system

Abstract

We provide the system. [Solution] A means of obtaining player location information and game object control information, A means of performing real-time data processing based on the acquired data to analyze information regarding player movements and the team's overall game strategy, Based on the analysis results, a means of applying a machine learning algorithm to predict the opposing team's tactical patterns, A means for presenting the optimal game strategy for the team based on the aforementioned prediction results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the current management of soccer tactics, coaches and managers need to instantly judge tactics during the game, and such judgment mainly relies on experience and intuition. Therefore, optimization of tactics based on objective data is required, but existing technologies have limitations in the ability to acquire and analyze tactical information in real time during the game and provide immediate feedback. In particular, there is a lack of a mechanism to predict the tactical patterns of the opposing team and present optimal tactics in real time based on the prediction, which is an issue.

Means for Solving the Problems

[0005] This invention provides means for acquiring player position information and game object control information, and means for processing the acquired data in real time to analyze player movements and the team's overall game strategy. Furthermore, it includes means for predicting the opposing team's tactical patterns using a machine learning algorithm based on the analysis results. By constructing a system that includes means for presenting the optimal game strategy to the team based on these prediction results, it becomes possible to support data-driven tactical decisions during matches, reduce the burden on managers and coaches, and contribute to improving team performance.

[0006] "Player location information" refers to data that shows the current coordinates of a player, and is information used to understand the player's position on the pitch in real time.

[0007] "Game object control information" refers to data that indicates the position and state of the ball and other controllable objects in a match, and is fundamental information for understanding the progress of the match.

[0008] "Data processing" refers to a series of operations that cleanse, format, and analyze acquired raw data, and is a process for obtaining objective insights based on data.

[0009] A "tactical pattern" refers to a typical set of actions or formations that a team or player might take under specific circumstances, and is a part of the highly strategic actions taken during a match.

[0010] A "machine learning algorithm" is a set of computational methods that learn patterns from past data and use them to make predictions and classifications on unknown data.

[0011] "Optimal game strategy" refers to the tactical choices recommended to achieve maximum efficiency and effectiveness in response to the game situation and anticipated opponent movements. [Brief explanation of the drawing]

[0012] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a tagged 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.

[0016] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, a tagged 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), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention provides a system that supports tactical decision-making in soccer matches in real time. This system supports the decision-making of managers and coaches by having a server, terminals, and users cooperate to collect and analyze information during the match and present optimal tactics.

[0034] The server acquires player position information and game object control information in real time from sensors and cameras placed on the field. The acquired data is first stored in a database, where initial data cleansing and formatting are performed. After that, real-time processing is performed to analyze player movements and the team's overall game strategy. This analysis includes calculating the average position of players and the team's overall ball possession rate.

[0035] Next, the server uses a machine learning algorithm to predict the opposing team's tactical patterns based on the analysis results. Based on this prediction, the server generates an optimal game strategy and sends it to the terminal. The terminal visually notifies the user of the received tactical information. For example, the terminal displays specific advice to the coach on the screen, such as "To strengthen the side attacks, you should advance the wingers' positions."

[0036] Users can refer to the presented tactical options and issue appropriate tactical instructions based on the match situation and the team's circumstances. This enables data-driven decision-making and real-time tactical changes during the match. As a concrete example of this system, if the opposing team has possession of the ball for an extended period, the user can select an instruction to strengthen pressing based on the presented defensive tactics.

[0037] As described above, the present invention provides a system that supports tactical decisions during a match based on objective data, thereby contributing to improved team performance while reducing the burden on managers and coaches.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server acquires real-time player location information and ball control information from sensor and camera systems installed in the stadium. The acquired data is immediately recorded in a database and made available for subsequent processing.

[0041] Step 2:

[0042] The server performs preprocessing on the recorded data. Specifically, it cleanses the data, removes outliers, and formats it into a consistent format. It also handles the imputation of missing data.

[0043] Step 3:

[0044] The server analyzes each player's movement paths and positional information based on the cleansed data. This analysis calculates the average position of each player and the team's overall ball possession rate, visualizing the progress of the match in real time.

[0045] Step 4:

[0046] The server applies a machine learning algorithm to the analysis results as input to predict the opposing team's tactical patterns. Here, it identifies tactical trends by comparing them with past data and estimates future trends.

[0047] Step 5:

[0048] The server generates the optimal game strategy based on predicted tactical patterns. This strategy includes specific guidelines for the team to adapt to the game situation.

[0049] Step 6:

[0050] The terminal receives information on optimal tactics transmitted from the server and notifies the user of its contents. The notification is delivered through a visual interface and presented in a format that is easily understood by the coach or manager.

[0051] Step 7:

[0052] Users consider the presented tactical options and make final decisions based on the match situation and their own tactical judgment. This information allows users to issue instructions to players and implement rapid tactical changes on the field.

[0053] (Example 1)

[0054] 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."

[0055] In modern sports, it is crucial to select and immediately implement the appropriate tactics in real time during a soccer match. However, analyzing the vast amount of information available during a match and presenting the optimal tactics is difficult, leading to an increased burden on coaches and managers. Solving this problem is essential.

[0056] 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.

[0057] In this invention, the server includes a device for acquiring player location information and game object control information, a device for performing real-time information processing based on the acquired information and analyzing information regarding player movements and the team's overall game strategy, and a device for applying a machine learning model to predict the opposing team's tactical patterns based on the analysis results. This makes it possible to analyze a vast amount of information during a match in real time and immediately provide optimal tactics.

[0058] "Player location information" refers to the coordinate data of each player on the field during a soccer match.

[0059] "Game object control information" refers to data that indicates the current state of the ball and other important elements on the field.

[0060] "Device" refers to an electronic or software configuration designed to perform a specific function.

[0061] "Real-time information processing" means analyzing and processing data almost simultaneously with its generation.

[0062] "Information regarding player movements and the team's overall game strategy" refers to information that shows players' movement paths and relative positions, as well as the team's positioning and tactical movements.

[0063] "Analysis results" refer to conclusions and insights derived from the collected data.

[0064] "Opponent team's tactical patterns" refers to the playing style and tactical tendencies that the opposing team frequently employs during a match.

[0065] A "machine learning model" is a mathematical algorithm that learns patterns and rules from data to perform predictions and classifications.

[0066] A "terminal" is an interface device used by a user to receive and manipulate information.

[0067] "Information processing" refers to a series of operations that involve collecting, organizing, and analyzing data.

[0068] "Average position" is a numerical value that represents a typical position calculated based on multiple positional data points of the players.

[0069] This invention provides a system that supports real-time tactical decision-making in soccer matches. The server uses sensors and cameras installed on the field to collect player location information and game object control information. GPS technology and image recognition technology are used to enable highly accurate data acquisition. The hardware used in this system includes a server computer capable of high-speed processing and wireless communication equipment that enables wide-area communication.

[0070] Next, the server stores the acquired information in a database and performs real-time processing after data cleansing. Data analysis software and machine learning libraries are used for this information processing. This analysis allows for the analysis of player movements and team tactics, enabling the aggregation of individual player movement paths and the calculation of average positions.

[0071] Furthermore, the server utilizes this analysis to predict the opposing team's tactical patterns using a machine learning model. Based on the predicted data, the server generates an optimal game strategy and sends it to the terminal. For example, based on the information received by the terminal, it provides the coach with specific advice such as, "To strengthen the side attacks, the wingers should advance their positions."

[0072] Users can refer to tactical information notified on their devices and make immediate decisions based on the match situation. This allows managers and coaches to make efficient, data-driven decisions and adjust tactics during matches.

[0073] As a concrete example, an example of a prompt sentence to be input to a generating AI model would be: "Please tell me more about a system that supports real-time tactical decision-making in soccer matches. This system has the function of collecting player position information and analyzing tactical patterns." It is expected that this system will support accurate tactical selection on the field and contribute to improving team performance.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server uses sensors and cameras placed on the field to acquire player location information and game object control information. It receives real-time data from sensors and cameras as input and stores it in a database. Sensor data consists of GPS coordinates and image data, outputting highly accurate location information.

[0077] Step 2:

[0078] The server performs data cleansing on the accumulated data. Using raw data stored in the database as input, it completes incomplete data and removes outliers. This process improves data quality, enabling accurate subsequent analysis. The output is formatted, clean data.

[0079] Step 3:

[0080] The server processes information in real time based on clean data, analyzing player movements and team tactics. Inputs include formatted player position data and game object data, which are used to aggregate player movement paths and calculate average positions and overall team ball possession rates. Outputs include statistical information and tactical analysis results.

[0081] Step 4:

[0082] The server utilizes the analysis results and uses a machine learning model to predict the opposing team's tactical patterns. It takes the statistical information and analysis results obtained in the previous step as input and learns patterns in the opposing team's playing style from this data. The output is the predicted tactical pattern of the opponent, which is used to formulate a strategy.

[0083] Step 5:

[0084] The server generates the optimal game strategy for its own team based on the prediction results and sends it to the terminal. The strategy generation algorithm is applied, taking into account the opponent team's tactical patterns and the team's own situation as input. The output consists of a specific game strategy suggestion and data notifying the terminal of that suggestion.

[0085] Step 6:

[0086] The terminal visually notifies the user of received tactical information. It displays the game strategy received as input on the screen and provides specific suggestions for tactical changes to the manager or coach. The output consists of visual instructions and notifications to the user, including suggestions such as "the wingers should be advanced to strengthen the side attack."

[0087] (Application Example 1)

[0088] 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."

[0089] Conventional autonomous driving technologies have limited ability to respond immediately to traffic conditions, making it difficult to provide optimal driving strategies, especially in the event of unpredictable congestion or accidents. This often hinders the efficient operation of vehicles, and improvements are needed to enhance passenger satisfaction and safety. The objective of this invention is to solve this problem.

[0090] 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.

[0091] In this invention, the server includes means for acquiring location information of an object and control information of a moving object; means for performing real-time data processing based on the acquired data and analyzing information regarding the movement of the object and the overall operational strategy; and means for applying a machine learning model to predict the operation patterns of competing objects from the analysis results. This makes it possible to present the optimal operational strategy for a moving object in real time according to traffic conditions.

[0092] "Object" refers to an object or entity from which location information can be obtained.

[0093] A "mobile object" refers to a device or equipment that is capable of moving, such as an automatically operating vehicle.

[0094] "Control information" refers to data and instructions used to manipulate the movement and functions of an object or moving body.

[0095] "Real-time data processing" refers to the process of instantly analyzing collected data and converting it into usable information.

[0096] "Operational strategy" refers to the optimal methods and plans for the operation and movement of objects or moving objects.

[0097] A "behavioral pattern" refers to a series of movements and behavioral tendencies exhibited by competing objects.

[0098] A "machine learning model" refers to an algorithm or mathematical structure used to learn and predict specific patterns from data.

[0099] "Operational strategy" refers to the plan and methods for ensuring that a moving object reaches its destination efficiently and safely.

[0100] The system implementing this invention consists of three main elements: a server, a terminal, and a user. The server utilizes hardware such as sensors and cameras to acquire location information of objects and control information of moving objects. This enables real-time data collection. The collected data is stored in a database on the server, and after initial data cleansing, detailed analysis is performed. For this analysis, machine learning models using Python or R are applied to predict the behavior patterns of objects and competitors. Machine learning libraries such as TENSORFLOW® are commonly used.

[0101] The server generates an optimal operational strategy based on the analysis results and transmits it to the terminal. The terminal receives this operational strategy and presents it visually to the user. The terminal is a smartphone, tablet, or dedicated in-vehicle device equipped with a user interface to display the strategy in an easy-to-understand manner. This allows the user to review the presented strategy and adjust the operation of the vehicle as needed.

[0102] As a concrete example, the server analyzes traffic data in urban areas on weekends and suggests an operational strategy to the vehicle, such as "a specific route is congested, so choose an alternative route." Users can receive this information on their devices and instantly optimize the vehicle's operation.

[0103] An example of a prompt to input into the generating AI model is, "Based on traffic congestion predictions, please suggest the best driving strategy to choose next. For example, how should the route be changed during the afternoon peak in urban areas?" This allows for obtaining appropriate information to ensure that vehicles operate efficiently and safely.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server acquires location and control information in real time from sensors and cameras. This input data includes the current position of the object and the speed of the moving object. The server receives this data and stores it in a database at high speed.

[0107] Step 2:

[0108] Within the server, the acquired data is cleansed to remove invalid data and noise. Data processing includes outlier removal and missing value imputation. This generates a clean, analyzable dataset.

[0109] Step 3:

[0110] The server uses a clean dataset and applies machine learning models using Python or R. It receives real-time location data as input and performs predictions of behavioral patterns. The output is a predictive model that includes the behavioral patterns of competing objects.

[0111] Step 4:

[0112] The server generates an optimal operational strategy based on the predictions of a machine learning model. This operational strategy is then concretized through data analysis and algorithmic calculations. The output is a set of action guidelines that the autonomous vehicle should take.

[0113] Step 5:

[0114] The generated operational strategy is sent from the server to the terminal. The terminal visually displays the operational strategy to the user. This data input and output allows the user to review the operational strategy in an easily understandable format.

[0115] Step 6:

[0116] The user checks the operational strategy on the terminal and adjusts the operational instructions for the autonomous vehicle as needed. The user's judgment is the input, and the operation of the autonomous vehicle is optimized as the output.

[0117] 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.

[0118] This invention combines a system that supports tactical decision-making in soccer matches with an emotion engine that recognizes the user's emotions. The system aims to collect and analyze information during a match through the cooperation of a server, terminal, and user, and to present the optimal tactics.

[0119] The server acquires real-time player location information and game object control information from sensors and camera systems installed on the field. This data is first stored in a database and preprocessed, such as data cleansing. Next, the server performs real-time processing to analyze player movements and the overall team strategy.

[0120] Furthermore, the server uses machine learning algorithms to predict the opposing team's tactical patterns based on the analysis results. Based on this prediction, the server generates an optimal game strategy and sends it to the terminal. The terminal visually notifies the user of this tactical information. For example, the terminal might display advice on the screen such as, "You should strengthen your defensive line in the next 10 minutes."

[0121] A key feature of this invention is that the server is equipped with an emotion engine. The emotion engine analyzes the user's facial expressions, voice, and body language to recognize their emotional state. Based on this, it generates and provides tactical options corresponding to the emotional state. For example, if the user is calm, it can suggest tactics based on established strategies, while if the user is excited, it can suggest aggressive tactics.

[0122] Users compare the presented tactical options with customized tactics based on sentiment analysis, and select the optimal tactic according to the match situation. This enables more appropriate tactical decisions and improves team performance.

[0123] By utilizing an emotion engine, it becomes possible to provide flexible tactical suggestions that respond to the user's emotional state, creating a system that can appropriately handle a variety of situations during a match.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The server acquires real-time player location information and game object control information from sensors and camera systems installed in the stadium. This data is stored in a database and made available for subsequent processing.

[0127] Step 2:

[0128] The server performs data cleansing preprocessing on the stored data. This removes outliers, fills in missing data, and formats the data into a consistent format. This enables highly accurate analysis.

[0129] Step 3:

[0130] The server analyzes player movements and the overall team strategy in real time based on cleansed data. It calculates player movement paths and the team's ball possession rate, providing a detailed understanding of the current match situation.

[0131] Step 4:

[0132] The server applies machine learning algorithms based on the analysis results to predict the opposing team's tactical patterns. It references past data to extract trends and infers future tactical developments.

[0133] Step 5:

[0134] The server generates the optimal game strategy based on the prediction results and sends that information to the terminal. The generated strategy includes specific tactical guidelines tailored to the match situation.

[0135] Step 6:

[0136] The terminal receives tactical information sent from the server and notifies the user. This notification is delivered through a visual interface, providing the manager or coach with an overview of the tactics.

[0137] Step 7:

[0138] The server collects information such as the user's facial expressions, voice, and body language from the terminal or separately installed sensors in order to recognize the user's emotions. The emotion engine processes this information to determine the user's current emotional state.

[0139] Step 8:

[0140] Based on the analysis results of the emotion engine, the server generates tactical options appropriate to the user's emotional state and sends them back to the terminal. For example, if the user is calm, it will suggest safe tactics, while if they are agitated, it will suggest more aggressive tactics.

[0141] Step 9:

[0142] The device notifies the user of customized tactical options, expanding the user's range of tactical choices. Based on the information provided, the user selects the optimal tactic according to the match situation and their own emotions.

[0143] Step 10:

[0144] Users can leverage information provided by the emotion engine to make better tactical decisions during matches. This allows users to make more flexible and objective decisions, ultimately improving team performance.

[0145] (Example 2)

[0146] 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".

[0147] In modern sports competitions, there are limited systems capable of understanding the movements of teams and athletes in real time and instantly formulating and presenting optimal tactics. Traditional systems focus on understanding the physical movements of athletes and making tactical decisions, making it difficult to reflect the user's emotional state in the strategy. As a result, decision-making in tense match situations tends to be inefficient.

[0148] 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.

[0149] In this invention, the server includes means for acquiring player location information and game object control information, means for performing real-time data processing to analyze information regarding player movements and the overall competitive strategy of the group, and means for recognizing the user's emotional state and adjusting the competitive strategy presented. This enables real-time strategic decision-making and flexible tactical reporting that responds to the user's emotional state.

[0150] "Player location information" refers to data that shows the physical coordinates of individual players on the field.

[0151] "Game object control information" refers to information about the movement and manipulation of objects used during a game, such as the ball.

[0152] "Real-time data processing" refers to the process of instantly analyzing data and providing information relevant to the current situation.

[0153] A "machine learning algorithm" is a computational method used to perform pattern recognition and prediction based on collected data.

[0154] "Methods for predicting a group's tactical patterns" refer to methods of analyzing the movements and behavioral tendencies of opposing groups to predict their future actions.

[0155] "Means for recognizing a user's emotional state" refers to technology that analyzes a user's emotions from their facial expressions, voice, and body movements, and determines their emotional state.

[0156] "Means of adjusting competitive strategy" refers to methods of adaptively changing the optimal strategic plan by taking into account the user's emotional state and the competitive situation.

[0157] This invention is a system for supporting tactical decision-making in sports competitions, including soccer, and is equipped with a function to recognize the user's emotional state. The system mainly consists of three components: a server, a terminal, and a user, and each component fulfills its role to achieve overall functionality.

[0158] The server acquires player location information and game object control information in real time using sensors and camera systems placed on the field. Specific hardware includes high-precision GPS sensors for acquiring location information and high-resolution cameras for recording movement. The collected information is stored in a database and then pre-processed by dedicated data cleansing software.

[0159] During the real-time data processing phase, the server runs machine learning algorithms on computers with high-performance processors. These algorithms are used to analyze player movements and overall team strategy patterns, and to predict collective tactical tendencies. In particular, generative AI models are used to learn from historical datasets and enhance knowledge about match-by-match tactics.

[0160] The server then utilizes an emotion recognition engine to estimate the user's emotions. This engine analyzes the user's facial expressions, voice tone, and gestures captured through the device's camera and microphone. The analysis software is based on complex emotion analysis algorithms, allowing it to precisely determine, for example, whether the user is calm or excited.

[0161] The terminal provides a user-accessible interface, visually displaying tactical information and sentiment-based tactical suggestions transmitted from the server. Based on this, users can select the appropriate strategy according to the match situation.

[0162] For example, if the server predicts that the opposing group is employing an aggressive playing style, the terminal will display a tactical message such as, "At this stage, you should strengthen your defensive line." In this case, if the user is overly stressed, an emotionally-based message such as, "Stay calm and strengthen your defense," may also be added.

[0163] The prompts used to input into the generative AI model include phrases such as, "Suggest appropriate soccer tactics when the user's emotional state is calm." The implementation of this system will support flexible and real-time tactical decision-making during matches.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The server acquires player position information and game object control information in real time from sensors and cameras installed on the field. Input data consists of camera footage and sensor information, while output is position coordinates and motion tracking data. Specifically, the server analyzes the video data and converts the positions of players and the ball into coordinate data.

[0167] Step 2:

[0168] The server stores the acquired data in a database and performs data cleansing. The input is raw position coordinate data, and the output is data that has been de-noised and formatted. Specifically, it performs operations such as imputing missing values ​​and filtering outliers.

[0169] Step 3:

[0170] The server performs real-time data processing to analyze player movements and the overall team strategy. Input is formatted position and movement data, and output is statistical information and strategic insights regarding player movements. The server applies machine learning algorithms to analyze, for example, player movement patterns and possession data.

[0171] Step 4:

[0172] The server predicts the opposing team's tactical patterns based on the analysis results. The input is statistical information on player movements, and the output is predictive data about future tactical patterns. A generative AI model is used to calculate the opponent's playing style and attack patterns.

[0173] Step 5:

[0174] The server generates the optimal game strategy based on the prediction results and sends it to the terminal. The input is prediction data, and the output is tactical suggestion information. As a specific example, a tactic of "strengthening defense" is created according to the match situation and sent to the terminal.

[0175] Step 6:

[0176] The terminal visually notifies the user of tactical information sent from the server. The input is tactical suggestion information, and the output is visual tactical instructions directed at the user. Messages such as "Strengthen the defensive line in the next 10 minutes" are displayed on the screen.

[0177] Step 7:

[0178] The server uses an emotion engine to recognize the user's emotional state. Input is the user's facial expressions and voice data, and output is an estimated emotional state. Specifically, it uses data acquired from the camera and microphone to determine whether the user is relaxed or excited.

[0179] Step 8:

[0180] The server adjusts tactical suggestions based on the user's emotional state. Inputs are the emotional state and tactical suggestion information, while output is the adjusted tactical instructions. For example, if the user is nervous, the server will suggest a tactic via the terminal recommending "play calmly."

[0181] (Application Example 2)

[0182] 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".

[0183] Conventional systems could only offer uniform tactical suggestions based solely on individual movements and strategies, in order to provide flexible tactical suggestions that respond to people's emotional states. In contrast, there is a need for a system that can consider individual emotional states and provide more personalized tactical suggestions, enabling appropriate responses depending on the situation.

[0184] 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.

[0185] In this invention, the server includes means for acquiring location information of individuals and control information of objects; means for performing real-time data processing based on the acquired data and analyzing information regarding the movements of individuals and the overall behavioral strategies of a group; means for applying a learning algorithm to predict the tactical patterns of others from the analysis results; and means for recognizing the emotional state of individuals using an emotion engine and generating emotion-based tactical options. This enables flexible and personalized tactical suggestions that respond to the emotions of individuals.

[0186] "Individual" refers to any single object within a particular set or system.

[0187] "Object" refers to a specific object or element with location information that is managed or manipulated within the system.

[0188] "Control information" refers to instruction data used to adjust and manage the movement and position of an object.

[0189] "Real-time data processing" refers to the process of immediately analyzing and interpreting acquired data and providing the results.

[0190] "Group-wide behavioral strategy" refers to the set of optimal actions that multiple individuals within a system should take.

[0191] A "learning algorithm" refers to a computational method used to learn from data, extract patterns, and make predictions about the future.

[0192] An "emotion engine" refers to a software or hardware component that recognizes and understands an individual's emotional state.

[0193] "Emotion-based tactical options" refer to multiple action strategies or policies that are generated by taking into account an individual's emotional state.

[0194] "Personalized tactical suggestions" refers to the process of providing tactics optimized to the characteristics and conditions of each individual.

[0195] This invention is specifically implemented as a customer service support system for physical stores. The system operates using various devices and software components.

[0196] First, the server uses sensors and smart cameras installed in the field to acquire location information and facial expression data of individuals (in this case, customers) in real time. Specifically, smart cameras and image sensors are used as hardware. These devices instantly acquire data on the customer's movements and facial expressions and transmit it to the server.

[0197] The server cleanses the received data, removes noise, and then uses machine learning algorithms (e.g., TensorFlow) to analyze the customer's emotional state. Based on the analyzed data, the server utilizes an emotion engine to generate optimal customer service tactics. This includes situational judgments, such as whether the customer is relaxed or in a hurry.

[0198] Next, the generated customer service tactics are notified to a terminal (in this case, the store staff's smartphone or smart glasses). The terminal provides the staff with the displayed tactical information, supporting them in providing customer service that is appropriate to the customer's current emotional state.

[0199] For example, if a customer appears relaxed, the terminal might display a message such as, "Encourage them to try our new product." In this way, tactics are personalized based on the group's emotional information, ensuring a better customer experience in the store.

[0200] An example of a prompt statement is as follows:

[0201] "If emotional data indicates that the customer is relaxed, provide recommended customer service advice."

[0202] This system enables personalized service in physical stores and improves employee support.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server acquires customer location and facial expression data from smart cameras and sensors. Input is raw data from cameras and sensors, while output is structured location and facial data. Data acquisition is performed in real time and transmitted to the server using a transfer protocol.

[0206] Step 2:

[0207] The server cleanses the acquired raw data, removing noise to improve accuracy. The input is the raw data obtained in step 1, and the output is the cleansed data for analysis. The cleansing process ensures reliable data.

[0208] Step 3:

[0209] The server applies machine learning algorithms for data analysis to identify the customer's emotional state. The input is cleansed data for analysis, and the output is a prediction of the customer's emotional state (e.g., relaxed, tense). At this stage, sentiment analysis is performed using a generative AI model such as TensorFlow.

[0210] Step 4:

[0211] The server utilizes an emotion engine to generate tactical options based on the customer's emotional state. The input is the emotional state data obtained in step 3, and the output is personalized customer service advice. The generated tactics reflect the user's emotions.

[0212] Step 5:

[0213] The terminal receives customer service advice from the server and notifies store staff in real time. The input is tactical option data sent from the server, and the output is customer service advice displayed on the terminal's screen. For example, advice such as "Encourage customers to try the new product" might be displayed.

[0214] Step 6:

[0215] Store staff, acting as users, respond appropriately to customers based on customer service advice provided by the terminal. The input is the information displayed on the terminal, and the output is the actual customer service action. This process enables personalized responses tailored to the customer's emotional state.

[0216] 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.

[0217] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention provides a system that supports tactical decision-making in soccer matches in real time. This system supports the decision-making of managers and coaches by having a server, terminals, and users cooperate to collect and analyze information during the match and present optimal tactics.

[0233] The server acquires player position information and game object control information in real time from sensors and cameras placed on the field. The acquired data is first stored in a database, where initial data cleansing and formatting are performed. After that, real-time processing is performed to analyze player movements and the team's overall game strategy. This analysis includes calculating the average position of players and the team's overall ball possession rate.

[0234] Next, the server uses a machine learning algorithm to predict the opposing team's tactical patterns based on the analysis results. Based on this prediction, the server generates an optimal game strategy and sends it to the terminal. The terminal visually notifies the user of the received tactical information. For example, the terminal displays specific advice to the coach on the screen, such as "To strengthen the side attacks, you should advance the wingers' positions."

[0235] Users can refer to the presented tactical options and issue appropriate tactical instructions based on the match situation and the team's circumstances. This enables data-driven decision-making and real-time tactical changes during the match. As a concrete example of this system, if the opposing team has possession of the ball for an extended period, the user can select an instruction to strengthen pressing based on the presented defensive tactics.

[0236] As described above, the present invention provides a system that supports tactical decisions during a match based on objective data, thereby contributing to improved team performance while reducing the burden on managers and coaches.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The server acquires real-time player location information and ball control information from sensor and camera systems installed in the stadium. The acquired data is immediately recorded in a database and made available for subsequent processing.

[0240] Step 2:

[0241] The server performs preprocessing on the recorded data. Specifically, it cleanses the data, removes outliers, and formats it into a consistent format. It also handles the imputation of missing data.

[0242] Step 3:

[0243] The server analyzes each player's movement paths and positional information based on the cleansed data. This analysis calculates the average position of each player and the team's overall ball possession rate, visualizing the progress of the match in real time.

[0244] Step 4:

[0245] The server applies a machine learning algorithm to the analysis results as input to predict the opposing team's tactical patterns. Here, it identifies tactical trends by comparing them with past data and estimates future trends.

[0246] Step 5:

[0247] The server generates the optimal game strategy based on predicted tactical patterns. This strategy includes specific guidelines for the team to adapt to the game situation.

[0248] Step 6:

[0249] The terminal receives information on optimal tactics transmitted from the server and notifies the user of its contents. The notification is delivered through a visual interface and presented in a format that is easily understood by the coach or manager.

[0250] Step 7:

[0251] Users consider the presented tactical options and make final decisions based on the match situation and their own tactical judgment. This information allows users to issue instructions to players and implement rapid tactical changes on the field.

[0252] (Example 1)

[0253] 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."

[0254] In modern sports, it is crucial to select and immediately implement the appropriate tactics in real time during a soccer match. However, analyzing the vast amount of information available during a match and presenting the optimal tactics is difficult, leading to an increased burden on coaches and managers. Solving this problem is essential.

[0255] 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.

[0256] In this invention, the server includes a device for acquiring player location information and game object control information, a device for performing real-time information processing based on the acquired information and analyzing information regarding player movements and the team's overall game strategy, and a device for applying a machine learning model to predict the opposing team's tactical patterns based on the analysis results. This makes it possible to analyze a vast amount of information during a match in real time and immediately provide optimal tactics.

[0257] "Player location information" refers to the coordinate data of each player on the field during a soccer match.

[0258] "Game object control information" refers to data that indicates the current state of the ball and other important elements on the field.

[0259] "Device" refers to an electronic or software configuration designed to perform a specific function.

[0260] "Real-time information processing" means analyzing and processing data almost simultaneously with its generation.

[0261] "Information regarding player movements and the team's overall game strategy" refers to information that shows players' movement paths and relative positions, as well as the team's positioning and tactical movements.

[0262] "Analysis results" refer to conclusions and insights derived from the collected data.

[0263] "Opponent team's tactical patterns" refers to the playing style and tactical tendencies that the opposing team frequently employs during a match.

[0264] A "machine learning model" is a mathematical algorithm that learns patterns and rules from data to perform predictions and classifications.

[0265] A "terminal" is an interface device used by a user to receive and manipulate information.

[0266] "Information processing" refers to a series of operations that involve collecting, organizing, and analyzing data.

[0267] "Average position" is a numerical value that represents a typical position calculated based on multiple positional data points of the players.

[0268] This invention provides a system that supports real-time tactical decision-making in soccer matches. The server uses sensors and cameras installed on the field to collect player location information and game object control information. GPS technology and image recognition technology are used to enable highly accurate data acquisition. The hardware used in this system includes a server computer capable of high-speed processing and wireless communication equipment that enables wide-area communication.

[0269] Next, the server stores the acquired information in a database and performs real-time processing after data cleansing. Data analysis software and machine learning libraries are used for this information processing. This analysis allows for the analysis of player movements and team tactics, enabling the aggregation of individual player movement paths and the calculation of average positions.

[0270] Furthermore, the server utilizes this analysis to predict the opposing team's tactical patterns using a machine learning model. Based on the predicted data, the server generates an optimal game strategy and sends it to the terminal. For example, based on the information received by the terminal, it provides the coach with specific advice such as, "To strengthen the side attacks, the wingers should advance their positions."

[0271] Users can refer to tactical information notified on their devices and make immediate decisions based on the match situation. This allows managers and coaches to make efficient, data-driven decisions and adjust tactics during matches.

[0272] As a concrete example, an example of a prompt sentence to be input to a generating AI model would be: "Please tell me more about a system that supports real-time tactical decision-making in soccer matches. This system has the function of collecting player position information and analyzing tactical patterns." It is expected that this system will support accurate tactical selection on the field and contribute to improving team performance.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The server uses sensors and cameras placed on the field to acquire player location information and game object control information. It receives real-time data from sensors and cameras as input and stores it in a database. Sensor data consists of GPS coordinates and image data, outputting highly accurate location information.

[0276] Step 2:

[0277] The server performs data cleansing on the accumulated data. Using raw data stored in the database as input, it completes incomplete data and removes outliers. This process improves data quality, enabling accurate subsequent analysis. The output is formatted, clean data.

[0278] Step 3:

[0279] The server processes information in real time based on clean data, analyzing player movements and team tactics. Inputs include formatted player position data and game object data, which are used to aggregate player movement paths and calculate average positions and overall team ball possession rates. Outputs include statistical information and tactical analysis results.

[0280] Step 4:

[0281] The server utilizes the analysis results and uses a machine learning model to predict the opposing team's tactical patterns. It takes the statistical information and analysis results obtained in the previous step as input and learns patterns in the opposing team's playing style from this data. The output is the predicted tactical pattern of the opponent, which is used to formulate a strategy.

[0282] Step 5:

[0283] The server generates an optimal game strategy for its own team based on the prediction results and transmits it to the terminal. As input, considering the tactical patterns of the opposing team and the situation of its own team, a strategy generation algorithm is applied. The output is a proposal for a specific game strategy and data for notifying the terminal of the proposal.

[0284] Step 6:

[0285] The terminal visually notifies the user of the received tactical information. It displays the received game strategy on the screen and provides specific proposals for tactical changes to the supervisor and coach. The output is visual instructions and notifications to the user, including proposals such as "the wing should be advanced to strengthen the side attack".

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] Conventional autonomous driving technologies have limited ability to respond immediately to traffic situations, and it has been difficult to provide an optimal operation strategy especially when traffic jams and accidents that are difficult to predict occur. Therefore, the efficient operation of the moving body is often hindered, and improvements for improving the passenger satisfaction and safety are required. Solving this problem is the object of the present invention.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0290] In this invention, the server includes means for acquiring the position information of the object and the control information of the moving body, means for performing real-time data processing based on the acquired data and analyzing information regarding the movement of the object and the overall operation strategy, and means for applying a machine learning model for predicting the operation patterns of competing objects from the analysis results. Thereby, it becomes possible to present an optimal operation strategy of the moving body in real time according to the traffic situation.

[0291] "Object" refers to an object or entity from which location information can be obtained.

[0292] A "mobile object" refers to a device or equipment that is capable of moving, such as an automatically operating vehicle.

[0293] "Control information" refers to data and instructions used to manipulate the movement and functions of an object or moving body.

[0294] "Real-time data processing" refers to the process of instantly analyzing collected data and converting it into usable information.

[0295] "Operational strategy" refers to the optimal methods and plans for the operation and movement of objects or moving objects.

[0296] A "behavioral pattern" refers to a series of movements and behavioral tendencies exhibited by competing objects.

[0297] A "machine learning model" refers to an algorithm or mathematical structure used to learn and predict specific patterns from data.

[0298] "Operational strategy" refers to the plan and methods for ensuring that a moving object reaches its destination efficiently and safely.

[0299] The system implementing this invention consists of three main elements: a server, a terminal, and a user. The server utilizes hardware such as sensors and cameras to acquire location information of objects and control information of moving objects. This enables real-time data collection. The collected data is stored in a database on the server, and after initial data cleansing, detailed analysis is performed. For this analysis, machine learning models using Python or R are applied to predict the behavior patterns of objects and competitors. Machine learning libraries such as TensorFlow are commonly used.

[0300] The server generates an optimal operation strategy based on the analysis results and transmits it to the terminal. The terminal receives this operation strategy and visually presents it to the user. The terminal is a smartphone, a tablet, or a dedicated in-vehicle device, and has a user interface for easily displaying the strategy. Thereby, the user can check the presented strategy and adjust the operation of the moving body as needed.

[0301] As a specific example, the server analyzes traffic data in the urban area on weekends and presents an operation strategy to the moving body, such as "Since a specific route is congested, select an alternative route". The user can receive this information on the terminal and immediately optimize the operation of the moving body.

[0302] Examples of prompt sentences input to the generation AI model include "Based on the prediction of traffic congestion, please propose the optimal driving tactics to be selected next. For example, how should the route be changed during the afternoon peak hours in the urban area?" Thereby, appropriate information for the efficient and safe operation of the moving body can be obtained.

[0303] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0304] Step 1:

[0305] The server acquires real-time position information and control information from sensors and cameras. This input data includes the current position of the object and the speed of the moving body. The server receives this data and accumulates it in the database at high speed.

[0306] Step 2:

[0307] In the server, the acquired data is cleansed to remove invalid data and noise. Data processing includes the exclusion of outliers and the complementation of missing values. Thereby, a clean and analyzable dataset is generated.

[0308] Step 3:

[0309] The server uses a clean dataset and applies machine learning models using Python or R. It receives real-time location data as input and performs predictions of behavioral patterns. The output is a predictive model that includes the behavioral patterns of competing objects.

[0310] Step 4:

[0311] The server generates an optimal operational strategy based on the predictions of a machine learning model. This operational strategy is then concretized through data analysis and algorithmic calculations. The output is a set of action guidelines that the autonomous vehicle should take.

[0312] Step 5:

[0313] The generated operational strategy is sent from the server to the terminal. The terminal visually displays the operational strategy to the user. This data input and output allows the user to review the operational strategy in an easily understandable format.

[0314] Step 6:

[0315] The user checks the operational strategy on the terminal and adjusts the operational instructions for the autonomous vehicle as needed. The user's judgment is the input, and the operation of the autonomous vehicle is optimized as the output.

[0316] 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.

[0317] This invention combines a system that supports tactical decision-making in soccer matches with an emotion engine that recognizes the user's emotions. The system aims to collect and analyze information during a match through the cooperation of a server, terminal, and user, and to present the optimal tactics.

[0318] The server acquires real-time player location information and game object control information from sensors and camera systems installed on the field. This data is first stored in a database and preprocessed, such as data cleansing. Next, the server performs real-time processing to analyze player movements and the overall team strategy.

[0319] Furthermore, the server uses machine learning algorithms to predict the opposing team's tactical patterns based on the analysis results. Based on this prediction, the server generates an optimal game strategy and sends it to the terminal. The terminal visually notifies the user of this tactical information. For example, the terminal might display advice on the screen such as, "You should strengthen your defensive line in the next 10 minutes."

[0320] A key feature of this invention is that the server is equipped with an emotion engine. The emotion engine analyzes the user's facial expressions, voice, and body language to recognize their emotional state. Based on this, it generates and provides tactical options corresponding to the emotional state. For example, if the user is calm, it can suggest tactics based on established strategies, while if the user is excited, it can suggest aggressive tactics.

[0321] Users compare the presented tactical options with customized tactics based on sentiment analysis, and select the optimal tactic according to the match situation. This enables more appropriate tactical decisions and improves team performance.

[0322] By utilizing an emotion engine, it becomes possible to provide flexible tactical suggestions that respond to the user's emotional state, creating a system that can appropriately handle a variety of situations during a match.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] The server acquires real-time player location information and game object control information from sensors and camera systems installed in the stadium. This data is stored in a database and made available for subsequent processing.

[0326] Step 2:

[0327] The server performs data cleansing preprocessing on the stored data. This removes outliers, fills in missing data, and formats the data into a consistent format. This enables highly accurate analysis.

[0328] Step 3:

[0329] The server analyzes player movements and the overall team strategy in real time based on cleansed data. It calculates player movement paths and the team's ball possession rate, providing a detailed understanding of the current match situation.

[0330] Step 4:

[0331] The server applies machine learning algorithms based on the analysis results to predict the opposing team's tactical patterns. It references past data to extract trends and infers future tactical developments.

[0332] Step 5:

[0333] The server generates the optimal game strategy based on the prediction results and sends that information to the terminal. The generated strategy includes specific tactical guidelines tailored to the match situation.

[0334] Step 6:

[0335] The terminal receives tactical information sent from the server and notifies the user. This notification is delivered through a visual interface, providing the manager or coach with an overview of the tactics.

[0336] Step 7:

[0337] The server collects information such as the user's facial expressions, voice, and body language from the terminal or separately installed sensors in order to recognize the user's emotions. The emotion engine processes this information to determine the user's current emotional state.

[0338] Step 8:

[0339] Based on the analysis results of the emotion engine, the server generates tactical options appropriate to the user's emotional state and sends them back to the terminal. For example, if the user is calm, it will suggest safe tactics, while if they are agitated, it will suggest more aggressive tactics.

[0340] Step 9:

[0341] The device notifies the user of customized tactical options, expanding the user's range of tactical choices. Based on the information provided, the user selects the optimal tactic according to the match situation and their own emotions.

[0342] Step 10:

[0343] Users can leverage information provided by the emotion engine to make better tactical decisions during matches. This allows users to make more flexible and objective decisions, ultimately improving team performance.

[0344] (Example 2)

[0345] 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".

[0346] In modern sports competitions, there are limited systems capable of understanding the movements of teams and athletes in real time and instantly formulating and presenting optimal tactics. Traditional systems focus on understanding the physical movements of athletes and making tactical decisions, making it difficult to reflect the user's emotional state in the strategy. As a result, decision-making in tense match situations tends to be inefficient.

[0347] 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.

[0348] In this invention, the server includes means for acquiring player location information and game object control information, means for performing real-time data processing to analyze information regarding player movements and the overall competitive strategy of the group, and means for recognizing the user's emotional state and adjusting the competitive strategy presented. This enables real-time strategic decision-making and flexible tactical reporting that responds to the user's emotional state.

[0349] "Player location information" refers to data that shows the physical coordinates of individual players on the field.

[0350] "Game object control information" refers to information about the movement and manipulation of objects used during a game, such as the ball.

[0351] "Real-time data processing" refers to the process of instantly analyzing data and providing information relevant to the current situation.

[0352] A "machine learning algorithm" is a computational method used to perform pattern recognition and prediction based on collected data.

[0353] "Methods for predicting a group's tactical patterns" refer to methods of analyzing the movements and behavioral tendencies of opposing groups to predict their future actions.

[0354] "Means for recognizing a user's emotional state" refers to technology that analyzes a user's emotions from their facial expressions, voice, and body movements, and determines their emotional state.

[0355] "Means of adjusting competitive strategy" refers to methods of adaptively changing the optimal strategic plan by taking into account the user's emotional state and the competitive situation.

[0356] This invention is a system for supporting tactical decision-making in sports competitions, including soccer, and is equipped with a function to recognize the user's emotional state. The system mainly consists of three components: a server, a terminal, and a user, and each component fulfills its role to achieve overall functionality.

[0357] The server acquires player location information and game object control information in real time using sensors and camera systems placed on the field. Specific hardware includes high-precision GPS sensors for acquiring location information and high-resolution cameras for recording movement. The collected information is stored in a database and then pre-processed by dedicated data cleansing software.

[0358] During the real-time data processing phase, the server runs machine learning algorithms on computers with high-performance processors. These algorithms are used to analyze player movements and overall team strategy patterns, and to predict collective tactical tendencies. In particular, generative AI models are used to learn from historical datasets and enhance knowledge about match-by-match tactics.

[0359] The server then utilizes an emotion recognition engine to estimate the user's emotions. This engine analyzes the user's facial expressions, voice tone, and gestures captured through the device's camera and microphone. The analysis software is based on complex emotion analysis algorithms, allowing it to precisely determine, for example, whether the user is calm or excited.

[0360] The terminal provides a user-accessible interface, visually displaying tactical information and sentiment-based tactical suggestions transmitted from the server. Based on this, users can select the appropriate strategy according to the match situation.

[0361] For example, if the server predicts that the opposing group is employing an aggressive playing style, the terminal will display a tactical message such as, "At this stage, you should strengthen your defensive line." In this case, if the user is overly stressed, an emotionally-based message such as, "Stay calm and strengthen your defense," may also be added.

[0362] The prompts used to input into the generative AI model include phrases such as, "Suggest appropriate soccer tactics when the user's emotional state is calm." The implementation of this system will support flexible and real-time tactical decision-making during matches.

[0363] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0364] Step 1:

[0365] The server acquires player position information and game object control information in real time from sensors and cameras installed on the field. Input data consists of camera footage and sensor information, while output is position coordinates and motion tracking data. Specifically, the server analyzes the video data and converts the positions of players and the ball into coordinate data.

[0366] Step 2:

[0367] The server stores the acquired data in a database and performs data cleansing. The input is raw position coordinate data, and the output is data that has been de-noised and formatted. Specifically, it performs operations such as imputing missing values ​​and filtering outliers.

[0368] Step 3:

[0369] The server performs real-time data processing to analyze player movements and the overall team strategy. Input is formatted position and movement data, and output is statistical information and strategic insights regarding player movements. The server applies machine learning algorithms to analyze, for example, player movement patterns and possession data.

[0370] Step 4:

[0371] The server predicts the opposing team's tactical patterns based on the analysis results. The input is statistical information on player movements, and the output is predictive data about future tactical patterns. A generative AI model is used to calculate the opponent's playing style and attack patterns.

[0372] Step 5:

[0373] The server generates the optimal game strategy based on the prediction results and sends it to the terminal. The input is prediction data, and the output is tactical suggestion information. As a specific example, a tactic of "strengthening defense" is created according to the match situation and sent to the terminal.

[0374] Step 6:

[0375] The terminal visually notifies the user of tactical information sent from the server. The input is tactical suggestion information, and the output is visual tactical instructions directed at the user. Messages such as "Strengthen the defensive line in the next 10 minutes" are displayed on the screen.

[0376] Step 7:

[0377] The server uses an emotion engine to recognize the user's emotional state. Input is the user's facial expressions and voice data, and output is an estimated emotional state. Specifically, it uses data acquired from the camera and microphone to determine whether the user is relaxed or excited.

[0378] Step 8:

[0379] The server adjusts tactical suggestions based on the user's emotional state. Inputs are the emotional state and tactical suggestion information, while output is the adjusted tactical instructions. For example, if the user is nervous, the server will suggest a tactic via the terminal recommending "play calmly."

[0380] (Application Example 2)

[0381] 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."

[0382] Conventional systems could only offer uniform tactical suggestions based solely on individual movements and strategies, in order to provide flexible tactical suggestions that respond to people's emotional states. In contrast, there is a need for a system that can consider individual emotional states and provide more personalized tactical suggestions, enabling appropriate responses depending on the situation.

[0383] 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.

[0384] In this invention, the server includes means for acquiring location information of individuals and control information of objects; means for performing real-time data processing based on the acquired data and analyzing information regarding the movements of individuals and the overall behavioral strategies of a group; means for applying a learning algorithm to predict the tactical patterns of others from the analysis results; and means for recognizing the emotional state of individuals using an emotion engine and generating emotion-based tactical options. This enables flexible and personalized tactical suggestions that respond to the emotions of individuals.

[0385] "Individual" refers to any single object within a particular set or system.

[0386] "Object" refers to a specific object or element with location information that is managed or manipulated within the system.

[0387] "Control information" refers to instruction data used to adjust and manage the movement and position of an object.

[0388] "Real-time data processing" refers to the process of immediately analyzing and interpreting acquired data and providing the results.

[0389] "Group-wide behavioral strategy" refers to the set of optimal actions that multiple individuals within a system should take.

[0390] A "learning algorithm" refers to a computational method used to learn from data, extract patterns, and make predictions about the future.

[0391] An "emotion engine" refers to a software or hardware component that recognizes and understands an individual's emotional state.

[0392] "Emotion-based tactical options" refer to multiple action strategies or policies that are generated by taking into account an individual's emotional state.

[0393] "Personalized tactical suggestions" refers to the process of providing tactics optimized to the characteristics and conditions of each individual.

[0394] This invention is specifically implemented as a customer service support system for physical stores. The system operates using various devices and software components.

[0395] First, the server uses sensors and smart cameras installed in the field to acquire location information and facial expression data of individuals (in this case, customers) in real time. Specifically, smart cameras and image sensors are used as hardware. These devices instantly acquire data on the customer's movements and facial expressions and transmit it to the server.

[0396] The server cleanses the received data, removes noise, and then uses machine learning algorithms (e.g., TensorFlow) to analyze the customer's emotional state. Based on the analyzed data, the server utilizes an emotion engine to generate optimal customer service tactics. This includes situational judgments, such as whether the customer is relaxed or in a hurry.

[0397] Next, the generated customer service tactics are notified to a terminal (in this case, the store staff's smartphone or smart glasses). The terminal provides the staff with the displayed tactical information, supporting them in providing customer service that is appropriate to the customer's current emotional state.

[0398] For example, if a customer appears relaxed, the terminal might display a message such as, "Encourage them to try our new product." In this way, tactics are personalized based on the group's emotional information, ensuring a better customer experience in the store.

[0399] An example of a prompt statement is as follows:

[0400] "If emotional data indicates that the customer is relaxed, provide recommended customer service advice."

[0401] This system enables personalized service in physical stores and improves employee support.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The server acquires customer location and facial expression data from smart cameras and sensors. Input is raw data from cameras and sensors, while output is structured location and facial data. Data acquisition is performed in real time and transmitted to the server using a transfer protocol.

[0405] Step 2:

[0406] The server cleanses the acquired raw data, removing noise to improve accuracy. The input is the raw data obtained in step 1, and the output is the cleansed data for analysis. The cleansing process ensures reliable data.

[0407] Step 3:

[0408] The server applies machine learning algorithms for data analysis to identify the customer's emotional state. The input is cleansed data for analysis, and the output is a prediction of the customer's emotional state (e.g., relaxed, tense). At this stage, sentiment analysis is performed using a generative AI model such as TensorFlow.

[0409] Step 4:

[0410] The server utilizes an emotion engine to generate tactical options based on the customer's emotional state. The input is the emotional state data obtained in step 3, and the output is personalized customer service advice. The generated tactics reflect the user's emotions.

[0411] Step 5:

[0412] The terminal receives customer service advice from the server and notifies store staff in real time. The input is tactical option data sent from the server, and the output is customer service advice displayed on the terminal's screen. For example, advice such as "Encourage customers to try the new product" might be displayed.

[0413] Step 6:

[0414] Store staff, acting as users, respond appropriately to customers based on customer service advice provided by the terminal. The input is the information displayed on the terminal, and the output is the actual customer service action. This process enables personalized responses tailored to the customer's emotional state.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] This invention provides a system that supports tactical decision-making in soccer matches in real time. This system supports the decision-making of managers and coaches by having a server, terminals, and users cooperate to collect and analyze information during the match and present optimal tactics.

[0432] The server acquires player position information and game object control information in real time from sensors and cameras placed on the field. The acquired data is first stored in a database, where initial data cleansing and formatting are performed. After that, real-time processing is performed to analyze player movements and the team's overall game strategy. This analysis includes calculating the average position of players and the team's overall ball possession rate.

[0433] Next, the server uses a machine learning algorithm to predict the opposing team's tactical patterns based on the analysis results. Based on this prediction, the server generates an optimal game strategy and sends it to the terminal. The terminal visually notifies the user of the received tactical information. For example, the terminal displays specific advice to the coach on the screen, such as "To strengthen the side attacks, you should advance the wingers' positions."

[0434] Users can refer to the presented tactical options and issue appropriate tactical instructions based on the match situation and the team's circumstances. This enables data-driven decision-making and real-time tactical changes during the match. As a concrete example of this system, if the opposing team has possession of the ball for an extended period, the user can select an instruction to strengthen pressing based on the presented defensive tactics.

[0435] As described above, the present invention provides a system that supports tactical decisions during a match based on objective data, thereby contributing to improved team performance while reducing the burden on managers and coaches.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] The server acquires real-time player location information and ball control information from sensor and camera systems installed in the stadium. The acquired data is immediately recorded in a database and made available for subsequent processing.

[0439] Step 2:

[0440] The server performs preprocessing on the recorded data. Specifically, it cleanses the data, removes outliers, and formats it into a consistent format. It also handles the imputation of missing data.

[0441] Step 3:

[0442] The server analyzes each player's movement paths and positional information based on the cleansed data. This analysis calculates the average position of each player and the team's overall ball possession rate, visualizing the progress of the match in real time.

[0443] Step 4:

[0444] The server applies a machine learning algorithm to the analysis results as input to predict the opposing team's tactical patterns. Here, it identifies tactical trends by comparing them with past data and estimates future trends.

[0445] Step 5:

[0446] The server generates the optimal game strategy based on predicted tactical patterns. This strategy includes specific guidelines for the team to adapt to the game situation.

[0447] Step 6:

[0448] The terminal receives information on optimal tactics transmitted from the server and notifies the user of its contents. The notification is delivered through a visual interface and presented in a format that is easily understood by the coach or manager.

[0449] Step 7:

[0450] Users consider the presented tactical options and make final decisions based on the match situation and their own tactical judgment. This information allows users to issue instructions to players and implement rapid tactical changes on the field.

[0451] (Example 1)

[0452] 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."

[0453] In modern sports, it is crucial to select and immediately implement the appropriate tactics in real time during a soccer match. However, analyzing the vast amount of information available during a match and presenting the optimal tactics is difficult, leading to an increased burden on coaches and managers. Solving this problem is essential.

[0454] 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.

[0455] In this invention, the server includes a device for acquiring player location information and game object control information, a device for performing real-time information processing based on the acquired information and analyzing information regarding player movements and the team's overall game strategy, and a device for applying a machine learning model to predict the opposing team's tactical patterns based on the analysis results. This makes it possible to analyze a vast amount of information during a match in real time and immediately provide optimal tactics.

[0456] "Player location information" refers to the coordinate data of each player on the field during a soccer match.

[0457] "Game object control information" refers to data that indicates the current state of the ball and other important elements on the field.

[0458] "Device" refers to an electronic or software configuration designed to perform a specific function.

[0459] "Real-time information processing" means analyzing and processing data almost simultaneously with its generation.

[0460] "Information regarding player movements and the team's overall game strategy" refers to information that shows players' movement paths and relative positions, as well as the team's positioning and tactical movements.

[0461] "Analysis results" refer to conclusions and insights derived from the collected data.

[0462] "Opponent team's tactical patterns" refers to the playing style and tactical tendencies that the opposing team frequently employs during a match.

[0463] A "machine learning model" is a mathematical algorithm that learns patterns and rules from data to perform predictions and classifications.

[0464] A "terminal" is an interface device used by a user to receive and manipulate information.

[0465] "Information processing" refers to a series of operations that involve collecting, organizing, and analyzing data.

[0466] "Average position" is a numerical value that represents a typical position calculated based on multiple positional data points of the players.

[0467] This invention provides a system that supports real-time tactical decision-making in soccer matches. The server uses sensors and cameras installed on the field to collect player location information and game object control information. GPS technology and image recognition technology are used to enable highly accurate data acquisition. The hardware used in this system includes a server computer capable of high-speed processing and wireless communication equipment that enables wide-area communication.

[0468] Next, the server stores the acquired information in a database and performs real-time processing after data cleansing. Data analysis software and machine learning libraries are used for this information processing. This analysis allows for the analysis of player movements and team tactics, enabling the aggregation of individual player movement paths and the calculation of average positions.

[0469] Furthermore, the server utilizes this analysis to predict the opposing team's tactical patterns using a machine learning model. Based on the predicted data, the server generates an optimal game strategy and sends it to the terminal. For example, based on the information received by the terminal, it provides the coach with specific advice such as, "To strengthen the side attacks, the wingers should advance their positions."

[0470] Users can refer to tactical information notified on their devices and make immediate decisions based on the match situation. This allows managers and coaches to make efficient, data-driven decisions and adjust tactics during matches.

[0471] As a concrete example, an example of a prompt sentence to be input to a generating AI model would be: "Please tell me more about a system that supports real-time tactical decision-making in soccer matches. This system has the function of collecting player position information and analyzing tactical patterns." It is expected that this system will support accurate tactical selection on the field and contribute to improving team performance.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The server uses sensors and cameras placed on the field to acquire player location information and game object control information. It receives real-time data from sensors and cameras as input and stores it in a database. Sensor data consists of GPS coordinates and image data, outputting highly accurate location information.

[0475] Step 2:

[0476] The server performs data cleansing on the accumulated data. Using raw data stored in the database as input, it completes incomplete data and removes outliers. This process improves data quality, enabling accurate subsequent analysis. The output is formatted, clean data.

[0477] Step 3:

[0478] The server processes information in real time based on clean data, analyzing player movements and team tactics. Inputs include formatted player position data and game object data, which are used to aggregate player movement paths and calculate average positions and overall team ball possession rates. Outputs include statistical information and tactical analysis results.

[0479] Step 4:

[0480] The server utilizes the analysis results and uses a machine learning model to predict the opposing team's tactical patterns. It takes the statistical information and analysis results obtained in the previous step as input and learns patterns in the opposing team's playing style from this data. The output is the predicted tactical pattern of the opponent, which is used to formulate a strategy.

[0481] Step 5:

[0482] The server generates the optimal game strategy for its own team based on the prediction results and sends it to the terminal. The strategy generation algorithm is applied, taking into account the opponent team's tactical patterns and the team's own situation as input. The output consists of a specific game strategy suggestion and data notifying the terminal of that suggestion.

[0483] Step 6:

[0484] The terminal visually notifies the user of received tactical information. It displays the game strategy received as input on the screen and provides specific suggestions for tactical changes to the manager or coach. The output consists of visual instructions and notifications to the user, including suggestions such as "the wingers should be advanced to strengthen the side attack."

[0485] (Application Example 1)

[0486] 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."

[0487] Conventional autonomous driving technologies have limited ability to respond immediately to traffic conditions, making it difficult to provide optimal driving strategies, especially in the event of unpredictable congestion or accidents. This often hinders the efficient operation of vehicles, and improvements are needed to enhance passenger satisfaction and safety. The objective of this invention is to solve this problem.

[0488] 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.

[0489] In this invention, the server includes means for acquiring location information of an object and control information of a moving object; means for performing real-time data processing based on the acquired data and analyzing information regarding the movement of the object and the overall operational strategy; and means for applying a machine learning model to predict the operation patterns of competing objects from the analysis results. This makes it possible to present the optimal operational strategy for a moving object in real time according to traffic conditions.

[0490] "Object" refers to an object or entity from which location information can be obtained.

[0491] A "mobile object" refers to a device or equipment that is capable of moving, such as an automatically operating vehicle.

[0492] "Control information" refers to data and instructions used to manipulate the movement and functions of an object or moving body.

[0493] "Real-time data processing" refers to the process of instantly analyzing collected data and converting it into usable information.

[0494] "Operational strategy" refers to the optimal methods and plans for the operation and movement of objects or moving objects.

[0495] A "behavioral pattern" refers to a series of movements and behavioral tendencies exhibited by competing objects.

[0496] A "machine learning model" refers to an algorithm or mathematical structure used to learn and predict specific patterns from data.

[0497] "Operational strategy" refers to the plan and methods for ensuring that a moving object reaches its destination efficiently and safely.

[0498] The system implementing this invention consists of three main elements: a server, a terminal, and a user. The server utilizes hardware such as sensors and cameras to acquire location information of objects and control information of moving objects. This enables real-time data collection. The collected data is stored in a database on the server, and after initial data cleansing, detailed analysis is performed. For this analysis, machine learning models using Python or R are applied to predict the behavior patterns of objects and competitors. Machine learning libraries such as TensorFlow are commonly used.

[0499] The server generates an optimal operational strategy based on the analysis results and transmits it to the terminal. The terminal receives this operational strategy and presents it visually to the user. The terminal is a smartphone, tablet, or dedicated in-vehicle device equipped with a user interface to display the strategy in an easy-to-understand manner. This allows the user to review the presented strategy and adjust the operation of the vehicle as needed.

[0500] As a concrete example, the server analyzes traffic data in urban areas on weekends and suggests an operational strategy to the vehicle, such as "a specific route is congested, so choose an alternative route." Users can receive this information on their devices and instantly optimize the vehicle's operation.

[0501] An example of a prompt to input into the generating AI model is, "Based on traffic congestion predictions, please suggest the best driving strategy to choose next. For example, how should the route be changed during the afternoon peak in urban areas?" This allows for obtaining appropriate information to ensure that vehicles operate efficiently and safely.

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] The server acquires location and control information in real time from sensors and cameras. This input data includes the current position of the object and the speed of the moving object. The server receives this data and stores it in a database at high speed.

[0505] Step 2:

[0506] Within the server, the acquired data is cleansed to remove invalid data and noise. Data processing includes outlier removal and missing value imputation. This generates a clean, analyzable dataset.

[0507] Step 3:

[0508] The server uses a clean dataset and applies machine learning models using Python or R. It receives real-time location data as input and performs predictions of behavioral patterns. The output is a predictive model that includes the behavioral patterns of competing objects.

[0509] Step 4:

[0510] The server generates an optimal operational strategy based on the predictions of a machine learning model. This operational strategy is then concretized through data analysis and algorithmic calculations. The output is a set of action guidelines that the autonomous vehicle should take.

[0511] Step 5:

[0512] The generated operational strategy is sent from the server to the terminal. The terminal visually displays the operational strategy to the user. This data input and output allows the user to review the operational strategy in an easily understandable format.

[0513] Step 6:

[0514] The user checks the operational strategy on the terminal and adjusts the operational instructions for the autonomous vehicle as needed. The user's judgment is the input, and the operation of the autonomous vehicle is optimized as the output.

[0515] 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.

[0516] This invention combines a system that supports tactical decision-making in soccer matches with an emotion engine that recognizes the user's emotions. The system aims to collect and analyze information during a match through the cooperation of a server, terminal, and user, and to present the optimal tactics.

[0517] The server acquires real-time player location information and game object control information from sensors and camera systems installed on the field. This data is first stored in a database and preprocessed, such as data cleansing. Next, the server performs real-time processing to analyze player movements and the overall team strategy.

[0518] Furthermore, the server uses machine learning algorithms to predict the opposing team's tactical patterns based on the analysis results. Based on this prediction, the server generates an optimal game strategy and sends it to the terminal. The terminal visually notifies the user of this tactical information. For example, the terminal might display advice on the screen such as, "You should strengthen your defensive line in the next 10 minutes."

[0519] A key feature of this invention is that the server is equipped with an emotion engine. The emotion engine analyzes the user's facial expressions, voice, and body language to recognize their emotional state. Based on this, it generates and provides tactical options corresponding to the emotional state. For example, if the user is calm, it can suggest tactics based on established strategies, while if the user is excited, it can suggest aggressive tactics.

[0520] Users compare the presented tactical options with customized tactics based on sentiment analysis, and select the optimal tactic according to the match situation. This enables more appropriate tactical decisions and improves team performance.

[0521] By utilizing an emotion engine, it becomes possible to provide flexible tactical suggestions that respond to the user's emotional state, creating a system that can appropriately handle a variety of situations during a match.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The server acquires real-time player location information and game object control information from sensors and camera systems installed in the stadium. This data is stored in a database and made available for subsequent processing.

[0525] Step 2:

[0526] The server performs data cleansing preprocessing on the stored data. This removes outliers, fills in missing data, and formats the data into a consistent format. This enables highly accurate analysis.

[0527] Step 3:

[0528] The server analyzes player movements and the overall team strategy in real time based on cleansed data. It calculates player movement paths and the team's ball possession rate, providing a detailed understanding of the current match situation.

[0529] Step 4:

[0530] The server applies machine learning algorithms based on the analysis results to predict the opposing team's tactical patterns. It references past data to extract trends and infers future tactical developments.

[0531] Step 5:

[0532] The server generates the optimal game strategy based on the prediction results and sends that information to the terminal. The generated strategy includes specific tactical guidelines tailored to the match situation.

[0533] Step 6:

[0534] The terminal receives tactical information sent from the server and notifies the user. This notification is delivered through a visual interface, providing the manager or coach with an overview of the tactics.

[0535] Step 7:

[0536] The server collects information such as the user's facial expressions, voice, and body language from the terminal or separately installed sensors in order to recognize the user's emotions. The emotion engine processes this information to determine the user's current emotional state.

[0537] Step 8:

[0538] Based on the analysis results of the emotion engine, the server generates tactical options appropriate to the user's emotional state and sends them back to the terminal. For example, if the user is calm, it will suggest safe tactics, while if they are agitated, it will suggest more aggressive tactics.

[0539] Step 9:

[0540] The device notifies the user of customized tactical options, expanding the user's range of tactical choices. Based on the information provided, the user selects the optimal tactic according to the match situation and their own emotions.

[0541] Step 10:

[0542] Users can leverage information provided by the emotion engine to make better tactical decisions during matches. This allows users to make more flexible and objective decisions, ultimately improving team performance.

[0543] (Example 2)

[0544] 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."

[0545] In modern sports competitions, there are limited systems capable of understanding the movements of teams and athletes in real time and instantly formulating and presenting optimal tactics. Traditional systems focus on understanding the physical movements of athletes and making tactical decisions, making it difficult to reflect the user's emotional state in the strategy. As a result, decision-making in tense match situations tends to be inefficient.

[0546] 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.

[0547] In this invention, the server includes means for acquiring player location information and game object control information, means for performing real-time data processing to analyze information regarding player movements and the overall competitive strategy of the group, and means for recognizing the user's emotional state and adjusting the competitive strategy presented. This enables real-time strategic decision-making and flexible tactical reporting that responds to the user's emotional state.

[0548] "Player location information" refers to data that shows the physical coordinates of individual players on the field.

[0549] "Game object control information" refers to information about the movement and manipulation of objects used during a game, such as the ball.

[0550] "Real-time data processing" refers to the process of instantly analyzing data and providing information relevant to the current situation.

[0551] A "machine learning algorithm" is a computational method used to perform pattern recognition and prediction based on collected data.

[0552] "Methods for predicting a group's tactical patterns" refer to methods of analyzing the movements and behavioral tendencies of opposing groups to predict their future actions.

[0553] "Means for recognizing a user's emotional state" refers to technology that analyzes a user's emotions from their facial expressions, voice, and body movements, and determines their emotional state.

[0554] "Means of adjusting competitive strategy" refers to methods of adaptively changing the optimal strategic plan by taking into account the user's emotional state and the competitive situation.

[0555] This invention is a system for supporting tactical decision-making in sports competitions, including soccer, and is equipped with a function to recognize the user's emotional state. The system mainly consists of three components: a server, a terminal, and a user, and each component fulfills its role to achieve overall functionality.

[0556] The server acquires player location information and game object control information in real time using sensors and camera systems placed on the field. Specific hardware includes high-precision GPS sensors for acquiring location information and high-resolution cameras for recording movement. The collected information is stored in a database and then pre-processed by dedicated data cleansing software.

[0557] During the real-time data processing phase, the server runs machine learning algorithms on computers with high-performance processors. These algorithms are used to analyze player movements and overall team strategy patterns, and to predict collective tactical tendencies. In particular, generative AI models are used to learn from historical datasets and enhance knowledge about match-by-match tactics.

[0558] The server then utilizes an emotion recognition engine to estimate the user's emotions. This engine analyzes the user's facial expressions, voice tone, and gestures captured through the device's camera and microphone. The analysis software is based on complex emotion analysis algorithms, allowing it to precisely determine, for example, whether the user is calm or excited.

[0559] The terminal provides a user-accessible interface, visually displaying tactical information and sentiment-based tactical suggestions transmitted from the server. Based on this, users can select the appropriate strategy according to the match situation.

[0560] For example, if the server predicts that the opposing group is employing an aggressive playing style, the terminal will display a tactical message such as, "At this stage, you should strengthen your defensive line." In this case, if the user is overly stressed, an emotionally-based message such as, "Stay calm and strengthen your defense," may also be added.

[0561] The prompts used to input into the generative AI model include phrases such as, "Suggest appropriate soccer tactics when the user's emotional state is calm." The implementation of this system will support flexible and real-time tactical decision-making during matches.

[0562] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0563] Step 1:

[0564] The server acquires player position information and game object control information in real time from sensors and cameras installed on the field. Input data consists of camera footage and sensor information, while output is position coordinates and motion tracking data. Specifically, the server analyzes the video data and converts the positions of players and the ball into coordinate data.

[0565] Step 2:

[0566] The server stores the acquired data in a database and performs data cleansing. The input is raw position coordinate data, and the output is data that has been de-noised and formatted. Specifically, it performs operations such as imputing missing values ​​and filtering outliers.

[0567] Step 3:

[0568] The server performs real-time data processing to analyze player movements and the overall team strategy. Input is formatted position and movement data, and output is statistical information and strategic insights regarding player movements. The server applies machine learning algorithms to analyze, for example, player movement patterns and possession data.

[0569] Step 4:

[0570] The server predicts the opposing team's tactical patterns based on the analysis results. The input is statistical information on player movements, and the output is predictive data about future tactical patterns. A generative AI model is used to calculate the opponent's playing style and attack patterns.

[0571] Step 5:

[0572] The server generates the optimal game strategy based on the prediction results and sends it to the terminal. The input is prediction data, and the output is tactical suggestion information. As a specific example, a tactic of "strengthening defense" is created according to the match situation and sent to the terminal.

[0573] Step 6:

[0574] The terminal visually notifies the user of tactical information sent from the server. The input is tactical suggestion information, and the output is visual tactical instructions directed at the user. Messages such as "Strengthen the defensive line in the next 10 minutes" are displayed on the screen.

[0575] Step 7:

[0576] The server uses an emotion engine to recognize the user's emotional state. Input is the user's facial expressions and voice data, and output is an estimated emotional state. Specifically, it uses data acquired from the camera and microphone to determine whether the user is relaxed or excited.

[0577] Step 8:

[0578] The server adjusts tactical suggestions based on the user's emotional state. Inputs are the emotional state and tactical suggestion information, while output is the adjusted tactical instructions. For example, if the user is nervous, the server will suggest a tactic via the terminal recommending "play calmly."

[0579] (Application Example 2)

[0580] 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."

[0581] Conventional systems could only offer uniform tactical suggestions based solely on individual movements and strategies, in order to provide flexible tactical suggestions that respond to people's emotional states. In contrast, there is a need for a system that can consider individual emotional states and provide more personalized tactical suggestions, enabling appropriate responses depending on the situation.

[0582] 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.

[0583] In this invention, the server includes means for acquiring location information of individuals and control information of objects; means for performing real-time data processing based on the acquired data and analyzing information regarding the movements of individuals and the overall behavioral strategies of a group; means for applying a learning algorithm to predict the tactical patterns of others from the analysis results; and means for recognizing the emotional state of individuals using an emotion engine and generating emotion-based tactical options. This enables flexible and personalized tactical suggestions that respond to the emotions of individuals.

[0584] "Individual" refers to any single object within a particular set or system.

[0585] "Object" refers to a specific object or element with location information that is managed or manipulated within the system.

[0586] "Control information" refers to instruction data used to adjust and manage the movement and position of an object.

[0587] "Real-time data processing" refers to the process of immediately analyzing and interpreting acquired data and providing the results.

[0588] "Group-wide behavioral strategy" refers to the set of optimal actions that multiple individuals within a system should take.

[0589] A "learning algorithm" refers to a computational method used to learn from data, extract patterns, and make predictions about the future.

[0590] An "emotion engine" refers to a software or hardware component that recognizes and understands an individual's emotional state.

[0591] "Emotion-based tactical options" refer to multiple action strategies or policies that are generated by taking into account an individual's emotional state.

[0592] "Personalized tactical suggestions" refers to the process of providing tactics optimized to the characteristics and conditions of each individual.

[0593] This invention is specifically implemented as a customer service support system for physical stores. The system operates using various devices and software components.

[0594] First, the server uses sensors and smart cameras installed in the field to acquire location information and facial expression data of individuals (in this case, customers) in real time. Specifically, smart cameras and image sensors are used as hardware. These devices instantly acquire data on the customer's movements and facial expressions and transmit it to the server.

[0595] The server cleanses the received data, removes noise, and then uses machine learning algorithms (e.g., TensorFlow) to analyze the customer's emotional state. Based on the analyzed data, the server utilizes an emotion engine to generate optimal customer service tactics. This includes situational judgments, such as whether the customer is relaxed or in a hurry.

[0596] Next, the generated customer service tactics are notified to a terminal (in this case, the store staff's smartphone or smart glasses). The terminal provides the staff with the displayed tactical information, supporting them in providing customer service that is appropriate to the customer's current emotional state.

[0597] For example, if a customer appears relaxed, the terminal might display a message such as, "Encourage them to try our new product." In this way, tactics are personalized based on the group's emotional information, ensuring a better customer experience in the store.

[0598] An example of a prompt statement is as follows:

[0599] "If emotional data indicates that the customer is relaxed, provide recommended customer service advice."

[0600] This system enables personalized service in physical stores and improves employee support.

[0601] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0602] Step 1:

[0603] The server acquires customer location and facial expression data from smart cameras and sensors. Input is raw data from cameras and sensors, while output is structured location and facial data. Data acquisition is performed in real time and transmitted to the server using a transfer protocol.

[0604] Step 2:

[0605] The server cleanses the acquired raw data, removing noise to improve accuracy. The input is the raw data obtained in step 1, and the output is the cleansed data for analysis. The cleansing process ensures reliable data.

[0606] Step 3:

[0607] The server applies machine learning algorithms for data analysis to identify the customer's emotional state. The input is cleansed data for analysis, and the output is a prediction of the customer's emotional state (e.g., relaxed, tense). At this stage, sentiment analysis is performed using a generative AI model such as TensorFlow.

[0608] Step 4:

[0609] The server utilizes an emotion engine to generate tactical options based on the customer's emotional state. The input is the emotional state data obtained in step 3, and the output is personalized customer service advice. The generated tactics reflect the user's emotions.

[0610] Step 5:

[0611] The terminal receives customer service advice from the server and notifies store staff in real time. The input is tactical option data sent from the server, and the output is customer service advice displayed on the terminal's screen. For example, advice such as "Encourage customers to try the new product" might be displayed.

[0612] Step 6:

[0613] Store staff, acting as users, respond appropriately to customers based on customer service advice provided by the terminal. The input is the information displayed on the terminal, and the output is the actual customer service action. This process enables personalized responses tailored to the customer's emotional state.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] [Fourth Embodiment]

[0618] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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".

[0631] This invention provides a system that supports tactical decision-making in soccer matches in real time. This system supports the decision-making of managers and coaches by having a server, terminals, and users cooperate to collect and analyze information during the match and present optimal tactics.

[0632] The server acquires player position information and game object control information in real time from sensors and cameras placed on the field. The acquired data is first stored in a database, where initial data cleansing and formatting are performed. After that, real-time processing is performed to analyze player movements and the team's overall game strategy. This analysis includes calculating the average position of players and the team's overall ball possession rate.

[0633] Next, the server uses a machine learning algorithm to predict the opposing team's tactical patterns based on the analysis results. Based on this prediction, the server generates an optimal game strategy and sends it to the terminal. The terminal visually notifies the user of the received tactical information. For example, the terminal displays specific advice to the coach on the screen, such as "To strengthen the side attacks, you should advance the wingers' positions."

[0634] Users can refer to the presented tactical options and issue appropriate tactical instructions based on the match situation and the team's circumstances. This enables data-driven decision-making and real-time tactical changes during the match. As a concrete example of this system, if the opposing team has possession of the ball for an extended period, the user can select an instruction to strengthen pressing based on the presented defensive tactics.

[0635] As described above, the present invention provides a system that supports tactical decisions during a match based on objective data, thereby contributing to improved team performance while reducing the burden on managers and coaches.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The server acquires real-time player location information and ball control information from sensor and camera systems installed in the stadium. The acquired data is immediately recorded in a database and made available for subsequent processing.

[0639] Step 2:

[0640] The server performs preprocessing on the recorded data. Specifically, it cleanses the data, removes outliers, and formats it into a consistent format. It also handles the imputation of missing data.

[0641] Step 3:

[0642] The server analyzes each player's movement paths and positional information based on the cleansed data. This analysis calculates the average position of each player and the team's overall ball possession rate, visualizing the progress of the match in real time.

[0643] Step 4:

[0644] The server applies a machine learning algorithm to the analysis results as input to predict the opposing team's tactical patterns. Here, it identifies tactical trends by comparing them with past data and estimates future trends.

[0645] Step 5:

[0646] The server generates the optimal game strategy based on predicted tactical patterns. This strategy includes specific guidelines for the team to adapt to the game situation.

[0647] Step 6:

[0648] The terminal receives information on optimal tactics transmitted from the server and notifies the user of its contents. The notification is delivered through a visual interface and presented in a format that is easily understood by the coach or manager.

[0649] Step 7:

[0650] Users consider the presented tactical options and make final decisions based on the match situation and their own tactical judgment. This information allows users to issue instructions to players and implement rapid tactical changes on the field.

[0651] (Example 1)

[0652] 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".

[0653] In modern sports, it is crucial to select and immediately implement the appropriate tactics in real time during a soccer match. However, analyzing the vast amount of information available during a match and presenting the optimal tactics is difficult, leading to an increased burden on coaches and managers. Solving this problem is essential.

[0654] 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.

[0655] In this invention, the server includes a device for acquiring player location information and game object control information, a device for performing real-time information processing based on the acquired information and analyzing information regarding player movements and the team's overall game strategy, and a device for applying a machine learning model to predict the opposing team's tactical patterns based on the analysis results. This makes it possible to analyze a vast amount of information during a match in real time and immediately provide optimal tactics.

[0656] "Player location information" refers to the coordinate data of each player on the field during a soccer match.

[0657] "Game object control information" refers to data that indicates the current state of the ball and other important elements on the field.

[0658] "Device" refers to an electronic or software configuration designed to perform a specific function.

[0659] "Real-time information processing" means analyzing and processing data almost simultaneously with its generation.

[0660] "Information regarding player movements and the team's overall game strategy" refers to information that shows players' movement paths and relative positions, as well as the team's positioning and tactical movements.

[0661] "Analysis results" refer to conclusions and insights derived from the collected data.

[0662] "Opponent team's tactical patterns" refers to the playing style and tactical tendencies that the opposing team frequently employs during a match.

[0663] A "machine learning model" is a mathematical algorithm that learns patterns and rules from data to perform predictions and classifications.

[0664] A "terminal" is an interface device used by a user to receive and manipulate information.

[0665] "Information processing" refers to a series of operations that involve collecting, organizing, and analyzing data.

[0666] "Average position" is a numerical value that represents a typical position calculated based on multiple positional data points of the players.

[0667] This invention provides a system that supports real-time tactical decision-making in soccer matches. The server uses sensors and cameras installed on the field to collect player location information and game object control information. GPS technology and image recognition technology are used to enable highly accurate data acquisition. The hardware used in this system includes a server computer capable of high-speed processing and wireless communication equipment that enables wide-area communication.

[0668] Next, the server stores the acquired information in a database and performs real-time processing after data cleansing. Data analysis software and machine learning libraries are used for this information processing. This analysis allows for the analysis of player movements and team tactics, enabling the aggregation of individual player movement paths and the calculation of average positions.

[0669] Furthermore, the server utilizes this analysis to predict the opposing team's tactical patterns using a machine learning model. Based on the predicted data, the server generates an optimal game strategy and sends it to the terminal. For example, based on the information received by the terminal, it provides the coach with specific advice such as, "To strengthen the side attacks, the wingers should advance their positions."

[0670] Users can refer to tactical information notified on their devices and make immediate decisions based on the match situation. This allows managers and coaches to make efficient, data-driven decisions and adjust tactics during matches.

[0671] As a concrete example, an example of a prompt sentence to be input to a generating AI model would be: "Please tell me more about a system that supports real-time tactical decision-making in soccer matches. This system has the function of collecting player position information and analyzing tactical patterns." It is expected that this system will support accurate tactical selection on the field and contribute to improving team performance.

[0672] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0673] Step 1:

[0674] The server uses sensors and cameras placed on the field to acquire player location information and game object control information. It receives real-time data from sensors and cameras as input and stores it in a database. Sensor data consists of GPS coordinates and image data, outputting highly accurate location information.

[0675] Step 2:

[0676] The server performs data cleansing on the accumulated data. Using raw data stored in the database as input, it completes incomplete data and removes outliers. This process improves data quality, enabling accurate subsequent analysis. The output is formatted, clean data.

[0677] Step 3:

[0678] The server processes information in real time based on clean data, analyzing player movements and team tactics. Inputs include formatted player position data and game object data, which are used to aggregate player movement paths and calculate average positions and overall team ball possession rates. Outputs include statistical information and tactical analysis results.

[0679] Step 4:

[0680] The server utilizes the analysis results and uses a machine learning model to predict the opposing team's tactical patterns. It takes the statistical information and analysis results obtained in the previous step as input and learns patterns in the opposing team's playing style from this data. The output is the predicted tactical pattern of the opponent, which is used to formulate a strategy.

[0681] Step 5:

[0682] The server generates the optimal game strategy for its own team based on the prediction results and sends it to the terminal. The strategy generation algorithm is applied, taking into account the opponent team's tactical patterns and the team's own situation as input. The output consists of a specific game strategy suggestion and data notifying the terminal of that suggestion.

[0683] Step 6:

[0684] The terminal visually notifies the user of received tactical information. It displays the game strategy received as input on the screen and provides specific suggestions for tactical changes to the manager or coach. The output consists of visual instructions and notifications to the user, including suggestions such as "the wingers should be advanced to strengthen the side attack."

[0685] (Application Example 1)

[0686] 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".

[0687] Conventional autonomous driving technologies have limited ability to respond immediately to traffic conditions, making it difficult to provide optimal driving strategies, especially in the event of unpredictable congestion or accidents. This often hinders the efficient operation of vehicles, and improvements are needed to enhance passenger satisfaction and safety. The objective of this invention is to solve this problem.

[0688] 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.

[0689] In this invention, the server includes means for acquiring location information of an object and control information of a moving object; means for performing real-time data processing based on the acquired data and analyzing information regarding the movement of the object and the overall operational strategy; and means for applying a machine learning model to predict the operation patterns of competing objects from the analysis results. This makes it possible to present the optimal operational strategy for a moving object in real time according to traffic conditions.

[0690] "Object" refers to an object or entity from which location information can be obtained.

[0691] A "mobile object" refers to a device or equipment that is capable of moving, such as an automatically operating vehicle.

[0692] "Control information" refers to data and instructions used to manipulate the movement and functions of an object or moving body.

[0693] "Real-time data processing" refers to the process of instantly analyzing collected data and converting it into usable information.

[0694] "Operational strategy" refers to the optimal methods and plans for the operation and movement of objects or moving objects.

[0695] A "behavioral pattern" refers to a series of movements and behavioral tendencies exhibited by competing objects.

[0696] A "machine learning model" refers to an algorithm or mathematical structure used to learn and predict specific patterns from data.

[0697] "Operational strategy" refers to the plan and methods for ensuring that a moving object reaches its destination efficiently and safely.

[0698] The system implementing this invention consists of three main elements: a server, a terminal, and a user. The server utilizes hardware such as sensors and cameras to acquire location information of objects and control information of moving objects. This enables real-time data collection. The collected data is stored in a database on the server, and after initial data cleansing, detailed analysis is performed. For this analysis, machine learning models using Python or R are applied to predict the behavior patterns of objects and competitors. Machine learning libraries such as TensorFlow are commonly used.

[0699] The server generates an optimal operational strategy based on the analysis results and transmits it to the terminal. The terminal receives this operational strategy and presents it visually to the user. The terminal is a smartphone, tablet, or dedicated in-vehicle device equipped with a user interface to display the strategy in an easy-to-understand manner. This allows the user to review the presented strategy and adjust the operation of the vehicle as needed.

[0700] As a concrete example, the server analyzes traffic data in urban areas on weekends and suggests an operational strategy to the vehicle, such as "a specific route is congested, so choose an alternative route." Users can receive this information on their devices and instantly optimize the vehicle's operation.

[0701] An example of a prompt to input into the generating AI model is, "Based on traffic congestion predictions, please suggest the best driving strategy to choose next. For example, how should the route be changed during the afternoon peak in urban areas?" This allows for obtaining appropriate information to ensure that vehicles operate efficiently and safely.

[0702] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0703] Step 1:

[0704] The server acquires location and control information in real time from sensors and cameras. This input data includes the current position of the object and the speed of the moving object. The server receives this data and stores it in a database at high speed.

[0705] Step 2:

[0706] Within the server, the acquired data is cleansed to remove invalid data and noise. Data processing includes outlier removal and missing value imputation. This generates a clean, analyzable dataset.

[0707] Step 3:

[0708] The server uses a clean dataset and applies machine learning models using Python or R. It receives real-time location data as input and performs predictions of behavioral patterns. The output is a predictive model that includes the behavioral patterns of competing objects.

[0709] Step 4:

[0710] The server generates an optimal operational strategy based on the predictions of a machine learning model. This operational strategy is then concretized through data analysis and algorithmic calculations. The output is a set of action guidelines that the autonomous vehicle should take.

[0711] Step 5:

[0712] The generated operational strategy is sent from the server to the terminal. The terminal visually displays the operational strategy to the user. This data input and output allows the user to review the operational strategy in an easily understandable format.

[0713] Step 6:

[0714] The user checks the operational strategy on the terminal and adjusts the operational instructions for the autonomous vehicle as needed. The user's judgment is the input, and the operation of the autonomous vehicle is optimized as the output.

[0715] 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.

[0716] This invention combines a system that supports tactical decision-making in soccer matches with an emotion engine that recognizes the user's emotions. The system aims to collect and analyze information during a match through the cooperation of a server, terminal, and user, and to present the optimal tactics.

[0717] The server acquires real-time player location information and game object control information from sensors and camera systems installed on the field. This data is first stored in a database and preprocessed, such as data cleansing. Next, the server performs real-time processing to analyze player movements and the overall team strategy.

[0718] Furthermore, the server uses machine learning algorithms to predict the opposing team's tactical patterns based on the analysis results. Based on this prediction, the server generates an optimal game strategy and sends it to the terminal. The terminal visually notifies the user of this tactical information. For example, the terminal might display advice on the screen such as, "You should strengthen your defensive line in the next 10 minutes."

[0719] A key feature of this invention is that the server is equipped with an emotion engine. The emotion engine analyzes the user's facial expressions, voice, and body language to recognize their emotional state. Based on this, it generates and provides tactical options corresponding to the emotional state. For example, if the user is calm, it can suggest tactics based on established strategies, while if the user is excited, it can suggest aggressive tactics.

[0720] Users compare the presented tactical options with customized tactics based on sentiment analysis, and select the optimal tactic according to the match situation. This enables more appropriate tactical decisions and improves team performance.

[0721] By utilizing an emotion engine, it becomes possible to provide flexible tactical suggestions that respond to the user's emotional state, creating a system that can appropriately handle a variety of situations during a match.

[0722] The following describes the processing flow.

[0723] Step 1:

[0724] The server acquires real-time player location information and game object control information from sensors and camera systems installed in the stadium. This data is stored in a database and made available for subsequent processing.

[0725] Step 2:

[0726] The server performs data cleansing preprocessing on the stored data. This removes outliers, fills in missing data, and formats the data into a consistent format. This enables highly accurate analysis.

[0727] Step 3:

[0728] The server analyzes player movements and the overall team strategy in real time based on cleansed data. It calculates player movement paths and the team's ball possession rate, providing a detailed understanding of the current match situation.

[0729] Step 4:

[0730] The server applies machine learning algorithms based on the analysis results to predict the opposing team's tactical patterns. It references past data to extract trends and infers future tactical developments.

[0731] Step 5:

[0732] The server generates the optimal game strategy based on the prediction results and sends that information to the terminal. The generated strategy includes specific tactical guidelines tailored to the match situation.

[0733] Step 6:

[0734] The terminal receives tactical information sent from the server and notifies the user. This notification is delivered through a visual interface, providing the manager or coach with an overview of the tactics.

[0735] Step 7:

[0736] The server collects information such as the user's facial expressions, voice, and body language from the terminal or separately installed sensors in order to recognize the user's emotions. The emotion engine processes this information to determine the user's current emotional state.

[0737] Step 8:

[0738] Based on the analysis results of the emotion engine, the server generates tactical options appropriate to the user's emotional state and sends them back to the terminal. For example, if the user is calm, it will suggest safe tactics, while if they are agitated, it will suggest more aggressive tactics.

[0739] Step 9:

[0740] The device notifies the user of customized tactical options, expanding the user's range of tactical choices. Based on the information provided, the user selects the optimal tactic according to the match situation and their own emotions.

[0741] Step 10:

[0742] Users can leverage information provided by the emotion engine to make better tactical decisions during matches. This allows users to make more flexible and objective decisions, ultimately improving team performance.

[0743] (Example 2)

[0744] 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".

[0745] In modern sports competitions, there are limited systems capable of understanding the movements of teams and athletes in real time and instantly formulating and presenting optimal tactics. Traditional systems focus on understanding the physical movements of athletes and making tactical decisions, making it difficult to reflect the user's emotional state in the strategy. As a result, decision-making in tense match situations tends to be inefficient.

[0746] 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.

[0747] In this invention, the server includes means for acquiring player location information and game object control information, means for performing real-time data processing to analyze information regarding player movements and the overall competitive strategy of the group, and means for recognizing the user's emotional state and adjusting the competitive strategy presented. This enables real-time strategic decision-making and flexible tactical reporting that responds to the user's emotional state.

[0748] "Player location information" refers to data that shows the physical coordinates of individual players on the field.

[0749] "Game object control information" refers to information about the movement and manipulation of objects used during a game, such as the ball.

[0750] "Real-time data processing" refers to the process of instantly analyzing data and providing information relevant to the current situation.

[0751] A "machine learning algorithm" is a computational method used to perform pattern recognition and prediction based on collected data.

[0752] "Methods for predicting a group's tactical patterns" refer to methods of analyzing the movements and behavioral tendencies of opposing groups to predict their future actions.

[0753] "Means for recognizing a user's emotional state" refers to technology that analyzes a user's emotions from their facial expressions, voice, and body movements, and determines their emotional state.

[0754] "Means of adjusting competitive strategy" refers to methods of adaptively changing the optimal strategic plan by taking into account the user's emotional state and the competitive situation.

[0755] This invention is a system for supporting tactical decision-making in sports competitions, including soccer, and is equipped with a function to recognize the user's emotional state. The system mainly consists of three components: a server, a terminal, and a user, and each component fulfills its role to achieve overall functionality.

[0756] The server acquires player location information and game object control information in real time using sensors and camera systems placed on the field. Specific hardware includes high-precision GPS sensors for acquiring location information and high-resolution cameras for recording movement. The collected information is stored in a database and then pre-processed by dedicated data cleansing software.

[0757] During the real-time data processing phase, the server runs machine learning algorithms on computers with high-performance processors. These algorithms are used to analyze player movements and overall team strategy patterns, and to predict collective tactical tendencies. In particular, generative AI models are used to learn from historical datasets and enhance knowledge about match-by-match tactics.

[0758] The server then utilizes an emotion recognition engine to estimate the user's emotions. This engine analyzes the user's facial expressions, voice tone, and gestures captured through the device's camera and microphone. The analysis software is based on complex emotion analysis algorithms, allowing it to precisely determine, for example, whether the user is calm or excited.

[0759] The terminal provides a user-accessible interface, visually displaying tactical information and sentiment-based tactical suggestions transmitted from the server. Based on this, users can select the appropriate strategy according to the match situation.

[0760] For example, if the server predicts that the opposing group is employing an aggressive playing style, the terminal will display a tactical message such as, "At this stage, you should strengthen your defensive line." In this case, if the user is overly stressed, an emotionally-based message such as, "Stay calm and strengthen your defense," may also be added.

[0761] The prompts used to input into the generative AI model include phrases such as, "Suggest appropriate soccer tactics when the user's emotional state is calm." The implementation of this system will support flexible and real-time tactical decision-making during matches.

[0762] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0763] Step 1:

[0764] The server acquires player position information and game object control information in real time from sensors and cameras installed on the field. Input data consists of camera footage and sensor information, while output is position coordinates and motion tracking data. Specifically, the server analyzes the video data and converts the positions of players and the ball into coordinate data.

[0765] Step 2:

[0766] The server stores the acquired data in a database and performs data cleansing. The input is raw position coordinate data, and the output is data that has been de-noised and formatted. Specifically, it performs operations such as imputing missing values ​​and filtering outliers.

[0767] Step 3:

[0768] The server performs real-time data processing to analyze player movements and the overall team strategy. Input is formatted position and movement data, and output is statistical information and strategic insights regarding player movements. The server applies machine learning algorithms to analyze, for example, player movement patterns and possession data.

[0769] Step 4:

[0770] The server predicts the opposing team's tactical patterns based on the analysis results. The input is statistical information on player movements, and the output is predictive data about future tactical patterns. A generative AI model is used to calculate the opponent's playing style and attack patterns.

[0771] Step 5:

[0772] The server generates the optimal game strategy based on the prediction results and sends it to the terminal. The input is prediction data, and the output is tactical suggestion information. As a specific example, a tactic of "strengthening defense" is created according to the match situation and sent to the terminal.

[0773] Step 6:

[0774] The terminal visually notifies the user of tactical information sent from the server. The input is tactical suggestion information, and the output is visual tactical instructions directed at the user. Messages such as "Strengthen the defensive line in the next 10 minutes" are displayed on the screen.

[0775] Step 7:

[0776] The server uses an emotion engine to recognize the user's emotional state. Input is the user's facial expressions and voice data, and output is an estimated emotional state. Specifically, it uses data acquired from the camera and microphone to determine whether the user is relaxed or excited.

[0777] Step 8:

[0778] The server adjusts tactical suggestions based on the user's emotional state. Inputs are the emotional state and tactical suggestion information, while output is the adjusted tactical instructions. For example, if the user is nervous, the server will suggest a tactic via the terminal recommending "play calmly."

[0779] (Application Example 2)

[0780] 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".

[0781] Conventional systems could only offer uniform tactical suggestions based solely on individual movements and strategies, in order to provide flexible tactical suggestions that respond to people's emotional states. In contrast, there is a need for a system that can consider individual emotional states and provide more personalized tactical suggestions, enabling appropriate responses depending on the situation.

[0782] 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.

[0783] In this invention, the server includes means for acquiring location information of individuals and control information of objects; means for performing real-time data processing based on the acquired data and analyzing information regarding the movements of individuals and the overall behavioral strategies of a group; means for applying a learning algorithm to predict the tactical patterns of others from the analysis results; and means for recognizing the emotional state of individuals using an emotion engine and generating emotion-based tactical options. This enables flexible and personalized tactical suggestions that respond to the emotions of individuals.

[0784] "Individual" refers to any single object within a particular set or system.

[0785] "Object" refers to a specific object or element with location information that is managed or manipulated within the system.

[0786] "Control information" refers to instruction data used to adjust and manage the movement and position of an object.

[0787] "Real-time data processing" refers to the process of immediately analyzing and interpreting acquired data and providing the results.

[0788] "Group-wide behavioral strategy" refers to the set of optimal actions that multiple individuals within a system should take.

[0789] A "learning algorithm" refers to a computational method used to learn from data, extract patterns, and make predictions about the future.

[0790] An "emotion engine" refers to a software or hardware component that recognizes and understands an individual's emotional state.

[0791] "Emotion-based tactical options" refer to multiple action strategies or policies that are generated by taking into account an individual's emotional state.

[0792] "Personalized tactical suggestions" refers to the process of providing tactics optimized to the characteristics and conditions of each individual.

[0793] This invention is specifically implemented as a customer service support system for physical stores. The system operates using various devices and software components.

[0794] First, the server uses sensors and smart cameras installed in the field to acquire location information and facial expression data of individuals (in this case, customers) in real time. Specifically, smart cameras and image sensors are used as hardware. These devices instantly acquire data on the customer's movements and facial expressions and transmit it to the server.

[0795] The server cleanses the received data, removes noise, and then uses machine learning algorithms (e.g., TensorFlow) to analyze the customer's emotional state. Based on the analyzed data, the server utilizes an emotion engine to generate optimal customer service tactics. This includes situational judgments, such as whether the customer is relaxed or in a hurry.

[0796] Next, the generated customer service tactics are notified to a terminal (in this case, the store staff's smartphone or smart glasses). The terminal provides the staff with the displayed tactical information, supporting them in providing customer service that is appropriate to the customer's current emotional state.

[0797] For example, if a customer appears relaxed, the terminal might display a message such as, "Encourage them to try our new product." In this way, tactics are personalized based on the group's emotional information, ensuring a better customer experience in the store.

[0798] An example of a prompt statement is as follows:

[0799] "If emotional data indicates that the customer is relaxed, provide recommended customer service advice."

[0800] This system enables personalized service in physical stores and improves employee support.

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The server acquires customer location and facial expression data from smart cameras and sensors. Input is raw data from cameras and sensors, while output is structured location and facial data. Data acquisition is performed in real time and transmitted to the server using a transfer protocol.

[0804] Step 2:

[0805] The server cleanses the acquired raw data, removing noise to improve accuracy. The input is the raw data obtained in step 1, and the output is the cleansed data for analysis. The cleansing process ensures reliable data.

[0806] Step 3:

[0807] The server applies machine learning algorithms for data analysis to identify the customer's emotional state. The input is cleansed data for analysis, and the output is a prediction of the customer's emotional state (e.g., relaxed, tense). At this stage, sentiment analysis is performed using a generative AI model such as TensorFlow.

[0808] Step 4:

[0809] The server utilizes an emotion engine to generate tactical options based on the customer's emotional state. The input is the emotional state data obtained in step 3, and the output is personalized customer service advice. The generated tactics reflect the user's emotions.

[0810] Step 5:

[0811] The terminal receives customer service advice from the server and notifies store staff in real time. The input is tactical option data sent from the server, and the output is customer service advice displayed on the terminal's screen. For example, advice such as "Encourage customers to try the new product" might be displayed.

[0812] Step 6:

[0813] Store staff, acting as users, respond appropriately to customers based on customer service advice provided by the terminal. The input is the information displayed on the terminal, and the output is the actual customer service action. This process enables personalized responses tailored to the customer's emotional state.

[0814] 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.

[0815] 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.

[0816] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] A means of obtaining player location information and game object control information,

[0838] A means of performing real-time data processing based on the acquired data to analyze information regarding player movements and the team's overall game strategy,

[0839] Based on the analysis results, a means of applying a machine learning algorithm to predict the opposing team's tactical patterns,

[0840] A means for presenting the optimal game strategy for the team based on the aforementioned prediction results,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising means for notifying the user of the presented game strategy.

[0844] (Claim 3)

[0845] The system according to claim 1, wherein the data processing includes an algorithm that aggregates the movement paths of each player and calculates the average position.

[0846] "Example 1"

[0847] (Claim 1)

[0848] A device that acquires player location information and game object control information,

[0849] A device that performs real-time information processing based on the acquired information to analyze information regarding player movements and the team's overall game strategy,

[0850] Based on the analysis results, a machine learning model is applied to predict the opposing team's tactical patterns.

[0851] A device that generates and presents an optimal game strategy for the team based on the aforementioned prediction results,

[0852] A terminal for displaying the generated game strategy,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, further comprising a device for visually notifying the user of the presented game strategy.

[0856] (Claim 3)

[0857] The system according to claim 1, wherein the information processing includes a method for aggregating the movement paths of each player and calculating the average position.

[0858] "Application Example 1"

[0859] (Claim 1)

[0860] Means for acquiring the position information of an object or control information of a moving object,

[0861] A means for performing real-time data processing based on the acquired data to analyze information regarding the movement of the object and the overall operational strategy,

[0862] Based on the analysis results, a means of applying a machine learning model to predict the behavior patterns of competing objects,

[0863] A means for presenting an optimal operation strategy for a moving object based on the aforementioned prediction results,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, further comprising means for notifying the operator of the presented operational strategy.

[0867] (Claim 3)

[0868] The system according to claim 1, wherein the data processing includes an algorithm that aggregates the movement paths of each object and calculates the average position.

[0869] "Example 2 of combining an emotion engine"

[0870] (Claim 1)

[0871] A means of obtaining player location information and game object control information,

[0872] A means of performing real-time data processing based on the acquired data to analyze information regarding the movements of athletes and the overall competitive strategy of the group,

[0873] Based on the analysis results, a means of applying a machine learning algorithm to predict the tactical patterns of the opposing group,

[0874] A means for presenting the optimal competitive strategy for the group based on the aforementioned prediction results,

[0875] A means of recognizing the user's emotional state,

[0876] A means of adjusting the presented game strategy based on the recognized emotional state of the user,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, further comprising means for notifying the user of the presented competition strategy.

[0880] (Claim 3)

[0881] The system according to claim 1, wherein the data processing includes an algorithm that aggregates the movement paths of each player and calculates the average position.

[0882] "Application example 2 when combining with an emotional engine"

[0883] (Claim 1)

[0884] Means for acquiring the location information of an individual or control information of an object,

[0885] A means for performing real-time data processing based on the acquired data to analyze information regarding the movements of individuals and the behavioral strategies of the entire group,

[0886] Based on the analysis results, a means of applying a learning algorithm to predict the tactical patterns of others,

[0887] A means for presenting an optimal action strategy for the group based on the aforementioned prediction results,

[0888] A means of generating emotion-based tactical options by utilizing an emotion engine that recognizes the emotional state of an individual,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, further comprising means for notifying the user of the proposed action strategy.

[0892] (Claim 3)

[0893] The system according to claim 1, wherein the data processing includes an algorithm that aggregates the movement paths of each individual and calculates the average position. [Explanation of symbols]

[0894] 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 obtaining player location information and game object control information, A means of performing real-time data processing based on the acquired data to analyze information regarding player movements and the team's overall game strategy, Based on the analysis results, a means of applying a machine learning algorithm to predict the opposing team's tactical patterns, A means for presenting the optimal game strategy for the team based on the aforementioned prediction results, A system that includes this.

2. The system according to claim 1, further comprising means for notifying the user of the presented game strategy.

3. The system according to claim 1, wherein the data processing includes an algorithm that aggregates the movement paths of each player and calculates the average position.

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