system

Abstract

Provide a system. [Solution] A means of collecting and analyzing performance data from top athletes; a means for collecting and analyzing athletic performance data of a user based on the performance data of the top-class athletes; A means for designing an optimal training menu for the user based on the analysis results; means for providing the training menu to a user terminal; means for monitoring the user's training progress and adjusting the training menu; A system including:

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] To improve their performance in track and field, particularly in the 100m sprint, athletes need not only to train but also to have an effective training plan. However, current training methods lack a system that utilizes data from top-class athletes to present optimal training methods for individual athletes. As a result, athletes are unable to clarify their performance issues, which can delay improvement. To solve this problem, it is necessary to develop a system that uses data from top-class athletes to provide training menus tailored to individual athletes and encourage improvement while monitoring their progress. [Means for solving the problem]

[0005] The present invention comprises a means for collecting and analyzing performance data of top-class athletes, a means for collecting and analyzing a user's athletic performance data based on the performance data of the top-class athletes, and a means for designing an optimal training menu for the user based on the analysis results. Furthermore, the system effectively supports the user's performance improvement by including a means for providing the training menu to a user terminal and a means for monitoring the user's training progress and adjusting the training menu. Furthermore, by including a sensor device for acquiring the performance data of the top-class athletes and an image analysis means for analyzing video of the user running, more precise data collection and analysis are possible.

[0006] A "top-class athlete" is an athlete who performs at a very high level of athletic performance and typically achieves excellent results in international or national competitions.

[0007] "Performance data" refers to quantitative data about an athlete's movements, speed, and application of force, as well as other actions and results during a competition.

[0008] "Means of analysis" refers to the combination of hardware and software used to analyze the collected data, thereby quantifying characteristics of an athlete's movements, form, speed, etc.

[0009] "User" refers to an athlete who uses this system to improve their own performance.

[0010] "Training menu" refers to specific training content or programs designed to improve a user's athletic performance.

[0011] "User terminal" refers to a device on which a user checks a training menu and inputs training results, and typically includes a smartphone, tablet, or the like.

[0012] "Sensor Device" refers to a hardware device that detects and collects data about an athlete's movements and the application of forces, including accelerometers and force sensors.

[0013] "Image analysis means" refers to a combination of software and hardware used to analyze image data such as videos and photographs, and to analyze athletes' movements and form. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention relates to a system that provides a means for collecting and analyzing performance data of top-class athletes. Specific embodiments of this system will be described below.

[0036] System configuration

[0037] This system mainly consists of a server, user terminals, and sensor devices.

[0038] server

[0039] The server is the main system that collects, stores, and analyzes performance data from top athletes and users, and generates training menus. The server performs the following processes:

[0040] Data collection: Receive and store video and sensor data from top athletes.

[0041] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate.

[0042] Data Comparison: Compare your performance data with that of top athletes to identify performance gaps.

[0043] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[0044] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[0045] User terminal

[0046] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[0047] Data entry: The user enters their own competition data and training results.

[0048] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc.

[0049] Display training menu: Displays the training menu sent from the server to the user.

[0050] Receive Feedback: Receive feedback from the server and training menu updates.

[0051] Sensor Device

[0052] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[0053] Acceleration data: Measures acceleration during movement.

[0054] Force data: Measures the force when touching the ground.

[0055] Location Data: Tracking the rider's location during a run.

[0056] Example of operation process

[0057] 1. Data Collection

[0058] The server collects and analyzes videos of top athletes and data from sensor devices.

[0059] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[0060] 2. Data analysis and comparison

[0061] The server compares the user's data with that of top athletes and analyzes the differences, for example, detecting that the user's starting sprint speed is slower than that of top athletes.

[0062] 3. Creation of training menu

[0063] Based on the analysis results, the server generates a training menu to improve the user's starting sprint, including starting sprint practice drills and short-distance sprint practice.

[0064] The server transmits the training menu to the user terminal.

[0065] 4. Training implementation and progress monitoring

[0066] The user uses the terminal to practice according to the training menu.

[0067] The user periodically inputs the results of training into the terminal and transmits them to the server.

[0068] The server analyzes the progress data sent by the user and evaluates the effectiveness of the training.

[0069] Based on the analysis results, the server adjusts the training menu as needed and sends updated information to the user's device.

[0070] By implementing this system, users can receive optimal training based on a scientific approach. Specifically, by utilizing data from top athletes, users can clarify individual performance issues and more effectively improve their athletic performance.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The server collects performance data from top athletes, including video footage and data from sensor devices, stores this data, and prepares it for analysis.

[0074] Step 2:

[0075] The server analyzes the collected data of top athletes, specifically extracting parameters such as stride length, ground contact time, and acceleration rate, and by extracting these parameters, it creates a performance profile of the athlete.

[0076] Step 3:

[0077] Users can record video of their own 100m sprint using a device such as a smartphone or tablet. The device then acquires the video data, converts it into the required format, and sends it to the server.

[0078] Step 4:

[0079] The device collects the user's running data using sensor devices (e.g., acceleration sensors and force sensors), analyzes the data, and extracts parameters such as stride length and acceleration rate. The data is then sent to a server.

[0080] Step 5:

[0081] The server receives the user's competitive performance data and compares it with that of top athletes. In particular, it analyzes differences in each phase, such as the start dash, acceleration, top speed, and finish. By visualizing the differences, it is possible to clarify the user's performance issues.

[0082] Step 6:

[0083] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting dash is an issue, starting dash practice drills and short distance sprint practice will be added to the menu.

[0084] Step 7:

[0085] The server sends the generated training menu to the user's device, which receives it and displays the training menu to the user.

[0086] Step 8:

[0087] The user uses the device to practice according to the training menu, inputs the results of the practice and feedback into the device, and sends the data to the server.

[0088] Step 9:

[0089] The server receives and analyzes the user's training progress data, and based on the analysis results, evaluates the effectiveness of the current training menu and adjusts the training menu as necessary.

[0090] Step 10:

[0091] The server sends the adjusted training menu to the user's device, which receives it and displays it to the user. This process is repeated until the user's performance is optimized.

[0092] Example 1

[0093] 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."

[0094] Conventional training systems lack scientific data collection and analysis to improve user performance, making it difficult to provide individually optimized training menus. Furthermore, performance improvement tends to be delayed due to ineffective monitoring and feedback of the user's training progress. The present invention aims to solve these problems by providing a system that compares the user's performance data with that of top athletes and provides an optimal training menu.

[0095] 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.

[0096] In this invention, the server includes means for collecting and analyzing performance data of top-class athletes, means for collecting and analyzing the user's athletic performance data based on the performance data of the top-class athletes, means for designing an optimal training menu for the user based on the analysis results, means for providing the training menu to a user terminal, means for monitoring the user's training progress and adjusting the training menu, means for extracting performance parameters such as stride length, ground contact time, and acceleration rate using the collected video data and sensor data, and means for comparing the user's performance data with data of top-class athletes and identifying performance differences. This allows the user to train based on an optimal training menu that is scientifically backed, thereby efficiently improving their athletic performance.

[0097] A "top-class athlete" is an outstanding athlete who demonstrates superior performance that exceeds certain standards and is recognized based on the evaluations of official organizations and their achievements in competitive events.

[0098] "Performance Data" refers to quantitative data relating to an athlete's physical movement and motion, including measurements such as stride length, ground contact time, and acceleration rate.

[0099] The "analyzing means" refers to a method or device for analyzing collected data and extracting useful information or parameters, and includes, for example, algorithms, software, hardware, and the like.

[0100] "User" refers to an individual or group who intends to improve their athletic performance by using this system.

[0101] A "user terminal" is an electronic device used by a user to operate the system, and specifically includes a smartphone, tablet, PC, etc.

[0102] "Training menu" refers to specific practice methods and drills designed to improve a user's athletic performance.

[0103] "Monitoring means" refers to methods or equipment for tracking a user's training progress and collecting and evaluating the necessary data, and includes, for example, software, sensor devices, etc.

[0104] "Video data" refers to video information captured on camera of an athlete's movements, which is usually stored in digital format and used for analysis.

[0105] "Sensor data" refers to various measurement data collected by sensor devices, and specifically includes acceleration, force, position information, etc.

[0106] "Performance parameters" refer to indicators that quantitatively show an athlete's movement characteristics, and specifically include stride length, ground contact time, acceleration speed, etc.

[0107] "Means for identifying differences" refers to methods or devices for comparing the performance data of a top-class athlete with that of the user and clarifying the differences or shortcomings between the two.

[0108] The present invention relates to a system that collects and analyzes performance data from top athletes to provide users with optimal training programs. This system is primarily composed of a server, user terminals, and sensor devices.

[0109] server

[0110] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus.

[0111] 1. Data Collection

[0112] The server receives and stores videos of top athletes and data from sensor devices, specifically MP4 videos and acceleration, force, and position data (e.g., in CSV format).

[0113] It also receives and stores driving data (video and sensor data) provided by the user, and transmits the data in real time via Bluetooth.

[0114] 2. Data Analysis

[0115] The server analyzes the collected data and extracts performance parameters such as stride length, ground contact time, and acceleration rate using Python's NumPy, Pandas, and OpenCV libraries.

[0116] For example, in video analysis, a skeleton detection algorithm using OpenCV is applied to calculate performance parameters from each frame.

[0117] 3. Data Comparison

[0118] The server compares the user's data with that of top athletes and identifies performance differences, for example detecting that the user's ground contact time is 0.15 seconds longer than that of top athletes.

[0119] The results of this comparison are statistically processed using libraries such as SciPy.

[0120] 4. Training menu generation

[0121] The server then generates the optimal training menu for the user based on the analysis results. For example, to improve starting speed, the server designs a menu that includes "reaction speed training" and "acceleration training."

[0122] The generated menu is sent to the user terminal in JSON format.

[0123] 5. Progress monitoring

[0124] The server analyzes the training progress data sent by the user, evaluates the effectiveness of the training, and provides feedback based on the results using statistical analysis and machine learning models.

[0125] For example, verify that the user's acceleration speed improved by 0.5 m / s over two weeks.

[0126] User terminal

[0127] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[0128] Data Entry

[0129] Users input their own competition data and training results, take videos using a dedicated app, and input sensor data.

[0130] The entered data is sent to the server in real time.

[0131] Video recording and analysis

[0132] The app automatically analyzes the user's stride length, ground contact time, and other data by recording video of the user's run. OpenCV technology is used for video analysis.

[0133] Training menu display

[0134] The training menu received from the server is displayed to the user, including specific training content and goals.

[0135] Receive feedback

[0136] Users receive feedback from the server and training menu updates, allowing them to adjust their training accordingly.

[0137] Sensor Device

[0138] The sensor device measures the athlete's movements and the force applied, and mainly collects the following data:

[0139] Acceleration Data

[0140] Acceleration during movement is measured and sent to the server in real time. A Bluetooth-enabled IMU sensor (e.g., MPU-6050) is used.

[0141] Force Data

[0142] Measure the force when the robot touches the ground. Use a force sensor (e.g., FSR sensor).

[0143] Location data

[0144] GPS tracking devices are used to obtain the location of athletes, for example GPS watches.

[0145] Specific Examples

[0146] Examples of data collection

[0147] An example of the data the server receives from a top athlete is a 10-second video of a sprint and acceleration data for the same period.

[0148] Examples of data analysis and comparison

[0149] The server compares the user's starting speed (e.g., 6.2 m / s) with the speed of a top-class athlete (e.g., 7.5 m / s) and detects that the user is behind.

[0150] Example of training menu generation

[0151] Examples of training menus generated by the server include "start dash strengthening drills" and "200m short distance sprint practice."

[0152] Prompt Sentence Examples

[0153] "Compare the starting speed of a top-class athlete with the user's speed and generate a specific training menu to improve the user's speed."

[0154] This system allows users to receive scientifically backed training plans using data from top athletes, allowing them to effectively improve their own athletic performance.

[0155] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0156] Step 1:

[0157] The server collects performance data from top athletes. Top athletes shoot videos using a dedicated app and upload them to the server in MP4 format. At the same time, data collected by sensor devices (accelerometers, force sensors, GPS tracking devices) is sent to the server in CSV format. The inputs are video files and sensor data, which the server saves. The output is the saved video and sensor data.

[0158] Step 2:

[0159] The server collects the user's performance data. The user records their own running video using a dedicated app and uploads it to the server in MP4 format. The server also receives acceleration data, force data, and position data from the sensor device and saves them in CSV format. The inputs are the user's video file and sensor data, which the server saves. The output is the saved user video and sensor data.

[0160] Step 3:

[0161] Performance parameters are extracted using the video data and sensor data collected by the server. The server uses Python's NumPy, Pandas, and OpenCV libraries to analyze the video and calculate stride length, ground contact time, and acceleration rate from each frame. It also processes the acceleration data with a low-pass filter and integrates the force data to calculate impact force. The input is the saved video data and sensor data, which the server analyzes to extract various performance parameters. The output is a list of each performance parameter.

[0162] Step 4:

[0163] The server compares the user's data with the performance data of top athletes. It compares the extracted performance parameters and identifies the difference in the user's performance. This comparison is performed statistically using the SciPy library. For example, it confirms that the user's ground contact time is 0.15 seconds longer than the top athletes. The input is a list of each performance parameter, and the server compares them to identify the difference. The output is a list of the differences.

[0164] Step 5:

[0165] The server generates an optimal training menu for the user based on the analysis results. The training menu is designed based on the areas the user needs to improve, and includes specific practice drills and training content. For example, improving a quick start could include "reaction speed training" and "acceleration training." The generated menu is sent to the user's device in JSON format. The input is a list of differences, and the server generates a training plan based on this. The output is a training menu JSON.

[0166] Step 6:

[0167] The user practices according to the training menu on the device. After each training session, the user enters the results into a dedicated app and sends the data to the server. For example, reaction time is entered as the result of "reaction speed training." The input is the user's training result data, which is sent to the server via the user's device. The output is the sent result data.

[0168] Step 7:

[0169] The server analyzes the user's progress data and evaluates the effectiveness of the training menu. It uses statistical analysis and machine learning models to evaluate the user's progress and adjusts the training menu as needed. For example, it verifies that acceleration speed has improved by 0.5 m / s over two weeks. The input is the user's progress data, which the server analyzes and evaluates the effectiveness. The output is the analysis results and, if necessary, an adjusted training menu.

[0170] Step 8:

[0171] The server sends the adjusted training menu to the user's terminal. The updated menu is provided to the user in the form of a notification to help them plan their next training. The input is the adjusted training menu, which the server sends to the user's terminal. The output is an updated notification that is displayed on the user's terminal.

[0172] (Application example 1)

[0173] 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."

[0174] The present invention aims to optimize the operational efficiency and maintenance effectiveness of users' automated equipment by utilizing top-class performance data. However, current automated equipment management systems are unable to effectively utilize top-class performance data, resulting in issues such as insufficient operational improvement and maintenance optimization. Furthermore, there is a lack of a mechanism for evaluating the effectiveness of maintenance performed by users in real time and providing appropriate feedback. This results in issues such as insufficient improvement of automated equipment efficiency and reduction of downtime.

[0175] 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.

[0176] In this invention, the server includes means for collecting and analyzing performance data of top-class automated equipment, means for collecting and analyzing performance data of a user's automated equipment, and means for designing an optimal operation improvement and maintenance menu for the user based on the analysis results, thereby enabling the operating efficiency of the user's automated equipment to approach that of top-class equipment.

[0177] "Top-class automated equipment" refers to an automated equipment system with the highest level of production capacity and efficiency.

[0178] "Performance Data" means data relating to the performance of automated equipment, such as its operating condition, efficiency, speed, and energy consumption.

[0179] "User's automated equipment" refers to the automated equipment system operated by the user.

[0180] A "sensor device" is a measuring device used to measure and collect operational data of automated equipment.

[0181] "Operational Improvement" refers to specific adjustments or modifications made to improve the performance of automated equipment.

[0182] A "maintenance menu" refers to a plan and instructions for a series of maintenance work such as inspection, repair, and cleaning of equipment.

[0183] "Analysis means" refers to technology or software used to analyze collected data and identify operational efficiency or abnormal behavior.

[0184] "Monitoring" refers to the continuous observation and recording of the performance and maintenance progress of a user's automated equipment.

[0185] A "user terminal" is a device (such as a smartphone or tablet) that displays maintenance menus and feedback and transmits input data to the server.

[0186] "Operational data" refers to detailed parameters such as speed, position, and force that automated equipment generates during operation.

[0187] MODE FOR CARRYING OUT THE INVENTION

[0188] System Program

[0189] The system program that realizes this application example consists of the following: The server collects and analyzes performance data of automated equipment, and generates and provides optimal operation improvement and maintenance menus to users. The user terminal has the function of displaying this menu and monitoring progress. Sensor devices also collect data.

[0190] Explanation of the generated program processing

[0191] The server interacts with sensor devices to collect performance data of the automated equipment. The sensor devices may include acceleration sensors, force sensors, position tracking devices, etc. These devices use common sensor devices provided by known hardware manufacturers.

[0192] The server uses data analysis libraries such as NumPy to analyze the collected performance data, calculating parameters such as operating speed, accuracy, and energy consumption, and comparing them with data from leading automated equipment.

[0193] The user terminal uses a Python program to display operation improvement and maintenance menus. Training menus and feedback are updated in real time based on instructions from the server. This allows the user terminal to accurately monitor the equipment status and instruct the user to carry out maintenance as necessary.

[0194] The server also monitors users' maintenance progress and evaluates the collected data in real time, including using generative AI models to predict and generate future improvement measures.

[0195] Specific examples

[0196] For example, operational data for a robot arm used in a factory is collected, analyzed, and compared with data for the most efficient robot arm. If delays in operation or a decrease in accuracy are found based on the analysis results, a menu for improving these issues is provided to the user's terminal. The user follows this menu to carry out maintenance on the robot arm, thereby improving the operational efficiency of the robot arm.

[0197] Prompt Sentence Examples

[0198] Generate a menu for optimizing the operation of a factory robot based on the following data:

[0199] User data: [example data]

[0200] Top-class robot data: [Data example]

[0201] Analyze the differences between each robot and design a maintenance menu based on that.

[0202] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0203] Step 1: Data collection

[0204] The server collects performance data of automated equipment from sensor devices. In this step, acceleration, force, and position data are collected. The input is real-time data from the sensor devices, and the output is the collected performance data. Specifically, the server sends a request to the sensor device API and receives the data as a response.

[0205] Step 2: Data analysis

[0206] Important parameters such as operating speed, accuracy, and energy consumption are extracted from the performance data collected by the server. In this step, data analysis libraries such as NumPy are used. The input is the collected performance data, and the output is the extracted parameters. Specifically, the data is filtered and each parameter is calculated.

[0207] Step 3: Data comparison

[0208] The server compares the performance data of the user's automated equipment with the data of top-class automated equipment. In this step, the difference for each parameter is calculated. The input is the user's performance data and the top-class data, and the output is the difference data for each parameter. Specifically, the server calculates the difference for each parameter and outputs it as a list.

[0209] Step 4: Generate maintenance menu

[0210] Based on the results of the data comparison, the server generates an optimal operation improvement and maintenance menu for the user. In this step, analysis is performed using a generative AI model. The input is differential data for each parameter, and the output is a specific maintenance menu. Specifically, a prompt sentence is input into the generative AI model, and the menu is output in text format.

[0211] Step 5: Provide maintenance menu

[0212] The server provides the generated maintenance menu to the user terminal. In this step, an HTTP request is used. The input is the maintenance menu, and the output is the menu information sent to the user terminal. Specifically, the server sends a request to the API of the user terminal and sends the menu.

[0213] Step 6: Perform and report maintenance

[0214] The user performs maintenance according to the maintenance menu provided by the user terminal. In this step, the user inputs the maintenance progress into the terminal. The input is the user's maintenance progress data, and the output is the progress data sent to the server. In specific operations, the user inputs data using the terminal interface and sends it to the server.

[0215] Step 7: Progress monitoring and menu adjustments

[0216] The server analyzes the maintenance progress data sent by the user and evaluates the effectiveness of the maintenance. In this step, the maintenance menu is adjusted as necessary. The input is the user's progress data, and the output is the adjusted maintenance menu. Specifically, the server analyzes the progress data and generates a new menu using a generative AI model.

[0217] 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.

[0218] The present invention relates to a system that provides a means for collecting and analyzing performance data of top athletes, and in particular to a system that incorporates an emotion engine that recognizes the emotions of users. Specific embodiments of this system will be described below.

[0219] System configuration

[0220] This system mainly consists of a server, a user terminal, a sensor device, and an emotion engine.

[0221] server

[0222] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus. It also receives user emotional data and adjusts training menus and feedback based on this data. The server performs the following processes:

[0223] Data collection: Receive and store video and sensor data from top athletes.

[0224] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate.

[0225] Data Comparison: Compare your performance data with that of top athletes to identify performance gaps.

[0226] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[0227] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[0228] Emotion data processing: Receives data from the emotion engine and provides training menus and feedback according to the user's emotional state.

[0229] User terminal

[0230] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[0231] Data entry: The user enters their own competition data and training results.

[0232] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc.

[0233] Display training menu: Displays the training menu sent from the server to the user.

[0234] Emotion data acquisition: Collect user's emotion data through the emotion engine and send it to the server.

[0235] Receive Feedback: Receive feedback from the server and training menu updates.

[0236] Sensor Device

[0237] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[0238] Acceleration data: Measures acceleration during movement.

[0239] Force data: Measures the force when touching the ground.

[0240] Location Data: Tracking the rider's location during a run.

[0241] Emotion Engine

[0242] The emotion engine is a system that recognizes the user's emotional state and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[0243] Example of operation process

[0244] 1. Data Collection

[0245] The server collects and analyzes videos of top athletes and data from sensor devices.

[0246] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[0247] 2. Data analysis and comparison

[0248] The server compares the user's data with that of top athletes and analyzes the differences, particularly in each phase of the race, such as the start, acceleration, top speed, and finish. By visualizing the differences, it is possible to clarify any issues with the user's performance.

[0249] 3. Creation of training menu

[0250] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting sprints are an issue, starting sprint practice drills and short-distance sprint practice drills will be added to the menu.

[0251] The server transmits the training menu to the user terminal.

[0252] 4. Training implementation and progress monitoring

[0253] The user uses the terminal to practice according to the training menu.

[0254] The user periodically inputs the results of training into the terminal and transmits the data to the server.

[0255] The server analyzes the progress data sent by the user and evaluates the effectiveness of the training.

[0256] Based on the analysis results, the server adjusts the training menu as needed and sends updated information to the user's device.

[0257] Example of using the emotion engine

[0258] 1. Acquiring Emotion Data

[0259] During training, the user uses the device's camera to recognize facial expressions, and the emotion engine analyzes the user's facial expressions to detect emotions such as joy, anger, and sadness.

[0260] The emotion engine uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[0261] 2. Customized feedback

[0262] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of training if the user feels fatigued.

[0263] The server customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[0264] As described above, this system utilizes data from top-class athletes to not only optimize individual users' performance, but also provide training menus and feedback that take the user's emotions into consideration, thereby achieving more effective performance improvement.

[0265] The processing flow will be explained below.

[0266] Step 1:

[0267] The server collects performance data from top athletes, including video footage and data from sensor devices, stores this data, and prepares it for analysis.

[0268] Step 2:

[0269] The server analyzes the collected data of top athletes, specifically extracting parameters such as stride length, ground contact time, and acceleration rate, and then creates a performance profile of the athlete.

[0270] Step 3:

[0271] Users record video of their own 100m sprint using a device such as a smartphone or tablet. The device acquires the video data, converts it into the required format, and sends it to the server.

[0272] Step 4:

[0273] The device collects the user's running data using sensor devices (e.g., acceleration sensors and force sensors). The device analyzes this data and extracts parameters such as stride length and acceleration rate. The data is then sent to a server.

[0274] Step 5:

[0275] The server receives the user's competitive performance data and compares it with that of top athletes. In particular, it analyzes differences in each phase, such as the start dash, acceleration, top speed, and finish. By visualizing the differences, it clarifies the user's performance issues.

[0276] Step 6:

[0277] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting dash training is an issue, starting dash training drills and short distance sprint training will be added to the menu.

[0278] Step 7:

[0279] The server transmits the generated training menu to the user terminal, which receives it and displays the training menu to the user.

[0280] Step 8:

[0281] The user uses the device to practice according to the training menu, inputs the practice results and feedback into the device, and sends the data to the server.

[0282] Step 9:

[0283] The server receives and analyzes the user's training progress data, evaluates the effectiveness of the current training program based on the analysis results, and adjusts the training program as necessary.

[0284] Step 10:

[0285] The server transmits the adjusted training menu to the user terminal, which receives it and displays it to the user.

[0286] Step 11:

[0287] While the user is training, facial expression recognition is performed using the device's camera. The emotion engine analyzes the user's facial expressions and detects emotions such as joy, anger, and sadness. The emotion engine also uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[0288] Step 12:

[0289] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of the training if the user feels fatigued. The server also customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[0290] Example 2

[0291] 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."

[0292] Conventional athlete training systems have limitations in their approach to collecting performance data from top athletes and providing feedback to users. In particular, they lack the functionality to monitor users' training progress and emotional state in real time and adjust training menus at appropriate times. As a result, users' motivation and results are not fully realized, leading to problems in which effective training cannot be achieved.

[0293] The specification processing by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and analyzing performance data of top-class athletes, means for collecting and analyzing performance data of the user, means for designing an optimal training menu for the user based on the analysis results, and means for collecting emotional data of the user and adjusting the training menu based on the collected data. This makes it possible to provide a detailed training menu that takes into account the user's training progress and emotional state.

[0294] A "top-class athlete" is an athlete who has outstanding performance or ability in a particular sport.

[0295] "Performance data" refers to data that includes an athlete's movements and physiological parameters, such as stride length, ground contact time, and acceleration rate.

[0296] "Collection" refers to the act of obtaining and storing the necessary data.

[0297] "Analysis" refers to the act of quantifying collected data and extracting specific parameters and trends.

[0298] "User" refers to an individual athlete who uses this system.

[0299] "Training menu" refers to a plan that specifies the content of the practice and training that an athlete should perform.

[0300] "User terminal" refers to a mobile device or computer used by a user to display the training menu and input data.

[0301] "Monitoring" refers to the act of continuously collecting data and tracking changes.

[0302] "Emotional data" refers to data that indicates the athlete's emotional state, such as joy, anger, or stress.

[0303] An "emotion engine" refers to a system that analyzes a user's emotional state from collected data and outputs it as emotional data.

[0304] MODE FOR CARRYING OUT THE INVENTION

[0305] The present invention relates to a system that provides a means for collecting and analyzing performance data of top athletes, and in particular to a system that incorporates an emotion engine that recognizes the emotions of users. Specific embodiments of this system will be described below.

[0306] System configuration

[0307] This system mainly consists of a server, a user terminal, a sensor device, and an emotion engine.

[0308] server

[0309] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus. It also receives user emotional data and adjusts training menus and feedback based on this data. The server performs the following processes:

[0310] Data collection: Receive and store video and sensor data from top athletes, specifically using a motion capture system and accelerometers.

[0311] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate. This analysis is performed using machine learning tools such as TENSORFLOW® and PyTorch.

[0312] Data comparison: Compare your performance data with that of top athletes to identify performance differences. Implement the analysis algorithm using Python.

[0313] Training menu generation: Based on the analysis results, we design an optimal training menu for the user. We utilize a reinforcement learning algorithm to generate an efficient training menu.

[0314] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[0315] Emotion data processing: Receives data from the emotion engine and provides training menus and feedback according to the user's emotional state.

[0316] User terminal

[0317] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[0318] Data entry: The user enters their own competition data and training results.

[0319] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc. using Google® Cloud Vision API.

[0320] Display training menu: Displays the training menu sent from the server to the user.

[0321] Emotion data acquisition: Collect user emotion data through the emotion engine and send it to the server. OpenCV is used for facial expression recognition.

[0322] Receive Feedback: Receive feedback from the server and training menu updates.

[0323] Sensor Device

[0324] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[0325] Acceleration data: Measures acceleration during movement.

[0326] Force data: Measures the force when touching the ground.

[0327] Location Data: Tracking the rider's location during a run.

[0328] Emotion Engine

[0329] The emotion engine is a system that recognizes the user's emotional state and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[0330] Example of operation process

[0331] 1. Data Collection

[0332] The server collects and stores videos of top athletes and data from sensor devices, including a Motion Capture system and accelerometers.

[0333] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[0334] 2. Data analysis and comparison

[0335] The server compares the data of the user with that of top athletes and analyzes the differences, particularly in terms of differences in each phase, such as the start dash, acceleration, top speed, and finish. TensorFlow and PyTorch are used for the analysis.

[0336] 3. Creation of training menu

[0337] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting sprints are an issue, starting sprint practice drills and short-distance sprint practice drills will be added to the menu. This is done using a reinforcement learning algorithm.

[0338] The server transmits the training menu to the user terminal.

[0339] 4. Training implementation and progress monitoring

[0340] The user uses the terminal to practice according to the training menu.

[0341] The user periodically inputs the results of training into the terminal and transmits the data to the server.

[0342] The server analyzes the progress data sent from the user, evaluates the effectiveness of the training, and adjusts the training menu based on the progress data.

[0343] Example of using the emotion engine

[0344] 1. Acquiring Emotion Data

[0345] During training, the user uses the device's camera to recognize facial expressions. The emotion engine analyzes the user's facial expressions and detects emotions such as joy, anger, and sadness. OpenCV is used for facial recognition.

[0346] The emotion engine uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[0347] 2. Customized feedback

[0348] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of training if the user feels fatigued.

[0349] The server customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[0350] Prompt Sentence Examples

[0351] Prompt statement to analyze top-class running speed data:

[0352] "top runner speed data analysis TensorFlow"

[0353] The prompt sentence that the server uses to analyze the user's emotion data:

[0354] "user emotion data analysis with OpenCV and Python"

[0355] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0356] System program processing flow

[0357] Step 1: Collecting top-tier athlete data

[0358] A server receives video of top athletes and data from sensor devices.

[0359] Input: Video data of top athletes, sensor device data (acceleration data, force data, position data).

[0360] Processing: Receives data from the Motion Capture system and accelerometer and stores it in a database.

[0361] Output: Saved performance data.

[0362] Step 2: Collect user data

[0363] The user inputs their own driving data (video and sensor device data) into the terminal.

[0364] The terminal transmits the input data to the server.

[0365] Input: User's driving data (video data, sensor data).

[0366] Processing: The user takes photos of their driving using a smartphone or other device and sends the data to the server, which then stores the received data in a database.

[0367] Output: Saved user driving data.

[0368] Step 3: Preprocessing the data

[0369] The server analyzes the collected video data frame by frame and converts it into a form that can be used for motion analysis.

[0370] Input: Video data.

[0371] Processing: Using Python, split the video data into frames and extract important frames.

[0372] Output: Preprocessed video data.

[0373] Step 4: Parameter extraction

[0374] The server extracts parameters such as stride length, ground contact time, and acceleration rate from the pre-processed data.

[0375] Input: Preprocessed video data and sensor data.

[0376] Processing: Using TensorFlow and PyTorch, we analyze and extract parameters such as stride length, ground contact time, and acceleration rate.

[0377] Output: The extracted parameter data.

[0378] Step 5: Data comparison

[0379] The server compares the data of top athletes with the user's data and analyzes differences and trends.

[0380] Input: Performance data of top athletes, performance data of the user.

[0381] Processing: Python scripts are used to compare the user's data with that of top athletes and analyze trends and differences.

[0382] Output: Data comparison results.

[0383] Step 6: Create a training menu

[0384] The server generates an optimal training menu for the user based on the analysis results.

[0385] Input: Data comparison results, extracted parameter data.

[0386] Processing: Using a reinforcement learning algorithm (e.g., Gym by OpenAI®), a personalized training menu is generated for the user.

[0387] Output: Training menu.

[0388] Step 7: Providing training menus

[0389] The server transmits the generated training menu to the user terminal.

[0390] Enter: Training Menu.

[0391] Processing: Format the training menu and send it to the user's device.

[0392] Output: Training menu displayed on the user's device.

[0393] Step 8: Implement training and collect progress data

[0394] The user performs training according to the training menu displayed on the terminal.

[0395] The user inputs the training result data into the terminal.

[0396] Input: Training implementation status and results data.

[0397] Processing: The user performs training using the device and inputs the results. The device then sends the data to the server.

[0398] Output: Progress data stored on the server.

[0399] Step 9: Analyze progress data and adjust menu

[0400] The server analyzes the progress data and evaluates the effectiveness of the training.

[0401] The server adjusts the training menu according to progress and sends updates to the user's terminal.

[0402] Input: Progress data.

[0403] Processing: Analytical algorithms are used to evaluate progress data and adjust training as needed.

[0404] Output: A tailored training menu.

[0405] Step 10: Collect and process emotion data

[0406] Facial expression recognition is performed using the device's camera while the user is training.

[0407] The emotion engine analyzes facial expression data to detect emotional states.

[0408] Input: User's facial expression data, biometric sensor data.

[0409] Processing: Analyzes the user's facial expressions using the OpenCV library to extract data on their emotional state, and measures their heart rate and other vital signs using biometric sensors.

[0410] Output: Emotion data.

[0411] Step 11: Feedback based on emotional data

[0412] The server adjusts the training menu based on the emotion data.

[0413] The server customizes the feedback based on the emotion data and transmits it to the user terminal.

[0414] Input: Emotion data.

[0415] Processing: Analyze emotional data and adjust training menus and feedback.

[0416] Output: Tailored training menus and customized feedback.

[0417] Prompt Sentence Examples

[0418] Prompt statement to analyze top-class running speed data:

[0419] "top runner speed data analysis TensorFlow"

[0420] The prompt sentence that the server uses to analyze the user's emotion data:

[0421] "user emotion data analysis with OpenCV and Python"

[0422] (Application example 2)

[0423] 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."

[0424] This invention relates to a system that provides a means for collecting and analyzing performance data of top workers. In particular, it aims to improve production efficiency by optimizing the operation of work robots in factories and dynamically adjusting work parameters based on the emotional state of the workers and robots. It also aims to improve the overall working environment by adjusting the work balance according to the worker's fatigue level and motivation.

[0425] The specific processing by the specific 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 and analyzing performance data of top athletes and workers, means for collecting and analyzing athletic performance data of users, means for designing an optimal training menu for users based on the analysis results, means for optimizing robot operation parameters based on worker performance data, and means for detecting the emotional states of workers and robots using an emotion engine and dynamically adjusting operation parameters. This makes it possible to establish an optimal work balance according to the state of workers and improve production efficiency.

[0426] A "top-class athlete" is someone who possesses outstanding skills and results in a particular sport and is highly regarded in the sporting community.

[0427] "Performance data" is a general term for data that includes dynamic and static information such as the efficiency of an athlete's or worker's movements and movements, how force is applied, and speed.

[0428] An "emotion engine" is a system that analyzes the emotional state of users or workers, extracts emotions such as joy, anger, and sadness as data, and provides that data in a usable form.

[0429] A "training menu" refers to a specific practice or training plan aimed at improving the performance of a user or athlete.

[0430] "User terminal" refers to a terminal device operated by a user, such as a smartphone, tablet, or PC, which is capable of communicating with the system and inputting data.

[0431] "Sensor device" refers to a device used to measure various data such as acceleration, position, and force, and is primarily used to obtain performance data.

[0432] A "robot" refers to an automated mechanical device that can perform programmed actions, and is used especially to perform tasks in factories.

[0433] "Movement parameters" include specific settings and indicators related to the robot's movements, and refer to information such as speed, position, and force.

[0434] "Emotional state" refers to the psychological and physiological state of a human or robot, including stress level, motivation, fatigue, etc.

[0435] The present invention relates to a system for collecting and analyzing performance data of top workers, particularly for optimizing robot operations in factories, and improving production efficiency by dynamically adjusting work parameters based on the emotional states of workers and robots.

[0436] System configuration

[0437] This system consists of a server, a user terminal, a sensor device, and an emotion engine.

[0438] server

[0439] The server mainly performs the following processes.

[0440] Performance Data Collection: Collect top worker performance data from sensor devices and videos.

[0441] Data analysis: Analyze the collected performance data and extract operating parameters.

[0442] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[0443] Optimization of robot motion: Optimize the robot's motion parameters based on the analysis results.

[0444] Emotion data processing: Receive data from the emotion engine and adjust the operating parameters according to the emotional state of the worker or robot.

[0445] User terminal

[0446] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[0447] Video recording and analysis: Workers' movements are videotaped and analyzed.

[0448] Display training menu: Displays the training menu sent from the server to the user.

[0449] Emotion data acquisition: Collect workers' emotion data through the emotion engine and send it to the server.

[0450] Receive Feedback: Receive feedback from the server and training menu updates.

[0451] Sensor Device

[0452] The sensor device is used to measure the movements and force applied to workers, and collects the following data:

[0453] Acceleration data: Measures acceleration during movement.

[0454] Force data: Measure the force applied during work.

[0455] Location data: Tracking worker locations.

[0456] Emotion Engine

[0457] The emotion engine is a system that recognizes the emotional state of workers and robots and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[0458] Example of operation process

[0459] Data collection

[0460] The server collects and analyzes videos of top workers and data from sensor devices, extracting operational parameters and providing data to optimize the robot's operation.

[0461] Data analysis and comparison

[0462] The server compares the data of top-class workers with other workers and analyzes the differences, especially differences in behavior in each phase, and visualizes the differences to clarify issues with the worker's performance.

[0463] Training menu generation

[0464] Based on the analysis results, the server generates the optimal training menu for the worker, and also optimizes the robot's operating parameters, enabling more efficient work.

[0465] Training implementation and progress monitoring

[0466] The user uses the device to practice according to the training menu. The server analyzes the progress data sent by the user and evaluates the effectiveness of the training. The server adjusts the training menu and the robot's operating parameters as necessary.

[0467] Example of using the emotion engine

[0468] Acquiring emotion data

[0469] While the user is training, facial expression recognition is performed using the device's camera. An emotion engine analyzes the worker's facial expressions and detects emotions such as joy, anger, and sadness. Biometric sensors (such as a heart rate monitor) are used to detect changes in stress levels and motivation.

[0470] Customized Feedback

[0471] The server adjusts the robot's operating parameters based on the emotional data. For example, if a worker feels tired, it will increase the robot's workload to reduce the worker's burden.

[0472] As described above, by dynamically adjusting parameters based on emotional states, it is possible to effectively improve work efficiency.

[0473] Prompt Sentence Examples

[0474] Collect work videos and sensor data of Employee A working on Line 1 in the factory and compare them with top-class employees. Based on the results, optimize the robot's operating parameters and make adjustments according to Employee A's fatigue level.

[0475] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0476] Step 1:

[0477] The server collects performance data of top workers from sensor devices and cameras. Specifically, it collects and stores videos of the workers' work movements and sensor data such as acceleration data, force data, and position data. In this process, data from the sensors and cameras is continuously sent to the server and stored appropriately.

[0478] Input: Video data and sensor data of workers at work

[0479] Output: Saved performance data

[0480] Step 2:

[0481] The server analyzes the collected performance data. Specifically, it uses a video analysis system to extract movement parameters (such as movement speed and accuracy), and a sensor data analysis system to analyze force application and position data. The analysis results are compiled as performance indicators.

[0482] Input: Stored performance data

[0483] Output: Analyzed operating parameters and performance indicators

[0484] Step 3:

[0485] The server adjusts the robot's operating parameters based on the analysis results. Specifically, it uses robot control software to reflect the analyzed operating parameters in the robot's control system and set the optimal speed and operating pattern.

[0486] Input: Analyzed operating parameters and performance indicators

[0487] Output: Adjusted robot movement parameters

[0488] Step 4:

[0489] The user terminal displays the training menu sent from the server, takes videos of the worker, and collects emotional data using an emotion engine. Specifically, the camera captures the worker's facial expressions and movements, and the emotion engine analyzes their emotional state (fatigue level, stress level, etc.).

[0490] Input: Worker training menu, emotion data collection

[0491] Output: Parsed emotion data

[0492] Step 5:

[0493] The server receives and analyzes the emotional data and training progress data sent from the user's device, and comprehensively evaluates the worker's emotional state and training progress, readjusting the training menu and robot's operating parameters as necessary.

[0494] Input: Parsed emotion data and training progress data

[0495] Output: Retuned training menu and robot movement parameters

[0496] Step 6:

[0497] The server sends the adjusted training menu and robot operating parameters to the user's terminal, which displays them to the worker. The worker then retrains based on this and sends the results to the server again.

[0498] Input: Retuned training menu and robot movement parameters

[0499] Output: New training menu and action instructions displayed on the user's device

[0500] 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.

[0501] 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.

[0502] 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.

[0503] [Second embodiment]

[0504] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0505] 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.

[0506] 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).

[0507] 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.

[0508] 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.

[0509] 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).

[0510] 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.

[0511] 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.

[0512] 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.

[0513] 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.

[0514] 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.

[0515] 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."

[0516] The present invention relates to a system that provides a means for collecting and analyzing performance data of top-class athletes. Specific embodiments of this system will be described below.

[0517] System configuration

[0518] This system mainly consists of a server, user terminals, and sensor devices.

[0519] server

[0520] The server is the main system that collects, stores, and analyzes performance data from top athletes and users, and generates training menus. The server performs the following processes:

[0521] Data collection: Receive and store video and sensor data from top athletes.

[0522] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate.

[0523] Data Comparison: Compare your performance data with that of top athletes to identify performance gaps.

[0524] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[0525] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[0526] User terminal

[0527] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[0528] Data entry: The user enters their own competition data and training results.

[0529] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc.

[0530] Display training menu: Displays the training menu sent from the server to the user.

[0531] Receive Feedback: Receive feedback from the server and training menu updates.

[0532] Sensor Device

[0533] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[0534] Acceleration data: Measures acceleration during movement.

[0535] Force data: Measures the force when touching the ground.

[0536] Location Data: Tracking the rider's location during a run.

[0537] Example of operation process

[0538] 1. Data Collection

[0539] The server collects and analyzes videos of top athletes and data from sensor devices.

[0540] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[0541] 2. Data analysis and comparison

[0542] The server compares the user's data with that of top athletes and analyzes the differences, for example, detecting that the user's starting sprint speed is slower than that of top athletes.

[0543] 3. Creation of training menu

[0544] Based on the analysis results, the server generates a training menu to improve the user's starting sprint, including starting sprint practice drills and short-distance sprint practice.

[0545] The server transmits the training menu to the user terminal.

[0546] 4. Training implementation and progress monitoring

[0547] The user uses the terminal to practice according to the training menu.

[0548] The user periodically inputs the results of training into the terminal and transmits them to the server.

[0549] The server analyzes the progress data sent by the user and evaluates the effectiveness of the training.

[0550] Based on the analysis results, the server adjusts the training menu as needed and sends updated information to the user's device.

[0551] By implementing this system, users can receive optimal training based on a scientific approach. Specifically, by utilizing data from top athletes, users can clarify individual performance issues and more effectively improve their athletic performance.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The server collects performance data from top athletes, including video footage and data from sensor devices, stores this data, and prepares it for analysis.

[0555] Step 2:

[0556] The server analyzes the collected data of top athletes, specifically extracting parameters such as stride length, ground contact time, and acceleration rate, and by extracting these parameters, it creates a performance profile of the athlete.

[0557] Step 3:

[0558] Users can record video of their own 100m sprint using a device such as a smartphone or tablet. The device then acquires the video data, converts it into the required format, and sends it to the server.

[0559] Step 4:

[0560] The device collects the user's running data using sensor devices (e.g., acceleration sensors and force sensors), analyzes the data, and extracts parameters such as stride length and acceleration rate. The data is then sent to a server.

[0561] Step 5:

[0562] The server receives the user's competitive performance data and compares it with that of top athletes. In particular, it analyzes differences in each phase, such as the start dash, acceleration, top speed, and finish. By visualizing the differences, it is possible to clarify the user's performance issues.

[0563] Step 6:

[0564] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting dash is an issue, starting dash practice drills and short distance sprint practice will be added to the menu.

[0565] Step 7:

[0566] The server sends the generated training menu to the user's device, which receives it and displays the training menu to the user.

[0567] Step 8:

[0568] The user uses the device to practice according to the training menu, inputs the results of the practice and feedback into the device, and sends the data to the server.

[0569] Step 9:

[0570] The server receives and analyzes the user's training progress data, and based on the analysis results, evaluates the effectiveness of the current training menu and adjusts the training menu as necessary.

[0571] Step 10:

[0572] The server sends the adjusted training menu to the user's device, which receives it and displays it to the user. This process is repeated until the user's performance is optimized.

[0573] Example 1

[0574] 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."

[0575] Conventional training systems lack scientific data collection and analysis to improve user performance, making it difficult to provide individually optimized training menus. Furthermore, performance improvement tends to be delayed due to ineffective monitoring and feedback of the user's training progress. The present invention aims to solve these problems by providing a system that compares the user's performance data with that of top athletes and provides an optimal training menu.

[0576] 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.

[0577] In this invention, the server includes means for collecting and analyzing performance data of top-class athletes, means for collecting and analyzing the user's athletic performance data based on the performance data of the top-class athletes, means for designing an optimal training menu for the user based on the analysis results, means for providing the training menu to a user terminal, means for monitoring the user's training progress and adjusting the training menu, means for extracting performance parameters such as stride length, ground contact time, and acceleration rate using the collected video data and sensor data, and means for comparing the user's performance data with data of top-class athletes and identifying performance differences. This allows the user to train based on an optimal training menu that is scientifically backed, thereby efficiently improving their athletic performance.

[0578] A "top-class athlete" is an outstanding athlete who demonstrates superior performance that exceeds certain standards and is recognized based on the evaluations of official organizations and their achievements in competitive events.

[0579] "Performance Data" refers to quantitative data relating to an athlete's physical movement and motion, including measurements such as stride length, ground contact time, and acceleration rate.

[0580] The "analyzing means" refers to a method or device for analyzing collected data and extracting useful information or parameters, and includes, for example, algorithms, software, hardware, and the like.

[0581] "User" refers to an individual or group who intends to improve their athletic performance by using this system.

[0582] A "user terminal" is an electronic device used by a user to operate the system, and specifically includes a smartphone, tablet, PC, etc.

[0583] "Training menu" refers to specific practice methods and drills designed to improve a user's athletic performance.

[0584] "Monitoring means" refers to methods or equipment for tracking a user's training progress and collecting and evaluating the necessary data, and includes, for example, software, sensor devices, etc.

[0585] "Video data" refers to video information captured on camera of an athlete's movements, which is usually stored in digital format and used for analysis.

[0586] "Sensor data" refers to various measurement data collected by sensor devices, and specifically includes acceleration, force, position information, etc.

[0587] "Performance parameters" refer to indicators that quantitatively show an athlete's movement characteristics, and specifically include stride length, ground contact time, acceleration speed, etc.

[0588] "Means for identifying differences" refers to methods or devices for comparing the performance data of a top-class athlete with that of the user and clarifying the differences or shortcomings between the two.

[0589] The present invention relates to a system that collects and analyzes performance data from top athletes to provide users with optimal training programs. This system is primarily composed of a server, user terminals, and sensor devices.

[0590] server

[0591] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus.

[0592] 1. Data Collection

[0593] The server receives and stores videos of top athletes and data from sensor devices, specifically MP4 videos and acceleration, force, and position data (e.g., in CSV format).

[0594] It also receives and stores driving data (video and sensor data) provided by the user, and transmits the data in real time via Bluetooth.

[0595] 2. Data Analysis

[0596] The server analyzes the collected data and extracts performance parameters such as stride length, ground contact time, and acceleration rate using Python's NumPy, Pandas, and OpenCV libraries.

[0597] For example, in video analysis, a skeleton detection algorithm using OpenCV is applied to calculate performance parameters from each frame.

[0598] 3. Data Comparison

[0599] The server compares the user's data with that of top athletes and identifies performance differences, for example detecting that the user's ground contact time is 0.15 seconds longer than that of top athletes.

[0600] The results of this comparison are statistically processed using libraries such as SciPy.

[0601] 4. Training menu generation

[0602] The server then generates the optimal training menu for the user based on the analysis results. For example, to improve starting speed, the server designs a menu that includes "reaction speed training" and "acceleration training."

[0603] The generated menu is sent to the user terminal in JSON format.

[0604] 5. Progress monitoring

[0605] The server analyzes the training progress data sent by the user, evaluates the effectiveness of the training, and provides feedback based on the results using statistical analysis and machine learning models.

[0606] For example, verify that the user's acceleration speed improved by 0.5 m / s over two weeks.

[0607] User terminal

[0608] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[0609] Data Entry

[0610] Users input their own competition data and training results, take videos using a dedicated app, and input sensor data.

[0611] The entered data is sent to the server in real time.

[0612] Video recording and analysis

[0613] The app automatically analyzes the user's stride length, ground contact time, and other data by recording video of the user's run. OpenCV technology is used for video analysis.

[0614] Training menu display

[0615] The training menu received from the server is displayed to the user, including specific training content and goals.

[0616] Receive feedback

[0617] Users receive feedback from the server and training menu updates, allowing them to adjust their training accordingly.

[0618] Sensor Device

[0619] The sensor device measures the athlete's movements and the force applied, and mainly collects the following data:

[0620] Acceleration Data

[0621] Acceleration during movement is measured and sent to the server in real time. A Bluetooth-enabled IMU sensor (e.g., MPU-6050) is used.

[0622] Force Data

[0623] Measure the force when the robot touches the ground. Use a force sensor (e.g., FSR sensor).

[0624] Location data

[0625] GPS tracking devices are used to obtain the location of athletes, for example GPS watches.

[0626] Specific Examples

[0627] Examples of data collection

[0628] An example of the data the server receives from a top athlete is a 10-second video of a sprint and acceleration data for the same period.

[0629] Examples of data analysis and comparison

[0630] The server compares the user's starting speed (e.g., 6.2 m / s) with the speed of a top-class athlete (e.g., 7.5 m / s) and detects that the user is behind.

[0631] Example of training menu generation

[0632] Examples of training menus generated by the server include "start dash strengthening drills" and "200m short distance sprint practice."

[0633] Prompt Sentence Examples

[0634] "Compare the starting speed of a top-class athlete with the user's speed and generate a specific training menu to improve the user's speed."

[0635] This system allows users to receive scientifically backed training plans using data from top athletes, allowing them to effectively improve their own athletic performance.

[0636] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0637] Step 1:

[0638] The server collects performance data from top athletes. Top athletes shoot videos using a dedicated app and upload them to the server in MP4 format. At the same time, data collected by sensor devices (accelerometers, force sensors, GPS tracking devices) is sent to the server in CSV format. The inputs are video files and sensor data, which the server saves. The output is the saved video and sensor data.

[0639] Step 2:

[0640] The server collects the user's performance data. The user records their own running video using a dedicated app and uploads it to the server in MP4 format. The server also receives acceleration data, force data, and position data from the sensor device and saves them in CSV format. The inputs are the user's video file and sensor data, which the server saves. The output is the saved user video and sensor data.

[0641] Step 3:

[0642] Performance parameters are extracted using the video data and sensor data collected by the server. The server uses Python's NumPy, Pandas, and OpenCV libraries to analyze the video and calculate stride length, ground contact time, and acceleration rate from each frame. It also processes the acceleration data with a low-pass filter and integrates the force data to calculate impact force. The input is the saved video data and sensor data, which the server analyzes to extract various performance parameters. The output is a list of each performance parameter.

[0643] Step 4:

[0644] The server compares the user's data with the performance data of top athletes. It compares the extracted performance parameters and identifies the difference in the user's performance. This comparison is performed statistically using the SciPy library. For example, it confirms that the user's ground contact time is 0.15 seconds longer than the top athletes. The input is a list of each performance parameter, and the server compares them to identify the difference. The output is a list of the differences.

[0645] Step 5:

[0646] The server generates an optimal training menu for the user based on the analysis results. The training menu is designed based on the areas the user needs to improve, and includes specific practice drills and training content. For example, improving a quick start could include "reaction speed training" and "acceleration training." The generated menu is sent to the user's device in JSON format. The input is a list of differences, and the server generates a training plan based on this. The output is a training menu JSON.

[0647] Step 6:

[0648] The user practices according to the training menu on the device. After each training session, the user enters the results into a dedicated app and sends the data to the server. For example, reaction time is entered as the result of "reaction speed training." The input is the user's training result data, which is sent to the server via the user's device. The output is the sent result data.

[0649] Step 7:

[0650] The server analyzes the user's progress data and evaluates the effectiveness of the training menu. It uses statistical analysis and machine learning models to evaluate the user's progress and adjusts the training menu as needed. For example, it verifies that acceleration speed has improved by 0.5 m / s over two weeks. The input is the user's progress data, which the server analyzes and evaluates the effectiveness. The output is the analysis results and, if necessary, an adjusted training menu.

[0651] Step 8:

[0652] The server sends the adjusted training menu to the user's terminal. The updated menu is provided to the user in the form of a notification to help them plan their next training. The input is the adjusted training menu, which the server sends to the user's terminal. The output is an updated notification that is displayed on the user's terminal.

[0653] (Application example 1)

[0654] 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."

[0655] The present invention aims to optimize the operational efficiency and maintenance effectiveness of users' automated equipment by utilizing top-class performance data. However, current automated equipment management systems are unable to effectively utilize top-class performance data, resulting in issues such as insufficient operational improvement and maintenance optimization. Furthermore, there is a lack of a mechanism for evaluating the effectiveness of maintenance performed by users in real time and providing appropriate feedback. This results in issues such as insufficient improvement of automated equipment efficiency and reduction of downtime.

[0656] 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.

[0657] In this invention, the server includes means for collecting and analyzing performance data of top-class automated equipment, means for collecting and analyzing performance data of a user's automated equipment, and means for designing an optimal operation improvement and maintenance menu for the user based on the analysis results, thereby enabling the operating efficiency of the user's automated equipment to approach that of top-class equipment.

[0658] "Top-class automated equipment" refers to an automated equipment system with the highest level of production capacity and efficiency.

[0659] "Performance Data" means data relating to the performance of automated equipment, such as its operating condition, efficiency, speed, and energy consumption.

[0660] "User's automated equipment" refers to the automated equipment system operated by the user.

[0661] A "sensor device" is a measuring device used to measure and collect operational data of automated equipment.

[0662] "Operational Improvement" refers to specific adjustments or modifications made to improve the performance of automated equipment.

[0663] A "maintenance menu" refers to a plan and instructions for a series of maintenance work such as inspection, repair, and cleaning of equipment.

[0664] "Analysis means" refers to technology or software used to analyze collected data and identify operational efficiency or abnormal behavior.

[0665] "Monitoring" refers to the continuous observation and recording of the performance and maintenance progress of a user's automated equipment.

[0666] A "user terminal" is a device (such as a smartphone or tablet) that displays maintenance menus and feedback and transmits input data to the server.

[0667] "Operational data" refers to detailed parameters such as speed, position, and force that automated equipment generates during operation.

[0668] MODE FOR CARRYING OUT THE INVENTION

[0669] System Program

[0670] The system program that realizes this application example consists of the following: The server collects and analyzes performance data of automated equipment, and generates and provides optimal operation improvement and maintenance menus to users. The user terminal has the function of displaying this menu and monitoring progress. Sensor devices also collect data.

[0671] Explanation of the generated program processing

[0672] The server interacts with sensor devices to collect performance data of the automated equipment. The sensor devices may include acceleration sensors, force sensors, position tracking devices, etc. These devices use common sensor devices provided by known hardware manufacturers.

[0673] The server uses data analysis libraries such as NumPy to analyze the collected performance data, calculating parameters such as operating speed, accuracy, and energy consumption, and comparing them with data from leading automated equipment.

[0674] The user terminal uses a Python program to display operation improvement and maintenance menus. Training menus and feedback are updated in real time based on instructions from the server. This allows the user terminal to accurately monitor the equipment status and instruct the user to carry out maintenance as necessary.

[0675] The server also monitors users' maintenance progress and evaluates the collected data in real time, including using generative AI models to predict and generate future improvement measures.

[0676] Specific examples

[0677] For example, operational data for a robot arm used in a factory is collected, analyzed, and compared with data for the most efficient robot arm. If delays in operation or a decrease in accuracy are found based on the analysis results, a menu for improving these issues is provided to the user's terminal. The user follows this menu to carry out maintenance on the robot arm, thereby improving the operational efficiency of the robot arm.

[0678] Prompt Sentence Examples

[0679] Generate a menu for optimizing the operation of a factory robot based on the following data:

[0680] User data: [example data]

[0681] Top-class robot data: [Data example]

[0682] Analyze the differences between each robot and design a maintenance menu based on that.

[0683] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0684] Step 1: Data collection

[0685] The server collects performance data of automated equipment from sensor devices. In this step, acceleration, force, and position data are collected. The input is real-time data from the sensor devices, and the output is the collected performance data. Specifically, the server sends a request to the sensor device API and receives the data as a response.

[0686] Step 2: Data analysis

[0687] Important parameters such as operating speed, accuracy, and energy consumption are extracted from the performance data collected by the server. In this step, data analysis libraries such as NumPy are used. The input is the collected performance data, and the output is the extracted parameters. Specifically, the data is filtered and each parameter is calculated.

[0688] Step 3: Data comparison

[0689] The server compares the performance data of the user's automated equipment with the data of top-class automated equipment. In this step, the difference for each parameter is calculated. The input is the user's performance data and the top-class data, and the output is the difference data for each parameter. Specifically, the server calculates the difference for each parameter and outputs it as a list.

[0690] Step 4: Generate maintenance menu

[0691] Based on the results of the data comparison, the server generates an optimal operation improvement and maintenance menu for the user. In this step, analysis is performed using a generative AI model. The input is differential data for each parameter, and the output is a specific maintenance menu. Specifically, a prompt sentence is input into the generative AI model, and the menu is output in text format.

[0692] Step 5: Provide maintenance menu

[0693] The server provides the generated maintenance menu to the user terminal. In this step, an HTTP request is used. The input is the maintenance menu, and the output is the menu information sent to the user terminal. Specifically, the server sends a request to the API of the user terminal and sends the menu.

[0694] Step 6: Perform and report maintenance

[0695] The user performs maintenance according to the maintenance menu provided by the user terminal. In this step, the user inputs the maintenance progress into the terminal. The input is the user's maintenance progress data, and the output is the progress data sent to the server. In specific operations, the user inputs data using the terminal interface and sends it to the server.

[0696] Step 7: Progress monitoring and menu adjustments

[0697] The server analyzes the maintenance progress data sent by the user and evaluates the effectiveness of the maintenance. In this step, the maintenance menu is adjusted as necessary. The input is the user's progress data, and the output is the adjusted maintenance menu. Specifically, the server analyzes the progress data and generates a new menu using a generative AI model.

[0698] 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.

[0699] The present invention relates to a system that provides a means for collecting and analyzing performance data of top athletes, and in particular to a system that incorporates an emotion engine that recognizes the emotions of users. Specific embodiments of this system will be described below.

[0700] System configuration

[0701] This system mainly consists of a server, a user terminal, a sensor device, and an emotion engine.

[0702] server

[0703] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus. It also receives user emotional data and adjusts training menus and feedback based on this data. The server performs the following processes:

[0704] Data collection: Receive and store video and sensor data from top athletes.

[0705] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate.

[0706] Data Comparison: Compare your performance data with that of top athletes to identify performance gaps.

[0707] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[0708] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[0709] Emotion data processing: Receives data from the emotion engine and provides training menus and feedback according to the user's emotional state.

[0710] User terminal

[0711] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[0712] Data entry: The user enters their own competition data and training results.

[0713] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc.

[0714] Display training menu: Displays the training menu sent from the server to the user.

[0715] Emotion data acquisition: Collect user's emotion data through the emotion engine and send it to the server.

[0716] Receive Feedback: Receive feedback from the server and training menu updates.

[0717] Sensor Device

[0718] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[0719] Acceleration data: Measures acceleration during movement.

[0720] Force data: Measures the force when touching the ground.

[0721] Location Data: Tracking the rider's location during a run.

[0722] Emotion Engine

[0723] The emotion engine is a system that recognizes the user's emotional state and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[0724] Example of operation process

[0725] 1. Data Collection

[0726] The server collects and analyzes videos of top athletes and data from sensor devices.

[0727] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[0728] 2. Data analysis and comparison

[0729] The server compares the user's data with that of top athletes and analyzes the differences, particularly in each phase of the race, such as the start, acceleration, top speed, and finish. By visualizing the differences, it is possible to clarify any issues with the user's performance.

[0730] 3. Creation of training menu

[0731] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting sprints are an issue, starting sprint practice drills and short-distance sprint practice drills will be added to the menu.

[0732] The server transmits the training menu to the user terminal.

[0733] 4. Training implementation and progress monitoring

[0734] The user uses the terminal to practice according to the training menu.

[0735] The user periodically inputs the results of training into the terminal and transmits the data to the server.

[0736] The server analyzes the progress data sent by the user and evaluates the effectiveness of the training.

[0737] Based on the analysis results, the server adjusts the training menu as needed and sends updated information to the user's device.

[0738] Example of using the emotion engine

[0739] 1. Acquiring Emotion Data

[0740] During training, the user uses the device's camera to recognize facial expressions, and the emotion engine analyzes the user's facial expressions to detect emotions such as joy, anger, and sadness.

[0741] The emotion engine uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[0742] 2. Customized feedback

[0743] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of training if the user feels fatigued.

[0744] The server customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[0745] As described above, this system utilizes data from top-class athletes to not only optimize individual users' performance, but also provide training menus and feedback that take the user's emotions into consideration, thereby achieving more effective performance improvement.

[0746] The processing flow will be explained below.

[0747] Step 1:

[0748] The server collects performance data from top athletes, including video footage and data from sensor devices, stores this data, and prepares it for analysis.

[0749] Step 2:

[0750] The server analyzes the collected data of top athletes, specifically extracting parameters such as stride length, ground contact time, and acceleration rate, and then creates a performance profile of the athlete.

[0751] Step 3:

[0752] Users record video of their own 100m sprint using a device such as a smartphone or tablet. The device acquires the video data, converts it into the required format, and sends it to the server.

[0753] Step 4:

[0754] The device collects the user's running data using sensor devices (e.g., acceleration sensors and force sensors). The device analyzes this data and extracts parameters such as stride length and acceleration rate. The data is then sent to a server.

[0755] Step 5:

[0756] The server receives the user's competitive performance data and compares it with that of top athletes. In particular, it analyzes differences in each phase, such as the start dash, acceleration, top speed, and finish. By visualizing the differences, it clarifies the user's performance issues.

[0757] Step 6:

[0758] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting dash training is an issue, starting dash training drills and short distance sprint training will be added to the menu.

[0759] Step 7:

[0760] The server transmits the generated training menu to the user terminal, which receives it and displays the training menu to the user.

[0761] Step 8:

[0762] The user uses the device to practice according to the training menu, inputs the practice results and feedback into the device, and sends the data to the server.

[0763] Step 9:

[0764] The server receives and analyzes the user's training progress data, evaluates the effectiveness of the current training program based on the analysis results, and adjusts the training program as necessary.

[0765] Step 10:

[0766] The server transmits the adjusted training menu to the user terminal, which receives it and displays it to the user.

[0767] Step 11:

[0768] While the user is training, facial expression recognition is performed using the device's camera. The emotion engine analyzes the user's facial expressions and detects emotions such as joy, anger, and sadness. The emotion engine also uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[0769] Step 12:

[0770] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of the training if the user feels fatigued. The server also customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[0771] Example 2

[0772] 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."

[0773] Conventional athlete training systems have limitations in their approach to collecting performance data from top athletes and providing feedback to users. In particular, they lack the functionality to monitor users' training progress and emotional state in real time and adjust training menus at appropriate times. As a result, users' motivation and results are not fully realized, leading to problems in which effective training cannot be achieved.

[0774] The specification processing by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and analyzing performance data of top-class athletes, means for collecting and analyzing performance data of the user, means for designing an optimal training menu for the user based on the analysis results, and means for collecting emotional data of the user and adjusting the training menu based on the collected data. This makes it possible to provide a detailed training menu that takes into account the user's training progress and emotional state.

[0775] A "top-class athlete" is an athlete who has outstanding performance or ability in a particular sport.

[0776] "Performance data" refers to data that includes an athlete's movements and physiological parameters, such as stride length, ground contact time, and acceleration rate.

[0777] "Collection" refers to the act of obtaining and storing the necessary data.

[0778] "Analysis" refers to the act of quantifying collected data and extracting specific parameters and trends.

[0779] "User" refers to an individual athlete who uses this system.

[0780] "Training menu" refers to a plan that specifies the content of the practice and training that an athlete should perform.

[0781] "User terminal" refers to a mobile device or computer used by a user to display the training menu and input data.

[0782] "Monitoring" refers to the act of continuously collecting data and tracking changes.

[0783] "Emotional data" refers to data that indicates the athlete's emotional state, such as joy, anger, or stress.

[0784] An "emotion engine" refers to a system that analyzes a user's emotional state from collected data and outputs it as emotional data.

[0785] MODE FOR CARRYING OUT THE INVENTION

[0786] The present invention relates to a system that provides a means for collecting and analyzing performance data of top athletes, and in particular to a system that incorporates an emotion engine that recognizes the emotions of users. Specific embodiments of this system will be described below.

[0787] System configuration

[0788] This system mainly consists of a server, a user terminal, a sensor device, and an emotion engine.

[0789] server

[0790] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus. It also receives user emotional data and adjusts training menus and feedback based on this data. The server performs the following processes:

[0791] Data collection: Receive and store video and sensor data from top athletes, specifically using a motion capture system and accelerometers.

[0792] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate. This analysis is performed using machine learning tools such as TensorFlow and PyTorch.

[0793] Data comparison: Compare your performance data with that of top athletes to identify performance differences. Implement the analysis algorithm using Python.

[0794] Training menu generation: Based on the analysis results, we design an optimal training menu for the user. We utilize a reinforcement learning algorithm to generate an efficient training menu.

[0795] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[0796] Emotion data processing: Receives data from the emotion engine and provides training menus and feedback according to the user's emotional state.

[0797] User terminal

[0798] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[0799] Data entry: The user enters their own competition data and training results.

[0800] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc. using the Google Cloud Vision API.

[0801] Display training menu: Displays the training menu sent from the server to the user.

[0802] Emotion data acquisition: Collect user emotion data through the emotion engine and send it to the server. OpenCV is used for facial expression recognition.

[0803] Receive Feedback: Receive feedback from the server and training menu updates.

[0804] Sensor Device

[0805] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[0806] Acceleration data: Measures acceleration during movement.

[0807] Force data: Measures the force when touching the ground.

[0808] Location Data: Tracking the rider's location during a run.

[0809] Emotion Engine

[0810] The emotion engine is a system that recognizes the user's emotional state and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[0811] Example of operation process

[0812] 1. Data Collection

[0813] The server collects and stores videos of top athletes and data from sensor devices, including a Motion Capture system and accelerometers.

[0814] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[0815] 2. Data analysis and comparison

[0816] The server compares the data of the user with that of top athletes and analyzes the differences, particularly in terms of differences in each phase, such as the start dash, acceleration, top speed, and finish. TensorFlow and PyTorch are used for the analysis.

[0817] 3. Creation of training menu

[0818] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting sprints are an issue, starting sprint practice drills and short-distance sprint practice drills will be added to the menu. This is done using a reinforcement learning algorithm.

[0819] The server transmits the training menu to the user terminal.

[0820] 4. Training implementation and progress monitoring

[0821] The user uses the terminal to practice according to the training menu.

[0822] The user periodically inputs the results of training into the terminal and transmits the data to the server.

[0823] The server analyzes the progress data sent from the user, evaluates the effectiveness of the training, and adjusts the training menu based on the progress data.

[0824] Example of using the emotion engine

[0825] 1. Acquiring Emotion Data

[0826] During training, the user uses the device's camera to recognize facial expressions. The emotion engine analyzes the user's facial expressions and detects emotions such as joy, anger, and sadness. OpenCV is used for facial recognition.

[0827] The emotion engine uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[0828] 2. Customized feedback

[0829] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of training if the user feels fatigued.

[0830] The server customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[0831] Prompt Sentence Examples

[0832] Prompt statement to analyze top-class running speed data:

[0833] "top runner speed data analysis TensorFlow"

[0834] The prompt sentence that the server uses to analyze the user's emotion data:

[0835] "user emotion data analysis with OpenCV and Python"

[0836] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0837] System program processing flow

[0838] Step 1: Collecting top-tier athlete data

[0839] A server receives video of top athletes and data from sensor devices.

[0840] Input: Video data of top athletes, sensor device data (acceleration data, force data, position data).

[0841] Processing: Receives data from the Motion Capture system and accelerometer and stores it in a database.

[0842] Output: Saved performance data.

[0843] Step 2: Collect user data

[0844] The user inputs their own driving data (video and sensor device data) into the terminal.

[0845] The terminal transmits the input data to the server.

[0846] Input: User's driving data (video data, sensor data).

[0847] Processing: The user takes photos of their driving using a smartphone or other device and sends the data to the server, which then stores the received data in a database.

[0848] Output: Saved user driving data.

[0849] Step 3: Preprocessing the data

[0850] The server analyzes the collected video data frame by frame and converts it into a form that can be used for motion analysis.

[0851] Input: Video data.

[0852] Processing: Using Python, split the video data into frames and extract important frames.

[0853] Output: Preprocessed video data.

[0854] Step 4: Parameter extraction

[0855] The server extracts parameters such as stride length, ground contact time, and acceleration rate from the pre-processed data.

[0856] Input: Preprocessed video data and sensor data.

[0857] Processing: Using TensorFlow and PyTorch, we analyze and extract parameters such as stride length, ground contact time, and acceleration rate.

[0858] Output: The extracted parameter data.

[0859] Step 5: Data comparison

[0860] The server compares the data of top athletes with the user's data and analyzes differences and trends.

[0861] Input: Performance data of top athletes, performance data of the user.

[0862] Processing: Python scripts are used to compare the user's data with that of top athletes and analyze trends and differences.

[0863] Output: Data comparison results.

[0864] Step 6: Create a training menu

[0865] The server generates an optimal training menu for the user based on the analysis results.

[0866] Input: Data comparison results, extracted parameter data.

[0867] Processing: Uses a reinforcement learning algorithm (e.g., OpenAI's Gym) to generate a personalized training menu for the user.

[0868] Output: Training menu.

[0869] Step 7: Providing training menus

[0870] The server transmits the generated training menu to the user terminal.

[0871] Enter: Training Menu.

[0872] Processing: Format the training menu and send it to the user's device.

[0873] Output: Training menu displayed on the user's device.

[0874] Step 8: Implement training and collect progress data

[0875] The user performs training according to the training menu displayed on the terminal.

[0876] The user inputs the training result data into the terminal.

[0877] Input: Training implementation status and results data.

[0878] Processing: The user performs training using the device and inputs the results. The device then sends the data to the server.

[0879] Output: Progress data stored on the server.

[0880] Step 9: Analyze progress data and adjust menu

[0881] The server analyzes the progress data and evaluates the effectiveness of the training.

[0882] The server adjusts the training menu according to progress and sends updates to the user's terminal.

[0883] Input: Progress data.

[0884] Processing: Analytical algorithms are used to evaluate progress data and adjust training as needed.

[0885] Output: A tailored training menu.

[0886] Step 10: Collect and process emotion data

[0887] Facial expression recognition is performed using the device's camera while the user is training.

[0888] The emotion engine analyzes facial expression data to detect emotional states.

[0889] Input: User's facial expression data, biometric sensor data.

[0890] Processing: Analyzes the user's facial expressions using the OpenCV library to extract data on their emotional state, and measures their heart rate and other vital signs using biometric sensors.

[0891] Output: Emotion data.

[0892] Step 11: Feedback based on emotional data

[0893] The server adjusts the training menu based on the emotion data.

[0894] The server customizes the feedback based on the emotion data and transmits it to the user terminal.

[0895] Input: Emotion data.

[0896] Processing: Analyze emotional data and adjust training menus and feedback.

[0897] Output: Tailored training menus and customized feedback.

[0898] Prompt Sentence Examples

[0899] Prompt statement to analyze top-class running speed data:

[0900] "top runner speed data analysis TensorFlow"

[0901] The prompt sentence that the server uses to analyze the user's emotion data:

[0902] "user emotion data analysis with OpenCV and Python"

[0903] (Application example 2)

[0904] 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."

[0905] This invention relates to a system that provides a means for collecting and analyzing performance data of top workers. In particular, it aims to improve production efficiency by optimizing the operation of work robots in factories and dynamically adjusting work parameters based on the emotional state of the workers and robots. It also aims to improve the overall working environment by adjusting the work balance according to the worker's fatigue level and motivation.

[0906] The specific processing by the specific 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 and analyzing performance data of top athletes and workers, means for collecting and analyzing athletic performance data of users, means for designing an optimal training menu for users based on the analysis results, means for optimizing robot operation parameters based on worker performance data, and means for detecting the emotional states of workers and robots using an emotion engine and dynamically adjusting operation parameters. This makes it possible to establish an optimal work balance according to the state of workers and improve production efficiency.

[0907] A "top-class athlete" is someone who possesses outstanding skills and results in a particular sport and is highly regarded in the sporting community.

[0908] "Performance data" is a general term for data that includes dynamic and static information such as the efficiency of an athlete's or worker's movements and movements, how force is applied, and speed.

[0909] An "emotion engine" is a system that analyzes the emotional state of users or workers, extracts emotions such as joy, anger, and sadness as data, and provides that data in a usable form.

[0910] A "training menu" refers to a specific practice or training plan aimed at improving the performance of a user or athlete.

[0911] "User terminal" refers to a terminal device operated by a user, such as a smartphone, tablet, or PC, which is capable of communicating with the system and inputting data.

[0912] "Sensor device" refers to a device used to measure various data such as acceleration, position, and force, and is primarily used to obtain performance data.

[0913] A "robot" refers to an automated mechanical device that can perform programmed actions, and is used especially to perform tasks in factories.

[0914] "Movement parameters" include specific settings and indicators related to the robot's movements, and refer to information such as speed, position, and force.

[0915] "Emotional state" refers to the psychological and physiological state of a human or robot, including stress level, motivation, fatigue, etc.

[0916] The present invention relates to a system for collecting and analyzing performance data of top workers, particularly for optimizing robot operations in factories, and improving production efficiency by dynamically adjusting work parameters based on the emotional states of workers and robots.

[0917] System configuration

[0918] This system consists of a server, a user terminal, a sensor device, and an emotion engine.

[0919] server

[0920] The server mainly performs the following processes.

[0921] Performance Data Collection: Collect top worker performance data from sensor devices and videos.

[0922] Data analysis: Analyze the collected performance data and extract operating parameters.

[0923] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[0924] Optimization of robot motion: Optimize the robot's motion parameters based on the analysis results.

[0925] Emotion data processing: Receive data from the emotion engine and adjust the operating parameters according to the emotional state of the worker or robot.

[0926] User terminal

[0927] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[0928] Video recording and analysis: Workers' movements are videotaped and analyzed.

[0929] Display training menu: Displays the training menu sent from the server to the user.

[0930] Emotion data acquisition: Collect workers' emotion data through the emotion engine and send it to the server.

[0931] Receive Feedback: Receive feedback from the server and training menu updates.

[0932] Sensor Device

[0933] The sensor device is used to measure the movements and force applied to workers, and collects the following data:

[0934] Acceleration data: Measures acceleration during movement.

[0935] Force data: Measure the force applied during work.

[0936] Location data: Tracking worker locations.

[0937] Emotion Engine

[0938] The emotion engine is a system that recognizes the emotional state of workers and robots and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[0939] Example of operation process

[0940] Data collection

[0941] The server collects and analyzes videos of top workers and data from sensor devices, extracting operational parameters and providing data to optimize the robot's operation.

[0942] Data analysis and comparison

[0943] The server compares the data of top-class workers with other workers and analyzes the differences, especially differences in behavior in each phase, and visualizes the differences to clarify issues with the worker's performance.

[0944] Training menu generation

[0945] Based on the analysis results, the server generates the optimal training menu for the worker, and also optimizes the robot's operating parameters, enabling more efficient work.

[0946] Training implementation and progress monitoring

[0947] The user uses the device to practice according to the training menu. The server analyzes the progress data sent by the user and evaluates the effectiveness of the training. The server adjusts the training menu and the robot's operating parameters as necessary.

[0948] Example of using the emotion engine

[0949] Acquiring emotion data

[0950] While the user is training, facial expression recognition is performed using the device's camera. An emotion engine analyzes the worker's facial expressions and detects emotions such as joy, anger, and sadness. Biometric sensors (such as a heart rate monitor) are used to detect changes in stress levels and motivation.

[0951] Customized Feedback

[0952] The server adjusts the robot's operating parameters based on the emotional data. For example, if a worker feels tired, it will increase the robot's workload to reduce the worker's burden.

[0953] As described above, by dynamically adjusting parameters based on emotional states, it is possible to effectively improve work efficiency.

[0954] Prompt Sentence Examples

[0955] Collect work videos and sensor data of Employee A working on Line 1 in the factory and compare them with top-class employees. Based on the results, optimize the robot's operating parameters and make adjustments according to Employee A's fatigue level.

[0956] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0957] Step 1:

[0958] The server collects performance data of top workers from sensor devices and cameras. Specifically, it collects and stores videos of the workers' work movements and sensor data such as acceleration data, force data, and position data. In this process, data from the sensors and cameras is continuously sent to the server and stored appropriately.

[0959] Input: Video data and sensor data of workers at work

[0960] Output: Saved performance data

[0961] Step 2:

[0962] The server analyzes the collected performance data. Specifically, it uses a video analysis system to extract movement parameters (such as movement speed and accuracy), and a sensor data analysis system to analyze force application and position data. The analysis results are compiled as performance indicators.

[0963] Input: Stored performance data

[0964] Output: Analyzed operating parameters and performance indicators

[0965] Step 3:

[0966] The server adjusts the robot's operating parameters based on the analysis results. Specifically, it uses robot control software to reflect the analyzed operating parameters in the robot's control system and set the optimal speed and operating pattern.

[0967] Input: Analyzed operating parameters and performance indicators

[0968] Output: Adjusted robot movement parameters

[0969] Step 4:

[0970] The user terminal displays the training menu sent from the server, takes videos of the worker, and collects emotional data using an emotion engine. Specifically, the camera captures the worker's facial expressions and movements, and the emotion engine analyzes their emotional state (fatigue level, stress level, etc.).

[0971] Input: Worker training menu, emotion data collection

[0972] Output: Parsed emotion data

[0973] Step 5:

[0974] The server receives and analyzes the emotional data and training progress data sent from the user's device, and comprehensively evaluates the worker's emotional state and training progress, readjusting the training menu and robot's operating parameters as necessary.

[0975] Input: Parsed emotion data and training progress data

[0976] Output: Retuned training menu and robot movement parameters

[0977] Step 6:

[0978] The server sends the adjusted training menu and robot operating parameters to the user's terminal, which displays them to the worker. The worker then retrains based on this and sends the results to the server again.

[0979] Input: Retuned training menu and robot movement parameters

[0980] Output: New training menu and action instructions displayed on the user's device

[0981] 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.

[0982] 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.

[0983] 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.

[0984] [Third embodiment]

[0985] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0986] 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.

[0987] 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).

[0988] 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.

[0989] 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.

[0990] 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).

[0991] 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.

[0992] 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.

[0993] 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.

[0994] 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.

[0995] 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.

[0996] 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."

[0997] The present invention relates to a system that provides a means for collecting and analyzing performance data of top-class athletes. Specific embodiments of this system will be described below.

[0998] System configuration

[0999] This system mainly consists of a server, user terminals, and sensor devices.

[1000] server

[1001] The server is the main system that collects, stores, and analyzes performance data from top athletes and users, and generates training menus. The server performs the following processes:

[1002] Data collection: Receive and store video and sensor data from top athletes.

[1003] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate.

[1004] Data Comparison: Compare your performance data with that of top athletes to identify performance gaps.

[1005] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[1006] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[1007] User terminal

[1008] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[1009] Data entry: The user enters their own competition data and training results.

[1010] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc.

[1011] Display training menu: Displays the training menu sent from the server to the user.

[1012] Receive Feedback: Receive feedback from the server and training menu updates.

[1013] Sensor Device

[1014] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[1015] Acceleration data: Measures acceleration during movement.

[1016] Force data: Measures the force when touching the ground.

[1017] Location Data: Tracking the rider's location during a run.

[1018] Example of operation process

[1019] 1. Data Collection

[1020] The server collects and analyzes videos of top athletes and data from sensor devices.

[1021] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[1022] 2. Data analysis and comparison

[1023] The server compares the user's data with that of top athletes and analyzes the differences, for example, detecting that the user's starting sprint speed is slower than that of top athletes.

[1024] 3. Creation of training menu

[1025] Based on the analysis results, the server generates a training menu to improve the user's starting sprint, including starting sprint practice drills and short-distance sprint practice.

[1026] The server transmits the training menu to the user terminal.

[1027] 4. Training implementation and progress monitoring

[1028] The user uses the terminal to practice according to the training menu.

[1029] The user periodically inputs the results of training into the terminal and transmits them to the server.

[1030] The server analyzes the progress data sent by the user and evaluates the effectiveness of the training.

[1031] Based on the analysis results, the server adjusts the training menu as needed and sends updated information to the user's device.

[1032] By implementing this system, users can receive optimal training based on a scientific approach. Specifically, by utilizing data from top athletes, users can clarify individual performance issues and more effectively improve their athletic performance.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] The server collects performance data from top athletes, including video footage and data from sensor devices, stores this data, and prepares it for analysis.

[1036] Step 2:

[1037] The server analyzes the collected data of top athletes, specifically extracting parameters such as stride length, ground contact time, and acceleration rate, and by extracting these parameters, it creates a performance profile of the athlete.

[1038] Step 3:

[1039] Users can record video of their own 100m sprint using a device such as a smartphone or tablet. The device then acquires the video data, converts it into the required format, and sends it to the server.

[1040] Step 4:

[1041] The device collects the user's running data using sensor devices (e.g., acceleration sensors and force sensors), analyzes the data, and extracts parameters such as stride length and acceleration rate. The data is then sent to a server.

[1042] Step 5:

[1043] The server receives the user's competitive performance data and compares it with that of top athletes. In particular, it analyzes differences in each phase, such as the start dash, acceleration, top speed, and finish. By visualizing the differences, it is possible to clarify the user's performance issues.

[1044] Step 6:

[1045] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting dash is an issue, starting dash practice drills and short distance sprint practice will be added to the menu.

[1046] Step 7:

[1047] The server sends the generated training menu to the user's device, which receives it and displays the training menu to the user.

[1048] Step 8:

[1049] The user uses the device to practice according to the training menu, inputs the results of the practice and feedback into the device, and sends the data to the server.

[1050] Step 9:

[1051] The server receives and analyzes the user's training progress data, and based on the analysis results, evaluates the effectiveness of the current training menu and adjusts the training menu as necessary.

[1052] Step 10:

[1053] The server sends the adjusted training menu to the user's device, which receives it and displays it to the user. This process is repeated until the user's performance is optimized.

[1054] Example 1

[1055] 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."

[1056] Conventional training systems lack scientific data collection and analysis to improve user performance, making it difficult to provide individually optimized training menus. Furthermore, performance improvement tends to be delayed due to ineffective monitoring and feedback of the user's training progress. The present invention aims to solve these problems by providing a system that compares the user's performance data with that of top athletes and provides an optimal training menu.

[1057] 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.

[1058] In this invention, the server includes means for collecting and analyzing performance data of top-class athletes, means for collecting and analyzing the user's athletic performance data based on the performance data of the top-class athletes, means for designing an optimal training menu for the user based on the analysis results, means for providing the training menu to a user terminal, means for monitoring the user's training progress and adjusting the training menu, means for extracting performance parameters such as stride length, ground contact time, and acceleration rate using the collected video data and sensor data, and means for comparing the user's performance data with data of top-class athletes and identifying performance differences. This allows the user to train based on an optimal training menu that is scientifically backed, thereby efficiently improving their athletic performance.

[1059] A "top-class athlete" is an outstanding athlete who demonstrates superior performance that exceeds certain standards and is recognized based on the evaluations of official organizations and their achievements in competitive events.

[1060] "Performance Data" refers to quantitative data relating to an athlete's physical movement and motion, including measurements such as stride length, ground contact time, and acceleration rate.

[1061] The "analyzing means" refers to a method or device for analyzing collected data and extracting useful information or parameters, and includes, for example, algorithms, software, hardware, and the like.

[1062] "User" refers to an individual or group who intends to improve their athletic performance by using this system.

[1063] A "user terminal" is an electronic device used by a user to operate the system, and specifically includes a smartphone, tablet, PC, etc.

[1064] "Training menu" refers to specific practice methods and drills designed to improve a user's athletic performance.

[1065] "Monitoring means" refers to methods or equipment for tracking a user's training progress and collecting and evaluating the necessary data, and includes, for example, software, sensor devices, etc.

[1066] "Video data" refers to video information captured on camera of an athlete's movements, which is usually stored in digital format and used for analysis.

[1067] "Sensor data" refers to various measurement data collected by sensor devices, and specifically includes acceleration, force, position information, etc.

[1068] "Performance parameters" refer to indicators that quantitatively show an athlete's movement characteristics, and specifically include stride length, ground contact time, acceleration speed, etc.

[1069] "Means for identifying differences" refers to methods or devices for comparing the performance data of a top-class athlete with that of the user and clarifying the differences or shortcomings between the two.

[1070] The present invention relates to a system that collects and analyzes performance data from top athletes to provide users with optimal training programs. This system is primarily composed of a server, user terminals, and sensor devices.

[1071] server

[1072] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus.

[1073] 1. Data Collection

[1074] The server receives and stores videos of top athletes and data from sensor devices, specifically MP4 videos and acceleration, force, and position data (e.g., in CSV format).

[1075] It also receives and stores driving data (video and sensor data) provided by the user, and transmits the data in real time via Bluetooth.

[1076] 2. Data Analysis

[1077] The server analyzes the collected data and extracts performance parameters such as stride length, ground contact time, and acceleration rate using Python's NumPy, Pandas, and OpenCV libraries.

[1078] For example, in video analysis, a skeleton detection algorithm using OpenCV is applied to calculate performance parameters from each frame.

[1079] 3. Data Comparison

[1080] The server compares the user's data with that of top athletes and identifies performance differences, for example detecting that the user's ground contact time is 0.15 seconds longer than that of top athletes.

[1081] The results of this comparison are statistically processed using libraries such as SciPy.

[1082] 4. Training menu generation

[1083] The server then generates the optimal training menu for the user based on the analysis results. For example, to improve starting speed, the server designs a menu that includes "reaction speed training" and "acceleration training."

[1084] The generated menu is sent to the user terminal in JSON format.

[1085] 5. Progress monitoring

[1086] The server analyzes the training progress data sent by the user, evaluates the effectiveness of the training, and provides feedback based on the results using statistical analysis and machine learning models.

[1087] For example, verify that the user's acceleration speed improved by 0.5 m / s over two weeks.

[1088] User terminal

[1089] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[1090] Data Entry

[1091] Users input their own competition data and training results, take videos using a dedicated app, and input sensor data.

[1092] The entered data is sent to the server in real time.

[1093] Video recording and analysis

[1094] The app automatically analyzes the user's stride length, ground contact time, and other data by recording video of the user's run. OpenCV technology is used for video analysis.

[1095] Training menu display

[1096] The training menu received from the server is displayed to the user, including specific training content and goals.

[1097] Receive feedback

[1098] Users receive feedback from the server and training menu updates, allowing them to adjust their training accordingly.

[1099] Sensor Device

[1100] The sensor device measures the athlete's movements and the force applied, and mainly collects the following data:

[1101] Acceleration Data

[1102] Acceleration during movement is measured and sent to the server in real time. A Bluetooth-enabled IMU sensor (e.g., MPU-6050) is used.

[1103] Force Data

[1104] Measure the force when the robot touches the ground. Use a force sensor (e.g., FSR sensor).

[1105] Location data

[1106] GPS tracking devices are used to obtain the location of athletes, for example GPS watches.

[1107] Specific Examples

[1108] Examples of data collection

[1109] An example of the data the server receives from a top athlete is a 10-second video of a sprint and acceleration data for the same period.

[1110] Examples of data analysis and comparison

[1111] The server compares the user's starting speed (e.g., 6.2 m / s) with the speed of a top-class athlete (e.g., 7.5 m / s) and detects that the user is behind.

[1112] Example of training menu generation

[1113] Examples of training menus generated by the server include "start dash strengthening drills" and "200m short distance sprint practice."

[1114] Prompt Sentence Examples

[1115] "Compare the starting speed of a top-class athlete with the user's speed and generate a specific training menu to improve the user's speed."

[1116] This system allows users to receive scientifically backed training plans using data from top athletes, allowing them to effectively improve their own athletic performance.

[1117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1118] Step 1:

[1119] The server collects performance data from top athletes. Top athletes shoot videos using a dedicated app and upload them to the server in MP4 format. At the same time, data collected by sensor devices (accelerometers, force sensors, GPS tracking devices) is sent to the server in CSV format. The inputs are video files and sensor data, which the server saves. The output is the saved video and sensor data.

[1120] Step 2:

[1121] The server collects the user's performance data. The user records their own running video using a dedicated app and uploads it to the server in MP4 format. The server also receives acceleration data, force data, and position data from the sensor device and saves them in CSV format. The inputs are the user's video file and sensor data, which the server saves. The output is the saved user video and sensor data.

[1122] Step 3:

[1123] Performance parameters are extracted using the video data and sensor data collected by the server. The server uses Python's NumPy, Pandas, and OpenCV libraries to analyze the video and calculate stride length, ground contact time, and acceleration rate from each frame. It also processes the acceleration data with a low-pass filter and integrates the force data to calculate impact force. The input is the saved video data and sensor data, which the server analyzes to extract various performance parameters. The output is a list of each performance parameter.

[1124] Step 4:

[1125] The server compares the user's data with the performance data of top athletes. It compares the extracted performance parameters and identifies the difference in the user's performance. This comparison is performed statistically using the SciPy library. For example, it confirms that the user's ground contact time is 0.15 seconds longer than the top athletes. The input is a list of each performance parameter, and the server compares them to identify the difference. The output is a list of the differences.

[1126] Step 5:

[1127] The server generates an optimal training menu for the user based on the analysis results. The training menu is designed based on the areas the user needs to improve, and includes specific practice drills and training content. For example, improving a quick start could include "reaction speed training" and "acceleration training." The generated menu is sent to the user's device in JSON format. The input is a list of differences, and the server generates a training plan based on this. The output is a training menu JSON.

[1128] Step 6:

[1129] The user practices according to the training menu on the device. After each training session, the user enters the results into a dedicated app and sends the data to the server. For example, reaction time is entered as the result of "reaction speed training." The input is the user's training result data, which is sent to the server via the user's device. The output is the sent result data.

[1130] Step 7:

[1131] The server analyzes the user's progress data and evaluates the effectiveness of the training menu. It uses statistical analysis and machine learning models to evaluate the user's progress and adjusts the training menu as needed. For example, it verifies that acceleration speed has improved by 0.5 m / s over two weeks. The input is the user's progress data, which the server analyzes and evaluates the effectiveness. The output is the analysis results and, if necessary, an adjusted training menu.

[1132] Step 8:

[1133] The server sends the adjusted training menu to the user's terminal. The updated menu is provided to the user in the form of a notification to help them plan their next training. The input is the adjusted training menu, which the server sends to the user's terminal. The output is an updated notification that is displayed on the user's terminal.

[1134] (Application example 1)

[1135] 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."

[1136] The present invention aims to optimize the operational efficiency and maintenance effectiveness of users' automated equipment by utilizing top-class performance data. However, current automated equipment management systems are unable to effectively utilize top-class performance data, resulting in issues such as insufficient operational improvement and maintenance optimization. Furthermore, there is a lack of a mechanism for evaluating the effectiveness of maintenance performed by users in real time and providing appropriate feedback. This results in issues such as insufficient improvement of automated equipment efficiency and reduction of downtime.

[1137] 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.

[1138] In this invention, the server includes means for collecting and analyzing performance data of top-class automated equipment, means for collecting and analyzing performance data of a user's automated equipment, and means for designing an optimal operation improvement and maintenance menu for the user based on the analysis results, thereby enabling the operating efficiency of the user's automated equipment to approach that of top-class equipment.

[1139] "Top-class automated equipment" refers to an automated equipment system with the highest level of production capacity and efficiency.

[1140] "Performance Data" means data relating to the performance of automated equipment, such as its operating condition, efficiency, speed, and energy consumption.

[1141] "User's automated equipment" refers to the automated equipment system operated by the user.

[1142] A "sensor device" is a measuring device used to measure and collect operational data of automated equipment.

[1143] "Operational Improvement" refers to specific adjustments or modifications made to improve the performance of automated equipment.

[1144] A "maintenance menu" refers to a plan and instructions for a series of maintenance work such as inspection, repair, and cleaning of equipment.

[1145] "Analysis means" refers to technology or software used to analyze collected data and identify operational efficiency or abnormal behavior.

[1146] "Monitoring" refers to the continuous observation and recording of the performance and maintenance progress of a user's automated equipment.

[1147] A "user terminal" is a device (such as a smartphone or tablet) that displays maintenance menus and feedback and transmits input data to the server.

[1148] "Operational data" refers to detailed parameters such as speed, position, and force that automated equipment generates during operation.

[1149] MODE FOR CARRYING OUT THE INVENTION

[1150] System Program

[1151] The system program that realizes this application example consists of the following: The server collects and analyzes performance data of automated equipment, and generates and provides optimal operation improvement and maintenance menus to users. The user terminal has the function of displaying this menu and monitoring progress. Sensor devices also collect data.

[1152] Explanation of the generated program processing

[1153] The server interacts with sensor devices to collect performance data of the automated equipment. The sensor devices may include acceleration sensors, force sensors, position tracking devices, etc. These devices use common sensor devices provided by known hardware manufacturers.

[1154] The server uses data analysis libraries such as NumPy to analyze the collected performance data, calculating parameters such as operating speed, accuracy, and energy consumption, and comparing them with data from leading automated equipment.

[1155] The user terminal uses a Python program to display operation improvement and maintenance menus. Training menus and feedback are updated in real time based on instructions from the server. This allows the user terminal to accurately monitor the equipment status and instruct the user to carry out maintenance as necessary.

[1156] The server also monitors users' maintenance progress and evaluates the collected data in real time, including using generative AI models to predict and generate future improvement measures.

[1157] Specific examples

[1158] For example, operational data for a robot arm used in a factory is collected, analyzed, and compared with data for the most efficient robot arm. If delays in operation or a decrease in accuracy are found based on the analysis results, a menu for improving these issues is provided to the user's terminal. The user follows this menu to carry out maintenance on the robot arm, thereby improving the operational efficiency of the robot arm.

[1159] Prompt Sentence Examples

[1160] Generate a menu for optimizing the operation of a factory robot based on the following data:

[1161] User data: [example data]

[1162] Top-class robot data: [Data example]

[1163] Analyze the differences between each robot and design a maintenance menu based on that.

[1164] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1165] Step 1: Data collection

[1166] The server collects performance data of automated equipment from sensor devices. In this step, acceleration, force, and position data are collected. The input is real-time data from the sensor devices, and the output is the collected performance data. Specifically, the server sends a request to the sensor device API and receives the data as a response.

[1167] Step 2: Data analysis

[1168] Important parameters such as operating speed, accuracy, and energy consumption are extracted from the performance data collected by the server. In this step, data analysis libraries such as NumPy are used. The input is the collected performance data, and the output is the extracted parameters. Specifically, the data is filtered and each parameter is calculated.

[1169] Step 3: Data comparison

[1170] The server compares the performance data of the user's automated equipment with the data of top-class automated equipment. In this step, the difference for each parameter is calculated. The input is the user's performance data and the top-class data, and the output is the difference data for each parameter. Specifically, the server calculates the difference for each parameter and outputs it as a list.

[1171] Step 4: Generate maintenance menu

[1172] Based on the results of the data comparison, the server generates an optimal operation improvement and maintenance menu for the user. In this step, analysis is performed using a generative AI model. The input is differential data for each parameter, and the output is a specific maintenance menu. Specifically, a prompt sentence is input into the generative AI model, and the menu is output in text format.

[1173] Step 5: Provide maintenance menu

[1174] The server provides the generated maintenance menu to the user terminal. In this step, an HTTP request is used. The input is the maintenance menu, and the output is the menu information sent to the user terminal. Specifically, the server sends a request to the API of the user terminal and sends the menu.

[1175] Step 6: Perform and report maintenance

[1176] The user performs maintenance according to the maintenance menu provided by the user terminal. In this step, the user inputs the maintenance progress into the terminal. The input is the user's maintenance progress data, and the output is the progress data sent to the server. In specific operations, the user inputs data using the terminal interface and sends it to the server.

[1177] Step 7: Progress monitoring and menu adjustments

[1178] The server analyzes the maintenance progress data sent by the user and evaluates the effectiveness of the maintenance. In this step, the maintenance menu is adjusted as necessary. The input is the user's progress data, and the output is the adjusted maintenance menu. Specifically, the server analyzes the progress data and generates a new menu using a generative AI model.

[1179] 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.

[1180] The present invention relates to a system that provides a means for collecting and analyzing performance data of top athletes, and in particular to a system that incorporates an emotion engine that recognizes the emotions of users. Specific embodiments of this system will be described below.

[1181] System configuration

[1182] This system mainly consists of a server, a user terminal, a sensor device, and an emotion engine.

[1183] server

[1184] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus. It also receives user emotional data and adjusts training menus and feedback based on this data. The server performs the following processes:

[1185] Data collection: Receive and store video and sensor data from top athletes.

[1186] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate.

[1187] Data Comparison: Compare your performance data with that of top athletes to identify performance gaps.

[1188] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[1189] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[1190] Emotion data processing: Receives data from the emotion engine and provides training menus and feedback according to the user's emotional state.

[1191] User terminal

[1192] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[1193] Data entry: The user enters their own competition data and training results.

[1194] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc.

[1195] Display training menu: Displays the training menu sent from the server to the user.

[1196] Emotion data acquisition: Collect user's emotion data through the emotion engine and send it to the server.

[1197] Receive Feedback: Receive feedback from the server and training menu updates.

[1198] Sensor Device

[1199] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[1200] Acceleration data: Measures acceleration during movement.

[1201] Force data: Measures the force when touching the ground.

[1202] Location Data: Tracking the rider's location during a run.

[1203] Emotion Engine

[1204] The emotion engine is a system that recognizes the user's emotional state and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[1205] Example of operation process

[1206] 1. Data Collection

[1207] The server collects and analyzes videos of top athletes and data from sensor devices.

[1208] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[1209] 2. Data analysis and comparison

[1210] The server compares the user's data with that of top athletes and analyzes the differences, particularly in each phase of the race, such as the start, acceleration, top speed, and finish. By visualizing the differences, it is possible to clarify any issues with the user's performance.

[1211] 3. Creation of training menu

[1212] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting sprints are an issue, starting sprint practice drills and short-distance sprint practice drills will be added to the menu.

[1213] The server transmits the training menu to the user terminal.

[1214] 4. Training implementation and progress monitoring

[1215] The user uses the terminal to practice according to the training menu.

[1216] The user periodically inputs the results of training into the terminal and transmits the data to the server.

[1217] The server analyzes the progress data sent by the user and evaluates the effectiveness of the training.

[1218] Based on the analysis results, the server adjusts the training menu as needed and sends updated information to the user's device.

[1219] Example of using the emotion engine

[1220] 1. Acquiring Emotion Data

[1221] During training, the user uses the device's camera to recognize facial expressions, and the emotion engine analyzes the user's facial expressions to detect emotions such as joy, anger, and sadness.

[1222] The emotion engine uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[1223] 2. Customized feedback

[1224] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of training if the user feels fatigued.

[1225] The server customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[1226] As described above, this system utilizes data from top-class athletes to not only optimize individual users' performance, but also provide training menus and feedback that take the user's emotions into consideration, thereby achieving more effective performance improvement.

[1227] The processing flow will be explained below.

[1228] Step 1:

[1229] The server collects performance data from top athletes, including video footage and data from sensor devices, stores this data, and prepares it for analysis.

[1230] Step 2:

[1231] The server analyzes the collected data of top athletes, specifically extracting parameters such as stride length, ground contact time, and acceleration rate, and then creates a performance profile of the athlete.

[1232] Step 3:

[1233] Users record video of their own 100m sprint using a device such as a smartphone or tablet. The device acquires the video data, converts it into the required format, and sends it to the server.

[1234] Step 4:

[1235] The device collects the user's running data using sensor devices (e.g., acceleration sensors and force sensors). The device analyzes this data and extracts parameters such as stride length and acceleration rate. The data is then sent to a server.

[1236] Step 5:

[1237] The server receives the user's competitive performance data and compares it with that of top athletes. In particular, it analyzes differences in each phase, such as the start dash, acceleration, top speed, and finish. By visualizing the differences, it clarifies the user's performance issues.

[1238] Step 6:

[1239] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting dash training is an issue, starting dash training drills and short distance sprint training will be added to the menu.

[1240] Step 7:

[1241] The server transmits the generated training menu to the user terminal, which receives it and displays the training menu to the user.

[1242] Step 8:

[1243] The user uses the device to practice according to the training menu, inputs the practice results and feedback into the device, and sends the data to the server.

[1244] Step 9:

[1245] The server receives and analyzes the user's training progress data, evaluates the effectiveness of the current training program based on the analysis results, and adjusts the training program as necessary.

[1246] Step 10:

[1247] The server transmits the adjusted training menu to the user terminal, which receives it and displays it to the user.

[1248] Step 11:

[1249] While the user is training, facial expression recognition is performed using the device's camera. The emotion engine analyzes the user's facial expressions and detects emotions such as joy, anger, and sadness. The emotion engine also uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[1250] Step 12:

[1251] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of the training if the user feels fatigued. The server also customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[1252] Example 2

[1253] 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."

[1254] Conventional athlete training systems have limitations in their approach to collecting performance data from top athletes and providing feedback to users. In particular, they lack the functionality to monitor users' training progress and emotional state in real time and adjust training menus at appropriate times. As a result, users' motivation and results are not fully realized, leading to problems in which effective training cannot be achieved.

[1255] The specification processing by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and analyzing performance data of top-class athletes, means for collecting and analyzing performance data of the user, means for designing an optimal training menu for the user based on the analysis results, and means for collecting emotional data of the user and adjusting the training menu based on the collected data. This makes it possible to provide a detailed training menu that takes into account the user's training progress and emotional state.

[1256] A "top-class athlete" is an athlete who has outstanding performance or ability in a particular sport.

[1257] "Performance data" refers to data that includes an athlete's movements and physiological parameters, such as stride length, ground contact time, and acceleration rate.

[1258] "Collection" refers to the act of obtaining and storing the necessary data.

[1259] "Analysis" refers to the act of quantifying collected data and extracting specific parameters and trends.

[1260] "User" refers to an individual athlete who uses this system.

[1261] "Training menu" refers to a plan that specifies the content of the practice and training that an athlete should perform.

[1262] "User terminal" refers to a mobile device or computer used by a user to display the training menu and input data.

[1263] "Monitoring" refers to the act of continuously collecting data and tracking changes.

[1264] "Emotional data" refers to data that indicates the athlete's emotional state, such as joy, anger, or stress.

[1265] An "emotion engine" refers to a system that analyzes a user's emotional state from collected data and outputs it as emotional data.

[1266] MODE FOR CARRYING OUT THE INVENTION

[1267] The present invention relates to a system that provides a means for collecting and analyzing performance data of top athletes, and in particular to a system that incorporates an emotion engine that recognizes the emotions of users. Specific embodiments of this system will be described below.

[1268] System configuration

[1269] This system mainly consists of a server, a user terminal, a sensor device, and an emotion engine.

[1270] server

[1271] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus. It also receives user emotional data and adjusts training menus and feedback based on this data. The server performs the following processes:

[1272] Data collection: Receive and store video and sensor data from top athletes, specifically using a motion capture system and accelerometers.

[1273] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate. This analysis is performed using machine learning tools such as TensorFlow and PyTorch.

[1274] Data comparison: Compare your performance data with that of top athletes to identify performance differences. Implement the analysis algorithm using Python.

[1275] Training menu generation: Based on the analysis results, we design an optimal training menu for the user. We utilize a reinforcement learning algorithm to generate an efficient training menu.

[1276] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[1277] Emotion data processing: Receives data from the emotion engine and provides training menus and feedback according to the user's emotional state.

[1278] User terminal

[1279] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[1280] Data entry: The user enters their own competition data and training results.

[1281] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc. using the Google Cloud Vision API.

[1282] Display training menu: Displays the training menu sent from the server to the user.

[1283] Emotion data acquisition: Collect user emotion data through the emotion engine and send it to the server. OpenCV is used for facial expression recognition.

[1284] Receive Feedback: Receive feedback from the server and training menu updates.

[1285] Sensor Device

[1286] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[1287] Acceleration data: Measures acceleration during movement.

[1288] Force data: Measures the force when touching the ground.

[1289] Location Data: Tracking the rider's location during a run.

[1290] Emotion Engine

[1291] The emotion engine is a system that recognizes the user's emotional state and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[1292] Example of operation process

[1293] 1. Data Collection

[1294] The server collects and stores videos of top athletes and data from sensor devices, including a Motion Capture system and accelerometers.

[1295] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[1296] 2. Data analysis and comparison

[1297] The server compares the data of the user with that of top athletes and analyzes the differences, particularly in terms of differences in each phase, such as the start dash, acceleration, top speed, and finish. TensorFlow and PyTorch are used for the analysis.

[1298] 3. Creation of training menu

[1299] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting sprints are an issue, starting sprint practice drills and short-distance sprint practice drills will be added to the menu. This is done using a reinforcement learning algorithm.

[1300] The server transmits the training menu to the user terminal.

[1301] 4. Training implementation and progress monitoring

[1302] The user uses the terminal to practice according to the training menu.

[1303] The user periodically inputs the results of training into the terminal and transmits the data to the server.

[1304] The server analyzes the progress data sent from the user, evaluates the effectiveness of the training, and adjusts the training menu based on the progress data.

[1305] Example of using the emotion engine

[1306] 1. Acquiring Emotion Data

[1307] During training, the user uses the device's camera to recognize facial expressions. The emotion engine analyzes the user's facial expressions and detects emotions such as joy, anger, and sadness. OpenCV is used for facial recognition.

[1308] The emotion engine uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[1309] 2. Customized feedback

[1310] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of training if the user feels fatigued.

[1311] The server customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[1312] Prompt Sentence Examples

[1313] Prompt statement to analyze top-class running speed data:

[1314] "top runner speed data analysis TensorFlow"

[1315] The prompt sentence that the server uses to analyze the user's emotion data:

[1316] "user emotion data analysis with OpenCV and Python"

[1317] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1318] System program processing flow

[1319] Step 1: Collecting top-tier athlete data

[1320] A server receives video of top athletes and data from sensor devices.

[1321] Input: Video data of top athletes, sensor device data (acceleration data, force data, position data).

[1322] Processing: Receives data from the Motion Capture system and accelerometer and stores it in a database.

[1323] Output: Saved performance data.

[1324] Step 2: Collect user data

[1325] The user inputs their own driving data (video and sensor device data) into the terminal.

[1326] The terminal transmits the input data to the server.

[1327] Input: User's driving data (video data, sensor data).

[1328] Processing: The user takes photos of their driving using a smartphone or other device and sends the data to the server, which then stores the received data in a database.

[1329] Output: Saved user driving data.

[1330] Step 3: Preprocessing the data

[1331] The server analyzes the collected video data frame by frame and converts it into a form that can be used for motion analysis.

[1332] Input: Video data.

[1333] Processing: Using Python, split the video data into frames and extract important frames.

[1334] Output: Preprocessed video data.

[1335] Step 4: Parameter extraction

[1336] The server extracts parameters such as stride length, ground contact time, and acceleration rate from the pre-processed data.

[1337] Input: Preprocessed video data and sensor data.

[1338] Processing: Using TensorFlow and PyTorch, we analyze and extract parameters such as stride length, ground contact time, and acceleration rate.

[1339] Output: The extracted parameter data.

[1340] Step 5: Data comparison

[1341] The server compares the data of top athletes with the user's data and analyzes differences and trends.

[1342] Input: Performance data of top athletes, performance data of the user.

[1343] Processing: Python scripts are used to compare the user's data with that of top athletes and analyze trends and differences.

[1344] Output: Data comparison results.

[1345] Step 6: Create a training menu

[1346] The server generates an optimal training menu for the user based on the analysis results.

[1347] Input: Data comparison results, extracted parameter data.

[1348] Processing: Uses a reinforcement learning algorithm (e.g., OpenAI's Gym) to generate a personalized training menu for the user.

[1349] Output: Training menu.

[1350] Step 7: Providing training menus

[1351] The server transmits the generated training menu to the user terminal.

[1352] Enter: Training Menu.

[1353] Processing: Format the training menu and send it to the user's device.

[1354] Output: Training menu displayed on the user's device.

[1355] Step 8: Implement training and collect progress data

[1356] The user performs training according to the training menu displayed on the terminal.

[1357] The user inputs the training result data into the terminal.

[1358] Input: Training implementation status and results data.

[1359] Processing: The user performs training using the device and inputs the results. The device then sends the data to the server.

[1360] Output: Progress data stored on the server.

[1361] Step 9: Analyze progress data and adjust menu

[1362] The server analyzes the progress data and evaluates the effectiveness of the training.

[1363] The server adjusts the training menu according to progress and sends updates to the user's terminal.

[1364] Input: Progress data.

[1365] Processing: Analytical algorithms are used to evaluate progress data and adjust training as needed.

[1366] Output: A tailored training menu.

[1367] Step 10: Collect and process emotion data

[1368] Facial expression recognition is performed using the device's camera while the user is training.

[1369] The emotion engine analyzes facial expression data to detect emotional states.

[1370] Input: User's facial expression data, biometric sensor data.

[1371] Processing: Analyzes the user's facial expressions using the OpenCV library to extract data on their emotional state, and measures their heart rate and other vital signs using biometric sensors.

[1372] Output: Emotion data.

[1373] Step 11: Feedback based on emotional data

[1374] The server adjusts the training menu based on the emotion data.

[1375] The server customizes the feedback based on the emotion data and transmits it to the user terminal.

[1376] Input: Emotion data.

[1377] Processing: Analyze emotional data and adjust training menus and feedback.

[1378] Output: Tailored training menus and customized feedback.

[1379] Prompt Sentence Examples

[1380] Prompt statement to analyze top-class running speed data:

[1381] "top runner speed data analysis TensorFlow"

[1382] The prompt sentence that the server uses to analyze the user's emotion data:

[1383] "user emotion data analysis with OpenCV and Python"

[1384] (Application example 2)

[1385] 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."

[1386] This invention relates to a system that provides a means for collecting and analyzing performance data of top workers. In particular, it aims to improve production efficiency by optimizing the operation of work robots in factories and dynamically adjusting work parameters based on the emotional state of the workers and robots. It also aims to improve the overall working environment by adjusting the work balance according to the worker's fatigue level and motivation.

[1387] The specific processing by the specific 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 and analyzing performance data of top athletes and workers, means for collecting and analyzing athletic performance data of users, means for designing an optimal training menu for users based on the analysis results, means for optimizing robot operation parameters based on worker performance data, and means for detecting the emotional states of workers and robots using an emotion engine and dynamically adjusting operation parameters. This makes it possible to establish an optimal work balance according to the state of workers and improve production efficiency.

[1388] A "top-class athlete" is someone who possesses outstanding skills and results in a particular sport and is highly regarded in the sporting community.

[1389] "Performance data" is a general term for data that includes dynamic and static information such as the efficiency of an athlete's or worker's movements and movements, how force is applied, and speed.

[1390] An "emotion engine" is a system that analyzes the emotional state of users or workers, extracts emotions such as joy, anger, and sadness as data, and provides that data in a usable form.

[1391] A "training menu" refers to a specific practice or training plan aimed at improving the performance of a user or athlete.

[1392] "User terminal" refers to a terminal device operated by a user, such as a smartphone, tablet, or PC, which is capable of communicating with the system and inputting data.

[1393] "Sensor device" refers to a device used to measure various data such as acceleration, position, and force, and is primarily used to obtain performance data.

[1394] A "robot" refers to an automated mechanical device that can perform programmed actions, and is used especially to perform tasks in factories.

[1395] "Movement parameters" include specific settings and indicators related to the robot's movements, and refer to information such as speed, position, and force.

[1396] "Emotional state" refers to the psychological and physiological state of a human or robot, including stress level, motivation, fatigue, etc.

[1397] The present invention relates to a system for collecting and analyzing performance data of top workers, particularly for optimizing robot operations in factories, and improving production efficiency by dynamically adjusting work parameters based on the emotional states of workers and robots.

[1398] System configuration

[1399] This system consists of a server, a user terminal, a sensor device, and an emotion engine.

[1400] server

[1401] The server mainly performs the following processes.

[1402] Performance Data Collection: Collect top worker performance data from sensor devices and videos.

[1403] Data analysis: Analyze the collected performance data and extract operating parameters.

[1404] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[1405] Optimization of robot motion: Optimize the robot's motion parameters based on the analysis results.

[1406] Emotion data processing: Receive data from the emotion engine and adjust the operating parameters according to the emotional state of the worker or robot.

[1407] User terminal

[1408] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[1409] Video recording and analysis: Workers' movements are videotaped and analyzed.

[1410] Display training menu: Displays the training menu sent from the server to the user.

[1411] Emotion data acquisition: Collect workers' emotion data through the emotion engine and send it to the server.

[1412] Receive Feedback: Receive feedback from the server and training menu updates.

[1413] Sensor Device

[1414] The sensor device is used to measure the movements and force applied to workers, and collects the following data:

[1415] Acceleration data: Measures acceleration during movement.

[1416] Force data: Measure the force applied during work.

[1417] Location data: Tracking worker locations.

[1418] Emotion Engine

[1419] The emotion engine is a system that recognizes the emotional state of workers and robots and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[1420] Example of operation process

[1421] Data collection

[1422] The server collects and analyzes videos of top workers and data from sensor devices, extracting operational parameters and providing data to optimize the robot's operation.

[1423] Data analysis and comparison

[1424] The server compares the data of top-class workers with other workers and analyzes the differences, especially differences in behavior in each phase, and visualizes the differences to clarify issues with the worker's performance.

[1425] Training menu generation

[1426] Based on the analysis results, the server generates the optimal training menu for the worker, and also optimizes the robot's operating parameters, enabling more efficient work.

[1427] Training implementation and progress monitoring

[1428] The user uses the device to practice according to the training menu. The server analyzes the progress data sent by the user and evaluates the effectiveness of the training. The server adjusts the training menu and the robot's operating parameters as necessary.

[1429] Example of using the emotion engine

[1430] Acquiring emotion data

[1431] While the user is training, facial expression recognition is performed using the device's camera. An emotion engine analyzes the worker's facial expressions and detects emotions such as joy, anger, and sadness. Biometric sensors (such as a heart rate monitor) are used to detect changes in stress levels and motivation.

[1432] Customized Feedback

[1433] The server adjusts the robot's operating parameters based on the emotional data. For example, if a worker feels tired, it will increase the robot's workload to reduce the worker's burden.

[1434] As described above, by dynamically adjusting parameters based on emotional states, it is possible to effectively improve work efficiency.

[1435] Prompt Sentence Examples

[1436] Collect work videos and sensor data of Employee A working on Line 1 in the factory and compare them with top-class employees. Based on the results, optimize the robot's operating parameters and make adjustments according to Employee A's fatigue level.

[1437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1438] Step 1:

[1439] The server collects performance data of top workers from sensor devices and cameras. Specifically, it collects and stores videos of the workers' work movements and sensor data such as acceleration data, force data, and position data. In this process, data from the sensors and cameras is continuously sent to the server and stored appropriately.

[1440] Input: Video data and sensor data of workers at work

[1441] Output: Saved performance data

[1442] Step 2:

[1443] The server analyzes the collected performance data. Specifically, it uses a video analysis system to extract movement parameters (such as movement speed and accuracy), and a sensor data analysis system to analyze force application and position data. The analysis results are compiled as performance indicators.

[1444] Input: Stored performance data

[1445] Output: Analyzed operating parameters and performance indicators

[1446] Step 3:

[1447] The server adjusts the robot's operating parameters based on the analysis results. Specifically, it uses robot control software to reflect the analyzed operating parameters in the robot's control system and set the optimal speed and operating pattern.

[1448] Input: Analyzed operating parameters and performance indicators

[1449] Output: Adjusted robot movement parameters

[1450] Step 4:

[1451] The user terminal displays the training menu sent from the server, takes videos of the worker, and collects emotional data using an emotion engine. Specifically, the camera captures the worker's facial expressions and movements, and the emotion engine analyzes their emotional state (fatigue level, stress level, etc.).

[1452] Input: Worker training menu, emotion data collection

[1453] Output: Parsed emotion data

[1454] Step 5:

[1455] The server receives and analyzes the emotional data and training progress data sent from the user's device, and comprehensively evaluates the worker's emotional state and training progress, readjusting the training menu and robot's operating parameters as necessary.

[1456] Input: Parsed emotion data and training progress data

[1457] Output: Retuned training menu and robot movement parameters

[1458] Step 6:

[1459] The server sends the adjusted training menu and robot operating parameters to the user's terminal, which displays them to the worker. The worker then retrains based on this and sends the results to the server again.

[1460] Input: Retuned training menu and robot movement parameters

[1461] Output: New training menu and action instructions displayed on the user's device

[1462] 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.

[1463] 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.

[1464] 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.

[1465] [Fourth embodiment]

[1466] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1467] 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.

[1468] 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).

[1469] 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.

[1470] 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.

[1471] 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).

[1472] 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.

[1473] 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.

[1474] 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.

[1475] 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.

[1476] 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.

[1477] 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.

[1478] 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."

[1479] The present invention relates to a system that provides a means for collecting and analyzing performance data of top-class athletes. Specific embodiments of this system will be described below.

[1480] System configuration

[1481] This system mainly consists of a server, user terminals, and sensor devices.

[1482] server

[1483] The server is the main system that collects, stores, and analyzes performance data from top athletes and users, and generates training menus. The server performs the following processes:

[1484] Data collection: Receive and store video and sensor data from top athletes.

[1485] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate.

[1486] Data Comparison: Compare your performance data with that of top athletes to identify performance gaps.

[1487] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[1488] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[1489] User terminal

[1490] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[1491] Data entry: The user enters their own competition data and training results.

[1492] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc.

[1493] Display training menu: Displays the training menu sent from the server to the user.

[1494] Receive Feedback: Receive feedback from the server and training menu updates.

[1495] Sensor Device

[1496] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[1497] Acceleration data: Measures acceleration during movement.

[1498] Force data: Measures the force when touching the ground.

[1499] Location Data: Tracking the rider's location during a run.

[1500] Example of operation process

[1501] 1. Data Collection

[1502] The server collects and analyzes videos of top athletes and data from sensor devices.

[1503] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[1504] 2. Data analysis and comparison

[1505] The server compares the user's data with that of top athletes and analyzes the differences, for example, detecting that the user's starting sprint speed is slower than that of top athletes.

[1506] 3. Creation of training menu

[1507] Based on the analysis results, the server generates a training menu to improve the user's starting sprint, including starting sprint practice drills and short-distance sprint practice.

[1508] The server transmits the training menu to the user terminal.

[1509] 4. Training implementation and progress monitoring

[1510] The user uses the terminal to practice according to the training menu.

[1511] The user periodically inputs the results of training into the terminal and transmits them to the server.

[1512] The server analyzes the progress data sent by the user and evaluates the effectiveness of the training.

[1513] Based on the analysis results, the server adjusts the training menu as needed and sends updated information to the user's device.

[1514] By implementing this system, users can receive optimal training based on a scientific approach. Specifically, by utilizing data from top athletes, users can clarify individual performance issues and more effectively improve their athletic performance.

[1515] The processing flow will be explained below.

[1516] Step 1:

[1517] The server collects performance data from top athletes, including video footage and data from sensor devices, stores this data, and prepares it for analysis.

[1518] Step 2:

[1519] The server analyzes the collected data of top athletes, specifically extracting parameters such as stride length, ground contact time, and acceleration rate, and by extracting these parameters, it creates a performance profile of the athlete.

[1520] Step 3:

[1521] Users can record video of their own 100m sprint using a device such as a smartphone or tablet. The device then acquires the video data, converts it into the required format, and sends it to the server.

[1522] Step 4:

[1523] The device collects the user's running data using sensor devices (e.g., acceleration sensors and force sensors), analyzes the data, and extracts parameters such as stride length and acceleration rate. The data is then sent to a server.

[1524] Step 5:

[1525] The server receives the user's competitive performance data and compares it with that of top athletes. In particular, it analyzes differences in each phase, such as the start dash, acceleration, top speed, and finish. By visualizing the differences, it is possible to clarify the user's performance issues.

[1526] Step 6:

[1527] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting dash is an issue, starting dash practice drills and short distance sprint practice will be added to the menu.

[1528] Step 7:

[1529] The server sends the generated training menu to the user's device, which receives it and displays the training menu to the user.

[1530] Step 8:

[1531] The user uses the device to practice according to the training menu, inputs the results of the practice and feedback into the device, and sends the data to the server.

[1532] Step 9:

[1533] The server receives and analyzes the user's training progress data, and based on the analysis results, evaluates the effectiveness of the current training menu and adjusts the training menu as necessary.

[1534] Step 10:

[1535] The server sends the adjusted training menu to the user's device, which receives it and displays it to the user. This process is repeated until the user's performance is optimized.

[1536] Example 1

[1537] 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."

[1538] Conventional training systems lack scientific data collection and analysis to improve user performance, making it difficult to provide individually optimized training menus. Furthermore, performance improvement tends to be delayed due to ineffective monitoring and feedback of the user's training progress. The present invention aims to solve these problems by providing a system that compares the user's performance data with that of top athletes and provides an optimal training menu.

[1539] 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.

[1540] In this invention, the server includes means for collecting and analyzing performance data of top-class athletes, means for collecting and analyzing the user's athletic performance data based on the performance data of the top-class athletes, means for designing an optimal training menu for the user based on the analysis results, means for providing the training menu to a user terminal, means for monitoring the user's training progress and adjusting the training menu, means for extracting performance parameters such as stride length, ground contact time, and acceleration rate using the collected video data and sensor data, and means for comparing the user's performance data with data of top-class athletes and identifying performance differences. This allows the user to train based on an optimal training menu that is scientifically backed, thereby efficiently improving their athletic performance.

[1541] A "top-class athlete" is an outstanding athlete who demonstrates superior performance that exceeds certain standards and is recognized based on the evaluations of official organizations and their achievements in competitive events.

[1542] "Performance Data" refers to quantitative data relating to an athlete's physical movement and motion, including measurements such as stride length, ground contact time, and acceleration rate.

[1543] The "analyzing means" refers to a method or device for analyzing collected data and extracting useful information or parameters, and includes, for example, algorithms, software, hardware, and the like.

[1544] "User" refers to an individual or group who intends to improve their athletic performance by using this system.

[1545] A "user terminal" is an electronic device used by a user to operate the system, and specifically includes a smartphone, tablet, PC, etc.

[1546] "Training menu" refers to specific practice methods and drills designed to improve a user's athletic performance.

[1547] "Monitoring means" refers to methods or equipment for tracking a user's training progress and collecting and evaluating the necessary data, and includes, for example, software, sensor devices, etc.

[1548] "Video data" refers to video information captured on camera of an athlete's movements, which is usually stored in digital format and used for analysis.

[1549] "Sensor data" refers to various measurement data collected by sensor devices, and specifically includes acceleration, force, position information, etc.

[1550] "Performance parameters" refer to indicators that quantitatively show an athlete's movement characteristics, and specifically include stride length, ground contact time, acceleration speed, etc.

[1551] "Means for identifying differences" refers to methods or devices for comparing the performance data of a top-class athlete with that of the user and clarifying the differences or shortcomings between the two.

[1552] The present invention relates to a system that collects and analyzes performance data from top athletes to provide users with optimal training programs. This system is primarily composed of a server, user terminals, and sensor devices.

[1553] server

[1554] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus.

[1555] 1. Data Collection

[1556] The server receives and stores videos of top athletes and data from sensor devices, specifically MP4 videos and acceleration, force, and position data (e.g., in CSV format).

[1557] It also receives and stores driving data (video and sensor data) provided by the user, and transmits the data in real time via Bluetooth.

[1558] 2. Data Analysis

[1559] The server analyzes the collected data and extracts performance parameters such as stride length, ground contact time, and acceleration rate using Python's NumPy, Pandas, and OpenCV libraries.

[1560] For example, in video analysis, a skeleton detection algorithm using OpenCV is applied to calculate performance parameters from each frame.

[1561] 3. Data Comparison

[1562] The server compares the user's data with that of top athletes and identifies performance differences, for example detecting that the user's ground contact time is 0.15 seconds longer than that of top athletes.

[1563] The results of this comparison are statistically processed using libraries such as SciPy.

[1564] 4. Training menu generation

[1565] The server then generates the optimal training menu for the user based on the analysis results. For example, to improve starting speed, the server designs a menu that includes "reaction speed training" and "acceleration training."

[1566] The generated menu is sent to the user terminal in JSON format.

[1567] 5. Progress monitoring

[1568] The server analyzes the training progress data sent by the user, evaluates the effectiveness of the training, and provides feedback based on the results using statistical analysis and machine learning models.

[1569] For example, verify that the user's acceleration speed improved by 0.5 m / s over two weeks.

[1570] User terminal

[1571] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[1572] Data Entry

[1573] Users input their own competition data and training results, take videos using a dedicated app, and input sensor data.

[1574] The entered data is sent to the server in real time.

[1575] Video recording and analysis

[1576] The app automatically analyzes the user's stride length, ground contact time, and other data by recording video of the user's run. OpenCV technology is used for video analysis.

[1577] Training menu display

[1578] The training menu received from the server is displayed to the user, including specific training content and goals.

[1579] Receive feedback

[1580] Users receive feedback from the server and training menu updates, allowing them to adjust their training accordingly.

[1581] Sensor Device

[1582] The sensor device measures the athlete's movements and the force applied, and mainly collects the following data:

[1583] Acceleration Data

[1584] Acceleration during movement is measured and sent to the server in real time. A Bluetooth-enabled IMU sensor (e.g., MPU-6050) is used.

[1585] Force Data

[1586] Measure the force when the robot touches the ground. Use a force sensor (e.g., FSR sensor).

[1587] Location data

[1588] GPS tracking devices are used to obtain the location of athletes, for example GPS watches.

[1589] Specific Examples

[1590] Examples of data collection

[1591] An example of the data the server receives from a top athlete is a 10-second video of a sprint and acceleration data for the same period.

[1592] Examples of data analysis and comparison

[1593] The server compares the user's starting speed (e.g., 6.2 m / s) with the speed of a top-class athlete (e.g., 7.5 m / s) and detects that the user is behind.

[1594] Example of training menu generation

[1595] Examples of training menus generated by the server include "start dash strengthening drills" and "200m short distance sprint practice."

[1596] Prompt Sentence Examples

[1597] "Compare the starting speed of a top-class athlete with the user's speed and generate a specific training menu to improve the user's speed."

[1598] This system allows users to receive scientifically backed training plans using data from top athletes, allowing them to effectively improve their own athletic performance.

[1599] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1600] Step 1:

[1601] The server collects performance data from top athletes. Top athletes shoot videos using a dedicated app and upload them to the server in MP4 format. At the same time, data collected by sensor devices (accelerometers, force sensors, GPS tracking devices) is sent to the server in CSV format. The inputs are video files and sensor data, which the server saves. The output is the saved video and sensor data.

[1602] Step 2:

[1603] The server collects the user's performance data. The user records their own running video using a dedicated app and uploads it to the server in MP4 format. The server also receives acceleration data, force data, and position data from the sensor device and saves them in CSV format. The inputs are the user's video file and sensor data, which the server saves. The output is the saved user video and sensor data.

[1604] Step 3:

[1605] Performance parameters are extracted using the video data and sensor data collected by the server. The server uses Python's NumPy, Pandas, and OpenCV libraries to analyze the video and calculate stride length, ground contact time, and acceleration rate from each frame. It also processes the acceleration data with a low-pass filter and integrates the force data to calculate impact force. The input is the saved video data and sensor data, which the server analyzes to extract various performance parameters. The output is a list of each performance parameter.

[1606] Step 4:

[1607] The server compares the user's data with the performance data of top athletes. It compares the extracted performance parameters and identifies the difference in the user's performance. This comparison is performed statistically using the SciPy library. For example, it confirms that the user's ground contact time is 0.15 seconds longer than the top athletes. The input is a list of each performance parameter, and the server compares them to identify the difference. The output is a list of the differences.

[1608] Step 5:

[1609] The server generates an optimal training menu for the user based on the analysis results. The training menu is designed based on the areas the user needs to improve, and includes specific practice drills and training content. For example, improving a quick start could include "reaction speed training" and "acceleration training." The generated menu is sent to the user's device in JSON format. The input is a list of differences, and the server generates a training plan based on this. The output is a training menu JSON.

[1610] Step 6:

[1611] The user practices according to the training menu on the device. After each training session, the user enters the results into a dedicated app and sends the data to the server. For example, reaction time is entered as the result of "reaction speed training." The input is the user's training result data, which is sent to the server via the user's device. The output is the sent result data.

[1612] Step 7:

[1613] The server analyzes the user's progress data and evaluates the effectiveness of the training menu. It uses statistical analysis and machine learning models to evaluate the user's progress and adjusts the training menu as needed. For example, it verifies that acceleration speed has improved by 0.5 m / s over two weeks. The input is the user's progress data, which the server analyzes and evaluates the effectiveness. The output is the analysis results and, if necessary, an adjusted training menu.

[1614] Step 8:

[1615] The server sends the adjusted training menu to the user's terminal. The updated menu is provided to the user in the form of a notification to help them plan their next training. The input is the adjusted training menu, which the server sends to the user's terminal. The output is an updated notification that is displayed on the user's terminal.

[1616] (Application example 1)

[1617] 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."

[1618] The present invention aims to optimize the operational efficiency and maintenance effectiveness of users' automated equipment by utilizing top-class performance data. However, current automated equipment management systems are unable to effectively utilize top-class performance data, resulting in issues such as insufficient operational improvement and maintenance optimization. Furthermore, there is a lack of a mechanism for evaluating the effectiveness of maintenance performed by users in real time and providing appropriate feedback. This results in issues such as insufficient improvement of automated equipment efficiency and reduction of downtime.

[1619] 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.

[1620] In this invention, the server includes means for collecting and analyzing performance data of top-class automated equipment, means for collecting and analyzing performance data of a user's automated equipment, and means for designing an optimal operation improvement and maintenance menu for the user based on the analysis results, thereby enabling the operating efficiency of the user's automated equipment to approach that of top-class equipment.

[1621] "Top-class automated equipment" refers to an automated equipment system with the highest level of production capacity and efficiency.

[1622] "Performance Data" means data relating to the performance of automated equipment, such as its operating condition, efficiency, speed, and energy consumption.

[1623] "User's automated equipment" refers to the automated equipment system operated by the user.

[1624] A "sensor device" is a measuring device used to measure and collect operational data of automated equipment.

[1625] "Operational Improvement" refers to specific adjustments or modifications made to improve the performance of automated equipment.

[1626] A "maintenance menu" refers to a plan and instructions for a series of maintenance work such as inspection, repair, and cleaning of equipment.

[1627] "Analysis means" refers to technology or software used to analyze collected data and identify operational efficiency or abnormal behavior.

[1628] "Monitoring" refers to the continuous observation and recording of the performance and maintenance progress of a user's automated equipment.

[1629] A "user terminal" is a device (such as a smartphone or tablet) that displays maintenance menus and feedback and transmits input data to the server.

[1630] "Operational data" refers to detailed parameters such as speed, position, and force that automated equipment generates during operation.

[1631] MODE FOR CARRYING OUT THE INVENTION

[1632] System Program

[1633] The system program that realizes this application example consists of the following: The server collects and analyzes performance data of automated equipment, and generates and provides optimal operation improvement and maintenance menus to users. The user terminal has the function of displaying this menu and monitoring progress. Sensor devices also collect data.

[1634] Explanation of the generated program processing

[1635] The server interacts with sensor devices to collect performance data of the automated equipment. The sensor devices may include acceleration sensors, force sensors, position tracking devices, etc. These devices use common sensor devices provided by known hardware manufacturers.

[1636] The server uses data analysis libraries such as NumPy to analyze the collected performance data, calculating parameters such as operating speed, accuracy, and energy consumption, and comparing them with data from leading automated equipment.

[1637] The user terminal uses a Python program to display operation improvement and maintenance menus. Training menus and feedback are updated in real time based on instructions from the server. This allows the user terminal to accurately monitor the equipment status and instruct the user to carry out maintenance as necessary.

[1638] The server also monitors users' maintenance progress and evaluates the collected data in real time, including using generative AI models to predict and generate future improvement measures.

[1639] Specific examples

[1640] For example, operational data for a robot arm used in a factory is collected, analyzed, and compared with data for the most efficient robot arm. If delays in operation or a decrease in accuracy are found based on the analysis results, a menu for improving these issues is provided to the user's terminal. The user follows this menu to carry out maintenance on the robot arm, thereby improving the operational efficiency of the robot arm.

[1641] Prompt Sentence Examples

[1642] Generate a menu for optimizing the operation of a factory robot based on the following data:

[1643] User data: [example data]

[1644] Top-class robot data: [Data example]

[1645] Analyze the differences between each robot and design a maintenance menu based on that.

[1646] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1647] Step 1: Data collection

[1648] The server collects performance data of automated equipment from sensor devices. In this step, acceleration, force, and position data are collected. The input is real-time data from the sensor devices, and the output is the collected performance data. Specifically, the server sends a request to the sensor device API and receives the data as a response.

[1649] Step 2: Data analysis

[1650] Important parameters such as operating speed, accuracy, and energy consumption are extracted from the performance data collected by the server. In this step, data analysis libraries such as NumPy are used. The input is the collected performance data, and the output is the extracted parameters. Specifically, the data is filtered and each parameter is calculated.

[1651] Step 3: Data comparison

[1652] The server compares the performance data of the user's automated equipment with the data of top-class automated equipment. In this step, the difference for each parameter is calculated. The input is the user's performance data and the top-class data, and the output is the difference data for each parameter. Specifically, the server calculates the difference for each parameter and outputs it as a list.

[1653] Step 4: Generate maintenance menu

[1654] Based on the results of the data comparison, the server generates an optimal operation improvement and maintenance menu for the user. In this step, analysis is performed using a generative AI model. The input is differential data for each parameter, and the output is a specific maintenance menu. Specifically, a prompt sentence is input into the generative AI model, and the menu is output in text format.

[1655] Step 5: Provide maintenance menu

[1656] The server provides the generated maintenance menu to the user terminal. In this step, an HTTP request is used. The input is the maintenance menu, and the output is the menu information sent to the user terminal. Specifically, the server sends a request to the API of the user terminal and sends the menu.

[1657] Step 6: Perform and report maintenance

[1658] The user performs maintenance according to the maintenance menu provided by the user terminal. In this step, the user inputs the maintenance progress into the terminal. The input is the user's maintenance progress data, and the output is the progress data sent to the server. In specific operations, the user inputs data using the terminal interface and sends it to the server.

[1659] Step 7: Progress monitoring and menu adjustments

[1660] The server analyzes the maintenance progress data sent by the user and evaluates the effectiveness of the maintenance. In this step, the maintenance menu is adjusted as necessary. The input is the user's progress data, and the output is the adjusted maintenance menu. Specifically, the server analyzes the progress data and generates a new menu using a generative AI model.

[1661] 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.

[1662] The present invention relates to a system that provides a means for collecting and analyzing performance data of top athletes, and in particular to a system that incorporates an emotion engine that recognizes the emotions of users. Specific embodiments of this system will be described below.

[1663] System configuration

[1664] This system mainly consists of a server, a user terminal, a sensor device, and an emotion engine.

[1665] server

[1666] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus. It also receives user emotional data and adjusts training menus and feedback based on this data. The server performs the following processes:

[1667] Data collection: Receive and store video and sensor data from top athletes.

[1668] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate.

[1669] Data Comparison: Compare your performance data with that of top athletes to identify performance gaps.

[1670] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[1671] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[1672] Emotion data processing: Receives data from the emotion engine and provides training menus and feedback according to the user's emotional state.

[1673] User terminal

[1674] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[1675] Data entry: The user enters their own competition data and training results.

[1676] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc.

[1677] Display training menu: Displays the training menu sent from the server to the user.

[1678] Emotion data acquisition: Collect user's emotion data through the emotion engine and send it to the server.

[1679] Receive Feedback: Receive feedback from the server and training menu updates.

[1680] Sensor Device

[1681] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[1682] Acceleration data: Measures acceleration during movement.

[1683] Force data: Measures the force when touching the ground.

[1684] Location Data: Tracking the rider's location during a run.

[1685] Emotion Engine

[1686] The emotion engine is a system that recognizes the user's emotional state and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[1687] Example of operation process

[1688] 1. Data Collection

[1689] The server collects and analyzes videos of top athletes and data from sensor devices.

[1690] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[1691] 2. Data analysis and comparison

[1692] The server compares the user's data with that of top athletes and analyzes the differences, particularly in each phase of the race, such as the start, acceleration, top speed, and finish. By visualizing the differences, it is possible to clarify any issues with the user's performance.

[1693] 3. Creation of training menu

[1694] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting sprints are an issue, starting sprint practice drills and short-distance sprint practice drills will be added to the menu.

[1695] The server transmits the training menu to the user terminal.

[1696] 4. Training implementation and progress monitoring

[1697] The user uses the terminal to practice according to the training menu.

[1698] The user periodically inputs the results of training into the terminal and transmits the data to the server.

[1699] The server analyzes the progress data sent by the user and evaluates the effectiveness of the training.

[1700] Based on the analysis results, the server adjusts the training menu as needed and sends updated information to the user's device.

[1701] Example of using the emotion engine

[1702] 1. Acquiring Emotion Data

[1703] During training, the user uses the device's camera to recognize facial expressions, and the emotion engine analyzes the user's facial expressions to detect emotions such as joy, anger, and sadness.

[1704] The emotion engine uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[1705] 2. Customized feedback

[1706] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of training if the user feels fatigued.

[1707] The server customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[1708] As described above, this system utilizes data from top-class athletes to not only optimize individual users' performance, but also provide training menus and feedback that take the user's emotions into consideration, thereby achieving more effective performance improvement.

[1709] The processing flow will be explained below.

[1710] Step 1:

[1711] The server collects performance data from top athletes, including video footage and data from sensor devices, stores this data, and prepares it for analysis.

[1712] Step 2:

[1713] The server analyzes the collected data of top athletes, specifically extracting parameters such as stride length, ground contact time, and acceleration rate, and then creates a performance profile of the athlete.

[1714] Step 3:

[1715] Users record video of their own 100m sprint using a device such as a smartphone or tablet. The device acquires the video data, converts it into the required format, and sends it to the server.

[1716] Step 4:

[1717] The device collects the user's running data using sensor devices (e.g., acceleration sensors and force sensors). The device analyzes this data and extracts parameters such as stride length and acceleration rate. The data is then sent to a server.

[1718] Step 5:

[1719] The server receives the user's competitive performance data and compares it with that of top athletes. In particular, it analyzes differences in each phase, such as the start dash, acceleration, top speed, and finish. By visualizing the differences, it clarifies the user's performance issues.

[1720] Step 6:

[1721] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting dash training is an issue, starting dash training drills and short distance sprint training will be added to the menu.

[1722] Step 7:

[1723] The server transmits the generated training menu to the user terminal, which receives it and displays the training menu to the user.

[1724] Step 8:

[1725] The user uses the device to practice according to the training menu, inputs the practice results and feedback into the device, and sends the data to the server.

[1726] Step 9:

[1727] The server receives and analyzes the user's training progress data, evaluates the effectiveness of the current training program based on the analysis results, and adjusts the training program as necessary.

[1728] Step 10:

[1729] The server transmits the adjusted training menu to the user terminal, which receives it and displays it to the user.

[1730] Step 11:

[1731] While the user is training, facial expression recognition is performed using the device's camera. The emotion engine analyzes the user's facial expressions and detects emotions such as joy, anger, and sadness. The emotion engine also uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[1732] Step 12:

[1733] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of the training if the user feels fatigued. The server also customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[1734] Example 2

[1735] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1736] Conventional athlete training systems have limitations in their approach to collecting performance data from top athletes and providing feedback to users. In particular, they lack the functionality to monitor users' training progress and emotional state in real time and adjust training menus at appropriate times. As a result, users' motivation and results are not fully realized, leading to problems in which effective training cannot be achieved.

[1737] The specification processing by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and analyzing performance data of top-class athletes, means for collecting and analyzing performance data of the user, means for designing an optimal training menu for the user based on the analysis results, and means for collecting emotional data of the user and adjusting the training menu based on the collected data. This makes it possible to provide a detailed training menu that takes into account the user's training progress and emotional state.

[1738] A "top-class athlete" is an athlete who has outstanding performance or ability in a particular sport.

[1739] "Performance data" refers to data that includes an athlete's movements and physiological parameters, such as stride length, ground contact time, and acceleration rate.

[1740] "Collection" refers to the act of obtaining and storing the necessary data.

[1741] "Analysis" refers to the act of quantifying collected data and extracting specific parameters and trends.

[1742] "User" refers to an individual athlete who uses this system.

[1743] "Training menu" refers to a plan that specifies the content of the practice and training that an athlete should perform.

[1744] "User terminal" refers to a mobile device or computer used by a user to display the training menu and input data.

[1745] "Monitoring" refers to the act of continuously collecting data and tracking changes.

[1746] "Emotional data" refers to data that indicates the athlete's emotional state, such as joy, anger, or stress.

[1747] An "emotion engine" refers to a system that analyzes a user's emotional state from collected data and outputs it as emotional data.

[1748] MODE FOR CARRYING OUT THE INVENTION

[1749] The present invention relates to a system that provides a means for collecting and analyzing performance data of top athletes, and in particular to a system that incorporates an emotion engine that recognizes the emotions of users. Specific embodiments of this system will be described below.

[1750] System configuration

[1751] This system mainly consists of a server, a user terminal, a sensor device, and an emotion engine.

[1752] server

[1753] The server is the main system that collects, stores, and analyzes performance data of top athletes and users, and generates training menus. It also receives user emotional data and adjusts training menus and feedback based on this data. The server performs the following processes:

[1754] Data collection: Receive and store video and sensor data from top athletes, specifically using a motion capture system and accelerometers.

[1755] Data analysis: The collected data is analyzed to extract parameters such as stride length, ground contact time, and acceleration rate. This analysis is performed using machine learning tools such as TensorFlow and PyTorch.

[1756] Data comparison: Compare your performance data with that of top athletes to identify performance differences. Implement the analysis algorithm using Python.

[1757] Training menu generation: Based on the analysis results, we design an optimal training menu for the user. We utilize a reinforcement learning algorithm to generate an efficient training menu.

[1758] Progress monitoring: Receive data that allows users to report their training progress and adjust their training menu based on the progress data.

[1759] Emotion data processing: Receives data from the emotion engine and provides training menus and feedback according to the user's emotional state.

[1760] User terminal

[1761] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[1762] Data entry: The user enters their own competition data and training results.

[1763] Video recording and analysis: Record a video of the user's running and extract stride length, ground contact time, acceleration rate, etc. using the Google Cloud Vision API.

[1764] Display training menu: Displays the training menu sent from the server to the user.

[1765] Emotion data acquisition: Collect user emotion data through the emotion engine and send it to the server. OpenCV is used for facial expression recognition.

[1766] Receive Feedback: Receive feedback from the server and training menu updates.

[1767] Sensor Device

[1768] The sensor device is used to measure the athlete's movements and the force applied, and collects the following data:

[1769] Acceleration data: Measures acceleration during movement.

[1770] Force data: Measures the force when touching the ground.

[1771] Location Data: Tracking the rider's location during a run.

[1772] Emotion Engine

[1773] The emotion engine is a system that recognizes the user's emotional state and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[1774] Example of operation process

[1775] 1. Data Collection

[1776] The server collects and stores videos of top athletes and data from sensor devices, including a Motion Capture system and accelerometers.

[1777] The server receives and analyzes the driving data (video and sensor data) provided by the user.

[1778] 2. Data analysis and comparison

[1779] The server compares the data of the user with that of top athletes and analyzes the differences, particularly in terms of differences in each phase, such as the start dash, acceleration, top speed, and finish. TensorFlow and PyTorch are used for the analysis.

[1780] 3. Creation of training menu

[1781] The server then generates the optimal training menu for the user based on the analysis results. For example, if starting sprints are an issue, starting sprint practice drills and short-distance sprint practice drills will be added to the menu. This is done using a reinforcement learning algorithm.

[1782] The server transmits the training menu to the user terminal.

[1783] 4. Training implementation and progress monitoring

[1784] The user uses the terminal to practice according to the training menu.

[1785] The user periodically inputs the results of training into the terminal and transmits the data to the server.

[1786] The server analyzes the progress data sent from the user, evaluates the effectiveness of the training, and adjusts the training menu based on the progress data.

[1787] Example of using the emotion engine

[1788] 1. Acquiring Emotion Data

[1789] During training, the user uses the device's camera to recognize facial expressions. The emotion engine analyzes the user's facial expressions and detects emotions such as joy, anger, and sadness. OpenCV is used for facial recognition.

[1790] The emotion engine uses biosensors (such as a heart rate monitor) to detect changes in stress levels and motivation.

[1791] 2. Customized feedback

[1792] The server adjusts the training menu based on the emotional data, for example, reducing the intensity of training if the user feels fatigued.

[1793] The server customizes the feedback based on the emotional data, for example, providing additional challenges if the user shows high motivation.

[1794] Prompt Sentence Examples

[1795] Prompt statement to analyze top-class running speed data:

[1796] "top runner speed data analysis TensorFlow"

[1797] The prompt sentence that the server uses to analyze the user's emotion data:

[1798] "user emotion data analysis with OpenCV and Python"

[1799] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1800] System program processing flow

[1801] Step 1: Collecting top-tier athlete data

[1802] A server receives video of top athletes and data from sensor devices.

[1803] Input: Video data of top athletes, sensor device data (acceleration data, force data, position data).

[1804] Processing: Receives data from the Motion Capture system and accelerometer and stores it in a database.

[1805] Output: Saved performance data.

[1806] Step 2: Collect user data

[1807] The user inputs their own driving data (video and sensor device data) into the terminal.

[1808] The terminal transmits the input data to the server.

[1809] Input: User's driving data (video data, sensor data).

[1810] Processing: The user takes photos of their driving using a smartphone or other device and sends the data to the server, which then stores the received data in a database.

[1811] Output: Saved user driving data.

[1812] Step 3: Preprocessing the data

[1813] The server analyzes the collected video data frame by frame and converts it into a form that can be used for motion analysis.

[1814] Input: Video data.

[1815] Processing: Using Python, split the video data into frames and extract important frames.

[1816] Output: Preprocessed video data.

[1817] Step 4: Parameter extraction

[1818] The server extracts parameters such as stride length, ground contact time, and acceleration rate from the pre-processed data.

[1819] Input: Preprocessed video data and sensor data.

[1820] Processing: Using TensorFlow and PyTorch, we analyze and extract parameters such as stride length, ground contact time, and acceleration rate.

[1821] Output: The extracted parameter data.

[1822] Step 5: Data comparison

[1823] The server compares the data of top athletes with the user's data and analyzes differences and trends.

[1824] Input: Performance data of top athletes, performance data of the user.

[1825] Processing: Python scripts are used to compare the user's data with that of top athletes and analyze trends and differences.

[1826] Output: Data comparison results.

[1827] Step 6: Create a training menu

[1828] The server generates an optimal training menu for the user based on the analysis results.

[1829] Input: Data comparison results, extracted parameter data.

[1830] Processing: Uses a reinforcement learning algorithm (e.g., OpenAI's Gym) to generate a personalized training menu for the user.

[1831] Output: Training menu.

[1832] Step 7: Providing training menus

[1833] The server transmits the generated training menu to the user terminal.

[1834] Enter: Training Menu.

[1835] Processing: Format the training menu and send it to the user's device.

[1836] Output: Training menu displayed on the user's device.

[1837] Step 8: Implement training and collect progress data

[1838] The user performs training according to the training menu displayed on the terminal.

[1839] The user inputs the training result data into the terminal.

[1840] Input: Training implementation status and results data.

[1841] Processing: The user performs training using the device and inputs the results. The device then sends the data to the server.

[1842] Output: Progress data stored on the server.

[1843] Step 9: Analyze progress data and adjust menu

[1844] The server analyzes the progress data and evaluates the effectiveness of the training.

[1845] The server adjusts the training menu according to progress and sends updates to the user's terminal.

[1846] Input: Progress data.

[1847] Processing: Analytical algorithms are used to evaluate progress data and adjust training as needed.

[1848] Output: A tailored training menu.

[1849] Step 10: Collect and process emotion data

[1850] Facial expression recognition is performed using the device's camera while the user is training.

[1851] The emotion engine analyzes facial expression data to detect emotional states.

[1852] Input: User's facial expression data, biometric sensor data.

[1853] Processing: Analyzes the user's facial expressions using the OpenCV library to extract data on their emotional state, and measures their heart rate and other vital signs using biometric sensors.

[1854] Output: Emotion data.

[1855] Step 11: Feedback based on emotional data

[1856] The server adjusts the training menu based on the emotion data.

[1857] The server customizes the feedback based on the emotion data and transmits it to the user terminal.

[1858] Input: Emotion data.

[1859] Processing: Analyze emotional data and adjust training menus and feedback.

[1860] Output: Tailored training menus and customized feedback.

[1861] Prompt Sentence Examples

[1862] Prompt statement to analyze top-class running speed data:

[1863] "top runner speed data analysis TensorFlow"

[1864] The prompt sentence that the server uses to analyze the user's emotion data:

[1865] "user emotion data analysis with OpenCV and Python"

[1866] (Application example 2)

[1867] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1868] This invention relates to a system that provides a means for collecting and analyzing performance data of top workers. In particular, it aims to improve production efficiency by optimizing the operation of work robots in factories and dynamically adjusting work parameters based on the emotional state of the workers and robots. It also aims to improve the overall working environment by adjusting the work balance according to the worker's fatigue level and motivation.

[1869] The specific processing by the specific 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 and analyzing performance data of top athletes and workers, means for collecting and analyzing athletic performance data of users, means for designing an optimal training menu for users based on the analysis results, means for optimizing robot operation parameters based on worker performance data, and means for detecting the emotional states of workers and robots using an emotion engine and dynamically adjusting operation parameters. This makes it possible to establish an optimal work balance according to the state of workers and improve production efficiency.

[1870] A "top-class athlete" is someone who possesses outstanding skills and results in a particular sport and is highly regarded in the sporting community.

[1871] "Performance data" is a general term for data that includes dynamic and static information such as the efficiency of an athlete's or worker's movements and movements, how force is applied, and speed.

[1872] An "emotion engine" is a system that analyzes the emotional state of users or workers, extracts emotions such as joy, anger, and sadness as data, and provides that data in a usable form.

[1873] A "training menu" refers to a specific practice or training plan aimed at improving the performance of a user or athlete.

[1874] "User terminal" refers to a terminal device operated by a user, such as a smartphone, tablet, or PC, which is capable of communicating with the system and inputting data.

[1875] "Sensor device" refers to a device used to measure various data such as acceleration, position, and force, and is primarily used to obtain performance data.

[1876] A "robot" refers to an automated mechanical device that can perform programmed actions, and is used especially to perform tasks in factories.

[1877] "Movement parameters" include specific settings and indicators related to the robot's movements, and refer to information such as speed, position, and force.

[1878] "Emotional state" refers to the psychological and physiological state of a human or robot, including stress level, motivation, fatigue, etc.

[1879] The present invention relates to a system for collecting and analyzing performance data of top workers, particularly for optimizing robot operations in factories, and improving production efficiency by dynamically adjusting work parameters based on the emotional states of workers and robots.

[1880] System configuration

[1881] This system consists of a server, a user terminal, a sensor device, and an emotion engine.

[1882] server

[1883] The server mainly performs the following processes.

[1884] Performance Data Collection: Collect top worker performance data from sensor devices and videos.

[1885] Data analysis: Analyze the collected performance data and extract operating parameters.

[1886] Training menu generation: Based on the analysis results, an optimal training menu is designed for the user.

[1887] Optimization of robot motion: Optimize the robot's motion parameters based on the analysis results.

[1888] Emotion data processing: Receive data from the emotion engine and adjust the operating parameters according to the emotional state of the worker or robot.

[1889] User terminal

[1890] The user terminal is a mobile device such as a smartphone or tablet, and has the following functions:

[1891] Video recording and analysis: Workers' movements are videotaped and analyzed.

[1892] Display training menu: Displays the training menu sent from the server to the user.

[1893] Emotion data acquisition: Collect workers' emotion data through the emotion engine and send it to the server.

[1894] Receive Feedback: Receive feedback from the server and training menu updates.

[1895] Sensor Device

[1896] The sensor device is used to measure the movements and force applied to workers, and collects the following data:

[1897] Acceleration data: Measures acceleration during movement.

[1898] Force data: Measure the force applied during work.

[1899] Location data: Tracking worker locations.

[1900] Emotion Engine

[1901] The emotion engine is a system that recognizes the emotional state of workers and robots and acquires it as data, using, for example, facial expression recognition technology and biometric sensors.

[1902] Example of operation process

[1903] Data collection

[1904] The server collects and analyzes videos of top workers and data from sensor devices, extracting operational parameters and providing data to optimize the robot's operation.

[1905] Data analysis and comparison

[1906] The server compares the data of top-class workers with other workers and analyzes the differences, especially differences in behavior in each phase, and visualizes the differences to clarify issues with the worker's performance.

[1907] Training menu generation

[1908] Based on the analysis results, the server generates the optimal training menu for the worker, and also optimizes the robot's operating parameters, enabling more efficient work.

[1909] Training implementation and progress monitoring

[1910] The user uses the device to practice according to the training menu. The server analyzes the progress data sent by the user and evaluates the effectiveness of the training. The server adjusts the training menu and the robot's operating parameters as necessary.

[1911] Example of using the emotion engine

[1912] Acquiring emotion data

[1913] While the user is training, facial expression recognition is performed using the device's camera. An emotion engine analyzes the worker's facial expressions and detects emotions such as joy, anger, and sadness. Biometric sensors (such as a heart rate monitor) are used to detect changes in stress levels and motivation.

[1914] Customized Feedback

[1915] The server adjusts the robot's operating parameters based on the emotional data. For example, if a worker feels tired, it will increase the robot's workload to reduce the worker's burden.

[1916] As described above, by dynamically adjusting parameters based on emotional states, it is possible to effectively improve work efficiency.

[1917] Prompt Sentence Examples

[1918] Collect work videos and sensor data of Employee A working on Line 1 in the factory and compare them with top-class employees. Based on the results, optimize the robot's operating parameters and make adjustments according to Employee A's fatigue level.

[1919] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1920] Step 1:

[1921] The server collects performance data of top workers from sensor devices and cameras. Specifically, it collects and stores videos of the workers' work movements and sensor data such as acceleration data, force data, and position data. In this process, data from the sensors and cameras is continuously sent to the server and stored appropriately.

[1922] Input: Video data and sensor data of workers at work

[1923] Output: Saved performance data

[1924] Step 2:

[1925] The server analyzes the collected performance data. Specifically, it uses a video analysis system to extract movement parameters (such as movement speed and accuracy), and a sensor data analysis system to analyze force application and position data. The analysis results are compiled as performance indicators.

[1926] Input: Stored performance data

[1927] Output: Analyzed operating parameters and performance indicators

[1928] Step 3:

[1929] The server adjusts the robot's operating parameters based on the analysis results. Specifically, it uses robot control software to reflect the analyzed operating parameters in the robot's control system and set the optimal speed and operating pattern.

[1930] Input: Analyzed operating parameters and performance indicators

[1931] Output: Adjusted robot movement parameters

[1932] Step 4:

[1933] The user terminal displays the training menu sent from the server, takes videos of the worker, and collects emotional data using an emotion engine. Specifically, the camera captures the worker's facial expressions and movements, and the emotion engine analyzes their emotional state (fatigue level, stress level, etc.).

[1934] Input: Worker training menu, emotion data collection

[1935] Output: Parsed emotion data

[1936] Step 5:

[1937] The server receives and analyzes the emotional data and training progress data sent from the user's device, and comprehensively evaluates the worker's emotional state and training progress, readjusting the training menu and robot's operating parameters as necessary.

[1938] Input: Parsed emotion data and training progress data

[1939] Output: Retuned training menu and robot movement parameters

[1940] Step 6:

[1941] The server sends the adjusted training menu and robot operating parameters to the user's terminal, which displays them to the worker. The worker then retrains based on this and sends the results to the server again.

[1942] Input: Retuned training menu and robot movement parameters

[1943] Output: New training menu and action instructions displayed on the user's device

[1944] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1945] 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.

[1946] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1947] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1948] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1949] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1950] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1951] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1952] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1953] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1954] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1955] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1956] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1957] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1958] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1959] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1960] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1961] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1962] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1963] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1964] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1965] The following is further disclosed regarding the above embodiment.

[1966] (Claim 1)

[1967] A means of collecting and analyzing performance data from top athletes;

[1968] a means for collecting and analyzing athletic performance data of a user based on the performance data of the top-class athletes;

[1969] A means for designing an optimal training menu for the user based on the analysis results;

[1970] means for providing the training menu to a user terminal;

[1971] means for monitoring the user's training progress and adjusting the training menu;

[1972] A system including:

[1973] (Claim 2)

[1974] The system of claim 1, further comprising a sensor device for acquiring performance data of the top athletes.

[1975] (Claim 3)

[1976] 2. The system according to claim 1, further comprising image analysis means for analyzing a video of the user while he is running.

[1977] "Example 1"

[1978] (Claim 1)

[1979] A means of collecting and analyzing performance data from top athletes;

[1980] a means for collecting and analyzing athletic performance data of a user based on the performance data of the top-class athletes;

[1981] A means for designing an optimal training menu for the user based on the analysis results;

[1982] means for providing the training menu to a user terminal;

[1983] means for monitoring the user's training progress and adjusting the training menu;

[1984] means for extracting performance parameters such as stride length, ground contact time, and acceleration rate using the collected video data and sensor data;

[1985] means for comparing said user's performance data with that of top athletes to identify performance differentials;

[1986] A system including:

[1987] (Claim 2)

[1988] 10. The system of claim 1, further comprising a detection device for acquiring performance data of the top athletes.

[1989] (Claim 3)

[1990] 2. The system according to claim 1, further comprising: an image analysis means for analyzing a video of the user running; and an analysis means for evaluating the effectiveness of training based on the analysis results.

[1991] "Application Example 1"

[1992] (Claim 1)

[1993] A means to collect and analyze performance data on top-tier automated equipment;

[1994] a means for collecting and analyzing performance data of a user's automated equipment based on the performance data of the top-class automated equipment;

[1995] A means for designing an optimal operation improvement and maintenance menu for the user based on the analysis results;

[1996] means for providing the maintenance menu to a user terminal;

[1997] a means for monitoring the progress of a user's maintenance and adjusting the maintenance menu;

[1998] A system including:

[1999] (Claim 2)

[2000] The system of claim 1 , further comprising a sensor device for acquiring performance data of the top-tier automated equipment.

[2001] (Claim 3)

[2002] 2. The system according to claim 1, further comprising data analysis means for analyzing data during operation of the user's automated equipment.

[2003] "Example 2: Combining Emotion Engines"

[2004] (Claim 1)

[2005] A means of collecting and analyzing performance data from top athletes;

[2006] a means for collecting and analyzing athletic performance data of a user based on the performance data of the top-class athletes;

[2007] A means for designing an optimal training menu for the user based on the analysis results;

[2008] means for providing the training menu to a user terminal;

[2009] means for monitoring the user's training progress and adjusting the training menu;

[2010] means for collecting emotional data of a user and adjusting a training menu based on the emotional data;

[2011] A system including:

[2012] (Claim 2)

[2013] The system of claim 1, further comprising a sensor device for acquiring performance data of the top athletes.

[2014] (Claim 3)

[2015] 2. The system according to claim 1, further comprising image analysis means for analyzing a video of the user while he is running.

[2016] "Application example 2 when combining emotion engines"

[2017] (Claim 1)

[2018] A means of collecting and analyzing performance data from top athletes;

[2019] a means for collecting and analyzing athletic performance data of a user based on the performance data of the top-class athletes;

[2020] A means for designing an optimal training menu for the user based on the analysis results;

[2021] means for providing the training menu to a user terminal;

[2022] means for monitoring the user's training progress and adjusting the training menu;

[2023] A means to collect and analyze performance data from top workers and apply it to work robots in factories;

[2024] means for optimizing the operating parameters of the robot based on the performance data of the worker;

[2025] a means for detecting the emotional states of the worker and the robot using an emotion engine and dynamically adjusting work parameters;

[2026] A system including:

[2027] (Claim 2)

[2028] The system of claim 1, further comprising a sensor device for acquiring performance data of the top athletes.

[2029] (Claim 3)

[2030] 2. The system according to claim 1, further comprising image analysis means for analyzing a video of the user while he is running. [Explanation of symbols]

[2031] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting and analyzing performance data from top athletes; a means for collecting and analyzing athletic performance data of a user based on the performance data of the top-class athletes; A means for designing an optimal training menu for the user based on the analysis results; means for providing the training menu to a user terminal; means for monitoring the user's training progress and adjusting the training menu; A system including:

2. The system of claim 1 , further comprising a sensor device for acquiring performance data of the top athletes.

3. 2. The system according to claim 1, further comprising image analysis means for analyzing a video of the user while he is running.

Images