system

Abstract

We provide the system. [Solution] A means for obtaining match information, A means for acquiring performance data of a motion device, Means for acquiring maintenance information for the aforementioned motion device, A means for analyzing acquired performance data and maintenance information, Means for generating a work plan based on the aforementioned analysis means, A means of presenting the generated work plan, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 operation of conventional sports teams, there are multiple problems such as formulating tactics, analyzing players' performances, managing the risk of injuries, and optimizing individual training. To effectively address these problems, analysis of a vast amount of data and immediate decision-making are required, which is a problem as it requires a great deal of time and effort.

Means for Solving the Problems

[0005] This invention provides a system that automatically acquires match information and player information, analyzes this information, and generates effective tactical plans. Furthermore, it predicts injury risk based on player health information and proposes management plans. In addition, by analyzing training information for each player and generating and presenting individually optimized training plans, it streamlines team operations and contributes to solving various challenges.

[0006] "Match information" is a general term for data related to the play situation, score, and player movements during a match.

[0007] "Player information" refers to a dataset containing performance data, statistical information, and physical data related to individual players.

[0008] "Analysis means" refers to a method or apparatus for processing and analyzing acquired data and deriving results that meet a specific purpose.

[0009] A "strategy plan" is a plan that defines the tactical policies, formations, and roles of each player in a match.

[0010] "Presentation means" refers to a method or device for visually or audibly communicating generated information or results to a user.

[0011] "Health information" refers to data that indicates the physical health status of an athlete, and includes medical data and biometric measurement data.

[0012] "Injury risk" refers to predictive data indicating the likelihood of an athlete being injured under specific circumstances.

[0013] A "management plan" is a plan that sets out policies and procedures aimed at preventing injuries and maintaining the health of athletes.

[0014] "Training information" refers to data related to an athlete's daily training activities, including their content, intensity, and frequency.

[0015] A "training plan" is a training menu and schedule individually formulated for the purpose of improving the abilities of athletes.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a labeled 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.

[0020] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a labeled 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.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system that utilizes generative AI to support the management and operation of sports teams. Through the following program processing, it analyzes match information and player information, provides optimal tactical plans and training plans, and manages injury risks.

[0038] The system broadly comprises the following functions: data collection, data analysis, tactical and training plan generation, and injury risk management.

[0039] 1. Data Collection

[0040] Users input player and match information into their terminals for each match and training session. This includes player data and health information during the match.

[0041] The terminal formats the entered data according to the specified format in order to send it to the server, and then sends it to the server.

[0042] 2. Data Analysis

[0043] The server performs analysis based on the received match and player information. Multiple algorithms are used to analyze tactical planning and individual player performance.

[0044] This allows us to identify the opposing team's tactical tendencies and clarify the strengths and weaknesses of our own team's players' performance.

[0045] 3. Generation of tactical and training plans

[0046] The server generates the optimal tactical plan for the match based on the analysis results. It also provides individually optimized training plans for each player.

[0047] The terminal displays the generated tactical and training plans to the user as a dashboard.

[0048] 4. Injury Risk Management

[0049] The server analyzes the players' health information and predicts the risk of injury based on this. Based on these results, it creates injury prevention measures and recovery plans.

[0050] The device displays injury risk predictions and management plans to physiotherapists and trainers.

[0051] Specific example

[0052] For example, consider a scenario where a match against a certain team is scheduled before a game. The user, acting as a coach, inputs data from the previous match, the players' health status, and the opponent's past data into a terminal. The server analyzes all the data, recommends a specific formation with strong offensive capabilities, and then presents a training menu tailored to player A. At the same time, based on player B's health status, it predicts an increased risk of injury and provides feedback on a plan to adjust the rest schedule.

[0053] Thus, by using the system of the present invention, it becomes possible to improve the tactical efficiency of the team and the health management of the players.

[0054] The following describes the processing flow.

[0055] Step 1:

[0056] Users input match results, player data, and information about their opponents into their devices. This includes statistical information and data indicating health status.

[0057] Step 2:

[0058] The terminal receives data entered by the user and converts it to the appropriate format. The converted data is then sent to the server.

[0059] Step 3:

[0060] The server receives data sent from the terminal and stores it in the database. When storing the data, it checks for data integrity and verifies that there are no outliers or duplicates.

[0061] Step 4:

[0062] The server extracts information stored in the database and executes data analysis algorithms. This allows for analysis of the opposing team's playing style and the performance of the team's own players.

[0063] Step 5:

[0064] The server generates an optimal tactical plan based on the analysis results. The generated plan includes specific strategies and formation suggestions that exploit the opponent's weaknesses.

[0065] Step 6:

[0066] The server creates a training plan optimized for each individual player based on their performance and physical data.

[0067] Step 7:

[0068] The terminal receives tactical and training plans sent from the server and presents them to the user in a dashboard format.

[0069] Step 8:

[0070] The server analyzes the players' health data and predicts their injury risk. If a risk is detected, it generates preventative measures and management plans.

[0071] Step 9:

[0072] The device displays injury risk predictions and related management plans to physiotherapists and trainers, enabling appropriate health management.

[0073] (Example 1)

[0074] 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."

[0075] In managing and operating sports teams, the effective use of match and player information is required, but collecting, analyzing, and utilizing this information in a timely manner to develop tactical and training plans is not easy. Furthermore, in player health management, there is a lack of concrete measures to predict and appropriately manage injury risks. Moreover, there is a need for an effective method to quickly share the plans generated through these processes with team personnel.

[0076] 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.

[0077] In this invention, the server includes means for collecting information about matches, means for collecting information about players, and means for performing analysis based on the collected match information and player information. This enables the formulation of effective tactical plans and the generation of training plans optimized for each player. Furthermore, it supports injury risk prediction and the creation of management plans for player health, and enables secure data communication throughout the entire process.

[0078] A "device for collecting information related to a match" refers to equipment or software used to acquire data related to a sports match, such as player performance, match results, and tactical data.

[0079] A "device for collecting information about athletes" refers to equipment or software used to acquire individual data on athletes, such as their health status, physical data, and individual performance.

[0080] "Means of analysis" refers to a function that uses statistical or machine learning methods to analyze data based on collected match information and player information, and derive meaningful conclusions.

[0081] A "tactical plan generating device" is a device or software that automatically creates optimal tactics and strategies for a match based on analysis results.

[0082] A "device for displaying tactical plans" is a device or interface that visually presents generated tactical plans or training plans to the user, providing information in an easily understandable format.

[0083] "Communication methods" refer to protocols and equipment for securely and efficiently sending and receiving data both within and outside a system, and they guarantee the confidentiality and integrity of the data.

[0084] A "device that uses analytical algorithms" is a device or software that applies multiple algorithms in data analysis, ultimately intended to derive useful tactics and performance indicators.

[0085] A "device for collecting information on athletes' health" refers to equipment or software that acquires information such as an athlete's health status, medical data, and past injury history.

[0086] A "device for predicting injury risk" is a device or software that uses an athlete's health information to evaluate and predict the likelihood of future injuries.

[0087] A "health management plan generating device" is a device or software that automatically creates the measures and plans necessary to maintain and improve the health of athletes.

[0088] A "training menu provider" refers to equipment or software that presents a training plan and schedule tailored to each athlete, thereby supporting performance improvement.

[0089] A "device for collecting training information" refers to equipment or software used to record the training content and results performed by athletes, and serves as a foundation for analyzing the effectiveness of the training.

[0090] A "simulation device" is a piece of equipment or software used to virtually evaluate the effectiveness of tactics and formations in a match and to find the optimal strategy.

[0091] This invention is a system that supports the management and operation of sports teams, analyzing match and player information to provide optimal tactical and training plans. Furthermore, it has a function to manage injury risk by analyzing player health information. This system is mainly implemented by server, terminal, and user components.

[0092] Data collection and formatting

[0093] Users use a terminal to input player information and match information related to matches and training. The terminal receives this information, converts it into accurately formatted data, and sends it to the server via a communication protocol (e.g., HTTPS).

[0094] Data Analysis

[0095] The server stores the received match and player information in a database and prepares it for analysis. Generative AI models are used for the analysis, and various data analysis algorithms are applied. This analysis generates individual player performance data and tactical plans for the entire team.

[0096] Tactical plan generation and display

[0097] The server automatically generates an optimal tactical plan based on the analysis results. The generated tactics are visually presented to the user via a terminal. This allows the coach to immediately verify the effectiveness of the tactics and make adjustments.

[0098] Injury risk management

[0099] Furthermore, the server analyzes the athletes' health information and runs an algorithm to predict injury risk. The prediction results are displayed to physiotherapists and trainers to help them develop appropriate preventative measures and recovery plans.

[0100] Specific example

[0101] For example, consider a scenario where a coach inputs data from the previous match, the players' health status, and the opponent's past tactical data into a terminal to prepare for the next game. The server analyzes this data to suggest an effective formation and provides a weekly training menu tailored to player A. It also suggests a rest schedule for player B based on their daily health data, addressing their increasing risk of injury. A concrete example of a prompt the user might input into the AI ​​model is, "Please suggest the optimal tactics for the next game and a training plan that takes into account the players' health status."

[0102] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0103] Step 1:

[0104] Users input player information and match information related to matches and training into the terminal. This input information includes individual player performance data, health status, and match results. The input data is supplemented using the terminal's checklist function to ensure that no information is missing.

[0105] Step 2:

[0106] The terminal receives information entered by the user and formats the data. This formatting process converts the input data into a unified format such as XML, maintaining data consistency. The formatted data is then sent to the server via a secure protocol (such as HTTPS).

[0107] Step 3:

[0108] The server stores the received data in a database. The stored data is prepared in a format suitable for analysis, with duplicates removed and organized in a way that allows for comparison with past data. Furthermore, backups are created simultaneously.

[0109] Step 4:

[0110] The server uses stored data to perform analysis using a generated AI model. The analysis applies various algorithms based on match and player information to analyze player performance and tactical tendencies. This reveals each player's strengths and weaknesses, as well as areas for overall team tactical improvement.

[0111] Step 5:

[0112] The server generates a tactical plan based on the analysis results. The generated tactical plan includes the most effective formations and approaches and is optimized based on evaluation in the simulation environment.

[0113] Step 6:

[0114] The terminal displays the generated tactical plan on a dashboard and presents it to the user. The displayed information is made easy for the user to understand and analyze using visualization tools such as graphs and heatmaps.

[0115] Step 7:

[0116] The server analyzes players' health information to predict injury risk. The prediction process calculates a risk score based on pre-configured health information and past injury data, and alerts players deemed to be at high risk.

[0117] Step 8:

[0118] The server generates a player's health management plan based on injury risk predictions. This includes appropriate rest schedules and physiotherapy plans, creating a detailed management plan to support the player's health.

[0119] Step 9:

[0120] The device provides physiotherapists and trainers with generated injury risk predictions and health management plans. The information provided is displayed as an alert if immediate action is required, enabling rapid intervention on-site.

[0121] (Application Example 1)

[0122] 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."

[0123] In modern manufacturing, efficient operation and maintenance of moving machinery are essential for improving productivity. However, properly analyzing performance data and maintenance information of moving machinery and predicting the risk of failure in advance is not easy. Furthermore, developing work plans and maintenance plans optimized for each piece of machinery requires considerable time and resources. Therefore, a new system is needed to solve these problems.

[0124] 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.

[0125] In this invention, the server includes means for acquiring performance data of a motion device, means for acquiring maintenance information of the motion device, and means for analyzing the acquired performance data and maintenance information. This enables the prediction of failure risk based on performance data and maintenance information, and the automatic generation of optimal work plans and maintenance plans.

[0126] "Motorized machinery" is a general term for machines and robots used to perform specific tasks in factories and production lines.

[0127] "Performance data" refers to various measured values ​​and output information acquired to indicate the operating status and efficiency of the motion control equipment.

[0128] "Maintenance information" refers to information necessary to maintain the proper condition of the equipment, such as the maintenance history, service records, and inspection results of the exercise equipment.

[0129] "Failure risk" refers to predictive information indicating the possibility that the motion control system may not function properly or may stop working.

[0130] A "work plan" is a guideline or schedule that shows the optimal operating schedule and work procedures for using the exercise equipment.

[0131] A "maintenance plan" is a plan that outlines regular inspections and maintenance to prevent malfunctions of the athletic equipment.

[0132] This invention is a system for optimizing the operation of exercise equipment. It uses a terminal such as a smartphone or tablet to acquire performance data and maintenance information from the exercise equipment, which is then analyzed on a server. The terminal transmits the data collected from the exercise equipment to the server. Communication methods such as Wi-Fi or wired connections are used for this data transmission.

[0133] The server uses programming languages ​​such as Python to analyze collected performance data and maintenance information, and utilizes a generated AI model to create optimal work plans and maintenance plans. The server uses machine learning algorithms to predict the failure risk for each piece of equipment and formulates plans based on that prediction. The data analysis results are returned to the terminal and presented to the person in charge in a dashboard format. In this way, the person in charge can quickly create an optimal work plan and perform the necessary maintenance.

[0134] As a concrete example, if a user inputs a command into a terminal for multiple motion machines within a factory, such as "Generate an optimal operating plan for each machine based on its current performance data and maintenance information," the server will analyze this and create an optimal plan. Using this prompt improves the operational efficiency of the factory.

[0135] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0136] Step 1:

[0137] The user uses a terminal to collect performance and maintenance data from the machinery in the factory. Inputs include operating status and maintenance history data obtained from the machinery's sensors, and output is formatted data. At this stage, the terminal prepares to format the data and transfer it to the server.

[0138] Step 2:

[0139] The server receives performance data and maintenance information sent from the terminal. It receives formatted data sent from the terminal as input and prepares datasets in a format suitable for data analysis as output. These datasets are stored in a database within the server and passed on to the next analysis step.

[0140] Step 3:

[0141] The server uses a generated AI model to analyze performance data and maintenance information. Using a prepared dataset as input, it predicts the failure risk of the motion equipment through data processing and calculations. The output generates predicted failure risk values ​​and analysis results. During this process, machine learning algorithms are employed to take into account the state and history of each motion equipment during the analysis.

[0142] Step 4:

[0143] The server generates optimal work plans and maintenance plans based on the analysis results. Using predicted failure risk values ​​and current data for each device as input, it applies a generated AI model to formulate an optimized plan. The output is a specific work plan and maintenance schedule for each device.

[0144] Step 5:

[0145] The server sends the generated plan information to the terminal. The terminal receives this information and presents it to the user in a dashboard format. It receives the plan information sent from the server as input and displays it to the user in a visually easy-to-understand format as output. Based on this, the user efficiently manages the operation and maintenance of the exercise equipment.

[0146] 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.

[0147] This invention is a sports team management system incorporating an emotion engine. It aims to improve player performance by comprehensively analyzing match information, player information, health information, and user emotional information. This system utilizes emotional data for strategic decision-making and player motivation management.

[0148] This system provides functions for data collection, sentiment analysis, tactical planning adjustments, player training, and motivation management.

[0149] 1. Data Collection

[0150] Users input match results and player data into their devices. The emotion engine acquires emotion data based on the user's reactions and feedback.

[0151] The terminal formats the entered information and sends it to the server.

[0152] 2. Emotion analysis

[0153] The server analyzes the acquired emotional data to determine the user's emotional state. This analysis includes a process of evaluating changes in emotions by comparing them with past emotional data.

[0154] 3. Adjustment of tactical plans

[0155] The server dynamically adjusts the strategy plan as needed based on the analyzed emotional data. This makes it possible to create an environment where players can play with the most motivation.

[0156] 4. Training and Motivation Management

[0157] The server optimizes the athlete's training plan based on emotion analysis. Individually tailored training plans and feedback are provided to boost the athlete's motivation.

[0158] 5. Information presentation

[0159] The terminal presents the user with tactical plans and motivation improvement plans generated from the server. This allows the user to take appropriate action based on the player's condition.

[0160] Specific example

[0161] Before a match, the user (coach) inputs match data into the system and evaluates factors that influenced emotions in past matches. The server analyzes the emotional data in real time and suggests tactics best suited to the players' performance. In addition, to boost player A's motivation, feedback generated by the emotion engine is provided via the terminal. This series of processes is expected to improve the overall team performance.

[0162] The following describes the processing flow.

[0163] Step 1:

[0164] The user inputs match data and player data into the terminal. At this time, they also input information regarding important match points and player condition.

[0165] Step 2:

[0166] Users provide emotional information by inputting reactions and feedback using their devices. The devices acquire this information in real time.

[0167] Step 3:

[0168] The terminal converts match data, player data, and sentiment data obtained from the user into the appropriate format and sends it to the server.

[0169] Step 4:

[0170] The server stores the received data in the database. During storage, it performs data integrity checks and removes invalid data.

[0171] Step 5:

[0172] The server analyzes emotional data based on the stored data to determine the user's emotional state. This is done using natural language processing and machine learning algorithms.

[0173] Step 6:

[0174] The server generates an optimal tactical plan for the match based on the results of the emotion analysis. This plan takes into account factors such as the players' emotional motivation.

[0175] Step 7:

[0176] The server uses the analysis results to optimize individual training plans for each player and generates feedback to boost their motivation.

[0177] Step 8:

[0178] The terminal receives tactical plans, training plans, and feedback information sent from the server, and presents them to the user in a dashboard format.

[0179] Step 9:

[0180] Based on the information provided, users can give appropriate instructions to players and decide how to respond to matches and training sessions. This improves the overall performance of the team.

[0181] (Example 2)

[0182] 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".

[0183] In modern sports teams, improving match performance and managing player health are crucial elements. However, conventional management systems are limited to analyzing match and player data, and cannot address the overall optimization of tactics or improvement of motivation, including player emotional data. A more precise coaching and strategy are needed by comprehensively considering the mental and physical state of players, and solving this problem is essential.

[0184] 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.

[0185] In this invention, the server includes means for acquiring match information, player information, and emotional information; means for analyzing the acquired match information, player information, and emotional information; and means for generating and presenting a tactical plan based on the analysis results. This makes it possible to formulate and present a tactical plan that takes into account the mental state of the players.

[0186] "Match information" refers to data related to sporting events, including scores, schedules, participating players, and match status.

[0187] "Player information" refers to data regarding each player's name, position, statistics, and profile.

[0188] "Emotional information" refers to data such as feedback and analysis results regarding the emotional state of players or users.

[0189] "Methods of analysis" refer to the process of analyzing acquired data to derive trends and relationships within that data.

[0190] A "tactical plan" is a plan that strategically defines the actions of players and teams in order to effectively conduct a match.

[0191] "Generative means" refers to the process of creating new data and plans based on the analysis results.

[0192] "Means of presentation" refers to the process of displaying generated data and plans in a way that users and players can understand.

[0193] "Health information" refers to data related to the athlete's physical condition and health risks.

[0194] A "management plan" is a set of policies and procedures established to maintain or improve the health and performance of athletes.

[0195] "Training information" refers to data about the content and results of an athlete's training.

[0196] This invention is a system for managing sports teams that integrates game information, player information, health information, and emotional information, and supports the optimization of player performance. The system mainly consists of the following processes: data collection, analysis, management plan generation, and information presentation.

[0197] Users input match data and player information using a terminal, which then transmits it to the server. The terminal is equipped with data formatting and network transmission capabilities. The server analyzes the collected data using an emotion engine and generates tactical plans. The analysis includes a process of comparing current data with past data and developing plans tailored to the current state of the players and team.

[0198] The emotion engine is a software module that uses diverse data to calculate emotional states, enabling customized coaching tailored to each player's condition. The generated tactical plans and training plans are presented visually to the user via a terminal, allowing the user to give appropriate instructions to players and matches in real time.

[0199] As a concrete example, a user (coach) inputs player A's psychological and physical performance data into the system before a match. The server then generates optimal feedback and tactics for player A based on this information and past match results, and presents them to the coach via a terminal. An example of such a prompt might be, "What kind of feedback would be effective in improving player A's motivation for today's match?"

[0200] This invention makes it possible to provide effective coaching and tactics that take into account the emotional state of the players, thereby contributing to the improvement of the overall team performance.

[0201] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0202] Step 1:

[0203] The user inputs match and player information using a terminal. During this process, the user uses a touchscreen or keyboard to input details such as match scores and player health and psychological states. Once input is complete, the user presses the submit button to send the data to the server. The input here consists of match and player information; the terminal converts the input data into a predetermined format and transfers it to the server via the network.

[0204] Step 2:

[0205] The server receives match information and player information sent from terminals. The server stores this data in a database and performs analysis using an emotion engine. The input for the analysis is the collected match information and player information. The data processing here involves comparing it with past emotion data of players and teams to find correlations. This result becomes the analysis output and is used to generate tactical plans.

[0206] Step 3:

[0207] The server generates a tactical plan using a generative AI model based on the analysis results. Here, the analysis results are the input, and the generative AI model dynamically constructs the optimal tactics based on them. Finally, the generated tactical plan is output and stored on the server as a customized plan that corresponds to the psychological and physical state of the players.

[0208] Step 4:

[0209] The server sends the generated tactical plan to the terminal. The transmitted data includes specific instructions and feedback, which are presented to the user as output on the terminal. The terminal provides a visual presentation, displaying the information on an interface that allows the user to easily understand and use the information for instruction.

[0210] Step 5:

[0211] Users provide player guidance and plan development based on tactical plans and feedback presented through their devices. Specifically, users can analyze the output information from their devices and provide direct feedback to players as needed. They can also input additional data into the server via their devices to obtain further feedback as required.

[0212] (Application Example 2)

[0213] 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".

[0214] In modern society, there is a need to comprehensively understand an individual's emotional state, health status, and environmental conditions, and to provide appropriate support and advice based on that understanding. However, conventional systems have the challenge of being unable to analyze emotional state, health status, and environmental information as an integrated whole, and to provide support optimized for each individual.

[0215] 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.

[0216] In this invention, the server includes means for acquiring match information, means for acquiring person information, and means for acquiring environmental information. This enables integrated analysis of emotional states, health conditions, and environmental conditions, making it possible to provide effective support and decision-making plans tailored to each individual.

[0217] "Match information" refers to all information related to sports and competitions, including data necessary for strategic decision-making, such as match schedules, opponents, and player statistics.

[0218] "Personal information" refers to all information about individual people managed by the system, including data such as basic personal information, health status, and past behavioral history.

[0219] "Environmental information" refers to information that describes the conditions surrounding the subject situation or place, and includes factors that may affect people, such as weather, temperature, and the condition of facilities.

[0220] A "decision-making plan" refers to a plan formulated to derive the most appropriate actions and policies for each specific situation, based on collected and analyzed information.

[0221] "Emotional information" refers to information about a person's emotional state and includes data obtained from facial expression analysis and voice analysis.

[0222] "Means of predicting risk" refers to methods for predicting potential risks that may arise in the future based on a person's health and behavior, and for providing warnings or preventative measures against those risks.

[0223] "Improvement suggestions" refer to specific actions and advice presented based on results derived from the analysis of collected data, with the aim of improving an individual's condition or increasing efficiency.

[0224] This invention is designed as a system incorporating an emotion engine. The server has multiple data acquisition means for collecting match information, person information, and environmental information. This information can be used to perform facial expression analysis and acquire health information using hardware and software such as Intel RealSense cameras and the OpenVINO toolkit. Furthermore, the server has the function to analyze this data comprehensively and formulate a decision plan from the data obtained using a generative AI model.

[0225] The terminal's role is to present the user with a decision plan transmitted from the server. Based on the user's emotional and health status, it provides optimal actions and policies. The user can then take appropriate action based on their individual circumstances.

[0226] As a concrete example, a scenario could be envisioned where a home robot, acting as a user, analyzes the emotional state of family members and, if it determines that stress levels are high, suggests appropriate relaxation methods. In this case, the server would input a prompt message such as "Please suggest effective ways for family member A to relax when they are feeling stressed" into the AI ​​model, which would then generate effective suggestions.

[0227] This configuration allows users to receive better health management and emotional care, thereby improving their quality of life.

[0228] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0229] Step 1:

[0230] The server collects match information, player information, and environmental information. The collected information is entered into a database, from which the necessary data is extracted for analysis. The entered data includes, for example, information about an individual's health status and emotional state.

[0231] Step 2:

[0232] The server analyzes the collected information. The main process here is determining the emotional state using an emotion engine. This analysis employs a method that evaluates current emotional changes by comparing them with past data. The analysis results are output as an evaluation of emotional state and health status.

[0233] Step 3:

[0234] The server generates a decision plan based on the analysis results. At this stage, a generative AI model is used to generate prompt statements and formulate appropriate actions and policies. For example, a prompt such as "Suggest effective ways for family member A to relax when they are feeling stressed" might be used. The generated plan is stored in a database.

[0235] Step 4:

[0236] The terminal receives a decision plan transmitted from the server. The received plan is presented to the user, who then makes a decision based on it. This process utilizes the terminal's display screen and audio output, and incorporates features to enhance user experience.

[0237] Step 5:

[0238] Users can take action based on the presented plan. Specifically, they will take actions in accordance with the plan, such as trying the suggested relaxation methods. This is expected to contribute to improvements in the user's health and emotional state.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] [Second Embodiment]

[0243] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0244] 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.

[0245] 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).

[0246] 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.

[0247] 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.

[0248] 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).

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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".

[0255] This invention is a system that utilizes generative AI to support the management and operation of sports teams. Through the following program processing, it analyzes match information and player information, provides optimal tactical plans and training plans, and manages injury risks.

[0256] The system broadly comprises the following functions: data collection, data analysis, tactical and training plan generation, and injury risk management.

[0257] 1. Data Collection

[0258] Users input player and match information into their terminals for each match and training session. This includes player data and health information during the match.

[0259] The terminal formats the entered data according to the specified format in order to send it to the server, and then sends it to the server.

[0260] 2. Data Analysis

[0261] The server performs analysis based on the received match and player information. Multiple algorithms are used to analyze tactical planning and individual player performance.

[0262] This allows us to identify the opposing team's tactical tendencies and clarify the strengths and weaknesses of our own team's players' performance.

[0263] 3. Generation of tactical and training plans

[0264] The server generates the optimal tactical plan for the match based on the analysis results. It also provides individually optimized training plans for each player.

[0265] The terminal displays the generated tactical and training plans to the user as a dashboard.

[0266] 4. Injury Risk Management

[0267] The server analyzes the players' health information and predicts the risk of injury based on this. Based on these results, it creates injury prevention measures and recovery plans.

[0268] The device displays injury risk predictions and management plans to physiotherapists and trainers.

[0269] Specific example

[0270] For example, consider a scenario where a match against a certain team is scheduled before a game. The user, acting as a coach, inputs data from the previous match, the players' health status, and the opponent's past data into a terminal. The server analyzes all the data, recommends a specific formation with strong offensive capabilities, and then presents a training menu tailored to player A. At the same time, based on player B's health status, it predicts an increased risk of injury and provides feedback on a plan to adjust the rest schedule.

[0271] Thus, by using the system of the present invention, it becomes possible to improve the tactical efficiency of the team and the health management of the players.

[0272] The following describes the processing flow.

[0273] Step 1:

[0274] Users input match results, player data, and information about their opponents into their devices. This includes statistical information and data indicating health status.

[0275] Step 2:

[0276] The terminal receives data entered by the user and converts it to the appropriate format. The converted data is then sent to the server.

[0277] Step 3:

[0278] The server receives data sent from the terminal and stores it in the database. When storing the data, it checks for data integrity and verifies that there are no outliers or duplicates.

[0279] Step 4:

[0280] The server extracts information stored in the database and executes data analysis algorithms. This allows for analysis of the opposing team's playing style and the performance of the team's own players.

[0281] Step 5:

[0282] Based on the analysis results, the server generates an optimal tactical plan. The generated plan includes specific strategies and formation proposals that target the opponent's weaknesses.

[0283] Step 6:

[0284] Based on the performance and physical data of individual players, the server creates a training plan optimized for specific players.

[0285] Step 7:

[0286] The terminal receives the tactical plan and training plan sent from the server and presents them to the user in the form of a dashboard.

[0287] Step 8:

[0288] The server analyzes the health data of the players and predicts the risk of injury. If there is a risk, it generates preventive measures and management plans.

[0289] Step 9:

[0290] The terminal presents the injury risk prediction results and related management plans to the physiotherapist and trainer. This enables appropriate health management.

[0291] (Example 1)

[0292] Next, 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".

[0293] In managing and operating sports teams, the effective use of match and player information is required, but collecting, analyzing, and utilizing this information in a timely manner to develop tactical and training plans is not easy. Furthermore, in player health management, there is a lack of concrete measures to predict and appropriately manage injury risks. Moreover, there is a need for an effective method to quickly share the plans generated through these processes with team personnel.

[0294] 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.

[0295] In this invention, the server includes means for collecting information about matches, means for collecting information about players, and means for performing analysis based on the collected match information and player information. This enables the formulation of effective tactical plans and the generation of training plans optimized for each player. Furthermore, it supports injury risk prediction and the creation of management plans for player health, and enables secure data communication throughout the entire process.

[0296] A "device for collecting information related to a match" refers to equipment or software used to acquire data related to a sports match, such as player performance, match results, and tactical data.

[0297] A "device for collecting information about athletes" refers to equipment or software used to acquire individual data on athletes, such as their health status, physical data, and individual performance.

[0298] "Means of analysis" refers to a function that uses statistical or machine learning methods to analyze data based on collected match information and player information, and derive meaningful conclusions.

[0299] A "tactical plan generating device" is a device or software that automatically creates optimal tactics and strategies for a match based on analysis results.

[0300] The "device for displaying tactical plans" is a device or interface for visually presenting the generated tactical plans and training plans to the user, providing information in an easy-to-understand form.

[0301] The "communication means" is a protocol or device for safely and efficiently transmitting and receiving data inside and outside the system, ensuring the confidentiality and integrity of the data.

[0302] The "device using analysis algorithms" is a device or software that applies multiple algorithms in data analysis, ultimately for deriving useful tactics and performance indicators.

[0303] The "device for collecting information on players' health" is a device or software for acquiring players' health status, medical data, past injury history, etc.

[0304] The "device for predicting injury risk" is a device or software for evaluating and predicting the likelihood of future injuries based on players' health information.

[0305] The "device for generating a health management plan" is a device or software for automatically creating measures and plans necessary to maintain and improve players' health.

[0306] The "device for providing a training menu" is a device or software for presenting a training plan and schedule suitable for each player and supporting performance improvement.

[0307] The "device for collecting information on training" is a device or software for recording the training content and training results of players, serving as a basis for analyzing the effectiveness of training.

[0308] A "simulation device" is a piece of equipment or software used to virtually evaluate the effectiveness of tactics and formations in a match and to find the optimal strategy.

[0309] This invention is a system that supports the management and operation of sports teams, analyzing match and player information to provide optimal tactical and training plans. Furthermore, it has a function to manage injury risk by analyzing player health information. This system is mainly implemented by server, terminal, and user components.

[0310] Data collection and formatting

[0311] Users use a terminal to input player information and match information related to matches and training. The terminal receives this information, converts it into accurately formatted data, and sends it to the server via a communication protocol (e.g., HTTPS).

[0312] Data Analysis

[0313] The server stores the received match and player information in a database and prepares it for analysis. Generative AI models are used for the analysis, and various data analysis algorithms are applied. This analysis generates individual player performance data and tactical plans for the entire team.

[0314] Tactical plan generation and display

[0315] The server automatically generates an optimal tactical plan based on the analysis results. The generated tactics are visually presented to the user via a terminal. This allows the coach to immediately verify the effectiveness of the tactics and make adjustments.

[0316] Injury risk management

[0317] Furthermore, the server analyzes the athletes' health information and runs an algorithm to predict injury risk. The prediction results are displayed to physiotherapists and trainers to help them develop appropriate preventative measures and recovery plans.

[0318] Specific example

[0319] For example, consider a scenario where a coach inputs data from the previous match, the players' health status, and the opponent's past tactical data into a terminal to prepare for the next game. The server analyzes this data to suggest an effective formation and provides a weekly training menu tailored to player A. It also suggests a rest schedule for player B based on their daily health data, addressing their increasing risk of injury. A concrete example of a prompt the user might input into the AI ​​model is, "Please suggest the optimal tactics for the next game and a training plan that takes into account the players' health status."

[0320] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0321] Step 1:

[0322] Users input player information and match information related to matches and training into the terminal. This input information includes individual player performance data, health status, and match results. The input data is supplemented using the terminal's checklist function to ensure that no information is missing.

[0323] Step 2:

[0324] The terminal receives information entered by the user and formats the data. This formatting process converts the input data into a unified format such as XML, maintaining data consistency. The formatted data is then sent to the server via a secure protocol (such as HTTPS).

[0325] Step 3:

[0326] The server stores the received data in a database. The stored data is prepared in a format suitable for analysis, with duplicates removed and organized in a way that allows for comparison with past data. Furthermore, backups are created simultaneously.

[0327] Step 4:

[0328] The server uses stored data to perform analysis using a generated AI model. The analysis applies various algorithms based on match and player information to analyze player performance and tactical tendencies. This reveals each player's strengths and weaknesses, as well as areas for overall team tactical improvement.

[0329] Step 5:

[0330] The server generates a tactical plan based on the analysis results. The generated tactical plan includes the most effective formations and approaches and is optimized based on evaluation in the simulation environment.

[0331] Step 6:

[0332] The terminal displays the generated tactical plan on a dashboard and presents it to the user. The displayed information is made easy for the user to understand and analyze using visualization tools such as graphs and heatmaps.

[0333] Step 7:

[0334] The server analyzes players' health information to predict injury risk. The prediction process calculates a risk score based on pre-configured health information and past injury data, and alerts players deemed to be at high risk.

[0335] Step 8:

[0336] The server generates a player's health management plan based on injury risk predictions. This includes appropriate rest schedules and physiotherapy plans, creating a detailed management plan to support the player's health.

[0337] Step 9:

[0338] The device provides physiotherapists and trainers with generated injury risk predictions and health management plans. The information provided is displayed as an alert if immediate action is required, enabling rapid intervention on-site.

[0339] (Application Example 1)

[0340] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0341] In modern manufacturing, efficient operation and maintenance of moving machinery are essential for improving productivity. However, properly analyzing performance data and maintenance information of moving machinery and predicting the risk of failure in advance is not easy. Furthermore, developing work plans and maintenance plans optimized for each piece of machinery requires considerable time and resources. Therefore, a new system is needed to solve these problems.

[0342] 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.

[0343] In this invention, the server includes means for acquiring performance data of a motion device, means for acquiring maintenance information of the motion device, and means for analyzing the acquired performance data and maintenance information. This enables the prediction of failure risk based on performance data and maintenance information, and the automatic generation of optimal work plans and maintenance plans.

[0344] "Motorized machinery" is a general term for machines and robots used to perform specific tasks in factories and production lines.

[0345] "Performance data" refers to various measured values ​​and output information acquired to indicate the operating status and efficiency of the motion control equipment.

[0346] "Maintenance information" refers to information necessary to maintain the proper condition of the equipment, such as the maintenance history, service records, and inspection results of the exercise equipment.

[0347] "Failure risk" refers to predictive information indicating the possibility that the motion control system may not function properly or may stop working.

[0348] A "work plan" is a guideline or schedule that shows the optimal operating schedule and work procedures for using the exercise equipment.

[0349] A "maintenance plan" is a plan that outlines regular inspections and maintenance to prevent malfunctions of the athletic equipment.

[0350] This invention is a system for optimizing the operation of exercise equipment. It uses a terminal such as a smartphone or tablet to acquire performance data and maintenance information from the exercise equipment, which is then analyzed on a server. The terminal transmits the data collected from the exercise equipment to the server. Communication methods such as Wi-Fi or wired connections are used for this data transmission.

[0351] The server uses programming languages ​​such as Python to analyze collected performance data and maintenance information, and utilizes a generated AI model to create optimal work plans and maintenance plans. The server uses machine learning algorithms to predict the failure risk for each piece of equipment and formulates plans based on that prediction. The data analysis results are returned to the terminal and presented to the person in charge in a dashboard format. In this way, the person in charge can quickly create an optimal work plan and perform the necessary maintenance.

[0352] As a concrete example, if a user inputs a command into a terminal for multiple motion machines within a factory, such as "Generate an optimal operating plan for each machine based on its current performance data and maintenance information," the server will analyze this and create an optimal plan. Using this prompt improves the operational efficiency of the factory.

[0353] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0354] Step 1:

[0355] The user uses a terminal to collect performance and maintenance data from the machinery in the factory. Inputs include operating status and maintenance history data obtained from the machinery's sensors, and output is formatted data. At this stage, the terminal prepares to format the data and transfer it to the server.

[0356] Step 2:

[0357] The server receives performance data and maintenance information sent from the terminal. It receives formatted data sent from the terminal as input and prepares datasets in a format suitable for data analysis as output. These datasets are stored in a database within the server and passed on to the next analysis step.

[0358] Step 3:

[0359] The server uses a generated AI model to analyze performance data and maintenance information. Using a prepared dataset as input, it predicts the failure risk of the motion equipment through data processing and calculations. The output generates predicted failure risk values ​​and analysis results. During this process, machine learning algorithms are employed to take into account the state and history of each motion equipment during the analysis.

[0360] Step 4:

[0361] The server generates optimal work plans and maintenance plans based on the analysis results. Using predicted failure risk values ​​and current data for each device as input, it applies a generated AI model to formulate an optimized plan. The output is a specific work plan and maintenance schedule for each device.

[0362] Step 5:

[0363] The server sends the generated plan information to the terminal. The terminal receives this information and presents it to the user in a dashboard format. It receives the plan information sent from the server as input and displays it to the user in a visually easy-to-understand format as output. Based on this, the user efficiently manages the operation and maintenance of the exercise equipment.

[0364] 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.

[0365] This invention is a sports team management system incorporating an emotion engine. It aims to improve player performance by comprehensively analyzing match information, player information, health information, and user emotional information. This system utilizes emotional data for strategic decision-making and player motivation management.

[0366] This system provides functions for data collection, sentiment analysis, tactical planning adjustments, player training, and motivation management.

[0367] 1. Data Collection

[0368] Users input match results and player data into their devices. The emotion engine acquires emotion data based on the user's reactions and feedback.

[0369] The terminal formats the entered information and sends it to the server.

[0370] 2. Emotion analysis

[0371] The server analyzes the acquired emotional data to determine the user's emotional state. This analysis includes a process of evaluating changes in emotions by comparing them with past emotional data.

[0372] 3. Adjustment of tactical plans

[0373] The server dynamically adjusts the strategy plan as needed based on the analyzed emotional data. This makes it possible to create an environment where players can play with the most motivation.

[0374] 4. Training and Motivation Management

[0375] The server optimizes the athlete's training plan based on emotion analysis. Individually tailored training plans and feedback are provided to boost the athlete's motivation.

[0376] 5. Information presentation

[0377] The terminal presents the user with tactical plans and motivation improvement plans generated from the server. This allows the user to take appropriate action based on the player's condition.

[0378] Specific example

[0379] Before a match, the user (coach) inputs match data into the system and evaluates factors that influenced emotions in past matches. The server analyzes the emotional data in real time and suggests tactics best suited to the players' performance. In addition, to boost player A's motivation, feedback generated by the emotion engine is provided via the terminal. This series of processes is expected to improve the overall team performance.

[0380] The following describes the processing flow.

[0381] Step 1:

[0382] The user inputs match data and player data into the terminal. At this time, they also input information regarding important match points and player condition.

[0383] Step 2:

[0384] Users provide emotional information by inputting reactions and feedback using their devices. The devices acquire this information in real time.

[0385] Step 3:

[0386] The terminal converts match data, player data, and sentiment data obtained from the user into the appropriate format and sends it to the server.

[0387] Step 4:

[0388] The server stores the received data in the database. During storage, it performs data integrity checks and removes invalid data.

[0389] Step 5:

[0390] The server analyzes emotional data based on the stored data to determine the user's emotional state. This is done using natural language processing and machine learning algorithms.

[0391] Step 6:

[0392] The server generates an optimal tactical plan for the match based on the results of the emotion analysis. This plan takes into account factors such as the players' emotional motivation.

[0393] Step 7:

[0394] The server uses the analysis results to optimize individual training plans for each player and generates feedback to boost their motivation.

[0395] Step 8:

[0396] The terminal receives tactical plans, training plans, and feedback information sent from the server, and presents them to the user in a dashboard format.

[0397] Step 9:

[0398] Based on the information provided, users can give appropriate instructions to players and decide how to respond to matches and training sessions. This improves the overall performance of the team.

[0399] (Example 2)

[0400] 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".

[0401] In modern sports teams, improving match performance and managing player health are crucial elements. However, conventional management systems are limited to analyzing match and player data, and cannot address the overall optimization of tactics or improvement of motivation, including player emotional data. A more precise coaching and strategy are needed by comprehensively considering the mental and physical state of players, and solving this problem is essential.

[0402] 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.

[0403] In this invention, the server includes means for acquiring match information, player information, and emotional information; means for analyzing the acquired match information, player information, and emotional information; and means for generating and presenting a tactical plan based on the analysis results. This makes it possible to formulate and present a tactical plan that takes into account the mental state of the players.

[0404] "Match information" refers to data related to sporting events, including scores, schedules, participating players, and match status.

[0405] "Player information" refers to data regarding each player's name, position, statistics, and profile.

[0406] "Emotional information" refers to data such as feedback and analysis results regarding the emotional state of players or users.

[0407] "Methods of analysis" refer to the process of analyzing acquired data to derive trends and relationships within that data.

[0408] A "tactical plan" is a plan that strategically defines the actions of players and teams in order to effectively conduct a match.

[0409] "Generative means" refers to the process of creating new data and plans based on the analysis results.

[0410] "Means of presentation" refers to the process of displaying generated data and plans in a way that users and players can understand.

[0411] "Health information" refers to data related to the athlete's physical condition and health risks.

[0412] A "management plan" is a set of policies and procedures established to maintain or improve the health and performance of athletes.

[0413] "Training information" refers to data about the content and results of an athlete's training.

[0414] This invention is a system for managing sports teams that integrates game information, player information, health information, and emotional information, and supports the optimization of player performance. The system mainly consists of the following processes: data collection, analysis, management plan generation, and information presentation.

[0415] Users input match data and player information using a terminal, which then transmits it to the server. The terminal is equipped with data formatting and network transmission capabilities. The server analyzes the collected data using an emotion engine and generates tactical plans. The analysis includes a process of comparing current data with past data and developing plans tailored to the current state of the players and team.

[0416] The emotion engine is a software module that uses diverse data to calculate emotional states, enabling customized coaching tailored to each player's condition. The generated tactical plans and training plans are presented visually to the user via a terminal, allowing the user to give appropriate instructions to players and matches in real time.

[0417] As a concrete example, a user (coach) inputs player A's psychological and physical performance data into the system before a match. The server then generates optimal feedback and tactics for player A based on this information and past match results, and presents them to the coach via a terminal. An example of such a prompt might be, "What kind of feedback would be effective in improving player A's motivation for today's match?"

[0418] This invention makes it possible to provide effective coaching and tactics that take into account the emotional state of the players, thereby contributing to the improvement of the overall team performance.

[0419] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0420] Step 1:

[0421] The user inputs match and player information using a terminal. During this process, the user uses a touchscreen or keyboard to input details such as match scores and player health and psychological states. Once input is complete, the user presses the submit button to send the data to the server. The input here consists of match and player information; the terminal converts the input data into a predetermined format and transfers it to the server via the network.

[0422] Step 2:

[0423] The server receives match information and player information sent from terminals. The server stores this data in a database and performs analysis using an emotion engine. The input for the analysis is the collected match information and player information. The data processing here involves comparing it with past emotion data of players and teams to find correlations. This result becomes the analysis output and is used to generate tactical plans.

[0424] Step 3:

[0425] The server generates a tactical plan using a generative AI model based on the analysis results. Here, the analysis results are the input, and the generative AI model dynamically constructs the optimal tactics based on them. Finally, the generated tactical plan is output and stored on the server as a customized plan that corresponds to the psychological and physical state of the players.

[0426] Step 4:

[0427] The server sends the generated tactical plan to the terminal. The transmitted data includes specific instructions and feedback, which are presented to the user as output on the terminal. The terminal provides a visual presentation, displaying the information on an interface that allows the user to easily understand and use the information for instruction.

[0428] Step 5:

[0429] Users provide player guidance and plan development based on tactical plans and feedback presented through their devices. Specifically, users can analyze the output information from their devices and provide direct feedback to players as needed. They can also input additional data into the server via their devices to obtain further feedback as required.

[0430] (Application Example 2)

[0431] 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."

[0432] In modern society, there is a need to comprehensively understand an individual's emotional state, health status, and environmental conditions, and to provide appropriate support and advice based on that understanding. However, conventional systems have the challenge of being unable to analyze emotional state, health status, and environmental information as an integrated whole, and to provide support optimized for each individual.

[0433] 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.

[0434] In this invention, the server includes means for acquiring match information, means for acquiring person information, and means for acquiring environmental information. This enables integrated analysis of emotional states, health conditions, and environmental conditions, making it possible to provide effective support and decision-making plans tailored to each individual.

[0435] "Match information" refers to all information related to sports and competitions, including data necessary for strategic decision-making, such as match schedules, opponents, and player statistics.

[0436] "Personal information" refers to all information about individual people managed by the system, including data such as basic personal information, health status, and past behavioral history.

[0437] "Environmental information" refers to information that describes the conditions surrounding the subject situation or place, and includes factors that may affect people, such as weather, temperature, and the condition of facilities.

[0438] A "decision-making plan" refers to a plan formulated to derive the most appropriate actions and policies for each specific situation, based on collected and analyzed information.

[0439] "Emotional information" refers to information about a person's emotional state and includes data obtained from facial expression analysis and voice analysis.

[0440] "Means of predicting risk" refers to methods for predicting potential risks that may arise in the future based on a person's health and behavior, and for providing warnings or preventative measures against those risks.

[0441] "Improvement suggestions" refer to specific actions and advice presented based on results derived from the analysis of collected data, with the aim of improving an individual's condition or increasing efficiency.

[0442] This invention is designed as a system incorporating an emotion engine. The server has multiple data acquisition means for collecting match information, person information, and environmental information. This information can be used to perform facial expression analysis and acquire health information using hardware and software such as Intel RealSense cameras and the OpenVINO toolkit. Furthermore, the server has the function to analyze this data comprehensively and formulate a decision plan from the data obtained using a generative AI model.

[0443] The terminal's role is to present the user with a decision plan transmitted from the server. Based on the user's emotional and health status, it provides optimal actions and policies. The user can then take appropriate action based on their individual circumstances.

[0444] As a concrete example, a scenario could be envisioned where a home robot, acting as a user, analyzes the emotional state of family members and, if it determines that stress levels are high, suggests appropriate relaxation methods. In this case, the server would input a prompt message such as "Please suggest effective ways for family member A to relax when they are feeling stressed" into the AI ​​model, which would then generate effective suggestions.

[0445] This configuration allows users to receive better health management and emotional care, thereby improving their quality of life.

[0446] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0447] Step 1:

[0448] The server collects match information, player information, and environmental information. The collected information is entered into a database, from which the necessary data is extracted for analysis. The entered data includes, for example, information about an individual's health status and emotional state.

[0449] Step 2:

[0450] The server analyzes the collected information. The main process here is determining the emotional state using an emotion engine. This analysis employs a method that evaluates current emotional changes by comparing them with past data. The analysis results are output as an evaluation of emotional state and health status.

[0451] Step 3:

[0452] The server generates a decision plan based on the analysis results. At this stage, a generative AI model is used to generate prompt statements and formulate appropriate actions and policies. For example, a prompt such as "Suggest effective ways for family member A to relax when they are feeling stressed" might be used. The generated plan is stored in a database.

[0453] Step 4:

[0454] The terminal receives a decision plan transmitted from the server. The received plan is presented to the user, who then makes a decision based on it. This process utilizes the terminal's display screen and audio output, and incorporates features to enhance user experience.

[0455] Step 5:

[0456] Users can take action based on the presented plan. Specifically, they will take actions in accordance with the plan, such as trying the suggested relaxation methods. This is expected to contribute to improvements in the user's health and emotional state.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] [Third Embodiment]

[0461] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0462] 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.

[0463] 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).

[0464] 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.

[0465] 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.

[0466] 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).

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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".

[0473] This invention is a system that utilizes generative AI to support the management and operation of sports teams. Through the following program processing, it analyzes match information and player information, provides optimal tactical plans and training plans, and manages injury risks.

[0474] The system broadly comprises the following functions: data collection, data analysis, tactical and training plan generation, and injury risk management.

[0475] 1. Data Collection

[0476] Users input player and match information into their terminals for each match and training session. This includes player data and health information during the match.

[0477] The terminal formats the entered data according to the specified format in order to send it to the server, and then sends it to the server.

[0478] 2. Data Analysis

[0479] The server performs analysis based on the received match and player information. Multiple algorithms are used to analyze tactical planning and individual player performance.

[0480] This allows us to identify the opposing team's tactical tendencies and clarify the strengths and weaknesses of our own team's players' performance.

[0481] 3. Generation of tactical and training plans

[0482] The server generates the optimal tactical plan for the match based on the analysis results. It also provides individually optimized training plans for each player.

[0483] The terminal displays the generated tactical and training plans to the user as a dashboard.

[0484] 4. Injury Risk Management

[0485] The server analyzes the players' health information and predicts the risk of injury based on this. Based on these results, it creates injury prevention measures and recovery plans.

[0486] The device displays injury risk predictions and management plans to physiotherapists and trainers.

[0487] Specific example

[0488] For example, consider a scenario where a match against a certain team is scheduled before a game. The user, acting as a coach, inputs data from the previous match, the players' health status, and the opponent's past data into a terminal. The server analyzes all the data, recommends a specific formation with strong offensive capabilities, and then presents a training menu tailored to player A. At the same time, based on player B's health status, it predicts an increased risk of injury and provides feedback on a plan to adjust the rest schedule.

[0489] Thus, by using the system of the present invention, it becomes possible to improve the tactical efficiency of the team and the health management of the players.

[0490] The following describes the processing flow.

[0491] Step 1:

[0492] Users input match results, player data, and information about their opponents into their devices. This includes statistical information and data indicating health status.

[0493] Step 2:

[0494] The terminal receives data entered by the user and converts it to the appropriate format. The converted data is then sent to the server.

[0495] Step 3:

[0496] The server receives data sent from the terminal and stores it in the database. When storing the data, it checks for data integrity and verifies that there are no outliers or duplicates.

[0497] Step 4:

[0498] The server extracts information stored in the database and executes data analysis algorithms. This allows for analysis of the opposing team's playing style and the performance of the team's own players.

[0499] Step 5:

[0500] The server generates an optimal tactical plan based on the analysis results. The generated plan includes specific strategies and formation suggestions that exploit the opponent's weaknesses.

[0501] Step 6:

[0502] The server creates a training plan optimized for each individual player based on their performance and physical data.

[0503] Step 7:

[0504] The terminal receives tactical and training plans sent from the server and presents them to the user in a dashboard format.

[0505] Step 8:

[0506] The server analyzes the players' health data and predicts their injury risk. If a risk is detected, it generates preventative measures and management plans.

[0507] Step 9:

[0508] The device displays injury risk predictions and related management plans to physiotherapists and trainers, enabling appropriate health management.

[0509] (Example 1)

[0510] 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."

[0511] In managing and operating sports teams, the effective use of match and player information is required, but collecting, analyzing, and utilizing this information in a timely manner to develop tactical and training plans is not easy. Furthermore, in player health management, there is a lack of concrete measures to predict and appropriately manage injury risks. Moreover, there is a need for an effective method to quickly share the plans generated through these processes with team personnel.

[0512] 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.

[0513] In this invention, the server includes means for collecting information about matches, means for collecting information about players, and means for performing analysis based on the collected match information and player information. This enables the formulation of effective tactical plans and the generation of training plans optimized for each player. Furthermore, it supports injury risk prediction and the creation of management plans for player health, and enables secure data communication throughout the entire process.

[0514] A "device for collecting information related to a match" refers to equipment or software used to acquire data related to a sports match, such as player performance, match results, and tactical data.

[0515] A "device for collecting information about athletes" refers to equipment or software used to acquire individual data on athletes, such as their health status, physical data, and individual performance.

[0516] "Means of analysis" refers to a function that uses statistical or machine learning methods to analyze data based on collected match information and player information, and derive meaningful conclusions.

[0517] A "tactical plan generating device" is a device or software that automatically creates optimal tactics and strategies for a match based on analysis results.

[0518] A "device for displaying tactical plans" is a device or interface that visually presents generated tactical plans or training plans to the user, providing information in an easily understandable format.

[0519] "Communication methods" refer to protocols and equipment for securely and efficiently sending and receiving data both within and outside a system, and they guarantee the confidentiality and integrity of the data.

[0520] A "device that uses analytical algorithms" is a device or software that applies multiple algorithms in data analysis, ultimately intended to derive useful tactics and performance indicators.

[0521] A "device for collecting information on athletes' health" refers to equipment or software that acquires information such as an athlete's health status, medical data, and past injury history.

[0522] A "device for predicting injury risk" is a device or software that uses an athlete's health information to evaluate and predict the likelihood of future injuries.

[0523] A "health management plan generating device" is a device or software that automatically creates the measures and plans necessary to maintain and improve the health of athletes.

[0524] A "training menu provider" refers to equipment or software that presents a training plan and schedule tailored to each athlete, thereby supporting performance improvement.

[0525] A "device for collecting training information" refers to equipment or software used to record the training content and results performed by athletes, and serves as a foundation for analyzing the effectiveness of the training.

[0526] A "simulation device" is a piece of equipment or software used to virtually evaluate the effectiveness of tactics and formations in a match and to find the optimal strategy.

[0527] This invention is a system that supports the management and operation of sports teams, analyzing match and player information to provide optimal tactical and training plans. Furthermore, it has a function to manage injury risk by analyzing player health information. This system is mainly implemented by server, terminal, and user components.

[0528] Data collection and formatting

[0529] Users use a terminal to input player information and match information related to matches and training. The terminal receives this information, converts it into accurately formatted data, and sends it to the server via a communication protocol (e.g., HTTPS).

[0530] Data Analysis

[0531] The server stores the received match and player information in a database and prepares it for analysis. Generative AI models are used for the analysis, and various data analysis algorithms are applied. This analysis generates individual player performance data and tactical plans for the entire team.

[0532] Tactical plan generation and display

[0533] The server automatically generates an optimal tactical plan based on the analysis results. The generated tactics are visually presented to the user via a terminal. This allows the coach to immediately verify the effectiveness of the tactics and make adjustments.

[0534] Injury risk management

[0535] Furthermore, the server analyzes the athletes' health information and runs an algorithm to predict injury risk. The prediction results are displayed to physiotherapists and trainers to help them develop appropriate preventative measures and recovery plans.

[0536] Specific example

[0537] For example, consider a scenario where a coach inputs data from the previous match, the players' health status, and the opponent's past tactical data into a terminal to prepare for the next game. The server analyzes this data to suggest an effective formation and provides a weekly training menu tailored to player A. It also suggests a rest schedule for player B based on their daily health data, addressing their increasing risk of injury. A concrete example of a prompt the user might input into the AI ​​model is, "Please suggest the optimal tactics for the next game and a training plan that takes into account the players' health status."

[0538] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0539] Step 1:

[0540] Users input player information and match information related to matches and training into the terminal. This input information includes individual player performance data, health status, and match results. The input data is supplemented using the terminal's checklist function to ensure that no information is missing.

[0541] Step 2:

[0542] The terminal receives information entered by the user and formats the data. This formatting process converts the input data into a unified format such as XML, maintaining data consistency. The formatted data is then sent to the server via a secure protocol (such as HTTPS).

[0543] Step 3:

[0544] The server stores the received data in a database. The stored data is prepared in a format suitable for analysis, with duplicates removed and organized in a way that allows for comparison with past data. Furthermore, backups are created simultaneously.

[0545] Step 4:

[0546] The server uses stored data to perform analysis using a generated AI model. The analysis applies various algorithms based on match and player information to analyze player performance and tactical tendencies. This reveals each player's strengths and weaknesses, as well as areas for overall team tactical improvement.

[0547] Step 5:

[0548] The server generates a tactical plan based on the analysis results. The generated tactical plan includes the most effective formations and approaches and is optimized based on evaluation in the simulation environment.

[0549] Step 6:

[0550] The terminal displays the generated tactical plan on a dashboard and presents it to the user. The displayed information is made easy for the user to understand and analyze using visualization tools such as graphs and heatmaps.

[0551] Step 7:

[0552] The server analyzes players' health information to predict injury risk. The prediction process calculates a risk score based on pre-configured health information and past injury data, and alerts players deemed to be at high risk.

[0553] Step 8:

[0554] The server generates a player's health management plan based on injury risk predictions. This includes appropriate rest schedules and physiotherapy plans, creating a detailed management plan to support the player's health.

[0555] Step 9:

[0556] The device provides physiotherapists and trainers with generated injury risk predictions and health management plans. The information provided is displayed as an alert if immediate action is required, enabling rapid intervention on-site.

[0557] (Application Example 1)

[0558] 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."

[0559] In modern manufacturing, efficient operation and maintenance of moving machinery are essential for improving productivity. However, properly analyzing performance data and maintenance information of moving machinery and predicting the risk of failure in advance is not easy. Furthermore, developing work plans and maintenance plans optimized for each piece of machinery requires considerable time and resources. Therefore, a new system is needed to solve these problems.

[0560] 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.

[0561] In this invention, the server includes means for acquiring performance data of a motion device, means for acquiring maintenance information of the motion device, and means for analyzing the acquired performance data and maintenance information. This enables the prediction of failure risk based on performance data and maintenance information, and the automatic generation of optimal work plans and maintenance plans.

[0562] "Motorized machinery" is a general term for machines and robots used to perform specific tasks in factories and production lines.

[0563] "Performance data" refers to various measured values ​​and output information acquired to indicate the operating status and efficiency of the motion control equipment.

[0564] "Maintenance information" refers to information necessary to maintain the proper condition of the equipment, such as the maintenance history, service records, and inspection results of the exercise equipment.

[0565] "Failure risk" refers to predictive information indicating the possibility that the motion control system may not function properly or may stop working.

[0566] A "work plan" is a guideline or schedule that shows the optimal operating schedule and work procedures for using the exercise equipment.

[0567] A "maintenance plan" is a plan that outlines regular inspections and maintenance to prevent malfunctions of the athletic equipment.

[0568] This invention is a system for optimizing the operation of exercise equipment. It uses a terminal such as a smartphone or tablet to acquire performance data and maintenance information from the exercise equipment, which is then analyzed on a server. The terminal transmits the data collected from the exercise equipment to the server. Communication methods such as Wi-Fi or wired connections are used for this data transmission.

[0569] The server uses programming languages ​​such as Python to analyze collected performance data and maintenance information, and utilizes a generated AI model to create optimal work plans and maintenance plans. The server uses machine learning algorithms to predict the failure risk for each piece of equipment and formulates plans based on that prediction. The data analysis results are returned to the terminal and presented to the person in charge in a dashboard format. In this way, the person in charge can quickly create an optimal work plan and perform the necessary maintenance.

[0570] As a concrete example, if a user inputs a command into a terminal for multiple motion machines within a factory, such as "Generate an optimal operating plan for each machine based on its current performance data and maintenance information," the server will analyze this and create an optimal plan. Using this prompt improves the operational efficiency of the factory.

[0571] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0572] Step 1:

[0573] The user uses a terminal to collect performance and maintenance data from the machinery in the factory. Inputs include operating status and maintenance history data obtained from the machinery's sensors, and output is formatted data. At this stage, the terminal prepares to format the data and transfer it to the server.

[0574] Step 2:

[0575] The server receives performance data and maintenance information sent from the terminal. It receives formatted data sent from the terminal as input and prepares datasets in a format suitable for data analysis as output. These datasets are stored in a database within the server and passed on to the next analysis step.

[0576] Step 3:

[0577] The server uses a generated AI model to analyze performance data and maintenance information. Using a prepared dataset as input, it predicts the failure risk of the motion equipment through data processing and calculations. The output generates predicted failure risk values ​​and analysis results. During this process, machine learning algorithms are employed to take into account the state and history of each motion equipment during the analysis.

[0578] Step 4:

[0579] The server generates optimal work plans and maintenance plans based on the analysis results. Using predicted failure risk values ​​and current data for each device as input, it applies a generated AI model to formulate an optimized plan. The output is a specific work plan and maintenance schedule for each device.

[0580] Step 5:

[0581] The server sends the generated plan information to the terminal. The terminal receives this information and presents it to the user in a dashboard format. It receives the plan information sent from the server as input and displays it to the user in a visually easy-to-understand format as output. Based on this, the user efficiently manages the operation and maintenance of the exercise equipment.

[0582] 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.

[0583] This invention is a sports team management system incorporating an emotion engine. It aims to improve player performance by comprehensively analyzing match information, player information, health information, and user emotional information. This system utilizes emotional data for strategic decision-making and player motivation management.

[0584] This system provides functions for data collection, sentiment analysis, tactical planning adjustments, player training, and motivation management.

[0585] 1. Data Collection

[0586] Users input match results and player data into their devices. The emotion engine acquires emotion data based on the user's reactions and feedback.

[0587] The terminal formats the entered information and sends it to the server.

[0588] 2. Emotion analysis

[0589] The server analyzes the acquired emotional data to determine the user's emotional state. This analysis includes a process of evaluating changes in emotions by comparing them with past emotional data.

[0590] 3. Adjustment of tactical plans

[0591] The server dynamically adjusts the strategy plan as needed based on the analyzed emotional data. This makes it possible to create an environment where players can play with the most motivation.

[0592] 4. Training and Motivation Management

[0593] The server optimizes the athlete's training plan based on emotion analysis. Individually tailored training plans and feedback are provided to boost the athlete's motivation.

[0594] 5. Information presentation

[0595] The terminal presents the user with tactical plans and motivation improvement plans generated from the server. This allows the user to take appropriate action based on the player's condition.

[0596] Specific example

[0597] Before a match, the user (coach) inputs match data into the system and evaluates factors that influenced emotions in past matches. The server analyzes the emotional data in real time and suggests tactics best suited to the players' performance. In addition, to boost player A's motivation, feedback generated by the emotion engine is provided via the terminal. This series of processes is expected to improve the overall team performance.

[0598] The following describes the processing flow.

[0599] Step 1:

[0600] The user inputs match data and player data into the terminal. At this time, they also input information regarding important match points and player condition.

[0601] Step 2:

[0602] Users provide emotional information by inputting reactions and feedback using their devices. The devices acquire this information in real time.

[0603] Step 3:

[0604] The terminal converts match data, player data, and sentiment data obtained from the user into the appropriate format and sends it to the server.

[0605] Step 4:

[0606] The server stores the received data in the database. During storage, it performs data integrity checks and removes invalid data.

[0607] Step 5:

[0608] The server analyzes emotional data based on the stored data to determine the user's emotional state. This is done using natural language processing and machine learning algorithms.

[0609] Step 6:

[0610] The server generates an optimal tactical plan for the match based on the results of the emotion analysis. This plan takes into account factors such as the players' emotional motivation.

[0611] Step 7:

[0612] The server uses the analysis results to optimize individual training plans for each player and generates feedback to boost their motivation.

[0613] Step 8:

[0614] The terminal receives tactical plans, training plans, and feedback information sent from the server, and presents them to the user in a dashboard format.

[0615] Step 9:

[0616] Based on the information provided, users can give appropriate instructions to players and decide how to respond to matches and training sessions. This improves the overall performance of the team.

[0617] (Example 2)

[0618] 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."

[0619] In modern sports teams, improving match performance and managing player health are crucial elements. However, conventional management systems are limited to analyzing match and player data, and cannot address the overall optimization of tactics or improvement of motivation, including player emotional data. A more precise coaching and strategy are needed by comprehensively considering the mental and physical state of players, and solving this problem is essential.

[0620] 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.

[0621] In this invention, the server includes means for acquiring match information, player information, and emotional information; means for analyzing the acquired match information, player information, and emotional information; and means for generating and presenting a tactical plan based on the analysis results. This makes it possible to formulate and present a tactical plan that takes into account the mental state of the players.

[0622] "Match information" refers to data related to sporting events, including scores, schedules, participating players, and match status.

[0623] "Player information" refers to data regarding each player's name, position, statistics, and profile.

[0624] "Emotional information" refers to data such as feedback and analysis results regarding the emotional state of players or users.

[0625] "Methods of analysis" refer to the process of analyzing acquired data to derive trends and relationships within that data.

[0626] A "tactical plan" is a plan that strategically defines the actions of players and teams in order to effectively conduct a match.

[0627] "Generative means" refers to the process of creating new data and plans based on the analysis results.

[0628] "Means of presentation" refers to the process of displaying generated data and plans in a way that users and players can understand.

[0629] "Health information" refers to data related to the athlete's physical condition and health risks.

[0630] A "management plan" is a set of policies and procedures established to maintain or improve the health and performance of athletes.

[0631] "Training information" refers to data about the content and results of an athlete's training.

[0632] This invention is a system for managing sports teams that integrates game information, player information, health information, and emotional information, and supports the optimization of player performance. The system mainly consists of the following processes: data collection, analysis, management plan generation, and information presentation.

[0633] Users input match data and player information using a terminal, which then transmits it to the server. The terminal is equipped with data formatting and network transmission capabilities. The server analyzes the collected data using an emotion engine and generates tactical plans. The analysis includes a process of comparing current data with past data and developing plans tailored to the current state of the players and team.

[0634] The emotion engine is a software module that uses diverse data to calculate emotional states, enabling customized coaching tailored to each player's condition. The generated tactical plans and training plans are presented visually to the user via a terminal, allowing the user to give appropriate instructions to players and matches in real time.

[0635] As a concrete example, a user (coach) inputs player A's psychological and physical performance data into the system before a match. The server then generates optimal feedback and tactics for player A based on this information and past match results, and presents them to the coach via a terminal. An example of such a prompt might be, "What kind of feedback would be effective in improving player A's motivation for today's match?"

[0636] This invention makes it possible to provide effective coaching and tactics that take into account the emotional state of the players, thereby contributing to the improvement of the overall team performance.

[0637] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0638] Step 1:

[0639] The user inputs match and player information using a terminal. During this process, the user uses a touchscreen or keyboard to input details such as match scores and player health and psychological states. Once input is complete, the user presses the submit button to send the data to the server. The input here consists of match and player information; the terminal converts the input data into a predetermined format and transfers it to the server via the network.

[0640] Step 2:

[0641] The server receives match information and player information sent from terminals. The server stores this data in a database and performs analysis using an emotion engine. The input for the analysis is the collected match information and player information. The data processing here involves comparing it with past emotion data of players and teams to find correlations. This result becomes the analysis output and is used to generate tactical plans.

[0642] Step 3:

[0643] The server generates a tactical plan using a generative AI model based on the analysis results. Here, the analysis results are the input, and the generative AI model dynamically constructs the optimal tactics based on them. Finally, the generated tactical plan is output and stored on the server as a customized plan that corresponds to the psychological and physical state of the players.

[0644] Step 4:

[0645] The server sends the generated tactical plan to the terminal. The transmitted data includes specific instructions and feedback, which are presented to the user as output on the terminal. The terminal provides a visual presentation, displaying the information on an interface that allows the user to easily understand and use the information for instruction.

[0646] Step 5:

[0647] Users provide player guidance and plan development based on tactical plans and feedback presented through their devices. Specifically, users can analyze the output information from their devices and provide direct feedback to players as needed. They can also input additional data into the server via their devices to obtain further feedback as required.

[0648] (Application Example 2)

[0649] 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."

[0650] In modern society, there is a need to comprehensively understand an individual's emotional state, health status, and environmental conditions, and to provide appropriate support and advice based on that understanding. However, conventional systems have the challenge of being unable to analyze emotional state, health status, and environmental information as an integrated whole, and to provide support optimized for each individual.

[0651] 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.

[0652] In this invention, the server includes means for acquiring match information, means for acquiring person information, and means for acquiring environmental information. This enables integrated analysis of emotional states, health conditions, and environmental conditions, making it possible to provide effective support and decision-making plans tailored to each individual.

[0653] "Match information" refers to all information related to sports and competitions, including data necessary for strategic decision-making, such as match schedules, opponents, and player statistics.

[0654] "Personal information" refers to all information about individual people managed by the system, including data such as basic personal information, health status, and past behavioral history.

[0655] "Environmental information" refers to information that describes the conditions surrounding the subject situation or place, and includes factors that may affect people, such as weather, temperature, and the condition of facilities.

[0656] A "decision-making plan" refers to a plan formulated to derive the most appropriate actions and policies for each specific situation, based on collected and analyzed information.

[0657] "Emotional information" refers to information about a person's emotional state and includes data obtained from facial expression analysis and voice analysis.

[0658] "Means of predicting risk" refers to methods for predicting potential risks that may arise in the future based on a person's health and behavior, and for providing warnings or preventative measures against those risks.

[0659] "Improvement suggestions" refer to specific actions and advice presented based on results derived from the analysis of collected data, with the aim of improving an individual's condition or increasing efficiency.

[0660] This invention is designed as a system incorporating an emotion engine. The server has multiple data acquisition means for collecting match information, person information, and environmental information. This information can be used to perform facial expression analysis and acquire health information using hardware and software such as Intel RealSense cameras and the OpenVINO toolkit. Furthermore, the server has the function to analyze this data comprehensively and formulate a decision plan from the data obtained using a generative AI model.

[0661] The terminal's role is to present the user with a decision plan transmitted from the server. Based on the user's emotional and health status, it provides optimal actions and policies. The user can then take appropriate action based on their individual circumstances.

[0662] As a concrete example, a scenario could be envisioned where a home robot, acting as a user, analyzes the emotional state of family members and, if it determines that stress levels are high, suggests appropriate relaxation methods. In this case, the server would input a prompt message such as "Please suggest effective ways for family member A to relax when they are feeling stressed" into the AI ​​model, which would then generate effective suggestions.

[0663] This configuration allows users to receive better health management and emotional care, thereby improving their quality of life.

[0664] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0665] Step 1:

[0666] The server collects match information, player information, and environmental information. The collected information is entered into a database, from which the necessary data is extracted for analysis. The entered data includes, for example, information about an individual's health status and emotional state.

[0667] Step 2:

[0668] The server analyzes the collected information. The main process here is determining the emotional state using an emotion engine. This analysis employs a method that evaluates current emotional changes by comparing them with past data. The analysis results are output as an evaluation of emotional state and health status.

[0669] Step 3:

[0670] The server generates a decision plan based on the analysis results. At this stage, a generative AI model is used to generate prompt statements and formulate appropriate actions and policies. For example, a prompt such as "Suggest effective ways for family member A to relax when they are feeling stressed" might be used. The generated plan is stored in a database.

[0671] Step 4:

[0672] The terminal receives a decision plan transmitted from the server. The received plan is presented to the user, who then makes a decision based on it. This process utilizes the terminal's display screen and audio output, and incorporates features to enhance user experience.

[0673] Step 5:

[0674] Users can take action based on the presented plan. Specifically, they will take actions in accordance with the plan, such as trying the suggested relaxation methods. This is expected to contribute to improvements in the user's health and emotional state.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] [Fourth Embodiment]

[0679] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0680] 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.

[0681] 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).

[0682] 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.

[0683] 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.

[0684] 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).

[0685] 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.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] 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".

[0692] This invention is a system that utilizes generative AI to support the management and operation of sports teams. Through the following program processing, it analyzes match information and player information, provides optimal tactical plans and training plans, and manages injury risks.

[0693] The system broadly comprises the following functions: data collection, data analysis, tactical and training plan generation, and injury risk management.

[0694] 1. Data Collection

[0695] Users input player and match information into their terminals for each match and training session. This includes player data and health information during the match.

[0696] The terminal formats the entered data according to the specified format in order to send it to the server, and then sends it to the server.

[0697] 2. Data Analysis

[0698] The server performs analysis based on the received match and player information. Multiple algorithms are used to analyze tactical planning and individual player performance.

[0699] This allows us to identify the opposing team's tactical tendencies and clarify the strengths and weaknesses of our own team's players' performance.

[0700] 3. Generation of tactical and training plans

[0701] The server generates the optimal tactical plan for the match based on the analysis results. It also provides individually optimized training plans for each player.

[0702] The terminal displays the generated tactical and training plans to the user as a dashboard.

[0703] 4. Injury Risk Management

[0704] The server analyzes the players' health information and predicts the risk of injury based on this. Based on these results, it creates injury prevention measures and recovery plans.

[0705] The device displays injury risk predictions and management plans to physiotherapists and trainers.

[0706] Specific example

[0707] For example, consider a scenario where a match against a certain team is scheduled before a game. The user, acting as a coach, inputs data from the previous match, the players' health status, and the opponent's past data into a terminal. The server analyzes all the data, recommends a specific formation with strong offensive capabilities, and then presents a training menu tailored to player A. At the same time, based on player B's health status, it predicts an increased risk of injury and provides feedback on a plan to adjust the rest schedule.

[0708] Thus, by using the system of the present invention, it becomes possible to improve the tactical efficiency of the team and the health management of the players.

[0709] The following describes the processing flow.

[0710] Step 1:

[0711] Users input match results, player data, and information about their opponents into their devices. This includes statistical information and data indicating health status.

[0712] Step 2:

[0713] The terminal receives data entered by the user and converts it to the appropriate format. The converted data is then sent to the server.

[0714] Step 3:

[0715] The server receives data sent from the terminal and stores it in the database. When storing the data, it checks for data integrity and verifies that there are no outliers or duplicates.

[0716] Step 4:

[0717] The server extracts information stored in the database and executes data analysis algorithms. This allows for analysis of the opposing team's playing style and the performance of the team's own players.

[0718] Step 5:

[0719] The server generates an optimal tactical plan based on the analysis results. The generated plan includes specific strategies and formation suggestions that exploit the opponent's weaknesses.

[0720] Step 6:

[0721] The server creates a training plan optimized for each individual player based on their performance and physical data.

[0722] Step 7:

[0723] The terminal receives tactical and training plans sent from the server and presents them to the user in a dashboard format.

[0724] Step 8:

[0725] The server analyzes the players' health data and predicts their injury risk. If a risk is detected, it generates preventative measures and management plans.

[0726] Step 9:

[0727] The device displays injury risk predictions and related management plans to physiotherapists and trainers, enabling appropriate health management.

[0728] (Example 1)

[0729] 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".

[0730] In managing and operating sports teams, the effective use of match and player information is required, but collecting, analyzing, and utilizing this information in a timely manner to develop tactical and training plans is not easy. Furthermore, in player health management, there is a lack of concrete measures to predict and appropriately manage injury risks. Moreover, there is a need for an effective method to quickly share the plans generated through these processes with team personnel.

[0731] 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.

[0732] In this invention, the server includes means for collecting information about matches, means for collecting information about players, and means for performing analysis based on the collected match information and player information. This enables the formulation of effective tactical plans and the generation of training plans optimized for each player. Furthermore, it supports injury risk prediction and the creation of management plans for player health, and enables secure data communication throughout the entire process.

[0733] A "device for collecting information related to a match" refers to equipment or software used to acquire data related to a sports match, such as player performance, match results, and tactical data.

[0734] A "device for collecting information about athletes" refers to equipment or software used to acquire individual data on athletes, such as their health status, physical data, and individual performance.

[0735] "Means of analysis" refers to a function that uses statistical or machine learning methods to analyze data based on collected match information and player information, and derive meaningful conclusions.

[0736] A "tactical plan generating device" is a device or software that automatically creates optimal tactics and strategies for a match based on analysis results.

[0737] A "device for displaying tactical plans" is a device or interface that visually presents generated tactical plans or training plans to the user, providing information in an easily understandable format.

[0738] "Communication methods" refer to protocols and equipment for securely and efficiently sending and receiving data both within and outside a system, and they guarantee the confidentiality and integrity of the data.

[0739] A "device that uses analytical algorithms" is a device or software that applies multiple algorithms in data analysis, ultimately intended to derive useful tactics and performance indicators.

[0740] A "device for collecting information on athletes' health" refers to equipment or software that acquires information such as an athlete's health status, medical data, and past injury history.

[0741] A "device for predicting injury risk" is a device or software that uses an athlete's health information to evaluate and predict the likelihood of future injuries.

[0742] A "health management plan generating device" is a device or software that automatically creates the measures and plans necessary to maintain and improve the health of athletes.

[0743] A "training menu provider" refers to equipment or software that presents a training plan and schedule tailored to each athlete, thereby supporting performance improvement.

[0744] A "device for collecting training information" refers to equipment or software used to record the training content and results performed by athletes, and serves as a foundation for analyzing the effectiveness of the training.

[0745] A "simulation device" is a piece of equipment or software used to virtually evaluate the effectiveness of tactics and formations in a match and to find the optimal strategy.

[0746] This invention is a system that supports the management and operation of sports teams, analyzing match and player information to provide optimal tactical and training plans. Furthermore, it has a function to manage injury risk by analyzing player health information. This system is mainly implemented by server, terminal, and user components.

[0747] Data collection and formatting

[0748] Users use a terminal to input player information and match information related to matches and training. The terminal receives this information, converts it into accurately formatted data, and sends it to the server via a communication protocol (e.g., HTTPS).

[0749] Data Analysis

[0750] The server stores the received match and player information in a database and prepares it for analysis. Generative AI models are used for the analysis, and various data analysis algorithms are applied. This analysis generates individual player performance data and tactical plans for the entire team.

[0751] Tactical plan generation and display

[0752] The server automatically generates an optimal tactical plan based on the analysis results. The generated tactics are visually presented to the user via a terminal. This allows the coach to immediately verify the effectiveness of the tactics and make adjustments.

[0753] Injury risk management

[0754] Furthermore, the server analyzes the athletes' health information and runs an algorithm to predict injury risk. The prediction results are displayed to physiotherapists and trainers to help them develop appropriate preventative measures and recovery plans.

[0755] Specific example

[0756] For example, consider a scenario where a coach inputs data from the previous match, the players' health status, and the opponent's past tactical data into a terminal to prepare for the next game. The server analyzes this data to suggest an effective formation and provides a weekly training menu tailored to player A. It also suggests a rest schedule for player B based on their daily health data, addressing their increasing risk of injury. A concrete example of a prompt the user might input into the AI ​​model is, "Please suggest the optimal tactics for the next game and a training plan that takes into account the players' health status."

[0757] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0758] Step 1:

[0759] Users input player information and match information related to matches and training into the terminal. This input information includes individual player performance data, health status, and match results. The input data is supplemented using the terminal's checklist function to ensure that no information is missing.

[0760] Step 2:

[0761] The terminal receives information entered by the user and formats the data. This formatting process converts the input data into a unified format such as XML, maintaining data consistency. The formatted data is then sent to the server via a secure protocol (such as HTTPS).

[0762] Step 3:

[0763] The server stores the received data in a database. The stored data is prepared in a format suitable for analysis, with duplicates removed and organized in a way that allows for comparison with past data. Furthermore, backups are created simultaneously.

[0764] Step 4:

[0765] The server uses stored data to perform analysis using a generated AI model. The analysis applies various algorithms based on match and player information to analyze player performance and tactical tendencies. This reveals each player's strengths and weaknesses, as well as areas for overall team tactical improvement.

[0766] Step 5:

[0767] The server generates a tactical plan based on the analysis results. The generated tactical plan includes the most effective formations and approaches and is optimized based on evaluation in the simulation environment.

[0768] Step 6:

[0769] The terminal displays the generated tactical plan on a dashboard and presents it to the user. The displayed information is made easy for the user to understand and analyze using visualization tools such as graphs and heatmaps.

[0770] Step 7:

[0771] The server analyzes players' health information to predict injury risk. The prediction process calculates a risk score based on pre-configured health information and past injury data, and alerts players deemed to be at high risk.

[0772] Step 8:

[0773] The server generates a player's health management plan based on injury risk predictions. This includes appropriate rest schedules and physiotherapy plans, creating a detailed management plan to support the player's health.

[0774] Step 9:

[0775] The device provides physiotherapists and trainers with generated injury risk predictions and health management plans. The information provided is displayed as an alert if immediate action is required, enabling rapid intervention on-site.

[0776] (Application Example 1)

[0777] 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".

[0778] In modern manufacturing, efficient operation and maintenance of moving machinery are essential for improving productivity. However, properly analyzing performance data and maintenance information of moving machinery and predicting the risk of failure in advance is not easy. Furthermore, developing work plans and maintenance plans optimized for each piece of machinery requires considerable time and resources. Therefore, a new system is needed to solve these problems.

[0779] 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.

[0780] In this invention, the server includes means for acquiring performance data of a motion device, means for acquiring maintenance information of the motion device, and means for analyzing the acquired performance data and maintenance information. This enables the prediction of failure risk based on performance data and maintenance information, and the automatic generation of optimal work plans and maintenance plans.

[0781] "Motorized machinery" is a general term for machines and robots used to perform specific tasks in factories and production lines.

[0782] "Performance data" refers to various measured values ​​and output information acquired to indicate the operating status and efficiency of the motion control equipment.

[0783] "Maintenance information" refers to information necessary to maintain the proper condition of the equipment, such as the maintenance history, service records, and inspection results of the exercise equipment.

[0784] "Failure risk" refers to predictive information indicating the possibility that the motion control system may not function properly or may stop working.

[0785] A "work plan" is a guideline or schedule that shows the optimal operating schedule and work procedures for using the exercise equipment.

[0786] A "maintenance plan" is a plan that outlines regular inspections and maintenance to prevent malfunctions of the athletic equipment.

[0787] This invention is a system for optimizing the operation of exercise equipment. It uses a terminal such as a smartphone or tablet to acquire performance data and maintenance information from the exercise equipment, which is then analyzed on a server. The terminal transmits the data collected from the exercise equipment to the server. Communication methods such as Wi-Fi or wired connections are used for this data transmission.

[0788] The server uses programming languages ​​such as Python to analyze collected performance data and maintenance information, and utilizes a generated AI model to create optimal work plans and maintenance plans. The server uses machine learning algorithms to predict the failure risk for each piece of equipment and formulates plans based on that prediction. The data analysis results are returned to the terminal and presented to the person in charge in a dashboard format. In this way, the person in charge can quickly create an optimal work plan and perform the necessary maintenance.

[0789] As a concrete example, if a user inputs a command into a terminal for multiple motion machines within a factory, such as "Generate an optimal operating plan for each machine based on its current performance data and maintenance information," the server will analyze this and create an optimal plan. Using this prompt improves the operational efficiency of the factory.

[0790] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0791] Step 1:

[0792] The user uses a terminal to collect performance and maintenance data from the machinery in the factory. Inputs include operating status and maintenance history data obtained from the machinery's sensors, and output is formatted data. At this stage, the terminal prepares to format the data and transfer it to the server.

[0793] Step 2:

[0794] The server receives performance data and maintenance information sent from the terminal. It receives formatted data sent from the terminal as input and prepares datasets in a format suitable for data analysis as output. These datasets are stored in a database within the server and passed on to the next analysis step.

[0795] Step 3:

[0796] The server uses a generated AI model to analyze performance data and maintenance information. Using a prepared dataset as input, it predicts the failure risk of the motion equipment through data processing and calculations. The output generates predicted failure risk values ​​and analysis results. During this process, machine learning algorithms are employed to take into account the state and history of each motion equipment during the analysis.

[0797] Step 4:

[0798] The server generates optimal work plans and maintenance plans based on the analysis results. Using predicted failure risk values ​​and current data for each device as input, it applies a generated AI model to formulate an optimized plan. The output is a specific work plan and maintenance schedule for each device.

[0799] Step 5:

[0800] The server sends the generated plan information to the terminal. The terminal receives this information and presents it to the user in a dashboard format. It receives the plan information sent from the server as input and displays it to the user in a visually easy-to-understand format as output. Based on this, the user efficiently manages the operation and maintenance of the exercise equipment.

[0801] 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.

[0802] This invention is a sports team management system incorporating an emotion engine. It aims to improve player performance by comprehensively analyzing match information, player information, health information, and user emotional information. This system utilizes emotional data for strategic decision-making and player motivation management.

[0803] This system provides functions for data collection, sentiment analysis, tactical planning adjustments, player training, and motivation management.

[0804] 1. Data Collection

[0805] Users input match results and player data into their devices. The emotion engine acquires emotion data based on the user's reactions and feedback.

[0806] The terminal formats the entered information and sends it to the server.

[0807] 2. Emotion analysis

[0808] The server analyzes the acquired emotional data to determine the user's emotional state. This analysis includes a process of evaluating changes in emotions by comparing them with past emotional data.

[0809] 3. Adjustment of tactical plans

[0810] The server dynamically adjusts the strategy plan as needed based on the analyzed emotional data. This makes it possible to create an environment where players can play with the most motivation.

[0811] 4. Training and Motivation Management

[0812] The server optimizes the athlete's training plan based on emotion analysis. Individually tailored training plans and feedback are provided to boost the athlete's motivation.

[0813] 5. Information presentation

[0814] The terminal presents the user with tactical plans and motivation improvement plans generated from the server. This allows the user to take appropriate action based on the player's condition.

[0815] Specific example

[0816] Before a match, the user (coach) inputs match data into the system and evaluates factors that influenced emotions in past matches. The server analyzes the emotional data in real time and suggests tactics best suited to the players' performance. In addition, to boost player A's motivation, feedback generated by the emotion engine is provided via the terminal. This series of processes is expected to improve the overall team performance.

[0817] The following describes the processing flow.

[0818] Step 1:

[0819] The user inputs match data and player data into the terminal. At this time, they also input information regarding important match points and player condition.

[0820] Step 2:

[0821] Users provide emotional information by inputting reactions and feedback using their devices. The devices acquire this information in real time.

[0822] Step 3:

[0823] The terminal converts match data, player data, and sentiment data obtained from the user into the appropriate format and sends it to the server.

[0824] Step 4:

[0825] The server stores the received data in the database. During storage, it performs data integrity checks and removes invalid data.

[0826] Step 5:

[0827] The server analyzes emotional data based on the stored data to determine the user's emotional state. This is done using natural language processing and machine learning algorithms.

[0828] Step 6:

[0829] The server generates an optimal tactical plan for the match based on the results of the emotion analysis. This plan takes into account factors such as the players' emotional motivation.

[0830] Step 7:

[0831] The server uses the analysis results to optimize individual training plans for each player and generates feedback to boost their motivation.

[0832] Step 8:

[0833] The terminal receives tactical plans, training plans, and feedback information sent from the server, and presents them to the user in a dashboard format.

[0834] Step 9:

[0835] Based on the information provided, users can give appropriate instructions to players and decide how to respond to matches and training sessions. This improves the overall performance of the team.

[0836] (Example 2)

[0837] 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".

[0838] In modern sports teams, improving match performance and managing player health are crucial elements. However, conventional management systems are limited to analyzing match and player data, and cannot address the overall optimization of tactics or improvement of motivation, including player emotional data. A more precise coaching and strategy are needed by comprehensively considering the mental and physical state of players, and solving this problem is essential.

[0839] 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.

[0840] In this invention, the server includes means for acquiring match information, player information, and emotional information; means for analyzing the acquired match information, player information, and emotional information; and means for generating and presenting a tactical plan based on the analysis results. This makes it possible to formulate and present a tactical plan that takes into account the mental state of the players.

[0841] "Match information" refers to data related to sporting events, including scores, schedules, participating players, and match status.

[0842] "Player information" refers to data regarding each player's name, position, statistics, and profile.

[0843] "Emotional information" refers to data such as feedback and analysis results regarding the emotional state of players or users.

[0844] "Methods of analysis" refer to the process of analyzing acquired data to derive trends and relationships within that data.

[0845] A "tactical plan" is a plan that strategically defines the actions of players and teams in order to effectively conduct a match.

[0846] "Generative means" refers to the process of creating new data and plans based on the analysis results.

[0847] "Means of presentation" refers to the process of displaying generated data and plans in a way that users and players can understand.

[0848] "Health information" refers to data related to the athlete's physical condition and health risks.

[0849] A "management plan" is a set of policies and procedures established to maintain or improve the health and performance of athletes.

[0850] "Training information" refers to data about the content and results of an athlete's training.

[0851] This invention is a system for managing sports teams that integrates game information, player information, health information, and emotional information, and supports the optimization of player performance. The system mainly consists of the following processes: data collection, analysis, management plan generation, and information presentation.

[0852] Users input match data and player information using a terminal, which then transmits it to the server. The terminal is equipped with data formatting and network transmission capabilities. The server analyzes the collected data using an emotion engine and generates tactical plans. The analysis includes a process of comparing current data with past data and developing plans tailored to the current state of the players and team.

[0853] The emotion engine is a software module that uses diverse data to calculate emotional states, enabling customized coaching tailored to each player's condition. The generated tactical plans and training plans are presented visually to the user via a terminal, allowing the user to give appropriate instructions to players and matches in real time.

[0854] As a concrete example, a user (coach) inputs player A's psychological and physical performance data into the system before a match. The server then generates optimal feedback and tactics for player A based on this information and past match results, and presents them to the coach via a terminal. An example of such a prompt might be, "What kind of feedback would be effective in improving player A's motivation for today's match?"

[0855] This invention makes it possible to provide effective coaching and tactics that take into account the emotional state of the players, thereby contributing to the improvement of the overall team performance.

[0856] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0857] Step 1:

[0858] The user inputs match and player information using a terminal. During this process, the user uses a touchscreen or keyboard to input details such as match scores and player health and psychological states. Once input is complete, the user presses the submit button to send the data to the server. The input here consists of match and player information; the terminal converts the input data into a predetermined format and transfers it to the server via the network.

[0859] Step 2:

[0860] The server receives match information and player information sent from terminals. The server stores this data in a database and performs analysis using an emotion engine. The input for the analysis is the collected match information and player information. The data processing here involves comparing it with past emotion data of players and teams to find correlations. This result becomes the analysis output and is used to generate tactical plans.

[0861] Step 3:

[0862] The server generates a tactical plan using a generative AI model based on the analysis results. Here, the analysis results are the input, and the generative AI model dynamically constructs the optimal tactics based on them. Finally, the generated tactical plan is output and stored on the server as a customized plan that corresponds to the psychological and physical state of the players.

[0863] Step 4:

[0864] The server sends the generated tactical plan to the terminal. The transmitted data includes specific instructions and feedback, which are presented to the user as output on the terminal. The terminal provides a visual presentation, displaying the information on an interface that allows the user to easily understand and use the information for instruction.

[0865] Step 5:

[0866] Users provide player guidance and plan development based on tactical plans and feedback presented through their devices. Specifically, users can analyze the output information from their devices and provide direct feedback to players as needed. They can also input additional data into the server via their devices to obtain further feedback as required.

[0867] (Application Example 2)

[0868] 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".

[0869] In modern society, there is a need to comprehensively understand an individual's emotional state, health status, and environmental conditions, and to provide appropriate support and advice based on that understanding. However, conventional systems have the challenge of being unable to analyze emotional state, health status, and environmental information as an integrated whole, and to provide support optimized for each individual.

[0870] 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.

[0871] In this invention, the server includes means for acquiring match information, means for acquiring person information, and means for acquiring environmental information. This enables integrated analysis of emotional states, health conditions, and environmental conditions, making it possible to provide effective support and decision-making plans tailored to each individual.

[0872] "Match information" refers to all information related to sports and competitions, including data necessary for strategic decision-making, such as match schedules, opponents, and player statistics.

[0873] "Personal information" refers to all information about individual people managed by the system, including data such as basic personal information, health status, and past behavioral history.

[0874] "Environmental information" refers to information that describes the conditions surrounding the subject situation or place, and includes factors that may affect people, such as weather, temperature, and the condition of facilities.

[0875] A "decision-making plan" refers to a plan formulated to derive the most appropriate actions and policies for each specific situation, based on collected and analyzed information.

[0876] "Emotional information" refers to information about a person's emotional state and includes data obtained from facial expression analysis and voice analysis.

[0877] "Means of predicting risk" refers to methods for predicting potential risks that may arise in the future based on a person's health and behavior, and for providing warnings or preventative measures against those risks.

[0878] "Improvement suggestions" refer to specific actions and advice presented based on results derived from the analysis of collected data, with the aim of improving an individual's condition or increasing efficiency.

[0879] This invention is designed as a system incorporating an emotion engine. The server has multiple data acquisition means for collecting match information, person information, and environmental information. This information can be used to perform facial expression analysis and acquire health information using hardware and software such as Intel RealSense cameras and the OpenVINO toolkit. Furthermore, the server has the function to analyze this data comprehensively and formulate a decision plan from the data obtained using a generative AI model.

[0880] The terminal's role is to present the user with a decision plan transmitted from the server. Based on the user's emotional and health status, it provides optimal actions and policies. The user can then take appropriate action based on their individual circumstances.

[0881] As a concrete example, a scenario could be envisioned where a home robot, acting as a user, analyzes the emotional state of family members and, if it determines that stress levels are high, suggests appropriate relaxation methods. In this case, the server would input a prompt message such as "Please suggest effective ways for family member A to relax when they are feeling stressed" into the AI ​​model, which would then generate effective suggestions.

[0882] This configuration allows users to receive better health management and emotional care, thereby improving their quality of life.

[0883] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0884] Step 1:

[0885] The server collects match information, player information, and environmental information. The collected information is entered into a database, from which the necessary data is extracted for analysis. The entered data includes, for example, information about an individual's health status and emotional state.

[0886] Step 2:

[0887] The server analyzes the collected information. The main process here is determining the emotional state using an emotion engine. This analysis employs a method that evaluates current emotional changes by comparing them with past data. The analysis results are output as an evaluation of emotional state and health status.

[0888] Step 3:

[0889] The server generates a decision plan based on the analysis results. At this stage, a generative AI model is used to generate prompt statements and formulate appropriate actions and policies. For example, a prompt such as "Suggest effective ways for family member A to relax when they are feeling stressed" might be used. The generated plan is stored in a database.

[0890] Step 4:

[0891] The terminal receives a decision plan transmitted from the server. The received plan is presented to the user, who then makes a decision based on it. This process utilizes the terminal's display screen and audio output, and incorporates features to enhance user experience.

[0892] Step 5:

[0893] Users can take action based on the presented plan. Specifically, they will take actions in accordance with the plan, such as trying the suggested relaxation methods. This is expected to contribute to improvements in the user's health and emotional state.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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."

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] The following is further disclosed regarding the embodiments described above.

[0916] (Claim 1)

[0917] Means of obtaining match information,

[0918] Means of obtaining player information,

[0919] A means for analyzing acquired match information and player information,

[0920] Means for generating an operational plan based on the aforementioned analysis means,

[0921] Means for presenting the generated operational plan,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] Means of obtaining athletes' health information,

[0925] A means for predicting the risk of injury based on the aforementioned health information,

[0926] A means of generating a player management plan based on prediction results,

[0927] The system according to claim 1, further comprising:

[0928] (Claim 3)

[0929] A means of obtaining training information for each player,

[0930] A means for analyzing the aforementioned training information and generating an optimized training plan for each athlete,

[0931] A means of presenting the generated training plan,

[0932] The system according to claim 1, further comprising:

[0933] "Example 1"

[0934] (Claim 1)

[0935] A device for collecting information about the match,

[0936] A device for collecting information about players,

[0937] A device that performs analysis based on collected match information and player information,

[0938] A device for generating a tactical plan based on the aforementioned analysis results,

[0939] A device that displays the generated tactical plan,

[0940] Communication means that enable secure transmission of data,

[0941] A device that uses multiple data analysis algorithms,

[0942] A system that includes this.

[0943] (Claim 2)

[0944] A device for collecting information about the athletes' health,

[0945] A device that predicts the risk of injury based on the aforementioned health information,

[0946] A device that generates a health management plan for athletes based on prediction results,

[0947] A device that provides training menus tailored to individual athletes,

[0948] The system according to claim 1, further comprising:

[0949] (Claim 3)

[0950] A device for collecting information on each athlete's training,

[0951] A device that analyzes the aforementioned training information to generate an optimal training plan for each athlete,

[0952] A device that presents the generated training plan,

[0953] A simulation device for evaluating tactical plans,

[0954] The system according to claim 1, further comprising:

[0955] "Application Example 1"

[0956] (Claim 1)

[0957] A means for acquiring performance data of a motion device,

[0958] Means for acquiring maintenance information for the aforementioned motion device,

[0959] A means for analyzing acquired performance data and maintenance information,

[0960] Means for generating a work plan based on the aforementioned analysis means,

[0961] A means of presenting the generated work plan,

[0962] A system that includes this.

[0963] (Claim 2)

[0964] A means for predicting the risk of failure based on the aforementioned maintenance information,

[0965] A means for generating a maintenance plan for the exercise equipment based on prediction results,

[0966] The system according to claim 1, further comprising:

[0967] (Claim 3)

[0968] A means for acquiring operational information for each motion device,

[0969] A means for analyzing the aforementioned operational information and generating individually optimized operational plans,

[0970] A means for presenting the generated operational plan,

[0971] The system according to claim 1, further comprising:

[0972] "Example 2 of combining an emotion engine"

[0973] (Claim 1)

[0974] Means of obtaining match information,

[0975] Means of obtaining player information,

[0976] Means of acquiring emotional information,

[0977] A means for analyzing acquired match information, player information, and sentiment information,

[0978] Means for generating a tactical plan based on the aforementioned analysis means,

[0979] A means of presenting the generated tactical plan,

[0980] A system that includes this.

[0981] (Claim 2)

[0982] Means of obtaining athletes' health information,

[0983] A means for predicting the risk of injury based on the aforementioned health information and emotional information,

[0984] A means of generating a player management plan based on prediction results,

[0985] The system according to claim 1, further comprising:

[0986] (Claim 3)

[0987] A means of obtaining training information for each player,

[0988] A means for analyzing the aforementioned training information and emotional information to generate an optimized training plan for each athlete,

[0989] A means of presenting the generated training plan and feedback for motivational improvement,

[0990] The system according to claim 1, further comprising:

[0991] "Application example 2 when combining with an emotional engine"

[0992] (Claim 1)

[0993] Means of obtaining match information,

[0994] Means of obtaining personal information,

[0995] Means of acquiring environmental information,

[0996] A means for analyzing acquired match information, person information, and environmental information,

[0997] Means for generating a decision-making plan based on the aforementioned analysis means,

[0998] A means of presenting the generated decision plan,

[0999] A system that includes this.

[1000] (Claim 2)

[1001] Means of obtaining a person's health status,

[1002] Means for predicting risk based on the aforementioned health status,

[1003] A means of generating a personnel management plan based on prediction results,

[1004] The system according to claim 1, further comprising:

[1005] (Claim 3)

[1006] A means of obtaining the training status of each individual,

[1007] A means for analyzing the training situation and generating an optimized training plan for each individual,

[1008] A means of presenting the generated training plan,

[1009] A means of analyzing acquired emotional information to evaluate a person's emotions,

[1010] A means of providing improvement suggestions based on the aforementioned evaluation,

[1011] The system according to claim 1, further comprising: [Explanation of symbols]

[1012] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring performance data of a motion device, Means for acquiring maintenance information for the aforementioned motion device, A means for analyzing acquired performance data and maintenance information, Means for generating a work plan based on the aforementioned analysis means, A means of presenting the generated work plan, A system that includes this.

2. A means for predicting the risk of failure based on the aforementioned maintenance information, A means for generating a maintenance plan for the exercise equipment based on prediction results, The system according to claim 1, further comprising:

3. A means for acquiring operational information for each motion device, A means for analyzing the aforementioned operational information and generating individually optimized operational plans, A means for presenting the generated operational plan, The system according to claim 1, further comprising:

Images