system

Abstract

The system according to the embodiment aims to objectively and efficiently evaluate the abilities of players. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data on players. The analysis unit analyzes the data collected by the collection unit. The evaluation unit assigns scores based on the analysis results obtained by the analysis unit. The provision unit provides the scores assigned by the evaluation unit to the players.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to objectively and efficiently evaluate a player's abilities.

[0005] The system according to the embodiment aims to objectively and efficiently evaluate the abilities of players. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data on the players. The analysis unit analyzes the data collected by the collection unit. The evaluation unit assigns a score based on the analysis result obtained by the analysis unit. The provision unit provides the scores assigned by the evaluation unit to the players. [Effects of the Invention]

[0007] The system according to the embodiment can objectively and efficiently evaluate the abilities of players. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The virtual tryout platform of an embodiment of the present invention is a system in which players use a smartphone or tablet to take various tests provided on an app. This system collects player data and uses AI developed by data specialists and veteran scouts to analyze and score the data. Examples of such data include dribbling speed and accuracy, shooting accuracy, and passing distance. These test results are sent to the app in real time, where the AI ​​analyzes the player's performance data and identifies talented and untalented players. For example, a player with fast dribbling speed and high shooting accuracy will receive a high score. This platform provides players with an opportunity to have their abilities objectively evaluated, allowing scouts to efficiently discover talent. Furthermore, the algorithm and evaluation criteria used by the AI ​​to assign scores are clearly explained, highlighting its advantages over other existing evaluation methods. This allows the virtual tryout platform to efficiently collect, analyze, evaluate, and provide player data.

[0029] A virtual tryout platform according to an embodiment includes a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data when a player takes a test using an app. The data when a player takes a test using the app includes, but is not limited to, dribbling speed, shooting accuracy, and pass distance. The collection unit records the player's movements in real time using, for example, a smartphone sensor. The collection unit can also collect environmental data (e.g., temperature, humidity, etc.) when the player takes the test. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the player's performance data using, for example, a machine learning algorithm. The analysis unit can statistically analyze the player's data to identify performance trends. For example, the analysis unit can analyze the correlation between a player's dribbling speed and shooting accuracy. The evaluation unit assigns a score based on the analysis results obtained by the analysis unit. For example, the evaluation unit converts the player's performance data into a numerical score. The evaluation unit can rank the player's scores to identify talented and untalented players. For example, the evaluation unit assigns a high score to a player with a fast dribbling speed and high shooting accuracy. The providing unit provides the scores assigned by the evaluation unit to the player. The providing unit displays the player's score on an app, for example. The providing unit can visually display the player's score as a graph or chart. For example, the providing unit can display the player's score in chronological order to grasp changes in performance. This allows the virtual tryout platform according to the embodiment to efficiently collect, analyze, evaluate, and provide player data.

[0030] The selection unit can select the type of test that the player will take. For example, the selection unit allows the player to select the type of test, such as a physical test or a mental test. The selection unit allows the player to select a test in which he or she is good at it. For example, the player can select a test that measures dribbling speed. The selection unit can also allow the player to select a test in which he or she is not good at it. For example, the player can select a test that measures shooting accuracy. This allows the player to maximize his or her abilities. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select the type of test using an AI model that suggests the optimal test based on the player's past performance data.

[0031] The collection unit can collect data when a player takes a test using the app. The collection unit, for example, uses a smartphone sensor to record the player's movements in real time. For example, the collection unit can measure the player's dribbling speed using an acceleration sensor. The collection unit can also measure the player's shooting accuracy using a gyro sensor. The collection unit can also measure the player's pass distance using a GPS sensor. For example, the collection unit can measure the speed and angle when the player kicks the ball and calculate the pass distance based on that data. This makes it possible to collect data when a player takes a test using the app. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the player's movements into a generation AI and have the generation AI perform movement analysis.

[0032] The analysis unit can analyze the collected data and pass it to the evaluation unit. The analysis unit can analyze the collected data using, for example, a machine learning algorithm. For example, the analysis unit can analyze the correlation between a player's dribbling speed and shooting accuracy. The analysis unit can also statistically analyze player performance data to identify performance trends. For example, the analysis unit can cluster player performance data to group players with similar performances. The analysis unit can also analyze player performance data over time to identify changes in performance. For example, the analysis unit can plot player performance data over time to visually display improvements or declines in performance. This allows the collected data to be analyzed and passed to the evaluation unit. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the data.

[0033] The evaluation unit can assign scores based on the analysis results. The evaluation unit, for example, converts the player's performance data into a numerical score. For example, the evaluation unit scores the player's dribbling speed and the accuracy of his / her shot. The evaluation unit can also rank the player's performance data to distinguish between talented and untalented players. For example, the evaluation unit assigns a high score to a player with a fast dribbling speed and high shooting accuracy. The evaluation unit can also comprehensively evaluate the player's performance data and calculate an overall score. For example, the evaluation unit calculates an overall score by combining scores such as dribbling speed, shooting accuracy, and pass distance. This allows the score to be assigned based on the analysis results. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the analysis results into a generation AI and have the generation AI calculate the score.

[0034] The providing unit can provide the score assigned by the evaluation unit to the player. The providing unit, for example, displays the player's score on an app. For example, the providing unit visually displays the player's score as a graph or chart. The providing unit can also display the player's score in chronological order to grasp changes in performance. For example, the providing unit plots the player's score in chronological order to visually display improvements or declines in performance. Furthermore, the providing unit can compare the player's score with other players. For example, the providing unit compares the player's score with the scores of other players to evaluate relative performance. In this way, the score assigned by the evaluation unit can be provided to the player. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the score assigned by the evaluation unit to a generation AI and cause the generation AI to display the score.

[0035] The collection unit can analyze the player's past performance data and select the optimal data collection method. For example, the collection unit prioritizes collection of test items in which the player excels from the past data. For example, the collection unit prioritizes collection of test items on which the player has previously scored high. The collection unit can also focus collection of test items in which the player is weak from the past data. For example, the collection unit focuses collection of test items on which the player has previously scored low. Furthermore, the collection unit can identify time periods in which the player's performance improves from the past data and collect data during those time periods. For example, the collection unit collects data during time periods in which the player's performance is highest. This makes it possible to analyze the player's past performance data and select the optimal data collection method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past performance data into a generation AI and cause the generation AI to select the optimal data collection method.

[0036] When collecting data, the collection unit can filter the data based on the player's current physical condition and environmental conditions. For example, if the player is in good physical condition, the collection unit performs all test items. For example, the collection unit measures the player's heart rate and blood pressure to confirm that the player is in good physical condition. Furthermore, if the player is in poor physical condition, the collection unit can perform only low-stress test items. For example, if the player is in poor physical condition, the collection unit can perform test items including light exercise and stretching. Furthermore, if the environmental conditions are poor (e.g., rainy weather), the collection unit can prioritize test items that can be performed indoors. For example, if the environmental conditions are poor, the collection unit selects test items that can be performed indoors. This makes it possible to filter data collection based on the player's current physical condition and environmental conditions. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without AI. For example, the collection unit can input the player's physical condition data and environmental condition data to the generation AI and have the generation AI perform the filtering.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the athlete. For example, if the athlete is at high altitude, the collection unit prioritizes collecting data related to high-altitude training. For example, the collection unit collects data by taking into account the effects of oxygen concentration and air pressure at high altitude. Furthermore, if the athlete is in an urban area, the collection unit can prioritize collecting performance data in an urban environment. For example, the collection unit collects data by taking into account the effects of noise and traffic in urban areas. Furthermore, if the athlete is at the seaside, the collection unit can prioritize collecting performance data on a sandy beach. For example, the collection unit collects data by taking into account the instability of footing on a sandy beach and the effects of wind. This makes it possible to prioritize collecting highly relevant data by taking into account the geographical location information of the athlete. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the geographical location information of the athlete to the generation AI and cause the generation AI to collect highly relevant data.

[0038] During data collection, the collection unit can analyze the social media activities of the players and collect related data. For example, the collection unit analyzes training videos posted by the players on social media and collects related data. For example, the collection unit performs motion analysis from the training videos posted by the players and collects performance data. The collection unit can also analyze game highlights shared by the players on social media and collect performance data. For example, the collection unit analyzes the players' movements and techniques from game highlight videos and collects data. Furthermore, the collection unit can also collect data by referring to training methods mentioned by the players on social media. For example, the collection unit collects related data based on the training methods mentioned by the players. In this way, the players' social media activities can be analyzed and related data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the players' social media data into the generation AI and cause the generation AI to collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis of data with high importance. For example, the analysis unit performs a detailed analysis of important data such as a player's dribbling speed and shooting accuracy. The analysis unit can also perform a simplified analysis of data with low importance. For example, the analysis unit simplifies and analyzes data on a player's warm-up and cool-down. Furthermore, the analysis unit can analyze data with a moderate level of detail for data with medium importance. For example, the analysis unit analyzes data such as the distance and accuracy of a player's passes with a moderate level of detail. This allows the level of detail of the analysis to be adjusted based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a technical analysis algorithm to technical data. For example, the analysis unit applies an algorithm for analyzing technical data such as a player's dribbling speed and shooting accuracy. The analysis unit can also apply a physical ability analysis algorithm to data related to physical abilities. For example, the analysis unit applies an algorithm for analyzing data related to physical abilities such as a player's jumping power and endurance. The analysis unit can also apply a mental analysis algorithm to data related to mental aspects. For example, the analysis unit applies an algorithm for analyzing data related to mental aspects such as a player's concentration level and stress level. This allows different analysis algorithms to be applied depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit prioritizes analyzing the player's most recent performance data and provides the most recent feedback. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit prioritizes analyzing the most recent data while referring to the player's past performance data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific period. For example, the analysis unit prioritizes analyzing data collected during a specific training period of the player. This makes it possible to determine the priority of analysis based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected to the generation AI and cause the generation AI to determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit analyzes the relevance between a player's dribbling speed and shooting accuracy, and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of a player's warm-up and cool-down data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit analyzes the relevance of a player's performance data in real time and dynamically adjusts the order of analysis. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between data during evaluation. The evaluation unit, for example, considers the correlation between dribbling speed and shooting accuracy when making an evaluation. For example, the evaluation unit analyzes the correlation between a player's dribbling speed and shooting accuracy and reflects this in the evaluation. The evaluation unit can also consider the correlation between pass distance and accuracy when making an evaluation. For example, the evaluation unit analyzes the correlation between a player's pass distance and accuracy and reflects this in the evaluation. Furthermore, the evaluation unit can also consider the balance between physical strength and technique when making an evaluation. For example, the evaluation unit analyzes the balance between a player's physical strength and technique and reflects this in the evaluation. This makes it possible to improve the accuracy of the evaluation by taking into account the interrelationships between data. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0044] The evaluation unit can perform evaluation taking into consideration the attribute information of the player when performing the evaluation. The evaluation unit applies evaluation criteria according to, for example, age. For example, the evaluation unit sets evaluation criteria according to the age of the player and performs the evaluation. The evaluation unit can also apply evaluation criteria according to the position. For example, the evaluation unit sets evaluation criteria according to the position of the player and performs the evaluation. Furthermore, the evaluation unit can also apply evaluation criteria according to the years of experience. For example, the evaluation unit sets evaluation criteria according to the years of experience of the player and performs the evaluation. This makes it possible to perform evaluation taking into consideration the attribute information of the player. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the attribute information of the player to the generation AI and have the generation AI perform the evaluation.

[0045] The evaluation unit can perform the evaluation taking into account the geographical distribution of the data. The evaluation unit, for example, performs the evaluation taking into account performance data at high altitudes. For example, the evaluation unit performs the evaluation taking into account the effects of oxygen concentration and air pressure at high altitudes. The evaluation unit can also perform the evaluation taking into account performance data in urban areas. For example, the evaluation unit performs the evaluation taking into account the effects of noise and traffic volume in urban areas. The evaluation unit can also perform the evaluation taking into account performance data at the seaside. For example, the evaluation unit performs the evaluation taking into account the effects of wind at the seaside and the instability of footing on sandy beaches. This makes it possible to perform the evaluation taking into account the geographical distribution of the data. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the geographical distribution of the data into the generation AI and have the generation AI perform the evaluation.

[0046] The evaluation unit can improve the accuracy of the evaluation by referring to related literature during the evaluation. The evaluation unit, for example, updates the evaluation criteria by referring to the latest research papers. For example, the evaluation unit reviews the evaluation criteria based on the latest research papers to improve the accuracy of the evaluation. The evaluation unit can also adjust the evaluation criteria by referring to past evaluation data. For example, the evaluation unit adjusts the evaluation criteria based on the past evaluation data to improve the accuracy of the evaluation. Furthermore, the evaluation unit can also perform the evaluation by referring to the evaluation criteria of other sports. For example, the evaluation unit performs the evaluation based on the evaluation criteria of other sports to improve the accuracy of the evaluation. This makes it possible to improve the accuracy of the evaluation by referring to related literature. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input related literature into the generation AI and cause the generation AI to update the evaluation criteria.

[0047] When providing scores, the providing unit can select the optimal display method by referring to the player's past score history. The providing unit, for example, displays the past score history in a graph format. For example, the providing unit displays the player's past score history in a graph format to visually grasp changes in performance. The providing unit can also display the past score history in a list format. For example, the providing unit displays the player's past score history in a list format to check detailed scores. The providing unit can also display the past score history in a comparative format. For example, the providing unit compares the player's past score history with the scores of other players to evaluate relative performance. This makes it possible to select the optimal display method by referring to the player's past score history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the player's past score history to the generation AI and cause the generation AI to select the optimal display method.

[0048] When providing scores, the providing unit can select the optimal display method by taking into account the player's device information. For example, when a smartphone is used, the providing unit provides a display method that matches the screen size. For example, the providing unit provides a display method optimized for the smartphone screen size to improve visibility. Furthermore, when a tablet is used, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit provides a display method optimized for the large screen of a tablet to display detailed information. Furthermore, when a personal computer is used, the providing unit can also provide a display method that includes detailed information. For example, the providing unit provides a display method optimized for the large screen of a personal computer to display detailed scores and graphs. This makes it possible to select the optimal display method by taking into account the player's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the player's device information to the generation AI and cause the generation AI to select the optimal display method.

[0049] When selecting a test, the selection unit can refer to the player's past test history to suggest the optimal test. For example, the selection unit prioritizes and suggests tests on which the player has scored highly in the past. For example, the selection unit prioritizes and suggests tests on which the player has scored highly in the past to strengthen his or her strengths. The selection unit can also focus on and suggest tests on which the player has scored poorly in the past. For example, the selection unit can focus on and suggest tests on which the player has scored poorly in the past to overcome his or her weaknesses. Furthermore, the selection unit can also suggest balanced tests based on the player's past test history. For example, the selection unit can suggest balanced tests based on the player's past test history to improve overall performance. This makes it possible to suggest the optimal test by referring to the player's past test history. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit can input the player's past test history into a generation AI and have the generation AI suggest the optimal test.

[0050] When selecting a test, the selection unit can propose an optimal test by taking into account the athlete's geographical location information. For example, if the athlete is at high altitude, the selection unit proposes a test related to high-altitude training. For example, the selection unit proposes a test by taking into account the effects of oxygen concentration and air pressure at high altitude. The selection unit can also propose a test in an urban environment if the athlete is in an urban area. For example, the selection unit proposes a test by taking into account the effects of noise and traffic in urban areas. Furthermore, the selection unit can also propose a test on a sandy beach if the athlete is at the seaside. For example, the selection unit proposes a test by taking into account the instability of footing on a sandy beach and the effects of wind. This makes it possible to propose an optimal test by taking into account the athlete's geographical location information. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit can input the athlete's geographical location information into the generation AI and cause the generation AI to execute the optimal test proposal.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When analyzing a player's performance data, the analysis unit can improve the accuracy of the analysis by referring to the player's past training data. For example, the analysis unit compares the player's past training data with current performance data. The analysis unit can also grasp trends in performance improvement or decline based on the player's past training data. Furthermore, the analysis unit can propose an optimal training plan based on the player's past training data. This makes it possible to improve the accuracy of the analysis by referring to the player's past training data.

[0053] When evaluating the performance data of a player, the evaluation unit can take into account the player's goal setting. For example, the evaluation unit evaluates the extent to which the player has achieved the goals set by the player. The evaluation unit can also quantify the degree of achievement based on the player's goal setting. Furthermore, the evaluation unit can also suggest the player's next step based on the player's goal setting. This allows the evaluation to take into account the player's goal setting.

[0054] When providing the player's performance data, the providing unit can provide feedback according to the player's learning style. For example, the providing unit can provide feedback using graphs or charts to a player with a visual learning style. The providing unit can also provide audio feedback to a player with an auditory learning style. Furthermore, the providing unit can also provide feedback using actual training simulations to a player with an experiential learning style. In this way, feedback according to the player's learning style can be provided.

[0055] When collecting the player's performance data, the collection unit can monitor the player's health condition and issue an alert if an abnormality is detected. For example, the collection unit can monitor the player's heart rate and blood pressure and issue an alert if an abnormality is detected. The collection unit can also monitor the player's body temperature and oxygen saturation level and issue an alert if an abnormality is detected. Furthermore, the collection unit can monitor the player's fatigue level and issue an alert if excessive fatigue is detected. In this way, the player's health condition can be monitored and an alert can be issued if an abnormality is detected.

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

[0057] Step 1: The collection unit collects data when the player uses the app to take the test. For example, it collects data such as dribbling speed, shooting accuracy, and passing distance. The collection unit uses the smartphone's sensors to record the player's movements in real time and can also collect environmental data (temperature, humidity, etc.) when the player takes the test. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses machine learning algorithms to analyze the player's performance data and statistically analyzes it to identify performance trends. For example, it can analyze the correlation between dribbling speed and shooting accuracy. Step 3: The evaluation unit assigns a score based on the analysis results obtained by the analysis unit. The evaluation unit converts the player's performance data into a numerical score and ranks them. For example, a player with a fast dribbling speed and high shooting accuracy will receive a high score. Step 4: The provider provides the athlete with the scores assigned by the evaluator. The provider can display the athlete's scores on the app and visually display them as graphs or charts. For example, the provider can display the athlete's scores over time to understand changes in performance.

[0058] (Example 2) The virtual tryout platform of an embodiment of the present invention is a system in which players use a smartphone or tablet to take various tests provided on an app. This system collects player data and uses AI developed by data specialists and veteran scouts to analyze and score the data. Examples of such data include dribbling speed and accuracy, shooting accuracy, and passing distance. These test results are sent to the app in real time, where the AI ​​analyzes the player's performance data and identifies talented and untalented players. For example, a player with fast dribbling speed and high shooting accuracy will receive a high score. This platform provides players with an opportunity to have their abilities objectively evaluated, allowing scouts to efficiently discover talent. Furthermore, the algorithm and evaluation criteria used by the AI ​​to assign scores are clearly explained, highlighting its advantages over other existing evaluation methods. This allows the virtual tryout platform to efficiently collect, analyze, evaluate, and provide player data.

[0059] A virtual tryout platform according to an embodiment includes a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data when a player takes a test using an app. The data when a player takes a test using the app includes, but is not limited to, dribbling speed, shooting accuracy, and pass distance. The collection unit records the player's movements in real time using, for example, a smartphone sensor. The collection unit can also collect environmental data (e.g., temperature, humidity, etc.) when the player takes the test. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the player's performance data using, for example, a machine learning algorithm. The analysis unit can statistically analyze the player's data to identify performance trends. For example, the analysis unit can analyze the correlation between a player's dribbling speed and shooting accuracy. The evaluation unit assigns a score based on the analysis results obtained by the analysis unit. For example, the evaluation unit converts the player's performance data into a numerical score. The evaluation unit can rank the player's scores to identify talented and untalented players. For example, the evaluation unit assigns a high score to a player with a fast dribbling speed and high shooting accuracy. The providing unit provides the scores assigned by the evaluation unit to the player. The providing unit displays the player's score on an app, for example. The providing unit can visually display the player's score as a graph or chart. For example, the providing unit can display the player's score in chronological order to grasp changes in performance. This allows the virtual tryout platform according to the embodiment to efficiently collect, analyze, evaluate, and provide player data.

[0060] The selection unit can select the type of test that the player will take. For example, the selection unit allows the player to select the type of test, such as a physical test or a mental test. The selection unit allows the player to select a test in which he or she is good at it. For example, the player can select a test that measures dribbling speed. The selection unit can also allow the player to select a test in which he or she is not good at it. For example, the player can select a test that measures shooting accuracy. This allows the player to maximize his or her abilities. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select the type of test using an AI model that suggests the optimal test based on the player's past performance data.

[0061] The collection unit can collect data when a player takes a test using the app. The collection unit, for example, uses a smartphone sensor to record the player's movements in real time. For example, the collection unit can measure the player's dribbling speed using an acceleration sensor. The collection unit can also measure the player's shooting accuracy using a gyro sensor. The collection unit can also measure the player's pass distance using a GPS sensor. For example, the collection unit can measure the speed and angle when the player kicks the ball and calculate the pass distance based on that data. This makes it possible to collect data when a player takes a test using the app. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the player's movements into a generation AI and have the generation AI perform movement analysis.

[0062] The analysis unit can analyze the collected data and pass it to the evaluation unit. The analysis unit can analyze the collected data using, for example, a machine learning algorithm. For example, the analysis unit can analyze the correlation between a player's dribbling speed and shooting accuracy. The analysis unit can also statistically analyze player performance data to identify performance trends. For example, the analysis unit can cluster player performance data to group players with similar performances. The analysis unit can also analyze player performance data over time to identify changes in performance. For example, the analysis unit can plot player performance data over time to visually display improvements or declines in performance. This allows the collected data to be analyzed and passed to the evaluation unit. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the data.

[0063] The evaluation unit can assign scores based on the analysis results. The evaluation unit, for example, converts the player's performance data into a numerical score. For example, the evaluation unit scores the player's dribbling speed and the accuracy of his / her shot. The evaluation unit can also rank the player's performance data to distinguish between talented and untalented players. For example, the evaluation unit assigns a high score to a player with a fast dribbling speed and high shooting accuracy. The evaluation unit can also comprehensively evaluate the player's performance data and calculate an overall score. For example, the evaluation unit calculates an overall score by combining scores such as dribbling speed, shooting accuracy, and pass distance. This allows the score to be assigned based on the analysis results. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the analysis results into a generation AI and have the generation AI calculate the score.

[0064] The providing unit can provide the score assigned by the evaluation unit to the player. The providing unit, for example, displays the player's score on an app. For example, the providing unit visually displays the player's score as a graph or chart. The providing unit can also display the player's score in chronological order to grasp changes in performance. For example, the providing unit plots the player's score in chronological order to visually display improvements or declines in performance. Furthermore, the providing unit can compare the player's score with other players. For example, the providing unit compares the player's score with the scores of other players to evaluate relative performance. In this way, the score assigned by the evaluation unit can be provided to the player. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the score assigned by the evaluation unit to a generation AI and cause the generation AI to display the score.

[0065] The collection unit can estimate a player's emotions and adjust the timing of data collection based on the estimated player's emotions. For example, if a player is nervous, the collection unit can start data collection after providing time for the player to relax. For example, the collection unit can provide a guided meditation to help the player relax. Furthermore, if a player is focused, the collection unit can continue data collection and record optimal performance. For example, the collection unit can monitor the player's concentration level in real time and collect data when the player is highly focused. Furthermore, if a player is tired, the collection unit can resume data collection after a break. For example, the collection unit can measure the player's fatigue level and suggest an appropriate break time. This allows the timing of data collection to be adjusted based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI or without AI. For example, the collection unit can input emotional data of a player into the generation AI and have the generation AI perform emotion estimation.

[0066] The collection unit can analyze the player's past performance data and select the optimal data collection method. For example, the collection unit prioritizes collection of test items in which the player excels from the past data. For example, the collection unit prioritizes collection of test items on which the player has previously scored high. The collection unit can also focus collection of test items in which the player is weak from the past data. For example, the collection unit focuses collection of test items on which the player has previously scored low. Furthermore, the collection unit can identify time periods in which the player's performance improves from the past data and collect data during those time periods. For example, the collection unit collects data during time periods in which the player's performance is highest. This makes it possible to analyze the player's past performance data and select the optimal data collection method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past performance data into a generation AI and cause the generation AI to select the optimal data collection method.

[0067] When collecting data, the collection unit can filter the data based on the player's current physical condition and environmental conditions. For example, if the player is in good physical condition, the collection unit performs all test items. For example, the collection unit measures the player's heart rate and blood pressure to confirm that the player is in good physical condition. Furthermore, if the player is in poor physical condition, the collection unit can perform only low-stress test items. For example, if the player is in poor physical condition, the collection unit can perform test items including light exercise and stretching. Furthermore, if the environmental conditions are poor (e.g., rainy weather), the collection unit can prioritize test items that can be performed indoors. For example, if the environmental conditions are poor, the collection unit selects test items that can be performed indoors. This makes it possible to filter data collection based on the player's current physical condition and environmental conditions. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without AI. For example, the collection unit can input the player's physical condition data and environmental condition data to the generation AI and have the generation AI perform the filtering.

[0068] The collection unit can estimate a player's emotions and determine the priority of data to be collected based on the estimated player's emotions. For example, when a player is relaxed, the collection unit prioritizes collection of technical test items. For example, the collection unit measures a player's dribbling speed and shooting accuracy when the player is relaxed. Furthermore, when a player is nervous, the collection unit can prioritize collection of test items related to physical ability. For example, the collection unit measures a player's jumping ability and endurance when the player is nervous. Furthermore, when a player is concentrating, the collection unit can prioritize collection of complex test items. For example, the collection unit measures a player's tactical judgment and reaction speed when the player is concentrating. This allows the priority of data to be collected to be determined based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input emotional data of players into the generation AI and have the generation AI determine the priority of the data.

[0069] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the athlete. For example, if the athlete is at high altitude, the collection unit prioritizes collecting data related to high-altitude training. For example, the collection unit collects data by taking into account the effects of oxygen concentration and air pressure at high altitude. Furthermore, if the athlete is in an urban area, the collection unit can prioritize collecting performance data in an urban environment. For example, the collection unit collects data by taking into account the effects of noise and traffic in urban areas. Furthermore, if the athlete is at the seaside, the collection unit can prioritize collecting performance data on a sandy beach. For example, the collection unit collects data by taking into account the instability of footing on a sandy beach and the effects of wind. This makes it possible to prioritize collecting highly relevant data by taking into account the geographical location information of the athlete. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the geographical location information of the athlete to the generation AI and cause the generation AI to collect highly relevant data.

[0070] During data collection, the collection unit can analyze the social media activities of the players and collect related data. For example, the collection unit analyzes training videos posted by the players on social media and collects related data. For example, the collection unit performs motion analysis from the training videos posted by the players and collects performance data. The collection unit can also analyze game highlights shared by the players on social media and collect performance data. For example, the collection unit analyzes the players' movements and techniques from game highlight videos and collects data. Furthermore, the collection unit can also collect data by referring to training methods mentioned by the players on social media. For example, the collection unit collects related data based on the training methods mentioned by the players. In this way, the players' social media activities can be analyzed and related data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the players' social media data into the generation AI and cause the generation AI to collect related data.

[0071] The analysis unit can estimate the player's emotions and adjust the way the analysis is presented based on the estimated player's emotions. For example, when the player is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit can analyze the player's performance data in detail and provide detailed feedback. Furthermore, when the player is nervous, the analysis unit can provide concise and to-the-point analysis results. For example, the analysis unit can briefly summarize the player's performance data and provide feedback on only the important points. Furthermore, when the player is focused, the analysis unit can provide complex analysis results. For example, the analysis unit can analyze the player's performance data in detail and provide complex feedback. This makes it possible to adjust the way the analysis is presented based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some 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 player's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0072] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis of data with high importance. For example, the analysis unit performs a detailed analysis of important data such as a player's dribbling speed and shooting accuracy. The analysis unit can also perform a simplified analysis of data with low importance. For example, the analysis unit simplifies and analyzes data on a player's warm-up and cool-down. Furthermore, the analysis unit can analyze data with a moderate level of detail for data with medium importance. For example, the analysis unit analyzes data such as the distance and accuracy of a player's passes with a moderate level of detail. This allows the level of detail of the analysis to be adjusted based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0073] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a technical analysis algorithm to technical data. For example, the analysis unit applies an algorithm for analyzing technical data such as a player's dribbling speed and shooting accuracy. The analysis unit can also apply a physical ability analysis algorithm to data related to physical abilities. For example, the analysis unit applies an algorithm for analyzing data related to physical abilities such as a player's jumping power and endurance. The analysis unit can also apply a mental analysis algorithm to data related to mental aspects. For example, the analysis unit applies an algorithm for analyzing data related to mental aspects such as a player's concentration level and stress level. This allows different analysis algorithms to be applied depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0074] The analysis unit can estimate the player's emotions and adjust the length of the analysis based on the estimated player's emotions. For example, if the player is relaxed, the analysis unit can perform a detailed analysis and provide a longer report. For example, the analysis unit can analyze the player's performance data in detail and provide detailed feedback. Furthermore, if the player is nervous, the analysis unit can perform a concise analysis and provide a shorter report. For example, the analysis unit can briefly summarize the player's performance data and provide feedback on only the important points. Furthermore, if the player is focused, the analysis unit can perform an analysis of moderate length and provide a balanced report. For example, the analysis unit can analyze the player's performance data with a moderate level of detail and provide balanced feedback. This allows the length of the analysis to be adjusted based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the player's emotional data into the generation AI and have the generation AI adjust the length of the analysis.

[0075] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit prioritizes analyzing the player's most recent performance data and provides the most recent feedback. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit prioritizes analyzing the most recent data while referring to the player's past performance data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific period. For example, the analysis unit prioritizes analyzing data collected during a specific training period of the player. This makes it possible to determine the priority of analysis based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected to the generation AI and cause the generation AI to determine the priority of analysis.

[0076] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit analyzes the relevance between a player's dribbling speed and shooting accuracy, and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of a player's warm-up and cool-down data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit analyzes the relevance of a player's performance data in real time and dynamically adjusts the order of analysis. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0077] The evaluation unit can estimate the player's emotions and adjust the evaluation criteria based on the estimated player's emotions. For example, when the player is relaxed, the evaluation unit applies normal evaluation criteria. For example, the evaluation unit evaluates the player's performance data based on normal evaluation criteria. Furthermore, when the player is nervous, the evaluation unit can apply relaxed evaluation criteria. For example, the evaluation unit evaluates the player's performance data based on relaxed evaluation criteria. Furthermore, when the player is focused, the evaluation unit can apply strict evaluation criteria. For example, the evaluation unit evaluates the player's performance data based on strict evaluation criteria. This makes it possible to adjust the evaluation criteria based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, an AI, or without using an AI. For example, the evaluation unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0078] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between data during evaluation. The evaluation unit, for example, considers the correlation between dribbling speed and shooting accuracy when making an evaluation. For example, the evaluation unit analyzes the correlation between a player's dribbling speed and shooting accuracy and reflects this in the evaluation. The evaluation unit can also consider the correlation between pass distance and accuracy when making an evaluation. For example, the evaluation unit analyzes the correlation between a player's pass distance and accuracy and reflects this in the evaluation. Furthermore, the evaluation unit can also consider the balance between physical strength and technique when making an evaluation. For example, the evaluation unit analyzes the balance between a player's physical strength and technique and reflects this in the evaluation. This makes it possible to improve the accuracy of the evaluation by taking into account the interrelationships between data. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0079] The evaluation unit can perform evaluation taking into consideration the attribute information of the player when performing the evaluation. The evaluation unit applies evaluation criteria according to, for example, age. For example, the evaluation unit sets evaluation criteria according to the age of the player and performs the evaluation. The evaluation unit can also apply evaluation criteria according to the position. For example, the evaluation unit sets evaluation criteria according to the position of the player and performs the evaluation. Furthermore, the evaluation unit can also apply evaluation criteria according to the years of experience. For example, the evaluation unit sets evaluation criteria according to the years of experience of the player and performs the evaluation. This makes it possible to perform evaluation taking into consideration the attribute information of the player. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the attribute information of the player to the generation AI and have the generation AI perform the evaluation.

[0080] The evaluation unit can estimate the player's emotions and adjust the order in which the evaluation results are displayed based on the estimated player's emotions. For example, if the player is relaxed, the evaluation unit can display detailed evaluation results first. For example, the evaluation unit can analyze the player's performance data in detail and display detailed feedback first. Furthermore, if the player is nervous, the evaluation unit can display concise evaluation results first. For example, the evaluation unit can summarize the player's performance data concisely and display only important points first. Furthermore, if the player is concentrating, the evaluation unit can display important evaluation results first. For example, the evaluation unit can analyze the player's performance data in detail and display important feedback first. This makes it possible to adjust the order in which the evaluation results are displayed based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, an AI, or without an AI. For example, the evaluation unit can input emotional data of the players into the generation AI and cause the generation AI to adjust the display order of the evaluation results.

[0081] The evaluation unit can perform the evaluation taking into account the geographical distribution of the data. The evaluation unit, for example, performs the evaluation taking into account performance data at high altitudes. For example, the evaluation unit performs the evaluation taking into account the effects of oxygen concentration and air pressure at high altitudes. The evaluation unit can also perform the evaluation taking into account performance data in urban areas. For example, the evaluation unit performs the evaluation taking into account the effects of noise and traffic volume in urban areas. The evaluation unit can also perform the evaluation taking into account performance data at the seaside. For example, the evaluation unit performs the evaluation taking into account the effects of wind at the seaside and the instability of footing on sandy beaches. This makes it possible to perform the evaluation taking into account the geographical distribution of the data. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the geographical distribution of the data into the generation AI and have the generation AI perform the evaluation.

[0082] The evaluation unit can improve the accuracy of the evaluation by referring to related literature during the evaluation. The evaluation unit, for example, updates the evaluation criteria by referring to the latest research papers. For example, the evaluation unit reviews the evaluation criteria based on the latest research papers to improve the accuracy of the evaluation. The evaluation unit can also adjust the evaluation criteria by referring to past evaluation data. For example, the evaluation unit adjusts the evaluation criteria based on the past evaluation data to improve the accuracy of the evaluation. Furthermore, the evaluation unit can also perform the evaluation by referring to the evaluation criteria of other sports. For example, the evaluation unit performs the evaluation based on the evaluation criteria of other sports to improve the accuracy of the evaluation. This makes it possible to improve the accuracy of the evaluation by referring to related literature. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input related literature into the generation AI and cause the generation AI to update the evaluation criteria.

[0083] The providing unit can estimate a player's emotions and adjust the score display method based on the estimated player's emotions. For example, if a player is relaxed, the providing unit displays a detailed score. For example, the providing unit analyzes the player's performance data in detail and displays a detailed score. Furthermore, if a player is nervous, the providing unit can display a concise score. For example, the providing unit summarizes the player's performance data concisely and displays only important points. Furthermore, if a player is concentrating, the providing unit can highlight important scores. For example, the providing unit analyzes the player's performance data in detail and highlights important scores. This makes it possible to adjust the score display method based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, or may be performed without an AI. For example, the providing unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the score display method.

[0084] When providing scores, the providing unit can select the optimal display method by referring to the player's past score history. The providing unit, for example, displays the past score history in a graph format. For example, the providing unit displays the player's past score history in a graph format to visually grasp changes in performance. The providing unit can also display the past score history in a list format. For example, the providing unit displays the player's past score history in a list format to check detailed scores. The providing unit can also display the past score history in a comparative format. For example, the providing unit compares the player's past score history with the scores of other players to evaluate relative performance. This makes it possible to select the optimal display method by referring to the player's past score history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the player's past score history to the generation AI and cause the generation AI to select the optimal display method.

[0085] The providing unit can estimate the player's emotions and determine the priority of scores based on the estimated player's emotions. For example, if the player is relaxed, the providing unit displays all scores evenly. For example, the providing unit displays the player's performance data evenly to evaluate the overall balance. Furthermore, if the player is nervous, the providing unit can display important scores first. For example, the providing unit displays important scores first among the player's performance data to highlight important points. Furthermore, if the player is concentrating, the providing unit can display detailed scores first. For example, the providing unit analyzes the player's performance data in detail and displays detailed scores first. This makes it possible to determine the priority of scores based on the player's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the emotional data of the players into the generating AI and have the generating AI determine the priority of the scores.

[0086] When providing scores, the providing unit can select the optimal display method by taking into account the player's device information. For example, when a smartphone is used, the providing unit provides a display method that matches the screen size. For example, the providing unit provides a display method optimized for the smartphone screen size to improve visibility. Furthermore, when a tablet is used, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit provides a display method optimized for the large screen of a tablet to display detailed information. Furthermore, when a personal computer is used, the providing unit can also provide a display method that includes detailed information. For example, the providing unit provides a display method optimized for the large screen of a personal computer to display detailed scores and graphs. This makes it possible to select the optimal display method by taking into account the player's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the player's device information to the generation AI and cause the generation AI to select the optimal display method.

[0087] The selection unit can estimate a player's emotions and adjust the test selection method based on the estimated player's emotions. For example, when a player is relaxed, the selection unit prioritizes technical tests. For example, the selection unit selects tests that measure dribbling speed and shooting accuracy when the player is relaxed. Furthermore, when a player is nervous, the selection unit can prioritize physical ability tests. For example, the selection unit selects tests that measure jumping power and endurance when the player is nervous. Furthermore, when a player is concentrating, the selection unit can prioritize complex tests. For example, the selection unit selects tests that measure tactical judgment and reaction speed when the player is concentrating. This allows the test selection method to be adjusted based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or without AI. For example, the selection unit can input the player's emotional data into the generation AI and cause the generation AI to adjust the test selection method.

[0088] When selecting a test, the selection unit can refer to the player's past test history to suggest the optimal test. For example, the selection unit prioritizes and suggests tests on which the player has scored highly in the past. For example, the selection unit prioritizes and suggests tests on which the player has scored highly in the past to strengthen his or her strengths. The selection unit can also focus on and suggest tests on which the player has scored poorly in the past. For example, the selection unit can focus on and suggest tests on which the player has scored poorly in the past to overcome his or her weaknesses. Furthermore, the selection unit can also suggest balanced tests based on the player's past test history. For example, the selection unit can suggest balanced tests based on the player's past test history to improve overall performance. This makes it possible to suggest the optimal test by referring to the player's past test history. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit can input the player's past test history into a generation AI and have the generation AI suggest the optimal test.

[0089] The selection unit can estimate the player's emotions and prioritize tests based on the estimated player's emotions. For example, if the player is relaxed, the selection unit selects all tests equally. For example, the selection unit collects the player's performance data equally and evaluates the player's overall balance. Furthermore, if the player is nervous, the selection unit can select important tests first. For example, the selection unit selects important tests first among the player's performance data and emphasizes important points. Furthermore, if the player is concentrating, the selection unit can select detailed tests first. For example, the selection unit analyzes the player's performance data in detail and selects detailed tests first. This makes it possible to prioritize tests based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without AI. For example, the selection unit can input emotional data of the players into the generation AI and have the generation AI determine the priority of the tests.

[0090] When selecting a test, the selection unit can propose an optimal test by taking into account the athlete's geographical location information. For example, if the athlete is at high altitude, the selection unit proposes a test related to high-altitude training. For example, the selection unit proposes a test by taking into account the effects of oxygen concentration and air pressure at high altitude. The selection unit can also propose a test in an urban environment if the athlete is in an urban area. For example, the selection unit proposes a test by taking into account the effects of noise and traffic in urban areas. Furthermore, the selection unit can also propose a test on a sandy beach if the athlete is at the seaside. For example, the selection unit proposes a test by taking into account the instability of footing on a sandy beach and the effects of wind. This makes it possible to propose an optimal test by taking into account the athlete's geographical location information. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit can input the athlete's geographical location information into the generation AI and cause the generation AI to execute the optimal test proposal. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, evaluation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit records the player's movements in real time using a sensor of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the player's performance data using a machine learning algorithm. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and assigns a score based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and displays the player's score on the app. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, evaluation unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit records the player's movements in real time using a sensor in the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the player's performance data using a machine learning algorithm. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and assigns a score based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and displays the player's score on the app. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, evaluation 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 records the player's movements in real time using a sensor of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the player's performance data using a machine learning algorithm. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and assigns a score based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and displays the player's score on the app. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, evaluation 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 records the player's movements in real time using a sensor of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the player's performance data using a machine learning algorithm. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and assigns a score based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the robot 414, and displays the player's score on the app.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When analyzing a player's performance data, the analysis unit can improve the accuracy of the analysis by referring to the player's past training data. For example, the analysis unit compares the player's past training data with current performance data. The analysis unit can also grasp trends in performance improvement or decline based on the player's past training data. Furthermore, the analysis unit can propose an optimal training plan based on the player's past training data. This makes it possible to improve the accuracy of the analysis by referring to the player's past training data.

[0093] When evaluating the performance data of a player, the evaluation unit can take into account the player's goal setting. For example, the evaluation unit evaluates the extent to which the player has achieved the goals set by the player. The evaluation unit can also quantify the degree of achievement based on the player's goal setting. Furthermore, the evaluation unit can also suggest the player's next step based on the player's goal setting. This allows the evaluation to take into account the player's goal setting.

[0094] When providing the player's performance data, the providing unit can provide feedback according to the player's learning style. For example, the providing unit can provide feedback using graphs or charts to a player with a visual learning style. The providing unit can also provide audio feedback to a player with an auditory learning style. Furthermore, the providing unit can also provide feedback using actual training simulations to a player with an experiential learning style. In this way, feedback according to the player's learning style can be provided.

[0095] When collecting the player's performance data, the collection unit can monitor the player's health condition and issue an alert if an abnormality is detected. For example, the collection unit can monitor the player's heart rate and blood pressure and issue an alert if an abnormality is detected. The collection unit can also monitor the player's body temperature and oxygen saturation level and issue an alert if an abnormality is detected. Furthermore, the collection unit can monitor the player's fatigue level and issue an alert if excessive fatigue is detected. In this way, the player's health condition can be monitored and an alert can be issued if an abnormality is detected.

[0096] When analyzing a player's performance data, the analysis unit can take the player's psychological factors into consideration. For example, the analysis unit can perform the analysis taking the player's stress level and motivation into consideration. The analysis unit can also analyze the impact of the player's psychological factors on performance. Furthermore, the analysis unit can provide advice for improving performance based on the player's psychological factors. This makes it possible to perform analysis taking the player's psychological factors into consideration.

[0097] When evaluating the performance data of a player, the evaluation unit can estimate the player's emotions and adjust the evaluation feedback based on the estimated emotions. For example, if the player is relaxed, the evaluation unit can provide detailed feedback. If the player is nervous, the evaluation unit can provide brief feedback. Furthermore, if the player is focused, the evaluation unit can provide feedback that emphasizes important points. This makes it possible to adjust the evaluation feedback based on the player's emotions.

[0098] When providing the player's performance data, the providing unit can estimate the player's emotions and adjust the way the data is displayed based on the estimated emotions. For example, the providing unit can display detailed data when the player is relaxed. Also, the providing unit can display concise data when the player is nervous. Furthermore, the providing unit can highlight important data when the player is concentrating. In this way, the way the data is displayed can be adjusted based on the player's emotions.

[0099] When collecting performance data of a player, the collection unit can estimate the player's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the player is relaxed, the collection unit can start data collection. Also, if the player is tense, the collection unit can allow the player time to relax before starting data collection. Furthermore, if the player is concentrating, the collection unit can continue data collection as is. In this way, the timing of data collection can be adjusted based on the player's emotions.

[0100] When analyzing a player's performance data, the analysis unit can estimate the player's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the player is relaxed, the analysis unit can provide detailed analysis results. If the player is nervous, the analysis unit can provide concise analysis results. Furthermore, if the player is focused, the analysis unit can provide analysis results that emphasize important points. This makes it possible to adjust the way the analysis is presented based on the player's emotions.

[0101] When evaluating the performance data of a player, the evaluation unit can estimate the player's emotions and adjust the evaluation criteria based on the estimated emotions. For example, the evaluation unit can apply normal evaluation criteria when the player is relaxed. Furthermore, the evaluation unit can apply lenient evaluation criteria when the player is nervous. Furthermore, the evaluation unit can apply strict evaluation criteria when the player is focused. In this way, the evaluation criteria can be adjusted based on the player's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects data when the player uses the app to take the test. For example, it collects data such as dribbling speed, shooting accuracy, and passing distance. The collection unit uses the smartphone's sensors to record the player's movements in real time and can also collect environmental data (temperature, humidity, etc.) when the player takes the test. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses machine learning algorithms to analyze the player's performance data and statistically analyzes it to identify performance trends. For example, it can analyze the correlation between dribbling speed and shooting accuracy. Step 3: The evaluation unit assigns a score based on the analysis results obtained by the analysis unit. The evaluation unit converts the player's performance data into a numerical score and ranks them. For example, a player with a fast dribbling speed and high shooting accuracy will receive a high score. Step 4: The provider provides the athlete with the scores assigned by the evaluator. The provider can display the athlete's scores on the app and visually display them as graphs or charts. For example, the provider can display the athlete's scores over time to understand changes in performance.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] 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.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] 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.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] 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.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] 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.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0167] 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.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 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 data on players; an analysis unit that analyzes the data collected by the collection unit; an evaluation unit that assigns a score based on the analysis result obtained by the analysis unit; a providing unit that provides the player with the score assigned by the evaluation unit; Equipped with A system characterized by:

2. A selection section is provided where players can choose the type of test they wish to attempt.

2. The system of claim 1.

3. The collecting unit Collect data as athletes use the app to take tests 2. The system of claim 1.

4. The analysis unit Analyze the collected data and pass it to the evaluation unit 2. The system of claim 1.

5. The evaluation unit Scoring based on analysis results 2. The system of claim 1.

6. The providing unit Providing the player with the score assigned by the evaluation unit 2. The system of claim 1.

7. The collecting unit Estimate the player's emotions and adjust the timing of data collection based on the estimated player's emotions.

2. The system of claim 1.

8. The collecting unit Analyze players' past performance data and select the most appropriate data collection method 2. The system of claim 1.

9. The collecting unit When collecting data, filtering is performed based on the athlete's current physical condition and environmental conditions.

2. The system of claim 1.

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