording practice results. For example, the recording unit applies different recording algorithms depending on the type of sport when recording practice results. For example, the recording unit can apply a recording algorithm dedicated to volleyball to record volleyball practice results. The recording unit can also apply a recording algorithm dedicated to baseball to record baseball practice results. The recording unit can also apply a recording algorithm dedicated to tennis to record tennis practice results. For example, the recording unit can apply a recording algorithm depending on the type of sport based on the design of the database and the selection of the algorithm. This enables more appropriate recording by applying a recording algorithm depending on the type of sport. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or without using AI.

[0067] The recording unit can improve the accuracy of the recording by referring to the user's past practice results when recording practice results. For example, the recording unit can improve the accuracy of the recording by referring to the user's past practice results when recording practice results. For example, the recording unit improves the accuracy of the recording based on the user's past practice results. The recording unit can also analyze the user's past practice data and suggest an optimal recording method. The recording unit can also adjust the level of detail of the recording by referring to the user's past practice results. For example, the recording unit can analyze the user's past practice data and improve the accuracy of the recording using an AI model that improves the accuracy of the recording. As a result, the accuracy of the recording is improved by referring to the user's past practice results. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI.

[0068] The providing unit can adjust the level of detail of the feedback based on the importance of the practice result when providing the feedback. For example, the providing unit adjusts the level of detail of the feedback based on the importance of the practice result when providing the feedback. For example, the providing unit provides detailed feedback based on an important practice result. The providing unit can also provide simplified feedback based on a less important practice result. The providing unit can also dynamically adjust the level of detail of the feedback according to the importance of the practice result. For example, the providing unit can adjust the level of detail of the feedback based on the reliability and relevance of the practice result. This enables more effective feedback by adjusting the level of detail of the feedback according to the importance of the practice result. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0069] The providing unit can apply different feedback algorithms depending on the type of sport when providing feedback. For example, the providing unit applies different feedback algorithms depending on the type of sport when providing feedback. For example, the providing unit applies a feedback algorithm dedicated to volleyball based on the results of volleyball practice. The providing unit can also apply a feedback algorithm dedicated to baseball based on the results of baseball practice. The providing unit can also apply a feedback algorithm dedicated to tennis based on the results of tennis practice. For example, the providing unit can apply a feedback algorithm depending on the type of sport based on the design of the database and the selection of the algorithm. This enables more appropriate feedback by applying a feedback algorithm depending on the type of sport. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0070] The providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit improves the accuracy of the feedback based on the user's past feedback results. The providing unit can also analyze the user's past feedback data and suggest an optimal feedback method. The providing unit can also adjust the level of detail of the feedback by referring to the user's past feedback results. For example, the providing unit can analyze the user's past feedback data and improve the accuracy of the feedback using an AI model that improves the accuracy of the feedback. As a result, the accuracy of the feedback is improved by referring to the user's past feedback results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0071] The providing unit, when providing feedback, can determine the priority of feedback based on the time of practice. For example, when providing feedback, the providing unit determines the priority of feedback based on the time of practice. For example, the providing unit provides feedback preferentially based on the results of the most recent practice. The providing unit can also provide feedback preferentially based on important practice results. The providing unit can also dynamically adjust the priority of feedback based on the time of practice. For example, the providing unit can determine the priority of feedback based on the season or the importance of practice. This enables more effective feedback by determining the priority of feedback based on the time of practice. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0072] The providing unit can adjust the order of feedback based on the relevance of practice when providing feedback. For example, the providing unit adjusts the order of feedback based on the relevance of practice when providing feedback. For example, the providing unit prioritizes providing feedback based on important practice results. The providing unit can also prioritize providing feedback based on highly relevant practice results. The providing unit can also dynamically adjust the order of feedback based on the relevance of practice. For example, the providing unit can adjust the order of feedback based on past practice content or the purpose of practice. This enables more effective feedback by adjusting the order of feedback based on the relevance of practice. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0073] The providing unit can adjust the use of technical terminology in the feedback according to the user's level of expertise when providing feedback. For example, the providing unit can adjust the use of technical terminology in the feedback according to the user's level of expertise when providing feedback. For example, if the user is a beginner, the providing unit can provide feedback using simple terminology. If the user is an intermediate user, the providing unit can provide feedback using appropriate technical terminology. If the user is an advanced user, the providing unit can provide feedback using detailed technical terminology. For example, the providing unit can adjust the use of technical terminology in the feedback based on the user's level of knowledge. This allows for feedback that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI.

[0074] The providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit improves the accuracy of the feedback based on the user's past feedback results. The providing unit can also analyze the user's past feedback data and suggest an optimal feedback method. The providing unit can also adjust the level of detail of the feedback by referring to the user's past feedback results. For example, the providing unit can analyze the user's past feedback data and improve the accuracy of the feedback using an AI model that improves the accuracy of the feedback. As a result, the accuracy of the feedback is improved by referring to the user's past feedback results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The reception unit inputs the content that the user wants to practice. The content that the user wants to practice includes, for example, catching a serve in volleyball or hitting a pitch in baseball. The reception unit supports multiple input methods, such as voice input and text input. Step 2: The generation unit uses AI to recreate the game situation based on the information input by the reception unit. The generation unit analyzes the game data and recreates the game situation based on the content the user wants to practice. For example, it analyzes volleyball serve data or baseball pitch data to recreate the game situation. Step 3: The recording unit records the user's practice results based on the match situation reproduced by the generation unit. The recording unit records the user's practice results as digital data and can also record them in chronological order to track the progress of the practice. Step 4: The providing unit provides the practice results recorded by the recording unit as feedback. The providing unit can also analyze the user's practice results, identify areas for improvement, and provide feedback by visually displaying them.

[0077] (Example 2) A practice support system according to an embodiment of the present invention allows a user to input what they want to practice, and an AI analyzes game data to recreate the game situation, records the user's practice results, and provides feedback. The practice support system allows a user to input what they want to practice, and an AI analyzes game data to recreate the game situation. The user can then practice based on the recreated game situation and receive feedback on the results. For example, in the practice support system, a user inputs "I want to receive a volleyball serve." This information is input to a reception unit of the system. The AI ​​then analyzes the input information and recreates the game situation based on the game data. The generation unit analyzes the game data and recreates the game situation based on the user's practice. For example, it analyzes volleyball serve data or baseball pitch data to recreate the game situation. The user then practices based on the recreated game situation. For example, the user can practice receiving a volleyball serve or hitting a baseball pitch. Furthermore, the user's practice results are recorded and provided as feedback. The provision unit records the user's practice results and provides feedback. This allows the user to review their practice results and identify areas for improvement. This allows the practice support system to improve the user's performance in a match and eliminate frustration. This allows the practice support system to improve the user's performance in a match and eliminate frustration. For example, by repeatedly practicing receiving serves in volleyball, the success rate of serve reception in a match can be improved. Similarly, by repeatedly practicing hitting pitched baseballs, the hitting power in a match can be improved.

[0078] A practice support system according to an embodiment includes a reception unit, a generation unit, a recording unit, and a providing unit. The reception unit inputs the content the user wants to practice. The content the user wants to practice includes, but is not limited to, receiving a volleyball serve or hitting a baseball pitch. For example, the reception unit can receive input such as "I want to receive a volleyball serve." The reception unit can also support multiple input methods, such as voice input and text input. The generation unit uses AI to recreate a game situation based on the information input by the reception unit. For example, the generation unit analyzes game data and recreates the game situation based on the content the user wants to practice. For example, the generation unit analyzes volleyball serve data or baseball pitch data to recreate the game situation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. The recording unit records the user's practice results based on the game situation recreated by the generation unit. For example, the recording unit records the user's practice results as digital data. The recording unit can also record the user's practice results in chronological order and track the progress of practice. Some or all of the above-described processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. The providing unit provides the practice results recorded by the recording unit as feedback. The providing unit, for example, analyzes the user's practice results and identifies areas for improvement. The providing unit can also visually display the user's practice results and provide feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. As a result, the practice support system according to the embodiment can improve the user's performance in a match.

[0079] The generation unit can analyze the match data and recreate the match situation. The match data includes, for example, video data of the match, statistical data, sensor data, etc., but is not limited to these examples. The generation unit, for example, analyzes the video data of the match and recreates the match situation. The generation unit can also analyze statistical data and recreate the match situation. The generation unit can also analyze sensor data and recreate the match situation. For example, the generation unit can input the video data of the match and recreate the match situation using an AI model that recreates the match situation. This allows for the reproduction of a more realistic match situation by analyzing the match data. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0080] The generation unit can analyze volleyball serve data or baseball pitch data to recreate a game situation. Volleyball serve data or baseball pitch data may include, but is not limited to, the type of sensor and the format of data. The generation unit can analyze, for example, volleyball serve data to recreate a game situation. The generation unit can also analyze baseball pitch data to recreate a game situation. The generation unit can also combine volleyball serve data and baseball pitch data to recreate a game situation. For example, the generation unit can input volleyball serve data and recreate a game situation using an AI model that recreates a game situation. This allows for more specific practice by recreating the game situation of a specific sport. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0081] The providing unit can analyze the user's practice results and identify areas for improvement. The providing unit, for example, analyzes the user's practice results and identifies areas for improvement. For example, the providing unit analyzes the user's practice results and identifies areas for improvement based on performance evaluation criteria. The providing unit can also analyze the user's practice results and identify areas for improvement using an analysis algorithm. The providing unit can also visually display the user's practice results and identify areas for improvement. For example, the providing unit displays the user's practice results as a graph or chart and identifies areas for improvement. In this way, specific areas for improvement can be identified by analyzing the user's practice results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI.

[0082] The reception unit can estimate the user's emotions and adjust the input method of the practice content based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the input method of the practice content based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface to minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of the practice content. For example, the reception unit can estimate the user's emotions using facial expression recognition technology. This allows for more appropriate practice by adjusting the input method of the practice content according to the user's emotions. Emotion estimation 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 reception unit can be performed using, for example, AI, or without AI.

[0083] The reception unit can analyze the user's past practice history and suggest appropriate practice content. The reception unit, for example, analyzes the user's past practice history and suggests appropriate practice content. For example, the reception unit automatically displays practice content that the user has frequently performed in the past as candidates. The reception unit can also predict and suggest practice content to be performed at a specific time period based on the user's past practice history. The reception unit can also analyze the user's past practice history and suggest the most effective practice content. For example, the reception unit can analyze the user's past practice data and suggest practice content using an AI model that suggests optimal practice content. In this way, the optimal practice content can be suggested to the user by analyzing the past practice history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0084] The reception unit can filter the practice content based on the user's current physical condition and fatigue level when inputting the practice content. For example, the reception unit can filter the practice content based on the user's current physical condition and fatigue level when inputting the practice content. For example, if the user is tired, the reception unit can suggest lighter practice content. Furthermore, if the user is feeling unwell, the reception unit can also suggest that the user prioritize rest. Furthermore, if the user is feeling well, the reception unit can suggest harder practice content. For example, the reception unit can analyze the user's vital signs data and evaluate the user's physical condition and fatigue level. This makes it possible to suggest more appropriate practice content by taking the user's physical condition and fatigue level into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0085] The reception unit can select the optimal input means depending on the user's input method when inputting practice content. For example, the reception unit can select the optimal input means depending on the user's input method when inputting practice content. For example, the reception unit can automatically set the practice content when the user simply inputs, by voice, "I want to receive a volleyball serve." The reception unit can also allow the user to easily set the practice content by performing a specific gesture on the smartphone screen. The reception unit can also allow the user to set the practice content more intuitively by combining voice input and gesture input. For example, the reception unit can use voice recognition technology to analyze the user's voice input and set the practice content. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI or without AI.

[0086] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prioritizes input of important practice content. Furthermore, if the user is relaxed, the reception unit can also input detailed practice content. Furthermore, if the user is in a hurry, the reception unit can prioritize input of simple practice content. For example, the reception unit can estimate the user's emotions using user voice analysis technology. This enables more effective practice by prioritizing input content according to the user's emotions. The emotion estimation is realized 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI.

[0087] When inputting practice content, the reception unit can prioritize suggesting highly relevant practice content by taking into account the user's geographical location information. For example, when inputting practice content, the reception unit prioritizes suggesting highly relevant practice content by taking into account the user's geographical location information. For example, when the user is at a specific sports facility, the reception unit can suggest practice content that can be done at the facility. Furthermore, when the user is at home, the reception unit can suggest practice content that can be done at home. Furthermore, when the user is in a park, the reception unit can suggest practice content that can be done in the park. For example, the reception unit can obtain the user's geographical location information using GPS data and suggest practice content. In this way, more appropriate practice content can be suggested by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without using AI.

[0088] The reception unit can analyze the user's social media activity when inputting practice content and suggest related practice content. For example, when inputting practice content, the reception unit can analyze the user's social media activity and suggest related practice content. For example, the reception unit can suggest practice content based on sports activities shared by the user on social media. The reception unit can also analyze the user's social media posts and suggest related practice content. The reception unit can also suggest related practice content by referring to the activities of the user's friends on social media. For example, the reception unit can analyze social media posting data and suggest practice content based on the user's interests. In this way, by analyzing the user's social media activity, more relevant practice content can be suggested. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI.

[0089] The reception unit can customize the input method by reflecting the user's past feedback when inputting practice content. The reception unit customizes the input method by reflecting the user's past feedback when inputting practice content, for example. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also suggest the optimal input method based on the user's past feedback. The reception unit can also customize the input method to avoid input methods that the user has previously been dissatisfied with. For example, the reception unit can analyze the user's past feedback data and suggest the optimal input method. In this way, a more appropriate input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0090] The generation unit can estimate the user's emotions and adjust the method of reproducing the game situation based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the method of reproducing the game situation based on the estimated user emotions. For example, if the user is relaxed, the generation unit can reproduce a game situation progressing at a leisurely pace. If the user is in a hurry, the generation unit can reproduce a game situation that emphasizes the shortest route. If the user is excited, the generation unit can reproduce a game situation that adds visually stimulating effects. For example, the generation unit can estimate the user's emotions using user facial expression recognition technology. This enables more effective practice by adjusting the method of reproducing the game situation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or 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 generation unit can be performed using, for example, AI, or without AI.

[0091] The generation unit can adjust the level of detail of the reproduction based on the importance of the match data when reproducing the match situation. For example, the generation unit adjusts the level of detail of the reproduction based on the importance of the match data when reproducing the match situation. For example, the generation unit performs detailed reproduction based on important match data. The generation unit can also perform simplified reproduction based on less important match data. The generation unit can also dynamically adjust the level of detail of the reproduction based on the importance of the match data. For example, the generation unit can adjust the level of detail of the reproduction based on the reliability and relevance of the match data. This allows for more effective practice by adjusting the level of detail of the reproduction based on the importance of the match data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0092] The generation unit can apply different reproduction algorithms depending on the type of sport when reproducing a game situation. For example, the generation unit applies different reproduction algorithms depending on the type of sport when reproducing a game situation. For example, the generation unit applies a reproduction algorithm dedicated to volleyball to reproduce a volleyball game situation. The generation unit can also apply a reproduction algorithm dedicated to baseball to reproduce a baseball game situation. The generation unit can also apply a reproduction algorithm dedicated to tennis to reproduce a tennis game situation. For example, the generation unit can apply a reproduction algorithm depending on the type of sport using a physical simulation or a machine learning algorithm. In this way, by applying a reproduction algorithm depending on the type of sport, more realistic game situations can be reproduced. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0093] The generation unit can improve the accuracy of the reproduction by referring to the user's past practice results when reproducing a match situation. For example, the generation unit can improve the accuracy of the reproduction by referring to the user's past practice results when reproducing a match situation. For example, the generation unit improves the accuracy of the reproduction based on the user's past practice results. The generation unit can also analyze the user's past practice data and propose an optimal reproduction method. The generation unit can also adjust the level of detail of the reproduction by referring to the user's past practice results. For example, the generation unit can analyze the user's past practice data and improve the accuracy of the reproduction using an AI model that improves the accuracy of the reproduction. As a result, the accuracy of the reproduction is improved by referring to the user's past practice results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without AI.

[0094] The generation unit can estimate the user's emotions and adjust the length of the game situation to be reproduced based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the game situation to be reproduced based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can reproduce a short, concise game situation. Furthermore, if the user is relaxed, the generation unit can reproduce a longer game situation including detailed explanations. Furthermore, if the user is excited, the generation unit can reproduce a game situation with visually stimulating effects. For example, the generation unit can analyze the user's biometric data to estimate the user's emotions. This allows for more effective practice by adjusting the length of the game situation according to the user's emotions. The emotion estimation is realized 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 generation unit can be performed using, for example, an AI or without an AI.

[0095] The generation unit can determine the priority of replay based on the time of the game when replaying the game situation. For example, the generation unit determines the priority of replay based on the time of the game when replaying the game situation. For example, the generation unit prioritizes replaying the most recent game data. The generation unit can also prioritize replaying important game data. The generation unit can also dynamically adjust the priority of replay based on the time of the game. For example, the generation unit can determine the priority of replay based on the season or the importance of the game. As a result, determining the priority of replay based on the time of the game enables more effective practice. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0096] The generation unit can adjust the order of reproduction based on the relevance of the match when reproducing the match situation. For example, the generation unit adjusts the order of reproduction based on the relevance of the match when reproducing the match situation. For example, the generation unit prioritizes reproducing important match data. The generation unit can also prioritize reproducing highly relevant match data. The generation unit can also dynamically adjust the order of reproduction based on the relevance of the match. For example, the generation unit can adjust the order of reproduction based on past match results and team tactics. This enables more effective practice by adjusting the order of reproduction based on the relevance of the match. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0097] The generation unit can adjust the use of technical terminology in the reproduction according to the user's level of expertise when reproducing a match situation. For example, the generation unit can adjust the use of technical terminology in the reproduction according to the user's level of expertise when reproducing a match situation. For example, if the user is a beginner, the generation unit can reproduce the situation using simple terminology. If the user is an intermediate player, the generation unit can also reproduce the situation using appropriate technical terminology. If the user is an advanced player, the generation unit can also reproduce the situation using detailed technical terminology. For example, the generation unit can adjust the use of technical terminology in the reproduction based on the user's level of knowledge. This allows for a more easily understandable reproduction by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI.

[0098] The recording unit can estimate the user's emotions and adjust the recording method of the practice results based on the estimated user emotions. For example, the recording unit can estimate the user's emotions and adjust the recording method of the practice results based on the estimated user emotions. For example, if the user is nervous, the recording unit can provide a simple, highly visible recording method. If the user is relaxed, the recording unit can provide a recording method that includes detailed information. If the user is in a hurry, the recording unit can provide a recording method that focuses on the main points. For example, the recording unit can estimate the user's emotions using user voice analysis technology. This enables more appropriate recording by adjusting the recording method according to the user'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 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 recording unit can be performed, for example, using AI or without AI.

[0099] The recording unit can adjust the level of detail of the recording based on the importance of the practice data when recording practice results. For example, the recording unit can adjust the level of detail of the recording based on the importance of the practice data when recording practice results. For example, the recording unit can record important practice data in detail. The recording unit can also simplify and record less important practice data. The recording unit can also dynamically adjust the level of detail of the recording based on the importance of the practice data. For example, the recording unit can adjust the level of detail of the recording based on the reliability and relevance of the practice data. This allows for more effective recording by adjusting the level of detail of the recording based on the importance of the practice data. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI.

[0100] The recording unit can apply different recording algorithms depending on the type of sport when recording practice results. For example, the recording unit applies different recording algorithms depending on the type of sport when recording practice results. For example, the recording unit can apply a recording algorithm dedicated to volleyball to record volleyball practice results. The recording unit can also apply a recording algorithm dedicated to baseball to record baseball practice results. The recording unit can also apply a recording algorithm dedicated to tennis to record tennis practice results. For example, the recording unit can apply a recording algorithm depending on the type of sport based on the design of the database and the selection of the algorithm. This enables more appropriate recording by applying a recording algorithm depending on the type of sport. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or without using AI.

[0101] The recording unit can improve the accuracy of the recording by referring to the user's past practice results when recording practice results. For example, the recording unit can improve the accuracy of the recording by referring to the user's past practice results when recording practice results. For example, the recording unit improves the accuracy of the recording based on the user's past practice results. The recording unit can also analyze the user's past practice data and suggest an optimal recording method. The recording unit can also adjust the level of detail of the recording by referring to the user's past practice results. For example, the recording unit can analyze the user's past practice data and improve the accuracy of the recording using an AI model that improves the accuracy of the recording. As a result, the accuracy of the recording is improved by referring to the user's past practice results. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI.

[0102] The recording unit can estimate the user's emotions and determine the priority of the practice results to be recorded based on the estimated user emotions. For example, the recording unit can estimate the user's emotions and determine the priority of the practice results to be recorded based on the estimated user emotions. For example, if the user is feeling stressed, the recording unit can prioritize recording important practice results. Furthermore, if the user is relaxed, the recording unit can also record detailed practice results. Furthermore, if the user is in a hurry, the recording unit can prioritize recording simple practice results. For example, the recording unit can estimate the user's emotions using facial expression recognition technology. This enables more effective recording by prioritizing the practice results to be recorded based on the user's emotions. Emotion estimation 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 recording unit can be performed using, for example, AI, or without AI.

[0103] The recording unit can determine the priority of recording when recording practice results based on the time when the practice was performed. For example, the recording unit determines the priority of recording when recording practice results based on the time when the practice was performed. For example, the recording unit prioritizes recording of the most recent practice results. The recording unit can also prioritize recording of important practice results. The recording unit can also dynamically adjust the priority of recording based on the time when the practice was performed. For example, the recording unit can determine the priority of recording based on the season or the importance of the practice. This enables more effective recording by determining the priority of recording based on the time when the practice was performed. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI.

[0104] The recording unit can adjust the order of recording based on the relevance of the practice when recording the practice results. For example, the recording unit can adjust the order of recording based on the relevance of the practice when recording the practice results. For example, the recording unit can prioritize recording important practice results. The recording unit can also prioritize recording highly relevant practice results. The recording unit can also dynamically adjust the order of recording based on the relevance of the practice. For example, the recording unit can adjust the order of recording based on the content of past practice or the purpose of practice. This enables more effective recording by adjusting the order of recording based on the relevance of the practice. Some or all of the above-mentioned processing in the recording unit can be performed, for example, using AI or without using AI.

[0105] The recording unit can adjust the use of technical terms in the recording according to the user's level of expertise when recording the practice results. For example, the recording unit can adjust the use of technical terms in the recording according to the user's level of expertise when recording the practice results. For example, if the user is a beginner, the recording unit can record using simple terms. If the user is an intermediate user, the recording unit can record using appropriate technical terms. If the user is an advanced user, the recording unit can record using detailed technical terms. For example, the recording unit can adjust the use of technical terms in the recording based on the user's level of knowledge. This allows for easier-to-understand recording by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI.

[0106] The providing unit can estimate the user's emotions and adjust the feedback provision method based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the feedback provision method based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible feedback. Furthermore, if the user is relaxed, the providing unit can provide feedback including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide feedback that focuses on the main points. For example, the providing unit can estimate the user's emotions using user speech analysis technology. This enables more effective feedback by adjusting the feedback provision method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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 providing unit can be performed, for example, using AI or without AI.

[0107] The providing unit can adjust the level of detail of the feedback based on the importance of the practice result when providing the feedback. For example, the providing unit adjusts the level of detail of the feedback based on the importance of the practice result when providing the feedback. For example, the providing unit provides detailed feedback based on an important practice result. The providing unit can also provide simplified feedback based on a less important practice result. The providing unit can also dynamically adjust the level of detail of the feedback according to the importance of the practice result. For example, the providing unit can adjust the level of detail of the feedback based on the reliability and relevance of the practice result. This enables more effective feedback by adjusting the level of detail of the feedback according to the importance of the practice result. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0108] The providing unit can apply different feedback algorithms depending on the type of sport when providing feedback. For example, the providing unit applies different feedback algorithms depending on the type of sport when providing feedback. For example, the providing unit applies a feedback algorithm dedicated to volleyball based on the results of volleyball practice. The providing unit can also apply a feedback algorithm dedicated to baseball based on the results of baseball practice. The providing unit can also apply a feedback algorithm dedicated to tennis based on the results of tennis practice. For example, the providing unit can apply a feedback algorithm depending on the type of sport based on the design of the database and the selection of the algorithm. This enables more appropriate feedback by applying a feedback algorithm depending on the type of sport. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0109] The providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit improves the accuracy of the feedback based on the user's past feedback results. The providing unit can also analyze the user's past feedback data and suggest an optimal feedback method. The providing unit can also adjust the level of detail of the feedback by referring to the user's past feedback results. For example, the providing unit can analyze the user's past feedback data and improve the accuracy of the feedback using an AI model that improves the accuracy of the feedback. As a result, the accuracy of the feedback is improved by referring to the user's past feedback results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0110] The providing unit can estimate the user's emotions and adjust the length of the feedback based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the length of the feedback based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point feedback. Furthermore, if the user is relaxed, the providing unit can provide longer feedback with detailed explanations. Furthermore, if the user is excited, the providing unit can provide feedback with visually stimulating effects. For example, the providing unit can analyze the user's biometric data to estimate the user's emotions. This allows for more effective feedback by adjusting the length of the feedback according to the user's emotions. The emotion estimation is realized 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 providing unit can be performed using, for example, an AI, or without an AI.

[0111] The providing unit, when providing feedback, can determine the priority of feedback based on the time of practice. For example, when providing feedback, the providing unit determines the priority of feedback based on the time of practice. For example, the providing unit provides feedback preferentially based on the results of the most recent practice. The providing unit can also provide feedback preferentially based on important practice results. The providing unit can also dynamically adjust the priority of feedback based on the time of practice. For example, the providing unit can determine the priority of feedback based on the season or the importance of practice. This enables more effective feedback by determining the priority of feedback based on the time of practice. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0112] The providing unit can adjust the order of feedback based on the relevance of practice when providing feedback. For example, the providing unit adjusts the order of feedback based on the relevance of practice when providing feedback. For example, the providing unit prioritizes providing feedback based on important practice results. The providing unit can also prioritize providing feedback based on highly relevant practice results. The providing unit can also dynamically adjust the order of feedback based on the relevance of practice. For example, the providing unit can adjust the order of feedback based on past practice content or the purpose of practice. This enables more effective feedback by adjusting the order of feedback based on the relevance of practice. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0113] The providing unit can adjust the use of technical terminology in the feedback according to the user's level of expertise when providing feedback. For example, the providing unit can adjust the use of technical terminology in the feedback according to the user's level of expertise when providing feedback. For example, if the user is a beginner, the providing unit can provide feedback using simple terminology. If the user is an intermediate user, the providing unit can provide feedback using appropriate technical terminology. If the user is an advanced user, the providing unit can provide feedback using detailed technical terminology. For example, the providing unit can adjust the use of technical terminology in the feedback based on the user's level of knowledge. This allows for feedback that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, recording unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and allows the user to input the content of the practice they want to do. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes match data to reproduce the match situation. The recording unit, for example, records the user's practice results in the storage 50 of the smart device 14. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides feedback to the user. === Hard Collateral 1-2 === For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and allows the user to voice-input the content of the practice he or she wants to do. Each of the above-mentioned multiple elements, including the reception unit, generation unit, recording unit, and providing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes match data to reproduce the match situation. The recording unit records the user's practice results in the storage 50 of the smart glasses 214, for example. The providing unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides feedback to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, recording unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314, and allows the user to voice-input the content of the practice he or she wants to do. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes match data to reproduce the match situation. The recording unit, for example, records the user's practice results in the storage 50 of the headset type terminal 314. The provision unit is realized, for example, by the display 343 of the headset type terminal 314, and provides feedback to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, recording unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and allows the user to voice-input the content of the practice they want to do. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes match data to reproduce the match situation. The recording unit, for example, records the user's practice results in the storage 50 of the robot 414. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides feedback to the user.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The reception unit can analyze the user's past practice history and suggest appropriate practice content. For example, the reception unit can automatically display practice content that the user has frequently performed in the past as candidates. The reception unit can also predict and suggest practice content to be performed at a specific time period based on the user's past practice history. The reception unit can also analyze the user's past practice history and suggest the most effective practice content. For example, the reception unit can analyze the user's past practice data and suggest practice content using an AI model that suggests optimal practice content. In this way, the optimal practice content can be suggested to the user by analyzing the past practice history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0116] The generation unit can analyze the match data and recreate the match situation. The match data includes, for example, video data of the match, statistical data, sensor data, etc., but is not limited to these examples. The generation unit, for example, analyzes the video data of the match and recreates the match situation. The generation unit can also analyze statistical data and recreate the match situation. The generation unit can also analyze sensor data and recreate the match situation. For example, the generation unit can input the video data of the match and recreate the match situation using an AI model that recreates the match situation. This allows for the reproduction of a more realistic match situation by analyzing the match data. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0117] The generation unit can analyze volleyball serve data or baseball pitch data to recreate a game situation. Volleyball serve data or baseball pitch data may include, but is not limited to, the type of sensor and the format of data. The generation unit can analyze, for example, volleyball serve data to recreate a game situation. The generation unit can also analyze baseball pitch data to recreate a game situation. The generation unit can also combine volleyball serve data and baseball pitch data to recreate a game situation. For example, the generation unit can input volleyball serve data and recreate a game situation using an AI model that recreates a game situation. This allows for more specific practice by recreating the game situation of a specific sport. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0118] The providing unit can analyze the user's practice results and identify areas for improvement. The providing unit, for example, analyzes the user's practice results and identifies areas for improvement. For example, the providing unit analyzes the user's practice results and identifies areas for improvement based on performance evaluation criteria. The providing unit can also analyze the user's practice results and identify areas for improvement using an analysis algorithm. The providing unit can also visually display the user's practice results and identify areas for improvement. For example, the providing unit displays the user's practice results as a graph or chart and identifies areas for improvement. In this way, specific areas for improvement can be identified by analyzing the user's practice results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI.

[0119] The reception unit can estimate the user's emotions and adjust the input method of the practice content based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the input method of the practice content based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface to minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of the practice content. For example, the reception unit can estimate the user's emotions using facial expression recognition technology. This allows for more appropriate practice by adjusting the input method of the practice content according to the user's emotions. Emotion estimation 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 reception unit can be performed using, for example, AI, or without AI.

[0120] The reception unit can filter the practice content based on the user's current physical condition and fatigue level when inputting the practice content. For example, the reception unit can filter the practice content based on the user's current physical condition and fatigue level when inputting the practice content. For example, if the user is tired, the reception unit can suggest lighter practice content. Furthermore, if the user is feeling unwell, the reception unit can also suggest that the user prioritize rest. Furthermore, if the user is feeling well, the reception unit can suggest harder practice content. For example, the reception unit can analyze the user's vital signs data and evaluate the user's physical condition and fatigue level. This makes it possible to suggest more appropriate practice content by taking the user's physical condition and fatigue level into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0121] The reception unit can select the optimal input means depending on the user's input method when inputting practice content. For example, the reception unit can select the optimal input means depending on the user's input method when inputting practice content. For example, the reception unit can automatically set the practice content when the user simply inputs, by voice, "I want to receive a volleyball serve." The reception unit can also allow the user to easily set the practice content by performing a specific gesture on the smartphone screen. The reception unit can also allow the user to set the practice content more intuitively by combining voice input and gesture input. For example, the reception unit can use voice recognition technology to analyze the user's voice input and set the practice content. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI or without AI.

[0122] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prioritizes input of important practice content. Furthermore, if the user is relaxed, the reception unit can also input detailed practice content. Furthermore, if the user is in a hurry, the reception unit can prioritize input of simple practice content. For example, the reception unit can estimate the user's emotions using user voice analysis technology. This enables more effective practice by prioritizing input content according to the user's emotions. The emotion estimation is realized 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI.

[0123] The generation unit can estimate the user's emotions and adjust the method of reproducing the game situation based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the method of reproducing the game situation based on the estimated user emotions. For example, if the user is relaxed, the generation unit can reproduce a game situation progressing at a leisurely pace. If the user is in a hurry, the generation unit can reproduce a game situation that emphasizes the shortest route. If the user is excited, the generation unit can reproduce a game situation that adds visually stimulating effects. For example, the generation unit can estimate the user's emotions using user facial expression recognition technology. This enables more effective practice by adjusting the method of reproducing the game situation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or 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 generation unit can be performed using, for example, AI, or without AI.

[0124] The generation unit can adjust the level of detail of the reproduction based on the importance of the match data when reproducing the match situation. For example, the generation unit adjusts the level of detail of the reproduction based on the importance of the match data when reproducing the match situation. For example, the generation unit performs detailed reproduction based on important match data. The generation unit can also perform simplified reproduction based on less important match data. The generation unit can also dynamically adjust the level of detail of the reproduction based on the importance of the match data. For example, the generation unit can adjust the level of detail of the reproduction based on the reliability and relevance of the match data. This allows for more effective practice by adjusting the level of detail of the reproduction based on the importance of the match data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0125] The recording unit can estimate the user's emotions and adjust the recording method of the practice results based on the estimated user emotions. For example, the recording unit can estimate the user's emotions and adjust the recording method of the practice results based on the estimated user emotions. For example, if the user is nervous, the recording unit can provide a simple, highly visible recording method. If the user is relaxed, the recording unit can provide a recording method that includes detailed information. If the user is in a hurry, the recording unit can provide a recording method that focuses on the main points. For example, the recording unit can estimate the user's emotions using user voice analysis technology. This enables more appropriate recording by adjusting the recording method according to the user'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 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 recording unit can be performed, for example, using AI or without AI.

[0126] The recording unit can adjust the level of detail of the recording based on the importance of the practice data when recording practice results. For example, the recording unit can adjust the level of detail of the recording based on the importance of the practice data when recording practice results. For example, the recording unit can record important practice data in detail. The recording unit can also simplify and record less important practice data. The recording unit can also dynamically adjust the level of detail of the recording based on the importance of the practice data. For example, the recording unit can adjust the level of detail of the recording based on the reliability and relevance of the practice data. This allows for more effective recording by adjusting the level of detail of the recording based on the importance of the practice data. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI.

[0127] The recording unit can apply different recording algorithms depending on the type of sport when recording practice results. For example, the recording unit applies different recording algorithms depending on the type of sport when recording practice results. For example, the recording unit can apply a recording algorithm dedicated to volleyball to record volleyball practice results. The recording unit can also apply a recording algorithm dedicated to baseball to record baseball practice results. The recording unit can also apply a recording algorithm dedicated to tennis to record tennis practice results. For example, the recording unit can apply a recording algorithm depending on the type of sport based on the design of the database and the selection of the algorithm. This enables more appropriate recording by applying a recording algorithm depending on the type of sport. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or without using AI.

[0128] The recording unit can improve the accuracy of the recording by referring to the user's past practice results when recording practice results. For example, the recording unit can improve the accuracy of the recording by referring to the user's past practice results when recording practice results. For example, the recording unit improves the accuracy of the recording based on the user's past practice results. The recording unit can also analyze the user's past practice data and suggest an optimal recording method. The recording unit can also adjust the level of detail of the recording by referring to the user's past practice results. For example, the recording unit can analyze the user's past practice data and improve the accuracy of the recording using an AI model that improves the accuracy of the recording. As a result, the accuracy of the recording is improved by referring to the user's past practice results. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI.

[0129] The recording unit can estimate the user's emotions and determine the priority of the practice results to be recorded based on the estimated user emotions. For example, the recording unit can estimate the user's emotions and determine the priority of the practice results to be recorded based on the estimated user emotions. For example, if the user is feeling stressed, the recording unit can prioritize recording important practice results. Furthermore, if the user is relaxed, the recording unit can also record detailed practice results. Furthermore, if the user is in a hurry, the recording unit can prioritize recording simple practice results. For example, the recording unit can estimate the user's emotions using facial expression recognition technology. This enables more effective recording by prioritizing the practice results to be recorded based on the user's emotions. Emotion estimation 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 recording unit can be performed using, for example, AI, or without AI.

[0130] The providing unit can estimate the user's emotions and adjust the feedback provision method based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the feedback provision method based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible feedback. Furthermore, if the user is relaxed, the providing unit can provide feedback including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide feedback that focuses on the main points. For example, the providing unit can estimate the user's emotions using user speech analysis technology. This enables more effective feedback by adjusting the feedback provision method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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 providing unit can be performed, for example, using AI or without AI.

[0131] The providing unit can adjust the level of detail of the feedback based on the importance of the practice result when providing the feedback. For example, the providing unit adjusts the level of detail of the feedback based on the importance of the practice result when providing the feedback. For example, the providing unit provides detailed feedback based on an important practice result. The providing unit can also provide simplified feedback based on a less important practice result. The providing unit can also dynamically adjust the level of detail of the feedback according to the importance of the practice result. For example, the providing unit can adjust the level of detail of the feedback based on the reliability and relevance of the practice result. This enables more effective feedback by adjusting the level of detail of the feedback according to the importance of the practice result. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0132] The providing unit can apply different feedback algorithms depending on the type of sport when providing feedback. For example, the providing unit applies different feedback algorithms depending on the type of sport when providing feedback. For example, the providing unit applies a feedback algorithm dedicated to volleyball based on the results of volleyball practice. The providing unit can also apply a feedback algorithm dedicated to baseball based on the results of baseball practice. The providing unit can also apply a feedback algorithm dedicated to tennis based on the results of tennis practice. For example, the providing unit can apply a feedback algorithm depending on the type of sport based on the design of the database and the selection of the algorithm. This enables more appropriate feedback by applying a feedback algorithm depending on the type of sport. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0133] The providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit improves the accuracy of the feedback based on the user's past feedback results. The providing unit can also analyze the user's past feedback data and suggest an optimal feedback method. The providing unit can also adjust the level of detail of the feedback by referring to the user's past feedback results. For example, the providing unit can analyze the user's past feedback data and improve the accuracy of the feedback using an AI model that improves the accuracy of the feedback. As a result, the accuracy of the feedback is improved by referring to the user's past feedback results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0134] The providing unit can estimate the user's emotions and adjust the length of the feedback based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the length of the feedback based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point feedback. Furthermore, if the user is relaxed, the providing unit can provide longer feedback with detailed explanations. Furthermore, if the user is excited, the providing unit can provide feedback with visually stimulating effects. For example, the providing unit can analyze the user's biometric data to estimate the user's emotions. This allows for more effective feedback by adjusting the length of the feedback according to the user's emotions. The emotion estimation is realized 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 providing unit can be performed using, for example, an AI, or without an AI.

[0135] The providing unit, when providing feedback, can determine the priority of feedback based on the time of practice. For example, when providing feedback, the providing unit determines the priority of feedback based on the time of practice. For example, the providing unit provides feedback preferentially based on the results of the most recent practice. The providing unit can also provide feedback preferentially based on important practice results. The providing unit can also dynamically adjust the priority of feedback based on the time of practice. For example, the providing unit can determine the priority of feedback based on the season or the importance of practice. This enables more effective feedback by determining the priority of feedback based on the time of practice. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0136] The providing unit can adjust the order of feedback based on the relevance of practice when providing feedback. For example, the providing unit adjusts the order of feedback based on the relevance of practice when providing feedback. For example, the providing unit prioritizes providing feedback based on important practice results. The providing unit can also prioritize providing feedback based on highly relevant practice results. The providing unit can also dynamically adjust the order of feedback based on the relevance of practice. For example, the providing unit can adjust the order of feedback based on past practice content or the purpose of practice. This enables more effective feedback by adjusting the order of feedback based on the relevance of practice. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI.

[0137] The providing unit can adjust the use of technical terminology in the feedback according to the user's level of expertise when providing feedback. For example, the providing unit can adjust the use of technical terminology in the feedback according to the user's level of expertise when providing feedback. For example, if the user is a beginner, the providing unit can provide feedback using simple terminology. If the user is an intermediate user, the providing unit can provide feedback using appropriate technical terminology. If the user is an advanced user, the providing unit can provide feedback using detailed technical terminology. For example, the providing unit can adjust the use of technical terminology in the feedback based on the user's level of knowledge. This allows for feedback that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI.

[0138] The providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the providing unit improves the accuracy of the feedback based on the user's past feedback results. The providing unit can also analyze the user's past feedback data and suggest an optimal feedback method. The providing unit can also adjust the level of detail of the feedback by referring to the user's past feedback results. For example, the providing unit can analyze the user's past feedback data and improve the accuracy of the feedback using an AI model that improves the accuracy of the feedback. As a result, the accuracy of the feedback is improved by referring to the user's past feedback results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0139] The processing flow of the second embodiment will be briefly explained below.

[0140] Step 1: The reception unit inputs the content that the user wants to practice. The content that the user wants to practice includes, for example, catching a serve in volleyball or hitting a pitch in baseball. The reception unit supports multiple input methods, such as voice input and text input. Step 2: The generation unit uses AI to recreate the game situation based on the information input by the reception unit. The generation unit analyzes the game data and recreates the game situation based on the content the user wants to practice. For example, it analyzes volleyball serve data or baseball pitch data to recreate the game situation. Step 3: The recording unit records the user's practice results based on the match situation reproduced by the generation unit. The recording unit records the user's practice results as digital data and can also record them in chronological order to track the progress of the practice. Step 4: The providing unit provides the practice results recorded by the recording unit as feedback. The providing unit can also analyze the user's practice results, identify areas for improvement, and provide feedback by visually displaying them.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0146] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 AI 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 AI 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.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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 AI 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.

[0192] 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.

[0193] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Explanation of symbols]

[0213] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting the content that the user wants to practice; a generation unit that reproduces a match situation based on the information input by the reception unit; a recording unit that records the user's practice results based on the match situation reproduced by the generation unit; a providing unit that provides the practice results recorded by the recording unit as feedback; Equipped with A system characterized by:

2. The generation unit Analyzing match data and recreating match situations 2. The system of claim 1.

3. The generation unit Analyzing volleyball serve or baseball pitch data to recreate game situations 2. The system of claim 1.

4. The providing unit Analyze user practice results and identify areas for improvement 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and adjust the input method of the practice content based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyzes the user's past practice history and suggests appropriate practice content 2. The system of claim 1.

7. The reception unit When entering exercise details, filtering is performed based on the user's current physical condition and fatigue level.

2. The system of claim 1.

8. The reception unit When inputting practice content, select the most appropriate input method according to the user's input method.

2. The system of claim 1.

Images