System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting game data, training data, and physical data of a player, a means for analyzing the collected data and specifying the strengths and weaknesses of the player, a means for generating a report based on the analysis result, and a means for outputting the generated report.SELECTED DRAWING: Figure 1

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] Optimally evaluating players' abilities and proposing strategies and managing schedules requires collecting and analyzing large amounts of data and making decisions based on the results. However, conventional methods often involve manually collecting and analyzing data, which is inefficient and lacks accuracy. Another problem is that it is difficult to accurately grasp each player's strengths and weaknesses and then use them to develop strategies and manage schedules. The present invention aims to solve these problems and provide a system for maximizing players' abilities. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including the following means. First, a means for collecting players' match data, training data, and physical data is provided, thereby enabling a detailed understanding of the players' condition. Next, a means for analyzing the collected data and identifying each player's strengths and weaknesses is provided. By combining a means for generating reports based on the analysis results and a means for outputting the generated reports, players' abilities can be objectively evaluated. Furthermore, a means for collecting and analyzing data from the opposing team and combining it with the player data of the team to generate an optimal strategy is provided, and a means for outputting the generated strategy is included, enabling efficient strategy planning. Furthermore, a means for periodically collecting players' physical data and generating a training schedule based on that data is provided, and a means for outputting and notifying the generated schedule is included, thereby optimizing the management of players' condition. In this way, a system is provided that maximizes players' abilities and realizes efficient team management.

[0006] 1. "Data collection means" refers to devices or systems for acquiring and storing necessary information such as players' match data, training data, and physical data.

[0007] 2. "Data Analysis Tools" means algorithms or software used to process collected data, evaluate athlete performance and condition, and identify strengths and weaknesses.

[0008] 3. "Report generation means" refers to devices or software that, based on the results of data analysis, creates detailed analytical results regarding an athlete's performance and condition in the form of documents, graphs, etc.

[0009] 4. "Output means" refers to devices or systems that display screens, output files, or provide notifications to users in order to provide generated reports, strategies, and schedules to users.

[0010] 5. "Opponent team data collection means" refers to a device or system for collecting and analyzing match data and player data related to the opposing team.

[0011] 6. "Strategy generation means" refers to algorithms or software that derive optimal match strategies based on data from the opposing team and one's own team.

[0012] 7. "Means for periodic physical data collection" refers to devices or systems for periodically collecting and storing physical data such as athletes' heart rate, fatigue level, and sleep data.

[0013] 8. "Training schedule generation means" refers to algorithms or software for creating optimal training schedules based on the athlete's physical data.

[0014] 9. "Schedule notification means" refers to a device or system for notifying athletes of information such as the generated training schedule and rest days.

[0015] The above definitions clarify the roles and functions of each means constituting the system of the present invention. [Brief explanation of the drawings]

[0016] [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 illustrating 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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention is a system for optimally evaluating the abilities of players, proposing strategies, and managing the schedules of players. The program processing of this system will be explained below in natural language.

[0038] Player ability analysis

[0039] Data collection

[0040] The server collects each player's match data, training data, and physical data from a database. The player's wearable device sends data such as heart rate, distance, and speed to the server in real time.

[0041] Data analysis

[0042] The server combines the collected data to calculate each player's performance metrics (e.g., passing success rate, shooting success rate, speed, stamina), and then applies machine learning algorithms to identify players' strengths and weaknesses.

[0043] Report Generation

[0044] The server generates a report for each player based on the analysis results. The report includes graphs and charts and is created in a visually easy-to-understand format. The generated report is sent to the terminal (coach's or player's device).

[0045] Specific examples

[0046] The server collects the match data of Player A and determines that his pass success rate is 90%, his shot success rate is 50%, and his speed is 28km / h. From this data, it is analyzed that Player A is good at passing, but his shooting accuracy has room for improvement.

[0047] Strategy proposals

[0048] Analysis of the opposing team

[0049] The server collects the opponent's match data, analyzes recent match results and player performance data, and uses machine learning algorithms to identify the opponent's strengths and weaknesses.

[0050] Analyzing your team's capabilities

[0051] The server collects and analyzes the performance data of each player on the team, identifying players who excel at dribbling and players with high passing accuracy.

[0052] Strategy Generation

[0053] The server generates an optimal strategy based on data from the team and the opposing team. The generated strategy is sent to the terminal (coach's device), where the user (coach) can review and adjust it.

[0054] Specific examples

[0055] The server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since player B on the team is good at dribbling, the server proposes a strategy of having player B attack from the center.

[0056] Player schedule management

[0057] Physical data analysis

[0058] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.), and the terminal (wearable device) transmits the data to the server in real time.

[0059] Schedule proposal

[0060] The server evaluates the player's current condition based on physical data and generates an optimal training schedule, which includes rest days and recovery training.

[0061] Schedule adjustment

[0062] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of each player and the team's schedule. After adjustments, the final schedule is fed back to the server.

[0063] Schedule Notifications

[0064] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[0065] Specific examples

[0066] The server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. The user (coach) checks the schedule and, after adjustments, the final schedule is sent to Player C's device.

[0067] As described above, the system of the present invention efficiently collects and analyzes player data, proposes strategies, and manages schedules, thereby maximizing player performance.

[0068] The processing flow will be explained below.

[0069] Player ability analysis

[0070] Data collection

[0071] Step 1:

[0072] The server collects each player's match data from the database. The server also collects performance data during the match (pass success rate, shot success rate, running distance, etc.).

[0073] Step 2:

[0074] The terminal (the athlete's wearable device) transmits training data such as heart rate, speed, and distance to the server in real time.

[0075] Step 3:

[0076] The server periodically collects physical data (e.g., heart rate, fatigue level, sleep time) from each player's device.

[0077] Data analysis

[0078] Step 4:

[0079] The server integrates the collected match data, training data, and physical data to calculate each player's performance indicators. The server also cleanses the data and fills in outliers and missing data.

[0080] Step 5:

[0081] The server applies machine learning algorithms to identify players' strengths and weaknesses, and performs clustering analysis based on each indicator to extract performance patterns.

[0082] Report Generation

[0083] Step 6:

[0084] The server generates a report for each player based on the analysis results, including graphs and charts in a visually easy-to-understand format.

[0085] Step 7:

[0086] The server sends the generated report to the terminal (the coach's or player's device). The server notifies the user of the report, and the user checks the report.

[0087] Strategy proposals

[0088] Analysis of the opposing team

[0089] Step 1:

[0090] The server collects the match data of the opposing team. The server retrieves the results of recent matches and player data of the opposing team from the database.

[0091] Step 2:

[0092] The server analyzes the strengths and weaknesses of the opposing team. The server uses machine learning algorithms to identify the weaknesses of the opposing team.

[0093] Analyzing your team's capabilities

[0094] Step 3:

[0095] The server collects and analyzes the performance data of each player on the team, evaluating skills such as dribbling and passing accuracy.

[0096] Strategy Generation

[0097] Step 4:

[0098] The server generates optimal match strategies based on the data collected, maximizing the strengths of the team and exploiting the weaknesses of the opposing team.

[0099] Strategic Notification

[0100] Step 5:

[0101] The server sends the generated strategy to the terminal (coach's device), where the user (coach) reviews the strategy and makes adjustments if necessary.

[0102] Player schedule management

[0103] Physical data analysis

[0104] Step 1:

[0105] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.), and the terminal (wearable device) sends the data to the server in real time.

[0106] Step 2:

[0107] The server analyzes the collected data to evaluate the player's current physical condition and cleanses outliers and inconsistencies.

[0108] Schedule proposal

[0109] Step 3:

[0110] The server generates an optimal training schedule based on the analysis results, incorporating rest days and recovery training as needed.

[0111] Schedule adjustment

[0112] Step 4:

[0113] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[0114] Schedule Notifications

[0115] Step 5:

[0116] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[0117] This allows one to understand the specific flow of how the system of the present invention efficiently collects and analyzes player data, proposes strategies, and manages schedules.

[0118] Example 1

[0119] 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."

[0120] In modern sports, accurately evaluating individual athletes' performance, formulating optimal strategies, and managing schedules efficiently are extremely difficult challenges. Conventional methods involve manual data collection and analysis, which is time-consuming, labor-intensive, and often inaccurate. Furthermore, training schedules and strategy proposals that incorporate athletes' real-time physical data are not adequately implemented. This makes it difficult to maximize athletes' potential. The present invention aims to solve these challenges by providing a system that automates effective data collection and analysis, strategy proposals, and schedule management.

[0121] 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.

[0122] In this invention, the server includes: means for collecting a player's match data, training data, and physical data; means for integrating the collected data to calculate a player's performance indicators; and means for identifying a player's strengths and weaknesses using a machine learning algorithm. This enables accurate evaluation of a player's abilities and rapid identification of their strengths and weaknesses. The server also includes means for generating reports using graphs and charts; means for outputting the generated reports to a terminal; and means for the terminal to transmit data to the server in real time. This allows evaluation results to be presented visually in an easy-to-understand manner, enabling players and coaches to respond quickly. The approach continues with means for collecting opponent team match data and applying a machine learning algorithm to analyze the opponent team's strengths and weaknesses; means for collecting and analyzing the player data of the team's own team to generate an optimal strategy; and means for outputting the generated strategy to a terminal for the user to review and adjust. This improves the accuracy and effectiveness of strategies, thereby increasing competitiveness in matches. The system also includes a means for periodically collecting athlete physical data and generating a training schedule using time-series analysis, a means for outputting the generated schedule to a terminal in PDF format, and a means for notifying and displaying the generated schedule to the athlete. This makes it possible to provide an optimal training schedule while maintaining the athlete's health and performance.

[0123] "Player's match data" refers to various statistical data obtained by a player in an actual match (e.g., pass success rate, shot success rate, number of assists, etc.).

[0124] "Athlete training data" refers to various numerical data recorded by athletes during training (e.g., training time, number of training sessions, load, etc.).

[0125] "Athlete's physical data" refers to numerical data relating to the athlete's physical condition and stamina (e.g., heart rate, speed, distance, sleep data, fatigue level, etc.).

[0126] "Synthesis" is the process of bringing together different types of data into a single dataset in a form that can be analyzed as a whole.

[0127] "Performance indicators" are quantitative indicators used to evaluate a player's abilities and condition, and include pass success rate, shooting success rate, speed, stamina, etc.

[0128] "Machine learning algorithm" is a general term for mathematical techniques that automatically learn from data and make predictions or classifications. Examples include TENSORFLOW (registered trademark) and Scikit-learn.

[0129] A "strength" is a characteristic or skill that makes a player's performance or ability superior to other aspects of their game.

[0130] A "weakness" is a characteristic or skill that is believed to need improvement in a player's performance or ability.

[0131] A "report" is a report that summarizes the results of data analysis in the form of documents, graphs, charts, etc.

[0132] "Device" means a device used by an athlete or coach (e.g., smartphone, tablet, computer, wearable device).

[0133] "Opponent team's match data" refers to various statistical data obtained by the opposing team during an actual match.

[0134] A "strategy" is a specific plan or policy for achieving a goal in a match or training.

[0135] "Time series analysis" is a method for analyzing data that changes over time and predicting future trends.

[0136] A "schedule" is a plan that systematically arranges the training, rest, and match dates of players.

[0137] "Notifications" are messages and alarms sent by the system to inform players and coaches of important information.

[0138] The present invention is a system for optimally evaluating the abilities of players, proposing strategies, and managing the schedules of players. The following describes how to implement the program of this system.

[0139] Player ability analysis system

[0140] Data collection

[0141] The server collects players' match and training data from a database. It also sends physical data measured in real time by the device, such as heart rate, distance, and speed, to the server. The specific software used is MySQL (registered trademark) for database management, and Python and Pandas for data collection and analysis scripts.

[0142] Data analysis

[0143] The server aggregates the collected data and calculates player performance indicators, such as passing success rate, shooting success rate, speed, stamina, etc. It then applies machine learning algorithms (such as TensorFlow and Scikit-learn) to identify players' strengths and weaknesses.

[0144] Report Generation

[0145] The server generates a visually easy-to-understand report using graphs and charts based on the analysis results. Specific libraries used are Matplotlib and Seaborn. This report is generated in PDF format and output to the terminal (the coach's or player's device).

[0146] Strategy Proposal System

[0147] Analysis of the opposing team

[0148] The server collects data on the opposing team's matches using web scraping techniques (Beautiful Soup and Selenium) and applies machine learning algorithms to analyze the opposing team's strengths and weaknesses.

[0149] Analyzing your team's capabilities

[0150] The server collects and analyzes data on the team's players, evaluating specific skills such as dribbling and passing accuracy, again using Python and the Sklearn library.

[0151] Strategy Generation

[0152] The server generates an optimal strategy based on data from the team and the opposing team. The generated strategy is output to the terminal (coach's device) and the user (coach) checks and adjusts it.

[0153] Athlete schedule management system

[0154] Physical data analysis

[0155] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.) and also receives real-time data from the device.

[0156] Schedule proposal

[0157] The server evaluates the player's current condition based on the collected physical data and uses time-series analysis to generate an optimal training schedule, including rest days and recovery training.

[0158] Scheduling and Notifications

[0159] The user (coach) reviews the proposed schedule and makes final adjustments. After adjustments, the final schedule is generated in PDF format and output to the device. The device receives a notification and displays the schedule to the players.

[0160] Specific examples

[0161] For example, suppose the server collects player A's match data and determines that his passing success rate is 90%, his shooting success rate is 50%, and his speed is 28km / h. Analyzing this data, it determines that player A is good at passing, but his shooting accuracy needs improvement. Based on these results, the server identifies player A's strengths and weaknesses, generates a visual report, and sends it to the coach's device.

[0162] Furthermore, when proposing a strategy, the server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since player B on the team is good at dribbling, the server proposes a strategy to have player B attack from the center and sends the result to the coach's device.

[0163] In schedule management, the server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. After the user (coach) checks and adjusts the schedule, the final schedule is sent to Player C's device.

[0164] Prompt Sentence Examples

[0165] "How would you rate Player A's match performance?"

[0166] The generative AI model generates an answer such as, "Player A's match performance was assessed as having a passing success rate of 90%, a shooting success rate of 50%, and a speed of 28 km / h. Based on this, it was determined that his passing ability is excellent, but his shooting accuracy needs improvement."

[0167] As described above, the present invention is a system that automates the effective collection and analysis of data, strategy proposals, and schedule management, and can bring out the best in players' performance.

[0168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0169] Player ability analysis system

[0170] Step 1: Data collection

[0171] Inputs: Real-time physical data (heart rate, speed, distance) and match and training data.

[0172] How it works: The wearable device collects the athlete's heart rate, speed, and distance data in real time. The server receives this data via Bluetooth or Wi-Fi and records it in a database. In addition, the server retrieves past match and training data from the database.

[0173] Output: The integrated dataset is stored in a database on the server.

[0174] Step 2: Data integration

[0175] Input: Match data, training data, physical data.

[0176] How it works: The server uses Python and the Pandas library to combine these data into a single unified dataset, clean and normalize the data, impute missing data, and remove outliers.

[0177] Output: A clean, consolidated dataset.

[0178] Step 3: Performance Metrics Calculation

[0179] Input: The integrated dataset.

[0180] How it works: The server applies statistical methods to calculate each player's performance metrics (passing success rate, shooting success rate, speed, stamina). Specifically, it uses the Python Scikit-learn library to perform the calculations.

[0181] Output: A list of calculated performance metrics.

[0182] Step 4: Machine learning analysis

[0183] Input: A list of performance metrics.

[0184] How it works: The server uses TensorFlow and Scikit-learn libraries to apply machine learning algorithms to identify players' strengths and weaknesses, and analyzes them using clustering methods (e.g., K-means) and classifiers.

[0185] Output: Identification of each player's strengths and weaknesses as a result of the analysis.

[0186] Step 5: Generate reports

[0187] Input: Analysis results.

[0188] How it works: The server uses Matplotlib and Seaborn libraries to generate graphs and charts based on the analysis results, which are then compiled into a PDF report.

[0189] Output: Report in PDF format.

[0190] Step 6: Print the report

[0191] Input: Report in PDF format.

[0192] How it works: The server sends the generated reports to the players' and coaches' devices via email or a dedicated app.

[0193] Output: Reports are displayed on players' and coaches' terminals.

[0194] Strategy Proposal System

[0195] Step 1: Gather data on the opposing team

[0196] Input: Opposing team's match data.

[0197] How it works: The server uses web scraping techniques (Beautiful Soup and Selenium) to retrieve opposing team match data, including match results and individual player performance data.

[0198] Output: The opponent team's match data is saved in the server database.

[0199] Step 2: Analyze the Opponent

[0200] Input: Opposing team's match data.

[0201] How it works: The server uses Python and the Scikit-learn library to analyze the strengths and weaknesses of the opposing team, applying machine learning algorithms to identify defensive weaknesses and attacking characteristics.

[0202] Output: Analysis of the opposing team's strengths and weaknesses.

[0203] Step 3: Collect and analyze your own data

[0204] Input: Player data for your team.

[0205] How it works: The server collects data on the team's players and analyzes it using Python and the Scikit-learn library. It evaluates the skills and performance data of each player.

[0206] Output: Analysis results of your team's player data.

[0207] Step 4: Strategy Generation

[0208] Input: Analysis results for your team and the opposing team.

[0209] How it works: The server generates optimal strategies based on the analysis results. Specifically, it proposes strategies that exploit the opponent's weaknesses and strategies that maximize the strengths of the player's own team.

[0210] Output: The generated strategic plan.

[0211] Step 5: Output and adjust your strategy

[0212] Enter: strategic planning.

[0213] How it works: The generated strategy is sent in PDF format to the coach's device, where the user (coach) can review the strategy and adjust it if necessary.

[0214] Output: The final strategic plan is displayed on the coach's terminal.

[0215] Athlete schedule management system

[0216] Step 1: Collecting physical data

[0217] Input: Real-time physical data.

[0218] How it works: The device collects the athlete's heart rate, fatigue level, and sleep data in real time and sends it to a server.

[0219] Output: The physical data is stored in the server database.

[0220] Step 2: Analysis of physical data and schedule proposal

[0221] Input: Physical data.

[0222] How it works: The server evaluates the player's condition based on the physical data collected. Using time series analysis, it generates a training schedule using a Python library.

[0223] Output: The generated training schedule.

[0224] Step 3: Export and adjust the schedule

[0225] Input: The generated training schedule.

[0226] Operation: The server sends the generated schedule in PDF format to the device. The user (coach) reviews the schedule and adjusts it as necessary.

[0227] Output: The final adjusted schedule is displayed on the terminal.

[0228] Step 4: Schedule Notification

[0229] Enter: Final Schedule.

[0230] What it does: The device notifies and displays the final schedule to the player. This notification can include pop-ups and alarms.

[0231] Output: The player checks the schedule and puts it into action.

[0232] These processing steps allow players to optimize their performance and strategically advance their matches and training.

[0233] (Application example 1)

[0234] 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."

[0235] In the conventional manufacturing industry, there has been a lack of systems that can properly evaluate the individual capabilities of factory employees and generate optimal production line allocations and work schedules. This has made it difficult to maximize employee capabilities, resulting in problems such as reduced production efficiency and employee overwork. Furthermore, it has been difficult to collect employee physical and work data in real time and then efficiently allocate and schedule employees based on that data. The present invention aims to solve these problems, optimize employee performance, and improve factory production efficiency.

[0236] 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.

[0237] In this invention, the server includes means for collecting and analyzing player match data, training data, and physical data, means for generating reports based on the evaluation results, means for outputting the generated reports, means for collecting and evaluating factory employee performance data, means for generating an optimal production line layout based on the evaluation results, and means for outputting the generated layout, thereby enabling optimal evaluation of factory employee capabilities and generation of efficient production line layouts and work schedules.

[0238] "Match data" refers to data that records various activities and performances performed by players during a match.

[0239] "Training Data" refers to data that records the performance of exercises and activities that an athlete performs during training.

[0240] "Physical data" refers to data that indicates the physical condition of athletes and factory workers, such as their heart rate, distance, speed, fatigue level, and sleep data.

[0241] "Means for generating reports" refers to systems or processes that automatically create reports based on analysis and evaluation results.

[0242] "Means for outputting reports" refers to a system or process that displays or transmits the generated reports to a terminal or device.

[0243] "Performance data" refers to data such as work speed, accuracy, and error rate used to evaluate the productivity and efficiency of factory employees while working.

[0244] "Means for generating optimal layout of production lines" refers to systems and processes for placing factory employees in optimal work positions based on collected and analyzed data.

[0245] "Means for generating strategies" refers to a system or process that analyzes information about one's own team and the opposing team and formulates optimal strategies and tactics.

[0246] "Means for generating schedules" refers to the systems and processes that create optimal training and work schedules based on the physical data of each player or employee.

[0247] "Means for notification" refers to a system or process that notifies the target person in real time about the generated schedule or allocation.

[0248] "Means for visualizing work speed and accuracy" refers to a system or process that visually displays each employee's work speed and accuracy in the form of graphs or charts.

[0249] The present invention is a system for optimally evaluating the performance of factory employees and generating optimal production line layouts and work schedules. The operation of this system will be described below.

[0250] Data collection and analysis process

[0251] The server collects performance data such as work speed, accuracy, number of breaks, and cumulative fatigue level from each employee's wearable device and sensors. This data is sent to the server in real time and stored in a database.

[0252] Data analysis

[0253] The server analyzes the collected data and calculates performance indicators for each employee. Specifically, it uses machine learning algorithms (such as Random Forest and SVM) to identify the employee's strengths and weaknesses. Based on the results of this analysis, it generates an employee evaluation report.

[0254] Report generation and output

[0255] The server generates evaluation reports in a visually easy-to-understand format, including graphs and charts. These reports are sent to the terminals of each employee and manager. For example, if Employee A's work speed is 45 units per minute and his accuracy is 98%, a report will be generated that displays his performance evaluation as "highly efficient."

[0256] Optimal production line layout and schedule generation

[0257] The server generates the optimal allocation of factory employees to production lines based on the evaluation results, and also generates an optimal individual work schedule based on each employee's physical data (heart rate, accumulated fatigue level, etc.), including allocation of break times and light work.

[0258] Scheduling and Placement Notification

[0259] The generated schedule and optimal allocation are notified to each employee's device in real time. Employees can check their daily schedule and allocation location through their own devices. For example, if Employee B reaches 75% of their normal work speed, they will be notified and assigned lighter tasks in the next day's work schedule.

[0260] Visualization

[0261] The server visualizes each employee's work speed and accuracy using graphs and charts, and this visualized data is provided to managers and upper management, allowing them to understand the overall performance status at a glance.

[0262] Hardware and software used

[0263] The present invention uses the following hardware and software:

[0264] Hardware: Smartphones, smart glasses, head-mounted displays, wearable sensors

[0265] Software: pandas (data frame manipulation), scikit-learn (machine learning algorithms), matplotlib (data visualization)

[0266] Prompt Sentence Examples

[0267] "Analyze Employee A's work data, evaluate his work speed and accuracy, and propose an optimal work schedule."

[0268] The present invention improves factory production efficiency and maximizes employee capabilities. It also prevents employees from overworking, makes health management easier, and improves overall work efficiency.

[0269] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0270] Step 1:

[0271] The server collects real-time performance data from the factory employees' wearable devices, such as work speed, accuracy, number of breaks, and cumulative fatigue. This data is sent to the server and stored in a database. The input is real-time data from the wearable devices, and the output is performance data stored in the database.

[0272] Step 2:

[0273] The server converts the collected performance data into a data frame and performs preprocessing for applying the machine learning algorithm. This preprocessing includes data normalization and missing value imputation. The input is the performance data collected in step 1, and the output is a preprocessed data frame.

[0274] Step 3:

[0275] The server uses the preprocessed data to apply machine learning algorithms (e.g., Random Forest) to evaluate each employee's performance. This evaluation includes a performance evaluation based on work speed and accuracy, and a fatigue evaluation based on cumulative fatigue. The input is the preprocessed data frame, and the output is the performance evaluation results for each employee.

[0276] Step 4:

[0277] The server generates a performance report for each employee based on the evaluation results. This report includes graphs and charts that visually represent the evaluation results. The input is the performance evaluation results, and the output is a visually represented performance report. Specifically, based on information that Employee A's work speed is 45 units per minute and his accuracy is 98%, a performance report is generated that evaluates him as "highly efficient."

[0278] Step 5:

[0279] The server sends the generated performance report to the terminals of each employee and manager, allowing employees to check their own performance and understand areas for improvement. The input is the generated performance report, and the output is the report displayed on the terminal.

[0280] Step 6:

[0281] The server generates the optimal layout of the production line based on the evaluation results of each employee. This layout includes the optimal work position and role of each employee. The input is the evaluation results, and the output is the optimal production line layout.

[0282] Step 7:

[0283] The server generates an individual optimal work schedule based on each employee's physical data. This schedule includes break times and light work assignments. The input is the physical data, and the output is the optimal work schedule for each employee.

[0284] Step 8:

[0285] The server notifies each employee's terminal of the generated schedule and production line allocation in real time, allowing the employee to check their own schedule and location. The input is the generated schedule and allocation, and the output is the schedule and allocation information displayed on the terminal. Specifically, if employee B reaches 75% of his normal work speed, he will be notified that he will be assigned a lighter task in the next day's work schedule.

[0286] Through the above processing steps, a system is realized that optimally evaluates the capabilities of factory employees and improves production efficiency.

[0287] 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.

[0288] The present invention aims to improve overall performance by combining a system for optimally evaluating a player's abilities, proposing strategies, and managing the player's schedule with an emotion engine that recognizes the user's emotions. The program processing of this system is explained below in natural language.

[0289] Player ability analysis

[0290] Data collection

[0291] The server collects each player's match data from a database. Performance data during the match includes pass success rate, shot success rate, and distance traveled. The device (player's wearable device) sends training data such as heart rate, speed, and distance to the server in real time. The server periodically obtains physical data (heart rate, fatigue level, and sleep time) from each player's device.

[0292] Data analysis

[0293] The server combines collected match data, training data, and physical data to calculate performance indicators for each player. The server cleanses the data and fills in outliers and missing data. The server applies machine learning algorithms to identify players' strengths and weaknesses. The server performs clustering analysis based on each indicator to extract performance patterns.

[0294] Report Generation

[0295] The server generates a report for each player based on the analysis results. The report includes graphs and charts and is created in a visually easy-to-understand format. The generated report is sent to the terminal (coach's or player's device). The server notifies the user of the report, and the user can check the report.

[0296] Specific examples

[0297] The server collects the match data of Player A and determines that his pass success rate is 90%, his shot success rate is 50%, and his speed is 28km / h. From this data, it is analyzed that Player A is good at passing, but his shooting accuracy has room for improvement.

[0298] Strategy proposals

[0299] Analysis of the opposing team

[0300] The server collects the opponent's match data. The server retrieves recent match results and opponent player data from the database. The server uses machine learning algorithms to identify the opponent's strengths and weaknesses.

[0301] Analyzing your team's capabilities

[0302] The server collects and analyzes the performance data of each player on the team, evaluating skills such as dribbling and passing accuracy.

[0303] Strategy Generation

[0304] The server generates optimal match strategies based on the data collected, maximizing the strengths of the team and exploiting the weaknesses of the opposing team.

[0305] Strategic Notification

[0306] The server sends the generated strategy to the terminal (coach's device), where the user (coach) reviews the strategy and makes adjustments if necessary.

[0307] Specific examples

[0308] The server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since player B on the team is good at dribbling, the server proposes a strategy of having player B attack from the center.

[0309] Player schedule management

[0310] Physical data analysis

[0311] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.). The terminal (wearable device) sends the data to the server in real time. The server analyzes the collected data and evaluates the player's current physical condition. The server cleans out abnormal values ​​and inconsistent data.

[0312] Schedule proposal

[0313] The server generates an optimal training schedule based on the analysis results, incorporating rest days and recovery training as needed.

[0314] Schedule adjustment

[0315] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[0316] Schedule Notifications

[0317] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[0318] Specific examples

[0319] The server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. The user (coach) checks the schedule and, after adjustments, the final schedule is sent to Player C's device.

[0320] User Emotion Recognition

[0321] Emotional Data Collection

[0322] The terminal (user's device) collects the user's facial expression data, voice data, or behavioral data and sends it to the server, which then stores this data.

[0323] Emotion analysis

[0324] The server uses an emotion engine to analyze the collected data and identify the user's emotions. The emotion engine uses algorithms for facial expression analysis, voice analysis, and behavior analysis. The server records the analysis results in an emotion database.

[0325] Emotional data usage

[0326] The server provides feedback to players and adjusts training menus based on the emotional data identified by the server. The server provides communication adapted to the user's emotions. The server includes a user interface for visually displaying the emotional data.

[0327] Specific examples

[0328] If the user (coach) feels stressed while instructing the player, the emotion engine detects this and the server displays suggestions for relaxation. Also, if the emotion engine detects that the player is highly motivated during training, the server will suggest a training menu to maintain that state.

[0329] As described above, the system of the present invention combines the collection, analysis, strategy proposal, and schedule management of player data with the recognition of user emotions to achieve overall performance improvement.

[0330] The processing flow will be explained below.

[0331] Player ability analysis

[0332] Data collection

[0333] Step 1:

[0334] The server collects each player's match data from a database, including performance data during the match, such as pass success rate, shot success rate, and distance traveled.

[0335] Step 2:

[0336] The terminal (the athlete's wearable device) transmits training data such as heart rate, speed, and distance to the server in real time.

[0337] Step 3:

[0338] The server periodically collects physical data (heart rate, fatigue level, sleep time) from each player's device.

[0339] Data analysis

[0340] Step 4:

[0341] The server integrates the match data, training data, and physical data collected.

[0342] Step 5:

[0343] The server calculates each player's performance indicators, such as passing success rate, shooting success rate, speed, and stamina.

[0344] Step 6:

[0345] The server applies machine learning algorithms to identify players' strengths and weaknesses, and performs clustering analysis based on each indicator to extract performance patterns.

[0346] Report Generation

[0347] Step 7:

[0348] The server generates a report for each player based on the analysis results, including easy-to-understand graphs and charts.

[0349] Step 8:

[0350] The server sends the generated report to the terminal (the coach's or player's device). The server notifies the user of the report, and the user (coach or player) checks the report.

[0351] Specific examples

[0352] Step 9:

[0353] The server collects the match data of Player A and determines that his passing success rate is 90%, his shooting success rate is 50%, and his speed is 28km / h. After analyzing, it is determined that Player A is good at passing, but his shooting accuracy has room for improvement.

[0354] Strategy proposals

[0355] Analysis of the opposing team

[0356] Step 1:

[0357] The server collects the match data of the opposing team, and retrieves the results of recent matches and player data of the opposing team from the database.

[0358] Step 2:

[0359] The server uses machine learning algorithms to analyze the strengths and weaknesses of the opposing team.

[0360] Analyzing your team's capabilities

[0361] Step 3:

[0362] The server collects and analyzes the performance data of each player on the team, evaluating skills such as dribbling and passing accuracy.

[0363] Strategy Generation

[0364] Step 4:

[0365] The server generates optimal match strategies based on the data collected, maximizing your team's strengths and exploiting the opponent's weaknesses.

[0366] Strategic Notification

[0367] Step 5:

[0368] The server sends the generated strategy to the terminal (coach's device), where the user (coach) reviews the strategy and makes adjustments if necessary.

[0369] Specific examples

[0370] Step 6:

[0371] The server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since the team's player B is good at dribbling, the server proposes a strategy of having player B attack from the center.

[0372] Player schedule management

[0373] Physical data analysis

[0374] Step 1:

[0375] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.), and the terminal (wearable device) transmits the data to the server in real time.

[0376] Step 2:

[0377] The server analyzes the collected data to evaluate the player's current physical condition and cleanses outliers and inconsistent data.

[0378] Schedule proposal

[0379] Step 3:

[0380] The server uses the analysis results to generate an optimal training schedule, incorporating rest days and recovery training as needed.

[0381] Schedule adjustment

[0382] Step 4:

[0383] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[0384] Schedule Notifications

[0385] Step 5:

[0386] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[0387] Specific examples

[0388] Step 6:

[0389] The server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. The user (coach) checks the schedule and, after adjustments, the final schedule is sent to Player C's device.

[0390] User Emotion Recognition

[0391] Emotional Data Collection

[0392] Step 1:

[0393] The terminal (user's device) collects the user's facial expression data, voice data, or behavioral data and sends it to the server, which then stores this data.

[0394] Emotion analysis

[0395] Step 2:

[0396] The server uses an emotion engine to analyze the collected data and identify the user's emotions. The emotion engine uses algorithms for facial expression analysis, voice analysis, and behavior analysis.

[0397] Emotional data usage

[0398] Step 3:

[0399] Based on the emotional data identified by the server, the server provides feedback to the player and adjusts the training menu, providing communication that is adapted to the user's emotions.

[0400] Emotion data display

[0401] Step 4:

[0402] The server provides a user interface for visually displaying emotional data on the device. The user (coach or player) can review this data and reflect it in training and strategies.

[0403] Specific examples

[0404] Step 5:

[0405] If the user (coach) feels stressed while instructing the player, the emotion engine detects this and the server displays suggestions for relaxation. Also, if the emotion engine detects that the player is highly motivated during training, the server will suggest a training menu to maintain that state.

[0406] As a result, the system of the present invention not only collects and analyzes player data, proposes consistent strategies, and manages schedules, but also incorporates a function to recognize user emotions, thereby achieving overall performance improvement.

[0407] Example 2

[0408] 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."

[0409] Conventional player performance evaluation systems are limited to collecting and analyzing match and training data, which is insufficient for achieving overall performance improvement. Furthermore, they do not take into account the emotions of users (coaches and players) when proposing strategies or managing schedules, making it difficult to optimize players' motivation and physical condition. Furthermore, the generated reports and strategies lack a visual display, making intuitive understanding difficult.

[0410] 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.

[0411] In this invention, the server includes means for collecting game data, training data, and physical data of players, means for analyzing the collected data and identifying the strengths and weaknesses of players, means for generating a report based on the analysis results, means for outputting and visually displaying the generated report, and means for calculating performance indicators for each player and performing clustering analysis. This makes it possible to improve overall performance by combining player data collection, analysis, strategy proposals, schedule management, and user emotion recognition.

[0412] "Match data" refers to data collected to evaluate a player's performance during a match, and specifically includes pass success rate, shooting success rate, distance traveled, etc.

[0413] "Training Data" is data recorded while an athlete is training, and includes information related to physical activity such as heart rate, speed, distance, etc.

[0414] "Physical data" refers to data used to evaluate the physical condition of a player, and specifically includes heart rate, fatigue level, sleep time, etc.

[0415] "Analysis" means processing information using techniques such as evaluation, judgment, and pattern identification based on collected data.

[0416] "Strengths and Weaknesses" refers to a player's strengths and areas for improvement, identified based on the player's performance data.

[0417] A "report" is a collection of documents and graphics generated based on the analysis results, including visual representations of player performance.

[0418] "Visually displaying" refers to presenting analysis results and reports in the form of graphs, charts, etc. so that users can intuitively understand them.

[0419] "Clustering analysis" refers to a technique that uses machine learning algorithms to classify collected data into multiple clusters and extract similar patterns.

[0420] "Strategy" means a plan or proposal for choosing the best course of action in a match, including one that takes into account the strengths and weaknesses of the opposing team.

[0421] A "schedule" is a plan for a player's training and rest, and is generated based on physical and performance data.

[0422] "Emotion data" refers to information indicating the emotional state of a user analyzed from facial expression data, voice data, and behavior data.

[0423] The present invention aims to improve overall performance by combining a system for optimally evaluating players' abilities, proposing strategies, and managing players' schedules with an emotion engine that recognizes the user's emotions. The system of the present invention is mainly composed of a server, terminals, and users.

[0424] Player ability analysis

[0425] Data collection

[0426] The server collects each player's match data from the database. This match data includes pass success rate, shot success rate, running distance, etc. The players' wearable devices also send training data such as heart rate, speed, and distance to the server in real time. The server periodically collects the players' physical data such as heart rate, fatigue level, and sleep time.

[0427] Data analysis

[0428] The server integrates the collected match data, training data, and physical data to calculate each player's performance indicators. It also cleanses the data and fills in outliers and missing data. It also applies machine learning algorithms to identify players' strengths and weaknesses. It also performs clustering analysis to extract performance patterns.

[0429] Report Generation

[0430] The server generates a report for each player based on the analysis results. The report includes graphs and charts and is created in a visually easy-to-understand format. The generated report is sent to the player's or coach's device, where the user can check the received report.

[0431] Specific examples

[0432] For example, if the server collects match data for Player A and determines that his pass success rate is 90%, his shot success rate is 50%, and his speed is 28km / h, it will analyze that Player A is good at passing, but that there is room for improvement in his shooting accuracy.

[0433] Strategy proposals

[0434] Analysis of the opposing team

[0435] The server collects the opponent's match data, retrieves recent match results and player data from the database, and uses machine learning algorithms to identify the opponent's strengths and weaknesses.

[0436] Analyzing your team's capabilities

[0437] The server collects and analyzes the performance data of each player on the team, including skill evaluations such as dribbling and passing accuracy.

[0438] Strategy Generation

[0439] The server uses the collected data to generate optimal match strategies that maximize the strengths of the team and exploit the weaknesses of the opposing team.

[0440] Strategic Notification

[0441] The generated strategy is sent from the server to the coach's device, where the user (coach) can review the strategy and make adjustments as necessary.

[0442] Specific examples

[0443] For example, if the server compares data from your team with that of the opposing team and discovers that the opposing team is weak in central defense, it will propose a strategy to have player B on your team attack from the center because he is good at dribbling.

[0444] Player schedule management

[0445] Physical data analysis

[0446] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.). The player's wearable device transmits the data to the server in real time. The server analyzes the collected data and evaluates the player's current physical condition.

[0447] Schedule proposal

[0448] The server generates an optimal training schedule based on the analysis results, incorporating rest days and recovery training as needed.

[0449] Schedule adjustment

[0450] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[0451] Schedule Notifications

[0452] The final schedule is sent from the server to each player's device, which receives the notification and displays the schedule to the player.

[0453] Specific examples

[0454] For example, if the server analyzes Player C's physical data and determines that he has become fatigued from recent training, it will suggest a day of rest and incorporate lighter training for the rest of the week. The user (coach) checks the schedule and, after making adjustments, the final schedule is sent to Player C's device.

[0455] User Emotion Recognition

[0456] Emotional Data Collection

[0457] The user's device collects facial expression data, voice data, or behavioral data and sends it to the server, which stores the data.

[0458] Emotion analysis

[0459] The server analyzes the collected data using an emotion engine to identify the user's emotions. The emotion engine uses algorithms for facial expression analysis, voice analysis, and behavior analysis. The analysis results are recorded in an emotion database.

[0460] Emotional data usage

[0461] The server provides feedback to the player and adjusts the training menu based on the identified emotion data. The server provides communication adapted to the user's emotions. The server includes a user interface for visually displaying the emotion data.

[0462] Specific examples

[0463] For example, if a user (coach) feels stressed while instructing a player, the emotion engine will detect this and the server will display suggestions for relaxation. Also, if the emotion engine detects that a player is highly motivated during training, the server will suggest a training menu to help the player maintain that state.

[0464] As described above, the system of the present invention combines the collection, analysis, strategy proposal, and schedule management of player data with the recognition of user emotions to achieve overall performance improvement.

[0465] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0466] Player ability analysis

[0467] Data collection

[0468] Step 1:

[0469] The server collects the match data from the database.

[0470] Input: Player ID

[0471] Data processing: Execute an SQL query to retrieve match data (pass success rate, shooting success rate, distance traveled, etc.) related to the player ID.

[0472] Output: Match data for each player

[0473] Specific example of operation: The server uses "MySQL" to execute the query "SELECT FROM match_data WHERE player_id = ?".

[0474] Step 2:

[0475] The athlete's wearable device (terminal) transmits training data to a server in real time.

[0476] Input: Real-time raw data (heart rate, speed, distance, etc.)

[0477] Data Processing: Data formatting and buffering

[0478] Output: Formatted training data

[0479] Specific example of operation: The device receives data via Bluetooth, converts it to JSON format, and sends an HTTP POST request to the server.

[0480] Step 3:

[0481] The server collects the physical data.

[0482] Input: Periodic data transmission from a wearable device

[0483] Data processing: Error correction and interpolation

[0484] Output: Unified physical data

[0485] Specific operation example: The server executes a scheduled job every day at 00:00 and collects data from the device with a GET request.

[0486] Data analysis

[0487] Step 4:

[0488] The server aggregates the data and calculates performance metrics for each player.

[0489] Input: Match data, training data, physical data

[0490] Data processing: data merging and statistical analysis

[0491] Output: Unified performance dataset

[0492] Specific example of operation: The server uses the "Pandas" library to merge data frames and calculate statistics.

[0493] Step 5:

[0494] The server performs data cleansing.

[0495] Input: Unified performance dataset

[0496] Data processing: Correction of outliers, completion of missing data

[0497] Output: A cleansed dataset

[0498] Specific example of operation: The server uses the "Scikit-learn" outlier detection algorithm to replace abnormal values ​​with the average value.

[0499] Step 6:

[0500] The server applies machine learning algorithms to identify players' strengths and weaknesses.

[0501] Input: Cleansed dataset

[0502] Data processing: Classification and regression analysis

[0503] Output: A list of strengths and weaknesses for each player

[0504] Specific example of operation: The server runs a "random forest" model and extracts important features.

[0505] Step 7:

[0506] The server performs the clustering analysis.

[0507] Input: List of strengths and weaknesses

[0508] Data processing: Applying clustering algorithms

[0509] Output: Clusters of performance patterns

[0510] Specific example of operation: The server classifies data into multiple clusters using "K-means".

[0511] Report Generation

[0512] Step 8:

[0513] The server generates a report based on the analysis results.

[0514] Input: Cluster of performance patterns

[0515] Data processing: Graph and chart generation

[0516] Output: Report document (PDF, etc.)

[0517] Specific example of operation: The server creates a graph using "Matplotlib" and exports it to PDF.

[0518] Step 9:

[0519] The server outputs the report and displays it visually.

[0520] Input: Generated report document

[0521] Data processing: Format conversion for report delivery and display

[0522] Output: Report notification

[0523] Specific example of how it works: The server sends a PDF via email via the SMTP protocol, and the report is displayed in the device's app.

[0524] Strategy proposals

[0525] Analysis of the opposing team

[0526] Step 10:

[0527] The server collects the match data of the opposing team.

[0528] Input: Match ID, Opponent Team ID

[0529] Data processing: Data collection through API queries

[0530] Output: Opponent team's match data

[0531] Specific example of operation: The server obtains the latest match data of the opposing team via the REST API.

[0532] Step 11:

[0533] The server analyzes the strengths and weaknesses of the opposing team.

[0534] Input: Opponent team's match data

[0535] Data processing: Analysis using machine learning algorithms

[0536] Output: A list of the opponent's strengths and weaknesses

[0537] Specific example of operation: The server uses a statistical model to analyze the opponent's defense success rate, attack patterns, etc.

[0538] Analyzing your team's capabilities

[0539] Step 12:

[0540] The server collects and analyzes the team's player data.

[0541] Input: Player ID, team ID

[0542] Data Transformation: API queries and data analysis

[0543] Output: Performance data of your team's players

[0544] Specific example of operation: The server retrieves the player's training log from the data warehouse and analyzes it using a data analysis tool.

[0545] Strategy Generation

[0546] Step 13:

[0547] The server generates the optimal strategy.

[0548] Input: Data analysis results for your team and the opposing team

[0549] Data Processing: Algorithmic Strategy Optimization

[0550] Output: Optimal strategic plan

[0551] Specific example of operation: The server optimizes the strategy using a genetic algorithm and outputs a strategic plan.

[0552] Strategic Notification

[0553] Step 14:

[0554] The server sends the generated strategy to the coach's device.

[0555] Input: Optimal strategic plan

[0556] Data processing: Strategic plan format conversion

[0557] Output: Strategy Notification

[0558] Specific example of operation: The server exports the generated strategy in JSON format and notifies the coach's device.

[0559] Player schedule management

[0560] Physical data analysis

[0561] Step 15:

[0562] The server collects the physical data.

[0563] Input: Periodic data from wearable devices

[0564] Data processing: data collection and formatting

[0565] Output: Unified physical data

[0566] Specific example of operation: The server schedules a periodic data collection job and retrieves the data with a GET request.

[0567] Step 16:

[0568] The server analyzes the physical data and evaluates the physical state.

[0569] Input: Unified physical data

[0570] Data processing: time series analysis and outlier correction

[0571] Output: Analysis results and physical state evaluation

[0572] Specific example of operation: The server performs "time series analysis" to identify the player's fatigue level trends.

[0573] Schedule proposal

[0574] Step 17:

[0575] The server generates the training schedule.

[0576] Input: Physical condition assessment results

[0577] Data processing: Applying schedule generation algorithms

[0578] Output: Optimal training schedule

[0579] Specific example of operation: The server combines the player's physical data and performance data to create a training schedule.

[0580] Schedule Notifications

[0581] Step 18:

[0582] The server sends the final schedule to the players' devices.

[0583] Input: Optimal training schedule

[0584] Data processing: Schedule format conversion

[0585] Output: Schedule notification

[0586] Specific example of operation: The server exports the schedule in JSON format and sends it to the player's app.

[0587] User Emotion Recognition

[0588] Emotional Data Collection

[0589] Step 19:

[0590] The user's device collects facial expression data, voice data, or behavioral data and transmits it to a server.

[0591] Input: Real-time facial, voice, and behavioral data

[0592] Data processing: data collection and format conversion

[0593] Output: Emotion data

[0594] Specific example of how it works: Collects data using the device's camera and microphone and streams it to a server in real time.

[0595] Emotion analysis

[0596] Step 20:

[0597] The server analyzes the emotion data using an emotion engine.

[0598] Input: Collected emotion data

[0599] Data processing: Applying sentiment analysis algorithms

[0600] Output: User's emotion rating

[0601] Specific example of operation: The server uses the emotion engine and executes a facial expression recognition algorithm to classify the user's emotion.

[0602] Emotional data usage

[0603] Step 21:

[0604] Feedback and training menus are adjusted based on the emotional data identified by the server.

[0605] Input: User's emotion rating

[0606] Data processing: Emotion-based training menu generation

[0607] Output: Adjusted training menu

[0608] Specific example of operation: The server detects the user's stress level and automatically generates a training menu based on that.

[0609] Step 22:

[0610] The server visually displays the emotion data.

[0611] Input: Emotion evaluation results

[0612] Data processing: graph generation for visualization

[0613] Output: Visual display of emotion data

[0614] Specific example of operation: The server generates various graphs and charts based on emotion data and displays them in real time on the user's device.

[0615] (Application example 2)

[0616] 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."

[0617] Conventional factory management systems lacked the functionality to efficiently collect, analyze, and optimize performance data from industrial robots and workers. Furthermore, safety management that took into account workers' physical data and emotions was insufficient, resulting in reduced work efficiency and health risks. Furthermore, a lack of real-time data analysis and feedback made it difficult to respond immediately. A new system is needed to resolve these issues and ensure worker safety while improving work efficiency within factories.

[0618] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting game data, training data, and physical data of players; means for analyzing the collected data and identifying the strengths and weaknesses of players; means for generating a report based on the analysis results; means for outputting the generated report; means for collecting and analyzing robot operation data and identifying performance indicators; means for collecting and analyzing physical data of workers and identifying stress and fatigue levels; means for collecting facial expression data, voice data, and behavioral data of workers and analyzing their emotions; means for generating and adjusting a work schedule based on the analysis results; and means for outputting the generated work schedule. This enables efficient operation of industrial robots in factories and optimization of work while ensuring the safety of workers.

[0619] "Athlete match data" refers to data that indicates the performance and achievements that an athlete has achieved during a match.

[0620] "Training data" refers to data such as heart rate, speed, and distance obtained by an athlete during training.

[0621] "Physical data" refers to data that indicates an athlete's physical strength and health, such as heart rate, fatigue level, and sleep time.

[0622] "Robot operation data" refers to data that indicates the operating status of an industrial robot, such as its work cycle time and energy consumption.

[0623] A "performance index" is an evaluation criterion calculated through data analysis that quantifies work efficiency and accuracy.

[0624] "Physical data of a worker" refers to data that indicates the physical condition of a worker, such as heart rate, movement, and fatigue level.

[0625] "Stress and fatigue" refers to the degree of psychological or physical strain or fatigue felt by workers.

[0626] "Worker facial expression data" is information obtained from the worker's facial expressions, and is data used to infer emotions and physical condition.

[0627] "Voice data" is data collected from the voices of workers and is used to analyze their emotions and state of consciousness.

[0628] "Behavioral data" is information collected about the movements and activity patterns of workers.

[0629] "Analyzing emotions" refers to the process of identifying a worker's emotional state based on facial, vocal, and behavioral data.

[0630] A "work schedule" is a plan that shows the order and timing of work that workers should perform.

[0631] An "optimal strategy" is the most efficient and effective plan of action generated by analyzing collected data.

[0632] The present invention relates to a system for efficiently managing industrial robots and workers in a factory and ensuring their safety. The configuration of the system and specific embodiments will be described below.

[0633] System configuration

[0634] The system consists of the following main components:

[0635] 1. Data Collection Methods

[0636] Industrial robots are used as a means for collecting operational data of the robots.

[0637] Wearable devices that measure heart rate and movement are used to collect workers' physical data.

[0638] Cameras and microphones are used to collect facial expression, voice, and behavioral data from workers.

[0639] 2. Data analysis methods

[0640] The server uses machine learning frameworks (e.g., TensorFlow or Scikit-learn) to synthesize the collected data and identify performance metrics.

[0641] This also includes a means of analyzing physical data to identify workers' stress and fatigue levels.

[0642] 3. Report generation and strategy proposal tool

[0643] The server generates a work schedule based on the analysis results and suggests appropriate breaks and workload adjustments.

[0644] An emotion engine is used to analyze workers' emotional data and suggest appropriate communication methods.

[0645] 4. Notification and Feedback Methods

[0646] Generated reports and proposed strategies are sent to each terminal, allowing workers and managers to review them.

[0647] Program processing explanation

[0648] The server does the following:

[0649] 1. Data Collection

[0650] Collecting data in real time from industrial robots and wearable devices.

[0651] Cameras and microphones are also used to collect facial expressions, voice and behavioral data from workers.

[0652] 2. Data Analysis

[0653] The server stores the collected data in a database (e.g., MySQL, MongoDB).

[0654] Machine learning algorithms are used to analyze the data and identify performance indicators and emotional states.

[0655] 3. Report generation and strategy proposals

[0656] Generate optimal work schedules and strategies based on performance data.

[0657] It analyzes emotional data and suggests feedback and communication methods based on that data.

[0658] 4. Notifications and Feedback

[0659] Generated reports and strategies are sent to terminals so that workers and managers can review them.

[0660] Specific examples

[0661] For example, a factory might have the following setup:

[0662] Collects operational data (work cycle time, energy consumption) from industrial robots to improve the efficiency of specific work processes.

[0663] Detects stress levels from workers' heart rate data and suggests appropriate breaks.

[0664] If a worker is highly fatigued, the analysis results will be used to suggest work to reduce fatigue.

[0665] Prompt Sentence Examples

[0666] Here are some examples of prompts for generative AI models:

[0667] prompt:

[0668] We are developing an application for efficient operation of industrial robots in a factory and for worker safety management. Please generate a program that meets the following conditions:

[0669] Collect and analyze robot operation data (cycle time, energy consumption)

[0670] Collect physical data (heart rate, movement) from workers' wearable devices and analyze their stress and fatigue levels.

[0671] Collecting facial expressions, voice, and behavioral data from workers and analyzing their emotions

[0672] Based on the analysis results, we propose optimization of work schedules and robot operations.

[0673] It has the function of notifying workers and managers of analysis results and suggestions.

[0674] Based on this requirement, generate the relevant code.

[0675] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0676] Step 1:

[0677] Data collection

[0678] The server collects real-time operational data from industrial robots (such as work cycle time and energy consumption), as well as data from wearable devices that measure workers' heart rates and movements, and from cameras and microphones that capture workers' facial expressions and voices, and stores this data in a database (e.g., MySQL, MongoDB).

[0679] input:

[0680] Industrial robot operation data

[0681] Worker physical data (heart rate, movement)

[0682] Facial expressions and voice data of workers

[0683] output:

[0684] Stored database records

[0685] Step 2:

[0686] Data analysis

[0687] The server analyzes the collected data. Specifically, it uses machine learning frameworks (e.g., TensorFlow, Scikit-learn) to identify robot performance indicators and calculates stress and fatigue levels from the worker's physical data. It also analyzes facial and voice data using an emotion engine to identify the worker's emotional state.

[0688] input:

[0689] Various data stored in the database

[0690] output:

[0691] Performance indicators, stress / fatigue levels, emotional state

[0692] Step 3:

[0693] Report generation and strategy recommendations

[0694] Based on the analysis results, the server generates an optimal schedule for efficient robot operation and workers, and also creates a report that suggests feedback and communication methods tailored to workers based on their emotional data.

[0695] input:

[0696] Performance Indicators

[0697] Stress and fatigue

[0698] Emotional state

[0699] output:

[0700] Work schedules, strategies, and feedback reports

[0701] Step 4:

[0702] Notifications and Feedback

[0703] The server then sends the generated reports and proposed work schedules to each terminal, allowing workers and managers to review them. This allows workers to check the schedules and proposals from their terminals and take appropriate action.

[0704] input:

[0705] Reports, strategies, and work schedules

[0706] output:

[0707] Notifications and displayed content on the device

[0708] Prompt Sentence Examples

[0709] Here are some examples of prompts for generative AI models:

[0710] prompt:

[0711] We are developing an application for efficient operation of industrial robots in a factory and for worker safety management. Please generate a program that meets the following conditions:

[0712] Collect and analyze robot operation data (cycle time, energy consumption)

[0713] Collect physical data (heart rate, movement) from workers' wearable devices and analyze their stress and fatigue levels.

[0714] Collecting facial expressions, voice, and behavioral data from workers and analyzing their emotions

[0715] Based on the analysis results, we propose optimization of work schedules and robot operations.

[0716] It has the function of notifying workers and managers of analysis results and suggestions.

[0717] Based on this requirement, generate the relevant code.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] [Second embodiment]

[0722] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0723] 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.

[0724] 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).

[0725] 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.

[0726] 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.

[0727] 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).

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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."

[0734] The present invention is a system for optimally evaluating the abilities of players, proposing strategies, and managing the schedules of players. The program processing of this system will be explained below in natural language.

[0735] Player ability analysis

[0736] Data collection

[0737] The server collects each player's match data, training data, and physical data from a database. The player's wearable device sends data such as heart rate, distance, and speed to the server in real time.

[0738] Data analysis

[0739] The server combines the collected data to calculate each player's performance metrics (e.g., passing success rate, shooting success rate, speed, stamina), and then applies machine learning algorithms to identify players' strengths and weaknesses.

[0740] Report Generation

[0741] The server generates a report for each player based on the analysis results. The report includes graphs and charts and is created in a visually easy-to-understand format. The generated report is sent to the terminal (coach's or player's device).

[0742] Specific examples

[0743] The server collects the match data of Player A and determines that his pass success rate is 90%, his shot success rate is 50%, and his speed is 28km / h. From this data, it is analyzed that Player A is good at passing, but his shooting accuracy has room for improvement.

[0744] Strategy proposals

[0745] Analysis of the opposing team

[0746] The server collects the opponent's match data, analyzes recent match results and player performance data, and uses machine learning algorithms to identify the opponent's strengths and weaknesses.

[0747] Analyzing your team's capabilities

[0748] The server collects and analyzes the performance data of each player on the team, identifying players who excel at dribbling and players with high passing accuracy.

[0749] Strategy Generation

[0750] The server generates an optimal strategy based on data from the team and the opposing team. The generated strategy is sent to the terminal (coach's device), where the user (coach) can review and adjust it.

[0751] Specific examples

[0752] The server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since player B on the team is good at dribbling, the server proposes a strategy of having player B attack from the center.

[0753] Player schedule management

[0754] Physical data analysis

[0755] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.), and the terminal (wearable device) transmits the data to the server in real time.

[0756] Schedule proposal

[0757] The server evaluates the player's current condition based on physical data and generates an optimal training schedule, which includes rest days and recovery training.

[0758] Schedule adjustment

[0759] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of each player and the team's schedule. After adjustments, the final schedule is fed back to the server.

[0760] Schedule Notifications

[0761] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[0762] Specific examples

[0763] The server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. The user (coach) checks the schedule and, after adjustments, the final schedule is sent to Player C's device.

[0764] As described above, the system of the present invention efficiently collects and analyzes player data, proposes strategies, and manages schedules, thereby maximizing player performance.

[0765] The processing flow will be explained below.

[0766] Player ability analysis

[0767] Data collection

[0768] Step 1:

[0769] The server collects each player's match data from the database. The server also collects performance data during the match (pass success rate, shot success rate, running distance, etc.).

[0770] Step 2:

[0771] The terminal (the athlete's wearable device) transmits training data such as heart rate, speed, and distance to the server in real time.

[0772] Step 3:

[0773] The server periodically collects physical data (e.g., heart rate, fatigue level, sleep time) from each player's device.

[0774] Data analysis

[0775] Step 4:

[0776] The server integrates the collected match data, training data, and physical data to calculate each player's performance indicators. The server also cleanses the data and fills in outliers and missing data.

[0777] Step 5:

[0778] The server applies machine learning algorithms to identify players' strengths and weaknesses, and performs clustering analysis based on each indicator to extract performance patterns.

[0779] Report Generation

[0780] Step 6:

[0781] The server generates a report for each player based on the analysis results, including graphs and charts in a visually easy-to-understand format.

[0782] Step 7:

[0783] The server sends the generated report to the terminal (the coach's or player's device). The server notifies the user of the report, and the user checks the report.

[0784] Strategy proposals

[0785] Analysis of the opposing team

[0786] Step 1:

[0787] The server collects the match data of the opposing team. The server retrieves the results of recent matches and player data of the opposing team from the database.

[0788] Step 2:

[0789] The server analyzes the strengths and weaknesses of the opposing team. The server uses machine learning algorithms to identify the weaknesses of the opposing team.

[0790] Analyzing your team's capabilities

[0791] Step 3:

[0792] The server collects and analyzes the performance data of each player on the team, evaluating skills such as dribbling and passing accuracy.

[0793] Strategy Generation

[0794] Step 4:

[0795] The server generates optimal match strategies based on the data collected, maximizing the strengths of the team and exploiting the weaknesses of the opposing team.

[0796] Strategic Notification

[0797] Step 5:

[0798] The server sends the generated strategy to the terminal (coach's device), where the user (coach) reviews the strategy and makes adjustments if necessary.

[0799] Player schedule management

[0800] Physical data analysis

[0801] Step 1:

[0802] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.), and the terminal (wearable device) sends the data to the server in real time.

[0803] Step 2:

[0804] The server analyzes the collected data to evaluate the player's current physical condition and cleanses outliers and inconsistencies.

[0805] Schedule proposal

[0806] Step 3:

[0807] The server generates an optimal training schedule based on the analysis results, incorporating rest days and recovery training as needed.

[0808] Schedule adjustment

[0809] Step 4:

[0810] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[0811] Schedule Notifications

[0812] Step 5:

[0813] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[0814] This allows one to understand the specific flow of how the system of the present invention efficiently collects and analyzes player data, proposes strategies, and manages schedules.

[0815] Example 1

[0816] 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."

[0817] In modern sports, accurately evaluating individual athletes' performance, formulating optimal strategies, and managing schedules efficiently are extremely difficult challenges. Conventional methods involve manual data collection and analysis, which is time-consuming, labor-intensive, and often inaccurate. Furthermore, training schedules and strategy proposals that incorporate athletes' real-time physical data are not adequately implemented. This makes it difficult to maximize athletes' potential. The present invention aims to solve these challenges by providing a system that automates effective data collection and analysis, strategy proposals, and schedule management.

[0818] 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.

[0819] In this invention, the server includes: means for collecting a player's match data, training data, and physical data; means for integrating the collected data to calculate a player's performance indicators; and means for identifying a player's strengths and weaknesses using a machine learning algorithm. This enables accurate evaluation of a player's abilities and rapid identification of their strengths and weaknesses. The server also includes means for generating reports using graphs and charts; means for outputting the generated reports to a terminal; and means for the terminal to transmit data to the server in real time. This allows evaluation results to be presented visually in an easy-to-understand manner, enabling players and coaches to respond quickly. The approach continues with means for collecting opponent team match data and applying a machine learning algorithm to analyze the opponent team's strengths and weaknesses; means for collecting and analyzing the player data of the team's own team to generate an optimal strategy; and means for outputting the generated strategy to a terminal for the user to review and adjust. This improves the accuracy and effectiveness of strategies, thereby increasing competitiveness in matches. The system also includes a means for periodically collecting athlete physical data and generating a training schedule using time-series analysis, a means for outputting the generated schedule to a terminal in PDF format, and a means for notifying and displaying the generated schedule to the athlete. This makes it possible to provide an optimal training schedule while maintaining the athlete's health and performance.

[0820] "Player's match data" refers to various statistical data obtained by a player in an actual match (e.g., pass success rate, shot success rate, number of assists, etc.).

[0821] "Athlete training data" refers to various numerical data recorded by athletes during training (e.g., training time, number of training sessions, load, etc.).

[0822] "Athlete's physical data" refers to numerical data relating to the athlete's physical condition and stamina (e.g., heart rate, speed, distance, sleep data, fatigue level, etc.).

[0823] "Synthesis" is the process of bringing together different types of data into a single dataset in a form that can be analyzed as a whole.

[0824] "Performance indicators" are quantitative indicators used to evaluate a player's abilities and condition, and include pass success rate, shooting success rate, speed, stamina, etc.

[0825] "Machine learning algorithm" is a general term for mathematical techniques that automatically learn from data and make predictions or classifications. Examples include TensorFlow and Scikit-learn.

[0826] A "strength" is a characteristic or skill that makes a player's performance or ability superior to other aspects of their game.

[0827] A "weakness" is a characteristic or skill that is believed to need improvement in a player's performance or ability.

[0828] A "report" is a report that summarizes the results of data analysis in the form of documents, graphs, charts, etc.

[0829] "Device" means a device used by an athlete or coach (e.g., smartphone, tablet, computer, wearable device).

[0830] "Opponent team's match data" refers to various statistical data obtained by the opposing team during an actual match.

[0831] A "strategy" is a specific plan or policy for achieving a goal in a match or training.

[0832] "Time series analysis" is a method for analyzing data that changes over time and predicting future trends.

[0833] A "schedule" is a plan that systematically arranges the training, rest, and match dates of players.

[0834] "Notifications" are messages and alarms sent by the system to inform players and coaches of important information.

[0835] The present invention is a system for optimally evaluating the abilities of players, proposing strategies, and managing the schedules of players. The following describes how to implement the program of this system.

[0836] Player ability analysis system

[0837] Data collection

[0838] The server collects players' match and training data from a database. It also sends physical data measured in real time by the devices, such as heart rate, distance, and speed, to the server. The specific software used is MySQL for database management, and Python and Pandas for data collection and analysis scripts.

[0839] Data analysis

[0840] The server aggregates the collected data and calculates player performance indicators, such as passing success rate, shooting success rate, speed, stamina, etc. It then applies machine learning algorithms (such as TensorFlow and Scikit-learn) to identify players' strengths and weaknesses.

[0841] Report Generation

[0842] The server generates a visually easy-to-understand report using graphs and charts based on the analysis results. Specific libraries used are Matplotlib and Seaborn. This report is generated in PDF format and output to the terminal (the coach's or player's device).

[0843] Strategy Proposal System

[0844] Analysis of the opposing team

[0845] The server collects data on the opposing team's matches using web scraping techniques (Beautiful Soup and Selenium) and applies machine learning algorithms to analyze the opposing team's strengths and weaknesses.

[0846] Analyzing your team's capabilities

[0847] The server collects and analyzes data on the team's players, evaluating specific skills such as dribbling and passing accuracy, again using Python and the Sklearn library.

[0848] Strategy Generation

[0849] The server generates an optimal strategy based on data from the team and the opposing team. The generated strategy is output to the terminal (coach's device) and the user (coach) checks and adjusts it.

[0850] Athlete schedule management system

[0851] Physical data analysis

[0852] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.) and also receives real-time data from the device.

[0853] Schedule proposal

[0854] The server evaluates the player's current condition based on the collected physical data and uses time-series analysis to generate an optimal training schedule, including rest days and recovery training.

[0855] Scheduling and Notifications

[0856] The user (coach) reviews the proposed schedule and makes final adjustments. After adjustments, the final schedule is generated in PDF format and output to the device. The device receives a notification and displays the schedule to the players.

[0857] Specific examples

[0858] For example, suppose the server collects player A's match data and determines that his passing success rate is 90%, his shooting success rate is 50%, and his speed is 28km / h. Analyzing this data, it determines that player A is good at passing, but his shooting accuracy needs improvement. Based on these results, the server identifies player A's strengths and weaknesses, generates a visual report, and sends it to the coach's device.

[0859] Furthermore, when proposing a strategy, the server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since player B on the team is good at dribbling, the server proposes a strategy to have player B attack from the center and sends the result to the coach's device.

[0860] In schedule management, the server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. After the user (coach) checks and adjusts the schedule, the final schedule is sent to Player C's device.

[0861] Prompt Sentence Examples

[0862] "How would you rate Player A's match performance?"

[0863] The generative AI model generates an answer such as, "Player A's match performance was assessed as having a passing success rate of 90%, a shooting success rate of 50%, and a speed of 28 km / h. Based on this, it was determined that his passing ability is excellent, but his shooting accuracy needs improvement."

[0864] As described above, the present invention is a system that automates the effective collection and analysis of data, strategy proposals, and schedule management, and can bring out the best in players' performance.

[0865] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0866] Player ability analysis system

[0867] Step 1: Data collection

[0868] Inputs: Real-time physical data (heart rate, speed, distance) and match and training data.

[0869] How it works: The wearable device collects the athlete's heart rate, speed, and distance data in real time. The server receives this data via Bluetooth or Wi-Fi and records it in a database. In addition, the server retrieves past match and training data from the database.

[0870] Output: The integrated dataset is stored in a database on the server.

[0871] Step 2: Data integration

[0872] Input: Match data, training data, physical data.

[0873] How it works: The server uses Python and the Pandas library to combine these data into a single unified dataset, clean and normalize the data, impute missing data, and remove outliers.

[0874] Output: A clean, consolidated dataset.

[0875] Step 3: Performance Metrics Calculation

[0876] Input: The integrated dataset.

[0877] How it works: The server applies statistical methods to calculate each player's performance metrics (passing success rate, shooting success rate, speed, stamina). Specifically, it uses the Python Scikit-learn library to perform the calculations.

[0878] Output: A list of calculated performance metrics.

[0879] Step 4: Machine learning analysis

[0880] Input: A list of performance metrics.

[0881] How it works: The server uses TensorFlow and Scikit-learn libraries to apply machine learning algorithms to identify players' strengths and weaknesses, and analyzes them using clustering methods (e.g., K-means) and classifiers.

[0882] Output: Identification of each player's strengths and weaknesses as a result of the analysis.

[0883] Step 5: Generate reports

[0884] Input: Analysis results.

[0885] How it works: The server uses Matplotlib and Seaborn libraries to generate graphs and charts based on the analysis results, which are then compiled into a PDF report.

[0886] Output: Report in PDF format.

[0887] Step 6: Print the report

[0888] Input: Report in PDF format.

[0889] How it works: The server sends the generated reports to the players' and coaches' devices via email or a dedicated app.

[0890] Output: Reports are displayed on players' and coaches' terminals.

[0891] Strategy Proposal System

[0892] Step 1: Gather data on the opposing team

[0893] Input: Opposing team's match data.

[0894] How it works: The server uses web scraping techniques (Beautiful Soup and Selenium) to retrieve opposing team match data, including match results and individual player performance data.

[0895] Output: The opponent team's match data is saved in the server database.

[0896] Step 2: Analyze the Opponent

[0897] Input: Opposing team's match data.

[0898] How it works: The server uses Python and the Scikit-learn library to analyze the strengths and weaknesses of the opposing team, applying machine learning algorithms to identify defensive weaknesses and attacking characteristics.

[0899] Output: Analysis of the opposing team's strengths and weaknesses.

[0900] Step 3: Collect and analyze your own data

[0901] Input: Player data for your team.

[0902] How it works: The server collects data on the team's players and analyzes it using Python and the Scikit-learn library. It evaluates the skills and performance data of each player.

[0903] Output: Analysis results of your team's player data.

[0904] Step 4: Strategy Generation

[0905] Input: Analysis results for your team and the opposing team.

[0906] How it works: The server generates optimal strategies based on the analysis results. Specifically, it proposes strategies that exploit the opponent's weaknesses and strategies that maximize the strengths of the player's own team.

[0907] Output: The generated strategic plan.

[0908] Step 5: Output and adjust your strategy

[0909] Enter: strategic planning.

[0910] How it works: The generated strategy is sent in PDF format to the coach's device, where the user (coach) can review the strategy and adjust it if necessary.

[0911] Output: The final strategic plan is displayed on the coach's terminal.

[0912] Athlete schedule management system

[0913] Step 1: Collecting physical data

[0914] Input: Real-time physical data.

[0915] How it works: The device collects the athlete's heart rate, fatigue level, and sleep data in real time and sends it to a server.

[0916] Output: The physical data is stored in the server database.

[0917] Step 2: Analysis of physical data and schedule proposal

[0918] Input: Physical data.

[0919] How it works: The server evaluates the player's condition based on the physical data collected. Using time series analysis, it generates a training schedule using a Python library.

[0920] Output: The generated training schedule.

[0921] Step 3: Export and adjust the schedule

[0922] Input: The generated training schedule.

[0923] Operation: The server sends the generated schedule in PDF format to the device. The user (coach) reviews the schedule and adjusts it as necessary.

[0924] Output: The final adjusted schedule is displayed on the terminal.

[0925] Step 4: Schedule Notification

[0926] Enter: Final Schedule.

[0927] What it does: The device notifies and displays the final schedule to the player. This notification can include pop-ups and alarms.

[0928] Output: The player checks the schedule and puts it into action.

[0929] These processing steps allow players to optimize their performance and strategically advance their matches and training.

[0930] (Application example 1)

[0931] 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."

[0932] In the conventional manufacturing industry, there has been a lack of systems that can properly evaluate the individual capabilities of factory employees and generate optimal production line allocations and work schedules. This has made it difficult to maximize employee capabilities, resulting in problems such as reduced production efficiency and employee overwork. Furthermore, it has been difficult to collect employee physical and work data in real time and then efficiently allocate and schedule employees based on that data. The present invention aims to solve these problems, optimize employee performance, and improve factory production efficiency.

[0933] 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.

[0934] In this invention, the server includes means for collecting and analyzing player match data, training data, and physical data, means for generating reports based on the evaluation results, means for outputting the generated reports, means for collecting and evaluating factory employee performance data, means for generating an optimal production line layout based on the evaluation results, and means for outputting the generated layout, thereby enabling optimal evaluation of factory employee capabilities and generation of efficient production line layouts and work schedules.

[0935] "Match data" refers to data that records various activities and performances performed by players during a match.

[0936] "Training Data" refers to data that records the performance of exercises and activities that an athlete performs during training.

[0937] "Physical data" refers to data that indicates the physical condition of athletes and factory workers, such as their heart rate, distance, speed, fatigue level, and sleep data.

[0938] "Means for generating reports" refers to systems or processes that automatically create reports based on analysis and evaluation results.

[0939] "Means for outputting reports" refers to a system or process that displays or transmits the generated reports to a terminal or device.

[0940] "Performance data" refers to data such as work speed, accuracy, and error rate used to evaluate the productivity and efficiency of factory employees while working.

[0941] "Means for generating optimal layout of production lines" refers to systems and processes for placing factory employees in optimal work positions based on collected and analyzed data.

[0942] "Means for generating strategies" refers to a system or process that analyzes information about one's own team and the opposing team and formulates optimal strategies and tactics.

[0943] "Means for generating schedules" refers to the systems and processes that create optimal training and work schedules based on the physical data of each player or employee.

[0944] "Means for notification" refers to a system or process that notifies the target person in real time about the generated schedule or allocation.

[0945] "Means for visualizing work speed and accuracy" refers to a system or process that visually displays each employee's work speed and accuracy in the form of graphs or charts.

[0946] The present invention is a system for optimally evaluating the performance of factory employees and generating optimal production line layouts and work schedules. The operation of this system will be described below.

[0947] Data collection and analysis process

[0948] The server collects performance data such as work speed, accuracy, number of breaks, and cumulative fatigue level from each employee's wearable device and sensors. This data is sent to the server in real time and stored in a database.

[0949] Data analysis

[0950] The server analyzes the collected data and calculates performance indicators for each employee. Specifically, it uses machine learning algorithms (such as Random Forest and SVM) to identify the employee's strengths and weaknesses. Based on the results of this analysis, it generates an employee evaluation report.

[0951] Report generation and output

[0952] The server generates evaluation reports in a visually easy-to-understand format, including graphs and charts. These reports are sent to the terminals of each employee and manager. For example, if Employee A's work speed is 45 units per minute and his accuracy is 98%, a report will be generated that displays his performance evaluation as "highly efficient."

[0953] Optimal production line layout and schedule generation

[0954] The server generates the optimal allocation of factory employees to production lines based on the evaluation results, and also generates an optimal individual work schedule based on each employee's physical data (heart rate, accumulated fatigue level, etc.), including allocation of break times and light work.

[0955] Scheduling and Placement Notification

[0956] The generated schedule and optimal allocation are notified to each employee's device in real time. Employees can check their daily schedule and allocation location through their own devices. For example, if Employee B reaches 75% of their normal work speed, they will be notified and assigned lighter tasks in the next day's work schedule.

[0957] Visualization

[0958] The server visualizes each employee's work speed and accuracy using graphs and charts, and this visualized data is provided to managers and upper management, allowing them to understand the overall performance status at a glance.

[0959] Hardware and software used

[0960] The present invention uses the following hardware and software:

[0961] Hardware: Smartphones, smart glasses, head-mounted displays, wearable sensors

[0962] Software: pandas (data frame manipulation), scikit-learn (machine learning algorithms), matplotlib (data visualization)

[0963] Prompt Sentence Examples

[0964] "Analyze Employee A's work data, evaluate his work speed and accuracy, and propose an optimal work schedule."

[0965] The present invention improves factory production efficiency and maximizes employee capabilities. It also prevents employees from overworking, makes health management easier, and improves overall work efficiency.

[0966] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0967] Step 1:

[0968] The server collects real-time performance data from the factory employees' wearable devices, such as work speed, accuracy, number of breaks, and cumulative fatigue. This data is sent to the server and stored in a database. The input is real-time data from the wearable devices, and the output is performance data stored in the database.

[0969] Step 2:

[0970] The server converts the collected performance data into a data frame and performs preprocessing for applying the machine learning algorithm. This preprocessing includes data normalization and missing value imputation. The input is the performance data collected in step 1, and the output is a preprocessed data frame.

[0971] Step 3:

[0972] The server uses the preprocessed data to apply machine learning algorithms (e.g., Random Forest) to evaluate each employee's performance. This evaluation includes a performance evaluation based on work speed and accuracy, and a fatigue evaluation based on cumulative fatigue. The input is the preprocessed data frame, and the output is the performance evaluation results for each employee.

[0973] Step 4:

[0974] The server generates a performance report for each employee based on the evaluation results. This report includes graphs and charts that visually represent the evaluation results. The input is the performance evaluation results, and the output is a visually represented performance report. Specifically, based on information that Employee A's work speed is 45 units per minute and his accuracy is 98%, a performance report is generated that evaluates him as "highly efficient."

[0975] Step 5:

[0976] The server sends the generated performance report to the terminals of each employee and manager, allowing employees to check their own performance and understand areas for improvement. The input is the generated performance report, and the output is the report displayed on the terminal.

[0977] Step 6:

[0978] The server generates the optimal layout of the production line based on the evaluation results of each employee. This layout includes the optimal work position and role of each employee. The input is the evaluation results, and the output is the optimal production line layout.

[0979] Step 7:

[0980] The server generates an individual optimal work schedule based on each employee's physical data. This schedule includes break times and light work assignments. The input is the physical data, and the output is the optimal work schedule for each employee.

[0981] Step 8:

[0982] The server notifies each employee's terminal of the generated schedule and production line allocation in real time, allowing the employee to check their own schedule and location. The input is the generated schedule and allocation, and the output is the schedule and allocation information displayed on the terminal. Specifically, if employee B reaches 75% of his normal work speed, he will be notified that he will be assigned a lighter task in the next day's work schedule.

[0983] Through the above processing steps, a system is realized that optimally evaluates the capabilities of factory employees and improves production efficiency.

[0984] 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.

[0985] The present invention aims to improve overall performance by combining a system for optimally evaluating a player's abilities, proposing strategies, and managing the player's schedule with an emotion engine that recognizes the user's emotions. The program processing of this system is explained below in natural language.

[0986] Player ability analysis

[0987] Data collection

[0988] The server collects each player's match data from a database. Performance data during the match includes pass success rate, shot success rate, and distance traveled. The device (player's wearable device) sends training data such as heart rate, speed, and distance to the server in real time. The server periodically obtains physical data (heart rate, fatigue level, and sleep time) from each player's device.

[0989] Data analysis

[0990] The server combines collected match data, training data, and physical data to calculate performance indicators for each player. The server cleanses the data and fills in outliers and missing data. The server applies machine learning algorithms to identify players' strengths and weaknesses. The server performs clustering analysis based on each indicator to extract performance patterns.

[0991] Report Generation

[0992] The server generates a report for each player based on the analysis results. The report includes graphs and charts and is created in a visually easy-to-understand format. The generated report is sent to the terminal (coach's or player's device). The server notifies the user of the report, and the user can check the report.

[0993] Specific examples

[0994] The server collects the match data of Player A and determines that his pass success rate is 90%, his shot success rate is 50%, and his speed is 28km / h. From this data, it is analyzed that Player A is good at passing, but his shooting accuracy has room for improvement.

[0995] Strategy proposals

[0996] Analysis of the opposing team

[0997] The server collects the opponent's match data. The server retrieves recent match results and opponent player data from the database. The server uses machine learning algorithms to identify the opponent's strengths and weaknesses.

[0998] Analyzing your team's capabilities

[0999] The server collects and analyzes the performance data of each player on the team, evaluating skills such as dribbling and passing accuracy.

[1000] Strategy Generation

[1001] The server generates optimal match strategies based on the data collected, maximizing the strengths of the team and exploiting the weaknesses of the opposing team.

[1002] Strategic Notification

[1003] The server sends the generated strategy to the terminal (coach's device), where the user (coach) reviews the strategy and makes adjustments if necessary.

[1004] Specific examples

[1005] The server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since player B on the team is good at dribbling, the server proposes a strategy of having player B attack from the center.

[1006] Player schedule management

[1007] Physical data analysis

[1008] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.). The terminal (wearable device) sends the data to the server in real time. The server analyzes the collected data and evaluates the player's current physical condition. The server cleans out abnormal values ​​and inconsistent data.

[1009] Schedule proposal

[1010] The server generates an optimal training schedule based on the analysis results, incorporating rest days and recovery training as needed.

[1011] Schedule adjustment

[1012] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[1013] Schedule Notifications

[1014] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[1015] Specific examples

[1016] The server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. The user (coach) checks the schedule and, after adjustments, the final schedule is sent to Player C's device.

[1017] User Emotion Recognition

[1018] Emotional Data Collection

[1019] The terminal (user's device) collects the user's facial expression data, voice data, or behavioral data and sends it to the server, which then stores this data.

[1020] Emotion analysis

[1021] The server uses an emotion engine to analyze the collected data and identify the user's emotions. The emotion engine uses algorithms for facial expression analysis, voice analysis, and behavior analysis. The server records the analysis results in an emotion database.

[1022] Emotional data usage

[1023] The server provides feedback to players and adjusts training menus based on the emotional data identified by the server. The server provides communication adapted to the user's emotions. The server includes a user interface for visually displaying the emotional data.

[1024] Specific examples

[1025] If the user (coach) feels stressed while instructing the player, the emotion engine detects this and the server displays suggestions for relaxation. Also, if the emotion engine detects that the player is highly motivated during training, the server will suggest a training menu to maintain that state.

[1026] As described above, the system of the present invention combines the collection, analysis, strategy proposal, and schedule management of player data with the recognition of user emotions to achieve overall performance improvement.

[1027] The processing flow will be explained below.

[1028] Player ability analysis

[1029] Data collection

[1030] Step 1:

[1031] The server collects each player's match data from a database, including performance data during the match, such as pass success rate, shot success rate, and distance traveled.

[1032] Step 2:

[1033] The terminal (the athlete's wearable device) transmits training data such as heart rate, speed, and distance to the server in real time.

[1034] Step 3:

[1035] The server periodically collects physical data (heart rate, fatigue level, sleep time) from each player's device.

[1036] Data analysis

[1037] Step 4:

[1038] The server integrates the match data, training data, and physical data collected.

[1039] Step 5:

[1040] The server calculates each player's performance indicators, such as passing success rate, shooting success rate, speed, and stamina.

[1041] Step 6:

[1042] The server applies machine learning algorithms to identify players' strengths and weaknesses, and performs clustering analysis based on each indicator to extract performance patterns.

[1043] Report Generation

[1044] Step 7:

[1045] The server generates a report for each player based on the analysis results, including easy-to-understand graphs and charts.

[1046] Step 8:

[1047] The server sends the generated report to the terminal (the coach's or player's device). The server notifies the user of the report, and the user (coach or player) checks the report.

[1048] Specific examples

[1049] Step 9:

[1050] The server collects the match data of Player A and determines that his passing success rate is 90%, his shooting success rate is 50%, and his speed is 28km / h. After analyzing, it is determined that Player A is good at passing, but his shooting accuracy has room for improvement.

[1051] Strategy proposals

[1052] Analysis of the opposing team

[1053] Step 1:

[1054] The server collects the match data of the opposing team, and retrieves the results of recent matches and player data of the opposing team from the database.

[1055] Step 2:

[1056] The server uses machine learning algorithms to analyze the strengths and weaknesses of the opposing team.

[1057] Analyzing your team's capabilities

[1058] Step 3:

[1059] The server collects and analyzes the performance data of each player on the team, evaluating skills such as dribbling and passing accuracy.

[1060] Strategy Generation

[1061] Step 4:

[1062] The server generates optimal match strategies based on the data collected, maximizing your team's strengths and exploiting the opponent's weaknesses.

[1063] Strategic Notification

[1064] Step 5:

[1065] The server sends the generated strategy to the terminal (coach's device), where the user (coach) reviews the strategy and makes adjustments if necessary.

[1066] Specific examples

[1067] Step 6:

[1068] The server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since the team's player B is good at dribbling, the server proposes a strategy of having player B attack from the center.

[1069] Player schedule management

[1070] Physical data analysis

[1071] Step 1:

[1072] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.), and the terminal (wearable device) transmits the data to the server in real time.

[1073] Step 2:

[1074] The server analyzes the collected data to evaluate the player's current physical condition and cleanses outliers and inconsistent data.

[1075] Schedule proposal

[1076] Step 3:

[1077] The server uses the analysis results to generate an optimal training schedule, incorporating rest days and recovery training as needed.

[1078] Schedule adjustment

[1079] Step 4:

[1080] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[1081] Schedule Notifications

[1082] Step 5:

[1083] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[1084] Specific examples

[1085] Step 6:

[1086] The server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. The user (coach) checks the schedule and, after adjustments, the final schedule is sent to Player C's device.

[1087] User Emotion Recognition

[1088] Emotional Data Collection

[1089] Step 1:

[1090] The terminal (user's device) collects the user's facial expression data, voice data, or behavioral data and sends it to the server, which then stores this data.

[1091] Emotion analysis

[1092] Step 2:

[1093] The server uses an emotion engine to analyze the collected data and identify the user's emotions. The emotion engine uses algorithms for facial expression analysis, voice analysis, and behavior analysis.

[1094] Emotional data usage

[1095] Step 3:

[1096] Based on the emotional data identified by the server, the server provides feedback to the player and adjusts the training menu, providing communication that is adapted to the user's emotions.

[1097] Emotion data display

[1098] Step 4:

[1099] The server provides a user interface for visually displaying emotional data on the device. The user (coach or player) can review this data and reflect it in training and strategies.

[1100] Specific examples

[1101] Step 5:

[1102] If the user (coach) feels stressed while instructing the player, the emotion engine detects this and the server displays suggestions for relaxation. Also, if the emotion engine detects that the player is highly motivated during training, the server will suggest a training menu to maintain that state.

[1103] As a result, the system of the present invention not only collects and analyzes player data, proposes consistent strategies, and manages schedules, but also incorporates a function to recognize user emotions, thereby achieving overall performance improvement.

[1104] Example 2

[1105] 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."

[1106] Conventional player performance evaluation systems are limited to collecting and analyzing match and training data, which is insufficient for achieving overall performance improvement. Furthermore, they do not take into account the emotions of users (coaches and players) when proposing strategies or managing schedules, making it difficult to optimize players' motivation and physical condition. Furthermore, the generated reports and strategies lack a visual display, making intuitive understanding difficult.

[1107] 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.

[1108] In this invention, the server includes means for collecting game data, training data, and physical data of players, means for analyzing the collected data and identifying the strengths and weaknesses of players, means for generating a report based on the analysis results, means for outputting and visually displaying the generated report, and means for calculating performance indicators for each player and performing clustering analysis. This makes it possible to improve overall performance by combining player data collection, analysis, strategy proposals, schedule management, and user emotion recognition.

[1109] "Match data" refers to data collected to evaluate a player's performance during a match, and specifically includes pass success rate, shooting success rate, distance traveled, etc.

[1110] "Training Data" is data recorded while an athlete is training, and includes information related to physical activity such as heart rate, speed, distance, etc.

[1111] "Physical data" refers to data used to evaluate the physical condition of a player, and specifically includes heart rate, fatigue level, sleep time, etc.

[1112] "Analysis" means processing information using techniques such as evaluation, judgment, and pattern identification based on collected data.

[1113] "Strengths and Weaknesses" refers to a player's strengths and areas for improvement, identified based on the player's performance data.

[1114] A "report" is a collection of documents and graphics generated based on the analysis results, including visual representations of player performance.

[1115] "Visually displaying" refers to presenting analysis results and reports in the form of graphs, charts, etc. so that users can intuitively understand them.

[1116] "Clustering analysis" refers to a technique that uses machine learning algorithms to classify collected data into multiple clusters and extract similar patterns.

[1117] "Strategy" means a plan or proposal for choosing the best course of action in a match, including one that takes into account the strengths and weaknesses of the opposing team.

[1118] A "schedule" is a plan for a player's training and rest, and is generated based on physical and performance data.

[1119] "Emotion data" refers to information indicating the emotional state of a user analyzed from facial expression data, voice data, and behavior data.

[1120] The present invention aims to improve overall performance by combining a system for optimally evaluating players' abilities, proposing strategies, and managing players' schedules with an emotion engine that recognizes the user's emotions. The system of the present invention is mainly composed of a server, terminals, and users.

[1121] Player ability analysis

[1122] Data collection

[1123] The server collects each player's match data from the database. This match data includes pass success rate, shot success rate, running distance, etc. The players' wearable devices also send training data such as heart rate, speed, and distance to the server in real time. The server periodically collects the players' physical data such as heart rate, fatigue level, and sleep time.

[1124] Data analysis

[1125] The server integrates the collected match data, training data, and physical data to calculate each player's performance indicators. It also cleanses the data and fills in outliers and missing data. It also applies machine learning algorithms to identify players' strengths and weaknesses. It also performs clustering analysis to extract performance patterns.

[1126] Report Generation

[1127] The server generates a report for each player based on the analysis results. The report includes graphs and charts and is created in a visually easy-to-understand format. The generated report is sent to the player's or coach's device, where the user can check the received report.

[1128] Specific examples

[1129] For example, if the server collects match data for Player A and determines that his pass success rate is 90%, his shot success rate is 50%, and his speed is 28km / h, it will analyze that Player A is good at passing, but that there is room for improvement in his shooting accuracy.

[1130] Strategy proposals

[1131] Analysis of the opposing team

[1132] The server collects the opponent's match data, retrieves recent match results and player data from the database, and uses machine learning algorithms to identify the opponent's strengths and weaknesses.

[1133] Analyzing your team's capabilities

[1134] The server collects and analyzes the performance data of each player on the team, including skill evaluations such as dribbling and passing accuracy.

[1135] Strategy Generation

[1136] The server uses the collected data to generate optimal match strategies that maximize the strengths of the team and exploit the weaknesses of the opposing team.

[1137] Strategic Notification

[1138] The generated strategy is sent from the server to the coach's device, where the user (coach) can review the strategy and make adjustments as necessary.

[1139] Specific examples

[1140] For example, if the server compares data from your team with that of the opposing team and discovers that the opposing team is weak in central defense, it will propose a strategy to have player B on your team attack from the center because he is good at dribbling.

[1141] Player schedule management

[1142] Physical data analysis

[1143] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.). The player's wearable device transmits the data to the server in real time. The server analyzes the collected data and evaluates the player's current physical condition.

[1144] Schedule proposal

[1145] The server generates an optimal training schedule based on the analysis results, incorporating rest days and recovery training as needed.

[1146] Schedule adjustment

[1147] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[1148] Schedule Notifications

[1149] The final schedule is sent from the server to each player's device, which receives the notification and displays the schedule to the player.

[1150] Specific examples

[1151] For example, if the server analyzes Player C's physical data and determines that he has become fatigued from recent training, it will suggest a day of rest and incorporate lighter training for the rest of the week. The user (coach) checks the schedule and, after making adjustments, the final schedule is sent to Player C's device.

[1152] User Emotion Recognition

[1153] Emotional Data Collection

[1154] The user's device collects facial expression data, voice data, or behavioral data and sends it to the server, which stores the data.

[1155] Emotion analysis

[1156] The server analyzes the collected data using an emotion engine to identify the user's emotions. The emotion engine uses algorithms for facial expression analysis, voice analysis, and behavior analysis. The analysis results are recorded in an emotion database.

[1157] Emotional data usage

[1158] The server provides feedback to the player and adjusts the training menu based on the identified emotion data. The server provides communication adapted to the user's emotions. The server includes a user interface for visually displaying the emotion data.

[1159] Specific examples

[1160] For example, if a user (coach) feels stressed while instructing a player, the emotion engine will detect this and the server will display suggestions for relaxation. Also, if the emotion engine detects that a player is highly motivated during training, the server will suggest a training menu to help the player maintain that state.

[1161] As described above, the system of the present invention combines the collection, analysis, strategy proposal, and schedule management of player data with the recognition of user emotions to achieve overall performance improvement.

[1162] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1163] Player ability analysis

[1164] Data collection

[1165] Step 1:

[1166] The server collects the match data from the database.

[1167] Input: Player ID

[1168] Data processing: Execute an SQL query to retrieve match data (pass success rate, shooting success rate, distance traveled, etc.) related to the player ID.

[1169] Output: Match data for each player

[1170] Specific example of operation: The server uses "MySQL" to execute the query "SELECT FROM match_data WHERE player_id = ?".

[1171] Step 2:

[1172] The athlete's wearable device (terminal) transmits training data to a server in real time.

[1173] Input: Real-time raw data (heart rate, speed, distance, etc.)

[1174] Data Processing: Data formatting and buffering

[1175] Output: Formatted training data

[1176] Specific example of operation: The device receives data via Bluetooth, converts it to JSON format, and sends an HTTP POST request to the server.

[1177] Step 3:

[1178] The server collects the physical data.

[1179] Input: Periodic data transmission from a wearable device

[1180] Data processing: Error correction and interpolation

[1181] Output: Unified physical data

[1182] Specific operation example: The server executes a scheduled job every day at 00:00 and collects data from the device with a GET request.

[1183] Data analysis

[1184] Step 4:

[1185] The server aggregates the data and calculates performance metrics for each player.

[1186] Input: Match data, training data, physical data

[1187] Data processing: data merging and statistical analysis

[1188] Output: Unified performance dataset

[1189] Specific example of operation: The server uses the "Pandas" library to merge data frames and calculate statistics.

[1190] Step 5:

[1191] The server performs data cleansing.

[1192] Input: Unified performance dataset

[1193] Data processing: Correction of outliers, completion of missing data

[1194] Output: A cleansed dataset

[1195] Specific example of operation: The server uses the "Scikit-learn" outlier detection algorithm to replace abnormal values ​​with the average value.

[1196] Step 6:

[1197] The server applies machine learning algorithms to identify players' strengths and weaknesses.

[1198] Input: Cleansed dataset

[1199] Data processing: Classification and regression analysis

[1200] Output: A list of strengths and weaknesses for each player

[1201] Specific example of operation: The server runs a "random forest" model and extracts important features.

[1202] Step 7:

[1203] The server performs the clustering analysis.

[1204] Input: List of strengths and weaknesses

[1205] Data processing: Applying clustering algorithms

[1206] Output: Clusters of performance patterns

[1207] Specific example of operation: The server classifies data into multiple clusters using "K-means".

[1208] Report Generation

[1209] Step 8:

[1210] The server generates a report based on the analysis results.

[1211] Input: Cluster of performance patterns

[1212] Data processing: Graph and chart generation

[1213] Output: Report document (PDF, etc.)

[1214] Specific example of operation: The server creates a graph using "Matplotlib" and exports it to PDF.

[1215] Step 9:

[1216] The server outputs the report and displays it visually.

[1217] Input: Generated report document

[1218] Data processing: Format conversion for report delivery and display

[1219] Output: Report notification

[1220] Specific example of how it works: The server sends a PDF via email via the SMTP protocol, and the report is displayed in the device's app.

[1221] Strategy proposals

[1222] Analysis of the opposing team

[1223] Step 10:

[1224] The server collects the match data of the opposing team.

[1225] Input: Match ID, Opponent Team ID

[1226] Data processing: Data collection through API queries

[1227] Output: Opponent team's match data

[1228] Specific example of operation: The server obtains the latest match data of the opposing team via the REST API.

[1229] Step 11:

[1230] The server analyzes the strengths and weaknesses of the opposing team.

[1231] Input: Opponent team's match data

[1232] Data processing: Analysis using machine learning algorithms

[1233] Output: A list of the opponent's strengths and weaknesses

[1234] Specific example of operation: The server uses a statistical model to analyze the opponent's defense success rate, attack patterns, etc.

[1235] Analyzing your team's capabilities

[1236] Step 12:

[1237] The server collects and analyzes the team's player data.

[1238] Input: Player ID, team ID

[1239] Data Transformation: API queries and data analysis

[1240] Output: Performance data of your team's players

[1241] Specific example of operation: The server retrieves the player's training log from the data warehouse and analyzes it using a data analysis tool.

[1242] Strategy Generation

[1243] Step 13:

[1244] The server generates the optimal strategy.

[1245] Input: Data analysis results for your team and the opposing team

[1246] Data Processing: Algorithmic Strategy Optimization

[1247] Output: Optimal strategic plan

[1248] Specific example of operation: The server optimizes the strategy using a genetic algorithm and outputs a strategic plan.

[1249] Strategic Notification

[1250] Step 14:

[1251] The server sends the generated strategy to the coach's device.

[1252] Input: Optimal strategic plan

[1253] Data processing: Strategic plan format conversion

[1254] Output: Strategy Notification

[1255] Specific example of operation: The server exports the generated strategy in JSON format and notifies the coach's device.

[1256] Player schedule management

[1257] Physical data analysis

[1258] Step 15:

[1259] The server collects the physical data.

[1260] Input: Periodic data from wearable devices

[1261] Data processing: data collection and formatting

[1262] Output: Unified physical data

[1263] Specific example of operation: The server schedules a periodic data collection job and retrieves the data with a GET request.

[1264] Step 16:

[1265] The server analyzes the physical data and evaluates the physical state.

[1266] Input: Unified physical data

[1267] Data processing: time series analysis and outlier correction

[1268] Output: Analysis results and physical state evaluation

[1269] Specific example of operation: The server performs "time series analysis" to identify the player's fatigue level trends.

[1270] Schedule proposal

[1271] Step 17:

[1272] The server generates the training schedule.

[1273] Input: Physical condition assessment results

[1274] Data processing: Applying schedule generation algorithms

[1275] Output: Optimal training schedule

[1276] Specific example of operation: The server combines the player's physical data and performance data to create a training schedule.

[1277] Schedule Notifications

[1278] Step 18:

[1279] The server sends the final schedule to the players' devices.

[1280] Input: Optimal training schedule

[1281] Data processing: Schedule format conversion

[1282] Output: Schedule notification

[1283] Specific example of operation: The server exports the schedule in JSON format and sends it to the player's app.

[1284] User Emotion Recognition

[1285] Emotional Data Collection

[1286] Step 19:

[1287] The user's device collects facial expression data, voice data, or behavioral data and transmits it to a server.

[1288] Input: Real-time facial, voice, and behavioral data

[1289] Data processing: data collection and format conversion

[1290] Output: Emotion data

[1291] Specific example of how it works: Collects data using the device's camera and microphone and streams it to a server in real time.

[1292] Emotion analysis

[1293] Step 20:

[1294] The server analyzes the emotion data using an emotion engine.

[1295] Input: Collected emotion data

[1296] Data processing: Applying sentiment analysis algorithms

[1297] Output: User's emotion rating

[1298] Specific example of operation: The server uses the emotion engine and executes a facial expression recognition algorithm to classify the user's emotion.

[1299] Emotional data usage

[1300] Step 21:

[1301] Feedback and training menus are adjusted based on the emotional data identified by the server.

[1302] Input: User's emotion rating

[1303] Data processing: Emotion-based training menu generation

[1304] Output: Adjusted training menu

[1305] Specific example of operation: The server detects the user's stress level and automatically generates a training menu based on that.

[1306] Step 22:

[1307] The server visually displays the emotion data.

[1308] Input: Emotion evaluation results

[1309] Data processing: graph generation for visualization

[1310] Output: Visual display of emotion data

[1311] Specific example of operation: The server generates various graphs and charts based on emotion data and displays them in real time on the user's device.

[1312] (Application example 2)

[1313] 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."

[1314] Conventional factory management systems lacked the functionality to efficiently collect, analyze, and optimize performance data from industrial robots and workers. Furthermore, safety management that took into account workers' physical data and emotions was insufficient, resulting in reduced work efficiency and health risks. Furthermore, a lack of real-time data analysis and feedback made it difficult to respond immediately. A new system is needed to resolve these issues and ensure worker safety while improving work efficiency within factories.

[1315] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting game data, training data, and physical data of players; means for analyzing the collected data and identifying the strengths and weaknesses of players; means for generating a report based on the analysis results; means for outputting the generated report; means for collecting and analyzing robot operation data and identifying performance indicators; means for collecting and analyzing physical data of workers and identifying stress and fatigue levels; means for collecting facial expression data, voice data, and behavioral data of workers and analyzing their emotions; means for generating and adjusting a work schedule based on the analysis results; and means for outputting the generated work schedule. This enables efficient operation of industrial robots in factories and optimization of work while ensuring the safety of workers.

[1316] "Athlete match data" refers to data that indicates the performance and achievements that an athlete has achieved during a match.

[1317] "Training data" refers to data such as heart rate, speed, and distance obtained by an athlete during training.

[1318] "Physical data" refers to data that indicates an athlete's physical strength and health, such as heart rate, fatigue level, and sleep time.

[1319] "Robot operation data" refers to data that indicates the operating status of an industrial robot, such as its work cycle time and energy consumption.

[1320] A "performance index" is an evaluation criterion calculated through data analysis that quantifies work efficiency and accuracy.

[1321] "Physical data of a worker" refers to data that indicates the physical condition of a worker, such as heart rate, movement, and fatigue level.

[1322] "Stress and fatigue" refers to the degree of psychological or physical strain or fatigue felt by workers.

[1323] "Worker facial expression data" is information obtained from the worker's facial expressions, and is data used to infer emotions and physical condition.

[1324] "Voice data" is data collected from the voices of workers and is used to analyze their emotions and state of consciousness.

[1325] "Behavioral data" is information collected about the movements and activity patterns of workers.

[1326] "Analyzing emotions" refers to the process of identifying a worker's emotional state based on facial, vocal, and behavioral data.

[1327] A "work schedule" is a plan that shows the order and timing of work that workers should perform.

[1328] An "optimal strategy" is the most efficient and effective plan of action generated by analyzing collected data.

[1329] The present invention relates to a system for efficiently managing industrial robots and workers in a factory and ensuring their safety. The configuration of the system and specific embodiments will be described below.

[1330] System configuration

[1331] The system consists of the following main components:

[1332] 1. Data Collection Methods

[1333] Industrial robots are used as a means for collecting operational data of the robots.

[1334] Wearable devices that measure heart rate and movement are used to collect workers' physical data.

[1335] Cameras and microphones are used to collect facial expression, voice, and behavioral data from workers.

[1336] 2. Data analysis methods

[1337] The server uses machine learning frameworks (e.g., TensorFlow or Scikit-learn) to synthesize the collected data and identify performance metrics.

[1338] This also includes a means of analyzing physical data to identify workers' stress and fatigue levels.

[1339] 3. Report generation and strategy proposal tool

[1340] The server generates a work schedule based on the analysis results and suggests appropriate breaks and workload adjustments.

[1341] An emotion engine is used to analyze workers' emotional data and suggest appropriate communication methods.

[1342] 4. Notification and Feedback Methods

[1343] Generated reports and proposed strategies are sent to each terminal, allowing workers and managers to review them.

[1344] Program processing explanation

[1345] The server does the following:

[1346] 1. Data Collection

[1347] Collecting data in real time from industrial robots and wearable devices.

[1348] Cameras and microphones are also used to collect facial expressions, voice and behavioral data from workers.

[1349] 2. Data Analysis

[1350] The server stores the collected data in a database (e.g., MySQL, MongoDB).

[1351] Machine learning algorithms are used to analyze the data and identify performance indicators and emotional states.

[1352] 3. Report generation and strategy proposals

[1353] Generate optimal work schedules and strategies based on performance data.

[1354] It analyzes emotional data and suggests feedback and communication methods based on that data.

[1355] 4. Notifications and Feedback

[1356] Generated reports and strategies are sent to terminals so that workers and managers can review them.

[1357] Specific examples

[1358] For example, a factory might have the following setup:

[1359] Collects operational data (work cycle time, energy consumption) from industrial robots to improve the efficiency of specific work processes.

[1360] Detects stress levels from workers' heart rate data and suggests appropriate breaks.

[1361] If a worker is highly fatigued, the analysis results will be used to suggest work to reduce fatigue.

[1362] Prompt Sentence Examples

[1363] Here are some examples of prompts for generative AI models:

[1364] prompt:

[1365] We are developing an application for efficient operation of industrial robots in a factory and for worker safety management. Please generate a program that meets the following conditions:

[1366] Collect and analyze robot operation data (cycle time, energy consumption)

[1367] Collect physical data (heart rate, movement) from workers' wearable devices and analyze their stress and fatigue levels.

[1368] Collecting facial expressions, voice, and behavioral data from workers and analyzing their emotions

[1369] Based on the analysis results, we propose optimization of work schedules and robot operations.

[1370] It has the function of notifying workers and managers of analysis results and suggestions.

[1371] Based on this requirement, generate the relevant code.

[1372] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1373] Step 1:

[1374] Data collection

[1375] The server collects real-time operational data from industrial robots (such as work cycle time and energy consumption), as well as data from wearable devices that measure workers' heart rates and movements, and from cameras and microphones that capture workers' facial expressions and voices, and stores this data in a database (e.g., MySQL, MongoDB).

[1376] input:

[1377] Industrial robot operation data

[1378] Worker physical data (heart rate, movement)

[1379] Facial expressions and voice data of workers

[1380] output:

[1381] Stored database records

[1382] Step 2:

[1383] Data analysis

[1384] The server analyzes the collected data. Specifically, it uses machine learning frameworks (e.g., TensorFlow, Scikit-learn) to identify robot performance indicators and calculates stress and fatigue levels from the worker's physical data. It also analyzes facial and voice data using an emotion engine to identify the worker's emotional state.

[1385] input:

[1386] Various data stored in the database

[1387] output:

[1388] Performance indicators, stress / fatigue levels, emotional state

[1389] Step 3:

[1390] Report generation and strategy recommendations

[1391] Based on the analysis results, the server generates an optimal schedule for efficient robot operation and workers, and also creates a report that suggests feedback and communication methods tailored to workers based on their emotional data.

[1392] input:

[1393] Performance Indicators

[1394] Stress and fatigue

[1395] Emotional state

[1396] output:

[1397] Work schedules, strategies, and feedback reports

[1398] Step 4:

[1399] Notifications and Feedback

[1400] The server then sends the generated reports and proposed work schedules to each terminal, allowing workers and managers to review them. This allows workers to check the schedules and proposals from their terminals and take appropriate action.

[1401] input:

[1402] Reports, strategies, and work schedules

[1403] output:

[1404] Notifications and displayed content on the device

[1405] Prompt Sentence Examples

[1406] Here are some examples of prompts for generative AI models:

[1407] prompt:

[1408] We are developing an application for efficient operation of industrial robots in a factory and for worker safety management. Please generate a program that meets the following conditions:

[1409] Collect and analyze robot operation data (cycle time, energy consumption)

[1410] Collect physical data (heart rate, movement) from workers' wearable devices and analyze their stress and fatigue levels.

[1411] Collecting facial expressions, voice, and behavioral data from workers and analyzing their emotions

[1412] Based on the analysis results, we propose optimization of work schedules and robot operations.

[1413] It has the function of notifying workers and managers of analysis results and suggestions.

[1414] Based on this requirement, generate the relevant code.

[1415] 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.

[1416] 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.

[1417] 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.

[1418] [Third embodiment]

[1419] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1420] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1421] 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).

[1422] 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.

[1423] 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.

[1424] 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).

[1425] 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.

[1426] 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.

[1427] 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.

[1428] 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.

[1429] 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.

[1430] 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."

[1431] The present invention is a system for optimally evaluating the abilities of players, proposing strategies, and managing the schedules of players. The program processing of this system will be explained below in natural language.

[1432] Player ability analysis

[1433] Data collection

[1434] The server collects each player's match data, training data, and physical data from a database. The player's wearable device sends data such as heart rate, distance, and speed to the server in real time.

[1435] Data analysis

[1436] The server combines the collected data to calculate each player's performance metrics (e.g., passing success rate, shooting success rate, speed, stamina), and then applies machine learning algorithms to identify players' strengths and weaknesses.

[1437] Report Generation

[1438] The server generates a report for each player based on the analysis results. The report includes graphs and charts and is created in a visually easy-to-understand format. The generated report is sent to the terminal (coach's or player's device).

[1439] Specific examples

[1440] The server collects the match data of Player A and determines that his pass success rate is 90%, his shot success rate is 50%, and his speed is 28km / h. From this data, it is analyzed that Player A is good at passing, but his shooting accuracy has room for improvement.

[1441] Strategy proposals

[1442] Analysis of the opposing team

[1443] The server collects the opponent's match data, analyzes recent match results and player performance data, and uses machine learning algorithms to identify the opponent's strengths and weaknesses.

[1444] Analyzing your team's capabilities

[1445] The server collects and analyzes the performance data of each player on the team, identifying players who excel at dribbling and players with high passing accuracy.

[1446] Strategy Generation

[1447] The server generates an optimal strategy based on data from the team and the opposing team. The generated strategy is sent to the terminal (coach's device), where the user (coach) can review and adjust it.

[1448] Specific examples

[1449] The server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since player B on the team is good at dribbling, the server proposes a strategy of having player B attack from the center.

[1450] Player schedule management

[1451] Physical data analysis

[1452] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.), and the terminal (wearable device) transmits the data to the server in real time.

[1453] Schedule proposal

[1454] The server evaluates the player's current condition based on physical data and generates an optimal training schedule, which includes rest days and recovery training.

[1455] Schedule adjustment

[1456] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of each player and the team's schedule. After adjustments, the final schedule is fed back to the server.

[1457] Schedule Notifications

[1458] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[1459] Specific examples

[1460] The server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. The user (coach) checks the schedule and, after adjustments, the final schedule is sent to Player C's device.

[1461] As described above, the system of the present invention efficiently collects and analyzes player data, proposes strategies, and manages schedules, thereby maximizing player performance.

[1462] The processing flow will be explained below.

[1463] Player ability analysis

[1464] Data collection

[1465] Step 1:

[1466] The server collects each player's match data from the database. The server also collects performance data during the match (pass success rate, shot success rate, running distance, etc.).

[1467] Step 2:

[1468] The terminal (the athlete's wearable device) transmits training data such as heart rate, speed, and distance to the server in real time.

[1469] Step 3:

[1470] The server periodically collects physical data (e.g., heart rate, fatigue level, sleep time) from each player's device.

[1471] Data analysis

[1472] Step 4:

[1473] The server integrates the collected match data, training data, and physical data to calculate each player's performance indicators. The server also cleanses the data and fills in outliers and missing data.

[1474] Step 5:

[1475] The server applies machine learning algorithms to identify players' strengths and weaknesses, and performs clustering analysis based on each indicator to extract performance patterns.

[1476] Report Generation

[1477] Step 6:

[1478] The server generates a report for each player based on the analysis results, including graphs and charts in a visually easy-to-understand format.

[1479] Step 7:

[1480] The server sends the generated report to the terminal (the coach's or player's device). The server notifies the user of the report, and the user checks the report.

[1481] Strategy proposals

[1482] Analysis of the opposing team

[1483] Step 1:

[1484] The server collects the match data of the opposing team. The server retrieves the results of recent matches and player data of the opposing team from the database.

[1485] Step 2:

[1486] The server analyzes the strengths and weaknesses of the opposing team. The server uses machine learning algorithms to identify the weaknesses of the opposing team.

[1487] Analyzing your team's capabilities

[1488] Step 3:

[1489] The server collects and analyzes the performance data of each player on the team, evaluating skills such as dribbling and passing accuracy.

[1490] Strategy Generation

[1491] Step 4:

[1492] The server generates optimal match strategies based on the data collected, maximizing the strengths of the team and exploiting the weaknesses of the opposing team.

[1493] Strategic Notification

[1494] Step 5:

[1495] The server sends the generated strategy to the terminal (coach's device), where the user (coach) reviews the strategy and makes adjustments if necessary.

[1496] Player schedule management

[1497] Physical data analysis

[1498] Step 1:

[1499] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.), and the terminal (wearable device) sends the data to the server in real time.

[1500] Step 2:

[1501] The server analyzes the collected data to evaluate the player's current physical condition and cleanses outliers and inconsistencies.

[1502] Schedule proposal

[1503] Step 3:

[1504] The server generates an optimal training schedule based on the analysis results, incorporating rest days and recovery training as needed.

[1505] Schedule adjustment

[1506] Step 4:

[1507] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[1508] Schedule Notifications

[1509] Step 5:

[1510] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[1511] This allows one to understand the specific flow of how the system of the present invention efficiently collects and analyzes player data, proposes strategies, and manages schedules.

[1512] Example 1

[1513] 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."

[1514] In modern sports, accurately evaluating individual athletes' performance, formulating optimal strategies, and managing schedules efficiently are extremely difficult challenges. Conventional methods involve manual data collection and analysis, which is time-consuming, labor-intensive, and often inaccurate. Furthermore, training schedules and strategy proposals that incorporate athletes' real-time physical data are not adequately implemented. This makes it difficult to maximize athletes' potential. The present invention aims to solve these challenges by providing a system that automates effective data collection and analysis, strategy proposals, and schedule management.

[1515] 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.

[1516] In this invention, the server includes: means for collecting a player's match data, training data, and physical data; means for integrating the collected data to calculate a player's performance indicators; and means for identifying a player's strengths and weaknesses using a machine learning algorithm. This enables accurate evaluation of a player's abilities and rapid identification of their strengths and weaknesses. The server also includes means for generating reports using graphs and charts; means for outputting the generated reports to a terminal; and means for the terminal to transmit data to the server in real time. This allows evaluation results to be presented visually in an easy-to-understand manner, enabling players and coaches to respond quickly. The approach continues with means for collecting opponent team match data and applying a machine learning algorithm to analyze the opponent team's strengths and weaknesses; means for collecting and analyzing the player data of the team's own team to generate an optimal strategy; and means for outputting the generated strategy to a terminal for the user to review and adjust. This improves the accuracy and effectiveness of strategies, thereby increasing competitiveness in matches. The system also includes a means for periodically collecting athlete physical data and generating a training schedule using time-series analysis, a means for outputting the generated schedule to a terminal in PDF format, and a means for notifying and displaying the generated schedule to the athlete. This makes it possible to provide an optimal training schedule while maintaining the athlete's health and performance.

[1517] "Player's match data" refers to various statistical data obtained by a player in an actual match (e.g., pass success rate, shot success rate, number of assists, etc.).

[1518] "Athlete training data" refers to various numerical data recorded by athletes during training (e.g., training time, number of training sessions, load, etc.).

[1519] "Athlete's physical data" refers to numerical data relating to the athlete's physical condition and stamina (e.g., heart rate, speed, distance, sleep data, fatigue level, etc.).

[1520] "Synthesis" is the process of bringing together different types of data into a single dataset in a form that can be analyzed as a whole.

[1521] "Performance indicators" are quantitative indicators used to evaluate a player's abilities and condition, and include pass success rate, shooting success rate, speed, stamina, etc.

[1522] "Machine learning algorithm" is a general term for mathematical techniques that automatically learn from data and make predictions or classifications. Examples include TensorFlow and Scikit-learn.

[1523] A "strength" is a characteristic or skill that makes a player's performance or ability superior to other aspects of their game.

[1524] A "weakness" is a characteristic or skill that is believed to need improvement in a player's performance or ability.

[1525] A "report" is a report that summarizes the results of data analysis in the form of documents, graphs, charts, etc.

[1526] "Device" means a device used by an athlete or coach (e.g., smartphone, tablet, computer, wearable device).

[1527] "Opponent team's match data" refers to various statistical data obtained by the opposing team during an actual match.

[1528] A "strategy" is a specific plan or policy for achieving a goal in a match or training.

[1529] "Time series analysis" is a method for analyzing data that changes over time and predicting future trends.

[1530] A "schedule" is a plan that systematically arranges the training, rest, and match dates of players.

[1531] "Notifications" are messages and alarms sent by the system to inform players and coaches of important information.

[1532] The present invention is a system for optimally evaluating the abilities of players, proposing strategies, and managing the schedules of players. The following describes how to implement the program of this system.

[1533] Player ability analysis system

[1534] Data collection

[1535] The server collects players' match and training data from a database. It also sends physical data measured in real time by the devices, such as heart rate, distance, and speed, to the server. The specific software used is MySQL for database management, and Python and Pandas for data collection and analysis scripts.

[1536] Data analysis

[1537] The server aggregates the collected data and calculates player performance indicators, such as passing success rate, shooting success rate, speed, stamina, etc. It then applies machine learning algorithms (such as TensorFlow and Scikit-learn) to identify players' strengths and weaknesses.

[1538] Report Generation

[1539] The server generates a visually easy-to-understand report using graphs and charts based on the analysis results. Specific libraries used are Matplotlib and Seaborn. This report is generated in PDF format and output to the terminal (the coach's or player's device).

[1540] Strategy Proposal System

[1541] Analysis of the opposing team

[1542] The server collects data on the opposing team's matches using web scraping techniques (Beautiful Soup and Selenium) and applies machine learning algorithms to analyze the opposing team's strengths and weaknesses.

[1543] Analyzing your team's capabilities

[1544] The server collects and analyzes data on the team's players, evaluating specific skills such as dribbling and passing accuracy, again using Python and the Sklearn library.

[1545] Strategy Generation

[1546] The server generates an optimal strategy based on data from the team and the opposing team. The generated strategy is output to the terminal (coach's device) and the user (coach) checks and adjusts it.

[1547] Athlete schedule management system

[1548] Physical data analysis

[1549] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.) and also receives real-time data from the device.

[1550] Schedule proposal

[1551] The server evaluates the player's current condition based on the collected physical data and uses time-series analysis to generate an optimal training schedule, including rest days and recovery training.

[1552] Scheduling and Notifications

[1553] The user (coach) reviews the proposed schedule and makes final adjustments. After adjustments, the final schedule is generated in PDF format and output to the device. The device receives a notification and displays the schedule to the players.

[1554] Specific examples

[1555] For example, suppose the server collects player A's match data and determines that his passing success rate is 90%, his shooting success rate is 50%, and his speed is 28km / h. Analyzing this data, it determines that player A is good at passing, but his shooting accuracy needs improvement. Based on these results, the server identifies player A's strengths and weaknesses, generates a visual report, and sends it to the coach's device.

[1556] Furthermore, when proposing a strategy, the server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since player B on the team is good at dribbling, the server proposes a strategy to have player B attack from the center and sends the result to the coach's device.

[1557] In schedule management, the server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. After the user (coach) checks and adjusts the schedule, the final schedule is sent to Player C's device.

[1558] Prompt Sentence Examples

[1559] "How would you rate Player A's match performance?"

[1560] The generative AI model generates an answer such as, "Player A's match performance was assessed as having a passing success rate of 90%, a shooting success rate of 50%, and a speed of 28 km / h. Based on this, it was determined that his passing ability is excellent, but his shooting accuracy needs improvement."

[1561] As described above, the present invention is a system that automates the effective collection and analysis of data, strategy proposals, and schedule management, and can bring out the best in players' performance.

[1562] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1563] Player ability analysis system

[1564] Step 1: Data collection

[1565] Inputs: Real-time physical data (heart rate, speed, distance) and match and training data.

[1566] How it works: The wearable device collects the athlete's heart rate, speed, and distance data in real time. The server receives this data via Bluetooth or Wi-Fi and records it in a database. In addition, the server retrieves past match and training data from the database.

[1567] Output: The integrated dataset is stored in a database on the server.

[1568] Step 2: Data integration

[1569] Input: Match data, training data, physical data.

[1570] How it works: The server uses Python and the Pandas library to combine these data into a single unified dataset, clean and normalize the data, impute missing data, and remove outliers.

[1571] Output: A clean, consolidated dataset.

[1572] Step 3: Performance Metrics Calculation

[1573] Input: The integrated dataset.

[1574] How it works: The server applies statistical methods to calculate each player's performance metrics (passing success rate, shooting success rate, speed, stamina). Specifically, it uses the Python Scikit-learn library to perform the calculations.

[1575] Output: A list of calculated performance metrics.

[1576] Step 4: Machine learning analysis

[1577] Input: A list of performance metrics.

[1578] How it works: The server uses TensorFlow and Scikit-learn libraries to apply machine learning algorithms to identify players' strengths and weaknesses, and analyzes them using clustering methods (e.g., K-means) and classifiers.

[1579] Output: Identification of each player's strengths and weaknesses as a result of the analysis.

[1580] Step 5: Generate reports

[1581] Input: Analysis results.

[1582] How it works: The server uses Matplotlib and Seaborn libraries to generate graphs and charts based on the analysis results, which are then compiled into a PDF report.

[1583] Output: Report in PDF format.

[1584] Step 6: Print the report

[1585] Input: Report in PDF format.

[1586] How it works: The server sends the generated reports to the players' and coaches' devices via email or a dedicated app.

[1587] Output: Reports are displayed on players' and coaches' terminals.

[1588] Strategy Proposal System

[1589] Step 1: Gather data on the opposing team

[1590] Input: Opposing team's match data.

[1591] How it works: The server uses web scraping techniques (Beautiful Soup and Selenium) to retrieve opposing team match data, including match results and individual player performance data.

[1592] Output: The opponent team's match data is saved in the server database.

[1593] Step 2: Analyze the Opponent

[1594] Input: Opposing team's match data.

[1595] How it works: The server uses Python and the Scikit-learn library to analyze the strengths and weaknesses of the opposing team, applying machine learning algorithms to identify defensive weaknesses and attacking characteristics.

[1596] Output: Analysis of the opposing team's strengths and weaknesses.

[1597] Step 3: Collect and analyze your own data

[1598] Input: Player data for your team.

[1599] How it works: The server collects data on the team's players and analyzes it using Python and the Scikit-learn library. It evaluates the skills and performance data of each player.

[1600] Output: Analysis results of your team's player data.

[1601] Step 4: Strategy Generation

[1602] Input: Analysis results for your team and the opposing team.

[1603] How it works: The server generates optimal strategies based on the analysis results. Specifically, it proposes strategies that exploit the opponent's weaknesses and strategies that maximize the strengths of the player's own team.

[1604] Output: The generated strategic plan.

[1605] Step 5: Output and adjust your strategy

[1606] Enter: strategic planning.

[1607] How it works: The generated strategy is sent in PDF format to the coach's device, where the user (coach) can review the strategy and adjust it if necessary.

[1608] Output: The final strategic plan is displayed on the coach's terminal.

[1609] Athlete schedule management system

[1610] Step 1: Collecting physical data

[1611] Input: Real-time physical data.

[1612] How it works: The device collects the athlete's heart rate, fatigue level, and sleep data in real time and sends it to a server.

[1613] Output: The physical data is stored in the server database.

[1614] Step 2: Analysis of physical data and schedule proposal

[1615] Input: Physical data.

[1616] How it works: The server evaluates the player's condition based on the physical data collected. Using time series analysis, it generates a training schedule using a Python library.

[1617] Output: The generated training schedule.

[1618] Step 3: Export and adjust the schedule

[1619] Input: The generated training schedule.

[1620] Operation: The server sends the generated schedule in PDF format to the device. The user (coach) reviews the schedule and adjusts it as necessary.

[1621] Output: The final adjusted schedule is displayed on the terminal.

[1622] Step 4: Schedule Notification

[1623] Enter: Final Schedule.

[1624] What it does: The device notifies and displays the final schedule to the player. This notification can include pop-ups and alarms.

[1625] Output: The player checks the schedule and puts it into action.

[1626] These processing steps allow players to optimize their performance and strategically advance their matches and training.

[1627] (Application example 1)

[1628] 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."

[1629] In the conventional manufacturing industry, there has been a lack of systems that can properly evaluate the individual capabilities of factory employees and generate optimal production line allocations and work schedules. This has made it difficult to maximize employee capabilities, resulting in problems such as reduced production efficiency and employee overwork. Furthermore, it has been difficult to collect employee physical and work data in real time and then efficiently allocate and schedule employees based on that data. The present invention aims to solve these problems, optimize employee performance, and improve factory production efficiency.

[1630] 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.

[1631] In this invention, the server includes means for collecting and analyzing player match data, training data, and physical data, means for generating reports based on the evaluation results, means for outputting the generated reports, means for collecting and evaluating factory employee performance data, means for generating an optimal production line layout based on the evaluation results, and means for outputting the generated layout, thereby enabling optimal evaluation of factory employee capabilities and generation of efficient production line layouts and work schedules.

[1632] "Match data" refers to data that records various activities and performances performed by players during a match.

[1633] "Training Data" refers to data that records the performance of exercises and activities that an athlete performs during training.

[1634] "Physical data" refers to data that indicates the physical condition of athletes and factory workers, such as their heart rate, distance, speed, fatigue level, and sleep data.

[1635] "Means for generating reports" refers to systems or processes that automatically create reports based on analysis and evaluation results.

[1636] "Means for outputting reports" refers to a system or process that displays or transmits the generated reports to a terminal or device.

[1637] "Performance data" refers to data such as work speed, accuracy, and error rate used to evaluate the productivity and efficiency of factory employees while working.

[1638] "Means for generating optimal layout of production lines" refers to systems and processes for placing factory employees in optimal work positions based on collected and analyzed data.

[1639] "Means for generating strategies" refers to a system or process that analyzes information about one's own team and the opposing team and formulates optimal strategies and tactics.

[1640] "Means for generating schedules" refers to the systems and processes that create optimal training and work schedules based on the physical data of each player or employee.

[1641] "Means for notification" refers to a system or process that notifies the target person in real time about the generated schedule or allocation.

[1642] "Means for visualizing work speed and accuracy" refers to a system or process that visually displays each employee's work speed and accuracy in the form of graphs or charts.

[1643] The present invention is a system for optimally evaluating the performance of factory employees and generating optimal production line layouts and work schedules. The operation of this system will be described below.

[1644] Data collection and analysis process

[1645] The server collects performance data such as work speed, accuracy, number of breaks, and cumulative fatigue level from each employee's wearable device and sensors. This data is sent to the server in real time and stored in a database.

[1646] Data analysis

[1647] The server analyzes the collected data and calculates performance indicators for each employee. Specifically, it uses machine learning algorithms (such as Random Forest and SVM) to identify the employee's strengths and weaknesses. Based on the results of this analysis, it generates an employee evaluation report.

[1648] Report generation and output

[1649] The server generates evaluation reports in a visually easy-to-understand format, including graphs and charts. These reports are sent to the terminals of each employee and manager. For example, if Employee A's work speed is 45 units per minute and his accuracy is 98%, a report will be generated that displays his performance evaluation as "highly efficient."

[1650] Optimal production line layout and schedule generation

[1651] The server generates the optimal allocation of factory employees to production lines based on the evaluation results, and also generates an optimal individual work schedule based on each employee's physical data (heart rate, accumulated fatigue level, etc.), including allocation of break times and light work.

[1652] Scheduling and Placement Notification

[1653] The generated schedule and optimal allocation are notified to each employee's device in real time. Employees can check their daily schedule and allocation location through their own devices. For example, if Employee B reaches 75% of their normal work speed, they will be notified and assigned lighter tasks in the next day's work schedule.

[1654] Visualization

[1655] The server visualizes each employee's work speed and accuracy using graphs and charts, and this visualized data is provided to managers and upper management, allowing them to understand the overall performance status at a glance.

[1656] Hardware and software used

[1657] The present invention uses the following hardware and software:

[1658] Hardware: Smartphones, smart glasses, head-mounted displays, wearable sensors

[1659] Software: pandas (data frame manipulation), scikit-learn (machine learning algorithms), matplotlib (data visualization)

[1660] Prompt Sentence Examples

[1661] "Analyze Employee A's work data, evaluate his work speed and accuracy, and propose an optimal work schedule."

[1662] The present invention improves factory production efficiency and maximizes employee capabilities. It also prevents employees from overworking, makes health management easier, and improves overall work efficiency.

[1663] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1664] Step 1:

[1665] The server collects real-time performance data from the factory employees' wearable devices, such as work speed, accuracy, number of breaks, and cumulative fatigue. This data is sent to the server and stored in a database. The input is real-time data from the wearable devices, and the output is performance data stored in the database.

[1666] Step 2:

[1667] The server converts the collected performance data into a data frame and performs preprocessing for applying the machine learning algorithm. This preprocessing includes data normalization and missing value imputation. The input is the performance data collected in step 1, and the output is a preprocessed data frame.

[1668] Step 3:

[1669] The server uses the preprocessed data to apply machine learning algorithms (e.g., Random Forest) to evaluate each employee's performance. This evaluation includes a performance evaluation based on work speed and accuracy, and a fatigue evaluation based on cumulative fatigue. The input is the preprocessed data frame, and the output is the performance evaluation results for each employee.

[1670] Step 4:

[1671] The server generates a performance report for each employee based on the evaluation results. This report includes graphs and charts that visually represent the evaluation results. The input is the performance evaluation results, and the output is a visually represented performance report. Specifically, based on information that Employee A's work speed is 45 units per minute and his accuracy is 98%, a performance report is generated that evaluates him as "highly efficient."

[1672] Step 5:

[1673] The server sends the generated performance report to the terminals of each employee and manager, allowing employees to check their own performance and understand areas for improvement. The input is the generated performance report, and the output is the report displayed on the terminal.

[1674] Step 6:

[1675] The server generates the optimal layout of the production line based on the evaluation results of each employee. This layout includes the optimal work position and role of each employee. The input is the evaluation results, and the output is the optimal production line layout.

[1676] Step 7:

[1677] The server generates an individual optimal work schedule based on each employee's physical data. This schedule includes break times and light work assignments. The input is the physical data, and the output is the optimal work schedule for each employee.

[1678] Step 8:

[1679] The server notifies each employee's terminal of the generated schedule and production line allocation in real time, allowing the employee to check their own schedule and location. The input is the generated schedule and allocation, and the output is the schedule and allocation information displayed on the terminal. Specifically, if employee B reaches 75% of his normal work speed, he will be notified that he will be assigned a lighter task in the next day's work schedule.

[1680] Through the above processing steps, a system is realized that optimally evaluates the capabilities of factory employees and improves production efficiency.

[1681] 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.

[1682] The present invention aims to improve overall performance by combining a system for optimally evaluating a player's abilities, proposing strategies, and managing the player's schedule with an emotion engine that recognizes the user's emotions. The program processing of this system is explained below in natural language.

[1683] Player ability analysis

[1684] Data collection

[1685] The server collects each player's match data from a database. Performance data during the match includes pass success rate, shot success rate, and distance traveled. The device (player's wearable device) sends training data such as heart rate, speed, and distance to the server in real time. The server periodically obtains physical data (heart rate, fatigue level, and sleep time) from each player's device.

[1686] Data analysis

[1687] The server combines collected match data, training data, and physical data to calculate performance indicators for each player. The server cleanses the data and fills in outliers and missing data. The server applies machine learning algorithms to identify players' strengths and weaknesses. The server performs clustering analysis based on each indicator to extract performance patterns.

[1688] Report Generation

[1689] The server generates a report for each player based on the analysis results. The report includes graphs and charts and is created in a visually easy-to-understand format. The generated report is sent to the terminal (coach's or player's device). The server notifies the user of the report, and the user can check the report.

[1690] Specific examples

[1691] The server collects the match data of Player A and determines that his pass success rate is 90%, his shot success rate is 50%, and his speed is 28km / h. From this data, it is analyzed that Player A is good at passing, but his shooting accuracy has room for improvement.

[1692] Strategy proposals

[1693] Analysis of the opposing team

[1694] The server collects the opponent's match data. The server retrieves recent match results and opponent player data from the database. The server uses machine learning algorithms to identify the opponent's strengths and weaknesses.

[1695] Analyzing your team's capabilities

[1696] The server collects and analyzes the performance data of each player on the team, evaluating skills such as dribbling and passing accuracy.

[1697] Strategy Generation

[1698] The server generates optimal match strategies based on the data collected, maximizing the strengths of the team and exploiting the weaknesses of the opposing team.

[1699] Strategic Notification

[1700] The server sends the generated strategy to the terminal (coach's device), where the user (coach) reviews the strategy and makes adjustments if necessary.

[1701] Specific examples

[1702] The server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since player B on the team is good at dribbling, the server proposes a strategy of having player B attack from the center.

[1703] Player schedule management

[1704] Physical data analysis

[1705] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.). The terminal (wearable device) sends the data to the server in real time. The server analyzes the collected data and evaluates the player's current physical condition. The server cleans out abnormal values ​​and inconsistent data.

[1706] Schedule proposal

[1707] The server generates an optimal training schedule based on the analysis results, incorporating rest days and recovery training as needed.

[1708] Schedule adjustment

[1709] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[1710] Schedule Notifications

[1711] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[1712] Specific examples

[1713] The server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. The user (coach) checks the schedule and, after adjustments, the final schedule is sent to Player C's device.

[1714] User Emotion Recognition

[1715] Emotional Data Collection

[1716] The terminal (user's device) collects the user's facial expression data, voice data, or behavioral data and sends it to the server, which then stores this data.

[1717] Emotion analysis

[1718] The server uses an emotion engine to analyze the collected data and identify the user's emotions. The emotion engine uses algorithms for facial expression analysis, voice analysis, and behavior analysis. The server records the analysis results in an emotion database.

[1719] Emotional data usage

[1720] The server provides feedback to players and adjusts training menus based on the emotional data identified by the server. The server provides communication adapted to the user's emotions. The server includes a user interface for visually displaying the emotional data.

[1721] Specific examples

[1722] If the user (coach) feels stressed while instructing the player, the emotion engine detects this and the server displays suggestions for relaxation. Also, if the emotion engine detects that the player is highly motivated during training, the server will suggest a training menu to maintain that state.

[1723] As described above, the system of the present invention combines the collection, analysis, strategy proposal, and schedule management of player data with the recognition of user emotions to achieve overall performance improvement.

[1724] The processing flow will be explained below.

[1725] Player ability analysis

[1726] Data collection

[1727] Step 1:

[1728] The server collects each player's match data from a database, including performance data during the match, such as pass success rate, shot success rate, and distance traveled.

[1729] Step 2:

[1730] The terminal (the athlete's wearable device) transmits training data such as heart rate, speed, and distance to the server in real time.

[1731] Step 3:

[1732] The server periodically collects physical data (heart rate, fatigue level, sleep time) from each player's device.

[1733] Data analysis

[1734] Step 4:

[1735] The server integrates the match data, training data, and physical data collected.

[1736] Step 5:

[1737] The server calculates each player's performance indicators, such as passing success rate, shooting success rate, speed, and stamina.

[1738] Step 6:

[1739] The server applies machine learning algorithms to identify players' strengths and weaknesses, and performs clustering analysis based on each indicator to extract performance patterns.

[1740] Report Generation

[1741] Step 7:

[1742] The server generates a report for each player based on the analysis results, including easy-to-understand graphs and charts.

[1743] Step 8:

[1744] The server sends the generated report to the terminal (the coach's or player's device). The server notifies the user of the report, and the user (coach or player) checks the report.

[1745] Specific examples

[1746] Step 9:

[1747] The server collects the match data of Player A and determines that his passing success rate is 90%, his shooting success rate is 50%, and his speed is 28km / h. After analyzing, it is determined that Player A is good at passing, but his shooting accuracy has room for improvement.

[1748] Strategy proposals

[1749] Analysis of the opposing team

[1750] Step 1:

[1751] The server collects the match data of the opposing team, and retrieves the results of recent matches and player data of the opposing team from the database.

[1752] Step 2:

[1753] The server uses machine learning algorithms to analyze the strengths and weaknesses of the opposing team.

[1754] Analyzing your team's capabilities

[1755] Step 3:

[1756] The server collects and analyzes the performance data of each player on the team, evaluating skills such as dribbling and passing accuracy.

[1757] Strategy Generation

[1758] Step 4:

[1759] The server generates optimal match strategies based on the data collected, maximizing your team's strengths and exploiting the opponent's weaknesses.

[1760] Strategic Notification

[1761] Step 5:

[1762] The server sends the generated strategy to the terminal (coach's device), where the user (coach) reviews the strategy and makes adjustments if necessary.

[1763] Specific examples

[1764] Step 6:

[1765] The server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since the team's player B is good at dribbling, the server proposes a strategy of having player B attack from the center.

[1766] Player schedule management

[1767] Physical data analysis

[1768] Step 1:

[1769] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.), and the terminal (wearable device) transmits the data to the server in real time.

[1770] Step 2:

[1771] The server analyzes the collected data to evaluate the player's current physical condition and cleanses outliers and inconsistent data.

[1772] Schedule proposal

[1773] Step 3:

[1774] The server uses the analysis results to generate an optimal training schedule, incorporating rest days and recovery training as needed.

[1775] Schedule adjustment

[1776] Step 4:

[1777] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[1778] Schedule Notifications

[1779] Step 5:

[1780] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[1781] Specific examples

[1782] Step 6:

[1783] The server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. The user (coach) checks the schedule and, after adjustments, the final schedule is sent to Player C's device.

[1784] User Emotion Recognition

[1785] Emotional Data Collection

[1786] Step 1:

[1787] The terminal (user's device) collects the user's facial expression data, voice data, or behavioral data and sends it to the server, which then stores this data.

[1788] Emotion analysis

[1789] Step 2:

[1790] The server uses an emotion engine to analyze the collected data and identify the user's emotions. The emotion engine uses algorithms for facial expression analysis, voice analysis, and behavior analysis.

[1791] Emotional data usage

[1792] Step 3:

[1793] Based on the emotional data identified by the server, the server provides feedback to the player and adjusts the training menu, providing communication that is adapted to the user's emotions.

[1794] Emotion data display

[1795] Step 4:

[1796] The server provides a user interface for visually displaying emotional data on the device. The user (coach or player) can review this data and reflect it in training and strategies.

[1797] Specific examples

[1798] Step 5:

[1799] If the user (coach) feels stressed while instructing the player, the emotion engine detects this and the server displays suggestions for relaxation. Also, if the emotion engine detects that the player is highly motivated during training, the server will suggest a training menu to maintain that state.

[1800] As a result, the system of the present invention not only collects and analyzes player data, proposes consistent strategies, and manages schedules, but also incorporates a function to recognize user emotions, thereby achieving overall performance improvement.

[1801] Example 2

[1802] 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."

[1803] Conventional player performance evaluation systems are limited to collecting and analyzing match and training data, which is insufficient for achieving overall performance improvement. Furthermore, they do not take into account the emotions of users (coaches and players) when proposing strategies or managing schedules, making it difficult to optimize players' motivation and physical condition. Furthermore, the generated reports and strategies lack a visual display, making intuitive understanding difficult.

[1804] 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.

[1805] In this invention, the server includes means for collecting game data, training data, and physical data of players, means for analyzing the collected data and identifying the strengths and weaknesses of players, means for generating a report based on the analysis results, means for outputting and visually displaying the generated report, and means for calculating performance indicators for each player and performing clustering analysis. This makes it possible to improve overall performance by combining player data collection, analysis, strategy proposals, schedule management, and user emotion recognition.

[1806] "Match data" refers to data collected to evaluate a player's performance during a match, and specifically includes pass success rate, shooting success rate, distance traveled, etc.

[1807] "Training Data" is data recorded while an athlete is training, and includes information related to physical activity such as heart rate, speed, distance, etc.

[1808] "Physical data" refers to data used to evaluate the physical condition of a player, and specifically includes heart rate, fatigue level, sleep time, etc.

[1809] "Analysis" means processing information using techniques such as evaluation, judgment, and pattern identification based on collected data.

[1810] "Strengths and Weaknesses" refers to a player's strengths and areas for improvement, identified based on the player's performance data.

[1811] A "report" is a collection of documents and graphics generated based on the analysis results, including visual representations of player performance.

[1812] "Visually displaying" refers to presenting analysis results and reports in the form of graphs, charts, etc. so that users can intuitively understand them.

[1813] "Clustering analysis" refers to a technique that uses machine learning algorithms to classify collected data into multiple clusters and extract similar patterns.

[1814] "Strategy" means a plan or proposal for choosing the best course of action in a match, including one that takes into account the strengths and weaknesses of the opposing team.

[1815] A "schedule" is a plan for a player's training and rest, and is generated based on physical and performance data.

[1816] "Emotion data" refers to information indicating the emotional state of a user analyzed from facial expression data, voice data, and behavior data.

[1817] The present invention aims to improve overall performance by combining a system for optimally evaluating players' abilities, proposing strategies, and managing players' schedules with an emotion engine that recognizes the user's emotions. The system of the present invention is mainly composed of a server, terminals, and users.

[1818] Player ability analysis

[1819] Data collection

[1820] The server collects each player's match data from the database. This match data includes pass success rate, shot success rate, running distance, etc. The players' wearable devices also send training data such as heart rate, speed, and distance to the server in real time. The server periodically collects the players' physical data such as heart rate, fatigue level, and sleep time.

[1821] Data analysis

[1822] The server integrates the collected match data, training data, and physical data to calculate each player's performance indicators. It also cleanses the data and fills in outliers and missing data. It also applies machine learning algorithms to identify players' strengths and weaknesses. It also performs clustering analysis to extract performance patterns.

[1823] Report Generation

[1824] The server generates a report for each player based on the analysis results. The report includes graphs and charts and is created in a visually easy-to-understand format. The generated report is sent to the player's or coach's device, where the user can check the received report.

[1825] Specific examples

[1826] For example, if the server collects match data for Player A and determines that his pass success rate is 90%, his shot success rate is 50%, and his speed is 28km / h, it will analyze that Player A is good at passing, but that there is room for improvement in his shooting accuracy.

[1827] Strategy proposals

[1828] Analysis of the opposing team

[1829] The server collects the opponent's match data, retrieves recent match results and player data from the database, and uses machine learning algorithms to identify the opponent's strengths and weaknesses.

[1830] Analyzing your team's capabilities

[1831] The server collects and analyzes the performance data of each player on the team, including skill evaluations such as dribbling and passing accuracy.

[1832] Strategy Generation

[1833] The server uses the collected data to generate optimal match strategies that maximize the strengths of the team and exploit the weaknesses of the opposing team.

[1834] Strategic Notification

[1835] The generated strategy is sent from the server to the coach's device, where the user (coach) can review the strategy and make adjustments as necessary.

[1836] Specific examples

[1837] For example, if the server compares data from your team with that of the opposing team and discovers that the opposing team is weak in central defense, it will propose a strategy to have player B on your team attack from the center because he is good at dribbling.

[1838] Player schedule management

[1839] Physical data analysis

[1840] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.). The player's wearable device transmits the data to the server in real time. The server analyzes the collected data and evaluates the player's current physical condition.

[1841] Schedule proposal

[1842] The server generates an optimal training schedule based on the analysis results, incorporating rest days and recovery training as needed.

[1843] Schedule adjustment

[1844] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[1845] Schedule Notifications

[1846] The final schedule is sent from the server to each player's device, which receives the notification and displays the schedule to the player.

[1847] Specific examples

[1848] For example, if the server analyzes Player C's physical data and determines that he has become fatigued from recent training, it will suggest a day of rest and incorporate lighter training for the rest of the week. The user (coach) checks the schedule and, after making adjustments, the final schedule is sent to Player C's device.

[1849] User Emotion Recognition

[1850] Emotional Data Collection

[1851] The user's device collects facial expression data, voice data, or behavioral data and sends it to the server, which stores the data.

[1852] Emotion analysis

[1853] The server analyzes the collected data using an emotion engine to identify the user's emotions. The emotion engine uses algorithms for facial expression analysis, voice analysis, and behavior analysis. The analysis results are recorded in an emotion database.

[1854] Emotional data usage

[1855] The server provides feedback to the player and adjusts the training menu based on the identified emotion data. The server provides communication adapted to the user's emotions. The server includes a user interface for visually displaying the emotion data.

[1856] Specific examples

[1857] For example, if a user (coach) feels stressed while instructing a player, the emotion engine will detect this and the server will display suggestions for relaxation. Also, if the emotion engine detects that a player is highly motivated during training, the server will suggest a training menu to help the player maintain that state.

[1858] As described above, the system of the present invention combines the collection, analysis, strategy proposal, and schedule management of player data with the recognition of user emotions to achieve overall performance improvement.

[1859] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1860] Player ability analysis

[1861] Data collection

[1862] Step 1:

[1863] The server collects the match data from the database.

[1864] Input: Player ID

[1865] Data processing: Execute an SQL query to retrieve match data (pass success rate, shooting success rate, distance traveled, etc.) related to the player ID.

[1866] Output: Match data for each player

[1867] Specific example of operation: The server uses "MySQL" to execute the query "SELECT FROM match_data WHERE player_id = ?".

[1868] Step 2:

[1869] The athlete's wearable device (terminal) transmits training data to a server in real time.

[1870] Input: Real-time raw data (heart rate, speed, distance, etc.)

[1871] Data Processing: Data formatting and buffering

[1872] Output: Formatted training data

[1873] Specific example of operation: The device receives data via Bluetooth, converts it to JSON format, and sends an HTTP POST request to the server.

[1874] Step 3:

[1875] The server collects the physical data.

[1876] Input: Periodic data transmission from a wearable device

[1877] Data processing: Error correction and interpolation

[1878] Output: Unified physical data

[1879] Specific operation example: The server executes a scheduled job every day at 00:00 and collects data from the device with a GET request.

[1880] Data analysis

[1881] Step 4:

[1882] The server aggregates the data and calculates performance metrics for each player.

[1883] Input: Match data, training data, physical data

[1884] Data processing: data merging and statistical analysis

[1885] Output: Unified performance dataset

[1886] Specific example of operation: The server uses the "Pandas" library to merge data frames and calculate statistics.

[1887] Step 5:

[1888] The server performs data cleansing.

[1889] Input: Unified performance dataset

[1890] Data processing: Correction of outliers, completion of missing data

[1891] Output: A cleansed dataset

[1892] Specific example of operation: The server uses the "Scikit-learn" outlier detection algorithm to replace abnormal values ​​with the average value.

[1893] Step 6:

[1894] The server applies machine learning algorithms to identify players' strengths and weaknesses.

[1895] Input: Cleansed dataset

[1896] Data processing: Classification and regression analysis

[1897] Output: A list of strengths and weaknesses for each player

[1898] Specific example of operation: The server runs a "random forest" model and extracts important features.

[1899] Step 7:

[1900] The server performs the clustering analysis.

[1901] Input: List of strengths and weaknesses

[1902] Data processing: Applying clustering algorithms

[1903] Output: Clusters of performance patterns

[1904] Specific example of operation: The server classifies data into multiple clusters using "K-means".

[1905] Report Generation

[1906] Step 8:

[1907] The server generates a report based on the analysis results.

[1908] Input: Cluster of performance patterns

[1909] Data processing: Graph and chart generation

[1910] Output: Report document (PDF, etc.)

[1911] Specific example of operation: The server creates a graph using "Matplotlib" and exports it to PDF.

[1912] Step 9:

[1913] The server outputs the report and displays it visually.

[1914] Input: Generated report document

[1915] Data processing: Format conversion for report delivery and display

[1916] Output: Report notification

[1917] Specific example of how it works: The server sends a PDF via email via the SMTP protocol, and the report is displayed in the device's app.

[1918] Strategy proposals

[1919] Analysis of the opposing team

[1920] Step 10:

[1921] The server collects the match data of the opposing team.

[1922] Input: Match ID, Opponent Team ID

[1923] Data processing: Data collection through API queries

[1924] Output: Opponent team's match data

[1925] Specific example of operation: The server obtains the latest match data of the opposing team via the REST API.

[1926] Step 11:

[1927] The server analyzes the strengths and weaknesses of the opposing team.

[1928] Input: Opponent team's match data

[1929] Data processing: Analysis using machine learning algorithms

[1930] Output: A list of the opponent's strengths and weaknesses

[1931] Specific example of operation: The server uses a statistical model to analyze the opponent's defense success rate, attack patterns, etc.

[1932] Analyzing your team's capabilities

[1933] Step 12:

[1934] The server collects and analyzes the team's player data.

[1935] Input: Player ID, team ID

[1936] Data Transformation: API queries and data analysis

[1937] Output: Performance data of your team's players

[1938] Specific example of operation: The server retrieves the player's training log from the data warehouse and analyzes it using a data analysis tool.

[1939] Strategy Generation

[1940] Step 13:

[1941] The server generates the optimal strategy.

[1942] Input: Data analysis results for your team and the opposing team

[1943] Data Processing: Algorithmic Strategy Optimization

[1944] Output: Optimal strategic plan

[1945] Specific example of operation: The server optimizes the strategy using a genetic algorithm and outputs a strategic plan.

[1946] Strategic Notification

[1947] Step 14:

[1948] The server sends the generated strategy to the coach's device.

[1949] Input: Optimal strategic plan

[1950] Data processing: Strategic plan format conversion

[1951] Output: Strategy Notification

[1952] Specific example of operation: The server exports the generated strategy in JSON format and notifies the coach's device.

[1953] Player schedule management

[1954] Physical data analysis

[1955] Step 15:

[1956] The server collects the physical data.

[1957] Input: Periodic data from wearable devices

[1958] Data processing: data collection and formatting

[1959] Output: Unified physical data

[1960] Specific example of operation: The server schedules a periodic data collection job and retrieves the data with a GET request.

[1961] Step 16:

[1962] The server analyzes the physical data and evaluates the physical state.

[1963] Input: Unified physical data

[1964] Data processing: time series analysis and outlier correction

[1965] Output: Analysis results and physical state evaluation

[1966] Specific example of operation: The server performs "time series analysis" to identify the player's fatigue level trends.

[1967] Schedule proposal

[1968] Step 17:

[1969] The server generates the training schedule.

[1970] Input: Physical condition assessment results

[1971] Data processing: Applying schedule generation algorithms

[1972] Output: Optimal training schedule

[1973] Specific example of operation: The server combines the player's physical data and performance data to create a training schedule.

[1974] Schedule Notifications

[1975] Step 18:

[1976] The server sends the final schedule to the players' devices.

[1977] Input: Optimal training schedule

[1978] Data processing: Schedule format conversion

[1979] Output: Schedule notification

[1980] Specific example of operation: The server exports the schedule in JSON format and sends it to the player's app.

[1981] User Emotion Recognition

[1982] Emotional Data Collection

[1983] Step 19:

[1984] The user's device collects facial expression data, voice data, or behavioral data and transmits it to a server.

[1985] Input: Real-time facial, voice, and behavioral data

[1986] Data processing: data collection and format conversion

[1987] Output: Emotion data

[1988] Specific example of how it works: Collects data using the device's camera and microphone and streams it to a server in real time.

[1989] Emotion analysis

[1990] Step 20:

[1991] The server analyzes the emotion data using an emotion engine.

[1992] Input: Collected emotion data

[1993] Data processing: Applying sentiment analysis algorithms

[1994] Output: User's emotion rating

[1995] Specific example of operation: The server uses the emotion engine and executes a facial expression recognition algorithm to classify the user's emotion.

[1996] Emotional data usage

[1997] Step 21:

[1998] Feedback and training menus are adjusted based on the emotional data identified by the server.

[1999] Input: User's emotion rating

[2000] Data processing: Emotion-based training menu generation

[2001] Output: Adjusted training menu

[2002] Specific example of operation: The server detects the user's stress level and automatically generates a training menu based on that.

[2003] Step 22:

[2004] The server visually displays the emotion data.

[2005] Input: Emotion evaluation results

[2006] Data processing: graph generation for visualization

[2007] Output: Visual display of emotion data

[2008] Specific example of operation: The server generates various graphs and charts based on emotion data and displays them in real time on the user's device.

[2009] (Application example 2)

[2010] 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."

[2011] Conventional factory management systems lacked the functionality to efficiently collect, analyze, and optimize performance data from industrial robots and workers. Furthermore, safety management that took into account workers' physical data and emotions was insufficient, resulting in reduced work efficiency and health risks. Furthermore, a lack of real-time data analysis and feedback made it difficult to respond immediately. A new system is needed to resolve these issues and ensure worker safety while improving work efficiency within factories.

[2012] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting game data, training data, and physical data of players; means for analyzing the collected data and identifying the strengths and weaknesses of players; means for generating a report based on the analysis results; means for outputting the generated report; means for collecting and analyzing robot operation data and identifying performance indicators; means for collecting and analyzing physical data of workers and identifying stress and fatigue levels; means for collecting facial expression data, voice data, and behavioral data of workers and analyzing their emotions; means for generating and adjusting a work schedule based on the analysis results; and means for outputting the generated work schedule. This enables efficient operation of industrial robots in factories and optimization of work while ensuring the safety of workers.

[2013] "Athlete match data" refers to data that indicates the performance and achievements that an athlete has achieved during a match.

[2014] "Training data" refers to data such as heart rate, speed, and distance obtained by an athlete during training.

[2015] "Physical data" refers to data that indicates an athlete's physical strength and health, such as heart rate, fatigue level, and sleep time.

[2016] "Robot operation data" refers to data that indicates the operating status of an industrial robot, such as its work cycle time and energy consumption.

[2017] A "performance index" is an evaluation criterion calculated through data analysis that quantifies work efficiency and accuracy.

[2018] "Physical data of a worker" refers to data that indicates the physical condition of a worker, such as heart rate, movement, and fatigue level.

[2019] "Stress and fatigue" refers to the degree of psychological or physical strain or fatigue felt by workers.

[2020] "Worker facial expression data" is information obtained from the worker's facial expressions, and is data used to infer emotions and physical condition.

[2021] "Voice data" is data collected from the voices of workers and is used to analyze their emotions and state of consciousness.

[2022] "Behavioral data" is information collected about the movements and activity patterns of workers.

[2023] "Analyzing emotions" refers to the process of identifying a worker's emotional state based on facial, vocal, and behavioral data.

[2024] A "work schedule" is a plan that shows the order and timing of work that workers should perform.

[2025] An "optimal strategy" is the most efficient and effective plan of action generated by analyzing collected data.

[2026] The present invention relates to a system for efficiently managing industrial robots and workers in a factory and ensuring their safety. The configuration of the system and specific embodiments will be described below.

[2027] System configuration

[2028] The system consists of the following main components:

[2029] 1. Data Collection Methods

[2030] Industrial robots are used as a means for collecting operational data of the robots.

[2031] Wearable devices that measure heart rate and movement are used to collect workers' physical data.

[2032] Cameras and microphones are used to collect facial expression, voice, and behavioral data from workers.

[2033] 2. Data analysis methods

[2034] The server uses machine learning frameworks (e.g., TensorFlow or Scikit-learn) to synthesize the collected data and identify performance metrics.

[2035] This also includes a means of analyzing physical data to identify workers' stress and fatigue levels.

[2036] 3. Report generation and strategy proposal tool

[2037] The server generates a work schedule based on the analysis results and suggests appropriate breaks and workload adjustments.

[2038] An emotion engine is used to analyze workers' emotional data and suggest appropriate communication methods.

[2039] 4. Notification and Feedback Methods

[2040] Generated reports and proposed strategies are sent to each terminal, allowing workers and managers to review them.

[2041] Program processing explanation

[2042] The server does the following:

[2043] 1. Data Collection

[2044] Collecting data in real time from industrial robots and wearable devices.

[2045] Cameras and microphones are also used to collect facial expressions, voice and behavioral data from workers.

[2046] 2. Data Analysis

[2047] The server stores the collected data in a database (e.g., MySQL, MongoDB).

[2048] Machine learning algorithms are used to analyze the data and identify performance indicators and emotional states.

[2049] 3. Report generation and strategy proposals

[2050] Generate optimal work schedules and strategies based on performance data.

[2051] It analyzes emotional data and suggests feedback and communication methods based on that data.

[2052] 4. Notifications and Feedback

[2053] Generated reports and strategies are sent to terminals so that workers and managers can review them.

[2054] Specific examples

[2055] For example, a factory might have the following setup:

[2056] Collects operational data (work cycle time, energy consumption) from industrial robots to improve the efficiency of specific work processes.

[2057] Detects stress levels from workers' heart rate data and suggests appropriate breaks.

[2058] If a worker is highly fatigued, the analysis results will be used to suggest work to reduce fatigue.

[2059] Prompt Sentence Examples

[2060] Here are some examples of prompts for generative AI models:

[2061] prompt:

[2062] We are developing an application for efficient operation of industrial robots in a factory and for worker safety management. Please generate a program that meets the following conditions:

[2063] Collect and analyze robot operation data (cycle time, energy consumption)

[2064] Collect physical data (heart rate, movement) from workers' wearable devices and analyze their stress and fatigue levels.

[2065] Collecting facial expressions, voice, and behavioral data from workers and analyzing their emotions

[2066] Based on the analysis results, we propose optimization of work schedules and robot operations.

[2067] It has the function of notifying workers and managers of analysis results and suggestions.

[2068] Based on this requirement, generate the relevant code.

[2069] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2070] Step 1:

[2071] Data collection

[2072] The server collects real-time operational data from industrial robots (such as work cycle time and energy consumption), as well as data from wearable devices that measure workers' heart rates and movements, and from cameras and microphones that capture workers' facial expressions and voices, and stores this data in a database (e.g., MySQL, MongoDB).

[2073] input:

[2074] Industrial robot operation data

[2075] Worker physical data (heart rate, movement)

[2076] Facial expressions and voice data of workers

[2077] output:

[2078] Stored database records

[2079] Step 2:

[2080] Data analysis

[2081] The server analyzes the collected data. Specifically, it uses machine learning frameworks (e.g., TensorFlow, Scikit-learn) to identify robot performance indicators and calculates stress and fatigue levels from the worker's physical data. It also analyzes facial and voice data using an emotion engine to identify the worker's emotional state.

[2082] input:

[2083] Various data stored in the database

[2084] output:

[2085] Performance indicators, stress / fatigue levels, emotional state

[2086] Step 3:

[2087] Report generation and strategy recommendations

[2088] Based on the analysis results, the server generates an optimal schedule for efficient robot operation and workers, and also creates a report that suggests feedback and communication methods tailored to workers based on their emotional data.

[2089] input:

[2090] Performance Indicators

[2091] Stress and fatigue

[2092] Emotional state

[2093] output:

[2094] Work schedules, strategies, and feedback reports

[2095] Step 4:

[2096] Notifications and Feedback

[2097] The server then sends the generated reports and proposed work schedules to each terminal, allowing workers and managers to review them. This allows workers to check the schedules and proposals from their terminals and take appropriate action.

[2098] input:

[2099] Reports, strategies, and work schedules

[2100] output:

[2101] Notifications and displayed content on the device

[2102] Prompt Sentence Examples

[2103] Here are some examples of prompts for generative AI models:

[2104] prompt:

[2105] We are developing an application for efficient operation of industrial robots in a factory and for worker safety management. Please generate a program that meets the following conditions:

[2106] Collect and analyze robot operation data (cycle time, energy consumption)

[2107] Collect physical data (heart rate, movement) from workers' wearable devices and analyze their stress and fatigue levels.

[2108] Collecting facial expressions, voice, and behavioral data from workers and analyzing their emotions

[2109] Based on the analysis results, we propose optimization of work schedules and robot operations.

[2110] It has the function of notifying workers and managers of analysis results and suggestions.

[2111] Based on this requirement, generate the relevant code.

[2112] 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.

[2113] 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.

[2114] 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.

[2115] [Fourth embodiment]

[2116] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2117] 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.

[2118] 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).

[2119] 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.

[2120] 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.

[2121] 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).

[2122] 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.

[2123] 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.

[2124] 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.

[2125] 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.

[2126] 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.

[2127] 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.

[2128] 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."

[2129] The present invention is a system for optimally evaluating the abilities of players, proposing strategies, and managing the schedules of players. The program processing of this system will be explained below in natural language.

[2130] Player ability analysis

[2131] Data collection

[2132] The server collects each player's match data, training data, and physical data from a database. The player's wearable device sends data such as heart rate, distance, and speed to the server in real time.

[2133] Data analysis

[2134] The server combines the collected data to calculate each player's performance metrics (e.g., passing success rate, shooting success rate, speed, stamina), and then applies machine learning algorithms to identify players' strengths and weaknesses.

[2135] Report Generation

[2136] The server generates a report for each player based on the analysis results. The report includes graphs and charts and is created in a visually easy-to-understand format. The generated report is sent to the terminal (coach's or player's device).

[2137] Specific examples

[2138] The server collects the match data of Player A and determines that his pass success rate is 90%, his shot success rate is 50%, and his speed is 28km / h. From this data, it is analyzed that Player A is good at passing, but his shooting accuracy has room for improvement.

[2139] Strategy proposals

[2140] Analysis of the opposing team

[2141] The server collects the opponent's match data, analyzes recent match results and player performance data, and uses machine learning algorithms to identify the opponent's strengths and weaknesses.

[2142] Analyzing your team's capabilities

[2143] The server collects and analyzes the performance data of each player on the team, identifying players who excel at dribbling and players with high passing accuracy.

[2144] Strategy Generation

[2145] The server generates an optimal strategy based on data from the team and the opposing team. The generated strategy is sent to the terminal (coach's device), where the user (coach) can review and adjust it.

[2146] Specific examples

[2147] The server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since player B on the team is good at dribbling, the server proposes a strategy of having player B attack from the center.

[2148] Player schedule management

[2149] Physical data analysis

[2150] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.), and the terminal (wearable device) transmits the data to the server in real time.

[2151] Schedule proposal

[2152] The server evaluates the player's current condition based on physical data and generates an optimal training schedule, which includes rest days and recovery training.

[2153] Schedule adjustment

[2154] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of each player and the team's schedule. After adjustments, the final schedule is fed back to the server.

[2155] Schedule Notifications

[2156] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[2157] Specific examples

[2158] The server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. The user (coach) checks the schedule and, after adjustments, the final schedule is sent to Player C's device.

[2159] As described above, the system of the present invention efficiently collects and analyzes player data, proposes strategies, and manages schedules, thereby maximizing player performance.

[2160] The processing flow will be explained below.

[2161] Player ability analysis

[2162] Data collection

[2163] Step 1:

[2164] The server collects each player's match data from the database. The server also collects performance data during the match (pass success rate, shot success rate, running distance, etc.).

[2165] Step 2:

[2166] The terminal (the athlete's wearable device) transmits training data such as heart rate, speed, and distance to the server in real time.

[2167] Step 3:

[2168] The server periodically collects physical data (e.g., heart rate, fatigue level, sleep time) from each player's device.

[2169] Data analysis

[2170] Step 4:

[2171] The server integrates the collected match data, training data, and physical data to calculate each player's performance indicators. The server also cleanses the data and fills in outliers and missing data.

[2172] Step 5:

[2173] The server applies machine learning algorithms to identify players' strengths and weaknesses, and performs clustering analysis based on each indicator to extract performance patterns.

[2174] Report Generation

[2175] Step 6:

[2176] The server generates a report for each player based on the analysis results, including graphs and charts in a visually easy-to-understand format.

[2177] Step 7:

[2178] The server sends the generated report to the terminal (the coach's or player's device). The server notifies the user of the report, and the user checks the report.

[2179] Strategy proposals

[2180] Analysis of the opposing team

[2181] Step 1:

[2182] The server collects the match data of the opposing team. The server retrieves the results of recent matches and player data of the opposing team from the database.

[2183] Step 2:

[2184] The server analyzes the strengths and weaknesses of the opposing team. The server uses machine learning algorithms to identify the weaknesses of the opposing team.

[2185] Analyzing your team's capabilities

[2186] Step 3:

[2187] The server collects and analyzes the performance data of each player on the team, evaluating skills such as dribbling and passing accuracy.

[2188] Strategy Generation

[2189] Step 4:

[2190] The server generates optimal match strategies based on the data collected, maximizing the strengths of the team and exploiting the weaknesses of the opposing team.

[2191] Strategic Notification

[2192] Step 5:

[2193] The server sends the generated strategy to the terminal (coach's device), where the user (coach) reviews the strategy and makes adjustments if necessary.

[2194] Player schedule management

[2195] Physical data analysis

[2196] Step 1:

[2197] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.), and the terminal (wearable device) sends the data to the server in real time.

[2198] Step 2:

[2199] The server analyzes the collected data to evaluate the player's current physical condition and cleanses outliers and inconsistencies.

[2200] Schedule proposal

[2201] Step 3:

[2202] The server generates an optimal training schedule based on the analysis results, incorporating rest days and recovery training as needed.

[2203] Schedule adjustment

[2204] Step 4:

[2205] The user (coach) checks the proposed schedule and makes final adjustments taking into account the availability of the players and the schedule of the entire team. The user then inputs the adjustment results into the server.

[2206] Schedule Notifications

[2207] Step 5:

[2208] The server sends the final schedule to each player's device, which receives the notification and displays the schedule to the player.

[2209] This allows one to understand the specific flow of how the system of the present invention efficiently collects and analyzes player data, proposes strategies, and manages schedules.

[2210] Example 1

[2211] 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."

[2212] In modern sports, accurately evaluating individual athletes' performance, formulating optimal strategies, and managing schedules efficiently are extremely difficult challenges. Conventional methods involve manual data collection and analysis, which is time-consuming, labor-intensive, and often inaccurate. Furthermore, training schedules and strategy proposals that incorporate athletes' real-time physical data are not adequately implemented. This makes it difficult to maximize athletes' potential. The present invention aims to solve these challenges by providing a system that automates effective data collection and analysis, strategy proposals, and schedule management.

[2213] 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.

[2214] In this invention, the server includes: means for collecting a player's match data, training data, and physical data; means for integrating the collected data to calculate a player's performance indicators; and means for identifying a player's strengths and weaknesses using a machine learning algorithm. This enables accurate evaluation of a player's abilities and rapid identification of their strengths and weaknesses. The server also includes means for generating reports using graphs and charts; means for outputting the generated reports to a terminal; and means for the terminal to transmit data to the server in real time. This allows evaluation results to be presented visually in an easy-to-understand manner, enabling players and coaches to respond quickly. The approach continues with means for collecting opponent team match data and applying a machine learning algorithm to analyze the opponent team's strengths and weaknesses; means for collecting and analyzing the player data of the team's own team to generate an optimal strategy; and means for outputting the generated strategy to a terminal for the user to review and adjust. This improves the accuracy and effectiveness of strategies, thereby increasing competitiveness in matches. The system also includes a means for periodically collecting athlete physical data and generating a training schedule using time-series analysis, a means for outputting the generated schedule to a terminal in PDF format, and a means for notifying and displaying the generated schedule to the athlete. This makes it possible to provide an optimal training schedule while maintaining the athlete's health and performance.

[2215] "Player's match data" refers to various statistical data obtained by a player in an actual match (e.g., pass success rate, shot success rate, number of assists, etc.).

[2216] "Athlete training data" refers to various numerical data recorded by athletes during training (e.g., training time, number of training sessions, load, etc.).

[2217] "Athlete's physical data" refers to numerical data relating to the athlete's physical condition and stamina (e.g., heart rate, speed, distance, sleep data, fatigue level, etc.).

[2218] "Synthesis" is the process of bringing together different types of data into a single dataset in a form that can be analyzed as a whole.

[2219] "Performance indicators" are quantitative indicators used to evaluate a player's abilities and condition, and include pass success rate, shooting success rate, speed, stamina, etc.

[2220] "Machine learning algorithm" is a general term for mathematical techniques that automatically learn from data and make predictions or classifications. Examples include TensorFlow and Scikit-learn.

[2221] A "strength" is a characteristic or skill that makes a player's performance or ability superior to other aspects of their game.

[2222] A "weakness" is a characteristic or skill that is believed to need improvement in a player's performance or ability.

[2223] A "report" is a report that summarizes the results of data analysis in the form of documents, graphs, charts, etc.

[2224] "Device" means a device used by an athlete or coach (e.g., smartphone, tablet, computer, wearable device).

[2225] "Opponent team's match data" refers to various statistical data obtained by the opposing team during an actual match.

[2226] A "strategy" is a specific plan or policy for achieving a goal in a match or training.

[2227] "Time series analysis" is a method for analyzing data that changes over time and predicting future trends.

[2228] A "schedule" is a plan that systematically arranges the training, rest, and match dates of players.

[2229] "Notifications" are messages and alarms sent by the system to inform players and coaches of important information.

[2230] The present invention is a system for optimally evaluating the abilities of players, proposing strategies, and managing the schedules of players. The following describes how to implement the program of this system.

[2231] Player ability analysis system

[2232] Data collection

[2233] The server collects players' match and training data from a database. It also sends physical data measured in real time by the devices, such as heart rate, distance, and speed, to the server. The specific software used is MySQL for database management, and Python and Pandas for data collection and analysis scripts.

[2234] Data analysis

[2235] The server aggregates the collected data and calculates player performance indicators, such as passing success rate, shooting success rate, speed, stamina, etc. It then applies machine learning algorithms (such as TensorFlow and Scikit-learn) to identify players' strengths and weaknesses.

[2236] Report Generation

[2237] The server generates a visually easy-to-understand report using graphs and charts based on the analysis results. Specific libraries used are Matplotlib and Seaborn. This report is generated in PDF format and output to the terminal (the coach's or player's device).

[2238] Strategy Proposal System

[2239] Analysis of the opposing team

[2240] The server collects data on the opposing team's matches using web scraping techniques (Beautiful Soup and Selenium) and applies machine learning algorithms to analyze the opposing team's strengths and weaknesses.

[2241] Analyzing your team's capabilities

[2242] The server collects and analyzes data on the team's players, evaluating specific skills such as dribbling and passing accuracy, again using Python and the Sklearn library.

[2243] Strategy Generation

[2244] The server generates an optimal strategy based on data from the team and the opposing team. The generated strategy is output to the terminal (coach's device) and the user (coach) checks and adjusts it.

[2245] Athlete schedule management system

[2246] Physical data analysis

[2247] The server periodically collects each player's physical data (heart rate, fatigue level, sleep data, etc.) and also receives real-time data from the device.

[2248] Schedule proposal

[2249] The server evaluates the player's current condition based on the collected physical data and uses time-series analysis to generate an optimal training schedule, including rest days and recovery training.

[2250] Scheduling and Notifications

[2251] The user (coach) reviews the proposed schedule and makes final adjustments. After adjustments, the final schedule is generated in PDF format and output to the device. The device receives a notification and displays the schedule to the players.

[2252] Specific examples

[2253] For example, suppose the server collects player A's match data and determines that his passing success rate is 90%, his shooting success rate is 50%, and his speed is 28km / h. Analyzing this data, it determines that player A is good at passing, but his shooting accuracy needs improvement. Based on these results, the server identifies player A's strengths and weaknesses, generates a visual report, and sends it to the coach's device.

[2254] Furthermore, when proposing a strategy, the server compares the data of the team and the opposing team and discovers that the opposing team is weak in central defense. Since player B on the team is good at dribbling, the server proposes a strategy to have player B attack from the center and sends the result to the coach's device.

[2255] In schedule management, the server analyzes Player C's physical data and determines that recent training has caused fatigue. Based on this result, the server suggests Player C take one day of rest and incorporates lighter training for the rest of the week. After the user (coach) checks and adjusts the schedule, the final schedule is sent to Player C's device.

[2256] Prompt Sentence Examples

[2257] "How would you rate Player A's match performance?"

[2258] The generative AI model generates an answer such as, "Player A's match performance was assessed as having a passing success rate of 90%, a shooting success rate of 50%, and a speed of 28 km / h. Based on this, it was determined that his passing ability is excellent, but his shooting accuracy needs improvement."

[2259] As described above, the present invention is a system that automates the effective collection and analysis of data, strategy proposals, and schedule management, and can bring out the best in players' performance.

[2260] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2261] Player ability analysis system

[2262] Step 1: Data collection

[2263] Inputs: Real-time physical data (heart rate, speed, distance) and match and training data.

[2264] How it works: The wearable device collects the athlete's heart rate, speed, and distance data in real time. The server receives this data via Bluetooth or Wi-Fi and records it in a database. In addition, the server retrieves past match and training data from the database.

[2265] Output: The integrated dataset is stored in a database on the server.

[2266] Step 2: Data integration

[2267] Input: Match data, training data, physical data.

[2268] How it works: The server uses Python and the Pandas library to combine these data into a single unified dataset, clean and normalize the data, impute missing data, and remove outliers.

[2269] Output: A clean, consolidated dataset.

[2270] Step 3: Performance Metrics Calculation

[2271] Input: The integrated dataset.

[2272] How it works: The server applies statistical methods to calculate each player's performance metrics (passing success rate, shooting success rate, speed, stamina). Specifically, it uses the Python Scikit-learn library to perform the calculations.

[2273] Output: A list of calculated performance metrics.

[2274] Step 4: Machine learning analysis

[2275] Input: A list of performance metrics.

[2276] How it works: The server uses TensorFlow and Scikit-learn libraries to apply machine learning algorithms to identify players' strengths and weaknesses, and analyzes them using clustering methods (e.g., K-means) and classifiers.

[2277] Output: Identification of each player's strengths and weaknesses as a result of the analysis.

[2278] Step 5: Generate reports

[2279] Input: Analysis results.

[2280] How it works: The server uses Matplotlib and Seaborn libraries to generate graphs and charts based on the analysis results, which are then compiled into a PDF report.

[2281] Output: Report in PDF format.

[2282] Step 6: Print the report

[2283] Input: Report in PDF format.

[2284] How it works: The server sends the generated reports to the players' and coaches' devices via email or a dedicated app.

[2285] Output: Reports are displayed on players' and coaches' terminals.

[2286] Strategy Proposal System

[2287] Step 1: Gather data on the opposing team

[2288] Input: Opposing team's match data.

[2289] How it works: The server uses web scraping techniques (Beautiful Soup and Selenium) to retrieve opposing team match data, including match results and individual player performance data.

[2290] Output: The opponent team's match data is saved in the server database.

[2291] Step 2: Analyze the Opponent

[2292] Input: Opposing team's match data.

[2293] How it works: The server uses Python and the Scikit-learn library to analyze the strengths and weaknesses of the opposing team, applying machine learning algorithms to identify defensive weaknesses and attacking characteristics.

[2294] Output: Analysis of the opposing team's strengths and weaknesses.

[2295] Step 3: Collect and analyze your own data

[2296] Input: Player data for your team.

[2297] How it works: The server collects data on the team's players and analyzes it using Python and the Scikit-learn library. It evaluates the skills and performance data of each player.

[2298] Output: Analysis results of your team's player data.

[2299] Step 4: Strategy Generation

[2300] Input: Analysis results for your team and the opposing team.

[2301] How it works: The server generates optimal strategies based on the analysis results. Specifically, it proposes strategies that exploit the opponent's weaknesses and strategies that maximize the strengths of the player's own team.

[2302] Output: The generated strategic plan.

[2303] Step 5: Output and adjust your strategy

[2304] Enter: strategic planning.

[2305] How it works: The generated strategy is sent in PDF format to the coach's device, where the user (coach) can review the strategy and adjust it if necessary.

[2306] Output: The final strategic plan is displayed on the coach's terminal.

[2307] Athlete schedule management system

[2308] Step 1: Collecting physical data

[2309] Input: Real-time physical data.

[2310] How it works: The device collects the athlete's heart rate, fatigue level, and sleep data in real time and sends it to a server.

[2311] Output: The physical data is stored in the server database.

[2312] Step 2: Analysis of physical data and schedule proposal

[2313] Input: Physical data.

[2314] How it works: The server evaluates the player's condition based on the physical data collected. Using time series analysis, it generates a training schedule using a Python library.

[2315] Output: The generated training schedule.

[2316] Step 3: Export and adjust the schedule

[2317] Input: The generated training schedule.

[2318] Operation: The server sends the generated schedule in PDF format to the device. The user (coach) reviews the schedule and adjusts it as necessary.

[2319] Output: The final adjusted schedule is displayed on the terminal.

[2320] Step 4: Schedule Notification

[2321] Enter: Final Schedule.

[2322] What it does: The device notifies and displays the final schedule to the player. This notification can include pop-ups and alarms.

[2323] Output: The player checks the schedule and puts it into action.

[2324] These processing steps allow players to optimize their performance and strategically advance their matches and training.

[2325] (Application example 1)

[2326] 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."

[2327] In the conventional manufacturing industry, there has been a lack of systems that can properly evaluate the individual capabilities of factory employees and generate optimal production line allocations and work schedules. This has made it difficult to maximize employee capabilities, resulting in problems such as reduced production efficiency and employee overwork. Furthermore, it has been difficult to collect employee physical and work data in real time and then efficiently allocate and schedule employees based on that data. The present invention aims to solve these problems, optimize employee performance, and improve factory production efficiency.

[2328] 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.

[2329] In this invention, the server includes means for collecting and analyzing player match data, training data, and physical data, means for generating reports based on the evaluation results, means for outputting the generated reports, means for collecting and evaluating factory employee performance data, means for generating an optimal production line layout based on the evaluation results, and means for outputting the generated layout, thereby enabling optimal evaluation of factory employee capabilities and generation of efficient production line layouts and work schedules.

[2330] "Match data" refers to data that records various activities and performances performed by players during a match.

[2331] "Training Data" refers to data that records the performance of exercises and activities that an athlete performs during training.

[2332] "Physical data" refers to data that indicates the physical condition of athletes and factory workers, such as their heart rate, distance, speed, fatigue level, and sleep data.

[2333] "Means for generating reports" refers to systems or processes that automatically create reports based on analysis and evaluation results.

[2334] "Means for outputting reports" refers to a system or process that displays or transmits the generated reports to a terminal or device.

[2335] "Performance data" refers to data such as work speed, accuracy, and error rate used to evaluate the productivity and efficiency of factory employees while working.

[2336] "Means for generating optimal layout of production lines" refers to systems and processes for placing factory employees in optimal work positions based on collected and analyzed data.

[2337] "Means for generating strategies" refers to a system or process that analyzes information about one's own team and the opposing team and formulates optimal strategies and tactics.

[2338] "Means for generating schedules" refers to the systems and processes that create optimal training and work schedules based on the physical data of each player or employee.

[2339] "Means for notification" refers to a system or process that notifies the target person in real time about the generated schedule or allocation.

[2340] "Means for visualizing work speed and accuracy" refers to a system or process that visually displays each employee's work speed and accuracy in the form of graphs or charts.

[2341] The present invention is a system for optimally evaluating the performance of factory employees and generating optimal production line layouts and work schedules. The operation of this system will be described below.

[2342] Data collection and analysis process

[2343] The server collects performance data such as work speed, accuracy, number of breaks, and cumulative fatigue level from each employee's wearable device and sensors. This data is sent to the server in real time and stored in a database.

[2344] Data analysis

[2345] The server analyzes the collected data and calculates performance indicators for each employee. Specifically, it uses machine learning algorithms (such as Random Forest and SVM) to identify the employee's strengths and weaknesses. Based on the results of this analysis, it generates an employee evaluation report.

[2346] Report generation and output

[2347] The server generates evaluation reports in a visually easy-to-understand format, including graphs and charts. These reports are sent to the terminals of each employee and manager. For example, if Employee A's work speed is 45 units per minute and his accuracy is 98%, a report will be generated that displays his performance evaluation as "highly efficient."

[2348] Optimal production line layout and schedule generation

[2349] The server generates the optimal allocation of factory employees to production lines based on the evaluation results, and also generates an optimal individual work schedule based on each employee's physical data (heart rate, accumulated fatigue level, etc.), including allocation of break times and light work.

[2350] Scheduling and Placement Notification

[2351] The generated schedule and optimal allocation are notified to each employee's device in real time. Employees can check their daily schedule and allocation location through their own devices. For example, if Employee B reaches 75% of their normal work speed, they will be notified and assigned lighter tasks in the next day's work schedule.

[2352] Visualization

[2353] The server visualizes each employee's work speed and accuracy using graphs and charts, and this visualized data is provided to managers and upper management, allowing them to understand the overall performance status at a glance.

[2354] Hardware and software used

[2355] The present invention uses the following hardware and software:

[2356] Hardware: Smartphones, smart glasses, head-mounted displays, wearable sensors

[2357] Software: pandas (data frame manipulation), scikit-learn (machine learning algorithms), matplotlib (data visualization)

[2358] Prompt Sentence Examples

[2359] "Analyze Employee A's work data, evaluate his work speed and accuracy, and propose an optimal work schedule."

[2360] The present invention improves factory production efficiency and maximizes employee capabilities. It also prevents employees from overworking, makes health management easier, and improves overall work efficiency.

[2361] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2362] Step 1:

[2363] The server collects real-time performance data from the factory employees' wearable devices, such as work speed, accuracy, number of breaks, and cumulative fatigue. This data is sent to the server and stored in a database. The input is real-time data from the wearable devices, and the output is performance data stored in the database.

[2364] Step 2:

[2365] The server converts the collected performance data into a data frame and performs preprocessing for applying the machine learning algorithm. This preprocessing includes data normalization and missing value imputation. The input is the performance data collected in step 1, and the output is a preprocessed data frame.

[2366] Step 3:

[2367] The server uses the preprocessed data to apply machine learning algorithms (e.g., Random Forest) to evaluate each employee's performance. This evaluation includes a performance evaluation based on work speed and accuracy, and a fatigue evaluation based on cumulative fatigue. The input is the preprocessed data frame, and the output is the performance evaluation results for each employee.

[2368] Step 4:

[2369] The server generates a performance report for each employee based on the evaluation results. This report includes graphs and charts that visually represent the evaluation results. The input is the performance evaluation results, and the output is a visually represented performance report. Specifically, based on information that Employee A's work speed is 45 units per minute and his accuracy is 98%, a performance report is generated that evaluates him as "highly efficient."

[2370] Step 5:

[2371] The server sends the generated performance report to the terminals of each employee and manager, allowing employees to check their own performance and understand areas for improvement. The input is the generated performance report, and the output is the report displayed on the terminal.

[2372] Step 6:

[2373] The server generates the optimal layout of the production line based on the evaluation results of each employee. This layout includes the optimal work position and role of each employee. The input is the evaluation results, and the output is the optimal production line layout.

[2374] Step 7:

[2375] The server generates an individual optimal work schedule based on each employee's physical data. This schedule includes break times and light work assignments. The input is the physical data, and the output is the optimal work schedule for each employee.

[2376] Step 8:

[2377] The server notifies each employee's terminal of the generated schedule and production line allocation in real time, allowing the employee to check their own schedule and location. The input is the generated schedule and allocation, and the output is the schedule and allocation information displayed on the terminal. Specifically, if employee B reaches 75% of his normal work speed, he will be notified that he will be assigned a lighter task in the next day's work schedule.

[2378] Through the above processing steps, a system is realized that optimally evaluates the capabilities of factory employees and improves production efficiency.

[2379] 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.

[2380] The present invention aims to improve overall performance by combining a system for optimally evaluating a player's abilities, proposing strategies, and managing the player's schedule with an emotion engine that recognizes the user's emotions. The program processing of this system is explained below in natural language.

[2381] Player ability analysis

[2382] Data collection

[2383] The server collects each player's match data from a database. Performance data during the match includes pass success rate, shot success rate, and distance traveled. The device (player's wearable device) sends training data such as heart rate, speed, and distance to the server in real time. The server periodically obtains physical data (heart rate, fatigue level, and sleep time) from each player's device.

[2384] Data analysis

[2385] The server combines collected match data, training data, and physical data to calculate performance indicators for each player. The server cleanses the data and fills in outliers and missing data. The server applies machine learning algorithms to identify players' strengths and weaknesses. The server performs clustering analysis based on each indicator to extract performance patterns.

[2386] Report Generation

[2387] The server generates a report for each player based on the analysis results. The report includes graphs and charts and is created in a visually easy-to-understand format. The generated report is sent to the terminal (coach's or player's device). The server notifies the user of the report, and the user can check the report.

[2388] Specific examples

[2389] The server collects the match data of Player A and determines that his pass success rate is 90%, his shot success rate is 50%, and his speed is 28km / h. From this data, it is analyzed that Player A is good at passing, but his shooting accuracy has room for improvement.

[2390] Strategy proposals

[2391] Analysis of the opposing team

[2392] ...

Claims

1. A means of collecting players' match data, training data, and physical data; a means for analyzing the collected data and identifying strengths and weaknesses of players; means for generating a report based on the analysis results; means for outputting the generated report; A system including:

2. A means of collecting data on the opposing team and analyzing their strengths and weaknesses; A means to collect and analyze data on your team's players and generate optimal strategies, means for outputting the generated strategy; The system of claim 1 further comprising:

3. A means for periodically collecting physical data of players and generating training schedules; means for outputting the generated schedule; a means for making a notification based on the generated schedule; The system of claim 1 further comprising:

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