system

Abstract

We provide the system. [Solution] A means for acquiring player status information, analyzing it, and generating an optimal player roster plan, A means of formulating an athlete development plan based on acquired status information and past competition records, A means of analyzing status information acquired during a match in real time and providing tactical suggestions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional sports coaching, coaches and trainers need to manually monitor the players' conditions, formulate strategies, and provide individual training, which requires a huge amount of time and professional experience. In addition, injury prevention and mental support for players may not be fully provided, which may affect the players' performance. As a result, small teams with limited funds and resources in particular are facing the problem of difficulty in providing high-quality coaching and management.

Means for Solving the Problems

[0005] This invention solves these problems by providing a system that has the function of acquiring and analyzing player status information. Specifically, it generates an optimal player composition plan based on player status information and formulates individual training plans according to the requirements of the sport. It also provides appropriate tactical suggestions through real-time data analysis during matches. Furthermore, it evaluates the health status of players and formulates injury prediction and recovery plans, thereby optimally managing players' performance and health. This reduces the burden on sports coaches and enables efficient and high-quality coaching and management.

[0006] "Player condition information" refers to data related to the player's physical and mental state, and specifically includes information such as heart rate, distance covered, fatigue level, and motivation.

[0007] "Analysis" is the process of evaluating acquired data using statistical methods and machine learning algorithms to derive meaningful information.

[0008] "Tactical suggestions" refer to providing instructions that indicate the optimal actions and positions in a particular competitive situation.

[0009] A "player lineup proposal" refers to a plan that suggests the optimal placement and roles of players in matches and training sessions.

[0010] A "development plan" is a plan that includes training methods and schedules aimed at improving specific abilities in order to enhance each player's performance.

[0011] "Health status" refers to the overall physical and mental health of an athlete, and includes information such as physical fitness, risk of muscle injury, and mental stress.

[0012] "Injury prediction" refers to estimating the risk of future injuries based on an athlete's past data and current physical condition.

[0013] A "recovery plan" refers to a rehabilitation schedule and methods designed to help injured athletes safely and efficiently regain their original performance level. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that provides advanced management of athlete conditions and supports tactical decision-making in matches and training. This system comprehensively handles everything from collecting and analyzing athlete information to making recommendations. The main embodiments are described below.

[0036] First, the device acquires data such as the athlete's heart rate, distance covered, and fatigue level in real time from sensors. This information, combined with frequent monitoring of various locations within the stadium and the athlete's physical condition, forms a precise database. This device may also take the form of a portable or wearable device that athletes and coaches can carry.

[0037] The acquired data is then sent to a server. The server stores this data and performs analysis using machine learning algorithms. This analysis evaluates the players' health and performance, and derives information to determine the optimal training plan and player placement in matches. For example, if data shows that a particular player made many mistakes due to fatigue in past matches, the server will suggest that the player take a rest.

[0038] Next, based on real-time data from the match, the server generates effective tactical suggestions. For example, if it determines that the defense in a particular area is weak during the match, it will suggest a formation to put pressure on that area. In this way, the ability to change tactics in real time allows for a rapid response to the unfolding of the match.

[0039] Furthermore, as part of athlete health management, the server assesses injury risk based on past injury data and current physical data, and provides guidance on appropriate countermeasures. This includes providing training menus to reduce the strain on the knees caused by excessive load during running.

[0040] Finally, the analysis results and tactical suggestions are presented in a format that users—that is, coaches and team staff—can intuitively understand. This information is accessible from anywhere via devices and PCs and is used for pre-match strategy meetings and training schedule development.

[0041] According to the embodiment of the present invention, competition management, performance improvement, and athlete health management can be carried out efficiently, and resource optimization can be achieved, especially in small teams.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The terminal collects data in real time from the athlete's wearable device. This data includes heart rate, distance covered, and body temperature, and is used to understand the athlete's detailed condition on the field.

[0045] Step 2:

[0046] Data collected from the device is transferred to the server via the internet. Here, the data is stored quickly and securely on the server.

[0047] Step 3:

[0048] The server analyzes accumulated player data using machine learning algorithms. Through this analysis, it evaluates player performance and generates information necessary for suggesting individual tactics and training programs.

[0049] Step 4:

[0050] Based on the analysis results, the server proposes the optimal formation and player placement. This result is then sent to the terminal to support decisions for upcoming matches and training sessions.

[0051] Step 5:

[0052] During the match, the terminal monitors player status data in real time and immediately sends any significant changes to the server.

[0053] Step 6:

[0054] The server instantly analyzes real-time data during the match and sends back advice on tactical changes and player substitutions as needed to the terminal. This enables strategic actions that respond to the flow of the game.

[0055] Step 7:

[0056] The server predicts injury risk and generates data-driven preventative measures and recovery plans. These suggestions are provided to the terminal to support efforts to protect players from injury.

[0057] Step 8:

[0058] Users receive suggestions and analysis results from the server and use them to develop strategic plans for players and teams. Team coaches can access this information via mobile devices or PCs.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Managing athletes and optimizing game strategies within sports teams requires considering athletes' health and performance, demanding complex and sophisticated decision-making. However, traditional methods struggle with real-time data analysis and tactical proposals, making them ineffective in the fast-paced environment of a game. Furthermore, injury prevention and the development of effective training plans are burdensome for some teams. Against this backdrop, there is a need for a comprehensive management system that utilizes athletes' biometric information.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] This invention includes a server that includes means for acquiring and analyzing players' biometric data to generate an optimal player placement plan, means for formulating a player development plan based on the acquired biometric data and past competition data, means for analyzing biometric data acquired during a match in real time using the generated AI model and making tactical suggestions, and means for generating prompt messages according to the situation and making appropriate modifications. This enables more efficient player management and rapid changes to tactics during matches. It also allows for the assessment of injury risks and promotes safe and effective training and participation in matches.

[0064] "Athlete biometric data" refers to information related to an athlete's physical function, such as heart rate, distance covered, and fatigue level, which is acquired in real time using sensors.

[0065] "Analysis" refers to the computational process of using acquired biometric data and related information to evaluate the athlete's performance and health status.

[0066] A "player placement plan" is a proposal for the most effective placement of players in matches and training sessions, and is a plan generated based on analysis results.

[0067] "Competition data" refers to records of matches and training sessions that an athlete has participated in in the past, and is used for performance evaluation and the development of training plans.

[0068] A "development plan" is a set of long-term and short-term training schedules and educational guidelines formulated with the aim of improving players' abilities and preventing injuries.

[0069] A "generated AI model" is an algorithm that learns from a large amount of competition data and generates optimal suggestions based on the player's condition and the match situation.

[0070] "Tactical suggestions" are recommendations regarding player positioning and tactical actions that are generated based on the situation during a match.

[0071] A "prompt" is the format of a question or instruction input to the generated AI model, and it determines the direction of the responses and suggestions that are generated.

[0072] "Real-time analysis" means performing analysis immediately based on the acquired data and providing rapid feedback of the results.

[0073] This invention's system optimizes management and tactical decision-making using players' biometric information. It mainly consists of three elements: a terminal, a server, and a user.

[0074] First, the terminal acquires biometric data such as heart rate, distance covered, and fatigue level in real time from various sensors attached to the athlete. These sensors are often worn by the athlete as wearable or portable devices. The terminal receives data from the sensors using Bluetooth communication and transmits this information to a server in the cloud via the internet.

[0075] Next, the server stores the received biometric data and analyzes it using a machine learning framework (e.g., TENSORFLOW®). The server then uses the generated AI model to evaluate the athletes' health and performance and develop optimal athlete placement and training plans. This analysis process references a large amount of competition data and past performance to make optimal decisions based on the athletes' current situations.

[0076] Furthermore, during a match, the server can perform real-time data analysis and generate tactical suggestions. These suggestions are generated in response to specific questions using prompts. For example, by entering a prompt such as, "What is the optimal training plan for player B for the next match?", the server can immediately generate a suggestion and provide it to the user.

[0077] Ultimately, users view the analysis results and tactical suggestions provided by the server and adjust player management and match strategies based on them. Users can access this information at any time via their devices or PCs and utilize it for pre-match strategy meetings and daily training schedule adjustments. In this way, not only is player performance improvement and health management carried out efficiently and effectively, but rapid tactical changes during matches are also possible.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The device acquires biometric data such as heart rate, distance covered, and fatigue level from various sensors attached to the athlete. The device receives data from the sensors using Bluetooth communication. Specifically, the device communicates with the sensors at regular intervals to collect data. This provides input data for understanding the athlete's physical condition in real time.

[0081] Step 2:

[0082] The device transmits the acquired biometric data to a server in the cloud. The device confirms its internet connection and transfers the data via a secure communication path using HTTPS. Specifically, the process involves generating data packets and then sending them to the server. This output provides the server with the basic data necessary for subsequent data analysis.

[0083] Step 3:

[0084] The server stores the received biometric data and analyzes it using Python and machine learning frameworks. Based on the input data, the server uses machine learning models to evaluate the athletes' health and performance. Specifically, this includes processes such as detecting outliers and analyzing performance trends. The results of this analysis serve as the basis for decision-making in the next step.

[0085] Step 4:

[0086] The server uses an AI model based on the analysis results to create optimal player placement plans and training plans. This process involves specific actions that propose the most effective placements and future training plans, taking into account past competition data. The output includes drafts of tactical proposals and training plans.

[0087] Step 5:

[0088] The server uses real-time data to provide tactical suggestions during a match. Using data acquired during the match as input, the server instantly analyzes the situation and generates tactical suggestions using a generated AI model. Specifically, this involves generating tactical solutions in response to specific questions using prompt statements.

[0089] Step 6:

[0090] Users review the analysis results and tactical suggestions provided by the server and adjust player management and match strategies accordingly. They access the outputted information using applications on their devices or PCs and quickly modify their actions as needed. This ultimately improves the overall performance of the team.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] Managing athletes' physical and mental states in real time and supporting tactical decision-making to achieve optimal performance is a crucial challenge in modern sports. A system that provides such information and support quickly and effectively is needed. Existing systems suffer from insufficient real-time information acquisition and rapid feedback on tactical suggestions, and are also ineffective in managing athletes' health and preventing injuries.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for acquiring player status information and analyzing it to generate an optimal player configuration plan; means for formulating a player development plan based on the acquired status information and past competition records; means for analyzing the status information acquired during a match in real time and making tactical suggestions; and means for acquiring the player's physical and mental information from sensors and providing advice via voice and visual means. This enables real-time monitoring of the player's condition and rapid tactical feedback.

[0096] "Athlete condition information" refers to various data that indicates the athlete's physical and mental state, such as heart rate, distance covered, and fatigue level.

[0097] "Analysis" is the process of using acquired player status information to evaluate the players' health and performance, and to derive the optimal training plan and player placement.

[0098] An "optimal player lineup proposal" is a plan that suggests the optimal placement and selection strategy for players, based on the analysis results, taking into account the players' physical condition and the opponent's tactics.

[0099] A "development plan" involves formulating training menus and training policies that align with each athlete's individual skill improvement and career plan, taking into account their past athletic records and current condition.

[0100] "Tactical suggestions" refer to providing advice based on real-time information about the players' condition, with the aim of adjusting strategic actions and positioning during matches and training.

[0101] "Voice and visual advice" refers to providing players and coaches with real-time instructions and information via visual displays and audio, based on analyzed data.

[0102] A "sensor" is a device or equipment used to accurately measure and acquire various data from athletes, such as heart rate and exercise volume.

[0103] This invention provides a system for acquiring and analyzing real-time status information of players to implement optimal tactics and health management. Specific embodiments for realizing this system are described below.

[0104] The server first receives real-time physical data such as heart rate, distance covered, and fatigue level from sensor devices attached to the athletes. This data is then transmitted to the server via Bluetooth or Wi-Fi. The server stores this data and performs data analysis using machine learning models with cloud services such as Google Cloud.

[0105] Based on the analysis results, the server evaluates the players' performance and determines the necessary training content and injury prevention measures. Furthermore, during matches, it generates tactical suggestions in real time based on the players' collective movements and notifies the user's device.

[0106] The device functions as a smartphone or tablet for players and coaches. Analysis results and tactical suggestions are provided to users through voice advice and visual displays. The interface is intuitive, allowing for quick access to information even during matches or training sessions.

[0107] For example, if a player's heart rate reaches its limit during a match, the server will quickly send strategies to reduce the strain and suggestions for player substitutions to the terminal. The terminal will then provide a real-time voice notification saying, "The player's heart rate is high; please consider substituting them."

[0108] Examples of prompts to input into a generative AI model:

[0109] "Using the heart rate and location information of players during a match, we recommend the next action they should take."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server receives data in real time from sensor devices worn by the athletes. Input data includes heart rate, distance covered, and fatigue level. The server receives this data via Bluetooth or Wi-Fi and stores it in storage.

[0113] Step 2:

[0114] The server inputs the received player data into a Google Cloud machine learning model for analysis. This input includes heart rate and distance covered. The analysis process involves processing time-series data and detecting outliers, ultimately outputting the player's fatigue level and performance status.

[0115] Step 3:

[0116] The server evaluates the players' performance based on the analysis results obtained from the machine learning model. From the analysis results, it generates information suggesting rest for players who are fatigued, for example, and uses this as input for the next step.

[0117] Step 4:

[0118] The server generates tactical suggestions based on real-time data during the match. Inputs are the latest player data and historical match data, while output is specific tactical actions. The generated suggestions optimize tactics by considering player position information.

[0119] Step 5:

[0120] The terminal displays analysis results and tactical suggestions sent from the server, either audibly or visually. The terminal receives the output data and intuitively displays or notifies players and coaches of instructions such as "Urgent tactical change required." This enables users to make quick decisions during a match.

[0121] Step 6:

[0122] Users adjust player placement and individual actions based on information provided by their devices. This allows them to manage player health, optimize performance, and make crucial decisions that influence the outcome of matches.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention is a system that incorporates an emotion engine to recognize user emotions in order to more efficiently manage player condition and formulate tactics. In addition to collecting and analyzing player condition information, this system provides optimal support to players and teams by analyzing user emotions.

[0125] First, the device collects the athlete's physical data from sensors. This includes heart rate, movement patterns, and fatigue levels. Simultaneously, it also collects data such as the user's voice and facial expressions and sends it to the emotion engine. This emotion engine has the ability to analyze the user's voice tone and subtle facial movements to infer the user's emotional state.

[0126] Next, the server receives this data, applies machine learning algorithms to the players' physical data to evaluate their performance, and generates necessary tactics and training plans. Meanwhile, it uses emotional information obtained from the emotion engine to dynamically adjust communication methods according to the user's emotions. For example, if the user is feeling anxious, it provides advice to help them relax.

[0127] Furthermore, during a match, the server monitors the status of both players and users in real time, and provides tactical suggestions and emotional support to the device as needed. For example, if a user is feeling anxious during a tense moment in the match, the server will provide a message encouraging calmness based on that emotion, helping to soothe the situation.

[0128] Finally, users can receive suggestions from the system and incorporate them into player placement and training plans. By using the emotion engine, coaches can provide more emotionally resonant support to players and matches, which is expected to improve the team's psychological well-being and performance.

[0129] In this form, the present invention not only improves the performance of athletes but also enables effective sports management while maintaining the mental health of the entire team.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device collects physical data from players via sensors before matches and during training. This data includes heart rate, body temperature, and muscle movement. Simultaneously, it also collects the user's (coach's or staff's) face and voice to send to the emotion engine.

[0133] Step 2:

[0134] The device transmits collected player data and user sentiment data to a server via the internet. This process is performed continuously in real time.

[0135] Step 3:

[0136] The server analyzes the received physical data of the players and generates performance indicators. Here, it evaluates each player's condition and extracts information useful for suggesting training plans and tactics.

[0137] Step 4:

[0138] The server uses an emotion engine to analyze the user's emotional data. This analysis detects emotional states such as stress, anxiety, and excitement, and generates appropriate mental advice for the user.

[0139] Step 5:

[0140] The server creates a digital plan based on the analysis results, proposing training and match tactics for the players, and sends it to the terminal.

[0141] Step 6:

[0142] During matches and training sessions, the device continuously transmits real-time physical and emotional data to the server, keeping the latest information readily available.

[0143] Step 7:

[0144] The server generates real-time feedback to optimize tactical changes and player substitutions during matches. It also provides users with timely advice based on their emotional state.

[0145] Step 8:

[0146] Users can receive suggestions and advice from the server and incorporate them into on-site coaching and player management. This will lead to more effective coaching for players.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] In modern sports management, understanding an athlete's physical condition is crucial, but so is providing mental support. However, current technology makes it difficult to comprehensively analyze an athlete's condition and the user's emotions in real time and provide appropriate tactics and training plans based on that analysis. Therefore, there is a need to simultaneously improve athlete performance and provide support that is sensitive to the user's emotions.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes means for acquiring and analyzing player status information, means for analyzing the user's emotional state and providing feedback, and means for evaluating player performance by applying machine learning algorithms. This enables effective sports management tailored to the physical and mental state of the players.

[0152] "Athlete condition information" refers to data related to an athlete's physical and athletic performance. This data is collected using sensors and includes heart rate, movement patterns, and other similar information.

[0153] "Emotional analysis" refers to the process of inferring and analyzing a user's emotional state from their voice and facial expressions. This is carried out using voice analysis and facial recognition technologies.

[0154] "Feedback" refers to advice and information provided to users and players based on analysis results. This facilitates appropriate responses depending on the situation.

[0155] A "machine learning algorithm" refers to a method in which a computer system learns from data and makes predictions and decisions based on future data. In sports, it is used to analyze the performance of athletes.

[0156] A "tactical plan" refers to a plan formulated to take effective actions during matches or training. It is dynamically adjusted based on the players' condition and the user's emotions.

[0157] To implement this invention, the system is configured as follows.

[0158] First, the device collects physical data from the athlete. Physical data such as the athlete's heart rate, movement patterns, and fatigue level are acquired using a heart rate monitor and accelerometer. In addition, microphones and cameras are used to collect data on the user's voice and facial expressions. This data is temporarily stored in an athlete information database and a user emotion database.

[0159] Next, the server receives the collected data and begins processing it. The server applies machine learning algorithms to the players' physical data. The software used includes open-source machine learning frameworks and data analysis software. This algorithm evaluates performance and detects anomalies based on the players' past and current data. Simultaneously, the server uses an emotion analysis engine to analyze the user's voice and facial expression data and infer their emotional state. This makes it possible to formulate appropriate communication methods based on the user's emotions.

[0160] A concrete example is real-time assistance during a match. For instance, if a user is feeling nervous, the server generates a message such as "Calm down and focus on the next play" and notifies the user through their device. This allows the user to regain their composure.

[0161] Furthermore, by using a generative AI model to provide prompts that offer feedback in response to user requests, such as "What tactical plan should be suggested when a player's fatigue level is high?", an appropriate plan can be generated.

[0162] This will enable detailed and effective support based on the physical and psychological condition of athletes, and is expected to improve the quality of sports management.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The device collects physical data from the athlete. It uses sensors to acquire heart rate and movement patterns, and sends this data to a server. The raw data obtained from the sensors is temporarily stored in a database. Specifically, a heart rate sensor is attached to the body, and heart rate data is acquired in real time. The input is raw physical data from the sensor, and the output is formatted physical data.

[0166] Step 2:

[0167] The device collects the user's voice and facial expression data. Using the microphone and camera, it collects the user's voice tone and facial expressions, and sends this data to the sentiment analysis engine. Specifically, the microphone captures voice data, and the camera tracks facial movements. The input is voice and video data, and the output is processed data for sentiment analysis.

[0168] Step 3:

[0169] The server processes the player's physical data received by the server into a machine learning algorithm. Using historical and real-time data, it evaluates performance and detects anomalies. Specifically, the algorithm models the player's condition and generates performance metrics. The input is the player's physical data, and the output is the performance evaluation result.

[0170] Step 4:

[0171] The server analyzes the user's emotional state using an emotion analysis engine. It utilizes voice analysis software and facial recognition algorithms to determine the user's emotions. Specifically, it generates emotion labels based on voice tone and subtle changes in facial expression. The input is processed voice and facial data, and the output is the user's emotional state.

[0172] Step 5:

[0173] The server generates tactical plans and feedback based on player performance evaluations and the user's emotional state. Prompt messages are input to the generating AI model, which then generates an appropriate plan. Specifically, the AI ​​proposes countermeasures for situations such as "when a player's fatigue level is high." The input is the performance evaluation results and emotional state, and the output is a specific tactical plan.

[0174] Step 6:

[0175] The terminal receives output from the server and notifies the user. The user checks the notification and incorporates it into player positioning and training plans. Specifically, the terminal displays a tactical plan on the screen and provides audio notifications. The input is the tactical plan from the server, and the output is information provided to the user.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] In community-based events, a challenge is to appropriately understand participants' emotional responses and improve event satisfaction. Traditional methods have made it difficult to grasp individual participants' emotions in real time and dynamically optimize event content and communication based on that information.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for acquiring player status information and analyzing it to generate an optimal player configuration plan, means for analyzing user emotional information and dynamically generating event-participation messages, and means for optimizing communication based on the analyzed emotional information. This enables flexible event management that responds to participants' emotions and provides a better civic experience.

[0181] "Athlete status information" refers to data that indicates an athlete's current physical and mental health and performance, including heart rate, movement patterns, and fatigue levels.

[0182] An "optimal player lineup proposal" is a suggestion that, based on player condition information, indicates the most effective placement and combination of players to maximize team performance.

[0183] A "development plan" refers to the systematic planning of training content and schedules aimed at the long-term growth and improvement of an athlete's abilities.

[0184] "Emotional information" is data primarily obtained from voice and facial expressions, and represents the user's emotional state. This allows us to capture changes in the user's mood and emotions.

[0185] "Event-oriented messaging" refers to personalized suggestions and activity recommendations sent to participants during an event based on their emotional information.

[0186] "Communication optimization" means improving the quality of communication between participants and with the system by utilizing users' emotional information and providing information to individual users at the optimal timing and with the right content.

[0187] The system that realizes this invention consists of terminals such as smartphones and smart glasses, and a server that processes the data. The terminals collect biometric information such as the user's heart rate, movement patterns, and fatigue level via sensors. In addition, they capture the user's voice and facial expressions with cameras and microphones to acquire emotional information. This data is transmitted to the server in real time.

[0188] On the server, machine learning algorithms are first applied to the players' biometric information to analyze their current state. Based on these results, player placement plans and training plans are dynamically generated. Meanwhile, the emotion engine performs speech recognition and facial expression analysis to extract the user's emotional information. This allows for the real-time generation of interactive messages tailored to the user's emotions and sent back to the terminal. Facial expression analysis is performed using OpenCV and dlib, and speech features can be captured using PocketSphinx.

[0189] Messages sent from the server will be reassuring to users and encourage participation in new activities. For example, if a participant at a music festival is deemed to be relaxed, they will be notified of upcoming artist performances or campaigns to further enhance their enjoyment.

[0190] By using a generative AI model, event-participation messages based on user emotions can be efficiently generated. In this system, for example, using a prompt such as "Generate event-participation messages based on participants' emotions" can improve event satisfaction.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The device collects biometric information such as the user's heart rate, movement patterns, and fatigue level using sensors. In addition, it captures facial expressions with a camera and records voice with a microphone. The collected data is transmitted to a server in real time for integrated analysis. The input is data from sensors, camera, and microphone, and the output is data transmission to the server.

[0194] Step 2:

[0195] The server inputs the received biometric information into a machine learning algorithm to analyze the player's current performance status. Here, biometric data is used as input, and performance evaluation results are output. Based on these evaluation results, the system generates optimal placement and development plans for the player. Specifically, it suggests training plans tailored to the player's current condition.

[0196] Step 3:

[0197] The server analyzes audio data received from the user using PocketSphinx, extracting emotions from tone and speech patterns. It also analyzes facial features using OpenCV and dlib to infer emotional states. Input is audio and facial data, and output is the analyzed emotional information. Based on this emotional information, it dynamically generates event-participation messages.

[0198] Step 4:

[0199] The server uses a generative AI model to create the optimal message using a prompt such as, "Generate an event-participation message based on participants' emotions." The input for this step is emotion information and the generative AI model, and the output is a customized message.

[0200] Step 5:

[0201] The server sends the generated message back to the terminal, notifying the user. Providing appropriate activities and information based on the user's state enhances the event experience. The input is the generated message, and the output is the notification delivered to the user.

[0202] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0209] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0213] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0218] This invention is a system that provides advanced management of athlete conditions and supports tactical decision-making in matches and training. This system comprehensively handles everything from collecting and analyzing athlete information to making recommendations. The main embodiments are described below.

[0219] First, the device acquires data such as the athlete's heart rate, distance covered, and fatigue level in real time from sensors. This information, combined with frequent monitoring of various locations within the stadium and the athlete's physical condition, forms a precise database. This device may also take the form of a portable or wearable device that athletes and coaches can carry.

[0220] The acquired data is then sent to a server. The server stores this data and performs analysis using machine learning algorithms. This analysis evaluates the players' health and performance, and derives information to determine the optimal training plan and player placement in matches. For example, if data shows that a particular player made many mistakes due to fatigue in past matches, the server will suggest that the player take a rest.

[0221] Next, based on real-time data from the match, the server generates effective tactical suggestions. For example, if it determines that the defense in a particular area is weak during the match, it will suggest a formation to put pressure on that area. In this way, the ability to change tactics in real time allows for a rapid response to the unfolding of the match.

[0222] Furthermore, as part of athlete health management, the server assesses injury risk based on past injury data and current physical data, and provides guidance on appropriate countermeasures. This includes providing training menus to reduce the strain on the knees caused by excessive load during running.

[0223] Finally, the analysis results and tactical suggestions are presented in a format that users—that is, coaches and team staff—can intuitively understand. This information is accessible from anywhere via devices and PCs and is used for pre-match strategy meetings and training schedule development.

[0224] According to the embodiment of the present invention, competition management, performance improvement, and athlete health management can be carried out efficiently, and resource optimization can be achieved, especially in small teams.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The terminal collects data in real time from the athlete's wearable device. This data includes heart rate, distance covered, and body temperature, and is used to understand the athlete's detailed condition on the field.

[0228] Step 2:

[0229] Data collected from the device is transferred to the server via the internet. Here, the data is stored quickly and securely on the server.

[0230] Step 3:

[0231] The server analyzes accumulated player data using machine learning algorithms. Through this analysis, it evaluates player performance and generates information necessary for suggesting individual tactics and training programs.

[0232] Step 4:

[0233] Based on the analysis results, the server proposes the optimal formation and player placement. This result is then sent to the terminal to support decisions for upcoming matches and training sessions.

[0234] Step 5:

[0235] During the match, the terminal monitors player status data in real time and immediately sends any significant changes to the server.

[0236] Step 6:

[0237] The server instantly analyzes real-time data during the match and sends back advice on tactical changes and player substitutions as needed to the terminal. This enables strategic actions that respond to the flow of the game.

[0238] Step 7:

[0239] The server predicts injury risk and generates data-driven preventative measures and recovery plans. These suggestions are provided to the terminal to support efforts to protect players from injury.

[0240] Step 8:

[0241] Users receive suggestions and analysis results from the server and use them to develop strategic plans for players and teams. Team coaches can access this information via mobile devices or PCs.

[0242] (Example 1)

[0243] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0244] Managing athletes and optimizing game strategies within sports teams requires considering athletes' health and performance, demanding complex and sophisticated decision-making. However, traditional methods struggle with real-time data analysis and tactical proposals, making them ineffective in the fast-paced environment of a game. Furthermore, injury prevention and the development of effective training plans are burdensome for some teams. Against this backdrop, there is a need for a comprehensive management system that utilizes athletes' biometric information.

[0245] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0246] This invention includes a server that includes means for acquiring and analyzing players' biometric data to generate an optimal player placement plan, means for formulating a player development plan based on the acquired biometric data and past competition data, means for analyzing biometric data acquired during a match in real time using the generated AI model and making tactical suggestions, and means for generating prompt messages according to the situation and making appropriate modifications. This enables more efficient player management and rapid changes to tactics during matches. It also allows for the assessment of injury risks and promotes safe and effective training and participation in matches.

[0247] "Athlete biometric data" refers to information related to an athlete's physical function, such as heart rate, distance covered, and fatigue level, which is acquired in real time using sensors.

[0248] "Analysis" refers to the computational process of using acquired biometric data and related information to evaluate the athlete's performance and health status.

[0249] A "player placement plan" is a proposal for the most effective placement of players in matches and training sessions, and is a plan generated based on analysis results.

[0250] "Competition data" refers to records of matches and training sessions that an athlete has participated in in the past, and is used for performance evaluation and the development of training plans.

[0251] A "development plan" is a set of long-term and short-term training schedules and educational guidelines formulated with the aim of improving players' abilities and preventing injuries.

[0252] A "generated AI model" is an algorithm that learns from a large amount of competition data and generates optimal suggestions based on the player's condition and the match situation.

[0253] "Tactical suggestions" are recommendations regarding player positioning and tactical actions that are generated based on the situation during a match.

[0254] A "prompt" is the format of a question or instruction input to the generated AI model, and it determines the direction of the responses and suggestions that are generated.

[0255] "Real-time analysis" means performing analysis immediately based on the acquired data and providing rapid feedback of the results.

[0256] This invention's system optimizes management and tactical decision-making using players' biometric information. It mainly consists of three elements: a terminal, a server, and a user.

[0257] First, the terminal acquires biometric data such as heart rate, distance covered, and fatigue level in real time from various sensors attached to the athlete. These sensors are often worn by the athlete as wearable or portable devices. The terminal receives data from the sensors using Bluetooth communication and transmits this information to a server in the cloud via the internet.

[0258] Next, the server stores the received biometric data and analyzes it using a machine learning framework (e.g., TensorFlow). The server then uses the generated AI model to evaluate the players' health and performance and formulate optimal player placement and development plans. This analysis process references a large amount of competition data and past performance to make optimal decisions based on the players' current situations.

[0259] Furthermore, during a match, the server can perform real-time data analysis and generate tactical suggestions. These suggestions are generated in response to specific questions using prompts. For example, by entering a prompt such as, "What is the optimal training plan for player B for the next match?", the server can immediately generate a suggestion and provide it to the user.

[0260] Ultimately, users view the analysis results and tactical suggestions provided by the server and adjust player management and match strategies based on them. Users can access this information at any time via their devices or PCs and utilize it for pre-match strategy meetings and daily training schedule adjustments. In this way, not only is player performance improvement and health management carried out efficiently and effectively, but rapid tactical changes during matches are also possible.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The device acquires biometric data such as heart rate, distance covered, and fatigue level from various sensors attached to the athlete. The device receives data from the sensors using Bluetooth communication. Specifically, the device communicates with the sensors at regular intervals to collect data. This provides input data for understanding the athlete's physical condition in real time.

[0264] Step 2:

[0265] The device transmits the acquired biometric data to a server in the cloud. The device confirms its internet connection and transfers the data via a secure communication path using HTTPS. Specifically, the process involves generating data packets and then sending them to the server. This output provides the server with the basic data necessary for subsequent data analysis.

[0266] Step 3:

[0267] The server stores the received biometric data and analyzes it using Python and machine learning frameworks. Based on the input data, the server uses machine learning models to evaluate the athletes' health and performance. Specifically, this includes processes such as detecting outliers and analyzing performance trends. The results of this analysis serve as the basis for decision-making in the next step.

[0268] Step 4:

[0269] The server uses an AI model based on the analysis results to create optimal player placement plans and training plans. This process involves specific actions that propose the most effective placements and future training plans, taking into account past competition data. The output includes drafts of tactical proposals and training plans.

[0270] Step 5:

[0271] The server uses real-time data to provide tactical suggestions during a match. Using data acquired during the match as input, the server instantly analyzes the situation and generates tactical suggestions using a generated AI model. Specifically, this involves generating tactical solutions in response to specific questions using prompt statements.

[0272] Step 6:

[0273] Users review the analysis results and tactical suggestions provided by the server and adjust player management and match strategies accordingly. They access the outputted information using applications on their devices or PCs and quickly modify their actions as needed. This ultimately improves the overall performance of the team.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] Managing athletes' physical and mental states in real time and supporting tactical decision-making to achieve optimal performance is a crucial challenge in modern sports. A system that provides such information and support quickly and effectively is needed. Existing systems suffer from insufficient real-time information acquisition and rapid feedback on tactical suggestions, and are also ineffective in managing athletes' health and preventing injuries.

[0277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes means for acquiring player status information and analyzing it to generate an optimal player configuration plan; means for formulating a player development plan based on the acquired status information and past competition records; means for analyzing the status information acquired during a match in real time and making tactical suggestions; and means for acquiring the player's physical and mental information from sensors and providing advice via voice and visual means. This enables real-time monitoring of the player's condition and rapid tactical feedback.

[0279] "Athlete condition information" refers to various data that indicates the athlete's physical and mental state, such as heart rate, distance covered, and fatigue level.

[0280] "Analysis" is the process of using acquired player status information to evaluate the players' health and performance, and to derive the optimal training plan and player placement.

[0281] An "optimal player lineup proposal" is a plan that suggests the optimal placement and selection strategy for players, based on the analysis results, taking into account the players' physical condition and the opponent's tactics.

[0282] A "development plan" involves formulating training menus and training policies that align with each athlete's individual skill improvement and career plan, taking into account their past athletic records and current condition.

[0283] A "tactical proposal" is to present advice aimed at adjusting strategic actions and arrangements in games and training based on the real-time status information of players.

[0284] "Advice via voice or vision" is to provide instructions and information in real time to players and coaches via visual displays or voice based on the analyzed data.

[0285] A "sensor" is a device or instrument for accurately measuring and acquiring various data such as heart rate and momentum from players.

[0286] This invention provides a system for obtaining, analyzing, and performing optimal tactics and health management based on the real-time status information of players. Specific embodiments for realizing this system are shown below.

[0287] The server first receives in real time physical data such as heart rate, running distance, and fatigue degree from the sensor device worn by the player. This data is transferred to the server via Bluetooth or Wi-Fi. The server accumulates these data and performs data analysis using a machine learning model by utilizing cloud services such as Google Cloud.

[0288] Based on the analysis results, the server evaluates the performance of the player, derives the necessary training content and injury prevention measures. Also, during the game, it generates tactical proposals in real time from the movements of the players as a group and notifies the terminal.

[0289] The terminal functions as a smartphone or tablet for players and coaches. It provides the analysis results and tactical proposals to the user through advice via voice or visual displays. This interface is intuitive and enables quick reception of information even during games and training.

[0290] For example, if a player's heart rate reaches its limit during a match, the server will quickly send strategies to reduce the strain and suggestions for player substitutions to the terminal. The terminal will then provide a real-time voice notification saying, "The player's heart rate is high; please consider substituting them."

[0291] Examples of prompts to input into a generative AI model:

[0292] "Using the heart rate and location information of players during a match, we recommend the next action they should take."

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The server receives data in real time from sensor devices worn by the athletes. Input data includes heart rate, distance covered, and fatigue level. The server receives this data via Bluetooth or Wi-Fi and stores it in storage.

[0296] Step 2:

[0297] The server inputs the received player data into a Google Cloud machine learning model for analysis. This input includes heart rate and distance covered. The analysis process involves processing time-series data and detecting outliers, ultimately outputting the player's fatigue level and performance status.

[0298] Step 3:

[0299] The server evaluates the players' performance based on the analysis results obtained from the machine learning model. From the analysis results, it generates information suggesting rest for players who are fatigued, for example, and uses this as input for the next step.

[0300] Step 4:

[0301] The server generates tactical proposals based on real-time data during the game. The input is the latest player data and past competition data, and the output is specific tactical actions. The generated proposals take into account the player's position information to optimize the tactics.

[0302] Step 5:

[0303] The terminal presents the analysis results and tactical proposals sent from the server audibly or visually. The terminal receives the output data and intuitively displays or notifies instructions such as "An urgent tactical change is needed" to the players and coaches. This enables the user to make quick decisions during the game.

[0304] Step 6:

[0305] The user adjusts the player's placement and their own actions based on the information provided by the terminal. This optimizes the health management and performance of the players and makes important decisions that affect the outcome of the game.

[0306] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0307] The present invention is a system incorporating an emotion engine for recognizing the user's emotions in order to more efficiently manage the player's condition and formulate tactics. In addition to collecting and analyzing the player's condition information, this system provides optimal support to the players and the team by analyzing the user's emotions.

[0308] First, the terminal collects the player's physical data from the sensors. This includes heart rate, movement patterns, fatigue level, etc. Also, data such as the user's voice and facial expressions are collected simultaneously and sent to the emotion engine. This emotion engine has the ability to analyze the tone of the user's voice and subtle facial movements to infer the user's emotional state.

[0309] Next, the server receives this data, applies machine learning algorithms to the players' physical data to evaluate their performance, and generates necessary tactics and training plans. Meanwhile, it uses emotional information obtained from the emotion engine to dynamically adjust communication methods according to the user's emotions. For example, if the user is feeling anxious, it provides advice to help them relax.

[0310] Furthermore, during a match, the server monitors the status of both players and users in real time, and provides tactical suggestions and emotional support to the device as needed. For example, if a user is feeling anxious during a tense moment in the match, the server will provide a message encouraging calmness based on that emotion, helping to soothe the situation.

[0311] Finally, users can receive suggestions from the system and incorporate them into player placement and training plans. By using the emotion engine, coaches can provide more emotionally resonant support to players and matches, which is expected to improve the team's psychological well-being and performance.

[0312] In this form, the present invention not only improves the performance of athletes but also enables effective sports management while maintaining the mental health of the entire team.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] The device collects physical data from players via sensors before matches and during training. This data includes heart rate, body temperature, and muscle movement. Simultaneously, it also collects the user's (coach's or staff's) face and voice to send to the emotion engine.

[0316] Step 2:

[0317] The device transmits collected player data and user sentiment data to a server via the internet. This process is performed continuously in real time.

[0318] Step 3:

[0319] The server analyzes the received physical data of the players and generates performance indicators. Here, it evaluates each player's condition and extracts information useful for suggesting training plans and tactics.

[0320] Step 4:

[0321] The server uses an emotion engine to analyze the user's emotional data. This analysis detects emotional states such as stress, anxiety, and excitement, and generates appropriate mental advice for the user.

[0322] Step 5:

[0323] The server creates a digital plan based on the analysis results, proposing training and match tactics for the players, and sends it to the terminal.

[0324] Step 6:

[0325] During matches and training sessions, the device continuously transmits real-time physical and emotional data to the server, keeping the latest information readily available.

[0326] Step 7:

[0327] The server generates real-time feedback to optimize tactical changes and player substitutions during matches. It also provides users with timely advice based on their emotional state.

[0328] Step 8:

[0329] Users can receive suggestions and advice from the server and incorporate them into on-site coaching and player management. This will lead to more effective coaching for players.

[0330] (Example 2)

[0331] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0332] In modern sports management, understanding an athlete's physical condition is crucial, but so is providing mental support. However, current technology makes it difficult to comprehensively analyze an athlete's condition and the user's emotions in real time and provide appropriate tactics and training plans based on that analysis. Therefore, there is a need to simultaneously improve athlete performance and provide support that is sensitive to the user's emotions.

[0333] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0334] In this invention, the server includes means for acquiring and analyzing player status information, means for analyzing the user's emotional state and providing feedback, and means for evaluating player performance by applying machine learning algorithms. This enables effective sports management tailored to the physical and mental state of the players.

[0335] "Athlete condition information" refers to data related to an athlete's physical and athletic performance. This data is collected using sensors and includes heart rate, movement patterns, and other similar information.

[0336] "Emotional analysis" refers to the process of inferring and analyzing a user's emotional state from their voice and facial expressions. This is carried out using voice analysis and facial recognition technologies.

[0337] "Feedback" refers to advice and information provided to users and players based on analysis results. This facilitates appropriate responses depending on the situation.

[0338] A "machine learning algorithm" refers to a method in which a computer system learns from data and makes predictions and decisions based on future data. In sports, it is used to analyze the performance of athletes.

[0339] A "tactical plan" refers to a plan formulated to take effective actions during matches or training. It is dynamically adjusted based on the players' condition and the user's emotions.

[0340] To implement this invention, the system is configured as follows.

[0341] First, the device collects physical data from the athlete. Physical data such as the athlete's heart rate, movement patterns, and fatigue level are acquired using a heart rate monitor and accelerometer. In addition, microphones and cameras are used to collect data on the user's voice and facial expressions. This data is temporarily stored in an athlete information database and a user emotion database.

[0342] Next, the server receives the collected data and begins processing it. The server applies machine learning algorithms to the players' physical data. The software used includes open-source machine learning frameworks and data analysis software. This algorithm evaluates performance and detects anomalies based on the players' past and current data. Simultaneously, the server uses an emotion analysis engine to analyze the user's voice and facial expression data and infer their emotional state. This makes it possible to formulate appropriate communication methods based on the user's emotions.

[0343] A concrete example is real-time assistance during a match. For instance, if a user is feeling nervous, the server generates a message such as "Calm down and focus on the next play" and notifies the user through their device. This allows the user to regain their composure.

[0344] Furthermore, by using a generative AI model to provide prompts that offer feedback in response to user requests, such as "What tactical plan should be suggested when a player's fatigue level is high?", an appropriate plan can be generated.

[0345] This will enable detailed and effective support based on the physical and psychological condition of athletes, and is expected to improve the quality of sports management.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The device collects physical data from the athlete. It uses sensors to acquire heart rate and movement patterns, and sends this data to a server. The raw data obtained from the sensors is temporarily stored in a database. Specifically, a heart rate sensor is attached to the body, and heart rate data is acquired in real time. The input is raw physical data from the sensor, and the output is formatted physical data.

[0349] Step 2:

[0350] The device collects the user's voice and facial expression data. Using the microphone and camera, it collects the user's voice tone and facial expressions, and sends this data to the sentiment analysis engine. Specifically, the microphone captures voice data, and the camera tracks facial movements. The input is voice and video data, and the output is processed data for sentiment analysis.

[0351] Step 3:

[0352] The server processes the player's physical data received by the server into a machine learning algorithm. Using historical and real-time data, it evaluates performance and detects anomalies. Specifically, the algorithm models the player's condition and generates performance metrics. The input is the player's physical data, and the output is the performance evaluation result.

[0353] Step 4:

[0354] The server analyzes the user's emotional state using an emotion analysis engine. It utilizes voice analysis software and facial recognition algorithms to determine the user's emotions. Specifically, it generates emotion labels based on voice tone and subtle changes in facial expression. The input is processed voice and facial data, and the output is the user's emotional state.

[0355] Step 5:

[0356] The server generates tactical plans and feedback based on player performance evaluations and the user's emotional state. Prompt messages are input to the generating AI model, which then generates an appropriate plan. Specifically, the AI ​​proposes countermeasures for situations such as "when a player's fatigue level is high." The input is the performance evaluation results and emotional state, and the output is a specific tactical plan.

[0357] Step 6:

[0358] The terminal receives output from the server and notifies the user. The user checks the notification and incorporates it into player positioning and training plans. Specifically, the terminal displays a tactical plan on the screen and provides audio notifications. The input is the tactical plan from the server, and the output is information provided to the user.

[0359] (Application Example 2)

[0360] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0361] In community-based events, a challenge is to appropriately understand participants' emotional responses and improve event satisfaction. Traditional methods have made it difficult to grasp individual participants' emotions in real time and dynamically optimize event content and communication based on that information.

[0362] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0363] In this invention, the server includes means for acquiring player status information and analyzing it to generate an optimal player configuration plan, means for analyzing user emotional information and dynamically generating event-participation messages, and means for optimizing communication based on the analyzed emotional information. This enables flexible event management that responds to participants' emotions and provides a better civic experience.

[0364] "Athlete status information" refers to data that indicates an athlete's current physical and mental health and performance, including heart rate, movement patterns, and fatigue levels.

[0365] An "optimal player lineup proposal" is a suggestion that, based on player condition information, indicates the most effective placement and combination of players to maximize team performance.

[0366] A "development plan" refers to the systematic planning of training content and schedules aimed at the long-term growth and improvement of an athlete's abilities.

[0367] "Emotional information" is data primarily obtained from voice and facial expressions, and represents the user's emotional state. This allows us to capture changes in the user's mood and emotions.

[0368] "Event-oriented messaging" refers to personalized suggestions and activity recommendations sent to participants during an event based on their emotional information.

[0369] "Communication optimization" means improving the quality of communication between participants and with the system by utilizing users' emotional information and providing information to individual users at the optimal timing and with the right content.

[0370] The system that realizes this invention consists of terminals such as smartphones and smart glasses, and a server that processes the data. The terminals collect biometric information such as the user's heart rate, movement patterns, and fatigue level via sensors. In addition, they capture the user's voice and facial expressions with cameras and microphones to acquire emotional information. This data is transmitted to the server in real time.

[0371] On the server, machine learning algorithms are first applied to the players' biometric information to analyze their current state. Based on these results, player placement plans and training plans are dynamically generated. Meanwhile, the emotion engine performs speech recognition and facial expression analysis to extract the user's emotional information. This allows for the real-time generation of interactive messages tailored to the user's emotions and sent back to the terminal. Facial expression analysis is performed using OpenCV and dlib, and speech features can be captured using PocketSphinx.

[0372] Messages sent from the server will be reassuring to users and encourage participation in new activities. For example, if a participant at a music festival is deemed to be relaxed, they will be notified of upcoming artist performances or campaigns to further enhance their enjoyment.

[0373] By using a generative AI model, event-participation messages based on user emotions can be efficiently generated. In this system, for example, using a prompt such as "Generate event-participation messages based on participants' emotions" can improve event satisfaction.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The device collects biometric information such as the user's heart rate, movement patterns, and fatigue level using sensors. In addition, it captures facial expressions with a camera and records voice with a microphone. The collected data is transmitted to a server in real time for integrated analysis. The input is data from sensors, camera, and microphone, and the output is data transmission to the server.

[0377] Step 2:

[0378] The server inputs the received biometric information into a machine learning algorithm to analyze the player's current performance status. Here, biometric data is used as input, and performance evaluation results are output. Based on these evaluation results, the system generates optimal placement and development plans for the player. Specifically, it suggests training plans tailored to the player's current condition.

[0379] Step 3:

[0380] The server analyzes audio data received from the user using PocketSphinx, extracting emotions from tone and speech patterns. It also analyzes facial features using OpenCV and dlib to infer emotional states. Input is audio and facial data, and output is the analyzed emotional information. Based on this emotional information, it dynamically generates event-participation messages.

[0381] Step 4:

[0382] The server uses a generative AI model to create the optimal message using a prompt such as, "Generate an event-participation message based on participants' emotions." The input for this step is emotion information and the generative AI model, and the output is a customized message.

[0383] Step 5:

[0384] The server sends the generated message back to the terminal, notifying the user. Providing appropriate activities and information based on the user's state enhances the event experience. The input is the generated message, and the output is the notification delivered to the user.

[0385] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0386] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0387] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0391] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0392] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0393] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0394] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0395] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0396] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0397] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0398] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0399] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0400] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0401] This invention is a system that provides advanced management of athlete conditions and supports tactical decision-making in matches and training. This system comprehensively handles everything from collecting and analyzing athlete information to making recommendations. The main embodiments are described below.

[0402] First, the device acquires data such as the athlete's heart rate, distance covered, and fatigue level in real time from sensors. This information, combined with frequent monitoring of various locations within the stadium and the athlete's physical condition, forms a precise database. This device may also take the form of a portable or wearable device that athletes and coaches can carry.

[0403] The acquired data is then sent to a server. The server stores this data and performs analysis using machine learning algorithms. This analysis evaluates the players' health and performance, and derives information to determine the optimal training plan and player placement in matches. For example, if data shows that a particular player made many mistakes due to fatigue in past matches, the server will suggest that the player take a rest.

[0404] Next, based on real-time data from the match, the server generates effective tactical suggestions. For example, if it determines that the defense in a particular area is weak during the match, it will suggest a formation to put pressure on that area. In this way, the ability to change tactics in real time allows for a rapid response to the unfolding of the match.

[0405] Furthermore, as part of athlete health management, the server assesses injury risk based on past injury data and current physical data, and provides guidance on appropriate countermeasures. This includes providing training menus to reduce the strain on the knees caused by excessive load during running.

[0406] Finally, the analysis results and tactical suggestions are presented in a format that users—that is, coaches and team staff—can intuitively understand. This information is accessible from anywhere via devices and PCs and is used for pre-match strategy meetings and training schedule development.

[0407] According to the embodiment of the present invention, competition management, performance improvement, and athlete health management can be carried out efficiently, and resource optimization can be achieved, especially in small teams.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The terminal collects data in real time from the athlete's wearable device. This data includes heart rate, distance covered, and body temperature, and is used to understand the athlete's detailed condition on the field.

[0411] Step 2:

[0412] Data collected from the device is transferred to the server via the internet. Here, the data is stored quickly and securely on the server.

[0413] Step 3:

[0414] The server analyzes accumulated player data using machine learning algorithms. Through this analysis, it evaluates player performance and generates information necessary for suggesting individual tactics and training programs.

[0415] Step 4:

[0416] Based on the analysis results, the server proposes the optimal formation and player placement. This result is then sent to the terminal to support decisions for upcoming matches and training sessions.

[0417] Step 5:

[0418] During the match, the terminal monitors player status data in real time and immediately sends any significant changes to the server.

[0419] Step 6:

[0420] The server instantly analyzes real-time data during the match and sends back advice on tactical changes and player substitutions as needed to the terminal. This enables strategic actions that respond to the flow of the game.

[0421] Step 7:

[0422] The server predicts injury risk and generates data-driven preventative measures and recovery plans. These suggestions are provided to the terminal to support efforts to protect players from injury.

[0423] Step 8:

[0424] Users receive suggestions and analysis results from the server and use them to develop strategic plans for players and teams. Team coaches can access this information via mobile devices or PCs.

[0425] (Example 1)

[0426] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0427] Managing athletes and optimizing game strategies within sports teams requires considering athletes' health and performance, demanding complex and sophisticated decision-making. However, traditional methods struggle with real-time data analysis and tactical proposals, making them ineffective in the fast-paced environment of a game. Furthermore, injury prevention and the development of effective training plans are burdensome for some teams. Against this backdrop, there is a need for a comprehensive management system that utilizes athletes' biometric information.

[0428] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0429] This invention includes a server that includes means for acquiring and analyzing players' biometric data to generate an optimal player placement plan, means for formulating a player development plan based on the acquired biometric data and past competition data, means for analyzing biometric data acquired during a match in real time using the generated AI model and making tactical suggestions, and means for generating prompt messages according to the situation and making appropriate modifications. This enables more efficient player management and rapid changes to tactics during matches. It also allows for the assessment of injury risks and promotes safe and effective training and participation in matches.

[0430] "Athlete biometric data" refers to information related to an athlete's physical function, such as heart rate, distance covered, and fatigue level, which is acquired in real time using sensors.

[0431] "Analysis" refers to the computational process of using acquired biometric data and related information to evaluate the athlete's performance and health status.

[0432] A "player placement plan" is a proposal for the most effective placement of players in matches and training sessions, and is a plan generated based on analysis results.

[0433] "Competition data" refers to records of matches and training sessions that an athlete has participated in in the past, and is used for performance evaluation and the development of training plans.

[0434] A "development plan" is a set of long-term and short-term training schedules and educational guidelines formulated with the aim of improving players' abilities and preventing injuries.

[0435] A "generated AI model" is an algorithm that learns from a large amount of competition data and generates optimal suggestions based on the player's condition and the match situation.

[0436] "Tactical suggestions" are recommendations regarding player positioning and tactical actions that are generated based on the situation during a match.

[0437] A "prompt" is the format of a question or instruction input to the generated AI model, and it determines the direction of the responses and suggestions that are generated.

[0438] "Real-time analysis" means performing analysis immediately based on the acquired data and providing rapid feedback of the results.

[0439] This invention's system optimizes management and tactical decision-making using players' biometric information. It mainly consists of three elements: a terminal, a server, and a user.

[0440] First, the terminal acquires biometric data such as heart rate, distance covered, and fatigue level in real time from various sensors attached to the athlete. These sensors are often worn by the athlete as wearable or portable devices. The terminal receives data from the sensors using Bluetooth communication and transmits this information to a server in the cloud via the internet.

[0441] Next, the server stores the received biometric data and analyzes it using a machine learning framework (e.g., TensorFlow). The server then uses the generated AI model to evaluate the players' health and performance and formulate optimal player placement and development plans. This analysis process references a large amount of competition data and past performance to make optimal decisions based on the players' current situations.

[0442] Furthermore, during a match, the server can perform real-time data analysis and generate tactical suggestions. These suggestions are generated in response to specific questions using prompts. For example, by entering a prompt such as, "What is the optimal training plan for player B for the next match?", the server can immediately generate a suggestion and provide it to the user.

[0443] Ultimately, users view the analysis results and tactical suggestions provided by the server and adjust player management and match strategies based on them. Users can access this information at any time via their devices or PCs and utilize it for pre-match strategy meetings and daily training schedule adjustments. In this way, not only is player performance improvement and health management carried out efficiently and effectively, but rapid tactical changes during matches are also possible.

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The device acquires biometric data such as heart rate, distance covered, and fatigue level from various sensors attached to the athlete. The device receives data from the sensors using Bluetooth communication. Specifically, the device communicates with the sensors at regular intervals to collect data. This provides input data for understanding the athlete's physical condition in real time.

[0447] Step 2:

[0448] The device transmits the acquired biometric data to a server in the cloud. The device confirms its internet connection and transfers the data via a secure communication path using HTTPS. Specifically, the process involves generating data packets and then sending them to the server. This output provides the server with the basic data necessary for subsequent data analysis.

[0449] Step 3:

[0450] The server stores the received biometric data and analyzes it using Python and machine learning frameworks. Based on the input data, the server uses machine learning models to evaluate the athletes' health and performance. Specifically, this includes processes such as detecting outliers and analyzing performance trends. The results of this analysis serve as the basis for decision-making in the next step.

[0451] Step 4:

[0452] The server uses an AI model based on the analysis results to create optimal player placement plans and training plans. This process involves specific actions that propose the most effective placements and future training plans, taking into account past competition data. The output includes drafts of tactical proposals and training plans.

[0453] Step 5:

[0454] The server uses real-time data to provide tactical suggestions during a match. Using data acquired during the match as input, the server instantly analyzes the situation and generates tactical suggestions using a generated AI model. Specifically, this involves generating tactical solutions in response to specific questions using prompt statements.

[0455] Step 6:

[0456] Users review the analysis results and tactical suggestions provided by the server and adjust player management and match strategies accordingly. They access the outputted information using applications on their devices or PCs and quickly modify their actions as needed. This ultimately improves the overall performance of the team.

[0457] (Application Example 1)

[0458] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0459] Managing athletes' physical and mental states in real time and supporting tactical decision-making to achieve optimal performance is a crucial challenge in modern sports. A system that provides such information and support quickly and effectively is needed. Existing systems suffer from insufficient real-time information acquisition and rapid feedback on tactical suggestions, and are also ineffective in managing athletes' health and preventing injuries.

[0460] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0461] In this invention, the server includes means for acquiring player status information and analyzing it to generate an optimal player configuration plan; means for formulating a player development plan based on the acquired status information and past competition records; means for analyzing the status information acquired during a match in real time and making tactical suggestions; and means for acquiring the player's physical and mental information from sensors and providing advice via voice and visual means. This enables real-time monitoring of the player's condition and rapid tactical feedback.

[0462] "Athlete condition information" refers to various data that indicates the athlete's physical and mental state, such as heart rate, distance covered, and fatigue level.

[0463] "Analysis" is the process of using acquired player status information to evaluate the players' health and performance, and to derive the optimal training plan and player placement.

[0464] An "optimal player lineup proposal" is a plan that suggests the optimal placement and selection strategy for players, based on the analysis results, taking into account the players' physical condition and the opponent's tactics.

[0465] A "development plan" involves formulating training menus and training policies that align with each athlete's individual skill improvement and career plan, taking into account their past athletic records and current condition.

[0466] "Tactical suggestions" refer to providing advice based on real-time information about the players' condition, with the aim of adjusting strategic actions and positioning during matches and training.

[0467] "Voice and visual advice" refers to providing players and coaches with real-time instructions and information via visual displays and audio, based on analyzed data.

[0468] A "sensor" is a device or equipment used to accurately measure and acquire various data from athletes, such as heart rate and exercise volume.

[0469] This invention provides a system for acquiring and analyzing real-time status information of players to implement optimal tactics and health management. Specific embodiments for realizing this system are described below.

[0470] The server first receives real-time physical data such as heart rate, distance covered, and fatigue level from sensor devices worn by the athletes. This data is then transmitted to the server via Bluetooth or Wi-Fi. The server stores this data and performs data analysis using machine learning models with cloud services such as Google Cloud.

[0471] Based on the analysis results, the server evaluates the players' performance and determines the necessary training content and injury prevention measures. Furthermore, during matches, it generates tactical suggestions in real time based on the players' collective movements and notifies the user's device.

[0472] The device functions as a smartphone or tablet for players and coaches. Analysis results and tactical suggestions are provided to users through voice advice and visual displays. The interface is intuitive, allowing for quick access to information even during matches or training sessions.

[0473] For example, if a player's heart rate reaches its limit during a match, the server will quickly send strategies to reduce the strain and suggestions for player substitutions to the terminal. The terminal will then provide a real-time voice notification saying, "The player's heart rate is high; please consider substituting them."

[0474] Examples of prompts to input into a generative AI model:

[0475] "Using the heart rate and location information of players during a match, we recommend the next action they should take."

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The server receives data in real time from sensor devices worn by the athletes. Input data includes heart rate, distance covered, and fatigue level. The server receives this data via Bluetooth or Wi-Fi and stores it in storage.

[0479] Step 2:

[0480] The server inputs the received player data into a Google Cloud machine learning model for analysis. This input includes heart rate and distance covered. The analysis process involves processing time-series data and detecting outliers, ultimately outputting the player's fatigue level and performance status.

[0481] Step 3:

[0482] The server evaluates the players' performance based on the analysis results obtained from the machine learning model. From the analysis results, it generates information suggesting rest for players who are fatigued, for example, and uses this as input for the next step.

[0483] Step 4:

[0484] The server generates tactical suggestions based on real-time data during the match. Inputs are the latest player data and historical match data, while output is specific tactical actions. The generated suggestions optimize tactics by considering player position information.

[0485] Step 5:

[0486] The terminal displays analysis results and tactical suggestions sent from the server, either audibly or visually. The terminal receives the output data and intuitively displays or notifies players and coaches of instructions such as "Urgent tactical change required." This enables users to make quick decisions during a match.

[0487] Step 6:

[0488] Users adjust player placement and individual actions based on information provided by their devices. This allows them to manage player health, optimize performance, and make crucial decisions that influence the outcome of matches.

[0489] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0490] This invention is a system that incorporates an emotion engine to recognize user emotions in order to more efficiently manage player condition and formulate tactics. In addition to collecting and analyzing player condition information, this system provides optimal support to players and teams by analyzing user emotions.

[0491] First, the device collects the athlete's physical data from sensors. This includes heart rate, movement patterns, and fatigue levels. Simultaneously, it also collects data such as the user's voice and facial expressions and sends it to the emotion engine. This emotion engine has the ability to analyze the user's voice tone and subtle facial movements to infer the user's emotional state.

[0492] Next, the server receives this data, applies machine learning algorithms to the players' physical data to evaluate their performance, and generates necessary tactics and training plans. Meanwhile, it uses emotional information obtained from the emotion engine to dynamically adjust communication methods according to the user's emotions. For example, if the user is feeling anxious, it provides advice to help them relax.

[0493] Furthermore, during a match, the server monitors the status of both players and users in real time, and provides tactical suggestions and emotional support to the device as needed. For example, if a user is feeling anxious during a tense moment in the match, the server will provide a message encouraging calmness based on that emotion, helping to soothe the situation.

[0494] Finally, users can receive suggestions from the system and incorporate them into player placement and training plans. By using the emotion engine, coaches can provide more emotionally resonant support to players and matches, which is expected to improve the team's psychological well-being and performance.

[0495] In this form, the present invention not only improves the performance of athletes but also enables effective sports management while maintaining the mental health of the entire team.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] The device collects physical data from players via sensors before matches and during training. This data includes heart rate, body temperature, and muscle movement. Simultaneously, it also collects the user's (coach's or staff's) face and voice to send to the emotion engine.

[0499] Step 2:

[0500] The device transmits collected player data and user sentiment data to a server via the internet. This process is performed continuously in real time.

[0501] Step 3:

[0502] The server analyzes the received physical data of the players and generates performance indicators. Here, it evaluates each player's condition and extracts information useful for suggesting training plans and tactics.

[0503] Step 4:

[0504] The server uses an emotion engine to analyze the user's emotional data. This analysis detects emotional states such as stress, anxiety, and excitement, and generates appropriate mental advice for the user.

[0505] Step 5:

[0506] The server creates a digital plan based on the analysis results, proposing training and match tactics for the players, and sends it to the terminal.

[0507] Step 6:

[0508] During matches and training sessions, the device continuously transmits real-time physical and emotional data to the server, keeping the latest information readily available.

[0509] Step 7:

[0510] The server generates real-time feedback to optimize tactical changes and player substitutions during matches. It also provides users with timely advice based on their emotional state.

[0511] Step 8:

[0512] Users can receive suggestions and advice from the server and incorporate them into on-site coaching and player management. This will lead to more effective coaching for players.

[0513] (Example 2)

[0514] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] In modern sports management, understanding an athlete's physical condition is crucial, but so is providing mental support. However, current technology makes it difficult to comprehensively analyze an athlete's condition and the user's emotions in real time and provide appropriate tactics and training plans based on that analysis. Therefore, there is a need to simultaneously improve athlete performance and provide support that is sensitive to the user's emotions.

[0516] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0517] In this invention, the server includes means for acquiring and analyzing player status information, means for analyzing the user's emotional state and providing feedback, and means for evaluating player performance by applying machine learning algorithms. This enables effective sports management tailored to the physical and mental state of the players.

[0518] "Athlete condition information" refers to data related to an athlete's physical and athletic performance. This data is collected using sensors and includes heart rate, movement patterns, and other similar information.

[0519] "Emotional analysis" refers to the process of inferring and analyzing a user's emotional state from their voice and facial expressions. This is carried out using voice analysis and facial recognition technologies.

[0520] "Feedback" refers to advice and information provided to users and players based on analysis results. This facilitates appropriate responses depending on the situation.

[0521] A "machine learning algorithm" refers to a method in which a computer system learns from data and makes predictions and decisions based on future data. In sports, it is used to analyze the performance of athletes.

[0522] A "tactical plan" refers to a plan formulated to take effective actions during matches or training. It is dynamically adjusted based on the players' condition and the user's emotions.

[0523] To implement this invention, the system is configured as follows.

[0524] First, the device collects physical data from the athlete. Physical data such as the athlete's heart rate, movement patterns, and fatigue level are acquired using a heart rate monitor and accelerometer. In addition, microphones and cameras are used to collect data on the user's voice and facial expressions. This data is temporarily stored in an athlete information database and a user emotion database.

[0525] Next, the server receives the collected data and begins processing it. The server applies machine learning algorithms to the players' physical data. The software used includes open-source machine learning frameworks and data analysis software. This algorithm evaluates performance and detects anomalies based on the players' past and current data. Simultaneously, the server uses an emotion analysis engine to analyze the user's voice and facial expression data and infer their emotional state. This makes it possible to formulate appropriate communication methods based on the user's emotions.

[0526] A concrete example is real-time assistance during a match. For instance, if a user is feeling nervous, the server generates a message such as "Calm down and focus on the next play" and notifies the user through their device. This allows the user to regain their composure.

[0527] Furthermore, by using a generative AI model to provide prompts that offer feedback in response to user requests, such as "What tactical plan should be suggested when a player's fatigue level is high?", an appropriate plan can be generated.

[0528] This will enable detailed and effective support based on the physical and psychological condition of athletes, and is expected to improve the quality of sports management.

[0529] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0530] Step 1:

[0531] The device collects physical data from the athlete. It uses sensors to acquire heart rate and movement patterns, and sends this data to a server. The raw data obtained from the sensors is temporarily stored in a database. Specifically, a heart rate sensor is attached to the body, and heart rate data is acquired in real time. The input is raw physical data from the sensor, and the output is formatted physical data.

[0532] Step 2:

[0533] The device collects the user's voice and facial expression data. Using the microphone and camera, it collects the user's voice tone and facial expressions, and sends this data to the sentiment analysis engine. Specifically, the microphone captures voice data, and the camera tracks facial movements. The input is voice and video data, and the output is processed data for sentiment analysis.

[0534] Step 3:

[0535] The server processes the player's physical data received by the server into a machine learning algorithm. Using historical and real-time data, it evaluates performance and detects anomalies. Specifically, the algorithm models the player's condition and generates performance metrics. The input is the player's physical data, and the output is the performance evaluation result.

[0536] Step 4:

[0537] The server analyzes the user's emotional state using an emotion analysis engine. It utilizes voice analysis software and facial recognition algorithms to determine the user's emotions. Specifically, it generates emotion labels based on voice tone and subtle changes in facial expression. The input is processed voice and facial data, and the output is the user's emotional state.

[0538] Step 5:

[0539] The server generates tactical plans and feedback based on player performance evaluations and the user's emotional state. Prompt messages are input to the generating AI model, which then generates an appropriate plan. Specifically, the AI ​​proposes countermeasures for situations such as "when a player's fatigue level is high." The input is the performance evaluation results and emotional state, and the output is a specific tactical plan.

[0540] Step 6:

[0541] The terminal receives output from the server and notifies the user. The user checks the notification and incorporates it into player positioning and training plans. Specifically, the terminal displays a tactical plan on the screen and provides audio notifications. The input is the tactical plan from the server, and the output is information provided to the user.

[0542] (Application Example 2)

[0543] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] In community-based events, a challenge is to appropriately understand participants' emotional responses and improve event satisfaction. Traditional methods have made it difficult to grasp individual participants' emotions in real time and dynamically optimize event content and communication based on that information.

[0545] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0546] In this invention, the server includes means for acquiring player status information and analyzing it to generate an optimal player configuration plan, means for analyzing user emotional information and dynamically generating event-participation messages, and means for optimizing communication based on the analyzed emotional information. This enables flexible event management that responds to participants' emotions and provides a better civic experience.

[0547] "Athlete status information" refers to data that indicates an athlete's current physical and mental health and performance, including heart rate, movement patterns, and fatigue levels.

[0548] An "optimal player lineup proposal" is a suggestion that, based on player condition information, indicates the most effective placement and combination of players to maximize team performance.

[0549] A "development plan" refers to the systematic planning of training content and schedules aimed at the long-term growth and improvement of an athlete's abilities.

[0550] "Emotional information" is data primarily obtained from voice and facial expressions, and represents the user's emotional state. This allows us to capture changes in the user's mood and emotions.

[0551] "Event-oriented messaging" refers to personalized suggestions and activity recommendations sent to participants during an event based on their emotional information.

[0552] "Communication optimization" means improving the quality of communication between participants and with the system by utilizing users' emotional information and providing information to individual users at the optimal timing and with the right content.

[0553] The system that realizes this invention consists of terminals such as smartphones and smart glasses, and a server that processes the data. The terminals collect biometric information such as the user's heart rate, movement patterns, and fatigue level via sensors. In addition, they capture the user's voice and facial expressions with cameras and microphones to acquire emotional information. This data is transmitted to the server in real time.

[0554] On the server, machine learning algorithms are first applied to the players' biometric information to analyze their current state. Based on these results, player placement plans and training plans are dynamically generated. Meanwhile, the emotion engine performs speech recognition and facial expression analysis to extract the user's emotional information. This allows for the real-time generation of interactive messages tailored to the user's emotions and sent back to the terminal. Facial expression analysis is performed using OpenCV and dlib, and speech features can be captured using PocketSphinx.

[0555] Messages sent from the server will be reassuring to users and encourage participation in new activities. For example, if a participant at a music festival is deemed to be relaxed, they will be notified of upcoming artist performances or campaigns to further enhance their enjoyment.

[0556] By using a generative AI model, event-participation messages based on user emotions can be efficiently generated. In this system, for example, using a prompt such as "Generate event-participation messages based on participants' emotions" can improve event satisfaction.

[0557] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0558] Step 1:

[0559] The device collects biometric information such as the user's heart rate, movement patterns, and fatigue level using sensors. In addition, it captures facial expressions with a camera and records voice with a microphone. The collected data is transmitted to a server in real time for integrated analysis. The input is data from sensors, camera, and microphone, and the output is data transmission to the server.

[0560] Step 2:

[0561] The server inputs the received biometric information into a machine learning algorithm to analyze the player's current performance status. Here, biometric data is used as input, and performance evaluation results are output. Based on these evaluation results, the system generates optimal placement and development plans for the player. Specifically, it suggests training plans tailored to the player's current condition.

[0562] Step 3:

[0563] The server analyzes audio data received from the user using PocketSphinx, extracting emotions from tone and speech patterns. It also analyzes facial features using OpenCV and dlib to infer emotional states. Input is audio and facial data, and output is the analyzed emotional information. Based on this emotional information, it dynamically generates event-participation messages.

[0564] Step 4:

[0565] The server uses a generative AI model to create the optimal message using a prompt such as, "Generate an event-participation message based on participants' emotions." The input for this step is emotion information and the generative AI model, and the output is a customized message.

[0566] Step 5:

[0567] The server sends the generated message back to the terminal, notifying the user. Providing appropriate activities and information based on the user's state enhances the event experience. The input is the generated message, and the output is the notification delivered to the user.

[0568] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0569] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0570] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0571] [Fourth Embodiment]

[0572] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0573] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0574] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0575] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0576] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0577] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0578] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0579] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0580] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0581] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0582] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0583] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0584] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0585] This invention is a system that provides advanced management of athlete conditions and supports tactical decision-making in matches and training. This system comprehensively handles everything from collecting and analyzing athlete information to making recommendations. The main embodiments are described below.

[0586] First, the device acquires data such as the athlete's heart rate, distance covered, and fatigue level in real time from sensors. This information, combined with frequent monitoring of various locations within the stadium and the athlete's physical condition, forms a precise database. This device may also take the form of a portable or wearable device that athletes and coaches can carry.

[0587] The acquired data is then sent to a server. The server stores this data and performs analysis using machine learning algorithms. This analysis evaluates the players' health and performance, and derives information to determine the optimal training plan and player placement in matches. For example, if data shows that a particular player made many mistakes due to fatigue in past matches, the server will suggest that the player take a rest.

[0588] Next, based on real-time data from the match, the server generates effective tactical suggestions. For example, if it determines that the defense in a particular area is weak during the match, it will suggest a formation to put pressure on that area. In this way, the ability to change tactics in real time allows for a rapid response to the unfolding of the match.

[0589] Furthermore, as part of athlete health management, the server assesses injury risk based on past injury data and current physical data, and provides guidance on appropriate countermeasures. This includes providing training menus to reduce the strain on the knees caused by excessive load during running.

[0590] Finally, the analysis results and tactical suggestions are presented in a format that users—that is, coaches and team staff—can intuitively understand. This information is accessible from anywhere via devices and PCs and is used for pre-match strategy meetings and training schedule development.

[0591] According to the embodiment of the present invention, competition management, performance improvement, and athlete health management can be carried out efficiently, and resource optimization can be achieved, especially in small teams.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The terminal collects data in real time from the athlete's wearable device. This data includes heart rate, distance covered, and body temperature, and is used to understand the athlete's detailed condition on the field.

[0595] Step 2:

[0596] Data collected from the device is transferred to the server via the internet. Here, the data is stored quickly and securely on the server.

[0597] Step 3:

[0598] The server analyzes accumulated player data using machine learning algorithms. Through this analysis, it evaluates player performance and generates information necessary for suggesting individual tactics and training programs.

[0599] Step 4:

[0600] Based on the analysis results, the server proposes the optimal formation and player placement. This result is then sent to the terminal to support decisions for upcoming matches and training sessions.

[0601] Step 5:

[0602] During the match, the terminal monitors player status data in real time and immediately sends any significant changes to the server.

[0603] Step 6:

[0604] The server instantly analyzes real-time data during the match and sends back advice on tactical changes and player substitutions as needed to the terminal. This enables strategic actions that respond to the flow of the game.

[0605] Step 7:

[0606] The server predicts injury risk and generates data-driven preventative measures and recovery plans. These suggestions are provided to the terminal to support efforts to protect players from injury.

[0607] Step 8:

[0608] Users receive suggestions and analysis results from the server and use them to develop strategic plans for players and teams. Team coaches can access this information via mobile devices or PCs.

[0609] (Example 1)

[0610] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] Managing athletes and optimizing game strategies within sports teams requires considering athletes' health and performance, demanding complex and sophisticated decision-making. However, traditional methods struggle with real-time data analysis and tactical proposals, making them ineffective in the fast-paced environment of a game. Furthermore, injury prevention and the development of effective training plans are burdensome for some teams. Against this backdrop, there is a need for a comprehensive management system that utilizes athletes' biometric information.

[0612] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0613] This invention includes a server that includes means for acquiring and analyzing players' biometric data to generate an optimal player placement plan, means for formulating a player development plan based on the acquired biometric data and past competition data, means for analyzing biometric data acquired during a match in real time using the generated AI model and making tactical suggestions, and means for generating prompt messages according to the situation and making appropriate modifications. This enables more efficient player management and rapid changes to tactics during matches. It also allows for the assessment of injury risks and promotes safe and effective training and participation in matches.

[0614] "Athlete biometric data" refers to information related to an athlete's physical function, such as heart rate, distance covered, and fatigue level, which is acquired in real time using sensors.

[0615] "Analysis" refers to the computational process of using acquired biometric data and related information to evaluate the athlete's performance and health status.

[0616] A "player placement plan" is a proposal for the most effective placement of players in matches and training sessions, and is a plan generated based on analysis results.

[0617] "Competition data" refers to records of matches and training sessions that an athlete has participated in in the past, and is used for performance evaluation and the development of training plans.

[0618] A "development plan" is a set of long-term and short-term training schedules and educational guidelines formulated with the aim of improving players' abilities and preventing injuries.

[0619] A "generated AI model" is an algorithm that learns from a large amount of competition data and generates optimal suggestions based on the player's condition and the match situation.

[0620] "Tactical suggestions" are recommendations regarding player positioning and tactical actions that are generated based on the situation during a match.

[0621] A "prompt" is the format of a question or instruction input to the generated AI model, and it determines the direction of the responses and suggestions that are generated.

[0622] "Real-time analysis" means performing analysis immediately based on the acquired data and providing rapid feedback of the results.

[0623] This invention's system optimizes management and tactical decision-making using players' biometric information. It mainly consists of three elements: a terminal, a server, and a user.

[0624] First, the terminal acquires biometric data such as heart rate, distance covered, and fatigue level in real time from various sensors attached to the athlete. These sensors are often worn by the athlete as wearable or portable devices. The terminal receives data from the sensors using Bluetooth communication and transmits this information to a server in the cloud via the internet.

[0625] Next, the server stores the received biometric data and analyzes it using a machine learning framework (e.g., TensorFlow). The server then uses the generated AI model to evaluate the players' health and performance and formulate optimal player placement and development plans. This analysis process references a large amount of competition data and past performance to make optimal decisions based on the players' current situations.

[0626] Furthermore, during a match, the server can perform real-time data analysis and generate tactical suggestions. These suggestions are generated in response to specific questions using prompts. For example, by entering a prompt such as, "What is the optimal training plan for player B for the next match?", the server can immediately generate a suggestion and provide it to the user.

[0627] Ultimately, users view the analysis results and tactical suggestions provided by the server and adjust player management and match strategies based on them. Users can access this information at any time via their devices or PCs and utilize it for pre-match strategy meetings and daily training schedule adjustments. In this way, not only is player performance improvement and health management carried out efficiently and effectively, but rapid tactical changes during matches are also possible.

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] The device acquires biometric data such as heart rate, distance covered, and fatigue level from various sensors attached to the athlete. The device receives data from the sensors using Bluetooth communication. Specifically, the device communicates with the sensors at regular intervals to collect data. This provides input data for understanding the athlete's physical condition in real time.

[0631] Step 2:

[0632] The device transmits the acquired biometric data to a server in the cloud. The device confirms its internet connection and transfers the data via a secure communication path using HTTPS. Specifically, the process involves generating data packets and then sending them to the server. This output provides the server with the basic data necessary for subsequent data analysis.

[0633] Step 3:

[0634] The server stores the received biometric data and analyzes it using Python and machine learning frameworks. Based on the input data, the server uses machine learning models to evaluate the athletes' health and performance. Specifically, this includes processes such as detecting outliers and analyzing performance trends. The results of this analysis serve as the basis for decision-making in the next step.

[0635] Step 4:

[0636] The server uses an AI model based on the analysis results to create optimal player placement plans and training plans. This process involves specific actions that propose the most effective placements and future training plans, taking into account past competition data. The output includes drafts of tactical proposals and training plans.

[0637] Step 5:

[0638] The server uses real-time data to provide tactical suggestions during a match. Using data acquired during the match as input, the server instantly analyzes the situation and generates tactical suggestions using a generated AI model. Specifically, this involves generating tactical solutions in response to specific questions using prompt statements.

[0639] Step 6:

[0640] Users review the analysis results and tactical suggestions provided by the server and adjust player management and match strategies accordingly. They access the outputted information using applications on their devices or PCs and quickly modify their actions as needed. This ultimately improves the overall performance of the team.

[0641] (Application Example 1)

[0642] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] Managing athletes' physical and mental states in real time and supporting tactical decision-making to achieve optimal performance is a crucial challenge in modern sports. A system that provides such information and support quickly and effectively is needed. Existing systems suffer from insufficient real-time information acquisition and rapid feedback on tactical suggestions, and are also ineffective in managing athletes' health and preventing injuries.

[0644] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0645] In this invention, the server includes means for acquiring player status information and analyzing it to generate an optimal player configuration plan; means for formulating a player development plan based on the acquired status information and past competition records; means for analyzing the status information acquired during a match in real time and making tactical suggestions; and means for acquiring the player's physical and mental information from sensors and providing advice via voice and visual means. This enables real-time monitoring of the player's condition and rapid tactical feedback.

[0646] "Athlete condition information" refers to various data that indicates the athlete's physical and mental state, such as heart rate, distance covered, and fatigue level.

[0647] "Analysis" is the process of using acquired player status information to evaluate the players' health and performance, and to derive the optimal training plan and player placement.

[0648] An "optimal player lineup proposal" is a plan that suggests the optimal placement and selection strategy for players, based on the analysis results, taking into account the players' physical condition and the opponent's tactics.

[0649] A "development plan" involves formulating training menus and training policies that align with each athlete's individual skill improvement and career plan, taking into account their past athletic records and current condition.

[0650] "Tactical suggestions" refer to providing advice based on real-time information about the players' condition, with the aim of adjusting strategic actions and positioning during matches and training.

[0651] "Voice and visual advice" refers to providing players and coaches with real-time instructions and information via visual displays and audio, based on analyzed data.

[0652] A "sensor" is a device or equipment used to accurately measure and acquire various data from athletes, such as heart rate and exercise volume.

[0653] This invention provides a system for acquiring and analyzing real-time status information of players to implement optimal tactics and health management. Specific embodiments for realizing this system are described below.

[0654] The server first receives real-time physical data such as heart rate, distance covered, and fatigue level from sensor devices worn by the athletes. This data is then transmitted to the server via Bluetooth or Wi-Fi. The server stores this data and performs data analysis using machine learning models with cloud services such as Google Cloud.

[0655] Based on the analysis results, the server evaluates the players' performance and determines the necessary training content and injury prevention measures. Furthermore, during matches, it generates tactical suggestions in real time based on the players' collective movements and notifies the user's device.

[0656] The device functions as a smartphone or tablet for players and coaches. Analysis results and tactical suggestions are provided to users through voice advice and visual displays. The interface is intuitive, allowing for quick access to information even during matches or training sessions.

[0657] For example, if a player's heart rate reaches its limit during a match, the server will quickly send strategies to reduce the strain and suggestions for player substitutions to the terminal. The terminal will then provide a real-time voice notification saying, "The player's heart rate is high; please consider substituting them."

[0658] Examples of prompts to input into a generative AI model:

[0659] "Using the heart rate and location information of players during a match, we recommend the next action they should take."

[0660] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0661] Step 1:

[0662] The server receives data in real time from sensor devices worn by the athletes. Input data includes heart rate, distance covered, and fatigue level. The server receives this data via Bluetooth or Wi-Fi and stores it in storage.

[0663] Step 2:

[0664] The server inputs the received player data into a Google Cloud machine learning model for analysis. This input includes heart rate and distance covered. The analysis process involves processing time-series data and detecting outliers, ultimately outputting the player's fatigue level and performance status.

[0665] Step 3:

[0666] The server evaluates the players' performance based on the analysis results obtained from the machine learning model. From the analysis results, it generates information suggesting rest for players who are fatigued, for example, and uses this as input for the next step.

[0667] Step 4:

[0668] The server generates tactical suggestions based on real-time data during the match. Inputs are the latest player data and historical match data, while output is specific tactical actions. The generated suggestions optimize tactics by considering player position information.

[0669] Step 5:

[0670] The terminal displays analysis results and tactical suggestions sent from the server, either audibly or visually. The terminal receives the output data and intuitively displays or notifies players and coaches of instructions such as "Urgent tactical change required." This enables users to make quick decisions during a match.

[0671] Step 6:

[0672] Users adjust player placement and individual actions based on information provided by their devices. This allows them to manage player health, optimize performance, and make crucial decisions that influence the outcome of matches.

[0673] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0674] This invention is a system that incorporates an emotion engine to recognize user emotions in order to more efficiently manage player condition and formulate tactics. In addition to collecting and analyzing player condition information, this system provides optimal support to players and teams by analyzing user emotions.

[0675] First, the device collects the athlete's physical data from sensors. This includes heart rate, movement patterns, and fatigue levels. Simultaneously, it also collects data such as the user's voice and facial expressions and sends it to the emotion engine. This emotion engine has the ability to analyze the user's voice tone and subtle facial movements to infer the user's emotional state.

[0676] Next, the server receives this data, applies machine learning algorithms to the players' physical data to evaluate their performance, and generates necessary tactics and training plans. Meanwhile, it uses emotional information obtained from the emotion engine to dynamically adjust communication methods according to the user's emotions. For example, if the user is feeling anxious, it provides advice to help them relax.

[0677] Furthermore, during a match, the server monitors the status of both players and users in real time, and provides tactical suggestions and emotional support to the device as needed. For example, if a user is feeling anxious during a tense moment in the match, the server will provide a message encouraging calmness based on that emotion, helping to soothe the situation.

[0678] Finally, users can receive suggestions from the system and incorporate them into player placement and training plans. By using the emotion engine, coaches can provide more emotionally resonant support to players and matches, which is expected to improve the team's psychological well-being and performance.

[0679] In this form, the present invention not only improves the performance of athletes but also enables effective sports management while maintaining the mental health of the entire team.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The device collects physical data from players via sensors before matches and during training. This data includes heart rate, body temperature, and muscle movement. Simultaneously, it also collects the user's (coach's or staff's) face and voice to send to the emotion engine.

[0683] Step 2:

[0684] The device transmits collected player data and user sentiment data to a server via the internet. This process is performed continuously in real time.

[0685] Step 3:

[0686] The server analyzes the received physical data of the players and generates performance indicators. Here, it evaluates each player's condition and extracts information useful for suggesting training plans and tactics.

[0687] Step 4:

[0688] The server uses an emotion engine to analyze the user's emotional data. This analysis detects emotional states such as stress, anxiety, and excitement, and generates appropriate mental advice for the user.

[0689] Step 5:

[0690] The server creates a digital plan based on the analysis results, proposing training and match tactics for the players, and sends it to the terminal.

[0691] Step 6:

[0692] During matches and training sessions, the device continuously transmits real-time physical and emotional data to the server, keeping the latest information readily available.

[0693] Step 7:

[0694] The server generates real-time feedback to optimize tactical changes and player substitutions during matches. It also provides users with timely advice based on their emotional state.

[0695] Step 8:

[0696] Users can receive suggestions and advice from the server and incorporate them into on-site coaching and player management. This will lead to more effective coaching for players.

[0697] (Example 2)

[0698] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] In modern sports management, understanding an athlete's physical condition is crucial, but so is providing mental support. However, current technology makes it difficult to comprehensively analyze an athlete's condition and the user's emotions in real time and provide appropriate tactics and training plans based on that analysis. Therefore, there is a need to simultaneously improve athlete performance and provide support that is sensitive to the user's emotions.

[0700] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0701] In this invention, the server includes means for acquiring and analyzing player status information, means for analyzing the user's emotional state and providing feedback, and means for evaluating player performance by applying machine learning algorithms. This enables effective sports management tailored to the physical and mental state of the players.

[0702] "Athlete condition information" refers to data related to an athlete's physical and athletic performance. This data is collected using sensors and includes heart rate, movement patterns, and other similar information.

[0703] "Emotional analysis" refers to the process of inferring and analyzing a user's emotional state from their voice and facial expressions. This is carried out using voice analysis and facial recognition technologies.

[0704] "Feedback" refers to advice and information provided to users and players based on analysis results. This facilitates appropriate responses depending on the situation.

[0705] A "machine learning algorithm" refers to a method in which a computer system learns from data and makes predictions and decisions based on future data. In sports, it is used to analyze the performance of athletes.

[0706] A "tactical plan" refers to a plan formulated to take effective actions during matches or training. It is dynamically adjusted based on the players' condition and the user's emotions.

[0707] To implement this invention, the system is configured as follows.

[0708] First, the device collects physical data from the athlete. Physical data such as the athlete's heart rate, movement patterns, and fatigue level are acquired using a heart rate monitor and accelerometer. In addition, microphones and cameras are used to collect data on the user's voice and facial expressions. This data is temporarily stored in an athlete information database and a user emotion database.

[0709] Next, the server receives the collected data and begins processing it. The server applies machine learning algorithms to the players' physical data. The software used includes open-source machine learning frameworks and data analysis software. This algorithm evaluates performance and detects anomalies based on the players' past and current data. Simultaneously, the server uses an emotion analysis engine to analyze the user's voice and facial expression data and infer their emotional state. This makes it possible to formulate appropriate communication methods based on the user's emotions.

[0710] A concrete example is real-time assistance during a match. For instance, if a user is feeling nervous, the server generates a message such as "Calm down and focus on the next play" and notifies the user through their device. This allows the user to regain their composure.

[0711] Furthermore, by using a generative AI model to provide prompts that offer feedback in response to user requests, such as "What tactical plan should be suggested when a player's fatigue level is high?", an appropriate plan can be generated.

[0712] This will enable detailed and effective support based on the physical and psychological condition of athletes, and is expected to improve the quality of sports management.

[0713] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0714] Step 1:

[0715] The device collects physical data from the athlete. It uses sensors to acquire heart rate and movement patterns, and sends this data to a server. The raw data obtained from the sensors is temporarily stored in a database. Specifically, a heart rate sensor is attached to the body, and heart rate data is acquired in real time. The input is raw physical data from the sensor, and the output is formatted physical data.

[0716] Step 2:

[0717] The device collects the user's voice and facial expression data. Using the microphone and camera, it collects the user's voice tone and facial expressions, and sends this data to the sentiment analysis engine. Specifically, the microphone captures voice data, and the camera tracks facial movements. The input is voice and video data, and the output is processed data for sentiment analysis.

[0718] Step 3:

[0719] The server processes the player's physical data received by the server into a machine learning algorithm. Using historical and real-time data, it evaluates performance and detects anomalies. Specifically, the algorithm models the player's condition and generates performance metrics. The input is the player's physical data, and the output is the performance evaluation result.

[0720] Step 4:

[0721] The server analyzes the user's emotional state using an emotion analysis engine. It utilizes voice analysis software and facial recognition algorithms to determine the user's emotions. Specifically, it generates emotion labels based on voice tone and subtle changes in facial expression. The input is processed voice and facial data, and the output is the user's emotional state.

[0722] Step 5:

[0723] The server generates tactical plans and feedback based on player performance evaluations and the user's emotional state. Prompt messages are input to the generating AI model, which then generates an appropriate plan. Specifically, the AI ​​proposes countermeasures for situations such as "when a player's fatigue level is high." The input is the performance evaluation results and emotional state, and the output is a specific tactical plan.

[0724] Step 6:

[0725] The terminal receives output from the server and notifies the user. The user checks the notification and incorporates it into player positioning and training plans. Specifically, the terminal displays a tactical plan on the screen and provides audio notifications. The input is the tactical plan from the server, and the output is information provided to the user.

[0726] (Application Example 2)

[0727] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0728] In community-based events, a challenge is to appropriately understand participants' emotional responses and improve event satisfaction. Traditional methods have made it difficult to grasp individual participants' emotions in real time and dynamically optimize event content and communication based on that information.

[0729] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0730] In this invention, the server includes means for acquiring player status information and analyzing it to generate an optimal player configuration plan, means for analyzing user emotional information and dynamically generating event-participation messages, and means for optimizing communication based on the analyzed emotional information. This enables flexible event management that responds to participants' emotions and provides a better civic experience.

[0731] "Athlete status information" refers to data that indicates an athlete's current physical and mental health and performance, including heart rate, movement patterns, and fatigue levels.

[0732] An "optimal player lineup proposal" is a suggestion that, based on player condition information, indicates the most effective placement and combination of players to maximize team performance.

[0733] A "development plan" refers to the systematic planning of training content and schedules aimed at the long-term growth and improvement of an athlete's abilities.

[0734] "Emotional information" is data primarily obtained from voice and facial expressions, and represents the user's emotional state. This allows us to capture changes in the user's mood and emotions.

[0735] "Event-oriented messaging" refers to personalized suggestions and activity recommendations sent to participants during an event based on their emotional information.

[0736] "Communication optimization" means improving the quality of communication between participants and with the system by utilizing users' emotional information and providing information to individual users at the optimal timing and with the right content.

[0737] The system that realizes this invention consists of terminals such as smartphones and smart glasses, and a server that processes the data. The terminals collect biometric information such as the user's heart rate, movement patterns, and fatigue level via sensors. In addition, they capture the user's voice and facial expressions with cameras and microphones to acquire emotional information. This data is transmitted to the server in real time.

[0738] On the server, machine learning algorithms are first applied to the players' biometric information to analyze their current state. Based on these results, player placement plans and training plans are dynamically generated. Meanwhile, the emotion engine performs speech recognition and facial expression analysis to extract the user's emotional information. This allows for the real-time generation of interactive messages tailored to the user's emotions and sent back to the terminal. Facial expression analysis is performed using OpenCV and dlib, and speech features can be captured using PocketSphinx.

[0739] Messages sent from the server will be reassuring to users and encourage participation in new activities. For example, if a participant at a music festival is deemed to be relaxed, they will be notified of upcoming artist performances or campaigns to further enhance their enjoyment.

[0740] By using a generative AI model, event-participation messages based on user emotions can be efficiently generated. In this system, for example, using a prompt such as "Generate event-participation messages based on participants' emotions" can improve event satisfaction.

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The device collects biometric information such as the user's heart rate, movement patterns, and fatigue level using sensors. In addition, it captures facial expressions with a camera and records voice with a microphone. The collected data is transmitted to a server in real time for integrated analysis. The input is data from sensors, camera, and microphone, and the output is data transmission to the server.

[0744] Step 2:

[0745] The server inputs the received biometric information into a machine learning algorithm to analyze the player's current performance status. Here, biometric data is used as input, and performance evaluation results are output. Based on these evaluation results, the system generates optimal placement and development plans for the player. Specifically, it suggests training plans tailored to the player's current condition.

[0746] Step 3:

[0747] The server analyzes audio data received from the user using PocketSphinx, extracting emotions from tone and speech patterns. It also analyzes facial features using OpenCV and dlib to infer emotional states. Input is audio and facial data, and output is the analyzed emotional information. Based on this emotional information, it dynamically generates event-participation messages.

[0748] Step 4:

[0749] The server uses a generative AI model to create the optimal message using a prompt such as, "Generate an event-participation message based on participants' emotions." The input for this step is emotion information and the generative AI model, and the output is a customized message.

[0750] Step 5:

[0751] The server sends the generated message back to the terminal, notifying the user. Providing appropriate activities and information based on the user's state enhances the event experience. The input is the generated message, and the output is the notification delivered to the user.

[0752] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0753] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0754] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0755] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0756] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0757] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0758] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0759] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0760] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0761] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0762] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0763] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0764] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0765] 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.

[0766] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0767] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0768] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0769] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0770] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0771] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0772] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0773] The following is further disclosed regarding the embodiments described above.

[0774] (Claim 1)

[0775] A means for acquiring player status information, analyzing it, and generating an optimal player roster plan,

[0776] A means of formulating an athlete development plan based on acquired status information and past competition records,

[0777] A means of analyzing status information acquired during a match in real time and providing tactical suggestions,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, comprising means for evaluating the health status of athletes and providing injury prediction and recovery plans.

[0781] (Claim 3)

[0782] The system according to claim 1, comprising means for publishing analysis results in a format usable by stakeholders and supporting communication among participants.

[0783] "Example 1"

[0784] (Claim 1)

[0785] A means for acquiring players' biometric data, analyzing it, and generating an optimal player placement plan,

[0786] A means of formulating an athlete development plan based on acquired biometric data and past competition data,

[0787] A method for analyzing biometric data acquired during a match in real time using a generated AI model and providing tactical suggestions,

[0788] A means of generating prompt statements according to the situation and modifying them as needed,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, comprising means for evaluating a player's health information and providing injury risk prediction and recovery plan.

[0792] (Claim 3)

[0793] The system according to claim 1, comprising means for publishing analysis results in a format accessible to participants and supporting information exchange among stakeholders.

[0794] "Application Example 1"

[0795] (Claim 1)

[0796] A means for acquiring player status information, analyzing it, and generating an optimal player roster plan,

[0797] A means of formulating an athlete development plan based on acquired status information and past competition records,

[0798] A means of analyzing status information acquired during a match in real time and providing tactical suggestions,

[0799] A method of acquiring information about the athlete's physical and mental state from sensors and providing advice through voice and visual means,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, comprising means for evaluating the health status of athletes and providing injury prediction and recovery plans.

[0803] (Claim 3)

[0804] The system according to claim 1, comprising means for publishing analysis results in a format usable by stakeholders and supporting communication among participants.

[0805] "Example 2 of combining an emotion engine"

[0806] (Claim 1)

[0807] A means for acquiring player status information, analyzing it, and generating an optimal player roster plan,

[0808] A means of formulating an athlete development plan based on acquired status information and past competition records,

[0809] A means of analyzing status information acquired during a match in real time and providing tactical suggestions,

[0810] A means of performing emotion analysis and providing situation-appropriate feedback based on the user's emotional state,

[0811] A method for evaluating player performance by applying machine learning algorithms based on collected data,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, comprising means for evaluating the health status of players and providing injury prediction and recovery plans, and means for generating tactical plans and training plans based on emotion analysis results.

[0815] (Claim 3)

[0816] The system according to claim 1, comprising means for publishing analysis results in a format usable by stakeholders, means for supporting communication among participants, and means for sending notifications to the terminal according to the user's emotional state.

[0817] "Application example 2 when combining with an emotional engine"

[0818] (Claim 1)

[0819] A means for acquiring player status information, analyzing it, and generating an optimal player roster plan,

[0820] A means of formulating an athlete development plan based on acquired status information and past competition records,

[0821] A means of analyzing status information acquired during a match in real time and providing tactical suggestions,

[0822] A means of analyzing user sentiment information and dynamically generating event-participation messages,

[0823] A means of optimizing communication based on analyzed emotional information,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, comprising means for evaluating the health status of athletes and providing injury prediction and recovery plans.

[0827] (Claim 3)

[0828] The system according to claim 1, comprising means for publishing analysis results in a format usable by stakeholders and supporting communication among participants. [Explanation of symbols]

[0829] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring player status information, analyzing it, and generating an optimal player roster plan, A means of formulating an athlete development plan based on acquired status information and past competition records, A means of analyzing status information acquired during a match in real time and providing tactical suggestions, A system that includes this.

2. The system according to claim 1, comprising means for evaluating the health status of athletes and providing injury prediction and recovery plans.

3. The system according to claim 1, comprising means for publishing analysis results in a format usable by stakeholders and supporting communication among participants.

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