system

Abstract

Provide a system. [Solution] means for storing the registration information entered by the user in a database; means for receiving and storing data uploaded by users; means for analyzing the stored data using a generative model to generate a result; a means for visually displaying the generated analysis results; A means for generating strategies and training programs according to the characteristics of teams and players using a generative model; A system including a means for analyzing real-time data sent by a user and providing feedback.

Description

[Technical Field]

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

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

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

[0004] Team sports enthusiasts, professional athletes, and their coaching staff face challenges in discovering effective strategies and new playing styles, and a lack of analysis of players and opponents limits their ability to take on new challenges and improve their performance. Furthermore, the lack of tools that provide real-time data analysis and visual feedback often delays tactical adjustments and training optimization. This creates a challenge for athletes, preventing them from fully realizing their potential. [Means for solving the problem]

[0005] The present invention provides a means for storing registration information entered by users in a database and preparing basic data for analysis. Next, a means for receiving and storing match data and training data uploaded by users is provided. By providing a means for analyzing the stored data using a generative model and generating results, detailed analysis based on the characteristics of teams and players becomes possible. Furthermore, by adding a means for visually displaying the generated analysis results, strategies, and training programs, users can receive feedback in an easily understandable format. Furthermore, by providing a means for analyzing real-time data sent by users using a generative model and providing immediate feedback, tactical adjustments can be made quickly during matches and training. This provides continuous support for improving athletes' abilities and contributes to improving their competitiveness in sports.

[0006] "User" refers to an individual or coaching staff member who uses the sports strategy planning system, and is the entity that inputs data into the system and checks the output results.

[0007] The "database" is a place where information entered by users, such as registration information, match data, and training data, is stored and processed by the system.

[0008] A "generative model" is an algorithm or program that uses AI technology to analyze input data and automatically generate strategies and training programs.

[0009] "Analysis" is the process by which a generative model analyzes data based on information stored in a database and derives conclusions or results for a specific purpose.

[0010] "Strategy" refers to a specific plan or policy to be adopted in a match or training session based on the characteristics of the team or players.

[0011] A "training program" is a practice menu and training method that is generated based on the performance data of each individual player and is aimed at improving the player's abilities.

[0012] "Feedback" refers to notifications and suggestions, including corrections and improvements, generated in real time based on data submitted by users.

[0013] "Visual display" refers to a means of presenting analysis results, strategies, training programs, etc. to users in a visually easy-to-understand format such as graphs or diagrams.

[0014] "Real-time data" refers to ongoing data sent by a user during a match or training session that is analyzed immediately and feedback is provided. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The sports strategy planning system of the present invention combines various functions centered on generation AI to provide a platform for users to optimize their game strategies and discover new playing styles. Specific embodiments for implementing the system are described below.

[0037] User Registration and Login

[0038] A user first creates an account using a terminal. The required information is entered as name, email address, password, and basic information about the sport they play (e.g., sport type and position). The server then receives this information and stores it in a database. When the user uses the system again, they are required to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, they are granted access to the main screen.

[0039] Data acquisition and analysis

[0040] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis results include player performance evaluations and opponent tactical analysis. The analysis results are visually displayed on the server and provided as a dashboard for users to easily understand.

[0041] Strategy and playing style suggestions

[0042] The server uses a generative AI model based on team and player characteristic data to generate optimal strategies and new playing styles. The generated strategies are then sent to the user's device. Specific suggestions, such as "This player is good at three-point shooting, so we'll build plays around him," are included. Users can review these suggestions and incorporate them into their own strategies.

[0043] Training program generation

[0044] The server generates an optimized training program based on the performance data of each player. This provides a training menu that will help each player maximize their potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. The user can check this training program on their device and incorporate it into their actual training.

[0045] Real-time feedback

[0046] During a match or training session, the user sends data from their device to the server in real time. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "You should strengthen your defensive line" can be sent to the user's device as visual feedback during a match. This allows the user to make tactical adjustments on the spot and improve their performance.

[0047] Specific examples

[0048] For example, when a basketball coach uses this system, he or she first uploads basic information about each player and past game data from their device. Based on this, the server uses a generative AI model to analyze the strengths and weaknesses of the entire team. Based on the analysis results, a strategy suggestion is made, such as "A strategy of launching fast breaks centered around Player A would be effective in this game." Individual training programs are also generated for each player, with instructions provided such as "Player B should focus on free throw practice." During the game, data is transmitted in real time, and feedback such as "Switch to zone defense" is instantly displayed on the device.

[0049] As described above, this invention is a system that utilizes generative AI to provide advanced support for users to select effective strategies and discover new playing styles. This system contributes to improving sports performance and helps players and teams to perform at their best.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The user enters account information on the device, such as name, email address, password, and basic information about the sport played (e.g., sport type and position).

[0053] Step 2:

[0054] The server receives the registration information sent by the user and stores it in a database, after which a confirmation message containing the user authentication information is generated.

[0055] Step 3:

[0056] The user enters their email address and password into the terminal and attempts to log in. This sends the entered information to the server.

[0057] Step 4:

[0058] The server checks the registration information against the database and performs authentication. If authentication is successful, the user is allowed to access the main screen.

[0059] Step 5:

[0060] Users upload files of match data and training data using their devices, and this data is sent to the server.

[0061] Step 6:

[0062] The server receives the uploaded data, stores it in a database, and then analyzes the stored data using a generative AI model.

[0063] Step 7:

[0064] The server visualizes the analysis results and generates a dashboard, which is created in a user-friendly format using charts and graphs.

[0065] Step 8:

[0066] The server uses the generative AI model to generate optimal strategies and new playing styles based on the characteristics of the team and players. For example, it may generate a specific strategy such as "a strategy of launching a fast attack centered around player A is effective."

[0067] Step 9:

[0068] The server notifies the user of the generated strategies and training programs and displays them visually. The user can then check them on their device and use them in actual matches and training.

[0069] Step 10:

[0070] Users send data in real time during a match or training session, which provides the server with information on the match status and training progress in real time.

[0071] Step 11:

[0072] The server receives real-time data and instantly analyzes it using generative AI models, enabling quick tactical adjustments during matches and immediate feedback during training.

[0073] Step 12:

[0074] The server generates real-time feedback and provides it to the user, with specific instructions such as "You should strengthen your defensive line" being visually displayed on the device.

[0075] Step 13:

[0076] The feedback received by users is used to instantly modify match and training tactics, improving player and team performance.

[0077] Example 1

[0078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0079] Conventional sports strategy planning systems often require manual analysis of match and training data, which is time-consuming and labor-intensive. Furthermore, the strategies and training programs generated may not be fully effective because they are not optimized for the characteristics of specific players or teams. Furthermore, the lack of real-time feedback makes it difficult to make timely tactical adjustments during matches or training.

[0080] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0081] In this invention, the server includes means for saving registration information entered by a user in a database, means for receiving and saving data uploaded by a user, means for analyzing the saved data using a generative AI model and generating results, means for visually displaying the generated analysis results, means for using the generative AI model to generate strategies and training programs tailored to the characteristics of teams and players, means for analyzing real-time data sent by users and providing feedback, means for notifying users of strategies and training programs based on the analysis results to their terminals, and means for users to confirm the proposed strategies and incorporate them into their own strategies. This allows users to easily automate data analysis and use strategies and training programs optimized for specific players and teams, and also enables timely tactical adjustments based on real-time feedback.

[0082] A "user" is a person who uses the system to plan sports strategies, upload various data, and check training programs.

[0083] "Server" means a computer system that manages and analyzes information received from users, generates results, and provides them to users.

[0084] "Terminal" means a device that allows a user to access the system and input and upload data and check analysis results.

[0085] A "database" is a storage device for storing user registration information, uploaded match data, training data, and the like.

[0086] A "generative AI model" is an artificial intelligence algorithm that analyzes input sports-related data and generates strategy suggestions and training programs.

[0087] "Registration information" refers to basic information such as name, email address, password, sport, and position that a user enters when registering with the system for the first time.

[0088] "Match Data" means information relating to the results of specific matches and player performances that is uploaded by a User to the System.

[0089] "Training Data" is information including measurement results related to an athlete's training status and performance.

[0090] "Analysis results" are deliverables such as player performance evaluations and opponent tactical analyses that are generated by the generative AI model after analyzing the uploaded data.

[0091] A "dashboard" is a user interface that allows the system to visually display analysis results.

[0092] "Strategy" refers to a game plan or approach optimized for the characteristics of a team or player in a match.

[0093] A "training program" is a training menu or drill optimized based on each player's performance data.

[0094] "Real-time data" refers to performance data that is collected in real time during a match or training session and sent to a server.

[0095] "Feedback" refers to specific instructions and improvements provided by the server based on the results of analyzing real-time data.

[0096] The sports strategy planning system of the present invention combines various functions centered on a generative AI model to provide a platform for users to optimize their game strategies and discover new playing styles. Specific embodiments for implementing the system are described below.

[0097] This system uses a server, terminals, and a database. The server analyzes information received from users and generates strategies and training programs using generative AI models. The terminal is an interface through which users input data and check the analysis results. The database functions as a storage device for saving user registration information and uploaded data.

[0098] First, a user creates an account using a terminal by entering basic information such as name, email address, password, sport, position, etc. This information is sent to the server and stored in a database.

[0099] Next, users can upload their own match and training data to the system via their devices. The server receives this data and stores it in a database. The stored data is then analyzed using generative AI models. The analysis results include player performance evaluations and opponent tactical analysis.

[0100] The analysis results are displayed visually and provided as a dashboard for easy understanding by users. For example, when basketball game data is analyzed, detailed statistical data such as points, assists, and rebounds for each player is displayed visually.

[0101] The server also uses generative AI models to generate optimal strategies and new playing styles based on team and player characteristics. The generated strategies are then sent to the user's device. For example, specific suggestions are made, such as, "This player is good at three-point shooting, so we'll build a play around him."

[0102] Furthermore, the server analyzes the performance data of each player and generates an optimized training program. For example, it may suggest specific drills for a certain player to improve their 3-point shooting success rate. Users can check this training program on their device and incorporate it into their actual training.

[0103] During a match or training session, the user sends real-time data from their device to the server, which instantly analyzes it and uses generative AI models to provide real-time feedback. For example, specific visual feedback such as "You should improve your defensive line" could be provided during a match.

[0104] Examples of specific prompts include:

[0105] "Please upload basketball game data, analyze player A's strengths, and suggest the optimal strategy."

[0106] "Generate a training program suitable for player B."

[0107] "Provide real-time feedback during the match."

[0108] In this way, the present invention is a system that utilizes generative AI to provide advanced support for users to select effective strategies and discover new playing styles, thereby improving sports performance and helping players and teams to perform at their best.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1:

[0111] The user uses their device to open the account creation page. They enter basic information such as their name, email address, password, sport, and position, and click the "Register" button. The entered information is sent to the server, which receives it and stores it in a database. Input data: "Name, email address, password, sport." Output data: "Registration information saved in database."

[0112] Step 2:

[0113] The user uses their device to open the login page, enter their email address and password, and click the "Login" button. The entered authentication information is sent to the server, which checks it against the information in its database. If it matches, authentication is successful and access to the main screen is permitted. If it does not match, an error message is displayed. Input data: "Email address, password". Output data: "Authentication result, permission to access the main screen or error message."

[0114] Step 3:

[0115] Users upload match data and training data from their devices. The user opens the "Data Upload" page, selects a file, and clicks the "Upload" button. The uploaded data is sent to the server, which receives it and stores it in a database. Input data: "Match data, training data." Output data: "Data saved to database."

[0116] Step 4:

[0117] The server passes saved match data and training data to the generative AI model and begins analyzing the data. The analysis involves evaluating player performance and analyzing opponent tactics. As a result, information such as each player's strengths and weaknesses and opponent tactical tendencies is generated. Input data: "Saved data." Output data: "Analysis results."

[0118] Step 5:

[0119] The analysis results are visually represented by the server and provided to the user in the form of a dashboard. The user opens the dashboard page on their device to check the analysis results. For example, statistical data such as points, assists, and rebounds for each player is displayed. Input data: "Analysis results". Output data: "Visualized dashboard".

[0120] Step 6:

[0121] The server uses a generative AI model to generate optimal strategies and new playing styles based on characteristic data of teams and players. The generated strategies are sent to the user's device. For example, they may include specific suggestions such as, "In this match, a strategy of launching quick attacks centered around player A would be effective." Input data: "Characteristic data." Output data: "Strategic proposals."

[0122] Step 7:

[0123] The user checks the strategy proposal on the terminal and decides whether to adopt the proposed strategy. If they do, they click the "Adopt" button. Input data: "Strategy proposal". Output data: "Decision on adopting the strategy".

[0124] Step 8:

[0125] The server analyzes the performance data of each player and generates an optimized training program using a generative AI model. For example, a specific menu may be generated, such as "Player B should focus on free throw practice." This is then sent to the user's device. Input data: "Performance data." Output data: "Training program proposal."

[0126] Step 9:

[0127] The user checks the training program on the device and reflects it in their actual training. For example, a detailed menu is displayed, such as "Player B will be given a specific free throw drill." Input data: "Training program proposal." Output data: "Training reflection."

[0128] Step 10:

[0129] During a match or training session, the user sends data from their device to the server in real time. The server immediately analyzes this data and provides real-time feedback using a generative AI model. For example, specific instructions such as "You should improve your defensive line" are fed back to the device in real time. Input data: "Real-time data." Output data: "Real-time analysis results and feedback."

[0130] (Application example 1)

[0131] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0132] In sports games and training, data analysis and real-time feedback are important for optimizing strategies and improving player performance. However, existing systems struggle to provide these in an integrated and efficient manner. Furthermore, there are limitations, particularly with regard to providing real-time feedback and strategy suggestions, creating a demand for technologies that enhance the user experience. Additionally, there is a lack of methods for providing real-time analysis and feedback in virtual environments.

[0133] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0134] In this invention, the server includes: means for storing registration information entered by a user in a database; means for receiving and storing data uploaded by a user; means for analyzing the stored data using a generative model to generate results; means for visually displaying the generated analysis results; means for generating strategies and training programs tailored to team and player characteristics using the generative model; means for analyzing real-time data sent by a user and providing feedback; means for acquiring data in real time from the smart glasses and notifying the user of the analysis results on the spot; and means for providing strategy suggestions and training feedback in real time using the generative AI model while running a virtual sports simulation. This allows users to receive strategy and training suggestions based on real-time analyzed data during sports games and training and immediately implement them. Real-time feedback and instant strategy adjustments can maximize player and team performance.

[0135] Definition of Terms

[0136] "Means for storing user-entered registration information in a database" refers to the process and system for securely storing basic information entered by users, such as name, email address, password, sport type, and position, in a database.

[0137] "Means for receiving and storing data uploaded by users" refers to the functions and system for receiving match data and training data uploaded by users from their terminals to the server and storing it in a database.

[0138] "Means for analyzing data and generating results using a generative model" refers to methods and technologies for analyzing uploaded match data or training data using a generative AI model and generating results.

[0139] "Means for visually displaying the generated analysis results" refers to an interface and system for visually displaying the results analyzed by the generative AI model in a format that is easy for users to understand.

[0140] "Means for using generative models to generate strategies and training programs tailored to the characteristics of teams and players" refers to generative AI models and systems for generating optimal strategies and training programs based on team and player data.

[0141] The "means for analyzing real-time data sent by a user and providing feedback" refers to a method and system for instantly analyzing real-time data sent by a user during a match or training session and providing that feedback.

[0142] "Means for acquiring data in real time from smart glasses and notifying the user of the analysis results on the spot" refers to technology and a system for acquiring data in real time through smart glasses and notifying the user of the analysis results by displaying them on the glasses.

[0143] "Means for providing strategy suggestions and training feedback in real time using a generative AI model while running a virtual sports simulation" refers to methods and technologies for using a generative AI model to provide strategy suggestions and training feedback to a user in real time during a sports simulation in a virtual environment.

[0144] MODE FOR CARRYING OUT THE INVENTION

[0145] A system for realizing the present invention includes the following configuration and means.

[0146] Basic configuration

[0147] 1. User Registration and Login

[0148] The user first creates an account using a terminal. The required information is entered as name, email address, password, and basic information about the sport (e.g., sport type and position). The server receives this information and stores it in a database. When the user uses the system again, they log in using their email address and password. The server verifies the authentication information, and if authentication is successful, they are granted access to the main screen.

[0149] 2. Data Acquisition and Analysis

[0150] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. The analysis includes evaluation of player performance and analysis of opponent tactics. The analysis results are presented as a visual dashboard. This dashboard has an interface that is easy for users to understand.

[0151] 3. Generating a Strategy and Training Program

[0152] The server uses a generative AI model based on team and player characteristic data to generate optimal strategies and training programs. The generated strategies are then sent to the user's device. For example, specific suggestions such as "This player is good at three-point shooting, so we'll build plays around him" are included. Users can review these suggestions and incorporate them into their own strategies.

[0153] 4. Real-time feedback

[0154] During a match or training session, users use smart glasses to capture real-time data and send it to a server. The server then uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "You should strengthen your defensive line" are displayed on the user's smart glasses as visual feedback during a match, allowing the user to make on-the-spot tactical adjustments and improve their performance.

[0155] Hardware / Software used

[0156] The system uses generative AI models that utilize the Tensorflow® and Keras libraries, and uses a SQLite3 database to store data. Appropriate network infrastructure is also used to acquire real-time data using smart glasses and communicate the analysis results to the user.

[0157] Examples of concrete examples and prompts

[0158] As a concrete example, the prompt sentence when basketball game data is input is shown below.

[0159] "Upload your basketball game data and evaluate your 3-point shooting performance."

[0160] "Use Player A's match data to generate the optimal training program for him."

[0161] "Use real-time match data to give us feedback to improve our defensive strategies."

[0162] The system based on this invention allows users to receive real-time data analysis and strategy suggestions to maximize their sports performance.

[0163] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0164] Processing Step Description

[0165] Step 1: User Registration and Login

[0166] Operation:

[0167] Input: The user enters basic information into the device, such as name, email address, password, sport type, and position.

[0168] Data processing: The server receives the registration information sent from the device and formats it into the required format.

[0169] Output: Save the formatted registration information to the database.

[0170] Specific behavior:

[0171] The user enters registration information into the terminal and sends it to the server.

[0172] The server stores the received information in an SQLite3 database.

[0173] When the user uses the system again, he or she sends a login request from the terminal using the email address and password.

[0174] The server checks the authentication information in its database and, if authentication is successful, allows the user access to the main screen.

[0175] Step 2: Data acquisition and analysis

[0176] Operation:

[0177] Input: Users upload match data and training data from their devices.

[0178] Data processing: The server receives and stores the uploaded data.

[0179] Output: The generative AI model analyzes the data and generates analytical results.

[0180] Specific behavior:

[0181] The user uploads match data and training data to the device.

[0182] The server receives this and stores it in a database.

[0183] The server analyzes the data using a generative AI model built using the TensorFlow and Keras libraries.

[0184] The analysis results are generated as a visually displayed dashboard.

[0185] Step 3: Generate a strategy and training program

[0186] Operation:

[0187] Input: The server receives team and player characteristic data.

[0188] Data Processing: Generative AI models are used to analyze trait data and generate optimal strategies and training programs.

[0189] Output: The generated strategies and training programs are sent to the user's terminal.

[0190] Specific behavior:

[0191] The server retrieves team and player characteristic data from a database.

[0192] The server uses a generative AI model to generate optimal strategies and training programs.

[0193] The system notifies the user's device of specific strategy suggestions and training programs, making suggestions such as, "This player is good at three-point shooting, so we'll build our play around him."

[0194] Step 4: Real-time feedback

[0195] Operation:

[0196] Input: Real-time data during a match or training session is acquired by the user using smart glasses and sent to the server.

[0197] Data processing: Receive real-time data and instantly analyze it using generative AI models.

[0198] Output: The analysis results are visually displayed as feedback on the user's smart glasses.

[0199] Specific behavior:

[0200] The user wears smart glasses and transmits data during a match or training session to a server in real time.

[0201] The server receives real-time data and instantly analyzes it using generative AI models.

[0202] As a result of the analysis, specific feedback such as "Your defensive line should be stronger" is displayed on the user's smart glasses.

[0203] Users receive instant visual feedback and can adjust their tactics accordingly.

[0204] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0205] The sports strategy planning system of the present invention provides advanced functionality that combines generative AI and an emotion engine, and is a platform that allows users to optimize their game strategies and discover new playing styles. An embodiment of this system will be described in detail.

[0206] User Registration and Login

[0207] A user first creates an account using a terminal. The required information is entered: name, email address, password, and basic information about the sport they play (e.g., sport type and position). The server then receives this information and stores it in a database. When the user uses the system again, they are required to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, the user is allowed to access the main screen.

[0208] Data acquisition and analysis

[0209] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis results include player performance evaluations and opponent tactical analysis. The analysis results are displayed visually as a dashboard for easy user understanding.

[0210] Strategy and playing style suggestions

[0211] The server uses a generative AI model to generate optimal strategies and new playing styles based on team and player characteristic data. For example, specific suggestions such as "A strategy of launching a fast break centered on player A is effective" are included. These suggestions are then sent to the user's device.

[0212] Training program generation

[0213] The server generates an optimized training program based on the performance data of each player. This provides a training menu that will help each player reach their full potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. The user can check this training program on their device and incorporate it into their actual training.

[0214] Real-time feedback

[0215] During a match or training session, the user sends real-time data from their device to the server. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "strengthen your defensive line" can be sent to the user's device as visual feedback during a match. This allows the user to make tactical adjustments on the spot and improve their performance.

[0216] Emotion engine integration

[0217] The system also integrates an emotion engine to collect and analyze users' emotional data. Specifically, the system detects the user's facial expressions and tone of voice during matches and training sessions, and collects emotional data. This data is then sent to the server in real time.

[0218] Emotional data analysis and feedback

[0219] The server analyzes the collected emotional data and adjusts strategies and training programs based on the results. For example, if it determines that the user is nervous, it will suggest relaxation training. If the user shows signs of impatience during a match, it will provide tactical feedback based on that. This allows for optimal instruction tailored to the user's mental state.

[0220] Specific examples

[0221] For example, when a soccer team coach uses this system, he or she uploads basic information about each player and past match data. Based on this data, the server uses a generative AI model to analyze the strengths and weaknesses of the entire team. As a result, a strategy suggestion is made, such as "A strategy of launching fast breaks centered on Player A is effective." Furthermore, the emotion engine collects each player's emotional data and suggests relaxation drills for players who are nervous. During the game, specific feedback is provided, such as "You need to play with more concentration," based on the emotional data collected in real time.

[0222] As described above, this invention is a system that combines generative AI and an emotion engine to provide users with advanced analysis and emotional support, thereby contributing to the improvement of sports performance and helping athletes and teams to perform at their best.

[0223] The processing flow will be explained below.

[0224] The sports strategy planning system of the present invention provides advanced functions that combine generative AI and an emotion engine, and is a platform for users to optimize their game strategies and discover new playing styles. The specific processing flow for implementing the present invention is described below.

[0225] User Registration and Login

[0226] Step 1:

[0227] The user enters account information on the device, specifically, name, email address, password, and basic information about the sport played (e.g., sport type and position).

[0228] Step 2:

[0229] The server receives the registration information sent by the user and stores it in the database. After the storage is complete, it generates the user authentication information.

[0230] Step 3:

[0231] The user enters their email address and password into the terminal and attempts to log in. This sends the entered information to the server.

[0232] Step 4:

[0233] The server checks the registration information against the database and performs authentication. If authentication is successful, the user is allowed to access the main screen.

[0234] Data acquisition and analysis

[0235] Step 5:

[0236] Users upload files of match data and training data using their devices, and this data is sent to the server.

[0237] Step 6:

[0238] The server receives the uploaded data, stores it in a database, and then analyzes the stored data using a generative AI model.

[0239] Step 7:

[0240] The server visualizes the analysis results and generates a dashboard, which is created in a user-friendly format using charts and graphs.

[0241] Strategy and playing style suggestions

[0242] Step 8:

[0243] The server uses the generative AI model to generate optimal strategies and new playing styles based on the characteristics of the team and players. For example, it may generate a specific strategy such as "a strategy of launching a fast attack centered around player A is effective."

[0244] Step 9:

[0245] The server notifies the user of the generated strategies and training programs and displays them visually. The user can then check them on their device and use them in actual matches and training.

[0246] Training program generation

[0247] Step 10:

[0248] The server generates an optimized training program based on each player's performance data. For example, it might suggest specific drills to improve a player's 3-point shooting success rate.

[0249] Step 11:

[0250] The user can check this training program on the terminal and apply it to their actual training.

[0251] Real-time feedback

[0252] Step 12:

[0253] Users send data in real time during matches and training, which provides the server with information on match status and training progress in real time.

[0254] Step 13:

[0255] The server receives real-time data and instantly analyzes it using generative AI models, enabling quick tactical adjustments during matches and immediate feedback during training.

[0256] Step 14:

[0257] The server generates real-time feedback and provides it to the user, with specific instructions such as "You should strengthen your defensive line" being visually displayed on the device.

[0258] Step 15:

[0259] The feedback received by users is used to instantly modify match and training tactics, improving player and team performance.

[0260] Emotion engine integration

[0261] Step 16:

[0262] During a match or training session, the device detects facial expressions and tone of voice to collect emotional data, which is then sent to a server in real time.

[0263] Step 17:

[0264] The server receives the collected emotional data and analyzes it using an emotion engine. Based on the analysis results, strategies and training programs are tailored to the user's mental state.

[0265] Step 18:

[0266] The server generates strategies and training programs based on the analyzed emotional data and provides them to the user, for example, suggesting training drills to help a tense user relax.

[0267] Step 19:

[0268] The server uses the user's emotional data to generate real-time feedback during a match or training session, providing the user with visual instructions such as "You need to play more focused."

[0269] As described above, this invention is a system that combines generative AI and an emotion engine to provide users with advanced analysis and emotional support, thereby contributing to the improvement of sports performance and helping athletes and teams to perform at their best.

[0270] Example 2

[0271] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0272] In modern sports, maximizing the performance of athletes and teams requires detailed analysis of match and training data and the optimization of strategies and training programs based on the results. However, conventional systems have difficulty analyzing data in real time and providing feedback, and are unable to provide training and strategy suggestions that take the user's emotional state into account. Therefore, there is a need for systems that provide advanced analysis and emotional support to users in order to improve their sports performance.

[0273] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0274] In this invention, the server includes means for saving registration information entered by the user in a database, means for receiving and saving data uploaded by the user, means for analyzing the saved data using a generative model to generate results, means for visually displaying the generated analysis results, means for generating strategies and training programs according to the characteristics of teams and players using the generative model, means for analyzing real-time data sent by the user and providing feedback, means for collecting user emotion data and integrating an emotion engine that is useful for analysis, and means for adjusting strategies and training programs based on the emotion data and providing feedback to the user. This enables detailed analysis of sports data in real time and can also suggest optimal strategies and training according to the user's emotional state.

[0275] "Means for storing registration information entered by the user in a database" refers to the system's function of receiving the data entered by the user when creating an account (e.g., name, email address, password, sport, position, etc.) and storing it in a database.

[0276] "Means for receiving and storing data uploaded by users" refers to a system function whereby the server receives and stores match data or training data uploaded by users from their terminals.

[0277] "Means for analyzing data using a generative model that analyzes stored data and generating results" refers to a function that analyzes data stored on a server using a generative AI model and generates the analysis results.

[0278] "Means for visually displaying the generated analysis results" refers to a system function that displays the results analyzed by the generative AI model in a visual format such as a dashboard or graph so that the user can easily understand them.

[0279] "Means for generating strategies and training programs according to the characteristics of teams and players using generative models" refers to a system function that uses generative AI models to analyze characteristic data of teams and players and automatically generates optimal strategies and training programs based on the results.

[0280] "Means for analyzing real-time data sent by users and providing feedback" refers to a system function that uses a generative AI model to instantly analyze real-time data sent by users from their devices during matches or training, and provides feedback based on the results.

[0281] "Means for collecting user emotional data and integrating an emotional engine to aid in analysis" refers to a function that integrates into the system an emotional engine that detects and collects the user's facial expressions and tone of voice, and uses that data for analysis.

[0282] "Means for adjusting strategies and training programs based on emotional data and providing feedback to users" refers to a system function that adjusts strategies and training programs in real time based on emotional data collected and analyzed by the emotion engine, and provides the content of these adjustments to users as feedback.

[0283] The sports strategy planning system of the present invention provides advanced functionality that combines generative AI and an emotion engine. Specific embodiments of this system are described in detail below.

[0284] User Registration and Login

[0285] A user first creates an account using a terminal. The required information is entered: name, email address, password, and basic information about the sport played (e.g., sport type and position). The server receives this information and stores it in a database (e.g., MySQL (registered trademark)). When the user uses the system again, they are required to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, the user is allowed to access the main screen.

[0286] Data acquisition and analysis

[0287] When a user uploads match data or training data using a device, the server receives and stores it. The uploaded data is stored in file storage (e.g., AWS (registered trademark) S3). The server then analyzes the data using a generative AI model (e.g., OpenAI (registered trademark) GPT) to evaluate player performance and analyze opponent tactics. The analysis results are displayed visually as a dashboard. For example, it visually displays "how the movements of the top scorer in this match were different."

[0288] Strategy and playing style suggestions

[0289] The server uses a generative AI model to generate optimal strategies and new playing styles based on team and player characteristic data. Specific suggestions include, for example, "A strategy of launching fast breaks centered around player A is effective." These suggestions are sent to the user's device. An example of a prompt is, "Please explain how the generative AI analyzes game data when the user uploads it."

[0290] Training program generation

[0291] The server generates an optimized training program based on each player's performance data. This provides a training menu that will help each player reach their full potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. Users can check this training program on their device and incorporate it into their actual training.

[0292] Real-time feedback

[0293] During a match or training session, the user sends real-time data from their device to the server. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "strengthen your defensive line" are sent to the user's device as visual feedback. This allows the user to make on-the-spot tactical adjustments and improve their performance.

[0294] Emotion engine integration

[0295] This system integrates an emotion engine to collect and analyze users' emotional data. For example, it detects the facial expressions and tone of voice that users show while using their devices during matches or training, and collects this as emotional data. Input devices such as cameras and microphones are used for detection. This data is sent to a server in real time and used for analysis.

[0296] Emotional data analysis and feedback

[0297] The server analyzes the collected emotional data and adjusts strategies and training programs based on the results. For example, if it determines that the user is nervous, it can suggest training to help them relax. Also, if the user shows signs of impatience during a match, it can provide tactical feedback based on that. This allows for optimal instruction tailored to the user's mental state.

[0298] Specific examples

[0299] An example of actual use of this system is when a soccer team coach uses it. The coach uploads basic information about each player and past match data. The server uses a generative AI model to analyze the strengths and weaknesses of the entire team, and as a result, suggests that "a strategy of launching a fast break centered on player A would be effective." Furthermore, the emotion engine collects each player's emotional data and suggests relaxation drills for players who are nervous. During the game, specific feedback such as "You need to play with more concentration" is provided based on the emotional data collected in real time.

[0300] In this way, by combining generative AI and an emotion engine, the present invention is a system that provides users with advanced analysis and emotional support, contributing to improving their athletic performance.

[0301] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0302] Program processing flow

[0303] User Registration and Login

[0304] Step 1:

[0305] The user creates an account on the device.

[0306] Input: User-entered name, email address, password, sport, and position.

[0307] Output: Input data is sent from the device to the server.

[0308] Specific actions: The user opens the application on the device, enters the required information, and clicks the send button.

[0309] Step 2:

[0310] The server receives the information and stores it in a database.

[0311] Input: Registration information sent from the device.

[0312] Output: User information stored in the database.

[0313] Specific operation: The server receives the information, validates it, and then saves it in a database (e.g., MySQL).

[0314] Step 3:

[0315] The user logs in and the server performs authentication.

[0316] Input: User's email address and password.

[0317] Output: The result of authentication, success or failure.

[0318] Specific operation: The user enters their email address and password on the login screen and clicks the login button. The server checks the information against the database, and if authentication is successful, allows access to the main screen.

[0319] Data acquisition and analysis

[0320] Step 4:

[0321] Users upload match data and training data on their devices.

[0322] Input: User selected match and training data files.

[0323] Output: Data transfer from the device to the server.

[0324] Specific behavior: The user clicks the upload button and selects a data file from the file selection dialog.

[0325] Step 5:

[0326] The server receives and stores the data.

[0327] Input: Match data and training data files sent from the device.

[0328] Output: Data stored in file storage (e.g. AWS S3).

[0329] Specific operation: The server confirms receipt of the file and saves it in the specified storage.

[0330] Step 6:

[0331] The server analyzes the data using a generative AI model.

[0332] Input: Saved match and training data.

[0333] Output: Analysis results.

[0334] How it works: The server inputs data into a generative AI model (e.g., OpenAI GPT) to evaluate player performance and analyze opponent tactics.

[0335] Strategy and playing style suggestions

[0336] Step 7:

[0337] The server generates strategies and playing styles based on the analysis results.

[0338] Input: Analysis results output by the generative AI model.

[0339] Output: Strategy and playing style suggestions.

[0340] Specific operation: The server analyzes the analysis results using an algorithm and generates the optimal strategy and playing style.

[0341] Step 8:

[0342] The server notifies the user of the proposal content.

[0343] Input: Generated strategy and playing style suggestions.

[0344] Output: The notification displayed on the user's device.

[0345] Specific operation: The server sends the suggestions to the user via push notification or email.

[0346] Training program generation

[0347] Step 9:

[0348] The server generates a training program based on the performance data.

[0349] Input: Player performance data.

[0350] Output: Optimized training program.

[0351] Specific operation: The server analyzes performance data and generates a training program for each player.

[0352] Step 10:

[0353] The user checks the training program on the terminal.

[0354] Input: The training program sent from the server.

[0355] Output: The training program displayed on the user's terminal.

[0356] What happens: The user receives a notification and learns about the training program.

[0357] Real-time feedback

[0358] Step 11:

[0359] The user sends real-time data on the terminal.

[0360] Input: Real-time data sent from your device (e.g., location, performance data).

[0361] Output: Data transfer to the server.

[0362] Specific operation: The user carries the device during a match or training session, and real-time data is automatically sent to the server.

[0363] Step 12:

[0364] The server analyzes the data in real time and provides feedback.

[0365] Input: Real-time data sent.

[0366] Output: Providing feedback.

[0367] How it works: The server uses a generative AI model to instantly analyze real-time data and send visual feedback to the user's device.

[0368] Emotion engine integration

[0369] Step 13:

[0370] The user collects emotion data on the device.

[0371] Input: Facial expressions and tone of voice collected from the camera and microphone.

[0372] Output: Emotion data sent to the server.

[0373] Specific operation: The user uses the device to collect emotional data by sensing facial expressions and tone of voice during a match or training session.

[0374] Step 14:

[0375] The server receives and analyzes the emotion data.

[0376] Input: User emotion data.

[0377] Output: Analysis results.

[0378] Specific operation: The server receives the transmitted emotion data and immediately analyzes it using the emotion engine.

[0379] Emotional data analysis and feedback

[0380] Step 15:

[0381] The server adjusts strategies and training programs based on the emotional data.

[0382] Input: Analysis results of emotion data.

[0383] Output: Tailored strategies and training programs.

[0384] Specific actions: Based on the analysis results, the server adjusts strategies and training programs as needed.

[0385] Step 16:

[0386] Send feedback to the user's device.

[0387] Input: Tailored strategies and training programs.

[0388] Output: Feedback displayed on the user's device.

[0389] Specific operation: The server sends the adjustments to the user's device and provides feedback.

[0390] (Application example 2)

[0391] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0392] Improving production efficiency in factories and managing workers' mental workload are important challenges in the manufacturing industry. Conventional systems have difficulty providing real-time optimization strategies and understanding workers' emotional states, making it difficult to improve production efficiency and the work environment. In particular, improving the operational efficiency of operating machines, reducing defective products, and adjusting workers' stress and fatigue levels are required, but it is difficult to achieve all of these simultaneously.

[0393] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0394] In this invention, the server includes means for saving registration information entered by a user in a database, means for receiving and saving data uploaded by a user, means for analyzing the saved data using a generative model to generate results, means for visually displaying the generated analysis results, means for generating optimization strategies and programs according to the characteristics of the operating machine and the worker using the generative model, means for analyzing real-time data sent from the user and providing feedback, and means for analyzing worker emotion data using an emotion recognition engine and adjusting the workload, thereby enabling improvement of production efficiency in the factory and management of the mental workload of workers.

[0395] "User" means the person or operator who operates the system and inputs or uploads data.

[0396] "Registration Information" refers to basic information such as name, email address, and password that a user enters when creating an account on the system.

[0397] "Database" refers to a storage device or software for storing user registration information and uploaded data within the system.

[0398] "Upload data" refers to information such as match data, training data, and operating machine data that users provide to the system.

[0399] A "generative model" is a model that analyzes user data and generates results such as strategies and programs. Specifically, it refers to a generative AI model.

[0400] "Analysis results" refers to information obtained as a result of analyzing data using a generative model, and includes strategy proposals, training programs, etc.

[0401] "Visual display means" refers to a method or device that displays the analysis results and generated strategies in graphs, charts, etc. so that the user can easily understand them.

[0402] An "optimization strategy" is the most efficient strategy or program generated by the generative model based on the characteristics of the operating machine or worker.

[0403] "Real-time data" refers to data collected in real time while a match or task is in progress, and is used for immediate analysis and feedback.

[0404] "Feedback means" refers to a method or system for notifying users of improvements or corrections based on the analysis results.

[0405] An "emotion recognition engine" is software or a device that analyzes workers' emotional data and adjusts strategies and training programs based on the results.

[0406] The "workload adjustment means" refers to a method or system for reducing the mental load of a worker and providing an optimized work environment based on the analysis results of the emotion recognition engine.

[0407] The system for implementing this invention is a comprehensive platform for improving production efficiency in factories and managing the mental workload of workers. The system provides advanced functionality combining generative AI models and emotion recognition engines, enabling users to utilize optimization strategies and training programs.

[0408] Hardware and software used

[0409] Hardware: moving machines (e.g., robotic arms), sensors (cameras, microphones, thermometers, hygrometers, etc.)

[0410] Software: Generative AI model (natural language generation AI model), emotion recognition engine (emotion analysis software)

[0411] Communication: Local server, cloud server

[0412] System configuration and operation

[0413] 1. User Registration and Login:

[0414] A user first creates an account using a terminal. Required information includes name, email address, and password. The server then receives this information and stores it in a database. When the user uses the system again, they are prompted to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, the user is allowed access to the main screen.

[0415] 2. Data Acquisition and Analysis:

[0416] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis content includes performance evaluation of operating machines and analysis of the production environment. The analysis results are displayed visually as a dashboard for easy user understanding.

[0417] 3. Optimization strategies and program generation:

[0418] The server uses a generative AI model to generate optimal strategies and programs based on the characteristic data of the operating machines and workers. For example, specific suggestions such as "A strategy to increase the operating speed of machine A by 10% is effective" are included. These suggestions are then sent to the user's device.

[0419] 4. Real-time feedback:

[0420] During production or work, users send data from their devices to a server in real time. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "adjust the machine's operating angle" are sent to the user's device as visual feedback during work. This allows the user to make on-the-spot corrections to improve efficiency.

[0421] 5. Emotion recognition engine integration:

[0422] The system also integrates an emotion recognition engine to collect and analyze user emotional data. Specifically, the system detects facial expressions and tone of voice while the user is working on the device, and collects emotional data. This data is then sent to the server in real time.

[0423] 6. Emotional data analysis and feedback:

[0424] The server analyzes the collected emotional data and adjusts work optimization strategies and training programs based on the results. For example, if it determines that the user is tired, it will suggest taking a break. Also, if it determines that the user is unable to concentrate while working, it will adjust the work load accordingly. This allows for optimal instruction tailored to the user's mental state.

[0425] Specific examples

[0426] For example, if a robot is introduced to assemble products in a factory, the system operates as follows.

[0427] Collects operation data and sensor data from factory robots in real time.

[0428] The generative AI model analyzes the data and generates specific suggestions such as "The supply speed of part A should be increased by 10%."

[0429] An emotion recognition engine analyzes the worker's level of fatigue based on their facial expressions and tone of voice. If it determines that the worker is highly fatigued, it will suggest break times and work adjustments.

[0430] Prompt Sentence Examples

[0431] "Propose an optimization strategy to increase production efficiency on a manufacturing line. Data should include the operating time of each work station, part feed rate, and environmental parameters. Also analyze worker fatigue and stress levels."

[0432] With the above-described configuration and operation, the present invention can improve production efficiency in a factory and manage the mental stress of workers.

[0433] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0434] Step 1:

[0435] The user creates an account using a device and enters the required information (name, email address, password, etc.). This generates registration information. The server saves this registration information in a database. After verifying the entered data, the server saves it in the database as registration information, creating the user's account.

[0436] Step 2:

[0437] The user uploads game data, training data, and operation data of the motion machine using the terminal. The uploaded data is received and stored by the server. After the input data is confirmed, the stored data can be used for analysis.

[0438] Step 3:

[0439] The server uses a generative AI model to analyze the stored data. This generative AI model analyzes the uploaded data (e.g., evaluating the performance of the operating machine, analyzing the production environment, etc.) and generates results. The generative AI model performs analysis based on the input data and obtains the analysis results as output.

[0440] Step 4:

[0441] The server generates a dashboard that visually displays the analysis results obtained by the generative AI model. The analysis results are converted into visual formats such as graphs and charts so that users can easily understand them. The server receives the analysis results as input, generates data for visual display, and sends it to the terminal.

[0442] Step 5:

[0443] The server uses the generative AI model to generate optimization strategies and programs according to the characteristics of the operating machine and worker. These optimization strategies and programs are created based on the generated analysis results and characteristic data of the operating machine and worker. Based on the input characteristic data and analysis results, the server generates and outputs the optimization strategies and programs.

[0444] Step 6:

[0445] The server notifies the user's device of the generated optimization strategy or program, allowing the user to receive specific suggestions. The server receives the generated suggestions as input, generates notification data, and sends it to the device.

[0446] Step 7:

[0447] Users send data from their devices in real time to the server, which receives this real-time data and immediately analyzes it using a generative AI model.The server receives real-time data as input, analyzes it using a generative AI model, and generates feedback.

[0448] Step 8:

[0449] The server provides the results of the analysis performed in real time as feedback. For example, specific instructions such as "adjust the machine's operating angle" are sent to the user's device during work. The server receives the generated feedback as input and sends it to the user's device.

[0450] Step 9:

[0451] The server uses an emotion recognition engine to collect and analyze the worker's emotional data, such as facial expressions and tone of voice while working.

[0452] Step 10:

[0453] The server analyzes the collected emotion data and generates a suggestion for adjusting the workload based on the analysis results. For example, if the server determines that the user is fatigued, it suggests taking a break. The server receives the analysis results of the emotion data as input, generates the suggestion for adjusting the workload, and outputs it.

[0454] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0455] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0456] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0457] [Second embodiment]

[0458] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0459] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0460] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0461] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0462] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0463] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0464] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0465] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0466] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0467] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0468] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0469] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0470] The sports strategy planning system of the present invention combines various functions centered on generation AI to provide a platform for users to optimize their game strategies and discover new playing styles. Specific embodiments for implementing the system are described below.

[0471] User Registration and Login

[0472] A user first creates an account using a terminal. The required information is entered as name, email address, password, and basic information about the sport they play (e.g., sport type and position). The server then receives this information and stores it in a database. When the user uses the system again, they are required to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, they are granted access to the main screen.

[0473] Data acquisition and analysis

[0474] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis results include player performance evaluations and opponent tactical analysis. The analysis results are visually displayed on the server and provided as a dashboard for users to easily understand.

[0475] Strategy and playing style suggestions

[0476] The server uses a generative AI model based on team and player characteristic data to generate optimal strategies and new playing styles. The generated strategies are then sent to the user's device. Specific suggestions, such as "This player is good at three-point shooting, so we'll build plays around him," are included. Users can review these suggestions and incorporate them into their own strategies.

[0477] Training program generation

[0478] The server generates an optimized training program based on the performance data of each player. This provides a training menu that will help each player maximize their potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. The user can check this training program on their device and incorporate it into their actual training.

[0479] Real-time feedback

[0480] During a match or training session, the user sends data from their device to the server in real time. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "You should strengthen your defensive line" can be sent to the user's device as visual feedback during a match. This allows the user to make tactical adjustments on the spot and improve their performance.

[0481] Specific examples

[0482] For example, when a basketball coach uses this system, he or she first uploads basic information about each player and past game data from their device. Based on this, the server uses a generative AI model to analyze the strengths and weaknesses of the entire team. Based on the analysis results, a strategy suggestion is made, such as "A strategy of launching fast breaks centered around Player A would be effective in this game." Individual training programs are also generated for each player, with instructions provided such as "Player B should focus on free throw practice." During the game, data is transmitted in real time, and feedback such as "Switch to zone defense" is instantly displayed on the device.

[0483] As described above, this invention is a system that utilizes generative AI to provide advanced support for users to select effective strategies and discover new playing styles. This system contributes to improving sports performance and helps players and teams to perform at their best.

[0484] The processing flow will be explained below.

[0485] Step 1:

[0486] The user enters account information on the device, such as name, email address, password, and basic information about the sport played (e.g., sport type and position).

[0487] Step 2:

[0488] The server receives the registration information sent by the user and stores it in a database, after which a confirmation message containing the user authentication information is generated.

[0489] Step 3:

[0490] The user enters their email address and password into the terminal and attempts to log in. This sends the entered information to the server.

[0491] Step 4:

[0492] The server checks the registration information against the database and performs authentication. If authentication is successful, the user is allowed to access the main screen.

[0493] Step 5:

[0494] Users upload files of match data and training data using their devices, and this data is sent to the server.

[0495] Step 6:

[0496] The server receives the uploaded data, stores it in a database, and then analyzes the stored data using a generative AI model.

[0497] Step 7:

[0498] The server visualizes the analysis results and generates a dashboard, which is created in a user-friendly format using charts and graphs.

[0499] Step 8:

[0500] The server uses the generative AI model to generate optimal strategies and new playing styles based on the characteristics of the team and players. For example, it may generate a specific strategy such as "a strategy of launching a fast attack centered around player A is effective."

[0501] Step 9:

[0502] The server notifies the user of the generated strategies and training programs and displays them visually. The user can then check them on their device and use them in actual matches and training.

[0503] Step 10:

[0504] Users send data in real time during a match or training session, which provides the server with information on the match status and training progress in real time.

[0505] Step 11:

[0506] The server receives real-time data and instantly analyzes it using generative AI models, enabling quick tactical adjustments during matches and immediate feedback during training.

[0507] Step 12:

[0508] The server generates real-time feedback and provides it to the user, with specific instructions such as "You should strengthen your defensive line" being visually displayed on the device.

[0509] Step 13:

[0510] The feedback received by users is used to instantly modify match and training tactics, improving player and team performance.

[0511] Example 1

[0512] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0513] Conventional sports strategy planning systems often require manual analysis of match and training data, which is time-consuming and labor-intensive. Furthermore, the strategies and training programs generated may not be fully effective because they are not optimized for the characteristics of specific players or teams. Furthermore, the lack of real-time feedback makes it difficult to make timely tactical adjustments during matches or training.

[0514] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0515] In this invention, the server includes means for saving registration information entered by a user in a database, means for receiving and saving data uploaded by a user, means for analyzing the saved data using a generative AI model and generating results, means for visually displaying the generated analysis results, means for using the generative AI model to generate strategies and training programs tailored to the characteristics of teams and players, means for analyzing real-time data sent by users and providing feedback, means for notifying users of strategies and training programs based on the analysis results to their terminals, and means for users to confirm the proposed strategies and incorporate them into their own strategies. This allows users to easily automate data analysis and use strategies and training programs optimized for specific players and teams, and also enables timely tactical adjustments based on real-time feedback.

[0516] A "user" is a person who uses the system to plan sports strategies, upload various data, and check training programs.

[0517] "Server" means a computer system that manages and analyzes information received from users, generates results, and provides them to users.

[0518] "Terminal" means a device that allows a user to access the system and input and upload data and check analysis results.

[0519] A "database" is a storage device for storing user registration information, uploaded match data, training data, and the like.

[0520] A "generative AI model" is an artificial intelligence algorithm that analyzes input sports-related data and generates strategy suggestions and training programs.

[0521] "Registration information" refers to basic information such as name, email address, password, sport, and position that a user enters when registering with the system for the first time.

[0522] "Match Data" means information relating to the results of specific matches and player performances that is uploaded by a User to the System.

[0523] "Training Data" is information including measurement results related to an athlete's training status and performance.

[0524] "Analysis results" are deliverables such as player performance evaluations and opponent tactical analyses that are generated by the generative AI model after analyzing the uploaded data.

[0525] A "dashboard" is a user interface that allows the system to visually display analysis results.

[0526] "Strategy" refers to a game plan or approach optimized for the characteristics of a team or player in a match.

[0527] A "training program" is a training menu or drill optimized based on each player's performance data.

[0528] "Real-time data" refers to performance data that is collected in real time during a match or training session and sent to a server.

[0529] "Feedback" refers to specific instructions and improvements provided by the server based on the results of analyzing real-time data.

[0530] The sports strategy planning system of the present invention combines various functions centered on a generative AI model to provide a platform for users to optimize their game strategies and discover new playing styles. Specific embodiments for implementing the system are described below.

[0531] This system uses a server, terminals, and a database. The server analyzes information received from users and generates strategies and training programs using generative AI models. The terminal is an interface through which users input data and check the analysis results. The database functions as a storage device for saving user registration information and uploaded data.

[0532] First, a user creates an account using a terminal by entering basic information such as name, email address, password, sport, position, etc. This information is sent to the server and stored in a database.

[0533] Next, users can upload their own match and training data to the system via their devices. The server receives this data and stores it in a database. The stored data is then analyzed using generative AI models. The analysis results include player performance evaluations and opponent tactical analysis.

[0534] The analysis results are displayed visually and provided as a dashboard for easy understanding by users. For example, when basketball game data is analyzed, detailed statistical data such as points, assists, and rebounds for each player is displayed visually.

[0535] The server also uses generative AI models to generate optimal strategies and new playing styles based on team and player characteristics. The generated strategies are then sent to the user's device. For example, specific suggestions are made, such as, "This player is good at three-point shooting, so we'll build a play around him."

[0536] Furthermore, the server analyzes the performance data of each player and generates an optimized training program. For example, it may suggest specific drills for a certain player to improve their 3-point shooting success rate. Users can check this training program on their device and incorporate it into their actual training.

[0537] During a match or training session, the user sends real-time data from their device to the server, which instantly analyzes it and uses generative AI models to provide real-time feedback. For example, specific visual feedback such as "You should improve your defensive line" could be provided during a match.

[0538] Examples of specific prompts include:

[0539] "Please upload basketball game data, analyze player A's strengths, and suggest the optimal strategy."

[0540] "Generate a training program suitable for player B."

[0541] "Provide real-time feedback during the match."

[0542] In this way, the present invention is a system that utilizes generative AI to provide advanced support for users to select effective strategies and discover new playing styles, thereby improving sports performance and helping players and teams to perform at their best.

[0543] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0544] Step 1:

[0545] The user uses their device to open the account creation page. They enter basic information such as their name, email address, password, sport, and position, and click the "Register" button. The entered information is sent to the server, which receives it and stores it in a database. Input data: "Name, email address, password, sport." Output data: "Registration information saved in database."

[0546] Step 2:

[0547] The user uses their device to open the login page, enter their email address and password, and click the "Login" button. The entered authentication information is sent to the server, which checks it against the information in its database. If it matches, authentication is successful and access to the main screen is permitted. If it does not match, an error message is displayed. Input data: "Email address, password". Output data: "Authentication result, permission to access the main screen or error message."

[0548] Step 3:

[0549] Users upload match data and training data from their devices. The user opens the "Data Upload" page, selects a file, and clicks the "Upload" button. The uploaded data is sent to the server, which receives it and stores it in a database. Input data: "Match data, training data." Output data: "Data saved to database."

[0550] Step 4:

[0551] The server passes saved match data and training data to the generative AI model and begins analyzing the data. The analysis involves evaluating player performance and analyzing opponent tactics. As a result, information such as each player's strengths and weaknesses and opponent tactical tendencies is generated. Input data: "Saved data." Output data: "Analysis results."

[0552] Step 5:

[0553] The analysis results are visually represented by the server and provided to the user in the form of a dashboard. The user opens the dashboard page on their device to check the analysis results. For example, statistical data such as points, assists, and rebounds for each player is displayed. Input data: "Analysis results". Output data: "Visualized dashboard".

[0554] Step 6:

[0555] The server uses a generative AI model to generate optimal strategies and new playing styles based on characteristic data of teams and players. The generated strategies are sent to the user's device. For example, they may include specific suggestions such as, "In this match, a strategy of launching quick attacks centered around player A would be effective." Input data: "Characteristic data." Output data: "Strategic proposals."

[0556] Step 7:

[0557] The user checks the strategy proposal on the terminal and decides whether to adopt the proposed strategy. If they do, they click the "Adopt" button. Input data: "Strategy proposal". Output data: "Decision on adopting the strategy".

[0558] Step 8:

[0559] The server analyzes the performance data of each player and generates an optimized training program using a generative AI model. For example, a specific menu may be generated, such as "Player B should focus on free throw practice." This is then sent to the user's device. Input data: "Performance data." Output data: "Training program proposal."

[0560] Step 9:

[0561] The user checks the training program on the device and reflects it in their actual training. For example, a detailed menu is displayed, such as "Player B will be given a specific free throw drill." Input data: "Training program proposal." Output data: "Training reflection."

[0562] Step 10:

[0563] During a match or training session, the user sends data from their device to the server in real time. The server immediately analyzes this data and provides real-time feedback using a generative AI model. For example, specific instructions such as "You should improve your defensive line" are fed back to the device in real time. Input data: "Real-time data." Output data: "Real-time analysis results and feedback."

[0564] (Application example 1)

[0565] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0566] In sports games and training, data analysis and real-time feedback are important for optimizing strategies and improving player performance. However, existing systems struggle to provide these in an integrated and efficient manner. Furthermore, there are limitations, particularly with regard to providing real-time feedback and strategy suggestions, creating a demand for technologies that enhance the user experience. Additionally, there is a lack of methods for providing real-time analysis and feedback in virtual environments.

[0567] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0568] In this invention, the server includes: means for storing registration information entered by a user in a database; means for receiving and storing data uploaded by a user; means for analyzing the stored data using a generative model to generate results; means for visually displaying the generated analysis results; means for generating strategies and training programs tailored to team and player characteristics using the generative model; means for analyzing real-time data sent by a user and providing feedback; means for acquiring data in real time from the smart glasses and notifying the user of the analysis results on the spot; and means for providing strategy suggestions and training feedback in real time using the generative AI model while running a virtual sports simulation. This allows users to receive strategy and training suggestions based on real-time analyzed data during sports games and training and immediately implement them. Real-time feedback and instant strategy adjustments can maximize player and team performance.

[0569] Definition of Terms

[0570] "Means for storing user-entered registration information in a database" refers to the process and system for securely storing basic information entered by users, such as name, email address, password, sport type, and position, in a database.

[0571] "Means for receiving and storing data uploaded by users" refers to the functions and system for receiving match data and training data uploaded by users from their terminals to the server and storing it in a database.

[0572] "Means for analyzing data and generating results using a generative model" refers to methods and technologies for analyzing uploaded match data or training data using a generative AI model and generating results.

[0573] "Means for visually displaying the generated analysis results" refers to an interface and system for visually displaying the results analyzed by the generative AI model in a format that is easy for users to understand.

[0574] "Means for using generative models to generate strategies and training programs tailored to the characteristics of teams and players" refers to generative AI models and systems for generating optimal strategies and training programs based on team and player data.

[0575] The "means for analyzing real-time data sent by a user and providing feedback" refers to a method and system for instantly analyzing real-time data sent by a user during a match or training session and providing that feedback.

[0576] "Means for acquiring data in real time from smart glasses and notifying the user of the analysis results on the spot" refers to technology and a system for acquiring data in real time through smart glasses and notifying the user of the analysis results by displaying them on the glasses.

[0577] "Means for providing strategy suggestions and training feedback in real time using a generative AI model while running a virtual sports simulation" refers to methods and technologies for using a generative AI model to provide strategy suggestions and training feedback to a user in real time during a sports simulation in a virtual environment.

[0578] MODE FOR CARRYING OUT THE INVENTION

[0579] A system for realizing the present invention includes the following configuration and means.

[0580] Basic configuration

[0581] 1. User Registration and Login

[0582] The user first creates an account using a terminal. The required information is entered as name, email address, password, and basic information about the sport (e.g., sport type and position). The server receives this information and stores it in a database. When the user uses the system again, they log in using their email address and password. The server verifies the authentication information, and if authentication is successful, they are granted access to the main screen.

[0583] 2. Data Acquisition and Analysis

[0584] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. The analysis includes evaluation of player performance and analysis of opponent tactics. The analysis results are presented as a visual dashboard. This dashboard has an interface that is easy for users to understand.

[0585] 3. Generating a Strategy and Training Program

[0586] The server uses a generative AI model based on team and player characteristic data to generate optimal strategies and training programs. The generated strategies are then sent to the user's device. For example, specific suggestions such as "This player is good at three-point shooting, so we'll build plays around him" are included. Users can review these suggestions and incorporate them into their own strategies.

[0587] 4. Real-time feedback

[0588] During a match or training session, users use smart glasses to capture real-time data and send it to a server. The server then uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "You should strengthen your defensive line" are displayed on the user's smart glasses as visual feedback during a match, allowing the user to make on-the-spot tactical adjustments and improve their performance.

[0589] Hardware / Software used

[0590] The system uses generative AI models that utilize the TensorFlow and Keras libraries, and uses a SQLite3 database to store data. Appropriate network infrastructure is also used to acquire real-time data using smart glasses and communicate the analysis results to the user.

[0591] Examples of concrete examples and prompts

[0592] As a concrete example, the prompt sentence when basketball game data is input is shown below.

[0593] "Upload your basketball game data and evaluate your 3-point shooting performance."

[0594] "Use Player A's match data to generate the optimal training program for him."

[0595] "Use real-time match data to give us feedback to improve our defensive strategies."

[0596] The system based on this invention allows users to receive real-time data analysis and strategy suggestions to maximize their sports performance.

[0597] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0598] Processing Step Description

[0599] Step 1: User Registration and Login

[0600] Operation:

[0601] Input: The user enters basic information into the device, such as name, email address, password, sport type, and position.

[0602] Data processing: The server receives the registration information sent from the device and formats it into the required format.

[0603] Output: Save the formatted registration information to the database.

[0604] Specific behavior:

[0605] The user enters registration information into the terminal and sends it to the server.

[0606] The server stores the received information in an SQLite3 database.

[0607] When the user uses the system again, he or she sends a login request from the terminal using the email address and password.

[0608] The server checks the authentication information in its database and, if authentication is successful, allows the user access to the main screen.

[0609] Step 2: Data acquisition and analysis

[0610] Operation:

[0611] Input: Users upload match data and training data from their devices.

[0612] Data processing: The server receives and stores the uploaded data.

[0613] Output: The generative AI model analyzes the data and generates analytical results.

[0614] Specific behavior:

[0615] The user uploads match data and training data to the device.

[0616] The server receives this and stores it in a database.

[0617] The server analyzes the data using a generative AI model built using the TensorFlow and Keras libraries.

[0618] The analysis results are generated as a visually displayed dashboard.

[0619] Step 3: Generate a strategy and training program

[0620] Operation:

[0621] Input: The server receives team and player characteristic data.

[0622] Data Processing: Generative AI models are used to analyze trait data and generate optimal strategies and training programs.

[0623] Output: The generated strategies and training programs are sent to the user's terminal.

[0624] Specific behavior:

[0625] The server retrieves team and player characteristic data from a database.

[0626] The server uses a generative AI model to generate optimal strategies and training programs.

[0627] The system notifies the user's device of specific strategy suggestions and training programs, making suggestions such as, "This player is good at three-point shooting, so we'll build our play around him."

[0628] Step 4: Real-time feedback

[0629] Operation:

[0630] Input: Real-time data during a match or training session is acquired by the user using smart glasses and sent to the server.

[0631] Data processing: Receive real-time data and instantly analyze it using generative AI models.

[0632] Output: The analysis results are visually displayed as feedback on the user's smart glasses.

[0633] Specific behavior:

[0634] The user wears smart glasses and transmits data during a match or training session to a server in real time.

[0635] The server receives real-time data and instantly analyzes it using generative AI models.

[0636] As a result of the analysis, specific feedback such as "Your defensive line should be stronger" is displayed on the user's smart glasses.

[0637] Users receive instant visual feedback and can adjust their tactics accordingly.

[0638] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0639] The sports strategy planning system of the present invention provides advanced functionality that combines generative AI and an emotion engine, and is a platform that allows users to optimize their game strategies and discover new playing styles. An embodiment of this system will be described in detail.

[0640] User Registration and Login

[0641] A user first creates an account using a terminal. The required information is entered: name, email address, password, and basic information about the sport they play (e.g., sport type and position). The server then receives this information and stores it in a database. When the user uses the system again, they are required to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, the user is allowed to access the main screen.

[0642] Data acquisition and analysis

[0643] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis results include player performance evaluations and opponent tactical analysis. The analysis results are displayed visually as a dashboard for easy user understanding.

[0644] Strategy and playing style suggestions

[0645] The server uses a generative AI model to generate optimal strategies and new playing styles based on team and player characteristic data. For example, specific suggestions such as "A strategy of launching a fast break centered on player A is effective" are included. These suggestions are then sent to the user's device.

[0646] Training program generation

[0647] The server generates an optimized training program based on the performance data of each player. This provides a training menu that will help each player reach their full potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. The user can check this training program on their device and incorporate it into their actual training.

[0648] Real-time feedback

[0649] During a match or training session, the user sends real-time data from their device to the server. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "strengthen your defensive line" can be sent to the user's device as visual feedback during a match. This allows the user to make tactical adjustments on the spot and improve their performance.

[0650] Emotion engine integration

[0651] The system also integrates an emotion engine to collect and analyze users' emotional data. Specifically, the system detects the user's facial expressions and tone of voice during matches and training sessions, and collects emotional data. This data is then sent to the server in real time.

[0652] Emotional data analysis and feedback

[0653] The server analyzes the collected emotional data and adjusts strategies and training programs based on the results. For example, if it determines that the user is nervous, it will suggest relaxation training. If the user shows signs of impatience during a match, it will provide tactical feedback based on that. This allows for optimal instruction tailored to the user's mental state.

[0654] Specific examples

[0655] For example, when a soccer team coach uses this system, he or she uploads basic information about each player and past match data. Based on this data, the server uses a generative AI model to analyze the strengths and weaknesses of the entire team. As a result, a strategy suggestion is made, such as "A strategy of launching fast breaks centered on Player A is effective." Furthermore, the emotion engine collects each player's emotional data and suggests relaxation drills for players who are nervous. During the game, specific feedback is provided, such as "You need to play with more concentration," based on the emotional data collected in real time.

[0656] As described above, this invention is a system that combines generative AI and an emotion engine to provide users with advanced analysis and emotional support, thereby contributing to the improvement of sports performance and helping athletes and teams to perform at their best.

[0657] The processing flow will be explained below.

[0658] The sports strategy planning system of the present invention provides advanced functions that combine generative AI and an emotion engine, and is a platform for users to optimize their game strategies and discover new playing styles. The specific processing flow for implementing the present invention is described below.

[0659] User Registration and Login

[0660] Step 1:

[0661] The user enters account information on the device, specifically, name, email address, password, and basic information about the sport played (e.g., sport type and position).

[0662] Step 2:

[0663] The server receives the registration information sent by the user and stores it in the database. After the storage is complete, it generates the user authentication information.

[0664] Step 3:

[0665] The user enters their email address and password into the terminal and attempts to log in. This sends the entered information to the server.

[0666] Step 4:

[0667] The server checks the registration information against the database and performs authentication. If authentication is successful, the user is allowed to access the main screen.

[0668] Data acquisition and analysis

[0669] Step 5:

[0670] Users upload files of match data and training data using their devices, and this data is sent to the server.

[0671] Step 6:

[0672] The server receives the uploaded data, stores it in a database, and then analyzes the stored data using a generative AI model.

[0673] Step 7:

[0674] The server visualizes the analysis results and generates a dashboard, which is created in a user-friendly format using charts and graphs.

[0675] Strategy and playing style suggestions

[0676] Step 8:

[0677] The server uses the generative AI model to generate optimal strategies and new playing styles based on the characteristics of the team and players. For example, it may generate a specific strategy such as "a strategy of launching a fast attack centered around player A is effective."

[0678] Step 9:

[0679] The server notifies the user of the generated strategies and training programs and displays them visually. The user can then check them on their device and use them in actual matches and training.

[0680] Training program generation

[0681] Step 10:

[0682] The server generates an optimized training program based on each player's performance data. For example, it might suggest specific drills to improve a player's 3-point shooting success rate.

[0683] Step 11:

[0684] The user can check this training program on the terminal and apply it to their actual training.

[0685] Real-time feedback

[0686] Step 12:

[0687] Users send data in real time during matches and training, which provides the server with information on match status and training progress in real time.

[0688] Step 13:

[0689] The server receives real-time data and instantly analyzes it using generative AI models, enabling quick tactical adjustments during matches and immediate feedback during training.

[0690] Step 14:

[0691] The server generates real-time feedback and provides it to the user, with specific instructions such as "You should strengthen your defensive line" being visually displayed on the device.

[0692] Step 15:

[0693] The feedback received by users is used to instantly modify match and training tactics, improving player and team performance.

[0694] Emotion engine integration

[0695] Step 16:

[0696] During a match or training session, the device detects facial expressions and tone of voice to collect emotional data, which is then sent to a server in real time.

[0697] Step 17:

[0698] The server receives the collected emotional data and analyzes it using an emotion engine. Based on the analysis results, strategies and training programs are tailored to the user's mental state.

[0699] Step 18:

[0700] The server generates strategies and training programs based on the analyzed emotional data and provides them to the user, for example, suggesting training drills to help a tense user relax.

[0701] Step 19:

[0702] The server uses the user's emotional data to generate real-time feedback during a match or training session, providing the user with visual instructions such as "You need to play more focused."

[0703] As described above, this invention is a system that combines generative AI and an emotion engine to provide users with advanced analysis and emotional support, thereby contributing to the improvement of sports performance and helping athletes and teams to perform at their best.

[0704] Example 2

[0705] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0706] In modern sports, maximizing the performance of athletes and teams requires detailed analysis of match and training data and the optimization of strategies and training programs based on the results. However, conventional systems have difficulty analyzing data in real time and providing feedback, and are unable to provide training and strategy suggestions that take the user's emotional state into account. Therefore, there is a need for systems that provide advanced analysis and emotional support to users in order to improve their sports performance.

[0707] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0708] In this invention, the server includes means for saving registration information entered by the user in a database, means for receiving and saving data uploaded by the user, means for analyzing the saved data using a generative model to generate results, means for visually displaying the generated analysis results, means for generating strategies and training programs according to the characteristics of teams and players using the generative model, means for analyzing real-time data sent by the user and providing feedback, means for collecting user emotion data and integrating an emotion engine that is useful for analysis, and means for adjusting strategies and training programs based on the emotion data and providing feedback to the user. This enables detailed analysis of sports data in real time and can also suggest optimal strategies and training according to the user's emotional state.

[0709] "Means for storing registration information entered by the user in a database" refers to the system's function of receiving the data entered by the user when creating an account (e.g., name, email address, password, sport, position, etc.) and storing it in a database.

[0710] "Means for receiving and storing data uploaded by users" refers to a system function whereby the server receives and stores match data or training data uploaded by users from their terminals.

[0711] "Means for analyzing data using a generative model that analyzes stored data and generating results" refers to a function that analyzes data stored on a server using a generative AI model and generates the analysis results.

[0712] "Means for visually displaying the generated analysis results" refers to a system function that displays the results analyzed by the generative AI model in a visual format such as a dashboard or graph so that the user can easily understand them.

[0713] "Means for generating strategies and training programs according to the characteristics of teams and players using generative models" refers to a system function that uses generative AI models to analyze characteristic data of teams and players and automatically generates optimal strategies and training programs based on the results.

[0714] "Means for analyzing real-time data sent by users and providing feedback" refers to a system function that uses a generative AI model to instantly analyze real-time data sent by users from their devices during matches or training, and provides feedback based on the results.

[0715] "Means for collecting user emotional data and integrating an emotional engine to aid in analysis" refers to a function that integrates into the system an emotional engine that detects and collects the user's facial expressions and tone of voice, and uses that data for analysis.

[0716] "Means for adjusting strategies and training programs based on emotional data and providing feedback to users" refers to a system function that adjusts strategies and training programs in real time based on emotional data collected and analyzed by the emotion engine, and provides the content of these adjustments to users as feedback.

[0717] The sports strategy planning system of the present invention provides advanced functionality that combines generative AI and an emotion engine. Specific embodiments of this system are described in detail below.

[0718] User Registration and Login

[0719] A user first creates an account using a terminal. The required information is entered: name, email address, password, and basic information about the sport they play (e.g., sport type and position). The server receives this information and stores it in a database (e.g., MySQL). When the user returns to the system, they are prompted to log in using their email address and password. The server verifies the authentication information, and if successful, allows the user to access the main screen.

[0720] Data acquisition and analysis

[0721] When a user uploads match or training data using their device, the server receives and stores it. The uploaded data is saved in file storage (e.g., AWS S3). The server then analyzes the data using a generative AI model (e.g., OpenAI GPT) to evaluate player performance and analyze opponent tactics. The analysis results are displayed visually as a dashboard. For example, it visually displays "how the movements of the top scorer in this match were different."

[0722] Strategy and playing style suggestions

[0723] The server uses a generative AI model to generate optimal strategies and new playing styles based on team and player characteristic data. Specific suggestions include, for example, "A strategy of launching fast breaks centered around player A is effective." These suggestions are sent to the user's device. An example of a prompt is, "Please explain how the generative AI analyzes game data when the user uploads it."

[0724] Training program generation

[0725] The server generates an optimized training program based on each player's performance data. This provides a training menu that will help each player reach their full potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. Users can check this training program on their device and incorporate it into their actual training.

[0726] Real-time feedback

[0727] During a match or training session, the user sends real-time data from their device to the server. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "strengthen your defensive line" are sent to the user's device as visual feedback. This allows the user to make on-the-spot tactical adjustments and improve their performance.

[0728] Emotion engine integration

[0729] This system integrates an emotion engine to collect and analyze users' emotional data. For example, it detects the facial expressions and tone of voice that users show while using their devices during matches or training, and collects this as emotional data. Input devices such as cameras and microphones are used for detection. This data is sent to a server in real time and used for analysis.

[0730] Emotional data analysis and feedback

[0731] The server analyzes the collected emotional data and adjusts strategies and training programs based on the results. For example, if it determines that the user is nervous, it can suggest training to help them relax. Also, if the user shows signs of impatience during a match, it can provide tactical feedback based on that. This allows for optimal instruction tailored to the user's mental state.

[0732] Specific examples

[0733] An example of actual use of this system is when a soccer team coach uses it. The coach uploads basic information about each player and past match data. The server uses a generative AI model to analyze the strengths and weaknesses of the entire team, and as a result, suggests that "a strategy of launching a fast break centered on player A would be effective." Furthermore, the emotion engine collects each player's emotional data and suggests relaxation drills for players who are nervous. During the game, specific feedback such as "You need to play with more concentration" is provided based on the emotional data collected in real time.

[0734] In this way, by combining generative AI and an emotion engine, the present invention is a system that provides users with advanced analysis and emotional support, contributing to improving their athletic performance.

[0735] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0736] Program processing flow

[0737] User Registration and Login

[0738] Step 1:

[0739] The user creates an account on the device.

[0740] Input: User-entered name, email address, password, sport, and position.

[0741] Output: Input data is sent from the device to the server.

[0742] Specific actions: The user opens the application on the device, enters the required information, and clicks the send button.

[0743] Step 2:

[0744] The server receives the information and stores it in a database.

[0745] Input: Registration information sent from the device.

[0746] Output: User information stored in the database.

[0747] Specific operation: The server receives the information, validates it, and then saves it in a database (e.g., MySQL).

[0748] Step 3:

[0749] The user logs in and the server performs authentication.

[0750] Input: User's email address and password.

[0751] Output: The result of authentication, success or failure.

[0752] Specific operation: The user enters their email address and password on the login screen and clicks the login button. The server checks the information against the database, and if authentication is successful, allows access to the main screen.

[0753] Data acquisition and analysis

[0754] Step 4:

[0755] Users upload match data and training data on their devices.

[0756] Input: User selected match and training data files.

[0757] Output: Data transfer from the device to the server.

[0758] Specific behavior: The user clicks the upload button and selects a data file from the file selection dialog.

[0759] Step 5:

[0760] The server receives and stores the data.

[0761] Input: Match data and training data files sent from the device.

[0762] Output: Data stored in file storage (e.g. AWS S3).

[0763] Specific operation: The server confirms receipt of the file and saves it in the specified storage.

[0764] Step 6:

[0765] The server analyzes the data using a generative AI model.

[0766] Input: Saved match and training data.

[0767] Output: Analysis results.

[0768] How it works: The server inputs data into a generative AI model (e.g., OpenAI GPT) to evaluate player performance and analyze opponent tactics.

[0769] Strategy and playing style suggestions

[0770] Step 7:

[0771] The server generates strategies and playing styles based on the analysis results.

[0772] Input: Analysis results output by the generative AI model.

[0773] Output: Strategy and playing style suggestions.

[0774] Specific operation: The server analyzes the analysis results using an algorithm and generates the optimal strategy and playing style.

[0775] Step 8:

[0776] The server notifies the user of the proposal content.

[0777] Input: Generated strategy and playing style suggestions.

[0778] Output: The notification displayed on the user's device.

[0779] Specific operation: The server sends the suggestions to the user via push notification or email.

[0780] Training program generation

[0781] Step 9:

[0782] The server generates a training program based on the performance data.

[0783] Input: Player performance data.

[0784] Output: Optimized training program.

[0785] Specific operation: The server analyzes performance data and generates a training program for each player.

[0786] Step 10:

[0787] The user checks the training program on the terminal.

[0788] Input: The training program sent from the server.

[0789] Output: The training program displayed on the user's terminal.

[0790] What happens: The user receives a notification and learns about the training program.

[0791] Real-time feedback

[0792] Step 11:

[0793] The user sends real-time data on the terminal.

[0794] Input: Real-time data sent from your device (e.g., location, performance data).

[0795] Output: Data transfer to the server.

[0796] Specific operation: The user carries the device during a match or training session, and real-time data is automatically sent to the server.

[0797] Step 12:

[0798] The server analyzes the data in real time and provides feedback.

[0799] Input: Real-time data sent.

[0800] Output: Providing feedback.

[0801] How it works: The server uses a generative AI model to instantly analyze real-time data and send visual feedback to the user's device.

[0802] Emotion engine integration

[0803] Step 13:

[0804] The user collects emotion data on the device.

[0805] Input: Facial expressions and tone of voice collected from the camera and microphone.

[0806] Output: Emotion data sent to the server.

[0807] Specific operation: The user uses the device to collect emotional data by sensing facial expressions and tone of voice during a match or training session.

[0808] Step 14:

[0809] The server receives and analyzes the emotion data.

[0810] Input: User emotion data.

[0811] Output: Analysis results.

[0812] Specific operation: The server receives the transmitted emotion data and immediately analyzes it using the emotion engine.

[0813] Emotional data analysis and feedback

[0814] Step 15:

[0815] The server adjusts strategies and training programs based on the emotional data.

[0816] Input: Analysis results of emotion data.

[0817] Output: Tailored strategies and training programs.

[0818] Specific actions: Based on the analysis results, the server adjusts strategies and training programs as needed.

[0819] Step 16:

[0820] Send feedback to the user's device.

[0821] Input: Tailored strategies and training programs.

[0822] Output: Feedback displayed on the user's device.

[0823] Specific operation: The server sends the adjustments to the user's device and provides feedback.

[0824] (Application example 2)

[0825] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0826] Improving production efficiency in factories and managing workers' mental workload are important challenges in the manufacturing industry. Conventional systems have difficulty providing real-time optimization strategies and understanding workers' emotional states, making it difficult to improve production efficiency and the work environment. In particular, improving the operational efficiency of operating machines, reducing defective products, and adjusting workers' stress and fatigue levels are required, but it is difficult to achieve all of these simultaneously.

[0827] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0828] In this invention, the server includes means for saving registration information entered by a user in a database, means for receiving and saving data uploaded by a user, means for analyzing the saved data using a generative model to generate results, means for visually displaying the generated analysis results, means for generating optimization strategies and programs according to the characteristics of the operating machine and the worker using the generative model, means for analyzing real-time data sent from the user and providing feedback, and means for analyzing worker emotion data using an emotion recognition engine and adjusting the workload, thereby enabling improvement of production efficiency in the factory and management of the mental workload of workers.

[0829] "User" means the person or operator who operates the system and inputs or uploads data.

[0830] "Registration Information" refers to basic information such as name, email address, and password that a user enters when creating an account on the system.

[0831] "Database" refers to a storage device or software for storing user registration information and uploaded data within the system.

[0832] "Upload data" refers to information such as match data, training data, and operating machine data that users provide to the system.

[0833] A "generative model" is a model that analyzes user data and generates results such as strategies and programs. Specifically, it refers to a generative AI model.

[0834] "Analysis results" refers to information obtained as a result of analyzing data using a generative model, and includes strategy proposals, training programs, etc.

[0835] "Visual display means" refers to a method or device that displays the analysis results and generated strategies in graphs, charts, etc. so that the user can easily understand them.

[0836] An "optimization strategy" is the most efficient strategy or program generated by the generative model based on the characteristics of the operating machine or worker.

[0837] "Real-time data" refers to data collected in real time while a match or task is in progress, and is used for immediate analysis and feedback.

[0838] "Feedback means" refers to a method or system for notifying users of improvements or corrections based on the analysis results.

[0839] An "emotion recognition engine" is software or a device that analyzes workers' emotional data and adjusts strategies and training programs based on the results.

[0840] The "workload adjustment means" refers to a method or system for reducing the mental load of a worker and providing an optimized work environment based on the analysis results of the emotion recognition engine.

[0841] The system for implementing this invention is a comprehensive platform for improving production efficiency in factories and managing the mental workload of workers. The system provides advanced functionality combining generative AI models and emotion recognition engines, enabling users to utilize optimization strategies and training programs.

[0842] Hardware and software used

[0843] Hardware: moving machines (e.g., robotic arms), sensors (cameras, microphones, thermometers, hygrometers, etc.)

[0844] Software: Generative AI model (natural language generation AI model), emotion recognition engine (emotion analysis software)

[0845] Communication: Local server, cloud server

[0846] System configuration and operation

[0847] 1. User Registration and Login:

[0848] A user first creates an account using a terminal. Required information includes name, email address, and password. The server then receives this information and stores it in a database. When the user uses the system again, they are prompted to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, the user is allowed access to the main screen.

[0849] 2. Data Acquisition and Analysis:

[0850] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis content includes performance evaluation of operating machines and analysis of the production environment. The analysis results are displayed visually as a dashboard for easy user understanding.

[0851] 3. Optimization strategies and program generation:

[0852] The server uses a generative AI model to generate optimal strategies and programs based on the characteristic data of the operating machines and workers. For example, specific suggestions such as "A strategy to increase the operating speed of machine A by 10% is effective" are included. These suggestions are then sent to the user's device.

[0853] 4. Real-time feedback:

[0854] During production or work, users send data from their devices to a server in real time. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "adjust the machine's operating angle" are sent to the user's device as visual feedback during work. This allows the user to make on-the-spot corrections to improve efficiency.

[0855] 5. Emotion recognition engine integration:

[0856] The system also integrates an emotion recognition engine to collect and analyze user emotional data. Specifically, the system detects facial expressions and tone of voice while the user is working on the device, and collects emotional data. This data is then sent to the server in real time.

[0857] 6. Emotional data analysis and feedback:

[0858] The server analyzes the collected emotional data and adjusts work optimization strategies and training programs based on the results. For example, if it determines that the user is tired, it will suggest taking a break. Also, if it determines that the user is unable to concentrate while working, it will adjust the work load accordingly. This allows for optimal instruction tailored to the user's mental state.

[0859] Specific examples

[0860] For example, if a robot is introduced to assemble products in a factory, the system operates as follows.

[0861] Collects operation data and sensor data from factory robots in real time.

[0862] The generative AI model analyzes the data and generates specific suggestions such as "The supply speed of part A should be increased by 10%."

[0863] An emotion recognition engine analyzes the worker's level of fatigue based on their facial expressions and tone of voice. If it determines that the worker is highly fatigued, it will suggest break times and work adjustments.

[0864] Prompt Sentence Examples

[0865] "Propose an optimization strategy to increase production efficiency on a manufacturing line. Data should include the operating time of each work station, part feed rate, and environmental parameters. Also analyze worker fatigue and stress levels."

[0866] With the above-described configuration and operation, the present invention can improve production efficiency in a factory and manage the mental stress of workers.

[0867] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0868] Step 1:

[0869] The user creates an account using a device and enters the required information (name, email address, password, etc.). This generates registration information. The server saves this registration information in a database. After verifying the entered data, the server saves it in the database as registration information, creating the user's account.

[0870] Step 2:

[0871] The user uploads game data, training data, and operation data of the motion machine using the terminal. The uploaded data is received and stored by the server. After the input data is confirmed, the stored data can be used for analysis.

[0872] Step 3:

[0873] The server uses a generative AI model to analyze the stored data. This generative AI model analyzes the uploaded data (e.g., evaluating the performance of the operating machine, analyzing the production environment, etc.) and generates results. The generative AI model performs analysis based on the input data and obtains the analysis results as output.

[0874] Step 4:

[0875] The server generates a dashboard that visually displays the analysis results obtained by the generative AI model. The analysis results are converted into visual formats such as graphs and charts so that users can easily understand them. The server receives the analysis results as input, generates data for visual display, and sends it to the terminal.

[0876] Step 5:

[0877] The server uses the generative AI model to generate optimization strategies and programs according to the characteristics of the operating machine and worker. These optimization strategies and programs are created based on the generated analysis results and characteristic data of the operating machine and worker. Based on the input characteristic data and analysis results, the server generates and outputs the optimization strategies and programs.

[0878] Step 6:

[0879] The server notifies the user's device of the generated optimization strategy or program, allowing the user to receive specific suggestions. The server receives the generated suggestions as input, generates notification data, and sends it to the device.

[0880] Step 7:

[0881] Users send data from their devices in real time to the server, which receives this real-time data and immediately analyzes it using a generative AI model.The server receives real-time data as input, analyzes it using a generative AI model, and generates feedback.

[0882] Step 8:

[0883] The server provides the results of the analysis performed in real time as feedback. For example, specific instructions such as "adjust the machine's operating angle" are sent to the user's device during work. The server receives the generated feedback as input and sends it to the user's device.

[0884] Step 9:

[0885] The server uses an emotion recognition engine to collect and analyze the worker's emotional data, such as facial expressions and tone of voice while working.

[0886] Step 10:

[0887] The server analyzes the collected emotion data and generates a suggestion for adjusting the workload based on the analysis results. For example, if the server determines that the user is fatigued, it suggests taking a break. The server receives the analysis results of the emotion data as input, generates the suggestion for adjusting the workload, and outputs it.

[0888] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0889] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0890] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0891] [Third embodiment]

[0892] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0893] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0894] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0895] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0896] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0897] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0898] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0899] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0900] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0901] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0902] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0903] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0904] The sports strategy planning system of the present invention combines various functions centered on generation AI to provide a platform for users to optimize their game strategies and discover new playing styles. Specific embodiments for implementing the system are described below.

[0905] User Registration and Login

[0906] A user first creates an account using a terminal. The required information is entered as name, email address, password, and basic information about the sport they play (e.g., sport type and position). The server then receives this information and stores it in a database. When the user uses the system again, they are required to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, they are granted access to the main screen.

[0907] Data acquisition and analysis

[0908] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis results include player performance evaluations and opponent tactical analysis. The analysis results are visually displayed on the server and provided as a dashboard for users to easily understand.

[0909] Strategy and playing style suggestions

[0910] The server uses a generative AI model based on team and player characteristic data to generate optimal strategies and new playing styles. The generated strategies are then sent to the user's device. Specific suggestions, such as "This player is good at three-point shooting, so we'll build plays around him," are included. Users can review these suggestions and incorporate them into their own strategies.

[0911] Training program generation

[0912] The server generates an optimized training program based on the performance data of each player. This provides a training menu that will help each player maximize their potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. The user can check this training program on their device and incorporate it into their actual training.

[0913] Real-time feedback

[0914] During a match or training session, the user sends data from their device to the server in real time. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "You should strengthen your defensive line" can be sent to the user's device as visual feedback during a match. This allows the user to make tactical adjustments on the spot and improve their performance.

[0915] Specific examples

[0916] For example, when a basketball coach uses this system, he or she first uploads basic information about each player and past game data from their device. Based on this, the server uses a generative AI model to analyze the strengths and weaknesses of the entire team. Based on the analysis results, a strategy suggestion is made, such as "A strategy of launching fast breaks centered around Player A would be effective in this game." Individual training programs are also generated for each player, with instructions provided such as "Player B should focus on free throw practice." During the game, data is transmitted in real time, and feedback such as "Switch to zone defense" is instantly displayed on the device.

[0917] As described above, this invention is a system that utilizes generative AI to provide advanced support for users to select effective strategies and discover new playing styles. This system contributes to improving sports performance and helps players and teams to perform at their best.

[0918] The processing flow will be explained below.

[0919] Step 1:

[0920] The user enters account information on the device, such as name, email address, password, and basic information about the sport played (e.g., sport type and position).

[0921] Step 2:

[0922] The server receives the registration information sent by the user and stores it in a database, after which a confirmation message containing the user authentication information is generated.

[0923] Step 3:

[0924] The user enters their email address and password into the terminal and attempts to log in. This sends the entered information to the server.

[0925] Step 4:

[0926] The server checks the registration information against the database and performs authentication. If authentication is successful, the user is allowed to access the main screen.

[0927] Step 5:

[0928] Users upload files of match data and training data using their devices, and this data is sent to the server.

[0929] Step 6:

[0930] The server receives the uploaded data, stores it in a database, and then analyzes the stored data using a generative AI model.

[0931] Step 7:

[0932] The server visualizes the analysis results and generates a dashboard, which is created in a user-friendly format using charts and graphs.

[0933] Step 8:

[0934] The server uses the generative AI model to generate optimal strategies and new playing styles based on the characteristics of the team and players. For example, it may generate a specific strategy such as "a strategy of launching a fast attack centered around player A is effective."

[0935] Step 9:

[0936] The server notifies the user of the generated strategies and training programs and displays them visually. The user can then check them on their device and use them in actual matches and training.

[0937] Step 10:

[0938] Users send data in real time during a match or training session, which provides the server with information on the match status and training progress in real time.

[0939] Step 11:

[0940] The server receives real-time data and instantly analyzes it using generative AI models, enabling quick tactical adjustments during matches and immediate feedback during training.

[0941] Step 12:

[0942] The server generates real-time feedback and provides it to the user, with specific instructions such as "You should strengthen your defensive line" being visually displayed on the device.

[0943] Step 13:

[0944] The feedback received by users is used to instantly modify match and training tactics, improving player and team performance.

[0945] Example 1

[0946] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0947] Conventional sports strategy planning systems often require manual analysis of match and training data, which is time-consuming and labor-intensive. Furthermore, the strategies and training programs generated may not be fully effective because they are not optimized for the characteristics of specific players or teams. Furthermore, the lack of real-time feedback makes it difficult to make timely tactical adjustments during matches or training.

[0948] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0949] In this invention, the server includes means for saving registration information entered by a user in a database, means for receiving and saving data uploaded by a user, means for analyzing the saved data using a generative AI model and generating results, means for visually displaying the generated analysis results, means for using the generative AI model to generate strategies and training programs tailored to the characteristics of teams and players, means for analyzing real-time data sent by users and providing feedback, means for notifying users of strategies and training programs based on the analysis results to their terminals, and means for users to confirm the proposed strategies and incorporate them into their own strategies. This allows users to easily automate data analysis and use strategies and training programs optimized for specific players and teams, and also enables timely tactical adjustments based on real-time feedback.

[0950] A "user" is a person who uses the system to plan sports strategies, upload various data, and check training programs.

[0951] "Server" means a computer system that manages and analyzes information received from users, generates results, and provides them to users.

[0952] "Terminal" means a device that allows a user to access the system and input and upload data and check analysis results.

[0953] A "database" is a storage device for storing user registration information, uploaded match data, training data, and the like.

[0954] A "generative AI model" is an artificial intelligence algorithm that analyzes input sports-related data and generates strategy suggestions and training programs.

[0955] "Registration information" refers to basic information such as name, email address, password, sport, and position that a user enters when registering with the system for the first time.

[0956] "Match Data" means information relating to the results of specific matches and player performances that is uploaded by a User to the System.

[0957] "Training Data" is information including measurement results related to an athlete's training status and performance.

[0958] "Analysis results" are deliverables such as player performance evaluations and opponent tactical analyses that are generated by the generative AI model after analyzing the uploaded data.

[0959] A "dashboard" is a user interface that allows the system to visually display analysis results.

[0960] "Strategy" refers to a game plan or approach optimized for the characteristics of a team or player in a match.

[0961] A "training program" is a training menu or drill optimized based on each player's performance data.

[0962] "Real-time data" refers to performance data that is collected in real time during a match or training session and sent to a server.

[0963] "Feedback" refers to specific instructions and improvements provided by the server based on the results of analyzing real-time data.

[0964] The sports strategy planning system of the present invention combines various functions centered on a generative AI model to provide a platform for users to optimize their game strategies and discover new playing styles. Specific embodiments for implementing the system are described below.

[0965] This system uses a server, terminals, and a database. The server analyzes information received from users and generates strategies and training programs using generative AI models. The terminal is an interface through which users input data and check the analysis results. The database functions as a storage device for saving user registration information and uploaded data.

[0966] First, a user creates an account using a terminal by entering basic information such as name, email address, password, sport, position, etc. This information is sent to the server and stored in a database.

[0967] Next, users can upload their own match and training data to the system via their devices. The server receives this data and stores it in a database. The stored data is then analyzed using generative AI models. The analysis results include player performance evaluations and opponent tactical analysis.

[0968] The analysis results are displayed visually and provided as a dashboard for easy understanding by users. For example, when basketball game data is analyzed, detailed statistical data such as points, assists, and rebounds for each player is displayed visually.

[0969] The server also uses generative AI models to generate optimal strategies and new playing styles based on team and player characteristics. The generated strategies are then sent to the user's device. For example, specific suggestions are made, such as, "This player is good at three-point shooting, so we'll build a play around him."

[0970] Furthermore, the server analyzes the performance data of each player and generates an optimized training program. For example, it may suggest specific drills for a certain player to improve their 3-point shooting success rate. Users can check this training program on their device and incorporate it into their actual training.

[0971] During a match or training session, the user sends real-time data from their device to the server, which instantly analyzes it and uses generative AI models to provide real-time feedback. For example, specific visual feedback such as "You should improve your defensive line" could be provided during a match.

[0972] Examples of specific prompts include:

[0973] "Please upload basketball game data, analyze player A's strengths, and suggest the optimal strategy."

[0974] "Generate a training program suitable for player B."

[0975] "Provide real-time feedback during the match."

[0976] In this way, the present invention is a system that utilizes generative AI to provide advanced support for users to select effective strategies and discover new playing styles, thereby improving sports performance and helping players and teams to perform at their best.

[0977] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0978] Step 1:

[0979] The user uses their device to open the account creation page. They enter basic information such as their name, email address, password, sport, and position, and click the "Register" button. The entered information is sent to the server, which receives it and stores it in a database. Input data: "Name, email address, password, sport." Output data: "Registration information saved in database."

[0980] Step 2:

[0981] The user uses their device to open the login page, enter their email address and password, and click the "Login" button. The entered authentication information is sent to the server, which checks it against the information in its database. If it matches, authentication is successful and access to the main screen is permitted. If it does not match, an error message is displayed. Input data: "Email address, password". Output data: "Authentication result, permission to access the main screen or error message."

[0982] Step 3:

[0983] Users upload match data and training data from their devices. The user opens the "Data Upload" page, selects a file, and clicks the "Upload" button. The uploaded data is sent to the server, which receives it and stores it in a database. Input data: "Match data, training data." Output data: "Data saved to database."

[0984] Step 4:

[0985] The server passes saved match data and training data to the generative AI model and begins analyzing the data. The analysis involves evaluating player performance and analyzing opponent tactics. As a result, information such as each player's strengths and weaknesses and opponent tactical tendencies is generated. Input data: "Saved data." Output data: "Analysis results."

[0986] Step 5:

[0987] The analysis results are visually represented by the server and provided to the user in the form of a dashboard. The user opens the dashboard page on their device to check the analysis results. For example, statistical data such as points, assists, and rebounds for each player is displayed. Input data: "Analysis results". Output data: "Visualized dashboard".

[0988] Step 6:

[0989] The server uses a generative AI model to generate optimal strategies and new playing styles based on characteristic data of teams and players. The generated strategies are sent to the user's device. For example, they may include specific suggestions such as, "In this match, a strategy of launching quick attacks centered around player A would be effective." Input data: "Characteristic data." Output data: "Strategic proposals."

[0990] Step 7:

[0991] The user checks the strategy proposal on the terminal and decides whether to adopt the proposed strategy. If they do, they click the "Adopt" button. Input data: "Strategy proposal". Output data: "Decision on adopting the strategy".

[0992] Step 8:

[0993] The server analyzes the performance data of each player and generates an optimized training program using a generative AI model. For example, a specific menu may be generated, such as "Player B should focus on free throw practice." This is then sent to the user's device. Input data: "Performance data." Output data: "Training program proposal."

[0994] Step 9:

[0995] The user checks the training program on the device and reflects it in their actual training. For example, a detailed menu is displayed, such as "Player B will be given a specific free throw drill." Input data: "Training program proposal." Output data: "Training reflection."

[0996] Step 10:

[0997] During a match or training session, the user sends data from their device to the server in real time. The server immediately analyzes this data and provides real-time feedback using a generative AI model. For example, specific instructions such as "You should improve your defensive line" are fed back to the device in real time. Input data: "Real-time data." Output data: "Real-time analysis results and feedback."

[0998] (Application example 1)

[0999] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1000] In sports games and training, data analysis and real-time feedback are important for optimizing strategies and improving player performance. However, existing systems struggle to provide these in an integrated and efficient manner. Furthermore, there are limitations, particularly with regard to providing real-time feedback and strategy suggestions, creating a demand for technologies that enhance the user experience. Additionally, there is a lack of methods for providing real-time analysis and feedback in virtual environments.

[1001] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1002] In this invention, the server includes: means for storing registration information entered by a user in a database; means for receiving and storing data uploaded by a user; means for analyzing the stored data using a generative model to generate results; means for visually displaying the generated analysis results; means for generating strategies and training programs tailored to team and player characteristics using the generative model; means for analyzing real-time data sent by a user and providing feedback; means for acquiring data in real time from the smart glasses and notifying the user of the analysis results on the spot; and means for providing strategy suggestions and training feedback in real time using the generative AI model while running a virtual sports simulation. This allows users to receive strategy and training suggestions based on real-time analyzed data during sports games and training and immediately implement them. Real-time feedback and instant strategy adjustments can maximize player and team performance.

[1003] Definition of Terms

[1004] "Means for storing user-entered registration information in a database" refers to the process and system for securely storing basic information entered by users, such as name, email address, password, sport type, and position, in a database.

[1005] "Means for receiving and storing data uploaded by users" refers to the functions and system for receiving match data and training data uploaded by users from their terminals to the server and storing it in a database.

[1006] "Means for analyzing data and generating results using a generative model" refers to methods and technologies for analyzing uploaded match data or training data using a generative AI model and generating results.

[1007] "Means for visually displaying the generated analysis results" refers to an interface and system for visually displaying the results analyzed by the generative AI model in a format that is easy for users to understand.

[1008] "Means for using generative models to generate strategies and training programs tailored to the characteristics of teams and players" refers to generative AI models and systems for generating optimal strategies and training programs based on team and player data.

[1009] The "means for analyzing real-time data sent by a user and providing feedback" refers to a method and system for instantly analyzing real-time data sent by a user during a match or training session and providing that feedback.

[1010] "Means for acquiring data in real time from smart glasses and notifying the user of the analysis results on the spot" refers to technology and a system for acquiring data in real time through smart glasses and notifying the user of the analysis results by displaying them on the glasses.

[1011] "Means for providing strategy suggestions and training feedback in real time using a generative AI model while running a virtual sports simulation" refers to methods and technologies for using a generative AI model to provide strategy suggestions and training feedback to a user in real time during a sports simulation in a virtual environment.

[1012] MODE FOR CARRYING OUT THE INVENTION

[1013] A system for realizing the present invention includes the following configuration and means.

[1014] Basic configuration

[1015] 1. User Registration and Login

[1016] The user first creates an account using a terminal. The required information is entered as name, email address, password, and basic information about the sport (e.g., sport type and position). The server receives this information and stores it in a database. When the user uses the system again, they log in using their email address and password. The server verifies the authentication information, and if authentication is successful, they are granted access to the main screen.

[1017] 2. Data Acquisition and Analysis

[1018] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. The analysis includes evaluation of player performance and analysis of opponent tactics. The analysis results are presented as a visual dashboard. This dashboard has an interface that is easy for users to understand.

[1019] 3. Generating a Strategy and Training Program

[1020] The server uses a generative AI model based on team and player characteristic data to generate optimal strategies and training programs. The generated strategies are then sent to the user's device. For example, specific suggestions such as "This player is good at three-point shooting, so we'll build plays around him" are included. Users can review these suggestions and incorporate them into their own strategies.

[1021] 4. Real-time feedback

[1022] During a match or training session, users use smart glasses to capture real-time data and send it to a server. The server then uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "You should strengthen your defensive line" are displayed on the user's smart glasses as visual feedback during a match, allowing the user to make on-the-spot tactical adjustments and improve their performance.

[1023] Hardware / Software used

[1024] The system uses generative AI models that utilize the TensorFlow and Keras libraries, and uses a SQLite3 database to store data. Appropriate network infrastructure is also used to acquire real-time data using smart glasses and communicate the analysis results to the user.

[1025] Examples of concrete examples and prompts

[1026] As a concrete example, the prompt sentence when basketball game data is input is shown below.

[1027] "Upload your basketball game data and evaluate your 3-point shooting performance."

[1028] "Use Player A's match data to generate the optimal training program for him."

[1029] "Use real-time match data to give us feedback to improve our defensive strategies."

[1030] The system based on this invention allows users to receive real-time data analysis and strategy suggestions to maximize their sports performance.

[1031] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1032] Processing Step Description

[1033] Step 1: User Registration and Login

[1034] Operation:

[1035] Input: The user enters basic information into the device, such as name, email address, password, sport type, and position.

[1036] Data processing: The server receives the registration information sent from the device and formats it into the required format.

[1037] Output: Save the formatted registration information to the database.

[1038] Specific behavior:

[1039] The user enters registration information into the terminal and sends it to the server.

[1040] The server stores the received information in an SQLite3 database.

[1041] When the user uses the system again, he or she sends a login request from the terminal using the email address and password.

[1042] The server checks the authentication information in its database and, if authentication is successful, allows the user access to the main screen.

[1043] Step 2: Data acquisition and analysis

[1044] Operation:

[1045] Input: Users upload match data and training data from their devices.

[1046] Data processing: The server receives and stores the uploaded data.

[1047] Output: The generative AI model analyzes the data and generates analytical results.

[1048] Specific behavior:

[1049] The user uploads match data and training data to the device.

[1050] The server receives this and stores it in a database.

[1051] The server analyzes the data using a generative AI model built using the TensorFlow and Keras libraries.

[1052] The analysis results are generated as a visually displayed dashboard.

[1053] Step 3: Generate a strategy and training program

[1054] Operation:

[1055] Input: The server receives team and player characteristic data.

[1056] Data Processing: Generative AI models are used to analyze trait data and generate optimal strategies and training programs.

[1057] Output: The generated strategies and training programs are sent to the user's terminal.

[1058] Specific behavior:

[1059] The server retrieves team and player characteristic data from a database.

[1060] The server uses a generative AI model to generate optimal strategies and training programs.

[1061] The system notifies the user's device of specific strategy suggestions and training programs, making suggestions such as, "This player is good at three-point shooting, so we'll build our play around him."

[1062] Step 4: Real-time feedback

[1063] Operation:

[1064] Input: Real-time data during a match or training session is acquired by the user using smart glasses and sent to the server.

[1065] Data processing: Receive real-time data and instantly analyze it using generative AI models.

[1066] Output: The analysis results are visually displayed as feedback on the user's smart glasses.

[1067] Specific behavior:

[1068] The user wears smart glasses and transmits data during a match or training session to a server in real time.

[1069] The server receives real-time data and instantly analyzes it using generative AI models.

[1070] As a result of the analysis, specific feedback such as "Your defensive line should be stronger" is displayed on the user's smart glasses.

[1071] Users receive instant visual feedback and can adjust their tactics accordingly.

[1072] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1073] The sports strategy planning system of the present invention provides advanced functionality that combines generative AI and an emotion engine, and is a platform that allows users to optimize their game strategies and discover new playing styles. An embodiment of this system will be described in detail.

[1074] User Registration and Login

[1075] A user first creates an account using a terminal. The required information is entered: name, email address, password, and basic information about the sport they play (e.g., sport type and position). The server then receives this information and stores it in a database. When the user uses the system again, they are required to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, the user is allowed to access the main screen.

[1076] Data acquisition and analysis

[1077] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis results include player performance evaluations and opponent tactical analysis. The analysis results are displayed visually as a dashboard for easy user understanding.

[1078] Strategy and playing style suggestions

[1079] The server uses a generative AI model to generate optimal strategies and new playing styles based on team and player characteristic data. For example, specific suggestions such as "A strategy of launching a fast break centered on player A is effective" are included. These suggestions are then sent to the user's device.

[1080] Training program generation

[1081] The server generates an optimized training program based on the performance data of each player. This provides a training menu that will help each player reach their full potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. The user can check this training program on their device and incorporate it into their actual training.

[1082] Real-time feedback

[1083] During a match or training session, the user sends real-time data from their device to the server. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "strengthen your defensive line" can be sent to the user's device as visual feedback during a match. This allows the user to make tactical adjustments on the spot and improve their performance.

[1084] Emotion engine integration

[1085] The system also integrates an emotion engine to collect and analyze users' emotional data. Specifically, the system detects the user's facial expressions and tone of voice during matches and training sessions, and collects emotional data. This data is then sent to the server in real time.

[1086] Emotional data analysis and feedback

[1087] The server analyzes the collected emotional data and adjusts strategies and training programs based on the results. For example, if it determines that the user is nervous, it will suggest relaxation training. If the user shows signs of impatience during a match, it will provide tactical feedback based on that. This allows for optimal instruction tailored to the user's mental state.

[1088] Specific examples

[1089] For example, when a soccer team coach uses this system, he or she uploads basic information about each player and past match data. Based on this data, the server uses a generative AI model to analyze the strengths and weaknesses of the entire team. As a result, a strategy suggestion is made, such as "A strategy of launching fast breaks centered on Player A is effective." Furthermore, the emotion engine collects each player's emotional data and suggests relaxation drills for players who are nervous. During the game, specific feedback is provided, such as "You need to play with more concentration," based on the emotional data collected in real time.

[1090] As described above, this invention is a system that combines generative AI and an emotion engine to provide users with advanced analysis and emotional support, thereby contributing to the improvement of sports performance and helping athletes and teams to perform at their best.

[1091] The processing flow will be explained below.

[1092] The sports strategy planning system of the present invention provides advanced functions that combine generative AI and an emotion engine, and is a platform for users to optimize their game strategies and discover new playing styles. The specific processing flow for implementing the present invention is described below.

[1093] User Registration and Login

[1094] Step 1:

[1095] The user enters account information on the device, specifically, name, email address, password, and basic information about the sport played (e.g., sport type and position).

[1096] Step 2:

[1097] The server receives the registration information sent by the user and stores it in the database. After the storage is complete, it generates the user authentication information.

[1098] Step 3:

[1099] The user enters their email address and password into the terminal and attempts to log in. This sends the entered information to the server.

[1100] Step 4:

[1101] The server checks the registration information against the database and performs authentication. If authentication is successful, the user is allowed to access the main screen.

[1102] Data acquisition and analysis

[1103] Step 5:

[1104] Users upload files of match data and training data using their devices, and this data is sent to the server.

[1105] Step 6:

[1106] The server receives the uploaded data, stores it in a database, and then analyzes the stored data using a generative AI model.

[1107] Step 7:

[1108] The server visualizes the analysis results and generates a dashboard, which is created in a user-friendly format using charts and graphs.

[1109] Strategy and playing style suggestions

[1110] Step 8:

[1111] The server uses the generative AI model to generate optimal strategies and new playing styles based on the characteristics of the team and players. For example, it may generate a specific strategy such as "a strategy of launching a fast attack centered around player A is effective."

[1112] Step 9:

[1113] The server notifies the user of the generated strategies and training programs and displays them visually. The user can then check them on their device and use them in actual matches and training.

[1114] Training program generation

[1115] Step 10:

[1116] The server generates an optimized training program based on each player's performance data. For example, it might suggest specific drills to improve a player's 3-point shooting success rate.

[1117] Step 11:

[1118] The user can check this training program on the terminal and apply it to their actual training.

[1119] Real-time feedback

[1120] Step 12:

[1121] Users send data in real time during matches and training, which provides the server with information on match status and training progress in real time.

[1122] Step 13:

[1123] The server receives real-time data and instantly analyzes it using generative AI models, enabling quick tactical adjustments during matches and immediate feedback during training.

[1124] Step 14:

[1125] The server generates real-time feedback and provides it to the user, with specific instructions such as "You should strengthen your defensive line" being visually displayed on the device.

[1126] Step 15:

[1127] The feedback received by users is used to instantly modify match and training tactics, improving player and team performance.

[1128] Emotion engine integration

[1129] Step 16:

[1130] During a match or training session, the device detects facial expressions and tone of voice to collect emotional data, which is then sent to a server in real time.

[1131] Step 17:

[1132] The server receives the collected emotional data and analyzes it using an emotion engine. Based on the analysis results, strategies and training programs are tailored to the user's mental state.

[1133] Step 18:

[1134] The server generates strategies and training programs based on the analyzed emotional data and provides them to the user, for example, suggesting training drills to help a tense user relax.

[1135] Step 19:

[1136] The server uses the user's emotional data to generate real-time feedback during a match or training session, providing the user with visual instructions such as "You need to play more focused."

[1137] As described above, this invention is a system that combines generative AI and an emotion engine to provide users with advanced analysis and emotional support, thereby contributing to the improvement of sports performance and helping athletes and teams to perform at their best.

[1138] Example 2

[1139] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1140] In modern sports, maximizing the performance of athletes and teams requires detailed analysis of match and training data and the optimization of strategies and training programs based on the results. However, conventional systems have difficulty analyzing data in real time and providing feedback, and are unable to provide training and strategy suggestions that take the user's emotional state into account. Therefore, there is a need for systems that provide advanced analysis and emotional support to users in order to improve their sports performance.

[1141] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1142] In this invention, the server includes means for saving registration information entered by the user in a database, means for receiving and saving data uploaded by the user, means for analyzing the saved data using a generative model to generate results, means for visually displaying the generated analysis results, means for generating strategies and training programs according to the characteristics of teams and players using the generative model, means for analyzing real-time data sent by the user and providing feedback, means for collecting user emotion data and integrating an emotion engine that is useful for analysis, and means for adjusting strategies and training programs based on the emotion data and providing feedback to the user. This enables detailed analysis of sports data in real time and can also suggest optimal strategies and training according to the user's emotional state.

[1143] "Means for storing registration information entered by the user in a database" refers to the system's function of receiving the data entered by the user when creating an account (e.g., name, email address, password, sport, position, etc.) and storing it in a database.

[1144] "Means for receiving and storing data uploaded by users" refers to a system function whereby the server receives and stores match data or training data uploaded by users from their terminals.

[1145] "Means for analyzing data using a generative model that analyzes stored data and generating results" refers to a function that analyzes data stored on a server using a generative AI model and generates the analysis results.

[1146] "Means for visually displaying the generated analysis results" refers to a system function that displays the results analyzed by the generative AI model in a visual format such as a dashboard or graph so that the user can easily understand them.

[1147] "Means for generating strategies and training programs according to the characteristics of teams and players using generative models" refers to a system function that uses generative AI models to analyze characteristic data of teams and players and automatically generates optimal strategies and training programs based on the results.

[1148] "Means for analyzing real-time data sent by users and providing feedback" refers to a system function that uses a generative AI model to instantly analyze real-time data sent by users from their devices during matches or training, and provides feedback based on the results.

[1149] "Means for collecting user emotional data and integrating an emotional engine to aid in analysis" refers to a function that integrates into the system an emotional engine that detects and collects the user's facial expressions and tone of voice, and uses that data for analysis.

[1150] "Means for adjusting strategies and training programs based on emotional data and providing feedback to users" refers to a system function that adjusts strategies and training programs in real time based on emotional data collected and analyzed by the emotion engine, and provides the content of these adjustments to users as feedback.

[1151] The sports strategy planning system of the present invention provides advanced functionality that combines generative AI and an emotion engine. Specific embodiments of this system are described in detail below.

[1152] User Registration and Login

[1153] A user first creates an account using a terminal. The required information is entered: name, email address, password, and basic information about the sport they play (e.g., sport type and position). The server receives this information and stores it in a database (e.g., MySQL). When the user returns to the system, they are prompted to log in using their email address and password. The server verifies the authentication information, and if successful, allows the user to access the main screen.

[1154] Data acquisition and analysis

[1155] When a user uploads match or training data using their device, the server receives and stores it. The uploaded data is saved in file storage (e.g., AWS S3). The server then analyzes the data using a generative AI model (e.g., OpenAI GPT) to evaluate player performance and analyze opponent tactics. The analysis results are displayed visually as a dashboard. For example, it visually displays "how the movements of the top scorer in this match were different."

[1156] Strategy and playing style suggestions

[1157] The server uses a generative AI model to generate optimal strategies and new playing styles based on team and player characteristic data. Specific suggestions include, for example, "A strategy of launching fast breaks centered around player A is effective." These suggestions are sent to the user's device. An example of a prompt is, "Please explain how the generative AI analyzes game data when the user uploads it."

[1158] Training program generation

[1159] The server generates an optimized training program based on each player's performance data. This provides a training menu that will help each player reach their full potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. Users can check this training program on their device and incorporate it into their actual training.

[1160] Real-time feedback

[1161] During a match or training session, the user sends real-time data from their device to the server. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "strengthen your defensive line" are sent to the user's device as visual feedback. This allows the user to make on-the-spot tactical adjustments and improve their performance.

[1162] Emotion engine integration

[1163] This system integrates an emotion engine to collect and analyze users' emotional data. For example, it detects the facial expressions and tone of voice that users show while using their devices during matches or training, and collects this as emotional data. Input devices such as cameras and microphones are used for detection. This data is sent to a server in real time and used for analysis.

[1164] Emotional data analysis and feedback

[1165] The server analyzes the collected emotional data and adjusts strategies and training programs based on the results. For example, if it determines that the user is nervous, it can suggest training to help them relax. Also, if the user shows signs of impatience during a match, it can provide tactical feedback based on that. This allows for optimal instruction tailored to the user's mental state.

[1166] Specific examples

[1167] An example of actual use of this system is when a soccer team coach uses it. The coach uploads basic information about each player and past match data. The server uses a generative AI model to analyze the strengths and weaknesses of the entire team, and as a result, suggests that "a strategy of launching a fast break centered on player A would be effective." Furthermore, the emotion engine collects each player's emotional data and suggests relaxation drills for players who are nervous. During the game, specific feedback such as "You need to play with more concentration" is provided based on the emotional data collected in real time.

[1168] In this way, by combining generative AI and an emotion engine, the present invention is a system that provides users with advanced analysis and emotional support, contributing to improving their athletic performance.

[1169] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1170] Program processing flow

[1171] User Registration and Login

[1172] Step 1:

[1173] The user creates an account on the device.

[1174] Input: User-entered name, email address, password, sport, and position.

[1175] Output: Input data is sent from the device to the server.

[1176] Specific actions: The user opens the application on the device, enters the required information, and clicks the send button.

[1177] Step 2:

[1178] The server receives the information and stores it in a database.

[1179] Input: Registration information sent from the device.

[1180] Output: User information stored in the database.

[1181] Specific operation: The server receives the information, validates it, and then saves it in a database (e.g., MySQL).

[1182] Step 3:

[1183] The user logs in and the server performs authentication.

[1184] Input: User's email address and password.

[1185] Output: The result of authentication, success or failure.

[1186] Specific operation: The user enters their email address and password on the login screen and clicks the login button. The server checks the information against the database, and if authentication is successful, allows access to the main screen.

[1187] Data acquisition and analysis

[1188] Step 4:

[1189] Users upload match data and training data on their devices.

[1190] Input: User selected match and training data files.

[1191] Output: Data transfer from the device to the server.

[1192] Specific behavior: The user clicks the upload button and selects a data file from the file selection dialog.

[1193] Step 5:

[1194] The server receives and stores the data.

[1195] Input: Match data and training data files sent from the device.

[1196] Output: Data stored in file storage (e.g. AWS S3).

[1197] Specific operation: The server confirms receipt of the file and saves it in the specified storage.

[1198] Step 6:

[1199] The server analyzes the data using a generative AI model.

[1200] Input: Saved match and training data.

[1201] Output: Analysis results.

[1202] How it works: The server inputs data into a generative AI model (e.g., OpenAI GPT) to evaluate player performance and analyze opponent tactics.

[1203] Strategy and playing style suggestions

[1204] Step 7:

[1205] The server generates strategies and playing styles based on the analysis results.

[1206] Input: Analysis results output by the generative AI model.

[1207] Output: Strategy and playing style suggestions.

[1208] Specific operation: The server analyzes the analysis results using an algorithm and generates the optimal strategy and playing style.

[1209] Step 8:

[1210] The server notifies the user of the proposal content.

[1211] Input: Generated strategy and playing style suggestions.

[1212] Output: The notification displayed on the user's device.

[1213] Specific operation: The server sends the suggestions to the user via push notification or email.

[1214] Training program generation

[1215] Step 9:

[1216] The server generates a training program based on the performance data.

[1217] Input: Player performance data.

[1218] Output: Optimized training program.

[1219] Specific operation: The server analyzes performance data and generates a training program for each player.

[1220] Step 10:

[1221] The user checks the training program on the terminal.

[1222] Input: The training program sent from the server.

[1223] Output: The training program displayed on the user's terminal.

[1224] What happens: The user receives a notification and learns about the training program.

[1225] Real-time feedback

[1226] Step 11:

[1227] The user sends real-time data on the terminal.

[1228] Input: Real-time data sent from your device (e.g., location, performance data).

[1229] Output: Data transfer to the server.

[1230] Specific operation: The user carries the device during a match or training session, and real-time data is automatically sent to the server.

[1231] Step 12:

[1232] The server analyzes the data in real time and provides feedback.

[1233] Input: Real-time data sent.

[1234] Output: Providing feedback.

[1235] How it works: The server uses a generative AI model to instantly analyze real-time data and send visual feedback to the user's device.

[1236] Emotion engine integration

[1237] Step 13:

[1238] The user collects emotion data on the device.

[1239] Input: Facial expressions and tone of voice collected from the camera and microphone.

[1240] Output: Emotion data sent to the server.

[1241] Specific operation: The user uses the device to collect emotional data by sensing facial expressions and tone of voice during a match or training session.

[1242] Step 14:

[1243] The server receives and analyzes the emotion data.

[1244] Input: User emotion data.

[1245] Output: Analysis results.

[1246] Specific operation: The server receives the transmitted emotion data and immediately analyzes it using the emotion engine.

[1247] Emotional data analysis and feedback

[1248] Step 15:

[1249] The server adjusts strategies and training programs based on the emotional data.

[1250] Input: Analysis results of emotion data.

[1251] Output: Tailored strategies and training programs.

[1252] Specific actions: Based on the analysis results, the server adjusts strategies and training programs as needed.

[1253] Step 16:

[1254] Send feedback to the user's device.

[1255] Input: Tailored strategies and training programs.

[1256] Output: Feedback displayed on the user's device.

[1257] Specific operation: The server sends the adjustments to the user's device and provides feedback.

[1258] (Application example 2)

[1259] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1260] Improving production efficiency in factories and managing workers' mental workload are important challenges in the manufacturing industry. Conventional systems have difficulty providing real-time optimization strategies and understanding workers' emotional states, making it difficult to improve production efficiency and the work environment. In particular, improving the operational efficiency of operating machines, reducing defective products, and adjusting workers' stress and fatigue levels are required, but it is difficult to achieve all of these simultaneously.

[1261] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1262] In this invention, the server includes means for saving registration information entered by a user in a database, means for receiving and saving data uploaded by a user, means for analyzing the saved data using a generative model to generate results, means for visually displaying the generated analysis results, means for generating optimization strategies and programs according to the characteristics of the operating machine and the worker using the generative model, means for analyzing real-time data sent from the user and providing feedback, and means for analyzing worker emotion data using an emotion recognition engine and adjusting the workload, thereby enabling improvement of production efficiency in the factory and management of the mental workload of workers.

[1263] "User" means the person or operator who operates the system and inputs or uploads data.

[1264] "Registration Information" refers to basic information such as name, email address, and password that a user enters when creating an account on the system.

[1265] "Database" refers to a storage device or software for storing user registration information and uploaded data within the system.

[1266] "Upload data" refers to information such as match data, training data, and operating machine data that users provide to the system.

[1267] A "generative model" is a model that analyzes user data and generates results such as strategies and programs. Specifically, it refers to a generative AI model.

[1268] "Analysis results" refers to information obtained as a result of analyzing data using a generative model, and includes strategy proposals, training programs, etc.

[1269] "Visual display means" refers to a method or device that displays the analysis results and generated strategies in graphs, charts, etc. so that the user can easily understand them.

[1270] An "optimization strategy" is the most efficient strategy or program generated by the generative model based on the characteristics of the operating machine or worker.

[1271] "Real-time data" refers to data collected in real time while a match or task is in progress, and is used for immediate analysis and feedback.

[1272] "Feedback means" refers to a method or system for notifying users of improvements or corrections based on the analysis results.

[1273] An "emotion recognition engine" is software or a device that analyzes workers' emotional data and adjusts strategies and training programs based on the results.

[1274] The "workload adjustment means" refers to a method or system for reducing the mental load of a worker and providing an optimized work environment based on the analysis results of the emotion recognition engine.

[1275] The system for implementing this invention is a comprehensive platform for improving production efficiency in factories and managing the mental workload of workers. The system provides advanced functionality combining generative AI models and emotion recognition engines, enabling users to utilize optimization strategies and training programs.

[1276] Hardware and software used

[1277] Hardware: moving machines (e.g., robotic arms), sensors (cameras, microphones, thermometers, hygrometers, etc.)

[1278] Software: Generative AI model (natural language generation AI model), emotion recognition engine (emotion analysis software)

[1279] Communication: Local server, cloud server

[1280] System configuration and operation

[1281] 1. User Registration and Login:

[1282] A user first creates an account using a terminal. Required information includes name, email address, and password. The server then receives this information and stores it in a database. When the user uses the system again, they are prompted to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, the user is allowed access to the main screen.

[1283] 2. Data Acquisition and Analysis:

[1284] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis content includes performance evaluation of operating machines and analysis of the production environment. The analysis results are displayed visually as a dashboard for easy user understanding.

[1285] 3. Optimization strategies and program generation:

[1286] The server uses a generative AI model to generate optimal strategies and programs based on the characteristic data of the operating machines and workers. For example, specific suggestions such as "A strategy to increase the operating speed of machine A by 10% is effective" are included. These suggestions are then sent to the user's device.

[1287] 4. Real-time feedback:

[1288] During production or work, users send data from their devices to a server in real time. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "adjust the machine's operating angle" are sent to the user's device as visual feedback during work. This allows the user to make on-the-spot corrections to improve efficiency.

[1289] 5. Emotion recognition engine integration:

[1290] The system also integrates an emotion recognition engine to collect and analyze user emotional data. Specifically, the system detects facial expressions and tone of voice while the user is working on the device, and collects emotional data. This data is then sent to the server in real time.

[1291] 6. Emotional data analysis and feedback:

[1292] The server analyzes the collected emotional data and adjusts work optimization strategies and training programs based on the results. For example, if it determines that the user is tired, it will suggest taking a break. Also, if it determines that the user is unable to concentrate while working, it will adjust the work load accordingly. This allows for optimal instruction tailored to the user's mental state.

[1293] Specific examples

[1294] For example, if a robot is introduced to assemble products in a factory, the system operates as follows.

[1295] Collects operation data and sensor data from factory robots in real time.

[1296] The generative AI model analyzes the data and generates specific suggestions such as "The supply speed of part A should be increased by 10%."

[1297] An emotion recognition engine analyzes the worker's level of fatigue based on their facial expressions and tone of voice. If it determines that the worker is highly fatigued, it will suggest break times and work adjustments.

[1298] Prompt Sentence Examples

[1299] "Propose an optimization strategy to increase production efficiency on a manufacturing line. Data should include the operating time of each work station, part feed rate, and environmental parameters. Also analyze worker fatigue and stress levels."

[1300] With the above-described configuration and operation, the present invention can improve production efficiency in a factory and manage the mental stress of workers.

[1301] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1302] Step 1:

[1303] The user creates an account using a device and enters the required information (name, email address, password, etc.). This generates registration information. The server saves this registration information in a database. After verifying the entered data, the server saves it in the database as registration information, creating the user's account.

[1304] Step 2:

[1305] The user uploads game data, training data, and operation data of the motion machine using the terminal. The uploaded data is received and stored by the server. After the input data is confirmed, the stored data can be used for analysis.

[1306] Step 3:

[1307] The server uses a generative AI model to analyze the stored data. This generative AI model analyzes the uploaded data (e.g., evaluating the performance of the operating machine, analyzing the production environment, etc.) and generates results. The generative AI model performs analysis based on the input data and obtains the analysis results as output.

[1308] Step 4:

[1309] The server generates a dashboard that visually displays the analysis results obtained by the generative AI model. The analysis results are converted into visual formats such as graphs and charts so that users can easily understand them. The server receives the analysis results as input, generates data for visual display, and sends it to the terminal.

[1310] Step 5:

[1311] The server uses the generative AI model to generate optimization strategies and programs according to the characteristics of the operating machine and worker. These optimization strategies and programs are created based on the generated analysis results and characteristic data of the operating machine and worker. Based on the input characteristic data and analysis results, the server generates and outputs the optimization strategies and programs.

[1312] Step 6:

[1313] The server notifies the user's device of the generated optimization strategy or program, allowing the user to receive specific suggestions. The server receives the generated suggestions as input, generates notification data, and sends it to the device.

[1314] Step 7:

[1315] Users send data from their devices in real time to the server, which receives this real-time data and immediately analyzes it using a generative AI model.The server receives real-time data as input, analyzes it using a generative AI model, and generates feedback.

[1316] Step 8:

[1317] The server provides the results of the analysis performed in real time as feedback. For example, specific instructions such as "adjust the machine's operating angle" are sent to the user's device during work. The server receives the generated feedback as input and sends it to the user's device.

[1318] Step 9:

[1319] The server uses an emotion recognition engine to collect and analyze the worker's emotional data, such as facial expressions and tone of voice while working.

[1320] Step 10:

[1321] The server analyzes the collected emotion data and generates a suggestion for adjusting the workload based on the analysis results. For example, if the server determines that the user is fatigued, it suggests taking a break. The server receives the analysis results of the emotion data as input, generates the suggestion for adjusting the workload, and outputs it.

[1322] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1323] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1324] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1325] [Fourth embodiment]

[1326] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1327] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1328] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1329] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1330] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1331] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1332] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1333] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1334] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1335] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1336] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1337] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1338] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1339] The sports strategy planning system of the present invention combines various functions centered on generation AI to provide a platform for users to optimize their game strategies and discover new playing styles. Specific embodiments for implementing the system are described below.

[1340] User Registration and Login

[1341] A user first creates an account using a terminal. The required information is entered as name, email address, password, and basic information about the sport they play (e.g., sport type and position). The server then receives this information and stores it in a database. When the user uses the system again, they are required to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, they are granted access to the main screen.

[1342] Data acquisition and analysis

[1343] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis results include player performance evaluations and opponent tactical analysis. The analysis results are visually displayed on the server and provided as a dashboard for users to easily understand.

[1344] Strategy and playing style suggestions

[1345] The server uses a generative AI model based on team and player characteristic data to generate optimal strategies and new playing styles. The generated strategies are then sent to the user's device. Specific suggestions, such as "This player is good at three-point shooting, so we'll build plays around him," are included. Users can review these suggestions and incorporate them into their own strategies.

[1346] Training program generation

[1347] The server generates an optimized training program based on the performance data of each player. This provides a training menu that will help each player maximize their potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. The user can check this training program on their device and incorporate it into their actual training.

[1348] Real-time feedback

[1349] During a match or training session, the user sends data from their device to the server in real time. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "You should strengthen your defensive line" can be sent to the user's device as visual feedback during a match. This allows the user to make tactical adjustments on the spot and improve their performance.

[1350] Specific examples

[1351] For example, when a basketball coach uses this system, he or she first uploads basic information about each player and past game data from their device. Based on this, the server uses a generative AI model to analyze the strengths and weaknesses of the entire team. Based on the analysis results, a strategy suggestion is made, such as "A strategy of launching fast breaks centered around Player A would be effective in this game." Individual training programs are also generated for each player, with instructions provided such as "Player B should focus on free throw practice." During the game, data is transmitted in real time, and feedback such as "Switch to zone defense" is instantly displayed on the device.

[1352] As described above, this invention is a system that utilizes generative AI to provide advanced support for users to select effective strategies and discover new playing styles. This system contributes to improving sports performance and helps players and teams to perform at their best.

[1353] The processing flow will be explained below.

[1354] Step 1:

[1355] The user enters account information on the device, such as name, email address, password, and basic information about the sport played (e.g., sport type and position).

[1356] Step 2:

[1357] The server receives the registration information sent by the user and stores it in a database, after which a confirmation message containing the user authentication information is generated.

[1358] Step 3:

[1359] The user enters their email address and password into the terminal and attempts to log in. This sends the entered information to the server.

[1360] Step 4:

[1361] The server checks the registration information against the database and performs authentication. If authentication is successful, the user is allowed to access the main screen.

[1362] Step 5:

[1363] Users upload files of match data and training data using their devices, and this data is sent to the server.

[1364] Step 6:

[1365] The server receives the uploaded data, stores it in a database, and then analyzes the stored data using a generative AI model.

[1366] Step 7:

[1367] The server visualizes the analysis results and generates a dashboard, which is created in a user-friendly format using charts and graphs.

[1368] Step 8:

[1369] The server uses the generative AI model to generate optimal strategies and new playing styles based on the characteristics of the team and players. For example, it may generate a specific strategy such as "a strategy of launching a fast attack centered around player A is effective."

[1370] Step 9:

[1371] The server notifies the user of the generated strategies and training programs and displays them visually. The user can then check them on their device and use them in actual matches and training.

[1372] Step 10:

[1373] Users send data in real time during a match or training session, which provides the server with information on the match status and training progress in real time.

[1374] Step 11:

[1375] The server receives real-time data and instantly analyzes it using generative AI models, enabling quick tactical adjustments during matches and immediate feedback during training.

[1376] Step 12:

[1377] The server generates real-time feedback and provides it to the user, with specific instructions such as "You should strengthen your defensive line" being visually displayed on the device.

[1378] Step 13:

[1379] The feedback received by users is used to instantly modify match and training tactics, improving player and team performance.

[1380] Example 1

[1381] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1382] Conventional sports strategy planning systems often require manual analysis of match and training data, which is time-consuming and labor-intensive. Furthermore, the strategies and training programs generated may not be fully effective because they are not optimized for the characteristics of specific players or teams. Furthermore, the lack of real-time feedback makes it difficult to make timely tactical adjustments during matches or training.

[1383] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1384] In this invention, the server includes means for saving registration information entered by a user in a database, means for receiving and saving data uploaded by a user, means for analyzing the saved data using a generative AI model and generating results, means for visually displaying the generated analysis results, means for using the generative AI model to generate strategies and training programs tailored to the characteristics of teams and players, means for analyzing real-time data sent by users and providing feedback, means for notifying users of strategies and training programs based on the analysis results to their terminals, and means for users to confirm the proposed strategies and incorporate them into their own strategies. This allows users to easily automate data analysis and use strategies and training programs optimized for specific players and teams, and also enables timely tactical adjustments based on real-time feedback.

[1385] A "user" is a person who uses the system to plan sports strategies, upload various data, and check training programs.

[1386] "Server" means a computer system that manages and analyzes information received from users, generates results, and provides them to users.

[1387] "Terminal" means a device that allows a user to access the system and input and upload data and check analysis results.

[1388] A "database" is a storage device for storing user registration information, uploaded match data, training data, and the like.

[1389] A "generative AI model" is an artificial intelligence algorithm that analyzes input sports-related data and generates strategy suggestions and training programs.

[1390] "Registration information" refers to basic information such as name, email address, password, sport, and position that a user enters when registering with the system for the first time.

[1391] "Match Data" means information relating to the results of specific matches and player performances that is uploaded by a User to the System.

[1392] "Training Data" is information including measurement results related to an athlete's training status and performance.

[1393] "Analysis results" are deliverables such as player performance evaluations and opponent tactical analyses that are generated by the generative AI model after analyzing the uploaded data.

[1394] A "dashboard" is a user interface that allows the system to visually display analysis results.

[1395] "Strategy" refers to a game plan or approach optimized for the characteristics of a team or player in a match.

[1396] A "training program" is a training menu or drill optimized based on each player's performance data.

[1397] "Real-time data" refers to performance data that is collected in real time during a match or training session and sent to a server.

[1398] "Feedback" refers to specific instructions and improvements provided by the server based on the results of analyzing real-time data.

[1399] The sports strategy planning system of the present invention combines various functions centered on a generative AI model to provide a platform for users to optimize their game strategies and discover new playing styles. Specific embodiments for implementing the system are described below.

[1400] This system uses a server, terminals, and a database. The server analyzes information received from users and generates strategies and training programs using generative AI models. The terminal is an interface through which users input data and check the analysis results. The database functions as a storage device for saving user registration information and uploaded data.

[1401] First, a user creates an account using a terminal by entering basic information such as name, email address, password, sport, position, etc. This information is sent to the server and stored in a database.

[1402] Next, users can upload their own match and training data to the system via their devices. The server receives this data and stores it in a database. The stored data is then analyzed using generative AI models. The analysis results include player performance evaluations and opponent tactical analysis.

[1403] The analysis results are displayed visually and provided as a dashboard for easy understanding by users. For example, when basketball game data is analyzed, detailed statistical data such as points, assists, and rebounds for each player is displayed visually.

[1404] The server also uses generative AI models to generate optimal strategies and new playing styles based on team and player characteristics. The generated strategies are then sent to the user's device. For example, specific suggestions are made, such as, "This player is good at three-point shooting, so we'll build a play around him."

[1405] Furthermore, the server analyzes the performance data of each player and generates an optimized training program. For example, it may suggest specific drills for a certain player to improve their 3-point shooting success rate. Users can check this training program on their device and incorporate it into their actual training.

[1406] During a match or training session, the user sends real-time data from their device to the server, which instantly analyzes it and uses generative AI models to provide real-time feedback. For example, specific visual feedback such as "You should improve your defensive line" could be provided during a match.

[1407] Examples of specific prompts include:

[1408] "Please upload basketball game data, analyze player A's strengths, and suggest the optimal strategy."

[1409] "Generate a training program suitable for player B."

[1410] "Provide real-time feedback during the match."

[1411] In this way, the present invention is a system that utilizes generative AI to provide advanced support for users to select effective strategies and discover new playing styles, thereby improving sports performance and helping players and teams to perform at their best.

[1412] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1413] Step 1:

[1414] The user uses their device to open the account creation page. They enter basic information such as their name, email address, password, sport, and position, and click the "Register" button. The entered information is sent to the server, which receives it and stores it in a database. Input data: "Name, email address, password, sport." Output data: "Registration information saved in database."

[1415] Step 2:

[1416] The user uses their device to open the login page, enter their email address and password, and click the "Login" button. The entered authentication information is sent to the server, which checks it against the information in its database. If it matches, authentication is successful and access to the main screen is permitted. If it does not match, an error message is displayed. Input data: "Email address, password". Output data: "Authentication result, permission to access the main screen or error message."

[1417] Step 3:

[1418] Users upload match data and training data from their devices. The user opens the "Data Upload" page, selects a file, and clicks the "Upload" button. The uploaded data is sent to the server, which receives it and stores it in a database. Input data: "Match data, training data." Output data: "Data saved to database."

[1419] Step 4:

[1420] The server passes saved match data and training data to the generative AI model and begins analyzing the data. The analysis involves evaluating player performance and analyzing opponent tactics. As a result, information such as each player's strengths and weaknesses and opponent tactical tendencies is generated. Input data: "Saved data." Output data: "Analysis results."

[1421] Step 5:

[1422] The analysis results are visually represented by the server and provided to the user in the form of a dashboard. The user opens the dashboard page on their device to check the analysis results. For example, statistical data such as points, assists, and rebounds for each player is displayed. Input data: "Analysis results". Output data: "Visualized dashboard".

[1423] Step 6:

[1424] The server uses a generative AI model to generate optimal strategies and new playing styles based on characteristic data of teams and players. The generated strategies are sent to the user's device. For example, they may include specific suggestions such as, "In this match, a strategy of launching quick attacks centered around player A would be effective." Input data: "Characteristic data." Output data: "Strategic proposals."

[1425] Step 7:

[1426] The user checks the strategy proposal on the terminal and decides whether to adopt the proposed strategy. If they do, they click the "Adopt" button. Input data: "Strategy proposal". Output data: "Decision on adopting the strategy".

[1427] Step 8:

[1428] The server analyzes the performance data of each player and generates an optimized training program using a generative AI model. For example, a specific menu may be generated, such as "Player B should focus on free throw practice." This is then sent to the user's device. Input data: "Performance data." Output data: "Training program proposal."

[1429] Step 9:

[1430] The user checks the training program on the device and reflects it in their actual training. For example, a detailed menu is displayed, such as "Player B will be given a specific free throw drill." Input data: "Training program proposal." Output data: "Training reflection."

[1431] Step 10:

[1432] During a match or training session, the user sends data from their device to the server in real time. The server immediately analyzes this data and provides real-time feedback using a generative AI model. For example, specific instructions such as "You should improve your defensive line" are fed back to the device in real time. Input data: "Real-time data." Output data: "Real-time analysis results and feedback."

[1433] (Application example 1)

[1434] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1435] In sports games and training, data analysis and real-time feedback are important for optimizing strategies and improving player performance. However, existing systems struggle to provide these in an integrated and efficient manner. Furthermore, there are limitations, particularly with regard to providing real-time feedback and strategy suggestions, creating a demand for technologies that enhance the user experience. Additionally, there is a lack of methods for providing real-time analysis and feedback in virtual environments.

[1436] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1437] In this invention, the server includes: means for storing registration information entered by a user in a database; means for receiving and storing data uploaded by a user; means for analyzing the stored data using a generative model to generate results; means for visually displaying the generated analysis results; means for generating strategies and training programs tailored to team and player characteristics using the generative model; means for analyzing real-time data sent by a user and providing feedback; means for acquiring data in real time from the smart glasses and notifying the user of the analysis results on the spot; and means for providing strategy suggestions and training feedback in real time using the generative AI model while running a virtual sports simulation. This allows users to receive strategy and training suggestions based on real-time analyzed data during sports games and training and immediately implement them. Real-time feedback and instant strategy adjustments can maximize player and team performance.

[1438] Definition of Terms

[1439] "Means for storing user-entered registration information in a database" refers to the process and system for securely storing basic information entered by users, such as name, email address, password, sport type, and position, in a database.

[1440] "Means for receiving and storing data uploaded by users" refers to the functions and system for receiving match data and training data uploaded by users from their terminals to the server and storing it in a database.

[1441] "Means for analyzing data and generating results using a generative model" refers to methods and technologies for analyzing uploaded match data or training data using a generative AI model and generating results.

[1442] "Means for visually displaying the generated analysis results" refers to an interface and system for visually displaying the results analyzed by the generative AI model in a format that is easy for users to understand.

[1443] "Means for using generative models to generate strategies and training programs tailored to the characteristics of teams and players" refers to generative AI models and systems for generating optimal strategies and training programs based on team and player data.

[1444] The "means for analyzing real-time data sent by a user and providing feedback" refers to a method and system for instantly analyzing real-time data sent by a user during a match or training session and providing that feedback.

[1445] "Means for acquiring data in real time from smart glasses and notifying the user of the analysis results on the spot" refers to technology and a system for acquiring data in real time through smart glasses and notifying the user of the analysis results by displaying them on the glasses.

[1446] "Means for providing strategy suggestions and training feedback in real time using a generative AI model while running a virtual sports simulation" refers to methods and technologies for using a generative AI model to provide strategy suggestions and training feedback to a user in real time during a sports simulation in a virtual environment.

[1447] MODE FOR CARRYING OUT THE INVENTION

[1448] A system for realizing the present invention includes the following configuration and means.

[1449] Basic configuration

[1450] 1. User Registration and Login

[1451] The user first creates an account using a terminal. The required information is entered as name, email address, password, and basic information about the sport (e.g., sport type and position). The server receives this information and stores it in a database. When the user uses the system again, they log in using their email address and password. The server verifies the authentication information, and if authentication is successful, they are granted access to the main screen.

[1452] 2. Data Acquisition and Analysis

[1453] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. The analysis includes evaluation of player performance and analysis of opponent tactics. The analysis results are presented as a visual dashboard. This dashboard has an interface that is easy for users to understand.

[1454] 3. Generating a Strategy and Training Program

[1455] The server uses a generative AI model based on team and player characteristic data to generate optimal strategies and training programs. The generated strategies are then sent to the user's device. For example, specific suggestions such as "This player is good at three-point shooting, so we'll build plays around him" are included. Users can review these suggestions and incorporate them into their own strategies.

[1456] 4. Real-time feedback

[1457] During a match or training session, users use smart glasses to capture real-time data and send it to a server. The server then uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "You should strengthen your defensive line" are displayed on the user's smart glasses as visual feedback during a match, allowing the user to make on-the-spot tactical adjustments and improve their performance.

[1458] Hardware / Software used

[1459] The system uses generative AI models that utilize the TensorFlow and Keras libraries, and uses a SQLite3 database to store data. Appropriate network infrastructure is also used to acquire real-time data using smart glasses and communicate the analysis results to the user.

[1460] Examples of concrete examples and prompts

[1461] As a concrete example, the prompt sentence when basketball game data is input is shown below.

[1462] "Upload your basketball game data and evaluate your 3-point shooting performance."

[1463] "Use Player A's match data to generate the optimal training program for him."

[1464] "Use real-time match data to give us feedback to improve our defensive strategies."

[1465] The system based on this invention allows users to receive real-time data analysis and strategy suggestions to maximize their sports performance.

[1466] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1467] Processing Step Description

[1468] Step 1: User Registration and Login

[1469] Operation:

[1470] Input: The user enters basic information into the device, such as name, email address, password, sport type, and position.

[1471] Data processing: The server receives the registration information sent from the device and formats it into the required format.

[1472] Output: Save the formatted registration information to the database.

[1473] Specific behavior:

[1474] The user enters registration information into the terminal and sends it to the server.

[1475] The server stores the received information in an SQLite3 database.

[1476] When the user uses the system again, he or she sends a login request from the terminal using the email address and password.

[1477] The server checks the authentication information in its database and, if authentication is successful, allows the user access to the main screen.

[1478] Step 2: Data acquisition and analysis

[1479] Operation:

[1480] Input: Users upload match data and training data from their devices.

[1481] Data processing: The server receives and stores the uploaded data.

[1482] Output: The generative AI model analyzes the data and generates analytical results.

[1483] Specific behavior:

[1484] The user uploads match data and training data to the device.

[1485] The server receives this and stores it in a database.

[1486] The server analyzes the data using a generative AI model built using the TensorFlow and Keras libraries.

[1487] The analysis results are generated as a visually displayed dashboard.

[1488] Step 3: Generate a strategy and training program

[1489] Operation:

[1490] Input: The server receives team and player characteristic data.

[1491] Data Processing: Generative AI models are used to analyze trait data and generate optimal strategies and training programs.

[1492] Output: The generated strategies and training programs are sent to the user's terminal.

[1493] Specific behavior:

[1494] The server retrieves team and player characteristic data from a database.

[1495] The server uses a generative AI model to generate optimal strategies and training programs.

[1496] The system notifies the user's device of specific strategy suggestions and training programs, making suggestions such as, "This player is good at three-point shooting, so we'll build our play around him."

[1497] Step 4: Real-time feedback

[1498] Operation:

[1499] Input: Real-time data during a match or training session is acquired by the user using smart glasses and sent to the server.

[1500] Data processing: Receive real-time data and instantly analyze it using generative AI models.

[1501] Output: The analysis results are visually displayed as feedback on the user's smart glasses.

[1502] Specific behavior:

[1503] The user wears smart glasses and transmits data during a match or training session to a server in real time.

[1504] The server receives real-time data and instantly analyzes it using generative AI models.

[1505] As a result of the analysis, specific feedback such as "Your defensive line should be stronger" is displayed on the user's smart glasses.

[1506] Users receive instant visual feedback and can adjust their tactics accordingly.

[1507] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1508] The sports strategy planning system of the present invention provides advanced functionality that combines generative AI and an emotion engine, and is a platform that allows users to optimize their game strategies and discover new playing styles. An embodiment of this system will be described in detail.

[1509] User Registration and Login

[1510] A user first creates an account using a terminal. The required information is entered: name, email address, password, and basic information about the sport they play (e.g., sport type and position). The server then receives this information and stores it in a database. When the user uses the system again, they are required to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, the user is allowed to access the main screen.

[1511] Data acquisition and analysis

[1512] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis results include player performance evaluations and opponent tactical analysis. The analysis results are displayed visually as a dashboard for easy user understanding.

[1513] Strategy and playing style suggestions

[1514] The server uses a generative AI model to generate optimal strategies and new playing styles based on team and player characteristic data. For example, specific suggestions such as "A strategy of launching a fast break centered on player A is effective" are included. These suggestions are then sent to the user's device.

[1515] Training program generation

[1516] The server generates an optimized training program based on the performance data of each player. This provides a training menu that will help each player reach their full potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. The user can check this training program on their device and incorporate it into their actual training.

[1517] Real-time feedback

[1518] During a match or training session, the user sends real-time data from their device to the server. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "strengthen your defensive line" can be sent to the user's device as visual feedback during a match. This allows the user to make tactical adjustments on the spot and improve their performance.

[1519] Emotion engine integration

[1520] The system also integrates an emotion engine to collect and analyze users' emotional data. Specifically, the system detects the user's facial expressions and tone of voice during matches and training sessions, and collects emotional data. This data is then sent to the server in real time.

[1521] Emotional data analysis and feedback

[1522] The server analyzes the collected emotional data and adjusts strategies and training programs based on the results. For example, if it determines that the user is nervous, it will suggest relaxation training. If the user shows signs of impatience during a match, it will provide tactical feedback based on that. This allows for optimal instruction tailored to the user's mental state.

[1523] Specific examples

[1524] For example, when a soccer team coach uses this system, he or she uploads basic information about each player and past match data. Based on this data, the server uses a generative AI model to analyze the strengths and weaknesses of the entire team. As a result, a strategy suggestion is made, such as "A strategy of launching fast breaks centered on Player A is effective." Furthermore, the emotion engine collects each player's emotional data and suggests relaxation drills for players who are nervous. During the game, specific feedback is provided, such as "You need to play with more concentration," based on the emotional data collected in real time.

[1525] As described above, this invention is a system that combines generative AI and an emotion engine to provide users with advanced analysis and emotional support, thereby contributing to the improvement of sports performance and helping athletes and teams to perform at their best.

[1526] The processing flow will be explained below.

[1527] The sports strategy planning system of the present invention provides advanced functions that combine generative AI and an emotion engine, and is a platform for users to optimize their game strategies and discover new playing styles. The specific processing flow for implementing the present invention is described below.

[1528] User Registration and Login

[1529] Step 1:

[1530] The user enters account information on the device, specifically, name, email address, password, and basic information about the sport played (e.g., sport type and position).

[1531] Step 2:

[1532] The server receives the registration information sent by the user and stores it in the database. After the storage is complete, it generates the user authentication information.

[1533] Step 3:

[1534] The user enters their email address and password into the terminal and attempts to log in. This sends the entered information to the server.

[1535] Step 4:

[1536] The server checks the registration information against the database and performs authentication. If authentication is successful, the user is allowed to access the main screen.

[1537] Data acquisition and analysis

[1538] Step 5:

[1539] Users upload files of match data and training data using their devices, and this data is sent to the server.

[1540] Step 6:

[1541] The server receives the uploaded data, stores it in a database, and then analyzes the stored data using a generative AI model.

[1542] Step 7:

[1543] The server visualizes the analysis results and generates a dashboard, which is created in a user-friendly format using charts and graphs.

[1544] Strategy and playing style suggestions

[1545] Step 8:

[1546] The server uses the generative AI model to generate optimal strategies and new playing styles based on the characteristics of the team and players. For example, it may generate a specific strategy such as "a strategy of launching a fast attack centered around player A is effective."

[1547] Step 9:

[1548] The server notifies the user of the generated strategies and training programs and displays them visually. The user can then check them on their device and use them in actual matches and training.

[1549] Training program generation

[1550] Step 10:

[1551] The server generates an optimized training program based on each player's performance data. For example, it might suggest specific drills to improve a player's 3-point shooting success rate.

[1552] Step 11:

[1553] The user can check this training program on the terminal and apply it to their actual training.

[1554] Real-time feedback

[1555] Step 12:

[1556] Users send data in real time during matches and training, which provides the server with information on match status and training progress in real time.

[1557] Step 13:

[1558] The server receives real-time data and instantly analyzes it using generative AI models, enabling quick tactical adjustments during matches and immediate feedback during training.

[1559] Step 14:

[1560] The server generates real-time feedback and provides it to the user, with specific instructions such as "You should strengthen your defensive line" being visually displayed on the device.

[1561] Step 15:

[1562] The feedback received by users is used to instantly modify match and training tactics, improving player and team performance.

[1563] Emotion engine integration

[1564] Step 16:

[1565] During a match or training session, the device detects facial expressions and tone of voice to collect emotional data, which is then sent to a server in real time.

[1566] Step 17:

[1567] The server receives the collected emotional data and analyzes it using an emotion engine. Based on the analysis results, strategies and training programs are tailored to the user's mental state.

[1568] Step 18:

[1569] The server generates strategies and training programs based on the analyzed emotional data and provides them to the user, for example, suggesting training drills to help a tense user relax.

[1570] Step 19:

[1571] The server uses the user's emotional data to generate real-time feedback during a match or training session, providing the user with visual instructions such as "You need to play more focused."

[1572] As described above, this invention is a system that combines generative AI and an emotion engine to provide users with advanced analysis and emotional support, thereby contributing to the improvement of sports performance and helping athletes and teams to perform at their best.

[1573] Example 2

[1574] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1575] In modern sports, maximizing the performance of athletes and teams requires detailed analysis of match and training data and the optimization of strategies and training programs based on the results. However, conventional systems have difficulty analyzing data in real time and providing feedback, and are unable to provide training and strategy suggestions that take the user's emotional state into account. Therefore, there is a need for systems that provide advanced analysis and emotional support to users in order to improve their sports performance.

[1576] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1577] In this invention, the server includes means for saving registration information entered by the user in a database, means for receiving and saving data uploaded by the user, means for analyzing the saved data using a generative model to generate results, means for visually displaying the generated analysis results, means for generating strategies and training programs according to the characteristics of teams and players using the generative model, means for analyzing real-time data sent by the user and providing feedback, means for collecting user emotion data and integrating an emotion engine that is useful for analysis, and means for adjusting strategies and training programs based on the emotion data and providing feedback to the user. This enables detailed analysis of sports data in real time and can also suggest optimal strategies and training according to the user's emotional state.

[1578] "Means for storing registration information entered by the user in a database" refers to the system's function of receiving the data entered by the user when creating an account (e.g., name, email address, password, sport, position, etc.) and storing it in a database.

[1579] "Means for receiving and storing data uploaded by users" refers to a system function whereby the server receives and stores match data or training data uploaded by users from their terminals.

[1580] "Means for analyzing data using a generative model that analyzes stored data and generating results" refers to a function that analyzes data stored on a server using a generative AI model and generates the analysis results.

[1581] "Means for visually displaying the generated analysis results" refers to a system function that displays the results analyzed by the generative AI model in a visual format such as a dashboard or graph so that the user can easily understand them.

[1582] "Means for generating strategies and training programs according to the characteristics of teams and players using generative models" refers to a system function that uses generative AI models to analyze characteristic data of teams and players and automatically generates optimal strategies and training programs based on the results.

[1583] "Means for analyzing real-time data sent by users and providing feedback" refers to a system function that uses a generative AI model to instantly analyze real-time data sent by users from their devices during matches or training, and provides feedback based on the results.

[1584] "Means for collecting user emotional data and integrating an emotional engine to aid in analysis" refers to a function that integrates into the system an emotional engine that detects and collects the user's facial expressions and tone of voice, and uses that data for analysis.

[1585] "Means for adjusting strategies and training programs based on emotional data and providing feedback to users" refers to a system function that adjusts strategies and training programs in real time based on emotional data collected and analyzed by the emotion engine, and provides the content of these adjustments to users as feedback.

[1586] The sports strategy planning system of the present invention provides advanced functionality that combines generative AI and an emotion engine. Specific embodiments of this system are described in detail below.

[1587] User Registration and Login

[1588] A user first creates an account using a terminal. The required information is entered: name, email address, password, and basic information about the sport they play (e.g., sport type and position). The server receives this information and stores it in a database (e.g., MySQL). When the user returns to the system, they are prompted to log in using their email address and password. The server verifies the authentication information, and if successful, allows the user to access the main screen.

[1589] Data acquisition and analysis

[1590] When a user uploads match or training data using their device, the server receives and stores it. The uploaded data is saved in file storage (e.g., AWS S3). The server then analyzes the data using a generative AI model (e.g., OpenAI GPT) to evaluate player performance and analyze opponent tactics. The analysis results are displayed visually as a dashboard. For example, it visually displays "how the movements of the top scorer in this match were different."

[1591] Strategy and playing style suggestions

[1592] The server uses a generative AI model to generate optimal strategies and new playing styles based on team and player characteristic data. Specific suggestions include, for example, "A strategy of launching fast breaks centered around player A is effective." These suggestions are sent to the user's device. An example of a prompt is, "Please explain how the generative AI analyzes game data when the user uploads it."

[1593] Training program generation

[1594] The server generates an optimized training program based on each player's performance data. This provides a training menu that will help each player reach their full potential. For example, a specific drill to improve the success rate of three-point shots may be suggested for a certain player. Users can check this training program on their device and incorporate it into their actual training.

[1595] Real-time feedback

[1596] During a match or training session, the user sends real-time data from their device to the server. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "strengthen your defensive line" are sent to the user's device as visual feedback. This allows the user to make on-the-spot tactical adjustments and improve their performance.

[1597] Emotion engine integration

[1598] This system integrates an emotion engine to collect and analyze users' emotional data. For example, it detects the facial expressions and tone of voice that users show while using their devices during matches or training, and collects this as emotional data. Input devices such as cameras and microphones are used for detection. This data is sent to a server in real time and used for analysis.

[1599] Emotional data analysis and feedback

[1600] The server analyzes the collected emotional data and adjusts strategies and training programs based on the results. For example, if it determines that the user is nervous, it can suggest training to help them relax. Also, if the user shows signs of impatience during a match, it can provide tactical feedback based on that. This allows for optimal instruction tailored to the user's mental state.

[1601] Specific examples

[1602] An example of actual use of this system is when a soccer team coach uses it. The coach uploads basic information about each player and past match data. The server uses a generative AI model to analyze the strengths and weaknesses of the entire team, and as a result, suggests that "a strategy of launching a fast break centered on player A would be effective." Furthermore, the emotion engine collects each player's emotional data and suggests relaxation drills for players who are nervous. During the game, specific feedback such as "You need to play with more concentration" is provided based on the emotional data collected in real time.

[1603] In this way, by combining generative AI and an emotion engine, the present invention is a system that provides users with advanced analysis and emotional support, contributing to improving their athletic performance.

[1604] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1605] Program processing flow

[1606] User Registration and Login

[1607] Step 1:

[1608] The user creates an account on the device.

[1609] Input: User-entered name, email address, password, sport, and position.

[1610] Output: Input data is sent from the device to the server.

[1611] Specific actions: The user opens the application on the device, enters the required information, and clicks the send button.

[1612] Step 2:

[1613] The server receives the information and stores it in a database.

[1614] Input: Registration information sent from the device.

[1615] Output: User information stored in the database.

[1616] Specific operation: The server receives the information, validates it, and then saves it in a database (e.g., MySQL).

[1617] Step 3:

[1618] The user logs in and the server performs authentication.

[1619] Input: User's email address and password.

[1620] Output: The result of authentication, success or failure.

[1621] Specific operation: The user enters their email address and password on the login screen and clicks the login button. The server checks the information against the database, and if authentication is successful, allows access to the main screen.

[1622] Data acquisition and analysis

[1623] Step 4:

[1624] Users upload match data and training data on their devices.

[1625] Input: User selected match and training data files.

[1626] Output: Data transfer from the device to the server.

[1627] Specific behavior: The user clicks the upload button and selects a data file from the file selection dialog.

[1628] Step 5:

[1629] The server receives and stores the data.

[1630] Input: Match data and training data files sent from the device.

[1631] Output: Data stored in file storage (e.g. AWS S3).

[1632] Specific operation: The server confirms receipt of the file and saves it in the specified storage.

[1633] Step 6:

[1634] The server analyzes the data using a generative AI model.

[1635] Input: Saved match and training data.

[1636] Output: Analysis results.

[1637] How it works: The server inputs data into a generative AI model (e.g., OpenAI GPT) to evaluate player performance and analyze opponent tactics.

[1638] Strategy and playing style suggestions

[1639] Step 7:

[1640] The server generates strategies and playing styles based on the analysis results.

[1641] Input: Analysis results output by the generative AI model.

[1642] Output: Strategy and playing style suggestions.

[1643] Specific operation: The server analyzes the analysis results using an algorithm and generates the optimal strategy and playing style.

[1644] Step 8:

[1645] The server notifies the user of the proposal content.

[1646] Input: Generated strategy and playing style suggestions.

[1647] Output: The notification displayed on the user's device.

[1648] Specific operation: The server sends the suggestions to the user via push notification or email.

[1649] Training program generation

[1650] Step 9:

[1651] The server generates a training program based on the performance data.

[1652] Input: Player performance data.

[1653] Output: Optimized training program.

[1654] Specific operation: The server analyzes performance data and generates a training program for each player.

[1655] Step 10:

[1656] The user checks the training program on the terminal.

[1657] Input: The training program sent from the server.

[1658] Output: The training program displayed on the user's terminal.

[1659] What happens: The user receives a notification and learns about the training program.

[1660] Real-time feedback

[1661] Step 11:

[1662] The user sends real-time data on the terminal.

[1663] Input: Real-time data sent from your device (e.g., location, performance data).

[1664] Output: Data transfer to the server.

[1665] Specific operation: The user carries the device during a match or training session, and real-time data is automatically sent to the server.

[1666] Step 12:

[1667] The server analyzes the data in real time and provides feedback.

[1668] Input: Real-time data sent.

[1669] Output: Providing feedback.

[1670] How it works: The server uses a generative AI model to instantly analyze real-time data and send visual feedback to the user's device.

[1671] Emotion engine integration

[1672] Step 13:

[1673] The user collects emotion data on the device.

[1674] Input: Facial expressions and tone of voice collected from the camera and microphone.

[1675] Output: Emotion data sent to the server.

[1676] Specific operation: The user uses the device to collect emotional data by sensing facial expressions and tone of voice during a match or training session.

[1677] Step 14:

[1678] The server receives and analyzes the emotion data.

[1679] Input: User emotion data.

[1680] Output: Analysis results.

[1681] Specific operation: The server receives the transmitted emotion data and immediately analyzes it using the emotion engine.

[1682] Emotional data analysis and feedback

[1683] Step 15:

[1684] The server adjusts strategies and training programs based on the emotional data.

[1685] Input: Analysis results of emotion data.

[1686] Output: Tailored strategies and training programs.

[1687] Specific actions: Based on the analysis results, the server adjusts strategies and training programs as needed.

[1688] Step 16:

[1689] Send feedback to the user's device.

[1690] Input: Tailored strategies and training programs.

[1691] Output: Feedback displayed on the user's device.

[1692] Specific operation: The server sends the adjustments to the user's device and provides feedback.

[1693] (Application example 2)

[1694] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1695] Improving production efficiency in factories and managing workers' mental workload are important challenges in the manufacturing industry. Conventional systems have difficulty providing real-time optimization strategies and understanding workers' emotional states, making it difficult to improve production efficiency and the work environment. In particular, improving the operational efficiency of operating machines, reducing defective products, and adjusting workers' stress and fatigue levels are required, but it is difficult to achieve all of these simultaneously.

[1696] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1697] In this invention, the server includes means for saving registration information entered by a user in a database, means for receiving and saving data uploaded by a user, means for analyzing the saved data using a generative model to generate results, means for visually displaying the generated analysis results, means for generating optimization strategies and programs according to the characteristics of the operating machine and the worker using the generative model, means for analyzing real-time data sent from the user and providing feedback, and means for analyzing worker emotion data using an emotion recognition engine and adjusting the workload, thereby enabling improvement of production efficiency in the factory and management of the mental workload of workers.

[1698] "User" means the person or operator who operates the system and inputs or uploads data.

[1699] "Registration Information" refers to basic information such as name, email address, and password that a user enters when creating an account on the system.

[1700] "Database" refers to a storage device or software for storing user registration information and uploaded data within the system.

[1701] "Upload data" refers to information such as match data, training data, and operating machine data that users provide to the system.

[1702] A "generative model" is a model that analyzes user data and generates results such as strategies and programs. Specifically, it refers to a generative AI model.

[1703] "Analysis results" refers to information obtained as a result of analyzing data using a generative model, and includes strategy proposals, training programs, etc.

[1704] "Visual display means" refers to a method or device that displays the analysis results and generated strategies in graphs, charts, etc. so that the user can easily understand them.

[1705] An "optimization strategy" is the most efficient strategy or program generated by the generative model based on the characteristics of the operating machine or worker.

[1706] "Real-time data" refers to data collected in real time while a match or task is in progress, and is used for immediate analysis and feedback.

[1707] "Feedback means" refers to a method or system for notifying users of improvements or corrections based on the analysis results.

[1708] An "emotion recognition engine" is software or a device that analyzes workers' emotional data and adjusts strategies and training programs based on the results.

[1709] The "workload adjustment means" refers to a method or system for reducing the mental load of a worker and providing an optimized work environment based on the analysis results of the emotion recognition engine.

[1710] The system for implementing this invention is a comprehensive platform for improving production efficiency in factories and managing the mental workload of workers. The system provides advanced functionality combining generative AI models and emotion recognition engines, enabling users to utilize optimization strategies and training programs.

[1711] Hardware and software used

[1712] Hardware: moving machines (e.g., robotic arms), sensors (cameras, microphones, thermometers, hygrometers, etc.)

[1713] Software: Generative AI model (natural language generation AI model), emotion recognition engine (emotion analysis software)

[1714] Communication: Local server, cloud server

[1715] System configuration and operation

[1716] 1. User Registration and Login:

[1717] A user first creates an account using a terminal. Required information includes name, email address, and password. The server then receives this information and stores it in a database. When the user uses the system again, they are prompted to log in using their email address and password. The server verifies the authentication information, and if authentication is successful, the user is allowed access to the main screen.

[1718] 2. Data Acquisition and Analysis:

[1719] When users upload match and training data using their devices, the server receives and stores it. The uploaded data is analyzed by a generative AI model. Analysis content includes performance evaluation of operating machines and analysis of the production environment. The analysis results are displayed visually as a dashboard for easy user understanding.

[1720] 3. Optimization strategies and program generation:

[1721] The server uses a generative AI model to generate optimal strategies and programs based on the characteristic data of the operating machines and workers. For example, specific suggestions such as "A strategy to increase the operating speed of machine A by 10% is effective" are included. These suggestions are then sent to the user's device.

[1722] 4. Real-time feedback:

[1723] During production or work, users send data from their devices to a server in real time. The server uses a generative AI model to instantly analyze the real-time data and provide feedback. For example, specific instructions such as "adjust the machine's operating angle" are sent to the user's device as visual feedback during work. This allows the user to make on-the-spot corrections to improve efficiency.

[1724] 5. Emotion recognition engine integration:

[1725] The system also integrates an emotion recognition engine to collect and analyze user emotional data. Specifically, the system detects facial expressions and tone of voice while the user is working on the device, and collects emotional data. This data is then sent to the server in real time.

[1726] 6. Emotional data analysis and feedback:

[1727] The server analyzes the collected emotional data and adjusts work optimization strategies and training programs based on the results. For example, if it determines that the user is tired, it will suggest taking a break. Also, if it determines that the user is unable to concentrate while working, it will adjust the work load accordingly. This allows for optimal instruction tailored to the user's mental state.

[1728] Specific examples

[1729] For example, if a robot is introduced to assemble products in a factory, the system operates as follows.

[1730] Collects operation data and sensor data from factory robots in real time.

[1731] The generative AI model analyzes the data and generates specific suggestions such as "The supply speed of part A should be increased by 10%."

[1732] An emotion recognition engine analyzes the worker's level of fatigue based on their facial expressions and tone of voice. If it determines that the worker is highly fatigued, it will suggest break times and work adjustments.

[1733] Prompt Sentence Examples

[1734] "Propose an optimization strategy to increase production efficiency on a manufacturing line. Data should include the operating time of each work station, part feed rate, and environmental parameters. Also analyze worker fatigue and stress levels."

[1735] With the above-described configuration and operation, the present invention can improve production efficiency in a factory and manage the mental stress of workers.

[1736] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1737] Step 1:

[1738] The user creates an account using a device and enters the required information (name, email address, password, etc.). This generates registration information. The server saves this registration information in a database. After verifying the entered data, the server saves it in the database as registration information, creating the user's account.

[1739] Step 2:

[1740] The user uploads game data, training data, and operation data of the motion machine using the terminal. The uploaded data is received and stored by the server. After the input data is confirmed, the stored data can be used for analysis.

[1741] Step 3:

[1742] The server uses a generative AI model to analyze the stored data. This generative AI model analyzes the uploaded data (e.g., evaluating the performance of the operating machine, analyzing the production environment, etc.) and generates results. The generative AI model performs analysis based on the input data and obtains the analysis results as output.

[1743] Step 4:

[1744] The server generates a dashboard that visually displays the analysis results obtained by the generative AI model. The analysis results are converted into visual formats such as graphs and charts so that users can easily understand them. The server receives the analysis results as input, generates data for visual display, and sends it to the terminal.

[1745] Step 5:

[1746] The server uses the generative AI model to generate optimization strategies and programs according to the characteristics of the operating machine and worker. These optimization strategies and programs are created based on the generated analysis results and characteristic data of the operating machine and worker. Based on the input characteristic data and analysis results, the server generates and outputs the optimization strategies and programs.

[1747] Step 6:

[1748] The server notifies the user's device of the generated optimization strategy or program, allowing the user to receive specific suggestions. The server receives the generated suggestions as input, generates notification data, and sends it to the device.

[1749] Step 7:

[1750] Users send data from their devices in real time to the server, which receives this real-time data and immediately analyzes it using a generative AI model.The server receives real-time data as input, analyzes it using a generative AI model, and generates feedback.

[1751] Step 8:

[1752] The server provides the results of the analysis performed in real time as feedback. For example, specific instructions such as "adjust the machine's operating angle" are sent to the user's device during work. The server receives the generated feedback as input and sends it to the user's device.

[1753] Step 9:

[1754] The server uses an emotion recognition engine to collect and analyze the worker's emotional data, such as facial expressions and tone of voice while working.

[1755] Step 10:

[1756] The server analyzes the collected emotion data and generates a suggestion for adjusting the workload based on the analysis results. For example, if the server determines that the user is fatigued, it suggests taking a break. The server receives the analysis results of the emotion data as input, generates the suggestion for adjusting the workload, and outputs it.

[1757] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1758] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1759] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1760] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1761] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1762] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1763] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1764] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1765] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1766] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1767] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1768] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1769] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1770] 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.

[1771] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1772] The hardware resource for executing a specific process can be any of the following pro...

Claims

1. means for storing the registration information entered by the user in a database; means for receiving and storing data uploaded by users; means for analyzing the stored data using a generative model to generate a result; a means for visually displaying the generated analysis results; A means for generating strategies and training programs according to the characteristics of teams and players using a generative model; A system including a means for analyzing real-time data sent by a user and providing feedback.

2. A means for analyzing match data and training data input from a user's device and generating a dashboard for displaying the data; 10. The system of claim 1, further comprising means for visually displaying the generated strategies and training programs.

3. 10. The system of claim 1, further comprising means for analyzing individual performance data of athletes to generate individually optimized training programs.

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