System

Abstract

To provide a system according to an embodiment that analyzes play data on a user, and provides specific feedback in comparison with play data on a professional player.SOLUTION: A system according to an embodiment comprises: a collection unit; an analysis unit; a comparison unit; and a providing unit. The collection unit is configured to collect play data on a user. The analysis unit is configured to analyze the data collected by the collection unit. The comparison unit is configured to compare the data analyzed by the analysis unit with play data on a professional player. The providing unit is configured to provide feedback for the user on the basis of a result obtained by the comparison unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to obtain specific feedback that would allow players to efficiently improve their gaming skills.

[0005] The system according to the embodiment aims to analyze a user's playing data, compare it with the playing data of professional players, and provide specific feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a comparison unit, and a provision unit. The collection unit collects play data of a user. The analysis unit analyzes the data collected by the collection unit. The comparison unit compares the data analyzed by the analysis unit with play data of professional players. The provision unit provides feedback to the user based on the results obtained by the comparison unit. Effect of the Invention

[0007] The system according to the embodiment can analyze the user's playing data, compare it with the playing data of professional players, and provide specific feedback. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The game skill improvement coaching system according to the embodiment of the present invention is a system that collects and analyzes the play data of a user, compares it with the play data of a professional player, and provides feedback. This system starts when the user inputs his / her own play data into the AI. For example, it includes game replay data and operation logs during play. This data is analyzed by the AI. Next, the AI ​​compares it with the play data of the professional player to identify where the user's play has issues. For example, reaction speed and strategy selection in a specific situation are evaluated. Furthermore, the AI ​​proposes specific correction methods for the identified issues. For example, it includes training methods for improving reaction speed and advice for improving strategy selection. This mechanism allows the user to objectively evaluate his / her own play and efficiently improve his / her skills. In addition, more advanced coaching is possible by utilizing the play data of the professional player. For example, it is possible to analyze in detail what operations the professional player is performing in a specific game and provide specific steps for imitating those operations. This allows the user to learn the play style of the professional player and incorporate it into his / her own play. Furthermore, the AI ​​continuously analyzes the user's play data and monitors the improvement of the skill. This allows users to realize their own progress while aiming to further improve their skills. As such, it is expected that the demand for AI-based coaching systems for improving game skills will continue to grow along with the growth of the e-sports market. This allows the game skill improvement coaching system to automatically analyze users' play data and provide efficient feedback.

[0029] The game skill improvement coaching system according to the embodiment includes a collection unit, an analysis unit, a comparison unit, and a provision unit. The collection unit collects play data of the user. The play data of the user includes, for example, game scores, operation logs, play time, and the like, but is not limited to such examples. The collection unit collects, for example, game replay data. The collection unit can also collect operation logs during play. Furthermore, the collection unit can record play time. For example, the collection unit automatically saves game replay data and transmits it to the analysis unit. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes data, for example, using AI. The analysis unit can also analyze play data of professional players. For example, the analysis unit analyzes the user's play data based on the play data of the professional players. The comparison unit compares the data analyzed by the analysis unit with the play data of the professional players. The comparison unit compares data, for example, using AI. The comparison unit can also compare the user's play data with the play data of the professional players. For example, the comparison unit calculates the difference in score between the play data of the user and the play data of the professional player. The provision unit provides feedback to the user based on the result obtained by the comparison unit. The provision unit generates the feedback using, for example, AI. The provision unit can also suggest a specific correction method to the user. For example, the provision unit suggests a training method for improving reaction speed. Thereby, the game skill improvement coaching system according to the embodiment can automatically analyze the play data of the user and provide efficient feedback. A part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can provide feedback using an AI model that analyzes the play data of the user and generates feedback.

[0030] The collection unit collects the user's play data. The user's play data includes, for example, game scores, operation logs, play times, and the like, but is not limited to such examples. Specifically, the collection unit automatically saves the game replay data and transmits it to the analysis unit. The replay data contains a detailed record of the user's movements and operations in the game, which allows the user's play style and strategy to be accurately understood. The collection unit can also collect operation logs during play. The operation log contains records of which buttons the user pressed at what timing and what operations the user performed, which allows the user's operation accuracy and reaction speed to be evaluated. Furthermore, the collection unit can also record the play time. The play time data is important for evaluating the user's degree of engagement in the game and the duration of concentration. For example, the collection unit automatically saves the game replay data and transmits it to the analysis unit. This allows the user to look back on his or her play and check where he or she made mistakes and where he or she succeeded. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis and comparison units. In addition, the frequency and accuracy of data collection can be adjusted to respond flexibly to specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI. Specifically, the AI ​​uses a machine learning algorithm to analyze the user's play data in detail and extract play styles and operation patterns. For example, the AI ​​analyzes the user's operation log and analyzes which buttons were pressed at what timing and what operations were performed. The AI ​​can also analyze the play data of professional players. The play data of professional players includes advanced strategies and operation techniques, and by analyzing this, a standard can be set for comparison with the user's play data. For example, the analysis unit analyzes the user's play data based on the play data of professional players. This makes it possible to evaluate the extent to which the user's play style and operation techniques differ from those of professional players. Furthermore, the analysis unit can also utilize past data and statistical information to improve long-term skills and perform trend analysis. For example, based on past play data, the analysis unit predicts fluctuations in the user's skills and develops a future training plan. The analysis unit can also use an anomaly detection algorithm to detect unusual play patterns and abnormal data and issue an early warning. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term skill improvement and anomaly detection, thereby improving the reliability and effectiveness of the entire system.

[0032] The comparison unit compares the data analyzed by the analysis unit with the play data of the professional player. The comparison unit uses, for example, AI to compare the data. Specifically, the AI ​​compares the play data of the user with the play data of the professional player in detail to clarify the differences in scores and operations. For example, the comparison unit calculates the difference in scores between the play data of the user and the play data of the professional player. This makes it possible to identify where the user is losing scores and where improvement is needed. The comparison unit also compares the operation logs to evaluate how much the user's operation accuracy and reaction speed differ from those of the professional player. For example, by comparing the operation of the user based on the timing and speed at which the professional player performs a specific operation, it is possible to clarify specific improvements. Furthermore, the comparison unit can analyze differences in play style and strategy and suggest what strategy the user should adopt. For example, it can analyze what strategy the professional player uses and suggest a way to incorporate that strategy to the user. This allows the comparison unit to compare the play data of the user in detail and suggest specific improvements and strategies. Furthermore, the comparison unit can continuously revise the comparison results based on data updated in real time to respond to the latest situation. This enables the comparison unit to always provide highly accurate comparisons based on the latest information, thereby helping users improve their skills.

[0033] The providing unit provides feedback to the user based on the results obtained by the comparing unit. The providing unit generates the feedback using, for example, AI. Specifically, the AI ​​proposes specific correction methods and training methods based on the results of the comparison between the user's play data and the play data of a professional player. For example, the providing unit proposes a training method for improving reaction speed. This includes training to repeatedly practice specific operations and mini-games for measuring reaction speed. The providing unit can also propose a method for improving strategies according to the user's play style. For example, if the user prefers an offensive play style, the providing unit proposes a method for improving the timing of attacks and combo combinations. Furthermore, the providing unit can monitor the user's progress and regularly update the feedback. This allows the user to always train based on the latest information and efficiently improve their skills. The providing unit can also set specific goals for the user and evaluate the degree of achievement of the goals. For example, a goal for achieving a specific score is set and the progress is regularly evaluated. This allows the user to continue training while feeling his or her own growth. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the feedback content. This enables the providing unit to provide the user with quick and effective feedback and assist in improving skills.

[0034] The analysis unit can analyze the play data of professional players. The play data of professional players includes, for example, data of a specific tournament, data of a specific player, etc., but is not limited to such examples. The analysis unit, for example, collects the play data of professional players and analyzes it using AI. For example, the analysis unit analyzes the play data of the user based on the play data of the professional players. By analyzing the play data of the professional players, it becomes possible to compare the play data with the play data of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that analyzes the play data of professional players and compares it with the play data of the user.

[0035] The comparison unit can compare the play data of the user with the play data of the professional player. The comparison unit, for example, compares the play data of the user with the play data of the professional player. Specific criteria and methods of comparison include, for example, comparison of scores, comparison of operation patterns, and the like, but are not limited to such examples. For example, the comparison unit calculates the difference in score between the play data of the user and the play data of the professional player. The comparison unit can also calculate the degree of agreement of the operation patterns. In this way, the user's play data and the play data of the professional player can be compared to identify issues. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can perform the comparison using an AI model that compares the play data of the user with the play data of the professional player.

[0036] The providing unit can suggest a specific correction method to the user. The providing unit, for example, suggests a specific correction method to the user. Specific contents and standards of the correction method include, for example, improvement of the operation method, change of strategy, etc., but are not limited to such examples. For example, the providing unit suggests a training method for improving reaction speed. The providing unit can also provide advice for improving the selection of a strategy. In this way, by suggesting a specific correction method, the user's skill improvement is supported. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the correction method using an AI model that analyzes the user's play data and suggests a correction method.

[0037] The providing unit can continuously analyze the play data of the user and monitor the improvement of the skill. The providing unit, for example, continuously analyzes the play data of the user and monitors the improvement of the skill. Specific methods and standards for continuous analysis include, for example, the frequency of analysis, the type of data to be analyzed, and the like, but are not limited to such examples. For example, the providing unit periodically collects and analyzes the play data of the user. The providing unit can also evaluate the improvement of the user's skill based on the analysis result. In this way, the improvement of the user's skill can be monitored by continuously analyzing. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can perform monitoring using an AI model that continuously analyzes the play data of the user and monitors the improvement of the skill.

[0038] The collection unit can analyze the user's past play data and select the optimal collection method. The collection unit, for example, analyzes the user's past play data and selects the optimal collection method. The past play data includes, for example, data from the past month, data from a specific game session, etc., but is not limited to such examples. For example, the collection unit collects data under similar conditions based on play data in which the user demonstrated high performance in the past. In addition, the collection unit can obtain the most effective data from the user's past play data by collecting it in a specific time period. Furthermore, the collection unit can analyze the user's past play data and prioritize data collection in a specific game mode or stage. In this way, the optimal collection method can be selected by analyzing the past data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past play data to the generation AI and cause the generation AI to select the optimal collection method.

[0039] The collection unit can perform filtering based on the user's current in-game situation and play style when collecting play data. The collection unit, for example, performs filtering based on the user's current in-game situation and play style when collecting play data. The in-game situation includes, for example, the game progress situation, the user's behavior pattern, etc., but is not limited to such examples. The play style includes, for example, an offensive play style, a defensive play style, etc., but is not limited to such examples. For example, when the user is using a specific character, the collection unit preferentially collects data related to the character. In addition, when the user adopts a specific strategy, the collection unit can also filter and collect data related to the strategy. Furthermore, when the user is playing in a specific game mode, the collection unit can also collect data related to the mode. In this way, by filtering data based on the in-game situation and play style, highly relevant data can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data of the user's in-game situation and play style to the generation AI and cause the generation AI to perform filtering.

[0040] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting play data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting play data. The geographical location information includes, for example, a country, a city, a region, and the like, but is not limited to such examples. For example, when the user is playing in a specific region, the collection unit preferentially collects data related to the region. In addition, when the user is participating in a specific event, the collection unit can also preferentially collect data related to the event. Furthermore, when the user is playing in a specific time period, the collection unit can also preferentially collect data related to the time period. In this way, highly relevant data can be collected by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0041] The collection unit can analyze the user's social media activities and collect related data when collecting play data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting play data. Social media activities include, for example, the content of posts, the number of likes, the content of comments, and the like, but are not limited to such examples. For example, the collection unit collects related data based on play videos shared by the user on social media. The collection unit can also collect data related to in-game events mentioned by the user on social media. Furthermore, the collection unit can also collect data related to the play style of professional players that the user follows on social media. In this way, the related data can be collected by analyzing social media activities. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the play data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the play data during the analysis. The importance of the play data includes, for example, the score and the length of the play time, but is not limited to such examples. For example, the analysis unit performs a detailed analysis on important play data. The analysis unit can also perform a simplified analysis on general play data. Furthermore, the analysis unit can perform a specialized analysis on play data related to a specific game mode or stage. This allows efficient analysis by adjusting the level of detail of the analysis based on the importance of the play data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the play data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the game category during analysis. The analysis unit applies different analysis algorithms depending on the game category during analysis, for example. Game categories include, but are not limited to, action games, puzzle games, and the like. For example, the analysis unit performs analysis with emphasis on reaction speed and aim accuracy for shooting games. In addition, the analysis unit can also perform analysis with emphasis on strategy selection and resource management for strategy games. Furthermore, the analysis unit can also perform analysis with emphasis on play timing and tactics for sports games. This makes it possible to perform highly accurate analysis by applying an analysis algorithm according to the game category. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input an analysis algorithm according to the game category to the generation AI and cause the generation AI to execute the analysis.

[0044] The analysis unit can determine the priority of analysis based on the submission time of the play data during analysis. The analysis unit, for example, determines the priority of analysis based on the submission time of the play data during analysis. The submission time includes, for example, the date and time of submission, the order of submission, and the like, but is not limited to such examples. For example, the analysis unit preferentially analyzes the latest play data. The analysis unit can also preferentially analyze play data submitted during a specific event period. Furthermore, the analysis unit can also preferentially analyze important play data specified by the user. This enables efficient analysis by determining the priority of analysis based on the submission time. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the play data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of play data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of play data during analysis. The relevance of play data includes, for example, data in the same game, data of the same player, and the like, but is not limited to such examples. For example, the analysis unit preferentially analyzes data related to the user's play style. The analysis unit can also preferentially analyze data related to a specific game mode or stage. Furthermore, the analysis unit can also preferentially analyze data that is highly related to play data submitted by the user in the past. This allows efficient analysis by adjusting the order of analysis based on the relevance. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of play data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The comparison unit can improve the accuracy of the comparison by considering the interrelationship of the play data at the time of comparison. For example, the comparison unit improves the accuracy of the comparison by considering the interrelationship of the play data at the time of comparison. The interrelationship of the play data includes, for example, data correlation, causal relationship, etc., but is not limited to such examples. For example, the comparison unit analyzes the interrelationship between the play data of the user and the play data of the professional player, and performs a highly accurate comparison. The comparison unit can also perform the comparison by considering the interrelationship of the play data in a specific game mode or stage. Furthermore, the comparison unit can analyze the interrelationship between the play style of the user and the play style of the professional player, and perform a highly accurate comparison. As a result, by considering the interrelationship of the play data, a highly accurate comparison is possible. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input the interrelationship of the play data to the generation AI, and cause the generation AI to execute the improvement of the accuracy of the comparison.

[0047] The comparison unit can make the comparison while considering the attribute information of the person who submitted the play data. For example, the comparison unit makes the comparison while considering the attribute information of the person who submitted the play data. The attribute information of the person who submitted the play data includes, for example, age, sex, region, etc., but is not limited to such examples. For example, the comparison unit compares the play data with that of an appropriate professional player based on the age and sex of the user. The comparison unit can also compare the play data with that of an appropriate professional player based on the game experience and skill level of the user. Furthermore, the comparison unit can compare the play data with that of an appropriate professional player based on the play style of the user. This makes it possible to make an appropriate comparison by considering the attribute information of the person who submitted the play data. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input the attribute information of the person who submitted the play data to the generation AI and cause the generation AI to perform the comparison.

[0048] The comparison unit can make the comparison taking into consideration the geographical distribution of the play data when making the comparison. For example, the comparison unit makes the comparison taking into consideration the geographical distribution of the play data when making the comparison. The geographical distribution includes, for example, countries, cities, regions, and the like, but is not limited to such examples. For example, when the user is playing in a specific region, the comparison unit compares the play data with the play data of a professional player related to that region. In addition, when the user is participating in a specific event, the comparison unit can also compare the play data with the play data of a professional player related to that event. Furthermore, when the user is playing in a specific time period, the comparison unit can also compare the play data with the play data of a professional player related to that time period. This makes it possible to make an appropriate comparison by taking the geographical distribution into consideration. Part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input the geographical distribution of the play data to the generation AI and cause the generation AI to execute the comparison.

[0049] The comparison unit can improve the accuracy of the comparison by referring to literature related to the play data at the time of comparison. The comparison unit can improve the accuracy of the comparison by referring to literature related to the play data at the time of comparison, for example. The related literature includes, but is not limited to, academic papers, technical reports, and the like. For example, the comparison unit can perform a highly accurate comparison by referring to research papers related to the play data of the user. The comparison unit can also perform a highly accurate comparison by referring to research papers related to the play data of a professional player. Furthermore, the comparison unit can also perform a highly accurate comparison by referring to research papers related to a specific game mode or stage. This makes it possible to perform a highly accurate comparison by referring to the related literature. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input literature related to the play data to the generation AI and cause the generation AI to perform an improvement in the accuracy of the comparison.

[0050] The providing unit can adjust the level of detail of the feedback based on the importance of the play data when providing the feedback. For example, the providing unit adjusts the level of detail of the feedback based on the importance of the play data when providing the feedback. The importance of the play data includes, for example, the score and the length of the play time, but is not limited to such examples. For example, the providing unit provides detailed feedback for important play data. In addition, the providing unit can also provide simplified feedback for general play data. Furthermore, the providing unit can also provide specialized feedback for play data related to a specific game mode or stage. As a result, by adjusting the level of detail of the feedback based on the importance of the play data, efficient feedback is possible. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the play data to the generating AI and cause the generating AI to adjust the level of detail of the feedback.

[0051] The providing unit can apply different feedback algorithms depending on the game category when providing feedback. For example, the providing unit applies different feedback algorithms depending on the game category when providing feedback. The game categories include, for example, action games, puzzle games, etc., but are not limited to such examples. For example, the providing unit provides feedback regarding reaction speed and aim accuracy for shooting games. In addition, the providing unit can also provide feedback regarding strategy selection and resource management for strategy games. Furthermore, the providing unit can also provide feedback regarding play timing and tactics for sports games. As a result, highly accurate feedback can be provided by applying a feedback algorithm according to the game category. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input a feedback algorithm according to the game category to the generating AI and cause the generating AI to execute the feedback.

[0052] The providing unit can determine the priority of the feedback based on the submission time of the play data when providing the feedback. The providing unit, for example, determines the priority of the feedback based on the submission time of the play data when providing the feedback. The submission time includes, for example, the date and time of submission, the order of submission, and the like, but is not limited to such examples. For example, the providing unit provides feedback preferentially to the latest play data. The providing unit can also provide feedback preferentially to the play data submitted during a specific event period. Furthermore, the providing unit can also provide feedback preferentially to important play data designated by the user. This enables efficient feedback by determining the priority of the feedback based on the submission time. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the submission time of the play data to the generating AI and cause the generating AI to execute the determination of the priority of the feedback.

[0053] The providing unit can adjust the order of feedback based on the relevance of play data when providing feedback. For example, the providing unit adjusts the order of feedback based on the relevance of play data when providing feedback. The relevance of play data includes, for example, data in the same game, data of the same player, and the like, but is not limited to such examples. For example, the providing unit provides feedback preferentially to data related to the user's play style. The providing unit can also provide feedback preferentially to data related to a specific game mode or stage. Furthermore, the providing unit can also provide feedback preferentially to data highly related to play data submitted by the user in the past. This enables efficient feedback by adjusting the order of feedback based on the relevance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of play data to the generating AI and cause the generating AI to adjust the order of feedback.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When collecting the user's play data, the collection unit can also collect performance data of the user's device at the same time. For example, the collection unit can collect data such as CPU usage, memory usage, and network delay of the user's PC or console. This makes it possible to analyze the impact of the user's play environment on game performance. The collection unit can also collect hardware data such as the temperature and fan rotation speed of the user's device. This makes it possible to evaluate the impact of the hardware state on game play. Furthermore, the collection unit can also collect setting information (resolution, graphics settings, etc.) of the user's device and suggest optimal settings. This makes it possible to provide feedback that takes into account the user's device performance.

[0056] When analyzing the user's play data, the analysis unit can also compare it with the user's past play data. For example, the analysis unit can analyze the difference between the user's current play data and the play data from the past month. This makes it possible to evaluate the improvement and change in the user's skill. The analysis unit can also compare the user's specific past game sessions with the current session to identify what kind of improvement can be seen. Furthermore, the analysis unit can analyze specific trends and patterns based on the user's past play data and provide advice that will be useful for future play. This makes it possible to support the user's long-term skill improvement.

[0057] When comparing the user's play data with the play data of professional players, the comparison unit can apply comparison criteria customized based on the user's play style. For example, if the user has an offensive play style, the comparison unit can compare the user's play data with data of professional players suitable for that style. In addition, for a user with a defensive play style, the comparison unit can compare the user's play data with data of professional players suitable for that style. Furthermore, if the user uses a specific character or role, the comparison unit can compare the user's play data with data of professional players specialized in that character or role. This makes it possible to provide appropriate feedback according to the user's play style.

[0058] When suggesting a specific correction method to the user, the providing unit can provide customized feedback based on the user's learning style. For example, the providing unit can provide feedback using diagrams or videos to a user with a visual learning style. Also, the providing unit can provide feedback in the form of audio guides or podcasts to a user with an auditory learning style. Furthermore, the providing unit can suggest a training session through actual game play to a user with a hands-on learning style. In this way, effective feedback according to the user's learning style can be provided.

[0059] The providing unit can perform customized monitoring based on the user's goal settings when continuously analyzing the user's play data and monitoring the improvement of skills. For example, if the user sets that he / she wants to improve a particular skill (e.g., reaction speed, strategic thinking), the providing unit can focus on analyzing data related to that skill. In addition, if the user sets that he / she wants to improve performance in a particular game mode or stage, the providing unit can also focus on analyzing data related to that mode or stage. Furthermore, the providing unit can periodically evaluate the degree of achievement of the goals set by the user and provide feedback on the progress. This enables effective monitoring and feedback according to the user's goals.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The collection unit collects the user's play data. The user's play data includes game scores, operation logs, play time, etc. For example, the collection unit automatically saves game replay data and transmits it to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using AI, and analyzes the user's play data based on the play data of professional players. Step 3: The comparison unit compares the data analyzed by the analysis unit with the play data of the professional player. The comparison unit, for example, uses AI to calculate the difference in score between the user's play data and the play data of the professional player. Step 4: The providing unit provides feedback to the user based on the results obtained by the comparing unit. The providing unit generates the feedback using, for example, AI, and suggests a specific correction method to the user.

[0062] (Example 2) The game skill improvement coaching system according to the embodiment of the present invention is a system that collects and analyzes the play data of a user, compares it with the play data of a professional player, and provides feedback. This system starts when the user inputs his / her own play data into the AI. For example, it includes game replay data and operation logs during play. This data is analyzed by the AI. Next, the AI ​​compares it with the play data of the professional player to identify where the user's play has issues. For example, reaction speed and strategy selection in a specific situation are evaluated. Furthermore, the AI ​​proposes specific correction methods for the identified issues. For example, it includes training methods for improving reaction speed and advice for improving strategy selection. This mechanism allows the user to objectively evaluate his / her own play and efficiently improve his / her skills. In addition, more advanced coaching is possible by utilizing the play data of the professional player. For example, it is possible to analyze in detail what operations the professional player is performing in a specific game and provide specific steps for imitating those operations. This allows the user to learn the play style of the professional player and incorporate it into his / her own play. Furthermore, the AI ​​continuously analyzes the user's play data and monitors the improvement of the skill. This allows users to realize their own progress while aiming to further improve their skills. As such, it is expected that the demand for AI-based coaching systems for improving game skills will continue to grow along with the growth of the e-sports market. This allows the game skill improvement coaching system to automatically analyze users' play data and provide efficient feedback.

[0063] The game skill improvement coaching system according to the embodiment includes a collection unit, an analysis unit, a comparison unit, and a provision unit. The collection unit collects play data of the user. The play data of the user includes, for example, game scores, operation logs, play time, and the like, but is not limited to such examples. The collection unit collects, for example, game replay data. The collection unit can also collect operation logs during play. Furthermore, the collection unit can record play time. For example, the collection unit automatically saves game replay data and transmits it to the analysis unit. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes data, for example, using AI. The analysis unit can also analyze play data of professional players. For example, the analysis unit analyzes the user's play data based on the play data of the professional players. The comparison unit compares the data analyzed by the analysis unit with the play data of the professional players. The comparison unit compares data, for example, using AI. The comparison unit can also compare the user's play data with the play data of the professional players. For example, the comparison unit calculates the difference in score between the play data of the user and the play data of the professional player. The provision unit provides feedback to the user based on the result obtained by the comparison unit. The provision unit generates the feedback using, for example, AI. The provision unit can also suggest a specific correction method to the user. For example, the provision unit suggests a training method for improving reaction speed. Thereby, the game skill improvement coaching system according to the embodiment can automatically analyze the play data of the user and provide efficient feedback. A part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can provide feedback using an AI model that analyzes the play data of the user and generates feedback.

[0064] The collection unit collects the user's play data. The user's play data includes, for example, game scores, operation logs, play times, and the like, but is not limited to such examples. Specifically, the collection unit automatically saves the game replay data and transmits it to the analysis unit. The replay data contains a detailed record of the user's movements and operations in the game, which allows the user's play style and strategy to be accurately understood. The collection unit can also collect operation logs during play. The operation log contains records of which buttons the user pressed at what timing and what operations the user performed, which allows the user's operation accuracy and reaction speed to be evaluated. Furthermore, the collection unit can also record the play time. The play time data is important for evaluating the user's degree of engagement in the game and the duration of concentration. For example, the collection unit automatically saves the game replay data and transmits it to the analysis unit. This allows the user to look back on his or her play and check where he or she made mistakes and where he or she succeeded. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis and comparison units. In addition, the frequency and accuracy of data collection can be adjusted to respond flexibly to specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0065] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI. Specifically, the AI ​​uses a machine learning algorithm to analyze the user's play data in detail and extract play styles and operation patterns. For example, the AI ​​analyzes the user's operation log and analyzes which buttons were pressed at what timing and what operations were performed. The AI ​​can also analyze the play data of professional players. The play data of professional players includes advanced strategies and operation techniques, and by analyzing this, a standard can be set for comparison with the user's play data. For example, the analysis unit analyzes the user's play data based on the play data of professional players. This makes it possible to evaluate the extent to which the user's play style and operation techniques differ from those of professional players. Furthermore, the analysis unit can also utilize past data and statistical information to improve long-term skills and perform trend analysis. For example, based on past play data, the analysis unit predicts fluctuations in the user's skills and develops a future training plan. The analysis unit can also use an anomaly detection algorithm to detect unusual play patterns and abnormal data and issue an early warning. This enables the analysis unit to not only grasp the situation in real time, but also handle long-term skill improvement and anomaly detection, thereby improving the reliability and effectiveness of the entire system.

[0066] The comparison unit compares the data analyzed by the analysis unit with the play data of the professional player. The comparison unit uses, for example, AI to compare the data. Specifically, the AI ​​compares the play data of the user with the play data of the professional player in detail to clarify the differences in scores and operations. For example, the comparison unit calculates the difference in scores between the play data of the user and the play data of the professional player. This makes it possible to identify where the user is losing scores and where improvement is needed. The comparison unit also compares the operation logs to evaluate how much the user's operation accuracy and reaction speed differ from those of the professional player. For example, by comparing the operation of the user based on the timing and speed at which the professional player performs a specific operation, it is possible to clarify specific improvements. Furthermore, the comparison unit can analyze differences in play style and strategy and suggest what strategy the user should adopt. For example, it can analyze what strategy the professional player uses and suggest a way to incorporate that strategy to the user. This allows the comparison unit to compare the play data of the user in detail and suggest specific improvements and strategies. Furthermore, the comparison unit can continuously revise the comparison results based on data updated in real time to respond to the latest situation. This enables the comparison unit to always provide highly accurate comparisons based on the latest information, thereby helping users improve their skills.

[0067] The providing unit provides feedback to the user based on the results obtained by the comparing unit. The providing unit generates the feedback using, for example, AI. Specifically, the AI ​​proposes specific correction methods and training methods based on the results of the comparison between the user's play data and the play data of a professional player. For example, the providing unit proposes a training method for improving reaction speed. This includes training to repeatedly practice specific operations and mini-games for measuring reaction speed. The providing unit can also propose a method for improving strategies according to the user's play style. For example, if the user prefers an offensive play style, the providing unit proposes a method for improving the timing of attacks and combo combinations. Furthermore, the providing unit can monitor the user's progress and regularly update the feedback. This allows the user to always train based on the latest information and efficiently improve their skills. The providing unit can also set specific goals for the user and evaluate the degree of achievement of the goals. For example, a goal for achieving a specific score is set and the progress is regularly evaluated. This allows the user to continue training while feeling his or her own growth. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the feedback content. This enables the providing unit to provide the user with quick and effective feedback and assist in improving skills.

[0068] The analysis unit can analyze the play data of professional players. The play data of professional players includes, for example, data of a specific tournament, data of a specific player, etc., but is not limited to such examples. The analysis unit, for example, collects the play data of professional players and analyzes it using AI. For example, the analysis unit analyzes the play data of the user based on the play data of the professional players. By analyzing the play data of the professional players, it becomes possible to compare the play data with the play data of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that analyzes the play data of professional players and compares it with the play data of the user.

[0069] The comparison unit can compare the play data of the user with the play data of the professional player. The comparison unit, for example, compares the play data of the user with the play data of the professional player. Specific criteria and methods of comparison include, for example, comparison of scores, comparison of operation patterns, and the like, but are not limited to such examples. For example, the comparison unit calculates the difference in score between the play data of the user and the play data of the professional player. The comparison unit can also calculate the degree of agreement of the operation patterns. In this way, the user's play data and the play data of the professional player can be compared to identify issues. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can perform the comparison using an AI model that compares the play data of the user with the play data of the professional player.

[0070] The providing unit can suggest a specific correction method to the user. The providing unit, for example, suggests a specific correction method to the user. Specific contents and standards of the correction method include, for example, improvement of the operation method, change of strategy, etc., but are not limited to such examples. For example, the providing unit suggests a training method for improving reaction speed. The providing unit can also provide advice for improving the selection of a strategy. In this way, by suggesting a specific correction method, the user's skill improvement is supported. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the correction method using an AI model that analyzes the user's play data and suggests a correction method.

[0071] The providing unit can continuously analyze the play data of the user and monitor the improvement of the skill. The providing unit, for example, continuously analyzes the play data of the user and monitors the improvement of the skill. Specific methods and standards for continuous analysis include, for example, the frequency of analysis, the type of data to be analyzed, and the like, but are not limited to such examples. For example, the providing unit periodically collects and analyzes the play data of the user. The providing unit can also evaluate the improvement of the user's skill based on the analysis result. In this way, the improvement of the user's skill can be monitored by continuously analyzing. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can perform monitoring using an AI model that continuously analyzes the play data of the user and monitors the improvement of the skill.

[0072] The collection unit can estimate the user's emotion and adjust the timing of collecting play data based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of collecting play data based on the estimated user's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the examples are not limited to these. For example, when the user is stressed, the collection unit temporarily stops collecting play data and resumes it in a relaxed state. In addition, when the user is concentrating, the collection unit can collect play data at that timing to obtain the most accurate data. Furthermore, when the user is tired, the collection unit can refrain from collecting play data and collect again after a break. In this way, optimal data can be collected by adjusting the collection timing according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the examples are not limited to these. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0073] The collection unit can analyze the user's past play data and select the optimal collection method. The collection unit, for example, analyzes the user's past play data and selects the optimal collection method. The past play data includes, for example, data from the past month, data from a specific game session, etc., but is not limited to such examples. For example, the collection unit collects data under similar conditions based on play data in which the user demonstrated high performance in the past. In addition, the collection unit can obtain the most effective data from the user's past play data by collecting it in a specific time period. Furthermore, the collection unit can analyze the user's past play data and prioritize data collection in a specific game mode or stage. In this way, the optimal collection method can be selected by analyzing the past data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past play data to the generation AI and cause the generation AI to select the optimal collection method.

[0074] The collection unit can perform filtering based on the user's current in-game situation and play style when collecting play data. The collection unit, for example, performs filtering based on the user's current in-game situation and play style when collecting play data. The in-game situation includes, for example, the game progress situation, the user's behavior pattern, etc., but is not limited to such examples. The play style includes, for example, an offensive play style, a defensive play style, etc., but is not limited to such examples. For example, when the user is using a specific character, the collection unit preferentially collects data related to the character. In addition, when the user adopts a specific strategy, the collection unit can also filter and collect data related to the strategy. Furthermore, when the user is playing in a specific game mode, the collection unit can also collect data related to the mode. In this way, by filtering data based on the in-game situation and play style, highly relevant data can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data of the user's in-game situation and play style to the generation AI and cause the generation AI to perform filtering.

[0075] The collection unit can estimate the user's emotion and determine the priority of the play data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of the play data to be collected based on the estimated user's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the present invention is not limited to such examples. For example, when the user is excited, the collection unit preferentially collects play data at that timing. In addition, when the user is relaxed, the collection unit can also preferentially collect play data in that state. Furthermore, when the user is concentrating, the collection unit can also preferentially collect play data at that timing. In this way, important data can be preferentially collected by determining the priority of data based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the present invention is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0076] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting play data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting play data. The geographical location information includes, for example, a country, a city, a region, and the like, but is not limited to such examples. For example, when the user is playing in a specific region, the collection unit preferentially collects data related to the region. In addition, when the user is participating in a specific event, the collection unit can also preferentially collect data related to the event. Furthermore, when the user is playing in a specific time period, the collection unit can also preferentially collect data related to the time period. In this way, highly relevant data can be collected by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0077] The collection unit can analyze the user's social media activities and collect related data when collecting play data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting play data. Social media activities include, for example, the content of posts, the number of likes, the content of comments, and the like, but are not limited to such examples. For example, the collection unit collects related data based on play videos shared by the user on social media. The collection unit can also collect data related to in-game events mentioned by the user on social media. Furthermore, the collection unit can also collect data related to the play style of professional players that the user follows on social media. In this way, the related data can be collected by analyzing social media activities. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data.

[0078] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. For example, facial expression recognition, voice analysis, and other technologies are used for emotion estimation, but the examples are not limited to these. For example, the analysis unit provides a simple and highly visible analysis result when the user is nervous. In addition, the analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a visually stimulating analysis result when the user is excited. In this way, the analysis unit can provide an optimal analysis result by adjusting the expression method of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the examples are not limited to these. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the play data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the play data during the analysis. The importance of the play data includes, for example, the score and the length of the play time, but is not limited to such examples. For example, the analysis unit performs a detailed analysis on important play data. The analysis unit can also perform a simplified analysis on general play data. Furthermore, the analysis unit can perform a specialized analysis on play data related to a specific game mode or stage. This allows efficient analysis by adjusting the level of detail of the analysis based on the importance of the play data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the play data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the game category during analysis. The analysis unit applies different analysis algorithms depending on the game category during analysis, for example. Game categories include, but are not limited to, action games, puzzle games, and the like. For example, the analysis unit performs analysis with emphasis on reaction speed and aim accuracy for shooting games. In addition, the analysis unit can also perform analysis with emphasis on strategy selection and resource management for strategy games. Furthermore, the analysis unit can also perform analysis with emphasis on play timing and tactics for sports games. This makes it possible to perform highly accurate analysis by applying an analysis algorithm according to the game category. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input an analysis algorithm according to the game category to the generation AI and cause the generation AI to execute the analysis.

[0081] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. For example, facial expression recognition, voice analysis, and other technologies are used to estimate the emotion, but the examples are not limited to these. For example, the analysis unit provides a short and to the point analysis result when the user is in a hurry. In addition, the analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a visually stimulating analysis result when the user is excited. In this way, the analysis unit can provide an optimal analysis result by adjusting the length of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to these examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0082] The analysis unit can determine the priority of analysis based on the submission time of the play data during analysis. The analysis unit, for example, determines the priority of analysis based on the submission time of the play data during analysis. The submission time includes, for example, the date and time of submission, the order of submission, and the like, but is not limited to such examples. For example, the analysis unit preferentially analyzes the latest play data. The analysis unit can also preferentially analyze play data submitted during a specific event period. Furthermore, the analysis unit can also preferentially analyze important play data specified by the user. This enables efficient analysis by determining the priority of analysis based on the submission time. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the play data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of play data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of play data during analysis. The relevance of play data includes, for example, data in the same game, data of the same player, and the like, but is not limited to such examples. For example, the analysis unit preferentially analyzes data related to the user's play style. The analysis unit can also preferentially analyze data related to a specific game mode or stage. Furthermore, the analysis unit can also preferentially analyze data that is highly related to play data submitted by the user in the past. This allows efficient analysis by adjusting the order of analysis based on the relevance. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of play data to the generation AI and cause the generation AI to adjust the order of analysis.

[0084] The comparison unit can estimate the user's emotion and adjust the comparison criteria based on the estimated user's emotion. The comparison unit, for example, estimates the user's emotion and adjusts the comparison criteria based on the estimated user's emotion. For example, facial expression recognition, voice analysis, and other technologies are used for estimating the emotion, but the examples are not limited to these. For example, the comparison unit provides a simple and highly visible comparison criterion when the user is nervous. Furthermore, the comparison unit can also provide a detailed comparison criterion when the user is relaxed. Furthermore, the comparison unit can also provide a visually stimulating comparison criterion when the user is excited. In this way, the comparison unit can adjust the comparison criteria based on the user's emotion and provide an optimal comparison result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the examples are not limited to these. A part or all of the above-mentioned processing in the comparison unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the comparison unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0085] The comparison unit can improve the accuracy of the comparison by considering the interrelationship of the play data at the time of comparison. For example, the comparison unit improves the accuracy of the comparison by considering the interrelationship of the play data at the time of comparison. The interrelationship of the play data includes, for example, data correlation, causal relationship, etc., but is not limited to such examples. For example, the comparison unit analyzes the interrelationship between the play data of the user and the play data of the professional player, and performs a highly accurate comparison. The comparison unit can also perform the comparison by considering the interrelationship of the play data in a specific game mode or stage. Furthermore, the comparison unit can analyze the interrelationship between the play style of the user and the play style of the professional player, and perform a highly accurate comparison. As a result, by considering the interrelationship of the play data, a highly accurate comparison is possible. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input the interrelationship of the play data to the generation AI, and cause the generation AI to execute the improvement of the accuracy of the comparison.

[0086] The comparison unit can make the comparison while considering the attribute information of the person who submitted the play data. For example, the comparison unit makes the comparison while considering the attribute information of the person who submitted the play data. The attribute information of the person who submitted the play data includes, for example, age, sex, region, etc., but is not limited to such examples. For example, the comparison unit compares the play data with that of an appropriate professional player based on the age and sex of the user. The comparison unit can also compare the play data with that of an appropriate professional player based on the game experience and skill level of the user. Furthermore, the comparison unit can compare the play data with that of an appropriate professional player based on the play style of the user. This makes it possible to make an appropriate comparison by considering the attribute information of the person who submitted the play data. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input the attribute information of the person who submitted the play data to the generation AI and cause the generation AI to perform the comparison.

[0087] The comparison unit can estimate the user's emotion and adjust the order of displaying the comparison results based on the estimated user's emotion. The comparison unit can estimate the user's emotion, for example, and adjust the order of displaying the comparison results based on the estimated user's emotion. For example, facial expression recognition, voice analysis, and other technologies are used for estimating the emotion, but the present invention is not limited to such examples. For example, the comparison unit displays important results first when the user is nervous. Furthermore, the comparison unit can also display detailed results sequentially when the user is relaxed. Furthermore, the comparison unit can also display visually stimulating results first when the user is excited. In this way, the display order of the results can be adjusted based on the user's emotion, thereby providing optimal comparison results. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but the present invention is not limited to such examples. A part or all of the above-mentioned processing in the comparison unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the comparison unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0088] The comparison unit can make the comparison taking into consideration the geographical distribution of the play data when making the comparison. For example, the comparison unit makes the comparison taking into consideration the geographical distribution of the play data when making the comparison. The geographical distribution includes, for example, countries, cities, regions, and the like, but is not limited to such examples. For example, when the user is playing in a specific region, the comparison unit compares the play data with the play data of a professional player related to that region. In addition, when the user is participating in a specific event, the comparison unit can also compare the play data with the play data of a professional player related to that event. Furthermore, when the user is playing in a specific time period, the comparison unit can also compare the play data with the play data of a professional player related to that time period. This makes it possible to make an appropriate comparison by taking the geographical distribution into consideration. Part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input the geographical distribution of the play data to the generation AI and cause the generation AI to execute the comparison.

[0089] The comparison unit can improve the accuracy of the comparison by referring to literature related to the play data at the time of comparison. The comparison unit can improve the accuracy of the comparison by referring to literature related to the play data at the time of comparison, for example. The related literature includes, but is not limited to, academic papers, technical reports, and the like. For example, the comparison unit can perform a highly accurate comparison by referring to research papers related to the play data of the user. The comparison unit can also perform a highly accurate comparison by referring to research papers related to the play data of a professional player. Furthermore, the comparison unit can also perform a highly accurate comparison by referring to research papers related to a specific game mode or stage. This makes it possible to perform a highly accurate comparison by referring to the related literature. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input literature related to the play data to the generation AI and cause the generation AI to perform an improvement in the accuracy of the comparison.

[0090] The providing unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the feedback expression method based on the estimated user's emotion. For example, facial expression recognition, voice analysis, and other technologies are used for estimating the emotion, but the examples are not limited to these. For example, when the user is nervous, the providing unit provides simple and highly visible feedback. Furthermore, when the user is relaxed, the providing unit can also provide detailed feedback. Furthermore, when the user is excited, the providing unit can also provide visually stimulating feedback. In this way, the feedback expression method can be adjusted based on the user's emotion, thereby providing optimal feedback. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the examples are not limited to these. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0091] The providing unit can adjust the level of detail of the feedback based on the importance of the play data when providing the feedback. For example, the providing unit adjusts the level of detail of the feedback based on the importance of the play data when providing the feedback. The importance of the play data includes, for example, the score and the length of the play time, but is not limited to such examples. For example, the providing unit provides detailed feedback for important play data. In addition, the providing unit can also provide simplified feedback for general play data. Furthermore, the providing unit can also provide specialized feedback for play data related to a specific game mode or stage. As a result, by adjusting the level of detail of the feedback based on the importance of the play data, efficient feedback is possible. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the play data to the generating AI and cause the generating AI to adjust the level of detail of the feedback.

[0092] The providing unit can apply different feedback algorithms depending on the game category when providing feedback. For example, the providing unit applies different feedback algorithms depending on the game category when providing feedback. The game categories include, for example, action games, puzzle games, etc., but are not limited to such examples. For example, the providing unit provides feedback regarding reaction speed and aim accuracy for shooting games. In addition, the providing unit can also provide feedback regarding strategy selection and resource management for strategy games. Furthermore, the providing unit can also provide feedback regarding play timing and tactics for sports games. As a result, highly accurate feedback can be provided by applying a feedback algorithm according to the game category. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input a feedback algorithm according to the game category to the generating AI and cause the generating AI to execute the feedback.

[0093] The providing unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the length of the feedback based on the estimated user's emotion. For example, facial expression recognition, voice analysis, and other technologies are used for estimating the emotion, but the present invention is not limited to such examples. For example, the providing unit provides short and to the point feedback when the user is in a hurry. In addition, the providing unit can also provide detailed feedback when the user is relaxed. Furthermore, the providing unit can also provide visually stimulating feedback when the user is excited. In this way, the feedback length can be adjusted based on the user's emotion, thereby providing optimal feedback. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but the present invention is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0094] The providing unit can determine the priority of the feedback based on the submission time of the play data when providing the feedback. The providing unit, for example, determines the priority of the feedback based on the submission time of the play data when providing the feedback. The submission time includes, for example, the date and time of submission, the order of submission, and the like, but is not limited to such examples. For example, the providing unit provides feedback preferentially to the latest play data. The providing unit can also provide feedback preferentially to the play data submitted during a specific event period. Furthermore, the providing unit can also provide feedback preferentially to important play data designated by the user. This enables efficient feedback by determining the priority of the feedback based on the submission time. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the submission time of the play data to the generating AI and cause the generating AI to execute the determination of the priority of the feedback.

[0095] The providing unit can adjust the order of feedback based on the relevance of play data when providing feedback. For example, the providing unit adjusts the order of feedback based on the relevance of play data when providing feedback. The relevance of play data includes, for example, data in the same game, data of the same player, and the like, but is not limited to such examples. For example, the providing unit provides feedback preferentially to data related to the user's play style. The providing unit can also provide feedback preferentially to data related to a specific game mode or stage. Furthermore, the providing unit can also provide feedback preferentially to data highly related to play data submitted by the user in the past. This enables efficient feedback by adjusting the order of feedback based on the relevance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of play data to the generating AI and cause the generating AI to adjust the order of feedback.

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

[0097] When collecting the user's play data, the collection unit can also collect performance data of the user's device at the same time. For example, the collection unit can collect data such as CPU usage, memory usage, and network delay of the user's PC or console. This makes it possible to analyze the impact of the user's play environment on game performance. The collection unit can also collect hardware data such as the temperature and fan rotation speed of the user's device. This makes it possible to evaluate the impact of the hardware state on game play. Furthermore, the collection unit can also collect setting information (resolution, graphics settings, etc.) of the user's device and suggest optimal settings. This makes it possible to provide feedback that takes into account the user's device performance.

[0098] When analyzing the user's play data, the analysis unit can also compare it with the user's past play data. For example, the analysis unit can analyze the difference between the user's current play data and the play data from the past month. This makes it possible to evaluate the improvement and change in the user's skill. The analysis unit can also compare the user's specific past game sessions with the current session to identify what kind of improvement can be seen. Furthermore, the analysis unit can analyze specific trends and patterns based on the user's past play data and provide advice that will be useful for future play. This makes it possible to support the user's long-term skill improvement.

[0099] When comparing the user's play data with the play data of professional players, the comparison unit can apply comparison criteria customized based on the user's play style. For example, if the user has an offensive play style, the comparison unit can compare the user's play data with data of professional players suitable for that style. In addition, for a user with a defensive play style, the comparison unit can compare the user's play data with data of professional players suitable for that style. Furthermore, if the user uses a specific character or role, the comparison unit can compare the user's play data with data of professional players specialized in that character or role. This makes it possible to provide appropriate feedback according to the user's play style.

[0100] When suggesting a specific correction method to the user, the providing unit can provide customized feedback based on the user's learning style. For example, the providing unit can provide feedback using diagrams or videos to a user with a visual learning style. Also, the providing unit can provide feedback in the form of audio guides or podcasts to a user with an auditory learning style. Furthermore, the providing unit can suggest a training session through actual game play to a user with a hands-on learning style. In this way, effective feedback according to the user's learning style can be provided.

[0101] The providing unit can perform customized monitoring based on the user's goal settings when continuously analyzing the user's play data and monitoring the improvement of skills. For example, if the user sets that he / she wants to improve a particular skill (e.g., reaction speed, strategic thinking), the providing unit can focus on analyzing data related to that skill. In addition, if the user sets that he / she wants to improve performance in a particular game mode or stage, the providing unit can also focus on analyzing data related to that mode or stage. Furthermore, the providing unit can periodically evaluate the degree of achievement of the goals set by the user and provide feedback on the progress. This enables effective monitoring and feedback according to the user's goals.

[0102] The collection unit can estimate the user's emotions and adjust the timing of collecting play data based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit temporarily stops collecting play data and resumes it when the user is relaxed. Also, when the user is concentrating, the collection unit can collect play data at that timing to obtain the most accurate data. Furthermore, when the user is tired, the collection unit can refrain from collecting play data and collect again after a break. In this way, optimal data can be collected by adjusting the collection timing according to the user's emotions.

[0103] The analysis unit can estimate the user's emotion and adjust the method of expressing the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, when the user is excited, the analysis unit can also provide a visually stimulating analysis result. In this way, by adjusting the method of expressing the analysis based on the user's emotion, it is possible to provide an optimal analysis result.

[0104] The comparison unit can estimate the user's emotion and adjust the comparison criteria based on the estimated user's emotion. For example, the comparison unit provides a simple and highly visible comparison criterion when the user is nervous. The comparison unit can also provide a detailed comparison criterion when the user is relaxed. Furthermore, the comparison unit can also provide a visually stimulating comparison criterion when the user is excited. In this way, the comparison criterion can be adjusted based on the user's emotion to provide an optimal comparison result.

[0105] The providing unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides simple and highly visible feedback. When the user is relaxed, the providing unit can also provide detailed feedback. Furthermore, when the user is excited, the providing unit can also provide visually stimulating feedback. In this way, the feedback expression method can be adjusted based on the user's emotion, thereby providing optimal feedback.

[0106] The providing unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. For example, the providing unit can provide short and to the point feedback when the user is in a hurry. The providing unit can also provide detailed feedback when the user is relaxed. Furthermore, the providing unit can provide visually stimulating feedback when the user is excited. In this way, the feedback length can be adjusted based on the user's emotion to provide optimal feedback.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The collection unit collects the user's play data. The user's play data includes game scores, operation logs, play time, etc. For example, the collection unit automatically saves game replay data and transmits it to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using AI, and analyzes the user's play data based on the play data of professional players. Step 3: The comparison unit compares the data analyzed by the analysis unit with the play data of the professional player. The comparison unit, for example, uses AI to calculate the difference in score between the user's play data and the play data of the professional player. Step 4: The providing unit provides feedback to the user based on the results obtained by the comparing unit. The providing unit generates the feedback using, for example, AI, and suggests a specific correction method to the user.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned collection unit, analysis unit, comparison unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's play data using the camera 42 and the communication I / F 44 of the smart device 14, and transmits the data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the analyzed data with the play data of a professional player. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides feedback to the user based on the comparison result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0115] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0120] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned collection unit, analysis unit, comparison unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's play data using the camera 42 and the communication I / F 44 of the smart glasses 214, and transmits the data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the analyzed data with the play data of a professional player. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides feedback to the user based on the comparison result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0131] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned collection unit, analysis unit, comparison unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the user's play data using the camera 42 and the communication I / F 44 of the headset type terminal 314, and transmits the data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the analyzed data with the play data of a professional player. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides feedback to the user based on the comparison result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] Each of the multiple elements including the above-mentioned collection unit, analysis unit, comparison unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's play data using the camera 42 and the communication I / F 44 of the robot 414, and transmits the data to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the analyzed data with the play data of a professional player. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides feedback to the user based on the comparison result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0163] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this 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 the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

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

[0171] In addition, 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 upon request from the data processing device 12.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

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

[0175] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) A collection unit that collects play data of users; an analysis unit that analyzes the data collected by the collection unit; A comparison unit that compares the data analyzed by the analysis unit with play data of professional players; A providing unit that provides feedback to a user based on the results obtained by the comparing unit. A system characterized by: (Appendix 2) The analysis unit is Analyzing the play data of professional players 2. The system of claim 1, (Appendix 3) The comparison unit is Compare your playing data with that of professional players 2. The system of claim 1, (Appendix 4) The providing unit is Providing specific correction suggestions to users 2. The system of claim 1, (Appendix 5) The providing unit is Continuously analyze user play data and monitor skill improvement 2. The system of claim 1, (Appendix 6) The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting play data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze users' past play data and select the optimal collection method 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting play data, filtering is performed based on the user's current in-game situation and play style. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Estimating a user's emotions and determining the priority of play data to be collected based on the estimated user's emotions 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting play data, the system takes into account the user's geographic location information and prioritizes the collection of data that is more relevant. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting play data, analyze users' social media activity and collect related data. 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, the level of detail is adjusted based on the importance of the play data. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, different analysis algorithms are applied depending on the game category. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, the analysis priority will be determined based on when the play data was submitted. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on the relevance of play data 2. The system of claim 1, (Appendix 18) The comparison unit is Inferring a user's emotions and adjusting the comparison criteria based on the estimated user's emotions 2. The system of claim 1, (Appendix 19) The comparison unit is When comparing, the accuracy of the comparison is improved by taking into account the correlation of play data. 2. The system of claim 1, (Appendix 20) The comparison unit is When comparing, the attribute information of the person submitting the play data is taken into consideration. 2. The system of claim 1, (Appendix 21) The comparison unit is Inferring user sentiment and adjusting the order in which comparison results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 22) The comparison unit is When making comparisons, take into account the geographical distribution of play data. 2. The system of claim 1, (Appendix 23) The comparison unit is When comparing, refer to related literature on play data to improve the accuracy of the comparison. 2. The system of claim 1, (Appendix 24) The providing unit is Infer user emotions and adjust feedback presentation based on the estimated user emotions 2. The system of claim 1, (Appendix 25) The providing unit is When providing feedback, adjust the level of detail based on the importance of the play data 2. The system of claim 1, (Appendix 26) The providing unit is When providing feedback, we apply different feedback algorithms depending on the game category. 2. The system of claim 1, (Appendix 27) The providing unit is Estimate the user's emotions and adjust the length of the feedback based on the estimated user emotions. 2. The system of claim 1, (Appendix 28) The providing unit is When you provide feedback, we will prioritize it based on when you submitted your gameplay data. 2. The system of claim 1, (Appendix 29) The providing unit is When providing feedback, adjust the order of feedback based on the relevance of your play data 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. A collection unit that collects play data of users; an analysis unit that analyzes the data collected by the collection unit; A comparison unit that compares the data analyzed by the analysis unit with play data of professional players; A providing unit that provides feedback to a user based on the results obtained by the comparing unit. A system characterized by:

2. The analysis unit is Analyzing the play data of professional players 2. The system of claim 1 .

3. The providing unit is Continuously analyze user play data and monitor skill improvement 2. The system of claim 1 .

4. The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting play data based on the estimated user's emotion.

2. The system of claim 1 .

5. The collecting unit includes: Analyze users' past play data and select the optimal collection method 2. The system of claim 1 .

6. The collecting unit includes: When collecting play data, filtering is performed based on the user's current in-game situation and play style.

2. The system of claim 1 .

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