system

Abstract

We provide the system. [Solution] A means of collecting and processing player position data and ball possession data in real time, A means for predicting the opponent's tactical patterns via a machine learning model using the aforementioned data, A means of dynamically generating and presenting optimal tactical options to coaches based on prediction results, A means of providing spectators with tactical analysis data of the match, A means of providing benefits to spectators upon the success of a specific event, linked to electronic transactions, 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 persona chatbot control method 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 a chatbot 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] Conventionally, during a soccer game, coaches and managers mainly relied on experience and intuition to select tactics, making it difficult to respond to rapid changes in the game situation. In addition, real-time data analysis during the game and prediction of the tactical patterns of the opposing team were insufficient, and there was a lack of objective indicators for making optimal tactical decisions.

Means for Solving the Problems

[0005] This invention includes means for collecting and processing player position data and ball possession data in real time, means for predicting the opposing team's tactical patterns using machine learning models based on this data, and means for immediately generating and presenting optimal tactical options to coaches based on the prediction results. This enables coaches and managers to make quick and accurate tactical decisions during a match and to respond flexibly to changes in the match situation.

[0006] "Player location data" refers to data that captures the geographical location information of players in real time during a match.

[0007] "Ball possession data" refers to data that shows the percentage of time a particular team has possession of the ball during a game.

[0008] "Means of real-time collection and processing" refers to a combination of software and hardware that allows for the immediate collection and instantaneous analysis of data during a match.

[0009] A "machine learning model" is a collection of algorithms that learn patterns from past data and use them to make predictions about new data.

[0010] A "tactical pattern" is a concept that refers to a series of offensive and defensive methods employed by a particular team during a match.

[0011] A "tactical option" refers to the optimal combination of strategies and tactics that can be selected depending on the situation of the match.

[0012] "Means of presenting to leaders" refers to a system that includes an interface for communicating tactical options to managers and coaches visually or audibly. [Brief explanation of the drawing]

[0013] [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the 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.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the 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, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention proposes a system for improving the accuracy of tactical decisions during soccer matches. This system collects real-time data on player movements and ball trajectory during a match and provides a means for analyzing this data. The system consists primarily of a server, terminals, and users (coaches and managers).

[0035] The server receives information from devices that collect real-time player position data and ball possession data during the match. This data includes players' movements in specific areas and ball possession status over time. Using this data as input, the server uses machine learning models to predict the opposing team's tactical patterns. This allows it to anticipate the opponent's offensive and defensive strategies.

[0036] Based on these predictions, the server dynamically generates tactical options. These options take into account the current match situation and provide the optimal strategy, such as strengthening offense or defense, or selecting specific players. The terminal provides an interface for presenting the tactical options sent from the server to the manager or coach. This allows users to enhance their real-time tactical decision-making.

[0037] For example, if a particular player is positioned high up the field near the opponent's goal during a match, the system can recommend utilizing that player to strengthen the attack. Conversely, if the system anticipates that the opposing team is strengthening its defense and preparing for a quick counterattack, the server can suggest defensive tactical options to encourage a swift defensive response.

[0038] In this way, the present invention has significance in enabling data-driven tactical decisions and supporting rapid and accurate decision-making during matches.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server prepares to collect match data in real time. Specifically, it uses network sockets to listen for connections from clients on a designated port, enabling it to receive data from devices involved in the match.

[0042] Step 2:

[0043] The server receives player position data and ball possession data during a match. This includes the players' current position coordinates and ball ownership information, and is transmitted in JSON format or similar.

[0044] Step 3:

[0045] The server analyzes the received data. This data analysis extracts player movement trajectories and ball possession tendencies, converting them into numerical formats so they can be used as input data for subsequent machine learning models.

[0046] Step 4:

[0047] The server uses the analyzed data as input to a machine learning model to predict the opposing team's tactical patterns. The model is based on a pre-trained algorithm and predicts the flow of the game and the opponent's tactical actions with high accuracy.

[0048] Step 5:

[0049] The server determines the optimal tactical options based on the generated predictions. These are generated as specific tactical choices, such as suggestions for strengthening offense or defense, providing countermeasures tailored to the current state of the match.

[0050] Step 6:

[0051] The server transmits the determined tactical options to the terminal. The terminal receives this information and presents it visually to the user, who is the manager or coach. This allows the user to immediately review the information and make tactical decisions.

[0052] (Example 1)

[0053] 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."

[0054] Conventional tactical decision-making systems have problems with effectively utilizing player positioning information and ball possession rates, resulting in insufficient real-time tactical prediction and suggestions. Furthermore, the inaccurate tactical suggestions to coaches made it difficult to make quick and appropriate tactical changes during a match. This created a challenge in understanding the flow of the game and flexibly adapting tactics.

[0055] 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.

[0056] In this invention, the server includes means for processing the location information of athletes and object retention rate information collected from a location information acquisition device in real time, means for predicting match tactical patterns via an information processing model using the said information, and means for dynamically generating and presenting the optimal tactical selection to the coach based on the prediction results. This makes it possible to utilize data in real time during a match and adjust tactics flexibly and quickly.

[0057] A "location information acquisition device" is a device that detects the geographical location of a person exercising and collects location data corresponding to time.

[0058] "Athlete location information" refers to data that indicates the current physical location of an athlete on the field.

[0059] "Object retention rate information" is data that indicates how long a particular entity is holding an object.

[0060] An "information processing model" is an algorithm or mathematical model that learns patterns based on input data and makes predictions.

[0061] A "battle tactical pattern" is a pattern of data that analyzes the tendencies of tactics and strategies that an opponent may employ.

[0062] "Tactical selection" refers to strategic action and positioning options suggested in response to the situation during a match.

[0063] A "leader" is a person who is responsible for making strategic decisions and supervising in a sport or business setting.

[0064] "Information transmission technology" refers to the technologies and means used to send, receive, and transmit information such as data and messages.

[0065] A "user device" is a device used by a user to receive or input information.

[0066] This invention relates to a system that improves the accuracy of tactics in soccer matches, and its main components are a server, terminals, and users. The server is a device that receives player location information and ball possession rate information in real time from location information acquisition devices and camera systems installed at the match venue. The received data is analyzed using information processing models such as TENSORFLOW® and PyTorch. This makes it possible to predict match tactical patterns.

[0067] The server generates dynamic tactical selections based on analysis. Using a generation AI model, it derives the optimal tactics based on current match data. These generated tactical selections are presented to the coach via a terminal. The terminal plays a role in providing the coach with real-time information necessary for player instructions and tactical adjustments. The terminal uses touch-enabled tablets or monitor devices, displaying tactical information in a visually easy-to-understand manner.

[0068] Users can evaluate the tactical options presented on their device and make appropriate choices based on the match situation. For example, a generative AI model will provide appropriate tactical suggestions in response to a prompt such as, "Suggest attacking patterns when player A is positioned on the right side." This allows coaches to make quick and accurate decisions during a match.

[0069] This system is expected to enhance competitiveness in the sport by enabling real-time monitoring and analysis of the flow of the game, and allowing for flexible adjustments to tactics.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server collects player location information and ball possession percentage information in real time from location acquisition devices and camera systems installed at the match venue. This information is updated every second, and includes the geographical location data of athletes and the control status of objects. Specifically, the server acquires movement data during the match and performs preprocessing to make it immediately available for analysis.

[0073] Step 2:

[0074] The server inputs the collected location and retention rate information into an information processing model for analysis. This information processing model is built using TensorFlow and PyTorch and is trained on past match data to predict the opposing team's tactical patterns. Based on the input behavioral data, the server infers tactical movements and tendencies, and outputs a match tactical pattern. Specifically, it analyzes the opposing team's intentions from the movements of each player and the ball, and predicts actions several seconds in advance.

[0075] Step 3:

[0076] The server generates tactical suggestions using prompts based on the analysis results in the AI ​​model. These prompts include specific tactical scenarios, such as "If player A is positioned on the right side, how should the attack be constructed?" The AI ​​model responds to these prompts by outputting the optimal tactical plan and returning it to the system. Specifically, the server generates tactical data and creates possible options for each scenario.

[0077] Step 4:

[0078] The terminal clearly presents the generated tactical selections to the user. A touch-enabled tablet or display is used for this presentation, and the tactical selections are communicated to the coach via a visual interface. The user can evaluate the tactical selections displayed on the terminal and make immediate decisions based on the match situation. Specifically, it is possible to simulate actions such as giving instructions to players and adjusting their positioning on the terminal.

[0079] Step 5:

[0080] Based on the presented tactical options, the user makes quick and accurate decisions and gives instructions to the team. These decisions, made instantly during the match, directly impact player positioning and strategic adjustments. After deciding on tactics, the user gives specific instructions to the players and makes adjustments to ensure their execution. Specifically, it is possible to monitor the match situation and give instructions for adopting or changing tactics in real time.

[0081] (Application Example 1)

[0082] 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."

[0083] In sports competitions, it is difficult for coaches to make optimal tactical decisions in real time, and there is a lack of interactive viewing experiences and benefits for spectators that correspond to the progress of the game. Therefore, there is a need for a means to quickly and accurately share insights about the game and enhance spectator engagement.

[0084] 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.

[0085] In this invention, the server includes means for collecting and processing player position data and ball possession data in real time; means for predicting opponent tactical patterns via a machine learning model using the data; means for dynamically generating and presenting optimal tactical options to coaches based on the prediction results; means for providing spectators with tactical analysis data of the match; and means for providing rewards to spectators upon the success of a specific event in conjunction with electronic trading. This enables coaches to make tactical decisions quickly, provides spectators with a new viewing experience, and enhances their sense of participation in sporting events.

[0086] "Player position data" refers to information that indicates the position of individual players on the field during a sports competition.

[0087] "Ball possession data" refers to information that measures ball possession during a match, showing the time and frequency of ball control for each player and team.

[0088] A "machine learning model" is a mathematical and statistical method that uses large amounts of data to enable computers to learn patterns and rules and automatically make predictions and decisions.

[0089] "Tactical options" refer to the choices of actions and strategies that players and coaches should take depending on the situation during a match.

[0090] In sports competitions, a "coach" refers to a manager or coach who is responsible for the team's tactics and the guidance of the players.

[0091] "Spectators" refer to people who seek enjoyment or information by watching competitions or matches.

[0092] "Electronic transactions" refer to methods of buying and selling goods and services through digital networks.

[0093] A "perk" is an additional benefit or reward offered when certain conditions are met.

[0094] This invention is a system designed to improve tactical decision-making in soccer matches, utilizing real-time player position data and ball possession data. The server collects and processes match data from sensors and cameras to understand player movements. Specifically, the server uses this data to predict the opposing team's tactics through a machine learning model. Platforms such as TensorFlow and PyTorch can be used for machine learning.

[0095] Next, the server dynamically generates tactical options based on the prediction results and presents them to the coach in real time. The coach can visually review the tactical options through their terminal and receive assistance in selecting the appropriate tactic on the spot.

[0096] Furthermore, this invention also provides new value to spectators. Spectators can use user terminals to interactively experience tactical analysis results and information on player movements during the match. In addition, rewards are provided when specific tactics are successful, linked to an electronic trading system on the spectator's terminal. For example, if a certain player scores an important goal, spectators may receive discount coupons for merchandise.

[0097] As a concrete example, the system observes scenes where a particular player maintains possession of the ball for extended periods, and based on the analysis results obtained, explanatory information for spectators is displayed on the terminal. In this case, a prompt utilizing a generative AI model might be: "Analyze the current tactical data of the match and provide interesting insights to the audience. Also, suggest a campaign strategy if a particular tactic is successful."

[0098] As described above, by linking servers, terminals, and users, it is possible to realize a deeper sports viewing experience based on detailed data-driven analysis and strongly support coaches' tactical decisions.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server uses sensors and cameras to collect real-time data on player position and ball possession during a match. This data, including player coordinates and ball control status, is entered into a database. The output at this stage is the raw data needed for the next processing step.

[0102] Step 2:

[0103] The server preprocesses the collected location and ball retention data, performing filtering to remove noise and improve reliability. The filtering algorithm transforms the input data into a clean, analyzable state. This results in output data free from excessive noise.

[0104] Step 3:

[0105] The server sends pre-processed data as input to a machine learning model to predict the opposing team's tactical patterns. This model is trained on past match data and performance, and uses a generative AI model to predict what might happen next. The output is predictive information about the opposing team's actions.

[0106] Step 4:

[0107] The server dynamically generates tactical options based on predictions from machine learning models and presents them to the coach. The optimal tactic is output as a selection of choices appropriate to the match situation, and the coach can review it through their terminal.

[0108] Step 5:

[0109] The terminal displays real-time tactical analysis and commentary on the match via a spectator interface. Spectators can obtain detailed information that matches the progress of the match by operating the terminal. Inputs are tactical options and commentary information, and outputs are various interactively displayed information.

[0110] Step 6:

[0111] Users use their devices to receive rewards linked to electronic trading functions. This process involves inputting event information triggered by the success of specific players or tactics, and then receiving reward notifications. For example, the server automatically delivers "discount coupons for special merchandise" to spectator devices.

[0112] 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.

[0113] This invention incorporates an emotion engine into a system that supports tactical decision-making in soccer matches, thereby enabling more effective tactical suggestions that take into account the emotional state of the user, such as a manager or coach. This system consists primarily of a server, a terminal, an emotion engine, and the user.

[0114] The server has the ability to collect player position data and ball possession data in real time during a match and analyze this data. The analyzed data is used to predict the opposing team's tactical patterns using machine learning models. This prediction includes dynamically generated tactical options and suggests appropriate strategies depending on the match situation.

[0115] The emotion engine detects the user's emotional state and evaluates how it affects tactical selection suggestions. For example, the emotion engine recognizes the user's emotions in real time by analyzing their facial expressions and tone of voice. This allows the system to understand the user's mental responses to the game situation and flexibly adjust tactical suggestions accordingly.

[0116] The terminal presents the user with tactical options and sentiment-based analysis results sent from the server. These tactical options are not simply data-driven choices, but are presented in a way that is appropriate to the user's current emotions, allowing the user to make more conscious and strategic decisions during the match.

[0117] For example, if the emotion engine recognizes the user's stress level, the tactical options generated by the server will be adjusted to reduce the user's psychological burden. The user can review strategies through their device and control the flow of the game with an emotion-sensitive approach. In this way, the present invention enables more personalized strategic suggestions by incorporating the user's emotions into tactical decisions.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] The server connects to the network as soon as the match starts and begins receiving player position data and ball possession data in real time. The data includes dynamic information about each player during the match and is transmitted in a specific format.

[0121] Step 2:

[0122] The server uses received location data and ball possession data to predict the opposing team's tactical patterns via a machine learning model. The model is trained on past match data and has learned various tactical scenarios, enabling highly accurate predictions.

[0123] Step 3:

[0124] The emotion engine collects information in real time through cameras and microphones to understand the user's emotions during a match. The emotion engine uses facial recognition and voice analysis technologies to recognize emotions such as stress and tension.

[0125] Step 4:

[0126] The server dynamically generates tactical options based on predicted tactical patterns and sentiment data provided by the sentiment engine. These options are adjusted to prioritize conservative tactics, for example, if the user is under stress and prefers calmer tactics.

[0127] Step 5:

[0128] The device visually presents the user with generated tactical options and sentiment analysis results. The device's interface is designed to allow users to intuitively understand the information.

[0129] Step 6:

[0130] The user reviews the tactical options presented on the device and makes a final decision on how to execute their team's tactics. At this time, they select the most appropriate strategy, taking into account the displayed sentiment analysis information.

[0131] (Example 2)

[0132] 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".

[0133] While conventional tactical decision-making systems could analyze dynamic data during matches, they struggled to provide tactical suggestions that took into account the emotional state of the coaches and managers who used them. This meant that tactical choices sometimes did not align with the users' psychological state, potentially hindering optimal strategic decisions.

[0134] 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.

[0135] In this invention, the server includes means for collecting and analyzing location information and ball possession information in real time, means for predicting the opponent's tactical patterns via a machine learning algorithm using the said information, and means for recognizing the user's emotional state and dynamically adjusting tactical options based on the recognition results. This allows tactical decisions during a match to be made in a way that takes the user's emotional state into account, enabling more personalized strategic suggestions.

[0136] "Location information" refers to data that indicates the geographical location of an object, and is used to understand the spatial arrangement of players.

[0137] "Ball possession information" refers to data that shows how much each player controls or possesses the ball during a game.

[0138] A "machine learning algorithm" is a technology that analyzes large amounts of data patterns and automatically makes predictions and classifications, and is used to predict the tactical patterns of the opposing team.

[0139] "User's emotional state" refers to the emotional state exhibited by users such as coaches and managers, and includes information such as psychological stress levels and relaxation levels.

[0140] "Tactical options" refer to multiple strategic choices depending on the game situation, and are suggestions for effectively managing the flow of the match.

[0141] "Display devices" are devices that provide digital information to users visually, and include monitors and tablets.

[0142] A "digital network" is a communication infrastructure used for sending and receiving digital data, and includes the internet and local area networks.

[0143] A "user terminal" is a device that a user directly operates to check information, and includes personal computers and smart devices.

[0144] This invention integrates an emotion analysis function into a system that supports tactical decision-making in soccer matches, thereby providing more effective tactical suggestions that take into account the emotional state of the user, such as a manager or coach. The system consists primarily of a server, terminals, an emotion recognition device, and the user.

[0145] The server is responsible for collecting player location information and ball possession information in real time. Location information is obtained through GPS devices equipped on the players, and ball possession information is captured by surveillance cameras and sensors. This data is processed on the server using programming languages ​​such as Python and Java (registered trademark), and machine learning algorithms are used to predict the opponent's tactical patterns.

[0146] The emotion recognition device analyzes the user's facial expressions and voice tone to recognize their emotional state in real time. Specifically, it uses the OpenCV library to analyze facial features and Google® Cloud Speech-to-Text for voice analysis. This allows the system to understand the user's psychological state and propose tactical solutions that take this into account.

[0147] The device displays tactical options and sentiment-based analysis results sent from the server in a way that is intuitively understandable to the user. Frameworks such as React and Vue.js are used for visualization, and users access this information via tablets, PCs, etc.

[0148] For example, if an emotion recognition device detects user stress, the server uses this information to generate tactical options aimed at reducing psychological burden. The user can then review these adjusted tactics via a terminal and provide appropriate guidance.

[0149] An example of a prompt message for the AI ​​model generated by this system is, "Please suggest the optimal match strategy considering the user's current emotional state."

[0150] As a result, it becomes possible to support strategic decision-making by dynamically integrating the match situation and the user's emotions.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The server collects player location and ball possession information during the match. Inputs include real-time location and ball data obtained from GPS devices and camera systems. This data is digitized and stored in a database, laying the foundation for the subsequent analysis steps.

[0154] Step 2:

[0155] The server analyzes the collected data and predicts the opposing team's tactical patterns. The inputs are the positional and ball possession information collected in the previous step. The data is processed using a generative AI model developed based on past match data, employing Python and machine learning libraries. The output is the prediction regarding the opposing team's tactical patterns.

[0156] Step 3:

[0157] The emotion recognition device recognizes the user's emotional state. Input is the user's facial expressions and voice tone, captured from a camera and microphone. Emotions are recognized in real time using OpenCV and speech analysis tools. Output is emotional data, such as the user's stress level and state of comfort.

[0158] Step 4:

[0159] The server generates optimal tactical options based on analyzed match data and user sentiment information. The inputs are the prediction results from step 2 and the sentiment data from step 3. A generative AI model integrates this data to dynamically create situation-appropriate tactical options. The output is a list of tactics that take the user's psychological state into account.

[0160] Step 5:

[0161] The terminal presents tactical options sent from the server to the user. The input is optimized tactical data from the server. A GUI framework is used to display the information on the screen in a visually easy-to-understand format. The output is a visual representation of the tactical options available to the user.

[0162] Step 6:

[0163] The user provides feedback on the presented tactical options. The input is the tactical options presented from the terminal. Based on their emotional state and tactical selection, the user makes decisions to adjust their strategy during the match and returns feedback to the system based on the results. The output is feedback data regarding the effectiveness of the tactics, which can be used for future improvements.

[0164] (Application Example 2)

[0165] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0166] In modern living spaces, optimally adjusting the environment according to the emotional state of residents is crucial for improving their comfort and mental health. However, conventional systems have struggled to accurately grasp residents' emotions and dynamically adjust the environment based on them. Therefore, there is a need for a life support system that can flexibly respond to emotions.

[0167] 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.

[0168] In this invention, the server includes means for detecting and analyzing the emotional state of the resident in real time, means for dynamically adjusting the environmental settings based on the emotional state, and means for appropriately presenting the adjustment results to the resident. This makes it possible to adjust the living space to the optimal level according to the emotional state of the resident.

[0169] The term "resident" refers to an individual or their family who resides in a specific living space or residence.

[0170] "Emotional state" refers to the mental and emotional reactions and tendencies exhibited by residents, including states such as joy, sadness, anger, and stress.

[0171] "Real-time detection and analysis methods" refer to technical means for instantly grasping the emotional state of residents, performing analysis based on that data, and obtaining results quickly.

[0172] "Means for dynamically adjusting environmental settings" refers to means that have the function of automatically and promptly changing living environment elements such as lighting, music, and temperature based on detected emotional states.

[0173] "Means of appropriately presenting adjustment results to residents" refers to methods and technologies for communicating the details of dynamically adjusted environmental settings to residents in an easily understandable manner.

[0174] To implement this invention, a server, a living environment control device, and a network environment connecting each of these devices are required.

[0175] The server utilizes sensor devices such as cameras and microphones to detect residents' emotional states in real time. Specifically, it uses emotion recognition software such as Microsoft® Azure® Cognitive Services to analyze residents' facial expressions and tone of voice to understand their emotional state. This data is processed immediately, and adjustments to the environment settings are required according to the residents' emotions.

[0176] Next, the living environment control device receives instructions from the server and appropriately adjusts lighting, music systems, air conditioning, and other settings. For example, if the server determines that a resident is feeling fatigued, it can play relaxing music and change the lighting to a softer color tone. It can also deliver messages tailored to the resident's emotional state through a voice device.

[0177] Users can choose whether or not to accept the adjustments, thereby enhancing their own comfort. Furthermore, by receiving environmental suggestions tailored to their emotions, users can improve their quality of life.

[0178] For example, if a resident returns home from work and is perceived as feeling stressed, the server can immediately switch the environment to relaxation mode and send a positive message to the resident such as, "Let's unwind after a tiring day."

[0179] An example of a prompt message for a generating AI model might be: "Please suggest the optimal music and lighting settings to create a relaxing environment that will satisfy the residents."

[0180] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0181] Step 1:

[0182] The server acquires video and audio data of residents in real time via cameras and microphones. This video and audio data, as input, is used as foundational data for detecting emotional states.

[0183] Step 2:

[0184] The server uses Microsoft Azure Cognitive Services to analyze the acquired video and audio data. The analysis estimates the emotional state (e.g., joy, sadness, stress) based on the resident's facial expressions and tone of voice. The analysis results are output as an emotional state.

[0185] Step 3:

[0186] The server generates adjustment instructions for the living environment control device based on the estimated emotional state. Specifically, it determines the brightness and color of the lighting, the temperature settings, and the type of music to play according to the emotion. This output is generated as an adjustment instruction.

[0187] Step 4:

[0188] The device executes adjustment instructions received from the server, dynamically changing the living environment according to the user's emotional state. For example, if an emotion indicating stress is detected, it will play relaxing music and adjust the lighting to a warmer color. It will also provide voice messages such as, "You seem tired today. Let's relax."

[0189] Step 5:

[0190] Users evaluate changes in the environment and make additional manual adjustments if necessary. This feedback may be incorporated into future environment adjustments. The server receives user input and logs the changes in the environment to learn and improve responses tailored to individual residents.

[0191] 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.

[0192] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), 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.

[0193] 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.

[0194] [Second Embodiment]

[0195] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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".

[0207] This invention proposes a system for improving the accuracy of tactical decisions during soccer matches. This system collects real-time data on player movements and ball trajectory during a match and provides a means for analyzing this data. The system consists primarily of a server, terminals, and users (coaches and managers).

[0208] The server receives information from devices that collect real-time player position data and ball possession data during the match. This data includes players' movements in specific areas and ball possession status over time. Using this data as input, the server uses machine learning models to predict the opposing team's tactical patterns. This allows it to anticipate the opponent's offensive and defensive strategies.

[0209] Based on these predictions, the server dynamically generates tactical options. These options take into account the current match situation and provide the optimal strategy, such as strengthening offense or defense, or selecting specific players. The terminal provides an interface for presenting the tactical options sent from the server to the manager or coach. This allows users to enhance their real-time tactical decision-making.

[0210] For example, if a particular player is positioned high up the field near the opponent's goal during a match, the system can recommend utilizing that player to strengthen the attack. Conversely, if the system anticipates that the opposing team is strengthening its defense and preparing for a quick counterattack, the server can suggest defensive tactical options to encourage a swift defensive response.

[0211] In this way, the present invention has significance in enabling data-driven tactical decisions and supporting rapid and accurate decision-making during matches.

[0212] The following describes the processing flow.

[0213] Step 1:

[0214] The server prepares to collect match data in real time. Specifically, it uses network sockets to listen for connections from clients on a designated port, enabling it to receive data from devices involved in the match.

[0215] Step 2:

[0216] The server receives player position data and ball possession data during a match. This includes the players' current position coordinates and ball ownership information, and is transmitted in JSON format or similar.

[0217] Step 3:

[0218] The server analyzes the received data. This data analysis extracts player movement trajectories and ball possession tendencies, converting them into numerical formats so they can be used as input data for subsequent machine learning models.

[0219] Step 4:

[0220] The server uses the analyzed data as input to a machine learning model to predict the opposing team's tactical patterns. The model is based on a pre-trained algorithm and predicts the flow of the game and the opponent's tactical actions with high accuracy.

[0221] Step 5:

[0222] The server determines the optimal tactical options based on the generated predictions. These are generated as specific tactical choices, such as suggestions for strengthening offense or defense, providing countermeasures tailored to the current state of the match.

[0223] Step 6:

[0224] The server transmits the determined tactical options to the terminal. The terminal receives this information and presents it visually to the user, who is the manager or coach. This allows the user to immediately review the information and make tactical decisions.

[0225] (Example 1)

[0226] 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."

[0227] Conventional tactical decision-making systems have problems with effectively utilizing player positioning information and ball possession rates, resulting in insufficient real-time tactical prediction and suggestions. Furthermore, the inaccurate tactical suggestions to coaches made it difficult to make quick and appropriate tactical changes during a match. This created a challenge in understanding the flow of the game and flexibly adapting tactics.

[0228] 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.

[0229] In this invention, the server includes means for processing the location information of athletes and object retention rate information collected from a location information acquisition device in real time, means for predicting match tactical patterns via an information processing model using the said information, and means for dynamically generating and presenting the optimal tactical selection to the coach based on the prediction results. This makes it possible to utilize data in real time during a match and adjust tactics flexibly and quickly.

[0230] A "location information acquisition device" is a device that detects the geographical location of a person exercising and collects location data corresponding to time.

[0231] "Athlete location information" refers to data that indicates the current physical location of an athlete on the field.

[0232] "Object retention rate information" is data that indicates how long a particular entity is holding an object.

[0233] An "information processing model" is an algorithm or mathematical model that learns patterns based on input data and makes predictions.

[0234] A "battle tactical pattern" is a pattern of data that analyzes the tendencies of tactics and strategies that an opponent may employ.

[0235] "Tactical selection" refers to strategic action and positioning options suggested in response to the situation during a match.

[0236] A "leader" is a person who is responsible for making strategic decisions and supervising in a sport or business setting.

[0237] "Information transmission technology" refers to the technologies and means used to send, receive, and transmit information such as data and messages.

[0238] A "user device" is a device used by a user to receive or input information.

[0239] This invention relates to a system that improves the accuracy of tactics in soccer matches, and its main components are a server, terminals, and users. The server is a device that receives player location information and ball possession rate information in real time from location information acquisition devices and camera systems installed at the match venue. The received data is analyzed using information processing models such as TensorFlow and PyTorch. This makes it possible to predict match tactical patterns.

[0240] The server generates dynamic tactical selections based on analysis. Using a generation AI model, it derives the optimal tactics based on current match data. These generated tactical selections are presented to the coach via a terminal. The terminal plays a role in providing the coach with real-time information necessary for player instructions and tactical adjustments. The terminal uses touch-enabled tablets or monitor devices, displaying tactical information in a visually easy-to-understand manner.

[0241] Users can evaluate the tactical options presented on their device and make appropriate choices based on the match situation. For example, a generative AI model will provide appropriate tactical suggestions in response to a prompt such as, "Suggest attacking patterns when player A is positioned on the right side." This allows coaches to make quick and accurate decisions during a match.

[0242] This system is expected to enhance competitiveness in the sport by enabling real-time monitoring and analysis of the flow of the game, and allowing for flexible adjustments to tactics.

[0243] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0244] Step 1:

[0245] The server collects player location information and ball possession percentage information in real time from location acquisition devices and camera systems installed at the match venue. This information is updated every second, and includes the geographical location data of athletes and the control status of objects. Specifically, the server acquires movement data during the match and performs preprocessing to make it immediately available for analysis.

[0246] Step 2:

[0247] The server inputs the collected location and retention rate information into an information processing model for analysis. This information processing model is built using TensorFlow and PyTorch and is trained on past match data to predict the opposing team's tactical patterns. Based on the input behavioral data, the server infers tactical movements and tendencies, and outputs a match tactical pattern. Specifically, it analyzes the opposing team's intentions from the movements of each player and the ball, and predicts actions several seconds in advance.

[0248] Step 3:

[0249] The server generates tactical suggestions using prompts based on the analysis results in the AI ​​model. These prompts include specific tactical scenarios, such as "If player A is positioned on the right side, how should the attack be constructed?" The AI ​​model responds to these prompts by outputting the optimal tactical plan and returning it to the system. Specifically, the server generates tactical data and creates possible options for each scenario.

[0250] Step 4:

[0251] The terminal clearly presents the generated tactical selections to the user. A touch-enabled tablet or display is used for this presentation, and the tactical selections are communicated to the coach via a visual interface. The user can evaluate the tactical selections displayed on the terminal and make immediate decisions based on the match situation. Specifically, it is possible to simulate actions such as giving instructions to players and adjusting their positioning on the terminal.

[0252] Step 5:

[0253] Based on the presented tactical options, the user makes quick and accurate decisions and gives instructions to the team. These decisions, made instantly during the match, directly impact player positioning and strategic adjustments. After deciding on tactics, the user gives specific instructions to the players and makes adjustments to ensure their execution. Specifically, it is possible to monitor the match situation and give instructions for adopting or changing tactics in real time.

[0254] (Application Example 1)

[0255] 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."

[0256] In sports competitions, it is difficult for coaches to make optimal tactical decisions in real time, and there is a lack of interactive viewing experiences and benefits for spectators that correspond to the progress of the game. Therefore, there is a need for a means to quickly and accurately share insights about the game and enhance spectator engagement.

[0257] 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.

[0258] In this invention, the server includes means for collecting and processing player position data and ball possession data in real time; means for predicting opponent tactical patterns via a machine learning model using the data; means for dynamically generating and presenting optimal tactical options to coaches based on the prediction results; means for providing spectators with tactical analysis data of the match; and means for providing rewards to spectators upon the success of a specific event in conjunction with electronic trading. This enables coaches to make tactical decisions quickly, provides spectators with a new viewing experience, and enhances their sense of participation in sporting events.

[0259] "Player position data" refers to information that indicates the position of individual players on the field during a sports competition.

[0260] "Ball possession data" refers to information that measures ball possession during a match, showing the time and frequency of ball control for each player and team.

[0261] A "machine learning model" is a mathematical and statistical method that uses large amounts of data to enable computers to learn patterns and rules and automatically make predictions and decisions.

[0262] "Tactical options" refer to the choices of actions and strategies that players and coaches should take depending on the situation during a match.

[0263] In sports competitions, a "coach" refers to a manager or coach who is responsible for the team's tactics and the guidance of the players.

[0264] "Spectators" refer to people who seek enjoyment or information by watching competitions or matches.

[0265] "Electronic transactions" refer to methods of buying and selling goods and services through digital networks.

[0266] A "perk" is an additional benefit or reward offered when certain conditions are met.

[0267] This invention is a system designed to improve tactical decision-making in soccer matches, utilizing real-time player position data and ball possession data. The server collects and processes match data from sensors and cameras to understand player movements. Specifically, the server uses this data to predict the opposing team's tactics through a machine learning model. Platforms such as TensorFlow and PyTorch can be used for machine learning.

[0268] Next, the server dynamically generates tactical options based on the prediction results and presents them to the coach in real time. The coach can visually review the tactical options through their terminal and receive assistance in selecting the appropriate tactic on the spot.

[0269] Furthermore, this invention also provides new value to spectators. Spectators can use user terminals to interactively experience tactical analysis results and information on player movements during the match. In addition, rewards are provided when specific tactics are successful, linked to an electronic trading system on the spectator's terminal. For example, if a certain player scores an important goal, spectators may receive discount coupons for merchandise.

[0270] As a concrete example, the system observes scenes where a particular player maintains possession of the ball for extended periods, and based on the analysis results obtained, explanatory information for spectators is displayed on the terminal. In this case, a prompt utilizing a generative AI model might be: "Analyze the current tactical data of the match and provide interesting insights to the audience. Also, suggest a campaign strategy if a particular tactic is successful."

[0271] As described above, by linking servers, terminals, and users, it is possible to realize a deeper sports viewing experience based on detailed data-driven analysis and strongly support coaches' tactical decisions.

[0272] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0273] Step 1:

[0274] The server uses sensors and cameras to collect real-time data on player position and ball possession during a match. This data, including player coordinates and ball control status, is entered into a database. The output at this stage is the raw data needed for the next processing step.

[0275] Step 2:

[0276] The server preprocesses the collected location and ball retention data, performing filtering to remove noise and improve reliability. The filtering algorithm transforms the input data into a clean, analyzable state. This results in output data free from excessive noise.

[0277] Step 3:

[0278] The server sends pre-processed data as input to a machine learning model to predict the opposing team's tactical patterns. This model is trained on past match data and performance, and uses a generative AI model to predict what might happen next. The output is predictive information about the opposing team's actions.

[0279] Step 4:

[0280] The server dynamically generates tactical options based on predictions from machine learning models and presents them to the coach. The optimal tactic is output as a selection of choices appropriate to the match situation, and the coach can review it through their terminal.

[0281] Step 5:

[0282] The terminal displays real-time tactical analysis and commentary on the game via an interface for spectators. Spectators can obtain detailed information adapted to the progress of the game by operating the terminal. The input is tactical options and commentary information, and the output is various types of information displayed interactively.

[0283] Step 6:

[0284] The user receives the provision of benefits linked to the e-commerce function using the terminal. In this process, event information triggered when a specific player or tactic succeeds is input, and a notice of the benefit is output to the user. For example, the server automatically distributes "discount coupons for special goods" etc. to the spectator terminal.

[0285] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0286] The present invention realizes a more effective tactical proposal considering the emotional state of the coach or manager who is the user by incorporating an emotion engine into a system that supports tactical judgment in a soccer game. This system is mainly composed of a server, a terminal, an emotion engine, and a user.

[0287] The server has the ability to collect the position data of players and the ball possession rate data in real time during the game and analyze these data. The analyzed data is used to predict the tactical pattern of the opposing team using a machine learning model. This prediction result includes dynamically generated tactical options and proposes an appropriate strategy according to the game situation.

[0288] The emotion engine detects the user's emotional state and evaluates how it affects tactical selection suggestions. For example, the emotion engine recognizes the user's emotions in real time by analyzing their facial expressions and tone of voice. This allows the system to understand the user's mental responses to the game situation and flexibly adjust tactical suggestions accordingly.

[0289] The terminal presents the user with tactical options and sentiment-based analysis results sent from the server. These tactical options are not simply data-driven choices, but are presented in a way that is appropriate to the user's current emotions, allowing the user to make more conscious and strategic decisions during the match.

[0290] For example, if the emotion engine recognizes the user's stress level, the tactical options generated by the server will be adjusted to reduce the user's psychological burden. The user can review strategies through their device and control the flow of the game with an emotion-sensitive approach. In this way, the present invention enables more personalized strategic suggestions by incorporating the user's emotions into tactical decisions.

[0291] The following describes the processing flow.

[0292] Step 1:

[0293] The server connects to the network as soon as the match starts and begins receiving player position data and ball possession data in real time. The data includes dynamic information about each player during the match and is transmitted in a specific format.

[0294] Step 2:

[0295] The server uses received location data and ball possession data to predict the opposing team's tactical patterns via a machine learning model. The model is trained on past match data and has learned various tactical scenarios, enabling highly accurate predictions.

[0296] Step 3:

[0297] The emotion engine collects information in real time through cameras and microphones to understand the user's emotions during a match. The emotion engine uses facial recognition and voice analysis technologies to recognize emotions such as stress and tension.

[0298] Step 4:

[0299] The server dynamically generates tactical options based on predicted tactical patterns and sentiment data provided by the sentiment engine. These options are adjusted to prioritize conservative tactics, for example, if the user is under stress and prefers calmer tactics.

[0300] Step 5:

[0301] The device visually presents the user with generated tactical options and sentiment analysis results. The device's interface is designed to allow users to intuitively understand the information.

[0302] Step 6:

[0303] The user reviews the tactical options presented on the device and makes a final decision on how to execute their team's tactics. At this time, they select the most appropriate strategy, taking into account the displayed sentiment analysis information.

[0304] (Example 2)

[0305] 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".

[0306] In the conventional tactical decision-making system, although analysis based on dynamic data during the game was possible, it was difficult to make tactical proposals considering the emotional states of the users, such as coaches and managers. As a result, the tactical choices might not match the psychological states of the users, which could prevent optimal strategic decisions.

[0307] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 2 is realized by the following means.

[0308] In this invention, the server includes means for collecting and analyzing position information and ball holding information in real time, means for predicting the opponent's tactical pattern through a machine learning algorithm using the information, and means for recognizing the emotional state of the user and dynamically adjusting tactical options based on the recognition result. Thereby, tactical decisions during the game are made in a form that takes into account the emotional state of the user, enabling more personalized strategic proposals.

[0309] The "position information" is data indicating the geographical position of an object and is the information used to grasp the spatial arrangement of players.

[0310] The "ball holding information" is data indicating the degree to which each player operates or holds the ball during the game.

[0311] The "machine learning algorithm" is a technology for analyzing a large number of data patterns and automatically making predictions and classifications, and is used to predict the tactical pattern of the opponent team.

[0312] The "emotional state of the user" is the emotional state shown by users such as coaches and managers and is information including psychological stress and relaxation level.

[0313] The "tactical option" refers to a plurality of strategic choices according to the game situation and is a proposal for effectively advancing the course of the game.

[0314] "Display devices" are devices that provide digital information to users visually, and include monitors and tablets.

[0315] A "digital network" is a communication infrastructure used for sending and receiving digital data, and includes the internet and local area networks.

[0316] A "user terminal" is a device that a user directly operates to check information, and includes personal computers and smart devices.

[0317] This invention integrates an emotion analysis function into a system that supports tactical decision-making in soccer matches, thereby providing more effective tactical suggestions that take into account the emotional state of the user, such as a manager or coach. The system consists primarily of a server, terminals, an emotion recognition device, and the user.

[0318] The server is responsible for collecting player location information and ball possession information in real time. Location information is obtained through GPS devices equipped on the players, and ball possession information is captured by surveillance cameras and sensors. This data is processed on the server using programming languages ​​such as Python and Java, and machine learning algorithms are used to predict the opponent's tactical patterns.

[0319] The emotion recognition device analyzes the user's facial expressions and voice tone to recognize their emotional state in real time. Specifically, it uses the OpenCV library to analyze facial features and Google Cloud Speech-to-Text for speech analysis. This allows for an understanding of the user's psychological state and enables the provision of tactical suggestions that take this into account.

[0320] The device displays tactical options and sentiment-based analysis results sent from the server in a way that is intuitively understandable to the user. Frameworks such as React and Vue.js are used for visualization, and users access this information via tablets, PCs, etc.

[0321] For example, if an emotion recognition device detects user stress, the server uses this information to generate tactical options aimed at reducing psychological burden. The user can then review these adjusted tactics via a terminal and provide appropriate guidance.

[0322] An example of a prompt message for the AI ​​model generated by this system is, "Please suggest the optimal match strategy considering the user's current emotional state."

[0323] As a result, it becomes possible to support strategic decision-making by dynamically integrating the match situation and the user's emotions.

[0324] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0325] Step 1:

[0326] The server collects player location and ball possession information during the match. Inputs include real-time location and ball data obtained from GPS devices and camera systems. This data is digitized and stored in a database, laying the foundation for the subsequent analysis steps.

[0327] Step 2:

[0328] The server analyzes the collected data and predicts the opposing team's tactical patterns. The inputs are the positional and ball possession information collected in the previous step. The data is processed using a generative AI model developed based on past match data, employing Python and machine learning libraries. The output is the prediction regarding the opposing team's tactical patterns.

[0329] Step 3:

[0330] The emotion recognition device recognizes the user's emotional state. Input is the user's facial expressions and voice tone, captured from a camera and microphone. Emotions are recognized in real time using OpenCV and speech analysis tools. Output is emotional data, such as the user's stress level and state of comfort.

[0331] Step 4:

[0332] The server generates optimal tactical options based on analyzed match data and user sentiment information. The inputs are the prediction results from step 2 and the sentiment data from step 3. A generative AI model integrates this data to dynamically create situation-appropriate tactical options. The output is a list of tactics that take the user's psychological state into account.

[0333] Step 5:

[0334] The terminal presents tactical options sent from the server to the user. The input is optimized tactical data from the server. A GUI framework is used to display the information on the screen in a visually easy-to-understand format. The output is a visual representation of the tactical options available to the user.

[0335] Step 6:

[0336] The user provides feedback on the presented tactical options. The input is the tactical options presented from the terminal. Based on their emotional state and tactical selection, the user makes decisions to adjust their strategy during the match and returns feedback to the system based on the results. The output is feedback data regarding the effectiveness of the tactics, which can be used for future improvements.

[0337] (Application Example 2)

[0338] 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 will be referred to as the "terminal."

[0339] In modern living spaces, optimally adjusting the environment according to the emotional state of residents is crucial for improving their comfort and mental health. However, conventional systems have struggled to accurately grasp residents' emotions and dynamically adjust the environment based on them. Therefore, there is a need for a life support system that can flexibly respond to emotions.

[0340] 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.

[0341] In this invention, the server includes means for detecting and analyzing the emotional state of the resident in real time, means for dynamically adjusting the environmental settings based on the emotional state, and means for appropriately presenting the adjustment results to the resident. This makes it possible to adjust the living space to the optimal level according to the emotional state of the resident.

[0342] The term "resident" refers to an individual or their family who resides in a specific living space or residence.

[0343] "Emotional state" refers to the mental and emotional reactions and tendencies exhibited by residents, including states such as joy, sadness, anger, and stress.

[0344] "Real-time detection and analysis methods" refer to technical means for instantly grasping the emotional state of residents, performing analysis based on that data, and obtaining results quickly.

[0345] "Means for dynamically adjusting environmental settings" refers to means that have the function of automatically and promptly changing living environment elements such as lighting, music, and temperature based on detected emotional states.

[0346] "Means of appropriately presenting adjustment results to residents" refers to methods and technologies for communicating the details of dynamically adjusted environmental settings to residents in an easily understandable manner.

[0347] To implement this invention, a server, a living environment control device, and a network environment connecting each of these devices are required.

[0348] The server utilizes sensor devices such as cameras and microphones to detect residents' emotional states in real time. Specifically, it uses emotion recognition software, such as Microsoft Azure Cognitive Services, to analyze residents' facial expressions and tone of voice to understand their emotional state. This data is processed immediately, and adjustments to the environment settings are required based on the residents' emotions.

[0349] Next, the living environment control device receives instructions from the server and appropriately adjusts lighting, music systems, air conditioning, and other settings. For example, if the server determines that a resident is feeling fatigued, it can play relaxing music and change the lighting to a softer color tone. It can also deliver messages tailored to the resident's emotional state through a voice device.

[0350] Users can choose whether or not to accept the adjustments, thereby enhancing their own comfort. Furthermore, by receiving environmental suggestions tailored to their emotions, users can improve their quality of life.

[0351] For example, if a resident returns home from work and is perceived as feeling stressed, the server can immediately switch the environment to relaxation mode and send a positive message to the resident such as, "Let's unwind after a tiring day."

[0352] An example of a prompt message for a generating AI model might be: "Please suggest the optimal music and lighting settings to create a relaxing environment that will satisfy the residents."

[0353] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0354] Step 1:

[0355] The server acquires video and audio data of residents in real time via cameras and microphones. This video and audio data, as input, is used as foundational data for detecting emotional states.

[0356] Step 2:

[0357] The server uses Microsoft Azure Cognitive Services to analyze the acquired video and audio data. The analysis estimates the emotional state (e.g., joy, sadness, stress) based on the resident's facial expressions and tone of voice. The analysis results are output as an emotional state.

[0358] Step 3:

[0359] The server generates adjustment instructions for the living environment control device based on the estimated emotional state. Specifically, it determines the brightness and color of the lighting, the temperature settings, and the type of music to play according to the emotion. This output is generated as an adjustment instruction.

[0360] Step 4:

[0361] The device executes adjustment instructions received from the server, dynamically changing the living environment according to the user's emotional state. For example, if an emotion indicating stress is detected, it will play relaxing music and adjust the lighting to a warmer color. It will also provide voice messages such as, "You seem tired today. Let's relax."

[0362] Step 5:

[0363] Users evaluate changes in the environment and make additional manual adjustments if necessary. This feedback may be incorporated into future environment adjustments. The server receives user input and logs the changes in the environment to learn and improve responses tailored to individual residents.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] [Third Embodiment]

[0368] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0369] 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.

[0370] 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).

[0371] 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.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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".

[0380] This invention proposes a system for improving the accuracy of tactical decisions during soccer matches. This system collects real-time data on player movements and ball trajectory during a match and provides a means for analyzing this data. The system consists primarily of a server, terminals, and users (coaches and managers).

[0381] The server receives information from devices that collect real-time player position data and ball possession data during the match. This data includes players' movements in specific areas and ball possession status over time. Using this data as input, the server uses machine learning models to predict the opposing team's tactical patterns. This allows it to anticipate the opponent's offensive and defensive strategies.

[0382] Based on these predictions, the server dynamically generates tactical options. These options take into account the current match situation and provide the optimal strategy, such as strengthening offense or defense, or selecting specific players. The terminal provides an interface for presenting the tactical options sent from the server to the manager or coach. This allows users to enhance their real-time tactical decision-making.

[0383] For example, if a particular player is positioned high up the field near the opponent's goal during a match, the system can recommend utilizing that player to strengthen the attack. Conversely, if the system anticipates that the opposing team is strengthening its defense and preparing for a quick counterattack, the server can suggest defensive tactical options to encourage a swift defensive response.

[0384] In this way, the present invention has significance in enabling data-driven tactical decisions and supporting rapid and accurate decision-making during matches.

[0385] The following describes the processing flow.

[0386] Step 1:

[0387] The server prepares to collect match data in real time. Specifically, it uses network sockets to listen for connections from clients on a designated port, enabling it to receive data from devices involved in the match.

[0388] Step 2:

[0389] The server receives player position data and ball possession data during a match. This includes the players' current position coordinates and ball ownership information, and is transmitted in JSON format or similar.

[0390] Step 3:

[0391] The server analyzes the received data. This data analysis extracts player movement trajectories and ball possession tendencies, converting them into numerical formats so they can be used as input data for subsequent machine learning models.

[0392] Step 4:

[0393] The server uses the analyzed data as input to a machine learning model to predict the opposing team's tactical patterns. The model is based on a pre-trained algorithm and predicts the flow of the game and the opponent's tactical actions with high accuracy.

[0394] Step 5:

[0395] The server determines the optimal tactical options based on the generated predictions. These are generated as specific tactical choices, such as suggestions for strengthening offense or defense, providing countermeasures tailored to the current state of the match.

[0396] Step 6:

[0397] The server transmits the determined tactical options to the terminal. The terminal receives this information and presents it visually to the user, who is the manager or coach. This allows the user to immediately review the information and make tactical decisions.

[0398] (Example 1)

[0399] 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."

[0400] Conventional tactical decision-making systems have problems with effectively utilizing player positioning information and ball possession rates, resulting in insufficient real-time tactical prediction and suggestions. Furthermore, the inaccurate tactical suggestions to coaches made it difficult to make quick and appropriate tactical changes during a match. This created a challenge in understanding the flow of the game and flexibly adapting tactics.

[0401] 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.

[0402] In this invention, the server includes means for processing the location information of athletes and object retention rate information collected from a location information acquisition device in real time, means for predicting match tactical patterns via an information processing model using the said information, and means for dynamically generating and presenting the optimal tactical selection to the coach based on the prediction results. This makes it possible to utilize data in real time during a match and adjust tactics flexibly and quickly.

[0403] A "location information acquisition device" is a device that detects the geographical location of a person exercising and collects location data corresponding to time.

[0404] "Athlete location information" refers to data that indicates the current physical location of an athlete on the field.

[0405] "Object retention rate information" is data that indicates how long a particular entity is holding an object.

[0406] An "information processing model" is an algorithm or mathematical model that learns patterns based on input data and makes predictions.

[0407] A "battle tactical pattern" is a pattern of data that analyzes the tendencies of tactics and strategies that an opponent may employ.

[0408] "Tactical selection" refers to strategic action and positioning options suggested in response to the situation during a match.

[0409] A "leader" is a person who is responsible for making strategic decisions and supervising in a sport or business setting.

[0410] "Information transmission technology" refers to the technologies and means used to send, receive, and transmit information such as data and messages.

[0411] A "user device" is a device used by a user to receive or input information.

[0412] This invention relates to a system that improves the accuracy of tactics in soccer matches, and its main components are a server, terminals, and users. The server is a device that receives player location information and ball possession rate information in real time from location information acquisition devices and camera systems installed at the match venue. The received data is analyzed using information processing models such as TensorFlow and PyTorch. This makes it possible to predict match tactical patterns.

[0413] The server generates dynamic tactical selections based on analysis. Using a generation AI model, it derives the optimal tactics based on current match data. These generated tactical selections are presented to the coach via a terminal. The terminal plays a role in providing the coach with real-time information necessary for player instructions and tactical adjustments. The terminal uses touch-enabled tablets or monitor devices, displaying tactical information in a visually easy-to-understand manner.

[0414] Users can evaluate the tactical options presented on their device and make appropriate choices based on the match situation. For example, a generative AI model will provide appropriate tactical suggestions in response to a prompt such as, "Suggest attacking patterns when player A is positioned on the right side." This allows coaches to make quick and accurate decisions during a match.

[0415] This system is expected to enhance competitiveness in the sport by enabling real-time monitoring and analysis of the flow of the game, and allowing for flexible adjustments to tactics.

[0416] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0417] Step 1:

[0418] The server collects player location information and ball possession percentage information in real time from location acquisition devices and camera systems installed at the match venue. This information is updated every second, and includes the geographical location data of athletes and the control status of objects. Specifically, the server acquires movement data during the match and performs preprocessing to make it immediately available for analysis.

[0419] Step 2:

[0420] The server inputs the collected location and retention rate information into an information processing model for analysis. This information processing model is built using TensorFlow and PyTorch and is trained on past match data to predict the opposing team's tactical patterns. Based on the input behavioral data, the server infers tactical movements and tendencies, and outputs a match tactical pattern. Specifically, it analyzes the opposing team's intentions from the movements of each player and the ball, and predicts actions several seconds in advance.

[0421] Step 3:

[0422] The server generates tactical suggestions using prompts based on the analysis results in the AI ​​model. These prompts include specific tactical scenarios, such as "If player A is positioned on the right side, how should the attack be constructed?" The AI ​​model responds to these prompts by outputting the optimal tactical plan and returning it to the system. Specifically, the server generates tactical data and creates possible options for each scenario.

[0423] Step 4:

[0424] The terminal clearly presents the generated tactical selections to the user. A touch-enabled tablet or display is used for this presentation, and the tactical selections are communicated to the coach via a visual interface. The user can evaluate the tactical selections displayed on the terminal and make immediate decisions based on the match situation. Specifically, it is possible to simulate actions such as giving instructions to players and adjusting their positioning on the terminal.

[0425] Step 5:

[0426] Based on the presented tactical options, the user makes quick and accurate decisions and gives instructions to the team. These decisions, made instantly during the match, directly impact player positioning and strategic adjustments. After deciding on tactics, the user gives specific instructions to the players and makes adjustments to ensure their execution. Specifically, it is possible to monitor the match situation and give instructions for adopting or changing tactics in real time.

[0427] (Application Example 1)

[0428] 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."

[0429] In sports competitions, it is difficult for coaches to make optimal tactical decisions in real time, and there is a lack of interactive viewing experiences and benefits for spectators that correspond to the progress of the game. Therefore, there is a need for a means to quickly and accurately share insights about the game and enhance spectator engagement.

[0430] 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.

[0431] In this invention, the server includes means for collecting and processing player position data and ball possession data in real time; means for predicting opponent tactical patterns via a machine learning model using the data; means for dynamically generating and presenting optimal tactical options to coaches based on the prediction results; means for providing spectators with tactical analysis data of the match; and means for providing rewards to spectators upon the success of a specific event in conjunction with electronic trading. This enables coaches to make tactical decisions quickly, provides spectators with a new viewing experience, and enhances their sense of participation in sporting events.

[0432] "Player position data" refers to information that indicates the position of individual players on the field during a sports competition.

[0433] "Ball possession data" refers to information that measures ball possession during a match, showing the time and frequency of ball control for each player and team.

[0434] A "machine learning model" is a mathematical and statistical method that uses large amounts of data to enable computers to learn patterns and rules and automatically make predictions and decisions.

[0435] "Tactical options" refer to the choices of actions and strategies that players and coaches should take depending on the situation during a match.

[0436] In sports competitions, a "coach" refers to a manager or coach who is responsible for the team's tactics and the guidance of the players.

[0437] "Spectators" refer to people who seek enjoyment or information by watching competitions or matches.

[0438] "Electronic transactions" refer to methods of buying and selling goods and services through digital networks.

[0439] A "perk" is an additional benefit or reward offered when certain conditions are met.

[0440] This invention is a system designed to improve tactical decision-making in soccer matches, utilizing real-time player position data and ball possession data. The server collects and processes match data from sensors and cameras to understand player movements. Specifically, the server uses this data to predict the opposing team's tactics through a machine learning model. Platforms such as TensorFlow and PyTorch can be used for machine learning.

[0441] Next, the server dynamically generates tactical options based on the prediction results and presents them to the coach in real time. The coach can visually review the tactical options through their terminal and receive assistance in selecting the appropriate tactic on the spot.

[0442] Furthermore, this invention also provides new value to spectators. Spectators can use user terminals to interactively experience tactical analysis results and information on player movements during the match. In addition, rewards are provided when specific tactics are successful, linked to an electronic trading system on the spectator's terminal. For example, if a certain player scores an important goal, spectators may receive discount coupons for merchandise.

[0443] As a concrete example, the system observes scenes where a particular player maintains possession of the ball for extended periods, and based on the analysis results obtained, explanatory information for spectators is displayed on the terminal. In this case, a prompt utilizing a generative AI model might be: "Analyze the current tactical data of the match and provide interesting insights to the audience. Also, suggest a campaign strategy if a particular tactic is successful."

[0444] As described above, by linking servers, terminals, and users, it is possible to realize a deeper sports viewing experience based on detailed data-driven analysis and strongly support coaches' tactical decisions.

[0445] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0446] Step 1:

[0447] The server uses sensors and cameras to collect real-time data on player position and ball possession during a match. This data, including player coordinates and ball control status, is entered into a database. The output at this stage is the raw data needed for the next processing step.

[0448] Step 2:

[0449] The server preprocesses the collected location and ball retention data, performing filtering to remove noise and improve reliability. The filtering algorithm transforms the input data into a clean, analyzable state. This results in output data free from excessive noise.

[0450] Step 3:

[0451] The server sends pre-processed data as input to a machine learning model to predict the opposing team's tactical patterns. This model is trained on past match data and performance, and uses a generative AI model to predict what might happen next. The output is predictive information about the opposing team's actions.

[0452] Step 4:

[0453] The server dynamically generates tactical options based on predictions from machine learning models and presents them to the coach. The optimal tactic is output as a selection of choices appropriate to the match situation, and the coach can review it through their terminal.

[0454] Step 5:

[0455] The terminal displays real-time tactical analysis and commentary on the match via a spectator interface. Spectators can obtain detailed information that matches the progress of the match by operating the terminal. Inputs are tactical options and commentary information, and outputs are various interactively displayed information.

[0456] Step 6:

[0457] Users use their devices to receive rewards linked to electronic trading functions. This process involves inputting event information triggered by the success of specific players or tactics, and then receiving reward notifications. For example, the server automatically delivers "discount coupons for special merchandise" to spectator devices.

[0458] 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.

[0459] This invention incorporates an emotion engine into a system that supports tactical decision-making in soccer matches, thereby enabling more effective tactical suggestions that take into account the emotional state of the user, such as a manager or coach. This system consists primarily of a server, a terminal, an emotion engine, and the user.

[0460] The server has the ability to collect player position data and ball possession data in real time during a match and analyze this data. The analyzed data is used to predict the opposing team's tactical patterns using machine learning models. This prediction includes dynamically generated tactical options and suggests appropriate strategies depending on the match situation.

[0461] The emotion engine detects the user's emotional state and evaluates how it affects tactical selection suggestions. For example, the emotion engine recognizes the user's emotions in real time by analyzing their facial expressions and tone of voice. This allows the system to understand the user's mental responses to the game situation and flexibly adjust tactical suggestions accordingly.

[0462] The terminal presents the user with tactical options and sentiment-based analysis results sent from the server. These tactical options are not simply data-driven choices, but are presented in a way that is appropriate to the user's current emotions, allowing the user to make more conscious and strategic decisions during the match.

[0463] For example, if the emotion engine recognizes the user's stress level, the tactical options generated by the server will be adjusted to reduce the user's psychological burden. The user can review strategies through their device and control the flow of the game with an emotion-sensitive approach. In this way, the present invention enables more personalized strategic suggestions by incorporating the user's emotions into tactical decisions.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] The server connects to the network as soon as the match starts and begins receiving player position data and ball possession data in real time. The data includes dynamic information about each player during the match and is transmitted in a specific format.

[0467] Step 2:

[0468] The server uses received location data and ball possession data to predict the opposing team's tactical patterns via a machine learning model. The model is trained on past match data and has learned various tactical scenarios, enabling highly accurate predictions.

[0469] Step 3:

[0470] The emotion engine collects information in real time through cameras and microphones to understand the user's emotions during a match. The emotion engine uses facial recognition and voice analysis technologies to recognize emotions such as stress and tension.

[0471] Step 4:

[0472] The server dynamically generates tactical options based on predicted tactical patterns and sentiment data provided by the sentiment engine. These options are adjusted to prioritize conservative tactics, for example, if the user is under stress and prefers calmer tactics.

[0473] Step 5:

[0474] The device visually presents the user with generated tactical options and sentiment analysis results. The device's interface is designed to allow users to intuitively understand the information.

[0475] Step 6:

[0476] The user reviews the tactical options presented on the device and makes a final decision on how to execute their team's tactics. At this time, they select the most appropriate strategy, taking into account the displayed sentiment analysis information.

[0477] (Example 2)

[0478] 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."

[0479] While conventional tactical decision-making systems could analyze dynamic data during matches, they struggled to provide tactical suggestions that took into account the emotional state of the coaches and managers who used them. This meant that tactical choices sometimes did not align with the users' psychological state, potentially hindering optimal strategic decisions.

[0480] 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.

[0481] In this invention, the server includes means for collecting and analyzing location information and ball possession information in real time, means for predicting the opponent's tactical patterns via a machine learning algorithm using the said information, and means for recognizing the user's emotional state and dynamically adjusting tactical options based on the recognition results. This allows tactical decisions during a match to be made in a way that takes the user's emotional state into account, enabling more personalized strategic suggestions.

[0482] "Location information" refers to data that indicates the geographical location of an object, and is used to understand the spatial arrangement of players.

[0483] "Ball possession information" refers to data that shows how much each player controls or possesses the ball during a game.

[0484] A "machine learning algorithm" is a technology that analyzes large amounts of data patterns and automatically makes predictions and classifications, and is used to predict the tactical patterns of the opposing team.

[0485] "User's emotional state" refers to the emotional state exhibited by users such as coaches and managers, and includes information such as psychological stress levels and relaxation levels.

[0486] "Tactical options" refer to multiple strategic choices depending on the game situation, and are suggestions for effectively managing the flow of the match.

[0487] "Display devices" are devices that provide digital information to users visually, and include monitors and tablets.

[0488] A "digital network" is a communication infrastructure used for sending and receiving digital data, and includes the internet and local area networks.

[0489] A "user terminal" is a device that a user directly operates to check information, and includes personal computers and smart devices.

[0490] This invention integrates an emotion analysis function into a system that supports tactical decision-making in soccer matches, thereby providing more effective tactical suggestions that take into account the emotional state of the user, such as a manager or coach. The system consists primarily of a server, terminals, an emotion recognition device, and the user.

[0491] The server is responsible for collecting player location information and ball possession information in real time. Location information is obtained through GPS devices equipped on the players, and ball possession information is captured by surveillance cameras and sensors. This data is processed on the server using programming languages ​​such as Python and Java, and machine learning algorithms are used to predict the opponent's tactical patterns.

[0492] The emotion recognition device analyzes the user's facial expressions and voice tone to recognize their emotional state in real time. Specifically, it uses the OpenCV library to analyze facial features and Google Cloud Speech-to-Text for speech analysis. This allows for an understanding of the user's psychological state and enables the provision of tactical suggestions that take this into account.

[0493] The device displays tactical options and sentiment-based analysis results sent from the server in a way that is intuitively understandable to the user. Frameworks such as React and Vue.js are used for visualization, and users access this information via tablets, PCs, etc.

[0494] For example, if an emotion recognition device detects user stress, the server uses this information to generate tactical options aimed at reducing psychological burden. The user can then review these adjusted tactics via a terminal and provide appropriate guidance.

[0495] An example of a prompt message for the AI ​​model generated by this system is, "Please suggest the optimal match strategy considering the user's current emotional state."

[0496] As a result, it becomes possible to support strategic decision-making by dynamically integrating the match situation and the user's emotions.

[0497] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0498] Step 1:

[0499] The server collects player location and ball possession information during the match. Inputs include real-time location and ball data obtained from GPS devices and camera systems. This data is digitized and stored in a database, laying the foundation for the subsequent analysis steps.

[0500] Step 2:

[0501] The server analyzes the collected data and predicts the opposing team's tactical patterns. The inputs are the positional and ball possession information collected in the previous step. The data is processed using a generative AI model developed based on past match data, employing Python and machine learning libraries. The output is the prediction regarding the opposing team's tactical patterns.

[0502] Step 3:

[0503] The emotion recognition device recognizes the user's emotional state. Input is the user's facial expressions and voice tone, captured from a camera and microphone. Emotions are recognized in real time using OpenCV and speech analysis tools. Output is emotional data, such as the user's stress level and state of comfort.

[0504] Step 4:

[0505] The server generates optimal tactical options based on analyzed match data and user sentiment information. The inputs are the prediction results from step 2 and the sentiment data from step 3. A generative AI model integrates this data to dynamically create situation-appropriate tactical options. The output is a list of tactics that take the user's psychological state into account.

[0506] Step 5:

[0507] The terminal presents tactical options sent from the server to the user. The input is optimized tactical data from the server. A GUI framework is used to display the information on the screen in a visually easy-to-understand format. The output is a visual representation of the tactical options available to the user.

[0508] Step 6:

[0509] The user provides feedback on the presented tactical options. The input is the tactical options presented from the terminal. Based on their emotional state and tactical selection, the user makes decisions to adjust their strategy during the match and returns feedback to the system based on the results. The output is feedback data regarding the effectiveness of the tactics, which can be used for future improvements.

[0510] (Application Example 2)

[0511] 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."

[0512] In modern living spaces, optimally adjusting the environment according to the emotional state of residents is crucial for improving their comfort and mental health. However, conventional systems have struggled to accurately grasp residents' emotions and dynamically adjust the environment based on them. Therefore, there is a need for a life support system that can flexibly respond to emotions.

[0513] 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.

[0514] In this invention, the server includes means for detecting and analyzing the emotional state of the resident in real time, means for dynamically adjusting the environmental settings based on the emotional state, and means for appropriately presenting the adjustment results to the resident. This makes it possible to adjust the living space to the optimal level according to the emotional state of the resident.

[0515] The term "resident" refers to an individual or their family who resides in a specific living space or residence.

[0516] "Emotional state" refers to the mental and emotional reactions and tendencies exhibited by residents, including states such as joy, sadness, anger, and stress.

[0517] "Real-time detection and analysis methods" refer to technical means for instantly grasping the emotional state of residents, performing analysis based on that data, and obtaining results quickly.

[0518] "Means for dynamically adjusting environmental settings" refers to means that have the function of automatically and promptly changing living environment elements such as lighting, music, and temperature based on detected emotional states.

[0519] "Means of appropriately presenting adjustment results to residents" refers to methods and technologies for communicating the details of dynamically adjusted environmental settings to residents in an easily understandable manner.

[0520] To implement this invention, a server, a living environment control device, and a network environment connecting each of these devices are required.

[0521] The server utilizes sensor devices such as cameras and microphones to detect residents' emotional states in real time. Specifically, it uses emotion recognition software, such as Microsoft Azure Cognitive Services, to analyze residents' facial expressions and tone of voice to understand their emotional state. This data is processed immediately, and adjustments to the environment settings are required based on the residents' emotions.

[0522] Next, the living environment control device receives instructions from the server and appropriately adjusts lighting, music systems, air conditioning, and other settings. For example, if the server determines that a resident is feeling fatigued, it can play relaxing music and change the lighting to a softer color tone. It can also deliver messages tailored to the resident's emotional state through a voice device.

[0523] Users can choose whether or not to accept the adjustments, thereby enhancing their own comfort. Furthermore, by receiving environmental suggestions tailored to their emotions, users can improve their quality of life.

[0524] For example, if a resident returns home from work and is perceived as feeling stressed, the server can immediately switch the environment to relaxation mode and send a positive message to the resident such as, "Let's unwind after a tiring day."

[0525] An example of a prompt message for a generating AI model might be: "Please suggest the optimal music and lighting settings to create a relaxing environment that will satisfy the residents."

[0526] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0527] Step 1:

[0528] The server acquires video and audio data of residents in real time via cameras and microphones. This video and audio data, as input, is used as foundational data for detecting emotional states.

[0529] Step 2:

[0530] The server uses Microsoft Azure Cognitive Services to analyze the acquired video and audio data. The analysis estimates the emotional state (e.g., joy, sadness, stress) based on the resident's facial expressions and tone of voice. The analysis results are output as an emotional state.

[0531] Step 3:

[0532] The server generates adjustment instructions for the living environment control device based on the estimated emotional state. Specifically, it determines the brightness and color of the lighting, the temperature settings, and the type of music to play according to the emotion. This output is generated as an adjustment instruction.

[0533] Step 4:

[0534] The device executes adjustment instructions received from the server, dynamically changing the living environment according to the user's emotional state. For example, if an emotion indicating stress is detected, it will play relaxing music and adjust the lighting to a warmer color. It will also provide voice messages such as, "You seem tired today. Let's relax."

[0535] Step 5:

[0536] Users evaluate changes in the environment and make additional manual adjustments if necessary. This feedback may be incorporated into future environment adjustments. The server receives user input and logs the changes in the environment to learn and improve responses tailored to individual residents.

[0537] 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.

[0538] 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.

[0539] 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.

[0540] [Fourth Embodiment]

[0541] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0542] 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.

[0543] 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).

[0544] 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.

[0545] 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.

[0546] 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).

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] 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".

[0554] This invention proposes a system for improving the accuracy of tactical decisions during soccer matches. This system collects real-time data on player movements and ball trajectory during a match and provides a means for analyzing this data. The system consists primarily of a server, terminals, and users (coaches and managers).

[0555] The server receives information from devices that collect real-time player position data and ball possession data during the match. This data includes players' movements in specific areas and ball possession status over time. Using this data as input, the server uses machine learning models to predict the opposing team's tactical patterns. This allows it to anticipate the opponent's offensive and defensive strategies.

[0556] Based on these predictions, the server dynamically generates tactical options. These options take into account the current match situation and provide the optimal strategy, such as strengthening offense or defense, or selecting specific players. The terminal provides an interface for presenting the tactical options sent from the server to the manager or coach. This allows users to enhance their real-time tactical decision-making.

[0557] For example, if a particular player is positioned high up the field near the opponent's goal during a match, the system can recommend utilizing that player to strengthen the attack. Conversely, if the system anticipates that the opposing team is strengthening its defense and preparing for a quick counterattack, the server can suggest defensive tactical options to encourage a swift defensive response.

[0558] In this way, the present invention has significance in enabling data-driven tactical decisions and supporting rapid and accurate decision-making during matches.

[0559] The following describes the processing flow.

[0560] Step 1:

[0561] The server prepares to collect match data in real time. Specifically, it uses network sockets to listen for connections from clients on a designated port, enabling it to receive data from devices involved in the match.

[0562] Step 2:

[0563] The server receives player position data and ball possession data during a match. This includes the players' current position coordinates and ball ownership information, and is transmitted in JSON format or similar.

[0564] Step 3:

[0565] The server analyzes the received data. This data analysis extracts player movement trajectories and ball possession tendencies, converting them into numerical formats so they can be used as input data for subsequent machine learning models.

[0566] Step 4:

[0567] The server uses the analyzed data as input to a machine learning model to predict the opposing team's tactical patterns. The model is based on a pre-trained algorithm and predicts the flow of the game and the opponent's tactical actions with high accuracy.

[0568] Step 5:

[0569] The server determines the optimal tactical options based on the generated predictions. These are generated as specific tactical choices, such as suggestions for strengthening offense or defense, providing countermeasures tailored to the current state of the match.

[0570] Step 6:

[0571] The server transmits the determined tactical options to the terminal. The terminal receives this information and presents it visually to the user, who is the manager or coach. This allows the user to immediately review the information and make tactical decisions.

[0572] (Example 1)

[0573] 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".

[0574] Conventional tactical decision-making systems have problems with effectively utilizing player positioning information and ball possession rates, resulting in insufficient real-time tactical prediction and suggestions. Furthermore, the inaccurate tactical suggestions to coaches made it difficult to make quick and appropriate tactical changes during a match. This created a challenge in understanding the flow of the game and flexibly adapting tactics.

[0575] 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.

[0576] In this invention, the server includes means for processing the location information of athletes and object retention rate information collected from a location information acquisition device in real time, means for predicting match tactical patterns via an information processing model using the said information, and means for dynamically generating and presenting the optimal tactical selection to the coach based on the prediction results. This makes it possible to utilize data in real time during a match and adjust tactics flexibly and quickly.

[0577] A "location information acquisition device" is a device that detects the geographical location of a person exercising and collects location data corresponding to time.

[0578] "Athlete location information" refers to data that indicates the current physical location of an athlete on the field.

[0579] "Object retention rate information" is data that indicates how long a particular entity is holding an object.

[0580] An "information processing model" is an algorithm or mathematical model that learns patterns based on input data and makes predictions.

[0581] A "battle tactical pattern" is a pattern of data that analyzes the tendencies of tactics and strategies that an opponent may employ.

[0582] "Tactical selection" refers to strategic action and positioning options suggested in response to the situation during a match.

[0583] A "leader" is a person who is responsible for making strategic decisions and supervising in a sport or business setting.

[0584] "Information transmission technology" refers to the technologies and means used to send, receive, and transmit information such as data and messages.

[0585] A "user device" is a device used by a user to receive or input information.

[0586] This invention relates to a system that improves the accuracy of tactics in soccer matches, and its main components are a server, terminals, and users. The server is a device that receives player location information and ball possession rate information in real time from location information acquisition devices and camera systems installed at the match venue. The received data is analyzed using information processing models such as TensorFlow and PyTorch. This makes it possible to predict match tactical patterns.

[0587] The server generates dynamic tactical selections based on analysis. Using a generation AI model, it derives the optimal tactics based on current match data. These generated tactical selections are presented to the coach via a terminal. The terminal plays a role in providing the coach with real-time information necessary for player instructions and tactical adjustments. The terminal uses touch-enabled tablets or monitor devices, displaying tactical information in a visually easy-to-understand manner.

[0588] Users can evaluate the tactical options presented on their device and make appropriate choices based on the match situation. For example, a generative AI model will provide appropriate tactical suggestions in response to a prompt such as, "Suggest attacking patterns when player A is positioned on the right side." This allows coaches to make quick and accurate decisions during a match.

[0589] This system is expected to enhance competitiveness in the sport by enabling real-time monitoring and analysis of the flow of the game, and allowing for flexible adjustments to tactics.

[0590] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0591] Step 1:

[0592] The server collects player location information and ball possession percentage information in real time from location acquisition devices and camera systems installed at the match venue. This information is updated every second, and includes the geographical location data of athletes and the control status of objects. Specifically, the server acquires movement data during the match and performs preprocessing to make it immediately available for analysis.

[0593] Step 2:

[0594] The server inputs the collected location and retention rate information into an information processing model for analysis. This information processing model is built using TensorFlow and PyTorch and is trained on past match data to predict the opposing team's tactical patterns. Based on the input behavioral data, the server infers tactical movements and tendencies, and outputs a match tactical pattern. Specifically, it analyzes the opposing team's intentions from the movements of each player and the ball, and predicts actions several seconds in advance.

[0595] Step 3:

[0596] The server generates tactical suggestions using prompts based on the analysis results in the AI ​​model. These prompts include specific tactical scenarios, such as "If player A is positioned on the right side, how should the attack be constructed?" The AI ​​model responds to these prompts by outputting the optimal tactical plan and returning it to the system. Specifically, the server generates tactical data and creates possible options for each scenario.

[0597] Step 4:

[0598] The terminal clearly presents the generated tactical selections to the user. A touch-enabled tablet or display is used for this presentation, and the tactical selections are communicated to the coach via a visual interface. The user can evaluate the tactical selections displayed on the terminal and make immediate decisions based on the match situation. Specifically, it is possible to simulate actions such as giving instructions to players and adjusting their positioning on the terminal.

[0599] Step 5:

[0600] Based on the presented tactical options, the user makes quick and accurate decisions and gives instructions to the team. These decisions, made instantly during the match, directly impact player positioning and strategic adjustments. After deciding on tactics, the user gives specific instructions to the players and makes adjustments to ensure their execution. Specifically, it is possible to monitor the match situation and give instructions for adopting or changing tactics in real time.

[0601] (Application Example 1)

[0602] 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".

[0603] In sports competitions, it is difficult for coaches to make optimal tactical decisions in real time, and there is a lack of interactive viewing experiences and benefits for spectators that correspond to the progress of the game. Therefore, there is a need for a means to quickly and accurately share insights about the game and enhance spectator engagement.

[0604] 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.

[0605] In this invention, the server includes means for collecting and processing player position data and ball possession data in real time; means for predicting opponent tactical patterns via a machine learning model using the data; means for dynamically generating and presenting optimal tactical options to coaches based on the prediction results; means for providing spectators with tactical analysis data of the match; and means for providing rewards to spectators upon the success of a specific event in conjunction with electronic trading. This enables coaches to make tactical decisions quickly, provides spectators with a new viewing experience, and enhances their sense of participation in sporting events.

[0606] "Player position data" refers to information that indicates the position of individual players on the field during a sports competition.

[0607] "Ball possession data" refers to information that measures ball possession during a match, showing the time and frequency of ball control for each player and team.

[0608] A "machine learning model" is a mathematical and statistical method that uses large amounts of data to enable computers to learn patterns and rules and automatically make predictions and decisions.

[0609] "Tactical options" refer to the choices of actions and strategies that players and coaches should take depending on the situation during a match.

[0610] In sports competitions, a "coach" refers to a manager or coach who is responsible for the team's tactics and the guidance of the players.

[0611] "Spectators" refer to people who seek enjoyment or information by watching competitions or matches.

[0612] "Electronic transactions" refer to methods of buying and selling goods and services through digital networks.

[0613] A "perk" is an additional benefit or reward offered when certain conditions are met.

[0614] This invention is a system designed to improve tactical decision-making in soccer matches, utilizing real-time player position data and ball possession data. The server collects and processes match data from sensors and cameras to understand player movements. Specifically, the server uses this data to predict the opposing team's tactics through a machine learning model. Platforms such as TensorFlow and PyTorch can be used for machine learning.

[0615] Next, the server dynamically generates tactical options based on the prediction results and presents them to the coach in real time. The coach can visually review the tactical options through their terminal and receive assistance in selecting the appropriate tactic on the spot.

[0616] Furthermore, this invention also provides new value to spectators. Spectators can use user terminals to interactively experience tactical analysis results and information on player movements during the match. In addition, rewards are provided when specific tactics are successful, linked to an electronic trading system on the spectator's terminal. For example, if a certain player scores an important goal, spectators may receive discount coupons for merchandise.

[0617] As a concrete example, the system observes scenes where a particular player maintains possession of the ball for extended periods, and based on the analysis results obtained, explanatory information for spectators is displayed on the terminal. In this case, a prompt utilizing a generative AI model might be: "Analyze the current tactical data of the match and provide interesting insights to the audience. Also, suggest a campaign strategy if a particular tactic is successful."

[0618] As described above, by linking servers, terminals, and users, it is possible to realize a deeper sports viewing experience based on detailed data-driven analysis and strongly support coaches' tactical decisions.

[0619] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0620] Step 1:

[0621] The server uses sensors and cameras to collect real-time data on player position and ball possession during a match. This data, including player coordinates and ball control status, is entered into a database. The output at this stage is the raw data needed for the next processing step.

[0622] Step 2:

[0623] The server preprocesses the collected location and ball retention data, performing filtering to remove noise and improve reliability. The filtering algorithm transforms the input data into a clean, analyzable state. This results in output data free from excessive noise.

[0624] Step 3:

[0625] The server sends pre-processed data as input to a machine learning model to predict the opposing team's tactical patterns. This model is trained on past match data and performance, and uses a generative AI model to predict what might happen next. The output is predictive information about the opposing team's actions.

[0626] Step 4:

[0627] The server dynamically generates tactical options based on predictions from machine learning models and presents them to the coach. The optimal tactic is output as a selection of choices appropriate to the match situation, and the coach can review it through their terminal.

[0628] Step 5:

[0629] The terminal displays real-time tactical analysis and commentary on the match via a spectator interface. Spectators can obtain detailed information that matches the progress of the match by operating the terminal. Inputs are tactical options and commentary information, and outputs are various interactively displayed information.

[0630] Step 6:

[0631] Users use their devices to receive rewards linked to electronic trading functions. This process involves inputting event information triggered by the success of specific players or tactics, and then receiving reward notifications. For example, the server automatically delivers "discount coupons for special merchandise" to spectator devices.

[0632] 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.

[0633] This invention incorporates an emotion engine into a system that supports tactical decision-making in soccer matches, thereby enabling more effective tactical suggestions that take into account the emotional state of the user, such as a manager or coach. This system consists primarily of a server, a terminal, an emotion engine, and the user.

[0634] The server has the ability to collect player position data and ball possession data in real time during a match and analyze this data. The analyzed data is used to predict the opposing team's tactical patterns using machine learning models. This prediction includes dynamically generated tactical options and suggests appropriate strategies depending on the match situation.

[0635] The emotion engine detects the user's emotional state and evaluates how it affects tactical selection suggestions. For example, the emotion engine recognizes the user's emotions in real time by analyzing their facial expressions and tone of voice. This allows the system to understand the user's mental responses to the game situation and flexibly adjust tactical suggestions accordingly.

[0636] The terminal presents the user with tactical options and sentiment-based analysis results sent from the server. These tactical options are not simply data-driven choices, but are presented in a way that is appropriate to the user's current emotions, allowing the user to make more conscious and strategic decisions during the match.

[0637] For example, if the emotion engine recognizes the user's stress level, the tactical options generated by the server will be adjusted to reduce the user's psychological burden. The user can review strategies through their device and control the flow of the game with an emotion-sensitive approach. In this way, the present invention enables more personalized strategic suggestions by incorporating the user's emotions into tactical decisions.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The server connects to the network as soon as the match starts and begins receiving player position data and ball possession data in real time. The data includes dynamic information about each player during the match and is transmitted in a specific format.

[0641] Step 2:

[0642] The server uses received location data and ball possession data to predict the opposing team's tactical patterns via a machine learning model. The model is trained on past match data and has learned various tactical scenarios, enabling highly accurate predictions.

[0643] Step 3:

[0644] The emotion engine collects information in real time through cameras and microphones to understand the user's emotions during a match. The emotion engine uses facial recognition and voice analysis technologies to recognize emotions such as stress and tension.

[0645] Step 4:

[0646] The server dynamically generates tactical options based on predicted tactical patterns and sentiment data provided by the sentiment engine. These options are adjusted to prioritize conservative tactics, for example, if the user is under stress and prefers calmer tactics.

[0647] Step 5:

[0648] The device visually presents the user with generated tactical options and sentiment analysis results. The device's interface is designed to allow users to intuitively understand the information.

[0649] Step 6:

[0650] The user reviews the tactical options presented on the device and makes a final decision on how to execute their team's tactics. At this time, they select the most appropriate strategy, taking into account the displayed sentiment analysis information.

[0651] (Example 2)

[0652] 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".

[0653] While conventional tactical decision-making systems could analyze dynamic data during matches, they struggled to provide tactical suggestions that took into account the emotional state of the coaches and managers who used them. This meant that tactical choices sometimes did not align with the users' psychological state, potentially hindering optimal strategic decisions.

[0654] 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.

[0655] In this invention, the server includes means for collecting and analyzing location information and ball possession information in real time, means for predicting the opponent's tactical patterns via a machine learning algorithm using the said information, and means for recognizing the user's emotional state and dynamically adjusting tactical options based on the recognition results. This allows tactical decisions during a match to be made in a way that takes the user's emotional state into account, enabling more personalized strategic suggestions.

[0656] "Location information" refers to data that indicates the geographical location of an object, and is used to understand the spatial arrangement of players.

[0657] "Ball possession information" refers to data that shows how much each player controls or possesses the ball during a game.

[0658] A "machine learning algorithm" is a technology that analyzes large amounts of data patterns and automatically makes predictions and classifications, and is used to predict the tactical patterns of the opposing team.

[0659] "User's emotional state" refers to the emotional state exhibited by users such as coaches and managers, and includes information such as psychological stress levels and relaxation levels.

[0660] "Tactical options" refer to multiple strategic choices depending on the game situation, and are suggestions for effectively managing the flow of the match.

[0661] "Display devices" are devices that provide digital information to users visually, and include monitors and tablets.

[0662] A "digital network" is a communication infrastructure used for sending and receiving digital data, and includes the internet and local area networks.

[0663] A "user terminal" is a device that a user directly operates to check information, and includes personal computers and smart devices.

[0664] This invention integrates an emotion analysis function into a system that supports tactical decision-making in soccer matches, thereby providing more effective tactical suggestions that take into account the emotional state of the user, such as a manager or coach. The system consists primarily of a server, terminals, an emotion recognition device, and the user.

[0665] The server is responsible for collecting player location information and ball possession information in real time. Location information is obtained through GPS devices equipped on the players, and ball possession information is captured by surveillance cameras and sensors. This data is processed on the server using programming languages ​​such as Python and Java, and machine learning algorithms are used to predict the opponent's tactical patterns.

[0666] The emotion recognition device analyzes the user's facial expressions and voice tone to recognize their emotional state in real time. Specifically, it uses the OpenCV library to analyze facial features and Google Cloud Speech-to-Text for speech analysis. This allows for an understanding of the user's psychological state and enables the provision of tactical suggestions that take this into account.

[0667] The device displays tactical options and sentiment-based analysis results sent from the server in a way that is intuitively understandable to the user. Frameworks such as React and Vue.js are used for visualization, and users access this information via tablets, PCs, etc.

[0668] For example, if an emotion recognition device detects user stress, the server uses this information to generate tactical options aimed at reducing psychological burden. The user can then review these adjusted tactics via a terminal and provide appropriate guidance.

[0669] An example of a prompt message for the AI ​​model generated by this system is, "Please suggest the optimal match strategy considering the user's current emotional state."

[0670] As a result, it becomes possible to support strategic decision-making by dynamically integrating the match situation and the user's emotions.

[0671] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0672] Step 1:

[0673] The server collects player location and ball possession information during the match. Inputs include real-time location and ball data obtained from GPS devices and camera systems. This data is digitized and stored in a database, laying the foundation for the subsequent analysis steps.

[0674] Step 2:

[0675] The server analyzes the collected data and predicts the opposing team's tactical patterns. The inputs are the positional and ball possession information collected in the previous step. The data is processed using a generative AI model developed based on past match data, employing Python and machine learning libraries. The output is the prediction regarding the opposing team's tactical patterns.

[0676] Step 3:

[0677] The emotion recognition device recognizes the user's emotional state. Input is the user's facial expressions and voice tone, captured from a camera and microphone. Emotions are recognized in real time using OpenCV and speech analysis tools. Output is emotional data, such as the user's stress level and state of comfort.

[0678] Step 4:

[0679] The server generates optimal tactical options based on analyzed match data and user sentiment information. The inputs are the prediction results from step 2 and the sentiment data from step 3. A generative AI model integrates this data to dynamically create situation-appropriate tactical options. The output is a list of tactics that take the user's psychological state into account.

[0680] Step 5:

[0681] The terminal presents tactical options sent from the server to the user. The input is optimized tactical data from the server. A GUI framework is used to display the information on the screen in a visually easy-to-understand format. The output is a visual representation of the tactical options available to the user.

[0682] Step 6:

[0683] The user provides feedback on the presented tactical options. The input is the tactical options presented from the terminal. Based on their emotional state and tactical selection, the user makes decisions to adjust their strategy during the match and returns feedback to the system based on the results. The output is feedback data regarding the effectiveness of the tactics, which can be used for future improvements.

[0684] (Application Example 2)

[0685] 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".

[0686] In modern living spaces, optimally adjusting the environment according to the emotional state of residents is crucial for improving their comfort and mental health. However, conventional systems have struggled to accurately grasp residents' emotions and dynamically adjust the environment based on them. Therefore, there is a need for a life support system that can flexibly respond to emotions.

[0687] 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.

[0688] In this invention, the server includes means for detecting and analyzing the emotional state of the resident in real time, means for dynamically adjusting the environmental settings based on the emotional state, and means for appropriately presenting the adjustment results to the resident. This makes it possible to adjust the living space to the optimal level according to the emotional state of the resident.

[0689] The term "resident" refers to an individual or their family who resides in a specific living space or residence.

[0690] "Emotional state" refers to the mental and emotional reactions and tendencies exhibited by residents, including states such as joy, sadness, anger, and stress.

[0691] "Real-time detection and analysis methods" refer to technical means for instantly grasping the emotional state of residents, performing analysis based on that data, and obtaining results quickly.

[0692] "Means for dynamically adjusting environmental settings" refers to means that have the function of automatically and promptly changing living environment elements such as lighting, music, and temperature based on detected emotional states.

[0693] "Means of appropriately presenting adjustment results to residents" refers to methods and technologies for communicating the details of dynamically adjusted environmental settings to residents in an easily understandable manner.

[0694] To implement this invention, a server, a living environment control device, and a network environment connecting each of these devices are required.

[0695] The server utilizes sensor devices such as cameras and microphones to detect residents' emotional states in real time. Specifically, it uses emotion recognition software, such as Microsoft Azure Cognitive Services, to analyze residents' facial expressions and tone of voice to understand their emotional state. This data is processed immediately, and adjustments to the environment settings are required based on the residents' emotions.

[0696] Next, the living environment control device receives instructions from the server and appropriately adjusts lighting, music systems, air conditioning, and other settings. For example, if the server determines that a resident is feeling fatigued, it can play relaxing music and change the lighting to a softer color tone. It can also deliver messages tailored to the resident's emotional state through a voice device.

[0697] Users can choose whether or not to accept the adjustments, thereby enhancing their own comfort. Furthermore, by receiving environmental suggestions tailored to their emotions, users can improve their quality of life.

[0698] For example, if a resident returns home from work and is perceived as feeling stressed, the server can immediately switch the environment to relaxation mode and send a positive message to the resident such as, "Let's unwind after a tiring day."

[0699] An example of a prompt message for a generating AI model might be: "Please suggest the optimal music and lighting settings to create a relaxing environment that will satisfy the residents."

[0700] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0701] Step 1:

[0702] The server acquires video and audio data of residents in real time via cameras and microphones. This video and audio data, as input, is used as foundational data for detecting emotional states.

[0703] Step 2:

[0704] The server uses Microsoft Azure Cognitive Services to analyze the acquired video and audio data. The analysis estimates the emotional state (e.g., joy, sadness, stress) based on the resident's facial expressions and tone of voice. The analysis results are output as an emotional state.

[0705] Step 3:

[0706] The server generates adjustment instructions for the living environment control device based on the estimated emotional state. Specifically, it determines the brightness and color of the lighting, the temperature settings, and the type of music to play according to the emotion. This output is generated as an adjustment instruction.

[0707] Step 4:

[0708] The device executes adjustment instructions received from the server, dynamically changing the living environment according to the user's emotional state. For example, if an emotion indicating stress is detected, it will play relaxing music and adjust the lighting to a warmer color. It will also provide voice messages such as, "You seem tired today. Let's relax."

[0709] Step 5:

[0710] Users evaluate changes in the environment and make additional manual adjustments if necessary. This feedback may be incorporated into future environment adjustments. The server receives user input and logs the changes in the environment to learn and improve responses tailored to individual residents.

[0711] 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.

[0712] 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.

[0713] 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 robot 414.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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."

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] The following is further disclosed regarding the embodiments described above.

[0733] (Claim 1)

[0734] A means of collecting and processing player position data and ball possession data in real time,

[0735] A means for predicting the opponent's tactical patterns via a machine learning model using the aforementioned data,

[0736] A means of dynamically generating and presenting optimal tactical options to coaches based on prediction results,

[0737] A system that includes this.

[0738] (Claim 2)

[0739] The system according to claim 1, which proposes measures to strengthen offense or defense based on the aforementioned prediction results.

[0740] (Claim 3)

[0741] The system according to claim 1, which transmits generated tactical options to a client device via network communication.

[0742] "Example 1"

[0743] (Claim 1)

[0744] A means for processing the location information of a person and object retention rate information collected from a location information acquisition device in real time,

[0745] A means for predicting battle tactical patterns via an information processing model using the aforementioned information,

[0746] A means of dynamically generating and presenting the optimal tactical selection to the coach based on the prediction results,

[0747] A means of outputting instructions to physically apply the tactics selected by the leader,

[0748] A system that includes this.

[0749] (Claim 2)

[0750] The system according to claim 1, which proposes measures to strengthen offense or defense based on the aforementioned prediction results.

[0751] (Claim 3)

[0752] The system according to claim 1, which transmits the generated tactical selection to a user device via information transmission technology.

[0753] "Application Example 1"

[0754] (Claim 1)

[0755] A means of collecting and processing player position data and ball possession data in real time,

[0756] A means for predicting the opponent's tactical patterns via a machine learning model using the aforementioned data,

[0757] A means of dynamically generating and presenting optimal tactical options to coaches based on prediction results,

[0758] A means of providing spectators with tactical analysis data of the match,

[0759] A means of providing benefits to spectators upon the success of a specific event, linked to electronic transactions,

[0760] A system that includes this.

[0761] (Claim 2)

[0762] The system according to claim 1, which proposes measures to strengthen offense or defense based on the prediction results and provides match commentary to spectators.

[0763] (Claim 3)

[0764] The system according to claim 1, which transmits generated tactical options and related information to a client device via network communication and provides reward information linked to electronic transactions.

[0765] "Example 2 of combining an emotion engine"

[0766] (Claim 1)

[0767] A means for collecting and analyzing location information and ball possession information in real time,

[0768] A means for predicting the opponent's tactical patterns using the aforementioned information via a machine learning algorithm,

[0769] A means of recognizing the user's emotional state and dynamically adjusting tactical options based on that recognition,

[0770] A means of presenting the generated tactical options to a display device,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, which proposes tactics to reduce psychological burden based on prediction results and sentiment data.

[0774] (Claim 3)

[0775] The system according to claim 1, which transmits generated tactical options to a user terminal using a digital network.

[0776] "Application example 2 when combining with an emotional engine"

[0777] (Claim 1)

[0778] A means of detecting and analyzing the emotional state of residents in real time,

[0779] Means for dynamically adjusting environmental settings based on the aforementioned emotional state,

[0780] Means for appropriately presenting the adjustment results to residents,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, which makes a proposal to improve the atmosphere of the living space based on the aforementioned adjustment results.

[0784] (Claim 3)

[0785] The system according to claim 1, wherein the generated adjustment results are transmitted to a home terminal via network communication. [Explanation of Symbols]

[0786] 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 of collecting and processing player position data and ball possession data in real time, A means for predicting the opponent's tactical patterns via a machine learning model using the aforementioned data, A means of dynamically generating and presenting optimal tactical options to coaches based on prediction results, A means of providing spectators with tactical analysis data of the match, A means of providing benefits to spectators upon the success of a specific event, linked to electronic transactions, A system that includes this.

2. The system according to claim 1, which proposes measures to strengthen offense or defense based on the prediction results and provides match commentary to spectators.

3. The system according to claim 1, which transmits generated tactical options and related information to a client device via network communication and provides reward information linked to electronic transactions.

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