system

Abstract

The system according to this embodiment aims to provide e-sports players with practice methods and feedback to help them improve effectively. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, a practice unit, and a feedback unit. The reception unit receives input of gameplay videos or questions. The analysis unit analyzes the data received by the reception unit and identifies areas for improvement from the player's gameplay videos. The proposal unit proposes effective practice methods or tasks based on the areas for improvement identified by the analysis unit. The practice unit executes the practice methods or tasks proposed by the proposal unit. The feedback unit analyzes gameplay videos and generates feedback.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a method for improving e-sports has not been established and the opportunity to receive professional coaching or feedback is scarce.

[0005] The system according to the embodiment aims to provide a practice method and feedback for an e-sports player to effectively improve.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, a practice unit, and a feedback unit. The reception unit receives input of gameplay videos or questions. The analysis unit analyzes the data received by the reception unit and identifies areas for improvement from the player's gameplay videos. The proposal unit proposes effective practice methods or tasks based on the areas for improvement identified by the analysis unit. The practice unit executes the practice methods or tasks proposed by the proposal unit. The feedback unit analyzes gameplay videos and generates feedback. [Effects of the Invention]

[0007] The system according to this embodiment can provide e-sports players with practice methods and feedback to help them improve effectively. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] <000009%>The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​eSports coaching system according to an embodiment of the present invention is a system aimed at improving the skills of eSports players. In this system, the user provides gameplay videos and questions, and the AI ​​analyzes this data to identify areas for improvement from the player's gameplay videos and proposes effective practice methods and tasks. It also analyzes match videos to generate feedback, which can be used to help in strategy planning. For example, a user can upload their own gameplay video and ask questions about their movements in specific situations. This information is input into the AI. Next, the AI ​​analyzes the input information. Based on knowledge such as professional gameplay videos, commentary videos, and articles, the AI ​​analyzes the user's gameplay video and identifies areas for improvement. For example, it may point out problems such as slow movements in specific situations or inappropriate strategies. Furthermore, the AI ​​proposes effective practice methods and tasks. For example, it may suggest practice methods to improve specific skills or tasks to be performed before a match. This allows the user to overcome their weaknesses and improve their skills. The AI ​​also analyzes match videos to generate feedback. For example, it may evaluate the overall flow and strategy of the match, or the movements of individual players. This allows the user to review their play style and improve their strategy. This service aims to improve the skill level of esports players worldwide. For example, players of all levels, from beginners to professionals, can receive specific advice to improve their skills. Furthermore, by taking a data-driven, systematic approach, effective strategies can be developed without relying on individual experience or intuition. In this way, the AI ​​esports coaching system can support the skill improvement of esports players.

[0029] The AI ​​eSports coaching system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a practice unit, and a feedback unit. The reception unit is where users provide gameplay videos and questions. For example, a user can upload a video of their gameplay and ask questions about their movements in a specific scene. This information is input to the AI. The analysis unit uses the AI ​​to analyze the data received by the reception unit and identify areas for improvement from the player's gameplay video. For example, it points out problems such as slow movements in a specific scene or inappropriate strategies. The suggestion unit proposes effective practice methods and tasks based on the areas for improvement identified by the analysis unit. For example, it proposes practice methods to improve specific skills or tasks to be performed before a match. The practice unit is where the practice methods and tasks proposed by the suggestion unit are executed. For example, a user can execute the proposed practice methods and improve their skills. The feedback unit analyzes match videos and generates feedback. For example, it evaluates the overall flow and strategy of the match, and the movements of individual players. This allows the user to review their play style and improve their strategy. This allows the AI ​​eSports coaching system according to the embodiment to support the skill improvement of eSports players.

[0030] The reception desk allows users to upload their gameplay videos and ask questions about specific actions in particular scenes. For example, users can upload their gameplay videos and ask questions about specific actions in particular scenes. This allows users to provide their gameplay videos to the AI ​​and ask questions about specific actions in particular scenes. This allows users to provide their gameplay videos to the AI ​​and ask questions about specific actions in particular scenes.

[0031] The analysis unit can analyze users' gameplay videos based on knowledge from professional gameplay videos, commentary videos, articles, etc., and identify areas for improvement. For example, the analysis unit can analyze users' gameplay videos based on knowledge from professional gameplay videos, commentary videos, articles, etc., and identify areas for improvement. For example, it can point out problems such as slow movement in certain situations or inappropriate strategy. In this way, the analysis unit can analyze users' gameplay videos based on knowledge from professional gameplay videos, commentary videos, articles, etc., and identify areas for improvement.

[0032] The proposal department can suggest practice methods to improve specific skills or tasks to be performed before a match. For example, the proposal department can suggest practice methods to improve specific skills or tasks to be performed before a match. For example, it can suggest practice methods to improve specific skills or tasks to be performed before a match. This allows for the suggestion of practice methods to improve specific skills or tasks to be performed before a match.

[0033] The feedback unit can evaluate the overall flow or strategy of the match, as well as the movements of individual players. For example, the feedback unit can evaluate the overall flow and strategy of the match, as well as the movements of individual players. For example, it can evaluate the overall flow and strategy of the match, as well as the movements of individual players. This allows for evaluation of the overall flow and strategy of the match, as well as the movements of individual players.

[0034] The reception desk can analyze a user's past gameplay videos and question history to select the most suitable reception method. For example, the reception desk can prioritize receiving relevant gameplay videos based on the user's frequently asked questions in the past. It can also analyze trends in a user's past gameplay videos and suggest the most suitable reception method. Furthermore, it can select a reception method for specific time slots based on the user's question history. In this way, by analyzing a user's past gameplay videos and question history, the most suitable reception method can be selected.

[0035] The reception desk can filter gameplay videos and questions based on the user's current in-game status and areas of interest. For example, if a user is in a specific in-game situation, the reception desk will prioritize receiving gameplay videos and questions related to that situation. It can also filter relevant gameplay videos and questions based on the user's areas of interest. Furthermore, if a user is using a specific character, it can prioritize receiving gameplay videos and questions related to that character. In this way, by filtering based on the user's current in-game status and areas of interest, the reception desk can prioritize receiving highly relevant data.

[0036] The reception desk can prioritize receiving highly relevant data, taking into account the user's geographical location, when receiving gameplay videos and questions. For example, if a user is in a specific region, the reception desk will prioritize receiving gameplay videos and questions related to that region. It can also prioritize receiving information on relevant events and tournaments based on the user's geographical location. Furthermore, if a user is in a specific country, it can prioritize receiving gameplay videos and questions related to players in that country. In this way, by considering the user's geographical location, it is possible to prioritize receiving highly relevant data.

[0037] The reception desk can analyze users' social media activity and receive relevant data when receiving gameplay videos and questions. For example, the reception desk can prioritize receiving gameplay videos and questions that users have shared on social media. It can also analyze users' social media activity and suggest relevant gameplay videos and questions. Furthermore, it can prioritize receiving gameplay videos and questions from professional players that users follow. In this way, by analyzing users' social media activity, it can prioritize receiving relevant data.

[0038] The analysis unit can adjust the level of detail in the analysis based on the importance of the gameplay video. For example, it can perform a detailed analysis of gameplay videos from important matches, and a simplified analysis of practice match videos. Furthermore, for gameplay videos focusing on specific skills, it can perform a detailed analysis related to those skills. By adjusting the level of detail in the analysis based on the importance of the gameplay video, it can provide appropriate analysis results.

[0039] The analysis unit can apply different analysis algorithms depending on the category of the gameplay video during analysis. For example, it can apply an algorithm that analyzes reaction speed to gameplay videos of shooting games, an algorithm that evaluates strategy to gameplay videos of strategy games, and an algorithm that analyzes the precision of movement to gameplay videos of sports games. By applying different analysis algorithms depending on the category of the gameplay video, it can provide appropriate analysis results.

[0040] The analysis unit can determine the priority of analysis based on when the gameplay videos were submitted. For example, it will prioritize the analysis of the most recent gameplay videos. Furthermore, if the user sets a specific deadline, the analysis unit can determine the priority based on that deadline. It can also prioritize the analysis of gameplay videos that the user submits frequently. By prioritizing analysis based on the submission timing of the gameplay videos, the system can provide appropriate analysis results.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the gameplay videos during the analysis process. For example, if a user submits gameplay videos related to a specific skill, the analysis unit will prioritize analyzing videos related to that skill. Similarly, if a user submits gameplay videos related to a specific game, the analysis unit can prioritize analyzing videos related to that game. Furthermore, if a user submits gameplay videos related to a specific character, the analysis unit can prioritize analyzing videos related to that character. By adjusting the order of analysis based on the relevance of the gameplay videos, the analysis unit can provide appropriate analysis results.

[0042] The proposal department can adjust the level of detail in its proposals based on the importance of the practice methods and tasks. For example, it can provide detailed proposals for practice methods related to important skills, and simplified proposals for practice methods related to general skills. Furthermore, for proposals related to specific tasks, it can adjust the level of detail based on the importance of those tasks. This allows for the provision of appropriate proposals by adjusting the level of detail based on the practice methods and task importance.

[0043] The proposal function can apply different proposal algorithms depending on the practice method and task category when making a proposal. For example, for a shooting game practice method, it can apply a proposal algorithm to improve reaction speed. For a strategy game practice method, it can apply a proposal algorithm to improve strategic planning. Furthermore, for a sports game practice method, it can apply a proposal algorithm to improve movement precision. In this way, by applying different proposal algorithms depending on the practice method and task category, it can provide appropriate proposals.

[0044] The proposal team can prioritize proposals based on the practice methods and task submission timings. For example, the team will prioritize the most recent practice methods and tasks. Furthermore, if a user sets a specific deadline, the team can prioritize proposals based on that deadline. Additionally, the team can prioritize practice methods and tasks that users frequently submit. This allows the team to provide appropriate suggestions by prioritizing proposals based on the submission timing of practice methods and tasks.

[0045] The suggestion department can adjust the order of suggestions based on the relevance of practice methods and tasks during the suggestion process. For example, if a user submits a practice method related to a specific skill, the suggestion department will prioritize suggestions related to that skill. Similarly, if a user submits a practice method related to a specific game, the suggestion department can prioritize suggestions related to that game. Furthermore, if a user submits a practice method related to a specific character, the suggestion department can prioritize suggestions related to that character. By adjusting the order of suggestions based on the relevance of practice methods and tasks, the department can provide appropriate suggestions.

[0046] The training section can customize training methods based on the user's current in-game situation. For example, if the user is in a specific in-game situation, the training section can provide training methods related to that situation. It can also customize the optimal training methods based on the user's current in-game situation. Furthermore, if the user is using a specific character, it can provide training methods related to that character. In this way, by customizing training methods based on the user's current in-game situation, it can provide appropriate training methods.

[0047] The training program can select the most suitable training method during practice sessions, taking into account the user's geographical location. For example, if a user is in a specific region, the program can provide training methods relevant to that region. It can also provide information on relevant events and tournaments based on the user's geographical location. Furthermore, if a user is in a specific country, it can provide training methods relevant to players in that country. In this way, appropriate training methods can be provided by considering the user's geographical location.

[0048] The training program can analyze users' social media activity during practice sessions and suggest appropriate training methods. For example, it can prioritize providing training methods shared by users on social media. It can also analyze users' social media activity and suggest relevant training methods. Furthermore, it can prioritize providing training methods from professional players that users follow. In this way, by analyzing users' social media activity, it can provide appropriate training methods.

[0049] The feedback unit can adjust the level of detail in the feedback based on the importance of the match video. For example, it can provide detailed feedback for videos of important matches, and simplified feedback for videos of practice matches. Furthermore, for match videos that focus on a specific skill, it can provide detailed feedback related to that skill. This allows for the provision of appropriate feedback by adjusting the level of detail based on the importance of the match video.

[0050] The feedback unit can apply different feedback algorithms depending on the category of the match video during feedback. For example, it can apply a feedback algorithm that evaluates reaction speed to match videos of shooting games, a feedback algorithm that evaluates strategy to match videos of strategy games, and a feedback algorithm that evaluates the precision of movement to match videos of sports games. By applying different feedback algorithms depending on the category of the match video, it can provide appropriate feedback.

[0051] The feedback system can prioritize feedback based on when the match video was submitted. For example, it will prioritize the most recent match video. Furthermore, if the user sets a specific deadline, the system can prioritize feedback based on that deadline. It can also prioritize feedback on match videos that the user submits frequently. This allows for the provision of appropriate feedback by prioritizing feedback based on the submission timing of the match videos.

[0052] The feedback system can adjust the order of feedback based on the relevance of the match videos. For example, if a user submits a match video related to a specific skill, the feedback system will prioritize providing feedback related to that skill. Similarly, if a user submits a match video related to a specific game, it can prioritize providing feedback related to that game. Furthermore, if a user submits a match video related to a specific character, it can prioritize providing feedback related to that character. This allows for the provision of appropriate feedback by adjusting the order of feedback based on the relevance of the match videos.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can access users' past gameplay data when receiving gameplay videos and questions, and compare it to data from other users with similar play styles. For example, if a user is using a specific character, the reception desk can refer to the gameplay data of other users using that character to identify common problems and areas for improvement. Similarly, if a user is using a specific strategy, the reception desk can refer to the data of other users using that strategy to evaluate its effectiveness. Furthermore, if a user wants to improve a particular skill, the reception desk can refer to the data of other users related to that skill to suggest effective practice methods. This allows for more specific advice tailored to the user's play style.

[0055] The analysis unit can provide real-time feedback when analyzing user gameplay videos. For example, when a user performs a specific move during gameplay, it can instantly evaluate whether that move is appropriate and suggest areas for improvement. Furthermore, when a user attempts a specific strategy, it can evaluate its effectiveness in real time and suggest necessary modifications. Additionally, when a user is practicing a specific skill, it can evaluate their progress in real time and suggest effective practice methods. This allows users to receive immediate feedback during gameplay and make rapid improvements.

[0056] The suggestion department can provide customized practice plans tailored to the user's play style and skill level. For example, it can suggest practice methods to improve basic skills for beginner users, and practice methods to learn more advanced strategies for intermediate users. It can also suggest practice methods to further refine specific skills for advanced users. Furthermore, it can provide individual advice and tasks according to the user's play style. This allows users to implement practice methods that suit their skill level and play style, and to effectively improve their skills.

[0057] The feedback section can analyze user gameplay videos and compare them to those of other players. For example, it can compare a user's gameplay videos to those of professional players to clearly identify differences. It can also compare them to videos of other players at the same level to identify common problems and areas for improvement. Furthermore, it can compare a user's gameplay videos to their past videos to evaluate their progress. This allows users to compare their play style to others and find specific areas for improvement.

[0058] The feedback system can analyze user gameplay videos and provide customized feedback based on the user's play style. For example, if a user has an aggressive play style, it can provide feedback tailored to that style, offering advice on improving attack timing and strategy. If a user has a defensive play style, it can provide feedback tailored to that style, offering advice on strengthening defenses and improving counter-attack timing. Furthermore, if a user has a balanced play style, it can provide feedback tailored to that style, offering advice on improving overall strategy. This allows for the provision of specific feedback based on the user's play style.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception department is where users can provide gameplay videos and questions. For example, users can upload their gameplay videos and ask questions about specific actions in particular scenes. This information is then fed into the AI. Step 2: The analysis unit uses AI to analyze the data received by the reception unit and identify areas for improvement from the player's gameplay videos. For example, it may point out problems such as slow movement in certain situations or inappropriate strategies. Step 3: The proposal team proposes effective practice methods and tasks based on the areas for improvement identified by the analysis team. For example, they might suggest practice methods to improve specific skills or tasks to be performed before a match. Step 4: The Practice Department is responsible for implementing the practice methods and tasks proposed by the Proposal Department. For example, users can implement the proposed practice methods and improve their skills. Step 5: The Feedback Department is responsible for analyzing match videos and generating feedback. For example, it evaluates the overall flow and strategy of the match, as well as the movements of individual players. This allows users to review their play style and improve their strategies.

[0061] (Example of form 2) The AI ​​eSports coaching system according to an embodiment of the present invention is a system aimed at improving the skills of eSports players. In this system, the user provides gameplay videos and questions, and the AI ​​analyzes this data to identify areas for improvement from the player's gameplay videos and proposes effective practice methods and tasks. It also analyzes match videos to generate feedback, which can be used to help in strategy planning. For example, a user can upload their own gameplay video and ask questions about their movements in specific situations. This information is input into the AI. Next, the AI ​​analyzes the input information. Based on knowledge such as professional gameplay videos, commentary videos, and articles, the AI ​​analyzes the user's gameplay video and identifies areas for improvement. For example, it may point out problems such as slow movements in specific situations or inappropriate strategies. Furthermore, the AI ​​proposes effective practice methods and tasks. For example, it may suggest practice methods to improve specific skills or tasks to be performed before a match. This allows the user to overcome their weaknesses and improve their skills. The AI ​​also analyzes match videos to generate feedback. For example, it may evaluate the overall flow and strategy of the match, or the movements of individual players. This allows the user to review their play style and improve their strategy. This service aims to improve the skill level of esports players worldwide. For example, players of all levels, from beginners to professionals, can receive specific advice to improve their skills. Furthermore, by taking a data-driven, systematic approach, effective strategies can be developed without relying on individual experience or intuition. In this way, the AI ​​esports coaching system can support the skill improvement of esports players.

[0062] The AI ​​eSports coaching system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a practice unit, and a feedback unit. The reception unit is where users provide gameplay videos and questions. For example, a user can upload a video of their gameplay and ask questions about their movements in a specific scene. This information is input to the AI. The analysis unit uses the AI ​​to analyze the data received by the reception unit and identify areas for improvement from the player's gameplay video. For example, it points out problems such as slow movements in a specific scene or inappropriate strategies. The suggestion unit proposes effective practice methods and tasks based on the areas for improvement identified by the analysis unit. For example, it proposes practice methods to improve specific skills or tasks to be performed before a match. The practice unit is where the practice methods and tasks proposed by the suggestion unit are executed. For example, a user can execute the proposed practice methods and improve their skills. The feedback unit analyzes match videos and generates feedback. For example, it evaluates the overall flow and strategy of the match, and the movements of individual players. This allows the user to review their play style and improve their strategy. This allows the AI ​​eSports coaching system according to the embodiment to support the skill improvement of eSports players.

[0063] The reception desk allows users to upload their gameplay videos and ask questions about specific actions in particular scenes. For example, users can upload their gameplay videos and ask questions about specific actions in particular scenes. This allows users to provide their gameplay videos to the AI ​​and ask questions about specific actions in particular scenes. This allows users to provide their gameplay videos to the AI ​​and ask questions about specific actions in particular scenes.

[0064] The analysis unit can analyze users' gameplay videos based on knowledge from professional gameplay videos, commentary videos, articles, etc., and identify areas for improvement. For example, the analysis unit can analyze users' gameplay videos based on knowledge from professional gameplay videos, commentary videos, articles, etc., and identify areas for improvement. For example, it can point out problems such as slow movement in certain situations or inappropriate strategy. In this way, the analysis unit can analyze users' gameplay videos based on knowledge from professional gameplay videos, commentary videos, articles, etc., and identify areas for improvement.

[0065] The proposal department can suggest practice methods to improve specific skills or tasks to be performed before a match. For example, the proposal department can suggest practice methods to improve specific skills or tasks to be performed before a match. For example, it can suggest practice methods to improve specific skills or tasks to be performed before a match. This allows for the suggestion of practice methods to improve specific skills or tasks to be performed before a match.

[0066] The feedback unit can evaluate the overall flow or strategy of the match, as well as the movements of individual players. For example, the feedback unit can evaluate the overall flow and strategy of the match, as well as the movements of individual players. For example, it can evaluate the overall flow and strategy of the match, as well as the movements of individual players. This allows for evaluation of the overall flow and strategy of the match, as well as the movements of individual players.

[0067] The reception desk can estimate the user's emotions and adjust the timing of receiving gameplay videos and questions based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the reception timing to provide time for relaxation. If the user is excited, it can immediately accept gameplay videos and questions for a quick response. Furthermore, if the user is tired, the reception timing can be adjusted to prioritize rest. In this way, by adjusting the timing of receiving gameplay videos and questions according to the user's emotions, reception can be performed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The reception desk can analyze a user's past gameplay videos and question history to select the most suitable reception method. For example, the reception desk can prioritize receiving relevant gameplay videos based on the user's frequently asked questions in the past. It can also analyze trends in a user's past gameplay videos and suggest the most suitable reception method. Furthermore, it can select a reception method for specific time slots based on the user's question history. In this way, by analyzing a user's past gameplay videos and question history, the most suitable reception method can be selected.

[0069] The reception desk can filter gameplay videos and questions based on the user's current in-game status and areas of interest. For example, if a user is in a specific in-game situation, the reception desk will prioritize receiving gameplay videos and questions related to that situation. It can also filter relevant gameplay videos and questions based on the user's areas of interest. Furthermore, if a user is using a specific character, it can prioritize receiving gameplay videos and questions related to that character. In this way, by filtering based on the user's current in-game status and areas of interest, the reception desk can prioritize receiving highly relevant data.

[0070] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of the gameplay videos and questions to be received. For example, if the user is nervous, the reception desk will prioritize receiving gameplay videos and questions with relaxing content. If the user is excited, it can prioritize receiving gameplay videos and questions with challenging content. Furthermore, if the user is tired, it can prioritize receiving gameplay videos and questions with simple and easy-to-understand content. In this way, by prioritizing gameplay videos and questions according to the user's emotions, more appropriate data can be received preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The reception desk can prioritize receiving highly relevant data, taking into account the user's geographical location, when receiving gameplay videos and questions. For example, if a user is in a specific region, the reception desk will prioritize receiving gameplay videos and questions related to that region. It can also prioritize receiving information on relevant events and tournaments based on the user's geographical location. Furthermore, if a user is in a specific country, it can prioritize receiving gameplay videos and questions related to players in that country. In this way, by considering the user's geographical location, it is possible to prioritize receiving highly relevant data.

[0072] The reception desk can analyze users' social media activity and receive relevant data when receiving gameplay videos and questions. For example, the reception desk can prioritize receiving gameplay videos and questions that users have shared on social media. It can also analyze users' social media activity and suggest relevant gameplay videos and questions. Furthermore, it can prioritize receiving gameplay videos and questions from professional players that users follow. In this way, by analyzing users' social media activity, it can prioritize receiving relevant data.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is excited, it can provide analysis results with visually stimulating effects. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The analysis unit can adjust the level of detail in the analysis based on the importance of the gameplay video. For example, it can perform a detailed analysis of gameplay videos from important matches, and a simplified analysis of practice match videos. Furthermore, for gameplay videos focusing on specific skills, it can perform a detailed analysis related to those skills. By adjusting the level of detail in the analysis based on the importance of the gameplay video, it can provide appropriate analysis results.

[0075] The analysis unit can apply different analysis algorithms depending on the category of the gameplay video during analysis. For example, it can apply an algorithm that analyzes reaction speed to gameplay videos of shooting games, an algorithm that evaluates strategy to gameplay videos of strategy games, and an algorithm that analyzes the precision of movement to gameplay videos of sports games. By applying different analysis algorithms depending on the category of the gameplay video, it can provide appropriate analysis results.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is excited, it can provide an analysis result with visually stimulating effects. In this way, by adjusting the length of the analysis according to the user's emotions, appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The analysis unit can determine the priority of analysis based on when the gameplay videos were submitted. For example, it will prioritize the analysis of the most recent gameplay videos. Furthermore, if the user sets a specific deadline, the analysis unit can determine the priority based on that deadline. It can also prioritize the analysis of gameplay videos that the user submits frequently. By prioritizing analysis based on the submission timing of the gameplay videos, the system can provide appropriate analysis results.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the gameplay videos during the analysis process. For example, if a user submits gameplay videos related to a specific skill, the analysis unit will prioritize analyzing videos related to that skill. Similarly, if a user submits gameplay videos related to a specific game, the analysis unit can prioritize analyzing videos related to that game. Furthermore, if a user submits gameplay videos related to a specific character, the analysis unit can prioritize analyzing videos related to that character. By adjusting the order of analysis based on the relevance of the gameplay videos, the analysis unit can provide appropriate analysis results.

[0079] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is tense, the suggestion function can provide simple and highly visible suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is excited, it can provide suggestions with visually stimulating effects. In this way, by adjusting the way suggestions are presented according to the user's emotions, appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The proposal department can adjust the level of detail in its proposals based on the importance of the practice methods and tasks. For example, it can provide detailed proposals for practice methods related to important skills, and simplified proposals for practice methods related to general skills. Furthermore, for proposals related to specific tasks, it can adjust the level of detail based on the importance of those tasks. This allows for the provision of appropriate proposals by adjusting the level of detail based on the practice methods and task importance.

[0081] The proposal function can apply different proposal algorithms depending on the practice method and task category when making a proposal. For example, for a shooting game practice method, it can apply a proposal algorithm to improve reaction speed. For a strategy game practice method, it can apply a proposal algorithm to improve strategic planning. Furthermore, for a sports game practice method, it can apply a proposal algorithm to improve movement precision. In this way, by applying different proposal algorithms depending on the practice method and task category, it can provide appropriate proposals.

[0082] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is excited, it can provide suggestions with visually stimulating effects. In this way, by adjusting the length of suggestions according to the user's emotions, appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The proposal team can prioritize proposals based on the practice methods and task submission timings. For example, the team will prioritize the most recent practice methods and tasks. Furthermore, if a user sets a specific deadline, the team can prioritize proposals based on that deadline. Additionally, the team can prioritize practice methods and tasks that users frequently submit. This allows the team to provide appropriate suggestions by prioritizing proposals based on the submission timing of practice methods and tasks.

[0084] The suggestion department can adjust the order of suggestions based on the relevance of practice methods and tasks during the suggestion process. For example, if a user submits a practice method related to a specific skill, the suggestion department will prioritize suggestions related to that skill. Similarly, if a user submits a practice method related to a specific game, the suggestion department can prioritize suggestions related to that game. Furthermore, if a user submits a practice method related to a specific character, the suggestion department can prioritize suggestions related to that character. By adjusting the order of suggestions based on the relevance of practice methods and tasks, the department can provide appropriate suggestions.

[0085] The practice unit can estimate the user's emotions and adjust the practice method based on those emotions. For example, if the user is nervous, the practice unit can provide a relaxing practice method. If the user is excited, it can provide a challenging practice method. Furthermore, if the user is tired, it can provide a simple and easy-to-understand practice method. In this way, by adjusting the practice method according to the user's emotions, the appropriate practice method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The training section can customize training methods based on the user's current in-game situation. For example, if the user is in a specific in-game situation, the training section can provide training methods related to that situation. It can also customize the optimal training methods based on the user's current in-game situation. Furthermore, if the user is using a specific character, it can provide training methods related to that character. In this way, by customizing training methods based on the user's current in-game situation, it can provide appropriate training methods.

[0087] The practice unit can estimate the user's emotions and determine the priority of practice based on those emotions. For example, if the user is nervous, the practice unit can prioritize providing relaxing practice methods. If the user is excited, it can prioritize providing challenging practice methods. Furthermore, if the user is tired, it can prioritize providing simple and easy-to-understand practice methods. In this way, by determining the priority of practice according to the user's emotions, the appropriate practice methods can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The training program can select the most suitable training method during practice sessions, taking into account the user's geographical location. For example, if a user is in a specific region, the program can provide training methods relevant to that region. It can also provide information on relevant events and tournaments based on the user's geographical location. Furthermore, if a user is in a specific country, it can provide training methods relevant to players in that country. In this way, appropriate training methods can be provided by considering the user's geographical location.

[0089] The training program can analyze users' social media activity during practice sessions and suggest appropriate training methods. For example, it can prioritize providing training methods shared by users on social media. It can also analyze users' social media activity and suggest relevant training methods. Furthermore, it can prioritize providing training methods from professional players that users follow. In this way, by analyzing users' social media activity, it can provide appropriate training methods.

[0090] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is tense, the feedback unit can provide simple and highly visual feedback. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is excited, it can provide feedback with visually stimulating effects. In this way, appropriate feedback can be provided by adjusting the feedback method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The feedback unit can adjust the level of detail in the feedback based on the importance of the match video. For example, it can provide detailed feedback for videos of important matches, and simplified feedback for videos of practice matches. Furthermore, for match videos that focus on a specific skill, it can provide detailed feedback related to that skill. This allows for the provision of appropriate feedback by adjusting the level of detail based on the importance of the match video.

[0092] The feedback unit can apply different feedback algorithms depending on the category of the match video during feedback. For example, it can apply a feedback algorithm that evaluates reaction speed to match videos of shooting games, a feedback algorithm that evaluates strategy to match videos of strategy games, and a feedback algorithm that evaluates the precision of movement to match videos of sports games. By applying different feedback algorithms depending on the category of the match video, it can provide appropriate feedback.

[0093] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is in a hurry, the feedback unit can provide short, concise feedback. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is excited, it can provide feedback with visually stimulating effects. In this way, appropriate feedback can be provided by adjusting the length of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The feedback system can prioritize feedback based on when the match video was submitted. For example, it will prioritize the most recent match video. Furthermore, if the user sets a specific deadline, the system can prioritize feedback based on that deadline. It can also prioritize feedback on match videos that the user submits frequently. This allows for the provision of appropriate feedback by prioritizing feedback based on the submission timing of the match videos.

[0095] The feedback system can adjust the order of feedback based on the relevance of the match videos. For example, if a user submits a match video related to a specific skill, the feedback system will prioritize providing feedback related to that skill. Similarly, if a user submits a match video related to a specific game, it can prioritize providing feedback related to that game. Furthermore, if a user submits a match video related to a specific character, it can prioritize providing feedback related to that character. This allows for the provision of appropriate feedback by adjusting the order of feedback based on the relevance of the match videos. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, practice unit, and feedback unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to provide gameplay videos and questions. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze gameplay videos and identify areas for improvement. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes effective practice methods and tasks based on the analysis results. The practice unit is implemented, for example, by the control unit 46A of the smart device 14 and provides an interface for executing the proposed practice methods and tasks. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes gameplay videos to generate feedback. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, practice unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to provide gameplay videos and questions. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze gameplay videos and identify areas for improvement. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes effective practice methods and tasks based on the analysis results. The practice unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides an interface for executing the proposed practice methods and tasks. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes game videos to generate feedback. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, practice unit, and feedback unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to provide gameplay videos and questions. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses AI to analyze gameplay videos and identify areas for improvement. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes effective practice methods and tasks based on the analysis results. The practice unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides an interface for executing the proposed practice methods and tasks. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes gameplay videos to generate feedback. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, practice unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to provide gameplay videos and questions. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze gameplay videos and identify areas for improvement. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes effective practice methods and tasks based on the analysis results. The practice unit is implemented, for example, by the control unit 46A of the robot 414 and provides an interface for executing the proposed practice methods and tasks. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes game videos to generate feedback.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception desk can access users' past gameplay data when receiving gameplay videos and questions, and compare it to data from other users with similar play styles. For example, if a user is using a specific character, the reception desk can refer to the gameplay data of other users using that character to identify common problems and areas for improvement. Similarly, if a user is using a specific strategy, the reception desk can refer to the data of other users using that strategy to evaluate its effectiveness. Furthermore, if a user wants to improve a particular skill, the reception desk can refer to the data of other users related to that skill to suggest effective practice methods. This allows for more specific advice tailored to the user's play style.

[0098] The analysis unit can provide real-time feedback when analyzing user gameplay videos. For example, when a user performs a specific move during gameplay, it can instantly evaluate whether that move is appropriate and suggest areas for improvement. Furthermore, when a user attempts a specific strategy, it can evaluate its effectiveness in real time and suggest necessary modifications. Additionally, when a user is practicing a specific skill, it can evaluate their progress in real time and suggest effective practice methods. This allows users to receive immediate feedback during gameplay and make rapid improvements.

[0099] The suggestion department can provide customized practice plans tailored to the user's play style and skill level. For example, it can suggest practice methods to improve basic skills for beginner users, and practice methods to learn more advanced strategies for intermediate users. It can also suggest practice methods to further refine specific skills for advanced users. Furthermore, it can provide individual advice and tasks according to the user's play style. This allows users to implement practice methods that suit their skill level and play style, and to effectively improve their skills.

[0100] The feedback section can analyze user gameplay videos and compare them to those of other players. For example, it can compare a user's gameplay videos to those of professional players to clearly identify differences. It can also compare them to videos of other players at the same level to identify common problems and areas for improvement. Furthermore, it can compare a user's gameplay videos to their past videos to evaluate their progress. This allows users to compare their play style to others and find specific areas for improvement.

[0101] The reception desk can estimate the user's emotions and customize the way gameplay videos and questions are handled based on those estimates. For example, if a user is stressed, the reception desk can provide a relaxing interface, creating an environment where the user feels comfortable asking questions. If a user is excited, the reception desk can respond quickly and provide an interface that helps maintain the user's excitement. Furthermore, if a user is tired, the reception desk can provide a simple and intuitive interface, allowing the user to ask questions smoothly. This allows for the provision of appropriate reception methods tailored to the user's emotions.

[0102] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple, highly visual analysis results that are easy for the user to understand. If the user is relaxed, the analysis unit can provide detailed analysis results that allow the user to understand more deeply. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects to maintain the user's excitement. In this way, the system can provide appropriate analysis results that correspond to the user's emotions.

[0103] The suggestion function can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is tense, the suggestion function will provide simple, highly visual suggestions that are easy for the user to understand. If the user is relaxed, the suggestion function can provide detailed suggestions that allow for deeper understanding. Furthermore, if the user is excited, the suggestion function can provide suggestions with visually stimulating effects to maintain the user's excitement. This allows the system to provide appropriate suggestions that match the user's emotions.

[0104] The practice unit can estimate the user's emotions and adjust the practice method based on those emotions. For example, if the user is nervous, the practice unit will provide a relaxing practice method to help the user practice with ease. If the user is excited, the practice unit will provide a challenging practice method to maintain the user's excitement. Furthermore, if the user is tired, the practice unit will provide a simple and easy-to-understand practice method to help the user practice smoothly. In this way, the system can provide appropriate practice methods that match the user's emotions.

[0105] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is tense, the feedback unit provides simple, highly visual feedback that is easy for the user to understand. If the user is relaxed, the feedback unit can provide detailed feedback that allows for deeper understanding. Furthermore, if the user is excited, the feedback unit can provide feedback with visually stimulating effects to maintain the user's excitement. This allows for the provision of appropriate feedback that matches the user's emotions.

[0106] The feedback system can analyze user gameplay videos and provide customized feedback based on the user's play style. For example, if a user has an aggressive play style, it can provide feedback tailored to that style, offering advice on improving attack timing and strategy. If a user has a defensive play style, it can provide feedback tailored to that style, offering advice on strengthening defenses and improving counter-attack timing. Furthermore, if a user has a balanced play style, it can provide feedback tailored to that style, offering advice on improving overall strategy. This allows for the provision of specific feedback based on the user's play style.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception department is where users can provide gameplay videos and questions. For example, users can upload their gameplay videos and ask questions about specific actions in particular scenes. This information is then fed into the AI. Step 2: The analysis unit uses AI to analyze the data received by the reception unit and identify areas for improvement from the player's gameplay videos. For example, it may point out problems such as slow movement in certain situations or inappropriate strategies. Step 3: The proposal team proposes effective practice methods and tasks based on the areas for improvement identified by the analysis team. For example, they might suggest practice methods to improve specific skills or tasks to be performed before a match. Step 4: The Practice Department is responsible for implementing the practice methods and tasks proposed by the Proposal Department. For example, users can implement the proposed practice methods and improve their skills. Step 5: The Feedback Department is responsible for analyzing match videos and generating feedback. For example, it evaluates the overall flow and strategy of the match, as well as the movements of individual players. This allows users to review their play style and improve their strategies.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts gameplay videos or questions, The analysis unit analyzes the data received by the reception unit and identifies areas for improvement from the player's gameplay video, Based on the areas for improvement identified by the analysis unit, the proposal unit proposes effective practice methods or tasks. A practice unit that performs the practice method or task proposed by the aforementioned proposal unit, It includes a feedback unit that analyzes match videos and generates feedback. A system characterized by the following features.

2. The aforementioned reception unit is Users can upload their gameplay videos and ask questions about specific actions in particular scenes. The system according to feature 1.

3. The aforementioned analysis unit, Based on professional gameplay videos, tutorials, articles, and other knowledge, we analyze user gameplay videos to identify areas for improvement. The system according to feature 1.

4. The aforementioned proposal section is, We suggest practice methods to improve specific skills and tasks to perform before a match. The system according to feature 1.

5. The aforementioned feedback unit is This involves evaluating the overall flow and strategy of the match, as well as the movements of individual players. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of submitting gameplay videos and questions based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is The system analyzes the user's past gameplay videos and question history to select the most suitable method of contact. The system according to feature 1.

8. The aforementioned reception unit is When receiving gameplay videos or questions, the system filters them based on the user's current in-game status and areas of interest. The system according to feature 1.

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