system

Abstract

We provide the system. [Solution] A determination means that receives physical information and determines the stage of growth, An analytical means for analyzing motion information and identifying issues related to shape and skills, A generation means that automatically generates individually optimized training plans based on analysis results, A predictive method that analyzes past injury history and training plans to predict health risks, A notification method that informs users of risk mitigation measures based on prediction results, A feedback provision method that provides real-time feedback based on physical information and analysis results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that there is a lack of an optimal training plan according to individual growth stages for children engaged in sports. Furthermore, there is anxiety about the risk of injuries peculiar to the growth period, and means for predicting this in advance and taking appropriate measures are required. It is difficult to sufficiently address these problems with conventional uniform training methods, and the need for an individually optimized approach is increasing.

Means for Solving the Problems

[0005] This invention provides a sports training system that incorporates a determination means to receive physical data and determine the child's growth stage, thereby enabling an approach tailored to the child's development. Furthermore, it uses an analysis means to analyze movement data to identify issues with form and technique, and a generation means to automatically generate an individually optimized training plan based on the analysis results. In addition, it is equipped with a prediction means that analyzes past injury history and training plans to predict injury risk, and a notification means that informs the user of risk mitigation measures, thereby providing a safe and effective training environment. This effectively solves the technical challenges in children's sports training.

[0006] "Determination means" refers to a device or function for identifying a growth stage based on information received from the user.

[0007] "Analysis means" refers to a device or function for capturing motion data and analyzing and identifying form and technical issues.

[0008] "Generation means" refers to a device or function for creating an individualized training plan based on the analyzed information.

[0009] A "predictive means" is a device or function used to analyze collected data and predict the risk of injury.

[0010] A "notification means" is a device or function that provides information to the user based on predicted risks and prompts them to take appropriate action.

[0011] A "correction mechanism" is a device or function used to adjust the training plan based on user feedback.

[0012] "Data storage means" refers to a device or function for storing continuously collected information and making it available for later analysis and system learning. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system for achieving individualized optimization in sports training, taking into account the growth and safety of children. This system is composed of the following various means and their coordination.

[0035] First, the user (parent or coach) inputs physical data such as the child's age, height, weight, and past injury history via a terminal. This information is sent from the terminal to the server. The server stores the received data in a database and uses it to determine the child's growth stage. The assessment system is activated and identifies the child's growth stage. This prepares the user for receiving suggestions for the most suitable training for their child.

[0036] Next, the user uploads a video of their child's training from their device. The server runs a motion analysis AI to analyze the uploaded video data. The analysis identifies the child's form and technical issues from the video and extracts specific areas for improvement based on that.

[0037] Based on the identified issues, the server automatically generates a training plan using a generation mechanism. This plan includes exercises and practice schedules tailored to the individual's growth stage and technical challenges. The generated plan is then notified to the user via their device.

[0038] Furthermore, the server assesses the risk of injury based on past injury history and the current training plan. When the prediction system detects an injury risk, it provides the user with advice on risk reduction and suggestions for rest through the notification system.

[0039] For example, suppose a 10-year-old child in a growth spurt plays soccer and has a history of knee injuries. In this case, the server would generate a training plan that includes soccer-specific balance-improving exercises and suggest increasing rest days if it determines that the stress on the knees is excessive.

[0040] Users can provide feedback on the provided training plan and submit revision requests as needed. The server has a mechanism to update the training plan based on that feedback.

[0041] Thus, by integrating the entire system, the present invention provides a specific form for conducting safe and effective sports training.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user inputs the child's physical data. Using a terminal, they enter information such as age, height, weight, and past injury history into the system. This data serves as basic information for determining the child's growth stage.

[0045] Step 2:

[0046] The device sends data to the server. The collected physical data is sent to the server and stored in a database. The stored data is used in subsequent analysis steps.

[0047] Step 3:

[0048] The server determines the child's growth stage. Using this determination method, it analyzes the received physical data to identify the child's growth stage. This information is crucial for developing a training plan.

[0049] Step 4:

[0050] Users upload training videos. By uploading videos of their children exercising from their devices to the system, it becomes possible to analyze their exercise form.

[0051] Step 5:

[0052] The server analyzes the video data. A motion analysis AI analyzes the uploaded video data to identify form and technical issues. Specific areas for improvement are then identified.

[0053] Step 6:

[0054] The server generates a training plan. Using the generation method, it creates an individually optimized training plan based on the growth stage and analysis results. The plan includes specific exercises and a schedule.

[0055] Step 7:

[0056] The server predicts the risk of injury. The prediction method analyzes past injury data and the current training plan to assess the risk. If the risk is determined to be high, the following actions are required.

[0057] Step 8:

[0058] The server notifies the user of risk mitigation measures. Through this notification system, the server suggests rest periods and warns users about high-risk activities. Based on this information, users can safely adjust their training.

[0059] Step 9:

[0060] Users provide feedback. They can send opinions and suggestions for improvements to the training plan from their device to the server. This feedback is used to improve the training plan.

[0061] Step 10:

[0062] The server updates the training plan. Based on user feedback, it regenerates the plan using corrective measures and provides the updated version. This ensures that the plan is always up-to-date.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] In sports training, there is a need to provide efficient training while ensuring safety, by achieving individualized optimization that takes into account the child's stage of development. However, conventional methods have not adequately considered children's growth and past injury history, making it difficult to achieve sufficient results.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes a determination means for receiving information about the body and determining the developmental stage; an analysis means for analyzing movement information and identifying issues with form and technique; a generation means for automatically generating an individually optimized exercise plan based on the analysis results; a prediction means for analyzing information about past injuries and the exercise plan and predicting the likelihood of injury; a notification means for notifying the user of risk reduction measures based on the prediction results; and a communication means for sending and receiving information from the user's terminal. This enables the user to perform safe and effective training adapted to the child's developmental stage.

[0068] "Physical information" refers to data that indicates an individual's physiological and health characteristics, such as age, height, weight, and past injury history.

[0069] A "developmental stage" refers to a specific stage in an individual's growth process, indicating the degree of physiological and psychological development.

[0070] "Motion information" refers to data about the body's posture and form during exercise, and is used for analyzing sports techniques.

[0071] "Analysis means" refers to a device or program that has the function of analyzing motion information and identifying problems in form or technology.

[0072] A "generation means" is a device or program that has the function of automatically creating individually optimized movement plans based on the analyzed information.

[0073] A "predictive means" is a device or program that has the function of analyzing and predicting the likelihood of injury using past injury information and exercise plans.

[0074] A "notification means" is a device or program that has the function of informing users of risk mitigation measures based on prediction results.

[0075] "Communication means" refers to a device or program equipped with an interface or protocol for sending and receiving data between a user's terminal and another device.

[0076] This invention is a system for achieving individualized optimization in sports training, taking into account the growth and safety of children. This system involves collaboration between a terminal, a server, and the user to collect and analyze various data, and to generate and provide training plans.

[0077] First, the user uses their device to input physical information such as age, height, weight, and past injury history. The device then sends this data to the server using a secure communication protocol. One example of such a method is data transfer using HTTPS.

[0078] The server uses database management systems such as MySQL® or PostgreSQL to store the received information in a relational database. The stored information is then used to determine the child's developmental stage using a determination method. The algorithm for determination utilizes statistical and machine learning models.

[0079] Next, the user uploads a training video of their child via their device. The server analyzes the video data using a motion analysis AI model. For example, a posture analysis model based on TENSORFLOW® is used. The results obtained from the analysis include points for form improvement and technical challenges.

[0080] The server automatically generates individually optimized exercise plans using a generation mechanism. These plans are designed based on the child's developmental stage and analyzed technical challenges, and include exercise and practice schedules. The generated plans are sent to and notified to the user via the terminal.

[0081] Furthermore, the server uses prediction tools to analyze information on past injuries and exercise plans to predict the likelihood of injury. If a risk is detected, a notification tool sends the user advice on how to mitigate the risk.

[0082] For example, if a user enters a prompt such as, "Please create an individualized training plan for a 10-year-old child who plays soccer. He is 140cm tall, weighs 35kg, and has a history of knee injuries," the server will generate an optimal training plan.

[0083] In this way, the system provides effective training while ensuring the safety of children and is tailored to their individual developmental stages.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user uses a device to input physical information about the child, such as age, height, weight, and past injury history. The device organizes this input data and sends it to the server using a secure communication protocol (e.g., HTTPS). Numerical data of physical information is provided as input, and the output data is sent to the server.

[0087] Step 2:

[0088] The server stores the body information received from the terminal into a relational database. Database systems such as MySQL or PostgreSQL are used for this purpose. The received data is provided as input, and registration into the database is performed, resulting in the output of accurate information storage.

[0089] Step 3:

[0090] The server activates a determination system based on stored information to assess the child's developmental stage. This is done using statistical algorithms and machine learning models. Physical information and existing growth data are used as input, and the child's developmental stage is identified as the output.

[0091] Step 4:

[0092] The user uploads training videos of their child using their device. The device compresses the video data and sends it to the server. The input is a video file, and the output is obtained when the transmission to the server is complete.

[0093] Step 5:

[0094] The server runs a motion analysis AI model (e.g., using TensorFlow) to analyze the received video data. The input is video data, and the model analyzes the motion, identifying metrics for form improvement and technical challenges as output.

[0095] Step 6:

[0096] The server automatically generates individually optimized exercise plans using a generation method based on the analysis results. The inputs used are the results of form analysis and developmental stage information, and the output is an optimized training plan.

[0097] Step 7:

[0098] The generated training plan is notified to the user via the device, and specific exercises and schedules are presented. An exercise plan is given as input, and the output is a notification to the user.

[0099] Step 8:

[0100] The server activates a prediction mechanism and analyzes the likelihood of injury based on past injury data and the generated exercise plan. The input includes injury data and the exercise plan, and the output is an injury risk assessment.

[0101] Step 9:

[0102] If the risk is high, the server will use a notification system to send advice to the user on risk mitigation measures. The input is the risk assessment result, and the output is a risk notification to the user.

[0103] Step 10:

[0104] Users can review the training plan generated on their device and submit feedback by pressing a button. The device sends this feedback to the server. The user's feedback is given as input, and the feedback is sent to the server as output.

[0105] Step 11:

[0106] The server uses correction mechanisms to improve the training plan based on feedback received from the user and resends it to the user. The input includes feedback information, and the output is the transmission of the improved exercise plan.

[0107] (Application Example 1)

[0108] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0109] In youth sports training, it is difficult to provide training plans that take into account individual developmental stages and safety considerations. Furthermore, there are limitations to providing real-time feedback, making it difficult to efficiently support children's growth and improve their form. Additionally, it is difficult to anticipate and address health risks during training. There is a need to provide a system that solves these problems and enables safer and more effective sports training.

[0110] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0111] In this invention, the server includes a determination means for receiving physical information and determining the growth stage, an analysis means for analyzing movement information and identifying challenges in shape and skills, a generation means for automatically generating an individually optimized training plan based on the analysis results, a prediction means for analyzing past injury history and training plans and predicting health risks, and a feedback provision means for providing real-time feedback based on physical information and analysis results. This enables the provision of training plans tailored to individual growth stages, real-time support for shape improvement, and proactive detection of health risks.

[0112] "Physical information" refers to data such as age, height, weight, and past injury history used to determine an individual's stage of development.

[0113] "Stage of development" refers to a state of physical and mental maturity identified based on an individual's development and age.

[0114] "Motion information" refers to data related to form and skill in sports and exercise, and is used to identify form and technical challenges.

[0115] "Analysis means" refers to a method or process for processing and analyzing received data to extract problems and areas for improvement.

[0116] A "training plan" refers to a plan that automatically generates information based on an individual's developmental stage and analysis results, outlining the schedule and content of specific exercises and workouts.

[0117] "Health risk" refers to an index that assesses the likelihood of injury or health problems occurring based on past injury history and current training content.

[0118] "Real-time feedback" refers to immediate feedback and improvement advice provided during exercise, helping users quickly correct their form and skills.

[0119] A server plays a central role in the system that realizes this invention. The server utilizes a database to collect physical information and determine the stage of growth. Users input information such as age, height, weight, and past injury history via a terminal and send it to the server. The server can use a determination means to identify the individual's stage of growth based on the collected information.

[0120] Next, the server takes on the role of analyzing the movement information. The user uses a terminal to record a video of themselves exercising and uploads it to the server. The server uses analysis tools to process and analyze the received video data. By running a deep learning model, it identifies the shape of the movement and any skill-related problems in real time. For example, if a 10-year-old child is practicing dribbling in soccer and their form needs improvement, the server provides immediate feedback.

[0121] The server generates the training plan. Based on the analysis results, the server automatically generates an individually optimized training plan. This includes exercises and schedules tailored to the user's stage of development and is provided to the user via their device. The server also compares past injury history with the current training plan to assess health risks and provides risk mitigation measures through predictive means. For example, it can make specific suggestions such as "increase rest days to reduce stress on the knees."

[0122] This system also incorporates means of providing real-time feedback via voice and display. The server uses these feedback means to provide guidance on improving posture during exercise, helping users make adjustments as they continue their training.

[0123] As an example of a prompt, one might give the generating AI model the instruction, "Design a program that explains how a sports training assistant robot can give real-time advice to a child on how to improve their form."

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] The user inputs physical information via a terminal and sends it to the server. The input data includes age, height, weight, and past injury history, and the server uses this to determine the user's growth stage. The server receives this data, stores it in a database, and performs processing to identify the growth stage using a growth stage determination means.

[0127] Step 2:

[0128] The user records a video of themselves exercising using their device and uploads it to the server. The server receives this video data as input and processes and analyzes the motion information using analytical tools. By analyzing the video frames, the server identifies shapes and skill challenges from the movements. The server saves the identified data to a temporary file and uses it to suggest areas for improvement.

[0129] Step 3:

[0130] The server automatically generates a training plan based on the analysis results. The server's generation method creates individually optimized training content and sends this data to the terminal. The training plan includes exercises and schedules adapted to the user's stage of development, and this information is provided to the user. The generated training plan is recorded in a database for later feedback.

[0131] Step 4:

[0132] The server analyzes past injury history and generated training plans to assess health risks. It uses predictive tools to analyze risk and extract risk reduction measures. This assessment process involves data calculations to quantify risk levels. If the risk is high, it suggests rest or changes to exercise and sends notifications to the user.

[0133] Step 5:

[0134] Users perform training based on the provided training plan and return feedback to the server. The server receives the feedback using correction tools and adjusts the training plan as needed. The feedback is analyzed, updated in the database, and reflected in the training plan. Specifically, suggestions for form improvements and health-related comments are made.

[0135] Step 6:

[0136] The server uses data storage methods to record training plans, growth stage information, and health risk information in a centralized data storage. This data is used to train subsequent analysis results and improve the system's accuracy. The data is anonymized and stored securely. Prompt statements necessary for the generated AI model are also created and stored here and used in subsequent analyses.

[0137] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0138] This invention provides a system for sports training that offers individualized optimization, taking into account each individual's stage of development and risk of injury, and further considers the user's emotional state, thereby realizing an effective and safe training environment. This system is composed of multiple means linked together, as shown below.

[0139] The user first enters basic data about their child via a device. This includes age, height, weight, and past injury history. This data is sent from the device to a server and recorded in a database. The server uses a determination tool to determine the child's growth stage based on this data and stores the results.

[0140] During training, users upload videos of their children's movements from their devices. This data is necessary for motion analysis. The server uses analysis tools to analyze the videos and identify form and technical issues. The analysis results are used by a generation tool to automatically generate individually optimized training plans.

[0141] A distinctive feature of this invention is the incorporation of an emotion engine. It recognizes the user's facial expressions and tone of voice to determine their current emotional state. Specifically, it collects data through the device's camera and microphone and performs emotion analysis. This information influences the content and advice of the training plan, dynamically adjusting the plan according to the user's motivation and fatigue level during training.

[0142] Furthermore, the server uses injury prediction tools to analyze past injury history and training plans to predict injury risk. It also considers user emotional data obtained through an emotion engine and presents appropriate risk mitigation measures to the user via notification tools. These notifications may include, for example, recommendations to take a break if there are signs of fatigue.

[0143] For example, if a 13-year-old basketball player in their growth phase reports feeling fatigued during post-game practice, the emotion engine will recognize this emotion and generate a training plan with longer rest periods.

[0144] Finally, users provide feedback from their devices, submitting information about the effectiveness of their training plan and areas for improvement. This feedback is stored on the server and used to refine future training plans. This process optimizes the system to continuously provide high-quality training.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] The user uses a device to enter basic data about their child. This includes age, height, weight, sport, and past injury history. This information forms the basis for developing a training plan.

[0148] Step 2:

[0149] The terminal sends the input data to the server. The server records the received data in a database and activates a determination mechanism to determine the growth stage.

[0150] Step 3:

[0151] The server analyzes the growth stage using a determination mechanism. The determined growth stage information is an important element for designing individual training plans.

[0152] Step 4:

[0153] Users record videos of their training sessions from their devices and upload them to the system. This video data is used for detailed analysis of their exercise form.

[0154] Step 5:

[0155] The server uses motion analysis AI to analyze video data. The analysis tool identifies form and technical challenges, and the results are used by the generation tool to create a training plan.

[0156] Step 6:

[0157] The server automatically generates individually optimized training plans using a generation mechanism. These plans include analysis results and specific exercises tailored to the user's progress stage.

[0158] Step 7:

[0159] The server recognizes the user's emotions. Based on data collected through the device's camera and microphone, the emotion engine determines the user's emotional state.

[0160] Step 8:

[0161] The server incorporates emotional data into the training plan. The content of the training plan is adjusted considering the emotional data, and a plan tailored to the user's motivation and fatigue level is developed.

[0162] Step 9:

[0163] The server predicts the risk of injury and notifies the user of risk mitigation measures. The prediction system assesses the risk based on past injury data and the current training plan, and the notification system presents safety guidelines.

[0164] Step 10:

[0165] After completing training, the user provides feedback from their device. The server incorporates the received feedback into the next plan generation and optimizes the training plan using corrective measures.

[0166] (Example 2)

[0167] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0168] Traditional sports training systems struggle to provide exercise plans tailored to individual developmental stages and emotional states, making it difficult to achieve consistent results. Furthermore, injury prevention measures are often inadequate, potentially compromising user motivation and safety. To address these challenges, it is necessary to optimize exercise plans by appropriately incorporating user information and emotional states.

[0169] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0170] In this invention, the server includes a determination means for receiving physical information and determining the growth stage, an analysis means for analyzing movement information and identifying technical problems, a generation means for automatically generating an individually optimized exercise plan based on the analysis results, a prediction means for analyzing past health history and exercise plans and predicting health risks, and an adjustment means for acquiring emotional data and dynamically adjusting the exercise plan according to motivation and fatigue levels. This makes it possible to provide a safe and effective exercise plan tailored to each individual user.

[0171] "Physical information" refers to information about an individual's physical and health status, such as age, height, weight, and past health history.

[0172] "Growth stage" is an indicator that shows the user's physical and age-related maturity, and is used to optimize exercise plans.

[0173] "Determination means" refers to elements used to identify the growth stage based on physical information and select an appropriate exercise plan.

[0174] "Motion information" refers to data related to the user's movements during exercise, and is collected to evaluate form and technical issues.

[0175] "Analysis means" refers to the process of analyzing operational information to identify the user's technical challenges.

[0176] The "generation means" refers to a means for automatically creating individually optimized motion plans based on the results obtained from the analysis means.

[0177] "Past health history" refers to records of health problems or injuries that the user has experienced in the past.

[0178] An "exercise plan" refers to a customized training schedule and exercise routine based on the user's abilities and goals.

[0179] A "predictive method" is a process for assessing future health risks based on past health history and current exercise plans.

[0180] A "notification method" is a system for informing users about health risks and countermeasures to address them.

[0181] "Emotional data" refers to information about the emotional state collected from the user's facial expressions and voice.

[0182] "Adjustment methods" refer to the process of dynamically modifying exercise plans based on acquired emotional data to improve user motivation and safety.

[0183] This invention is a system that provides sports training optimized for each individual user. The system accepts personal data provided by the user via a terminal and transmits it to a server for processing.

[0184] The server first acquires physical information, including age, height, weight, and past health history. This data is sent from the terminal to the server, which uses a determination tool to assess the individual's growth stage. Based on this stage determination, the server evaluates what kind of training is appropriate for each individual.

[0185] Next, the user captures footage of their movements during training using their device and sends it to the server. The server uses various analytical tools to analyze this movement data in detail, identifying technical problems and areas for improvement. This analysis is performed quickly and accurately using cloud-based analysis software.

[0186] The server then uses a generation mechanism to create an individually optimized exercise plan based on the analysis results and growth stage information. A generation AI model is utilized to provide a dynamic training plan that responds to user interaction. This exercise plan includes specific exercises and rest periods.

[0187] Furthermore, the server evaluates past health data and the generated exercise plan, and uses predictive tools to forecast health risks. This can reduce the potential health risks that the planned exercise may pose.

[0188] Emotional data is also collected using the camera and microphone functions on the user's device. The server analyzes this data using adjustment mechanisms and makes fine adjustments to the exercise plan based on the user's emotional state.

[0189] As a concrete example, consider a 13-year-old basketball player. If this user provides feedback that they feel fatigued after a game, the server can take their emotional state and self-report into consideration and use a generation mechanism to adjust their exercise plan to reduce fatigue.

[0190] Examples of prompts include: "Propose an optimal training plan for a 13-year-old basketball player who is feeling fatigued after a game. Please take into account emotional data and past injury history."

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The user enters personal physical information using a terminal. Specifically, they enter age, height, weight, and past health history. The entered data is sent from the terminal to the server. The server processes this physical information using a determination mechanism to determine the user's growth stage. The determined growth stage is recorded in a database.

[0194] Step 2:

[0195] The user uses a device to record their movements during training and uploads the video to the server. The server processes the received video using an analysis device to analyze the form of the movements and any technical challenges. In this process, the video data is analyzed frame by frame, and technical challenges are identified using skeletal analysis software. The analysis results are then passed to a generation device.

[0196] Step 3:

[0197] The server automatically generates an individually optimized training plan using a generation method based on the analysis results and information on the user's growth stage. This process utilizes a generation AI model to propose an exercise plan tailored to the user's technical challenges and growth stage. The generated training plan includes exercise menus and rest timings.

[0198] Step 4:

[0199] The user's current emotional data is collected using the device's camera and microphone. As the user expresses their emotions through the device during training, this data is sent to the server. The server uses adjustment mechanisms to analyze the emotional data and dynamically adjust the training plan. This analysis allows for adjustments to the plan to increase motivation and take fatigue into consideration.

[0200] Step 5:

[0201] The server evaluates past health history and generated training plans, and uses predictive tools to forecast future health risks. This forecast involves analysis to assess the impact of specific actions on health. The forecast results are used to notify the user of the risks and issue warnings.

[0202] Step 6:

[0203] Users input feedback on the effectiveness of their training and areas for improvement into their device. This feedback information is sent to the server and stored in a database. The server uses corrective measures to incorporate this information into subsequent training plans, thereby ensuring continuous improvement.

[0204] (Application Example 2)

[0205] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0206] In recent years, with the increasing diversity of individual lifestyles and health conditions, there has been a growing demand for individually optimized methods of maintaining health. However, conventional health maintenance systems only provide general guidelines and have struggled to offer dynamic, personalized plans tailored to individual growth stages and health conditions. Furthermore, they lacked mechanisms to adjust activity plans to take into account changes in emotional states. A system is needed to address these challenges and support health maintenance efficiently and effectively.

[0207] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0208] In this invention, the server includes a determination means for receiving physical information and determining the growth stage; an analysis means for analyzing movement information and identifying technical problems; a generation means for automatically generating individually optimized health maintenance plans based on the analysis results; a prediction means for analyzing past health status history and activity plans and predicting health risks; and an adjustment means for determining health-related biometric information and emotional states and dynamically adjusting the activity plan. This enables efficient health maintenance according to the growth stage and emotional state of each individual user.

[0209] "Physical information" is a general term for health-related data such as the user's heart rate, steps taken, and body fat percentage.

[0210] "Growth stage" is a concept that refers to the stages that indicate the process of a user's physical and age-related development.

[0211] "Determination means" refers to the process or technology used to identify and determine the growth stage based on the input information.

[0212] "Movement information" refers to data related to the user's exercise and activity patterns, and specifically includes steps taken, distance traveled, posture, etc.

[0213] "Analysis methods" refer to the processes and techniques used to analyze acquired data and identify technical problems and areas for improvement.

[0214] "Generation method" refers to the process or technology for automatically creating individually optimized health maintenance plans based on analysis results.

[0215] A "health maintenance plan" refers to a plan that outlines a series of activities and dietary patterns recommended for users to maintain or improve their health.

[0216] "Health history" refers to a collection of records and data related to past health conditions.

[0217] An "activity plan" is a set of elements that make up the guidelines and schedules for a user's daily activities.

[0218] "Health risk" refers to the possibility or circumstances under which a user's health may be harmed, and it is an evaluation criterion aimed at detecting such risks proactively.

[0219] "Predictive methods" refer to processes and technologies used to foresee future health risks based on past data and trends.

[0220] "Adjustment methods" refer to processes and techniques for dynamically changing activity plans, taking into account the user's current health and emotional state.

[0221] "Emotional state" is a concept that describes a user's current psychological and emotional condition, which can sometimes affect their health.

[0222] "Biometric information" is a general term for data related to the user's physical responses and condition, such as heart rate and body temperature.

[0223] This invention is a system for supporting health maintenance, and it has a configuration that dynamically generates an individualized health maintenance plan from the user's physical information, movement information, and emotional state.

[0224] The system includes a server and user terminals. The server is equipped with hardware for determining physical information and growth stage, and software for analysis and generation. Specifically, it processes data using the Python data analysis libraries Pandas and NumPy, and performs sentiment analysis using TensorFlow. Furthermore, it uses machine learning algorithms based on historical data to predict health risks.

[0225] Users use their smartphones to input or automatically collect various data from their daily lives (e.g., heart rate, steps taken). The collected data is sent to a server and stored in a database.

[0226] The user's emotional state is captured through the device's camera and microphone, and emotion analysis is performed on the server. Based on the analysis results, a specific health maintenance plan is individually generated and provided to the user. The generating AI model takes into account the user's current lifestyle and goals, and recommends various health maintenance activities.

[0227] For example, when a user is monitoring their daily walks, the app might send a notification such as, "Your step count has increased, so we recommend adding a new stretching routine as your next step."

[0228] An example of a prompt for a generative AI model might be: "Analyze what health changes the user needs based on their heart rate and emotional state during recent activities."

[0229] In this way, the system can provide personalized and continuous health support to individual users.

[0230] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0231] Step 1:

[0232] The user's device collects physical information such as heart rate, steps taken, and body fat percentage. This includes data from sensors built into the device and from wearable devices. It receives physical information as input and sends it to the server.

[0233] Step 2:

[0234] The server uses the received physical information to determine the growth stage. It analyzes the data using Pandas and identifies the appropriate growth stage based on the user's age and physical data. The server then generates the identified growth stage information as output.

[0235] Step 3:

[0236] The user's device uses its camera and microphone to collect data on the user's facial expressions and voice. It receives facial expression information as input and sends it to the server.

[0237] Step 4:

[0238] The server uses collected facial and voice information to perform emotion analysis using TensorFlow. It takes the data as input to determine the user's emotional state and generates this as output.

[0239] Step 5:

[0240] The server analyzes operational information to identify technical problems and areas where the user needs assistance. It performs data calculations using NumPy and outputs specific technical challenges.

[0241] Step 6:

[0242] The server automatically generates an individually optimized health maintenance plan using an AI model based on the determined growth stage, emotional state, and technical challenges. This results in the output of the most suitable health maintenance plan for the user.

[0243] Step 7:

[0244] The server analyzes past health history and generated plans to predict health risks. It uses machine learning algorithms to build a predictive model and obtains a health risk assessment as output.

[0245] Step 8:

[0246] The generated health maintenance plan and health risk assessment are notified to the user's device. The device then presents these results to the user and allows them to adjust their activity plan as needed.

[0247] This sequence of steps allows the system to provide dynamic support tailored to the user's health condition.

[0248] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0249] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0250] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0251] [Second Embodiment]

[0252] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0253] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0254] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0255] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0256] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0257] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0258] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0259] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0260] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0261] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0262] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0263] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0264] This invention is a system for achieving individualized optimization in sports training, taking into account the growth and safety of children. This system is composed of the following various means and their coordination.

[0265] First, the user (parent or coach) inputs physical data such as the child's age, height, weight, and past injury history via a terminal. This information is sent from the terminal to the server. The server stores the received data in a database and uses it to determine the child's growth stage. The assessment system is activated and identifies the child's growth stage. This prepares the user for receiving suggestions for the most suitable training for their child.

[0266] Next, the user uploads a video of their child's training from their device. The server runs a motion analysis AI to analyze the uploaded video data. The analysis identifies the child's form and technical issues from the video and extracts specific areas for improvement based on that.

[0267] Based on the identified issues, the server automatically generates a training plan using a generation mechanism. This plan includes exercises and practice schedules tailored to the individual's growth stage and technical challenges. The generated plan is then notified to the user via their device.

[0268] Furthermore, the server assesses the risk of injury based on past injury history and the current training plan. When the prediction system detects an injury risk, it provides the user with advice on risk reduction and suggestions for rest through the notification system.

[0269] For example, suppose a 10-year-old child in a growth spurt plays soccer and has a history of knee injuries. In this case, the server would generate a training plan that includes soccer-specific balance-improving exercises and suggest increasing rest days if it determines that the stress on the knees is excessive.

[0270] Users can provide feedback on the provided training plan and submit revision requests as needed. The server has a mechanism to update the training plan based on that feedback.

[0271] Thus, by integrating the entire system, the present invention provides a specific form for conducting safe and effective sports training.

[0272] The following describes the processing flow.

[0273] Step 1:

[0274] The user inputs the physical data of the child. Using the terminal, information such as age, height, weight, and past injury history is input into the system. This data serves as the basic information for determining the child's growth stage.

[0275] Step 2:

[0276] The terminal sends the data to the server. The collected physical data is sent to the server and stored in the database. The stored data is used in subsequent analysis steps.

[0277] Step 3:

[0278] The server determines the growth stage. Using the determination means, the received physical data is analyzed to identify the child's growth stage. This information is important in formulating a training plan.

[0279] Step 4:

[0280] The user uploads a training video. By uploading a video of the child during exercise from the terminal to the system, analysis of the exercise form becomes possible.

[0281] Step 5:

[0282] The server analyzes the video data. The motion analysis AI analyzes the uploaded video data to identify the form and technical issues. Specific improvement points are extracted.

[0283] Step 6:

[0284] The server generates a training plan. Using the generation means, an individually optimized training plan based on the growth stage and analysis results is created. The plan includes specific exercise content and schedule.

[0285] Step 7:

[0286] The server predicts the risk of injury. Using prediction means, it analyzes past injury information and the current training plan to evaluate the risk. If the risk is determined to be high, the following actions are necessary.

[0287] Step 8:

[0288] The server notifies the user of risk mitigation measures. Through the notification means, it makes suggestions for rest and alerts the user about high-risk operations to the user. Based on this information, the user can safely change the training.

[0289] Step 9:

[0290] The user provides feedback. Opinions and points for modification regarding the training plan can be sent from the terminal to the server. The feedback is utilized for improving the training plan.

[0291] Step 10:

[0292] The server updates the training plan. Using the modification means based on the user's feedback, it regenerates the plan and provides an updated version. Thereby, the plan is always kept in the latest state.

[0293] (Example 1)

[0294] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0295] In sports training, it is required to achieve individual optimization based on the growth stage, ensure safety, and provide efficient training. However, in the conventional methods, the consideration for children's growth and past injury history was insufficient, and it was difficult to obtain sufficient effects.

[0296] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0297] In this invention, the server includes a determination means for receiving information about the body and determining the developmental stage; an analysis means for analyzing movement information and identifying issues with form and technique; a generation means for automatically generating an individually optimized exercise plan based on the analysis results; a prediction means for analyzing information about past injuries and the exercise plan and predicting the likelihood of injury; a notification means for notifying the user of risk reduction measures based on the prediction results; and a communication means for sending and receiving information from the user's terminal. This enables the user to perform safe and effective training adapted to the child's developmental stage.

[0298] "Physical information" refers to data that indicates an individual's physiological and health characteristics, such as age, height, weight, and past injury history.

[0299] A "developmental stage" refers to a specific stage in an individual's growth process, indicating the degree of physiological and psychological development.

[0300] "Motion information" refers to data about the body's posture and form during exercise, and is used for analyzing sports techniques.

[0301] "Analysis means" refers to a device or program that has the function of analyzing motion information and identifying problems in form or technology.

[0302] A "generation means" is a device or program that has the function of automatically creating individually optimized movement plans based on the analyzed information.

[0303] A "predictive means" is a device or program that has the function of analyzing and predicting the likelihood of injury using past injury information and exercise plans.

[0304] A "notification means" is a device or program that has the function of informing users of risk mitigation measures based on prediction results.

[0305] "Communication means" refers to a device or program equipped with an interface and protocol for transmitting and receiving data with the user's terminal.

[0306] This invention is a system for realizing individual optimization considering the growth and safety of children in sports training. This system coordinates among the terminal, server, and user, collects and analyzes various data, and generates and provides a training plan.

[0307] First, the user uses the terminal to input information related to the body, such as age, height, weight, and past injury history. The terminal transmits this data to the server using a secure communication protocol. As an example, a data transfer method using HTTPS can be cited.

[0308] The server uses a database management system such as MySQL or PostgreSQL to store the received information in a relational database. The stored information is used by the determination means to determine the growth stage of the child. Statistical and machine learning models are utilized for the determination algorithm.

[0309] Next, the user uploads a training video of the child via the terminal. The server analyzes the video data using an operation analysis AI model. As an example, a posture analysis model based on TensorFlow is used. The results obtained by the analysis means include points for improving form and technical issues.

[0310] The server automatically generates an individually optimized exercise plan using the generation means. This exercise plan is designed based on the growth stage of the child and the analyzed technical issues, and includes exercises and practice schedules. The generated plan is transmitted and notified to the user via the terminal.

[0311] Furthermore, the server uses prediction tools to analyze information on past injuries and exercise plans to predict the likelihood of injury. If a risk is detected, a notification tool sends the user advice on how to mitigate the risk.

[0312] For example, if a user enters a prompt such as, "Please create an individualized training plan for a 10-year-old child who plays soccer. He is 140cm tall, weighs 35kg, and has a history of knee injuries," the server will generate an optimal training plan.

[0313] In this way, the system provides effective training while ensuring the safety of children and is tailored to their individual developmental stages.

[0314] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0315] Step 1:

[0316] The user uses a device to input physical information about the child, such as age, height, weight, and past injury history. The device organizes this input data and sends it to the server using a secure communication protocol (e.g., HTTPS). Numerical data of physical information is provided as input, and the output data is sent to the server.

[0317] Step 2:

[0318] The server stores the body information received from the terminal into a relational database. Database systems such as MySQL or PostgreSQL are used for this purpose. The received data is provided as input, and registration into the database is performed, resulting in the output of accurate information storage.

[0319] Step 3:

[0320] The server activates a determination system based on stored information to assess the child's developmental stage. This is done using statistical algorithms and machine learning models. Physical information and existing growth data are used as input, and the child's developmental stage is identified as the output.

[0321] Step 4:

[0322] The user uploads training videos of their child using their device. The device compresses the video data and sends it to the server. The input is a video file, and the output is obtained when the transmission to the server is complete.

[0323] Step 5:

[0324] The server runs a motion analysis AI model (e.g., using TensorFlow) to analyze the received video data. The input is video data, and the model analyzes the motion, identifying metrics for form improvement and technical challenges as output.

[0325] Step 6:

[0326] The server automatically generates individually optimized exercise plans using a generation method based on the analysis results. The inputs used are the results of form analysis and developmental stage information, and the output is an optimized training plan.

[0327] Step 7:

[0328] The generated training plan is notified to the user via the device, and specific exercises and schedules are presented. An exercise plan is given as input, and the output is a notification to the user.

[0329] Step 8:

[0330] The server activates a prediction mechanism and analyzes the likelihood of injury based on past injury data and the generated exercise plan. The input includes injury data and the exercise plan, and the output is an injury risk assessment.

[0331] Step 9:

[0332] If the risk is high, the server will use a notification system to send advice to the user on risk mitigation measures. The input is the risk assessment result, and the output is a risk notification to the user.

[0333] Step 10:

[0334] Users can review the training plan generated on their device and submit feedback by pressing a button. The device sends this feedback to the server. The user's feedback is given as input, and the feedback is sent to the server as output.

[0335] Step 11:

[0336] The server uses correction mechanisms to improve the training plan based on feedback received from the user and resends it to the user. The input includes feedback information, and the output is the transmission of the improved exercise plan.

[0337] (Application Example 1)

[0338] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0339] In youth sports training, it is difficult to provide training plans that take into account individual developmental stages and safety considerations. Furthermore, there are limitations to providing real-time feedback, making it difficult to efficiently support children's growth and improve their form. Additionally, it is difficult to anticipate and address health risks during training. There is a need to provide a system that solves these problems and enables safer and more effective sports training.

[0340] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0341] In this invention, the server includes a determination means for receiving physical information and determining the growth stage, an analysis means for analyzing movement information and identifying challenges in shape and skills, a generation means for automatically generating an individually optimized training plan based on the analysis results, a prediction means for analyzing past injury history and training plans and predicting health risks, and a feedback provision means for providing real-time feedback based on physical information and analysis results. This enables the provision of training plans tailored to individual growth stages, real-time support for shape improvement, and proactive detection of health risks.

[0342] "Physical information" refers to data such as age, height, weight, and past injury history used to determine an individual's stage of development.

[0343] "Stage of development" refers to a state of physical and mental maturity identified based on an individual's development and age.

[0344] "Motion information" refers to data related to form and skill in sports and exercise, and is used to identify form and technical challenges.

[0345] "Analysis means" refers to a method or process for processing and analyzing received data to extract problems and areas for improvement.

[0346] A "training plan" refers to a plan that automatically generates information based on an individual's developmental stage and analysis results, outlining the schedule and content of specific exercises and workouts.

[0347] "Health risk" refers to an index that assesses the likelihood of injury or health problems occurring based on past injury history and current training content.

[0348] "Real-time feedback" refers to immediate feedback and improvement advice provided during exercise, helping users quickly correct their form and skills.

[0349] A server plays a central role in the system that realizes this invention. The server utilizes a database to collect physical information and determine the stage of growth. Users input information such as age, height, weight, and past injury history via a terminal and send it to the server. The server can use a determination means to identify the individual's stage of growth based on the collected information.

[0350] Next, the server takes on the role of analyzing the movement information. The user uses a terminal to record a video of themselves exercising and uploads it to the server. The server uses analysis tools to process and analyze the received video data. By running a deep learning model, it identifies the shape of the movement and any skill-related problems in real time. For example, if a 10-year-old child is practicing dribbling in soccer and their form needs improvement, the server provides immediate feedback.

[0351] The server generates the training plan. Based on the analysis results, the server automatically generates an individually optimized training plan. This includes exercises and schedules tailored to the user's stage of development and is provided to the user via their device. The server also compares past injury history with the current training plan to assess health risks and provides risk mitigation measures through predictive means. For example, it can make specific suggestions such as "increase rest days to reduce stress on the knees."

[0352] This system also incorporates means of providing real-time feedback via voice and display. The server uses these feedback means to provide guidance on improving posture during exercise, helping users make adjustments as they continue their training.

[0353] As an example of a prompt, one might give the generating AI model the instruction, "Design a program that explains how a sports training assistant robot can give real-time advice to a child on how to improve their form."

[0354] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0355] Step 1:

[0356] The user inputs physical information via a terminal and sends it to the server. The input data includes age, height, weight, and past injury history, and the server uses this to determine the user's growth stage. The server receives this data, stores it in a database, and performs processing to identify the growth stage using a growth stage determination means.

[0357] Step 2:

[0358] The user records a video of themselves exercising using their device and uploads it to the server. The server receives this video data as input and processes and analyzes the motion information using analytical tools. By analyzing the video frames, the server identifies shapes and skill challenges from the movements. The server saves the identified data to a temporary file and uses it to suggest areas for improvement.

[0359] Step 3:

[0360] The server automatically generates a training plan based on the analysis results. The server's generation method creates individually optimized training content and sends this data to the terminal. The training plan includes exercises and schedules adapted to the user's stage of development, and this information is provided to the user. The generated training plan is recorded in a database for later feedback.

[0361] Step 4:

[0362] The server analyzes past injury history and generated training plans to assess health risks. It uses predictive tools to analyze risk and extract risk reduction measures. This assessment process involves data calculations to quantify risk levels. If the risk is high, it suggests rest or changes to exercise and sends notifications to the user.

[0363] Step 5:

[0364] Users perform training based on the provided training plan and return feedback to the server. The server receives the feedback using correction tools and adjusts the training plan as needed. The feedback is analyzed, updated in the database, and reflected in the training plan. Specifically, suggestions for form improvements and health-related comments are made.

[0365] Step 6:

[0366] The server uses data storage methods to record training plans, growth stage information, and health risk information in a centralized data storage. This data is used to train subsequent analysis results and improve the system's accuracy. The data is anonymized and stored securely. Prompt statements necessary for the generated AI model are also created and stored here and used in subsequent analyses.

[0367] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0368] This invention provides a system for sports training that offers individualized optimization, taking into account each individual's stage of development and risk of injury, and further considers the user's emotional state, thereby realizing an effective and safe training environment. This system is composed of multiple means linked together, as shown below.

[0369] The user first enters basic data about their child via a device. This includes age, height, weight, and past injury history. This data is sent from the device to a server and recorded in a database. The server uses a determination tool to determine the child's growth stage based on this data and stores the results.

[0370] During training, users upload videos of their children's movements from their devices. This data is necessary for motion analysis. The server uses analysis tools to analyze the videos and identify form and technical issues. The analysis results are used by a generation tool to automatically generate individually optimized training plans.

[0371] A distinctive feature of this invention is the incorporation of an emotion engine. It recognizes the user's facial expressions and tone of voice to determine their current emotional state. Specifically, it collects data through the device's camera and microphone and performs emotion analysis. This information influences the content and advice of the training plan, dynamically adjusting the plan according to the user's motivation and fatigue level during training.

[0372] Furthermore, the server uses injury prediction tools to analyze past injury history and training plans to predict injury risk. It also considers user emotional data obtained through an emotion engine and presents appropriate risk mitigation measures to the user via notification tools. These notifications may include, for example, recommendations to take a break if there are signs of fatigue.

[0373] For example, if a 13-year-old basketball player in their growth phase reports feeling fatigued during post-game practice, the emotion engine will recognize this emotion and generate a training plan with longer rest periods.

[0374] Finally, users provide feedback from their devices, submitting information about the effectiveness of their training plan and areas for improvement. This feedback is stored on the server and used to refine future training plans. This process optimizes the system to continuously provide high-quality training.

[0375] The following describes the processing flow.

[0376] Step 1:

[0377] The user uses a device to enter basic data about their child. This includes age, height, weight, sport, and past injury history. This information forms the basis for developing a training plan.

[0378] Step 2:

[0379] The terminal sends the input data to the server. The server records the received data in a database and activates a determination mechanism to determine the growth stage.

[0380] Step 3:

[0381] The server analyzes the growth stage using a determination mechanism. The determined growth stage information is an important element for designing individual training plans.

[0382] Step 4:

[0383] Users record videos of their training sessions from their devices and upload them to the system. This video data is used for detailed analysis of their exercise form.

[0384] Step 5:

[0385] The server uses motion analysis AI to analyze video data. The analysis tool identifies form and technical challenges, and the results are used by the generation tool to create a training plan.

[0386] Step 6:

[0387] The server automatically generates individually optimized training plans using a generation mechanism. These plans include analysis results and specific exercises tailored to the user's progress stage.

[0388] Step 7:

[0389] The server recognizes the user's emotions. Based on data collected through the device's camera and microphone, the emotion engine determines the user's emotional state.

[0390] Step 8:

[0391] The server incorporates emotional data into the training plan. The content of the training plan is adjusted considering the emotional data, and a plan tailored to the user's motivation and fatigue level is developed.

[0392] Step 9:

[0393] The server predicts the risk of injury and notifies the user of risk mitigation measures. The prediction system assesses the risk based on past injury data and the current training plan, and the notification system presents safety guidelines.

[0394] Step 10:

[0395] After completing training, the user provides feedback from their device. The server incorporates the received feedback into the next plan generation and optimizes the training plan using corrective measures.

[0396] (Example 2)

[0397] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0398] Traditional sports training systems struggle to provide exercise plans tailored to individual developmental stages and emotional states, making it difficult to achieve consistent results. Furthermore, injury prevention measures are often inadequate, potentially compromising user motivation and safety. To address these challenges, it is necessary to optimize exercise plans by appropriately incorporating user information and emotional states.

[0399] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0400] In this invention, the server includes a determination means for receiving physical information and determining the growth stage, an analysis means for analyzing movement information and identifying technical problems, a generation means for automatically generating an individually optimized exercise plan based on the analysis results, a prediction means for analyzing past health history and exercise plans and predicting health risks, and an adjustment means for acquiring emotional data and dynamically adjusting the exercise plan according to motivation and fatigue levels. This makes it possible to provide a safe and effective exercise plan tailored to each individual user.

[0401] "Physical information" refers to information about an individual's physical and health status, such as age, height, weight, and past health history.

[0402] "Growth stage" is an indicator that shows the user's physical and age-related maturity, and is used to optimize exercise plans.

[0403] "Determination means" refers to elements used to identify the growth stage based on physical information and select an appropriate exercise plan.

[0404] "Motion information" refers to data related to the user's movements during exercise, and is collected to evaluate form and technical issues.

[0405] "Analysis means" refers to the process of analyzing operational information to identify the user's technical challenges.

[0406] The "generation means" refers to a means for automatically creating individually optimized motion plans based on the results obtained from the analysis means.

[0407] "Past health history" refers to records of health problems or injuries that the user has experienced in the past.

[0408] An "exercise plan" refers to a customized training schedule and exercise routine based on the user's abilities and goals.

[0409] A "predictive method" is a process for assessing future health risks based on past health history and current exercise plans.

[0410] A "notification method" is a system for informing users about health risks and countermeasures to address them.

[0411] "Emotional data" refers to information about the emotional state collected from the user's facial expressions and voice.

[0412] "Adjustment methods" refer to the process of dynamically modifying exercise plans based on acquired emotional data to improve user motivation and safety.

[0413] This invention is a system that provides sports training optimized for each individual user. The system accepts personal data provided by the user via a terminal and transmits it to a server for processing.

[0414] The server first acquires physical information, including age, height, weight, and past health history. This data is sent from the terminal to the server, which uses a determination tool to assess the individual's growth stage. Based on this stage determination, the server evaluates what kind of training is appropriate for each individual.

[0415] Next, the user captures footage of their movements during training using their device and sends it to the server. The server uses various analytical tools to analyze this movement data in detail, identifying technical problems and areas for improvement. This analysis is performed quickly and accurately using cloud-based analysis software.

[0416] The server then uses a generation mechanism to create an individually optimized exercise plan based on the analysis results and growth stage information. A generation AI model is utilized to provide a dynamic training plan that responds to user interaction. This exercise plan includes specific exercises and rest periods.

[0417] Furthermore, the server evaluates past health data and the generated exercise plan, and uses predictive tools to forecast health risks. This can reduce the potential health risks that the planned exercise may pose.

[0418] Emotional data is also collected using the camera and microphone functions on the user's device. The server analyzes this data using adjustment mechanisms and makes fine adjustments to the exercise plan based on the user's emotional state.

[0419] As a concrete example, consider a 13-year-old basketball player. If this user provides feedback that they feel fatigued after a game, the server can take their emotional state and self-report into consideration and use a generation mechanism to adjust their exercise plan to reduce fatigue.

[0420] Examples of prompts include: "Propose an optimal training plan for a 13-year-old basketball player who is feeling fatigued after a game. Please take into account emotional data and past injury history."

[0421] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0422] Step 1:

[0423] The user enters personal physical information using a terminal. Specifically, they enter age, height, weight, and past health history. The entered data is sent from the terminal to the server. The server processes this physical information using a determination mechanism to determine the user's growth stage. The determined growth stage is recorded in a database.

[0424] Step 2:

[0425] The user uses a device to record their movements during training and uploads the video to the server. The server processes the received video using an analysis device to analyze the form of the movements and any technical challenges. In this process, the video data is analyzed frame by frame, and technical challenges are identified using skeletal analysis software. The analysis results are then passed to a generation device.

[0426] Step 3:

[0427] The server automatically generates an individually optimized training plan using a generation method based on the analysis results and information on the user's growth stage. This process utilizes a generation AI model to propose an exercise plan tailored to the user's technical challenges and growth stage. The generated training plan includes exercise menus and rest timings.

[0428] Step 4:

[0429] The user's current emotional data is collected using the device's camera and microphone. As the user expresses their emotions through the device during training, this data is sent to the server. The server uses adjustment mechanisms to analyze the emotional data and dynamically adjust the training plan. This analysis allows for adjustments to the plan to increase motivation and take fatigue into consideration.

[0430] Step 5:

[0431] The server evaluates past health history and generated training plans, and uses predictive tools to forecast future health risks. This forecast involves analysis to assess the impact of specific actions on health. The forecast results are used to notify the user of the risks and issue warnings.

[0432] Step 6:

[0433] Users input feedback on the effectiveness of their training and areas for improvement into their device. This feedback information is sent to the server and stored in a database. The server uses corrective measures to incorporate this information into subsequent training plans, thereby ensuring continuous improvement.

[0434] (Application Example 2)

[0435] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0436] In recent years, with the increasing diversity of individual lifestyles and health conditions, there has been a growing demand for individually optimized methods of maintaining health. However, conventional health maintenance systems only provide general guidelines and have struggled to offer dynamic, personalized plans tailored to individual growth stages and health conditions. Furthermore, they lacked mechanisms to adjust activity plans to take into account changes in emotional states. A system is needed to address these challenges and support health maintenance efficiently and effectively.

[0437] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0438] In this invention, the server includes a determination means for receiving physical information and determining the growth stage; an analysis means for analyzing movement information and identifying technical problems; a generation means for automatically generating individually optimized health maintenance plans based on the analysis results; a prediction means for analyzing past health status history and activity plans and predicting health risks; and an adjustment means for determining health-related biometric information and emotional states and dynamically adjusting the activity plan. This enables efficient health maintenance according to the growth stage and emotional state of each individual user.

[0439] "Physical information" is a general term for health-related data such as the user's heart rate, steps taken, and body fat percentage.

[0440] "Growth stage" is a concept that refers to the stages that indicate the process of a user's physical and age-related development.

[0441] "Determination means" refers to the process or technology used to identify and determine the growth stage based on the input information.

[0442] "Movement information" refers to data related to the user's exercise and activity patterns, and specifically includes steps taken, distance traveled, posture, etc.

[0443] "Analysis methods" refer to the processes and techniques used to analyze acquired data and identify technical problems and areas for improvement.

[0444] "Generation method" refers to the process or technology for automatically creating individually optimized health maintenance plans based on analysis results.

[0445] A "health maintenance plan" refers to a plan that outlines a series of activities and dietary patterns recommended for users to maintain or improve their health.

[0446] "Health history" refers to a collection of records and data related to past health conditions.

[0447] An "activity plan" is a set of elements that make up the guidelines and schedules for a user's daily activities.

[0448] "Health risk" refers to the possibility or circumstances under which a user's health may be harmed, and it is an evaluation criterion aimed at detecting such risks proactively.

[0449] "Predictive methods" refer to processes and technologies used to foresee future health risks based on past data and trends.

[0450] "Adjustment methods" refer to processes and techniques for dynamically changing activity plans, taking into account the user's current health and emotional state.

[0451] "Emotional state" is a concept that describes a user's current psychological and emotional condition, which can sometimes affect their health.

[0452] "Biometric information" is a general term for data related to the user's physical responses and condition, such as heart rate and body temperature.

[0453] This invention is a system for supporting health maintenance, and it has a configuration that dynamically generates an individualized health maintenance plan from the user's physical information, movement information, and emotional state.

[0454] The system includes a server and user terminals. The server is equipped with hardware for determining physical information and growth stage, and software for analysis and generation. Specifically, it processes data using the Python data analysis libraries Pandas and NumPy, and performs sentiment analysis using TensorFlow. Furthermore, it uses machine learning algorithms based on historical data to predict health risks.

[0455] Users use their smartphones to input or automatically collect various data from their daily lives (e.g., heart rate, steps taken). The collected data is sent to a server and stored in a database.

[0456] The user's emotional state is captured through the device's camera and microphone, and emotion analysis is performed on the server. Based on the analysis results, a specific health maintenance plan is individually generated and provided to the user. The generating AI model takes into account the user's current lifestyle and goals, and recommends various health maintenance activities.

[0457] For example, when a user is monitoring their daily walks, the app might send a notification such as, "Your step count has increased, so we recommend adding a new stretching routine as your next step."

[0458] An example of a prompt for a generative AI model might be: "Analyze what health changes the user needs based on their heart rate and emotional state during recent activities."

[0459] In this way, the system can provide personalized and continuous health support to individual users.

[0460] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0461] Step 1:

[0462] The user's device collects physical information such as heart rate, steps taken, and body fat percentage. This includes data from sensors built into the device and from wearable devices. It receives physical information as input and sends it to the server.

[0463] Step 2:

[0464] The server uses the received physical information to determine the growth stage. It analyzes the data using Pandas and identifies the appropriate growth stage based on the user's age and physical data. The server then generates the identified growth stage information as output.

[0465] Step 3:

[0466] The user's device uses its camera and microphone to collect data on the user's facial expressions and voice. It receives facial expression information as input and sends it to the server.

[0467] Step 4:

[0468] The server uses collected facial and voice information to perform emotion analysis using TensorFlow. It takes the data as input to determine the user's emotional state and generates this as output.

[0469] Step 5:

[0470] The server analyzes operational information to identify technical problems and areas where the user needs assistance. It performs data calculations using NumPy and outputs specific technical challenges.

[0471] Step 6:

[0472] The server automatically generates an individually optimized health maintenance plan using an AI model based on the determined growth stage, emotional state, and technical challenges. This results in the output of the most suitable health maintenance plan for the user.

[0473] Step 7:

[0474] The server analyzes past health history and generated plans to predict health risks. It uses machine learning algorithms to build a predictive model and obtains a health risk assessment as output.

[0475] Step 8:

[0476] The generated health maintenance plan and health risk assessment are notified to the user's device. The device then presents these results to the user and allows them to adjust their activity plan as needed.

[0477] This sequence of steps allows the system to provide dynamic support tailored to the user's health condition.

[0478] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0479] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0480] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0481] [Third Embodiment]

[0482] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0483] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0484] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0485] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0486] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0487] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0488] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0489] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0490] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0491] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0492] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0493] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0494] This invention is a system for achieving individualized optimization in sports training, taking into account the growth and safety of children. This system is composed of the following various means and their coordination.

[0495] First, the user (parent or coach) inputs physical data such as the child's age, height, weight, and past injury history via a terminal. This information is sent from the terminal to the server. The server stores the received data in a database and uses it to determine the child's growth stage. The assessment system is activated and identifies the child's growth stage. This prepares the user for receiving suggestions for the most suitable training for their child.

[0496] Next, the user uploads a video of their child's training from their device. The server runs a motion analysis AI to analyze the uploaded video data. The analysis identifies the child's form and technical issues from the video and extracts specific areas for improvement based on that.

[0497] Based on the identified issues, the server automatically generates a training plan using a generation mechanism. This plan includes exercises and practice schedules tailored to the individual's growth stage and technical challenges. The generated plan is then notified to the user via their device.

[0498] Furthermore, the server assesses the risk of injury based on past injury history and the current training plan. When the prediction system detects an injury risk, it provides the user with advice on risk reduction and suggestions for rest through the notification system.

[0499] For example, suppose a 10-year-old child in a growth spurt plays soccer and has a history of knee injuries. In this case, the server would generate a training plan that includes soccer-specific balance-improving exercises and suggest increasing rest days if it determines that the stress on the knees is excessive.

[0500] Users can provide feedback on the provided training plan and submit revision requests as needed. The server has a mechanism to update the training plan based on that feedback.

[0501] Thus, by integrating the entire system, the present invention provides a specific form for conducting safe and effective sports training.

[0502] The following describes the processing flow.

[0503] Step 1:

[0504] The user inputs the child's physical data. Using a terminal, they enter information such as age, height, weight, and past injury history into the system. This data serves as basic information for determining the child's growth stage.

[0505] Step 2:

[0506] The device sends data to the server. The collected physical data is sent to the server and stored in a database. The stored data is used in subsequent analysis steps.

[0507] Step 3:

[0508] The server determines the child's growth stage. Using this determination method, it analyzes the received physical data to identify the child's growth stage. This information is crucial for developing a training plan.

[0509] Step 4:

[0510] Users upload training videos. By uploading videos of their children exercising from their devices to the system, it becomes possible to analyze their exercise form.

[0511] Step 5:

[0512] The server analyzes the video data. A motion analysis AI analyzes the uploaded video data to identify form and technical issues. Specific areas for improvement are then identified.

[0513] Step 6:

[0514] The server generates a training plan. Using the generation method, it creates an individually optimized training plan based on the growth stage and analysis results. The plan includes specific exercises and a schedule.

[0515] Step 7:

[0516] The server predicts the risk of injury. The prediction method analyzes past injury data and the current training plan to assess the risk. If the risk is determined to be high, the following actions are required.

[0517] Step 8:

[0518] The server notifies the user of risk mitigation measures. Through this notification system, the server suggests rest periods and warns users about high-risk activities. Based on this information, users can safely adjust their training.

[0519] Step 9:

[0520] Users provide feedback. They can send opinions and suggestions for improvements to the training plan from their device to the server. This feedback is used to improve the training plan.

[0521] Step 10:

[0522] The server updates the training plan. Based on user feedback, it regenerates the plan using corrective measures and provides the updated version. This ensures that the plan is always up-to-date.

[0523] (Example 1)

[0524] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0525] In sports training, there is a need to provide efficient training while ensuring safety, by achieving individualized optimization that takes into account the child's stage of development. However, conventional methods have not adequately considered children's growth and past injury history, making it difficult to achieve sufficient results.

[0526] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0527] In this invention, the server includes a determination means for receiving information about the body and determining the developmental stage; an analysis means for analyzing movement information and identifying issues with form and technique; a generation means for automatically generating an individually optimized exercise plan based on the analysis results; a prediction means for analyzing information about past injuries and the exercise plan and predicting the likelihood of injury; a notification means for notifying the user of risk reduction measures based on the prediction results; and a communication means for sending and receiving information from the user's terminal. This enables the user to perform safe and effective training adapted to the child's developmental stage.

[0528] "Physical information" refers to data that indicates an individual's physiological and health characteristics, such as age, height, weight, and past injury history.

[0529] A "developmental stage" refers to a specific stage in an individual's growth process, indicating the degree of physiological and psychological development.

[0530] "Motion information" refers to data about the body's posture and form during exercise, and is used for analyzing sports techniques.

[0531] "Analysis means" refers to a device or program that has the function of analyzing motion information and identifying problems in form or technology.

[0532] A "generation means" is a device or program that has the function of automatically creating individually optimized movement plans based on the analyzed information.

[0533] A "predictive means" is a device or program that has the function of analyzing and predicting the likelihood of injury using past injury information and exercise plans.

[0534] A "notification means" is a device or program that has the function of informing users of risk mitigation measures based on prediction results.

[0535] "Communication means" refers to a device or program equipped with an interface or protocol for sending and receiving data between a user's terminal and another device.

[0536] This invention is a system for achieving individualized optimization in sports training, taking into account the growth and safety of children. This system involves collaboration between a terminal, a server, and the user to collect and analyze various data, and to generate and provide training plans.

[0537] First, the user uses their device to input physical information such as age, height, weight, and past injury history. The device then sends this data to the server using a secure communication protocol. One example of such a method is data transfer using HTTPS.

[0538] The server uses database management systems such as MySQL or PostgreSQL to store the received information in a relational database. The stored information is then used to determine the child's developmental stage using a determination tool. The algorithm for determination utilizes statistical and machine learning models.

[0539] Next, the user uploads a training video of their child via their device. The server analyzes the video data using a motion analysis AI model. For example, a posture analysis model based on TensorFlow is used. The results obtained from the analysis include points for form improvement and technical challenges.

[0540] The server automatically generates individually optimized exercise plans using a generation mechanism. These plans are designed based on the child's developmental stage and analyzed technical challenges, and include exercise and practice schedules. The generated plans are sent to and notified to the user via the terminal.

[0541] Furthermore, the server uses prediction tools to analyze information on past injuries and exercise plans to predict the likelihood of injury. If a risk is detected, a notification tool sends the user advice on how to mitigate the risk.

[0542] For example, if a user enters a prompt such as, "Please create an individualized training plan for a 10-year-old child who plays soccer. He is 140cm tall, weighs 35kg, and has a history of knee injuries," the server will generate an optimal training plan.

[0543] In this way, the system provides effective training while ensuring the safety of children and is tailored to their individual developmental stages.

[0544] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0545] Step 1:

[0546] The user uses a device to input physical information about the child, such as age, height, weight, and past injury history. The device organizes this input data and sends it to the server using a secure communication protocol (e.g., HTTPS). Numerical data of physical information is provided as input, and the output data is sent to the server.

[0547] Step 2:

[0548] The server stores the body information received from the terminal into a relational database. Database systems such as MySQL or PostgreSQL are used for this purpose. The received data is provided as input, and registration into the database is performed, resulting in the output of accurate information storage.

[0549] Step 3:

[0550] The server activates a determination system based on stored information to assess the child's developmental stage. This is done using statistical algorithms and machine learning models. Physical information and existing growth data are used as input, and the child's developmental stage is identified as the output.

[0551] Step 4:

[0552] The user uploads training videos of their child using their device. The device compresses the video data and sends it to the server. The input is a video file, and the output is obtained when the transmission to the server is complete.

[0553] Step 5:

[0554] The server runs a motion analysis AI model (e.g., using TensorFlow) to analyze the received video data. The input is video data, and the model analyzes the motion, identifying metrics for form improvement and technical challenges as output.

[0555] Step 6:

[0556] The server automatically generates individually optimized exercise plans using a generation method based on the analysis results. The inputs used are the results of form analysis and developmental stage information, and the output is an optimized training plan.

[0557] Step 7:

[0558] The generated training plan is notified to the user via the device, and specific exercises and schedules are presented. An exercise plan is given as input, and the output is a notification to the user.

[0559] Step 8:

[0560] The server activates a prediction mechanism and analyzes the likelihood of injury based on past injury data and the generated exercise plan. The input includes injury data and the exercise plan, and the output is an injury risk assessment.

[0561] Step 9:

[0562] If the risk is high, the server will use a notification system to send advice to the user on risk mitigation measures. The input is the risk assessment result, and the output is a risk notification to the user.

[0563] Step 10:

[0564] Users can review the training plan generated on their device and submit feedback by pressing a button. The device sends this feedback to the server. The user's feedback is given as input, and the feedback is sent to the server as output.

[0565] Step 11:

[0566] The server uses correction mechanisms to improve the training plan based on feedback received from the user and resends it to the user. The input includes feedback information, and the output is the transmission of the improved exercise plan.

[0567] (Application Example 1)

[0568] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0569] In youth sports training, it is difficult to provide training plans that take into account individual developmental stages and safety considerations. Furthermore, there are limitations to providing real-time feedback, making it difficult to efficiently support children's growth and improve their form. Additionally, it is difficult to anticipate and address health risks during training. There is a need to provide a system that solves these problems and enables safer and more effective sports training.

[0570] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0571] In this invention, the server includes a determination means for receiving physical information and determining the growth stage, an analysis means for analyzing movement information and identifying challenges in shape and skills, a generation means for automatically generating an individually optimized training plan based on the analysis results, a prediction means for analyzing past injury history and training plans and predicting health risks, and a feedback provision means for providing real-time feedback based on physical information and analysis results. This enables the provision of training plans tailored to individual growth stages, real-time support for shape improvement, and proactive detection of health risks.

[0572] "Physical information" refers to data such as age, height, weight, and past injury history used to determine an individual's stage of development.

[0573] "Stage of development" refers to a state of physical and mental maturity identified based on an individual's development and age.

[0574] "Motion information" refers to data related to form and skill in sports and exercise, and is used to identify form and technical challenges.

[0575] "Analysis means" refers to a method or process for processing and analyzing received data to extract problems and areas for improvement.

[0576] A "training plan" refers to a plan that automatically generates information based on an individual's developmental stage and analysis results, outlining the schedule and content of specific exercises and workouts.

[0577] "Health risk" refers to an index that assesses the likelihood of injury or health problems occurring based on past injury history and current training content.

[0578] "Real-time feedback" refers to immediate feedback and improvement advice provided during exercise, helping users quickly correct their form and skills.

[0579] A server plays a central role in the system that realizes this invention. The server utilizes a database to collect physical information and determine the stage of growth. Users input information such as age, height, weight, and past injury history via a terminal and send it to the server. The server can use a determination means to identify the individual's stage of growth based on the collected information.

[0580] Next, the server takes on the role of analyzing the movement information. The user uses a terminal to record a video of themselves exercising and uploads it to the server. The server uses analysis tools to process and analyze the received video data. By running a deep learning model, it identifies the shape of the movement and any skill-related problems in real time. For example, if a 10-year-old child is practicing dribbling in soccer and their form needs improvement, the server provides immediate feedback.

[0581] The server generates the training plan. Based on the analysis results, the server automatically generates an individually optimized training plan. This includes exercises and schedules tailored to the user's stage of development and is provided to the user via their device. The server also compares past injury history with the current training plan to assess health risks and provides risk mitigation measures through predictive means. For example, it can make specific suggestions such as "increase rest days to reduce stress on the knees."

[0582] This system also incorporates means of providing real-time feedback via voice and display. The server uses these feedback means to provide guidance on improving posture during exercise, helping users make adjustments as they continue their training.

[0583] As an example of a prompt, one might give the generating AI model the instruction, "Design a program that explains how a sports training assistant robot can give real-time advice to a child on how to improve their form."

[0584] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0585] Step 1:

[0586] The user inputs physical information via a terminal and sends it to the server. The input data includes age, height, weight, and past injury history, and the server uses this to determine the user's growth stage. The server receives this data, stores it in a database, and performs processing to identify the growth stage using a growth stage determination means.

[0587] Step 2:

[0588] The user records a video of themselves exercising using their device and uploads it to the server. The server receives this video data as input and processes and analyzes the motion information using analytical tools. By analyzing the video frames, the server identifies shapes and skill challenges from the movements. The server saves the identified data to a temporary file and uses it to suggest areas for improvement.

[0589] Step 3:

[0590] The server automatically generates a training plan based on the analysis results. The server's generation method creates individually optimized training content and sends this data to the terminal. The training plan includes exercises and schedules adapted to the user's stage of development, and this information is provided to the user. The generated training plan is recorded in a database for later feedback.

[0591] Step 4:

[0592] The server analyzes past injury history and generated training plans to assess health risks. It uses predictive tools to analyze risk and extract risk reduction measures. This assessment process involves data calculations to quantify risk levels. If the risk is high, it suggests rest or changes to exercise and sends notifications to the user.

[0593] Step 5:

[0594] Users perform training based on the provided training plan and return feedback to the server. The server receives the feedback using correction tools and adjusts the training plan as needed. The feedback is analyzed, updated in the database, and reflected in the training plan. Specifically, suggestions for form improvements and health-related comments are made.

[0595] Step 6:

[0596] The server uses data storage methods to record training plans, growth stage information, and health risk information in a centralized data storage. This data is used to train subsequent analysis results and improve the system's accuracy. The data is anonymized and stored securely. Prompt statements necessary for the generated AI model are also created and stored here and used in subsequent analyses.

[0597] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0598] This invention provides a system for sports training that offers individualized optimization, taking into account each individual's stage of development and risk of injury, and further considers the user's emotional state, thereby realizing an effective and safe training environment. This system is composed of multiple means linked together, as shown below.

[0599] The user first enters basic data about their child via a device. This includes age, height, weight, and past injury history. This data is sent from the device to a server and recorded in a database. The server uses a determination tool to determine the child's growth stage based on this data and stores the results.

[0600] During training, users upload videos of their children's movements from their devices. This data is necessary for motion analysis. The server uses analysis tools to analyze the videos and identify form and technical issues. The analysis results are used by a generation tool to automatically generate individually optimized training plans.

[0601] A distinctive feature of this invention is the incorporation of an emotion engine. It recognizes the user's facial expressions and tone of voice to determine their current emotional state. Specifically, it collects data through the device's camera and microphone and performs emotion analysis. This information influences the content and advice of the training plan, dynamically adjusting the plan according to the user's motivation and fatigue level during training.

[0602] Furthermore, the server uses injury prediction tools to analyze past injury history and training plans to predict injury risk. It also considers user emotional data obtained through an emotion engine and presents appropriate risk mitigation measures to the user via notification tools. These notifications may include, for example, recommendations to take a break if there are signs of fatigue.

[0603] For example, if a 13-year-old basketball player in their growth phase reports feeling fatigued during post-game practice, the emotion engine will recognize this emotion and generate a training plan with longer rest periods.

[0604] Finally, users provide feedback from their devices, submitting information about the effectiveness of their training plan and areas for improvement. This feedback is stored on the server and used to refine future training plans. This process optimizes the system to continuously provide high-quality training.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] The user uses a device to enter basic data about their child. This includes age, height, weight, sport, and past injury history. This information forms the basis for developing a training plan.

[0608] Step 2:

[0609] The terminal sends the input data to the server. The server records the received data in a database and activates a determination mechanism to determine the growth stage.

[0610] Step 3:

[0611] The server analyzes the growth stage using a determination mechanism. The determined growth stage information is an important element for designing individual training plans.

[0612] Step 4:

[0613] Users record videos of their training sessions from their devices and upload them to the system. This video data is used for detailed analysis of their exercise form.

[0614] Step 5:

[0615] The server uses motion analysis AI to analyze video data. The analysis tool identifies form and technical challenges, and the results are used by the generation tool to create a training plan.

[0616] Step 6:

[0617] The server automatically generates individually optimized training plans using a generation mechanism. These plans include analysis results and specific exercises tailored to the user's progress stage.

[0618] Step 7:

[0619] The server recognizes the user's emotions. Based on data collected through the device's camera and microphone, the emotion engine determines the user's emotional state.

[0620] Step 8:

[0621] The server incorporates emotional data into the training plan. The content of the training plan is adjusted considering the emotional data, and a plan tailored to the user's motivation and fatigue level is developed.

[0622] Step 9:

[0623] The server predicts the risk of injury and notifies the user of risk mitigation measures. The prediction system assesses the risk based on past injury data and the current training plan, and the notification system presents safety guidelines.

[0624] Step 10:

[0625] After completing training, the user provides feedback from their device. The server incorporates the received feedback into the next plan generation and optimizes the training plan using corrective measures.

[0626] (Example 2)

[0627] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0628] Traditional sports training systems struggle to provide exercise plans tailored to individual developmental stages and emotional states, making it difficult to achieve consistent results. Furthermore, injury prevention measures are often inadequate, potentially compromising user motivation and safety. To address these challenges, it is necessary to optimize exercise plans by appropriately incorporating user information and emotional states.

[0629] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0630] In this invention, the server includes a determination means for receiving physical information and determining the growth stage, an analysis means for analyzing movement information and identifying technical problems, a generation means for automatically generating an individually optimized exercise plan based on the analysis results, a prediction means for analyzing past health history and exercise plans and predicting health risks, and an adjustment means for acquiring emotional data and dynamically adjusting the exercise plan according to motivation and fatigue levels. This makes it possible to provide a safe and effective exercise plan tailored to each individual user.

[0631] "Physical information" refers to information about an individual's physical and health status, such as age, height, weight, and past health history.

[0632] "Growth stage" is an indicator that shows the user's physical and age-related maturity, and is used to optimize exercise plans.

[0633] "Determination means" refers to elements used to identify the growth stage based on physical information and select an appropriate exercise plan.

[0634] "Motion information" refers to data related to the user's movements during exercise, and is collected to evaluate form and technical issues.

[0635] "Analysis means" refers to the process of analyzing operational information to identify the user's technical challenges.

[0636] The "generation means" refers to a means for automatically creating individually optimized motion plans based on the results obtained from the analysis means.

[0637] "Past health history" refers to records of health problems or injuries that the user has experienced in the past.

[0638] An "exercise plan" refers to a customized training schedule and exercise routine based on the user's abilities and goals.

[0639] A "predictive method" is a process for assessing future health risks based on past health history and current exercise plans.

[0640] A "notification method" is a system for informing users about health risks and countermeasures to address them.

[0641] "Emotional data" refers to information about the emotional state collected from the user's facial expressions and voice.

[0642] "Adjustment methods" refer to the process of dynamically modifying exercise plans based on acquired emotional data to improve user motivation and safety.

[0643] This invention is a system that provides sports training optimized for each individual user. The system accepts personal data provided by the user via a terminal and transmits it to a server for processing.

[0644] The server first acquires physical information, including age, height, weight, and past health history. This data is sent from the terminal to the server, which uses a determination tool to assess the individual's growth stage. Based on this stage determination, the server evaluates what kind of training is appropriate for each individual.

[0645] Next, the user captures footage of their movements during training using their device and sends it to the server. The server uses various analytical tools to analyze this movement data in detail, identifying technical problems and areas for improvement. This analysis is performed quickly and accurately using cloud-based analysis software.

[0646] The server then uses a generation mechanism to create an individually optimized exercise plan based on the analysis results and growth stage information. A generation AI model is utilized to provide a dynamic training plan that responds to user interaction. This exercise plan includes specific exercises and rest periods.

[0647] Furthermore, the server evaluates past health data and the generated exercise plan, and uses predictive tools to forecast health risks. This can reduce the potential health risks that the planned exercise may pose.

[0648] Emotional data is also collected using the camera and microphone functions on the user's device. The server analyzes this data using adjustment mechanisms and makes fine adjustments to the exercise plan based on the user's emotional state.

[0649] As a concrete example, consider a 13-year-old basketball player. If this user provides feedback that they feel fatigued after a game, the server can take their emotional state and self-report into consideration and use a generation mechanism to adjust their exercise plan to reduce fatigue.

[0650] Examples of prompts include: "Propose an optimal training plan for a 13-year-old basketball player who is feeling fatigued after a game. Please take into account emotional data and past injury history."

[0651] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0652] Step 1:

[0653] The user enters personal physical information using a terminal. Specifically, they enter age, height, weight, and past health history. The entered data is sent from the terminal to the server. The server processes this physical information using a determination mechanism to determine the user's growth stage. The determined growth stage is recorded in a database.

[0654] Step 2:

[0655] The user uses a device to record their movements during training and uploads the video to the server. The server processes the received video using an analysis device to analyze the form of the movements and any technical challenges. In this process, the video data is analyzed frame by frame, and technical challenges are identified using skeletal analysis software. The analysis results are then passed to a generation device.

[0656] Step 3:

[0657] The server automatically generates an individually optimized training plan using a generation method based on the analysis results and information on the user's growth stage. This process utilizes a generation AI model to propose an exercise plan tailored to the user's technical challenges and growth stage. The generated training plan includes exercise menus and rest timings.

[0658] Step 4:

[0659] The user's current emotional data is collected using the device's camera and microphone. As the user expresses their emotions through the device during training, this data is sent to the server. The server uses adjustment mechanisms to analyze the emotional data and dynamically adjust the training plan. This analysis allows for adjustments to the plan to increase motivation and take fatigue into consideration.

[0660] Step 5:

[0661] The server evaluates past health history and generated training plans, and uses predictive tools to forecast future health risks. This forecast involves analysis to assess the impact of specific actions on health. The forecast results are used to notify the user of the risks and issue warnings.

[0662] Step 6:

[0663] Users input feedback on the effectiveness of their training and areas for improvement into their device. This feedback information is sent to the server and stored in a database. The server uses corrective measures to incorporate this information into subsequent training plans, thereby ensuring continuous improvement.

[0664] (Application Example 2)

[0665] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0666] In recent years, with the increasing diversity of individual lifestyles and health conditions, there has been a growing demand for individually optimized methods of maintaining health. However, conventional health maintenance systems only provide general guidelines and have struggled to offer dynamic, personalized plans tailored to individual growth stages and health conditions. Furthermore, they lacked mechanisms to adjust activity plans to take into account changes in emotional states. A system is needed to address these challenges and support health maintenance efficiently and effectively.

[0667] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0668] In this invention, the server includes a determination means for receiving physical information and determining the growth stage; an analysis means for analyzing movement information and identifying technical problems; a generation means for automatically generating individually optimized health maintenance plans based on the analysis results; a prediction means for analyzing past health status history and activity plans and predicting health risks; and an adjustment means for determining health-related biometric information and emotional states and dynamically adjusting the activity plan. This enables efficient health maintenance according to the growth stage and emotional state of each individual user.

[0669] "Physical information" is a general term for health-related data such as the user's heart rate, steps taken, and body fat percentage.

[0670] "Growth stage" is a concept that refers to the stages that indicate the process of a user's physical and age-related development.

[0671] "Determination means" refers to the process or technology used to identify and determine the growth stage based on the input information.

[0672] "Movement information" refers to data related to the user's exercise and activity patterns, and specifically includes steps taken, distance traveled, posture, etc.

[0673] "Analysis methods" refer to the processes and techniques used to analyze acquired data and identify technical problems and areas for improvement.

[0674] "Generation method" refers to the process or technology for automatically creating individually optimized health maintenance plans based on analysis results.

[0675] A "health maintenance plan" refers to a plan that outlines a series of activities and dietary patterns recommended for users to maintain or improve their health.

[0676] "Health history" refers to a collection of records and data related to past health conditions.

[0677] An "activity plan" is a set of elements that make up the guidelines and schedules for a user's daily activities.

[0678] "Health risk" refers to the possibility or circumstances under which a user's health may be harmed, and it is an evaluation criterion aimed at detecting such risks proactively.

[0679] "Predictive methods" refer to processes and technologies used to foresee future health risks based on past data and trends.

[0680] "Adjustment methods" refer to processes and techniques for dynamically changing activity plans, taking into account the user's current health and emotional state.

[0681] "Emotional state" is a concept that describes a user's current psychological and emotional condition, which can sometimes affect their health.

[0682] "Biometric information" is a general term for data related to the user's physical responses and condition, such as heart rate and body temperature.

[0683] This invention is a system for supporting health maintenance, and it has a configuration that dynamically generates an individualized health maintenance plan from the user's physical information, movement information, and emotional state.

[0684] The system includes a server and user terminals. The server is equipped with hardware for determining physical information and growth stage, and software for analysis and generation. Specifically, it processes data using the Python data analysis libraries Pandas and NumPy, and performs sentiment analysis using TensorFlow. Furthermore, it uses machine learning algorithms based on historical data to predict health risks.

[0685] Users use their smartphones to input or automatically collect various data from their daily lives (e.g., heart rate, steps taken). The collected data is sent to a server and stored in a database.

[0686] The user's emotional state is captured through the device's camera and microphone, and emotion analysis is performed on the server. Based on the analysis results, a specific health maintenance plan is individually generated and provided to the user. The generating AI model takes into account the user's current lifestyle and goals, and recommends various health maintenance activities.

[0687] For example, when a user is monitoring their daily walks, the app might send a notification such as, "Your step count has increased, so we recommend adding a new stretching routine as your next step."

[0688] An example of a prompt for a generative AI model might be: "Analyze what health changes the user needs based on their heart rate and emotional state during recent activities."

[0689] In this way, the system can provide personalized and continuous health support to individual users.

[0690] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0691] Step 1:

[0692] The user's device collects physical information such as heart rate, steps taken, and body fat percentage. This includes data from sensors built into the device and from wearable devices. It receives physical information as input and sends it to the server.

[0693] Step 2:

[0694] The server uses the received physical information to determine the growth stage. It analyzes the data using Pandas and identifies the appropriate growth stage based on the user's age and physical data. The server then generates the identified growth stage information as output.

[0695] Step 3:

[0696] The user's device uses its camera and microphone to collect data on the user's facial expressions and voice. It receives facial expression information as input and sends it to the server.

[0697] Step 4:

[0698] The server uses collected facial and voice information to perform emotion analysis using TensorFlow. It takes the data as input to determine the user's emotional state and generates this as output.

[0699] Step 5:

[0700] The server analyzes operational information to identify technical problems and areas where the user needs assistance. It performs data calculations using NumPy and outputs specific technical challenges.

[0701] Step 6:

[0702] The server automatically generates an individually optimized health maintenance plan using an AI model based on the determined growth stage, emotional state, and technical challenges. This results in the output of the most suitable health maintenance plan for the user.

[0703] Step 7:

[0704] The server analyzes past health history and generated plans to predict health risks. It uses machine learning algorithms to build a predictive model and obtains a health risk assessment as output.

[0705] Step 8:

[0706] The generated health maintenance plan and health risk assessment are notified to the user's device. The device then presents these results to the user and allows them to adjust their activity plan as needed.

[0707] This sequence of steps allows the system to provide dynamic support tailored to the user's health condition.

[0708] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0709] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0710] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0711] [Fourth Embodiment]

[0712] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0713] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0714] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0715] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0716] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0717] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0718] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0719] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0720] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0721] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0722] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0723] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0724] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] This invention is a system for achieving individualized optimization in sports training, taking into account the growth and safety of children. This system is composed of the following various means and their coordination.

[0726] First, the user (parent or coach) inputs physical data such as the child's age, height, weight, and past injury history via a terminal. This information is sent from the terminal to the server. The server stores the received data in a database and uses it to determine the child's growth stage. The assessment system is activated and identifies the child's growth stage. This prepares the user for receiving suggestions for the most suitable training for their child.

[0727] Next, the user uploads a video of their child's training from their device. The server runs a motion analysis AI to analyze the uploaded video data. The analysis identifies the child's form and technical issues from the video and extracts specific areas for improvement based on that.

[0728] Based on the identified issues, the server automatically generates a training plan using a generation mechanism. This plan includes exercises and practice schedules tailored to the individual's growth stage and technical challenges. The generated plan is then notified to the user via their device.

[0729] Furthermore, the server assesses the risk of injury based on past injury history and the current training plan. When the prediction system detects an injury risk, it provides the user with advice on risk reduction and suggestions for rest through the notification system.

[0730] For example, suppose a 10-year-old child in a growth spurt plays soccer and has a history of knee injuries. In this case, the server would generate a training plan that includes soccer-specific balance-improving exercises and suggest increasing rest days if it determines that the stress on the knees is excessive.

[0731] Users can provide feedback on the provided training plan and submit revision requests as needed. The server has a mechanism to update the training plan based on that feedback.

[0732] Thus, by integrating the entire system, the present invention provides a specific form for conducting safe and effective sports training.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] The user inputs the child's physical data. Using a terminal, they enter information such as age, height, weight, and past injury history into the system. This data serves as basic information for determining the child's growth stage.

[0736] Step 2:

[0737] The device sends data to the server. The collected physical data is sent to the server and stored in a database. The stored data is used in subsequent analysis steps.

[0738] Step 3:

[0739] The server determines the child's growth stage. Using this determination method, it analyzes the received physical data to identify the child's growth stage. This information is crucial for developing a training plan.

[0740] Step 4:

[0741] Users upload training videos. By uploading videos of their children exercising from their devices to the system, it becomes possible to analyze their exercise form.

[0742] Step 5:

[0743] The server analyzes the video data. A motion analysis AI analyzes the uploaded video data to identify form and technical issues. Specific areas for improvement are then identified.

[0744] Step 6:

[0745] The server generates a training plan. Using the generation method, it creates an individually optimized training plan based on the growth stage and analysis results. The plan includes specific exercises and a schedule.

[0746] Step 7:

[0747] The server predicts the risk of injury. The prediction method analyzes past injury data and the current training plan to assess the risk. If the risk is determined to be high, the following actions are required.

[0748] Step 8:

[0749] The server notifies the user of risk mitigation measures. Through this notification system, the server suggests rest periods and warns users about high-risk activities. Based on this information, users can safely adjust their training.

[0750] Step 9:

[0751] Users provide feedback. They can send opinions and suggestions for improvements to the training plan from their device to the server. This feedback is used to improve the training plan.

[0752] Step 10:

[0753] The server updates the training plan. Based on user feedback, it regenerates the plan using corrective measures and provides the updated version. This ensures that the plan is always up-to-date.

[0754] (Example 1)

[0755] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] In sports training, there is a need to provide efficient training while ensuring safety, by achieving individualized optimization that takes into account the child's stage of development. However, conventional methods have not adequately considered children's growth and past injury history, making it difficult to achieve sufficient results.

[0757] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0758] In this invention, the server includes a determination means for receiving information about the body and determining the developmental stage; an analysis means for analyzing movement information and identifying issues with form and technique; a generation means for automatically generating an individually optimized exercise plan based on the analysis results; a prediction means for analyzing information about past injuries and the exercise plan and predicting the likelihood of injury; a notification means for notifying the user of risk reduction measures based on the prediction results; and a communication means for sending and receiving information from the user's terminal. This enables the user to perform safe and effective training adapted to the child's developmental stage.

[0759] "Physical information" refers to data that indicates an individual's physiological and health characteristics, such as age, height, weight, and past injury history.

[0760] A "developmental stage" refers to a specific stage in an individual's growth process, indicating the degree of physiological and psychological development.

[0761] "Motion information" refers to data about the body's posture and form during exercise, and is used for analyzing sports techniques.

[0762] "Analysis means" refers to a device or program that has the function of analyzing motion information and identifying problems in form or technology.

[0763] A "generation means" is a device or program that has the function of automatically creating individually optimized movement plans based on the analyzed information.

[0764] A "predictive means" is a device or program that has the function of analyzing and predicting the likelihood of injury using past injury information and exercise plans.

[0765] A "notification means" is a device or program that has the function of informing users of risk mitigation measures based on prediction results.

[0766] "Communication means" refers to a device or program equipped with an interface or protocol for sending and receiving data between a user's terminal and another device.

[0767] This invention is a system for achieving individualized optimization in sports training, taking into account the growth and safety of children. This system involves collaboration between a terminal, a server, and the user to collect and analyze various data, and to generate and provide training plans.

[0768] First, the user uses their device to input physical information such as age, height, weight, and past injury history. The device then sends this data to the server using a secure communication protocol. One example of such a method is data transfer using HTTPS.

[0769] The server uses database management systems such as MySQL or PostgreSQL to store the received information in a relational database. The stored information is then used to determine the child's developmental stage using a determination tool. The algorithm for determination utilizes statistical and machine learning models.

[0770] Next, the user uploads a training video of their child via their device. The server analyzes the video data using a motion analysis AI model. For example, a posture analysis model based on TensorFlow is used. The results obtained from the analysis include points for form improvement and technical challenges.

[0771] The server automatically generates individually optimized exercise plans using a generation mechanism. These plans are designed based on the child's developmental stage and analyzed technical challenges, and include exercise and practice schedules. The generated plans are sent to and notified to the user via the terminal.

[0772] Furthermore, the server uses prediction tools to analyze information on past injuries and exercise plans to predict the likelihood of injury. If a risk is detected, a notification tool sends the user advice on how to mitigate the risk.

[0773] For example, if a user enters a prompt such as, "Please create an individualized training plan for a 10-year-old child who plays soccer. He is 140cm tall, weighs 35kg, and has a history of knee injuries," the server will generate an optimal training plan.

[0774] In this way, the system provides effective training while ensuring the safety of children and is tailored to their individual developmental stages.

[0775] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0776] Step 1:

[0777] The user uses a device to input physical information about the child, such as age, height, weight, and past injury history. The device organizes this input data and sends it to the server using a secure communication protocol (e.g., HTTPS). Numerical data of physical information is provided as input, and the output data is sent to the server.

[0778] Step 2:

[0779] The server stores the body information received from the terminal into a relational database. Database systems such as MySQL or PostgreSQL are used for this purpose. The received data is provided as input, and registration into the database is performed, resulting in the output of accurate information storage.

[0780] Step 3:

[0781] The server activates a determination system based on stored information to assess the child's developmental stage. This is done using statistical algorithms and machine learning models. Physical information and existing growth data are used as input, and the child's developmental stage is identified as the output.

[0782] Step 4:

[0783] The user uploads training videos of their child using their device. The device compresses the video data and sends it to the server. The input is a video file, and the output is obtained when the transmission to the server is complete.

[0784] Step 5:

[0785] The server runs a motion analysis AI model (e.g., using TensorFlow) to analyze the received video data. The input is video data, and the model analyzes the motion, identifying metrics for form improvement and technical challenges as output.

[0786] Step 6:

[0787] The server automatically generates individually optimized exercise plans using a generation method based on the analysis results. The inputs used are the results of form analysis and developmental stage information, and the output is an optimized training plan.

[0788] Step 7:

[0789] The generated training plan is notified to the user via the device, and specific exercises and schedules are presented. An exercise plan is given as input, and the output is a notification to the user.

[0790] Step 8:

[0791] The server activates a prediction mechanism and analyzes the likelihood of injury based on past injury data and the generated exercise plan. The input includes injury data and the exercise plan, and the output is an injury risk assessment.

[0792] Step 9:

[0793] If the risk is high, the server will use a notification system to send advice to the user on risk mitigation measures. The input is the risk assessment result, and the output is a risk notification to the user.

[0794] Step 10:

[0795] Users can review the training plan generated on their device and submit feedback by pressing a button. The device sends this feedback to the server. The user's feedback is given as input, and the feedback is sent to the server as output.

[0796] Step 11:

[0797] The server uses correction mechanisms to improve the training plan based on feedback received from the user and resends it to the user. The input includes feedback information, and the output is the transmission of the improved exercise plan.

[0798] (Application Example 1)

[0799] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0800] In youth sports training, it is difficult to provide training plans that take into account individual developmental stages and safety considerations. Furthermore, there are limitations to providing real-time feedback, making it difficult to efficiently support children's growth and improve their form. Additionally, it is difficult to anticipate and address health risks during training. There is a need to provide a system that solves these problems and enables safer and more effective sports training.

[0801] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0802] In this invention, the server includes a determination means for receiving physical information and determining the growth stage, an analysis means for analyzing movement information and identifying challenges in shape and skills, a generation means for automatically generating an individually optimized training plan based on the analysis results, a prediction means for analyzing past injury history and training plans and predicting health risks, and a feedback provision means for providing real-time feedback based on physical information and analysis results. This enables the provision of training plans tailored to individual growth stages, real-time support for shape improvement, and proactive detection of health risks.

[0803] "Physical information" refers to data such as age, height, weight, and past injury history used to determine an individual's stage of development.

[0804] "Stage of development" refers to a state of physical and mental maturity identified based on an individual's development and age.

[0805] "Motion information" refers to data related to form and skill in sports and exercise, and is used to identify form and technical challenges.

[0806] "Analysis means" refers to a method or process for processing and analyzing received data to extract problems and areas for improvement.

[0807] A "training plan" refers to a plan that automatically generates information based on an individual's developmental stage and analysis results, outlining the schedule and content of specific exercises and workouts.

[0808] "Health risk" refers to an index that assesses the likelihood of injury or health problems occurring based on past injury history and current training content.

[0809] "Real-time feedback" refers to immediate feedback and improvement advice provided during exercise, helping users quickly correct their form and skills.

[0810] A server plays a central role in the system that realizes this invention. The server utilizes a database to collect physical information and determine the stage of growth. Users input information such as age, height, weight, and past injury history via a terminal and send it to the server. The server can use a determination means to identify the individual's stage of growth based on the collected information.

[0811] Next, the server takes on the role of analyzing the movement information. The user uses a terminal to record a video of themselves exercising and uploads it to the server. The server uses analysis tools to process and analyze the received video data. By running a deep learning model, it identifies the shape of the movement and any skill-related problems in real time. For example, if a 10-year-old child is practicing dribbling in soccer and their form needs improvement, the server provides immediate feedback.

[0812] The server generates the training plan. Based on the analysis results, the server automatically generates an individually optimized training plan. This includes exercises and schedules tailored to the user's stage of development and is provided to the user via their device. The server also compares past injury history with the current training plan to assess health risks and provides risk mitigation measures through predictive means. For example, it can make specific suggestions such as "increase rest days to reduce stress on the knees."

[0813] This system also incorporates means of providing real-time feedback via voice and display. The server uses these feedback means to provide guidance on improving posture during exercise, helping users make adjustments as they continue their training.

[0814] As an example of a prompt, one might give the generating AI model the instruction, "Design a program that explains how a sports training assistant robot can give real-time advice to a child on how to improve their form."

[0815] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0816] Step 1:

[0817] The user inputs physical information via a terminal and sends it to the server. The input data includes age, height, weight, and past injury history, and the server uses this to determine the user's growth stage. The server receives this data, stores it in a database, and performs processing to identify the growth stage using a growth stage determination means.

[0818] Step 2:

[0819] The user records a video of themselves exercising using their device and uploads it to the server. The server receives this video data as input and processes and analyzes the motion information using analytical tools. By analyzing the video frames, the server identifies shapes and skill challenges from the movements. The server saves the identified data to a temporary file and uses it to suggest areas for improvement.

[0820] Step 3:

[0821] The server automatically generates a training plan based on the analysis results. The server's generation method creates individually optimized training content and sends this data to the terminal. The training plan includes exercises and schedules adapted to the user's stage of development, and this information is provided to the user. The generated training plan is recorded in a database for later feedback.

[0822] Step 4:

[0823] The server analyzes past injury history and generated training plans to assess health risks. It uses predictive tools to analyze risk and extract risk reduction measures. This assessment process involves data calculations to quantify risk levels. If the risk is high, it suggests rest or changes to exercise and sends notifications to the user.

[0824] Step 5:

[0825] Users perform training based on the provided training plan and return feedback to the server. The server receives the feedback using correction tools and adjusts the training plan as needed. The feedback is analyzed, updated in the database, and reflected in the training plan. Specifically, suggestions for form improvements and health-related comments are made.

[0826] Step 6:

[0827] The server uses data storage methods to record training plans, growth stage information, and health risk information in a centralized data storage. This data is used to train subsequent analysis results and improve the system's accuracy. The data is anonymized and stored securely. Prompt statements necessary for the generated AI model are also created and stored here and used in subsequent analyses.

[0828] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0829] This invention provides a system for sports training that offers individualized optimization, taking into account each individual's stage of development and risk of injury, and further considers the user's emotional state, thereby realizing an effective and safe training environment. This system is composed of multiple means linked together, as shown below.

[0830] The user first enters basic data about their child via a device. This includes age, height, weight, and past injury history. This data is sent from the device to a server and recorded in a database. The server uses a determination tool to determine the child's growth stage based on this data and stores the results.

[0831] During training, users upload videos of their children's movements from their devices. This data is necessary for motion analysis. The server uses analysis tools to analyze the videos and identify form and technical issues. The analysis results are used by a generation tool to automatically generate individually optimized training plans.

[0832] A distinctive feature of this invention is the incorporation of an emotion engine. It recognizes the user's facial expressions and tone of voice to determine their current emotional state. Specifically, it collects data through the device's camera and microphone and performs emotion analysis. This information influences the content and advice of the training plan, dynamically adjusting the plan according to the user's motivation and fatigue level during training.

[0833] Furthermore, the server uses injury prediction tools to analyze past injury history and training plans to predict injury risk. It also considers user emotional data obtained through an emotion engine and presents appropriate risk mitigation measures to the user via notification tools. These notifications may include, for example, recommendations to take a break if there are signs of fatigue.

[0834] For example, if a 13-year-old basketball player in their growth phase reports feeling fatigued during post-game practice, the emotion engine will recognize this emotion and generate a training plan with longer rest periods.

[0835] Finally, users provide feedback from their devices, submitting information about the effectiveness of their training plan and areas for improvement. This feedback is stored on the server and used to refine future training plans. This process optimizes the system to continuously provide high-quality training.

[0836] The following describes the processing flow.

[0837] Step 1:

[0838] The user uses a device to enter basic data about their child. This includes age, height, weight, sport, and past injury history. This information forms the basis for developing a training plan.

[0839] Step 2:

[0840] The terminal sends the input data to the server. The server records the received data in a database and activates a determination mechanism to determine the growth stage.

[0841] Step 3:

[0842] The server analyzes the growth stage using a determination mechanism. The determined growth stage information is an important element for designing individual training plans.

[0843] Step 4:

[0844] Users record videos of their training sessions from their devices and upload them to the system. This video data is used for detailed analysis of their exercise form.

[0845] Step 5:

[0846] The server uses motion analysis AI to analyze video data. The analysis tool identifies form and technical challenges, and the results are used by the generation tool to create a training plan.

[0847] Step 6:

[0848] The server automatically generates individually optimized training plans using a generation mechanism. These plans include analysis results and specific exercises tailored to the user's progress stage.

[0849] Step 7:

[0850] The server recognizes the user's emotions. Based on data collected through the device's camera and microphone, the emotion engine determines the user's emotional state.

[0851] Step 8:

[0852] The server incorporates emotional data into the training plan. The content of the training plan is adjusted considering the emotional data, and a plan tailored to the user's motivation and fatigue level is developed.

[0853] Step 9:

[0854] The server predicts the risk of injury and notifies the user of risk mitigation measures. The prediction system assesses the risk based on past injury data and the current training plan, and the notification system presents safety guidelines.

[0855] Step 10:

[0856] After completing training, the user provides feedback from their device. The server incorporates the received feedback into the next plan generation and optimizes the training plan using corrective measures.

[0857] (Example 2)

[0858] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0859] Traditional sports training systems struggle to provide exercise plans tailored to individual developmental stages and emotional states, making it difficult to achieve consistent results. Furthermore, injury prevention measures are often inadequate, potentially compromising user motivation and safety. To address these challenges, it is necessary to optimize exercise plans by appropriately incorporating user information and emotional states.

[0860] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0861] In this invention, the server includes a determination means for receiving physical information and determining the growth stage, an analysis means for analyzing movement information and identifying technical problems, a generation means for automatically generating an individually optimized exercise plan based on the analysis results, a prediction means for analyzing past health history and exercise plans and predicting health risks, and an adjustment means for acquiring emotional data and dynamically adjusting the exercise plan according to motivation and fatigue levels. This makes it possible to provide a safe and effective exercise plan tailored to each individual user.

[0862] "Physical information" refers to information about an individual's physical and health status, such as age, height, weight, and past health history.

[0863] "Growth stage" is an indicator that shows the user's physical and age-related maturity, and is used to optimize exercise plans.

[0864] "Determination means" refers to elements used to identify the growth stage based on physical information and select an appropriate exercise plan.

[0865] "Motion information" refers to data related to the user's movements during exercise, and is collected to evaluate form and technical issues.

[0866] "Analysis means" refers to the process of analyzing operational information to identify the user's technical challenges.

[0867] The "generation means" refers to a means for automatically creating individually optimized motion plans based on the results obtained from the analysis means.

[0868] "Past health history" refers to records of health problems or injuries that the user has experienced in the past.

[0869] An "exercise plan" refers to a customized training schedule and exercise routine based on the user's abilities and goals.

[0870] A "predictive method" is a process for assessing future health risks based on past health history and current exercise plans.

[0871] A "notification method" is a system for informing users about health risks and countermeasures to address them.

[0872] "Emotional data" refers to information about the emotional state collected from the user's facial expressions and voice.

[0873] "Adjustment methods" refer to the process of dynamically modifying exercise plans based on acquired emotional data to improve user motivation and safety.

[0874] This invention is a system that provides sports training optimized for each individual user. The system accepts personal data provided by the user via a terminal and transmits it to a server for processing.

[0875] The server first acquires physical information, including age, height, weight, and past health history. This data is sent from the terminal to the server, which uses a determination tool to assess the individual's growth stage. Based on this stage determination, the server evaluates what kind of training is appropriate for each individual.

[0876] Next, the user captures footage of their movements during training using their device and sends it to the server. The server uses various analytical tools to analyze this movement data in detail, identifying technical problems and areas for improvement. This analysis is performed quickly and accurately using cloud-based analysis software.

[0877] The server then uses a generation mechanism to create an individually optimized exercise plan based on the analysis results and growth stage information. A generation AI model is utilized to provide a dynamic training plan that responds to user interaction. This exercise plan includes specific exercises and rest periods.

[0878] Furthermore, the server evaluates past health data and the generated exercise plan, and uses predictive tools to forecast health risks. This can reduce the potential health risks that the planned exercise may pose.

[0879] Emotional data is also collected using the camera and microphone functions on the user's device. The server analyzes this data using adjustment mechanisms and makes fine adjustments to the exercise plan based on the user's emotional state.

[0880] As a concrete example, consider a 13-year-old basketball player. If this user provides feedback that they feel fatigued after a game, the server can take their emotional state and self-report into consideration and use a generation mechanism to adjust their exercise plan to reduce fatigue.

[0881] Examples of prompts include: "Propose an optimal training plan for a 13-year-old basketball player who is feeling fatigued after a game. Please take into account emotional data and past injury history."

[0882] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0883] Step 1:

[0884] The user enters personal physical information using a terminal. Specifically, they enter age, height, weight, and past health history. The entered data is sent from the terminal to the server. The server processes this physical information using a determination mechanism to determine the user's growth stage. The determined growth stage is recorded in a database.

[0885] Step 2:

[0886] The user uses a device to record their movements during training and uploads the video to the server. The server processes the received video using an analysis device to analyze the form of the movements and any technical challenges. In this process, the video data is analyzed frame by frame, and technical challenges are identified using skeletal analysis software. The analysis results are then passed to a generation device.

[0887] Step 3:

[0888] The server automatically generates an individually optimized training plan using a generation method based on the analysis results and information on the user's growth stage. This process utilizes a generation AI model to propose an exercise plan tailored to the user's technical challenges and growth stage. The generated training plan includes exercise menus and rest timings.

[0889] Step 4:

[0890] The user's current emotional data is collected using the device's camera and microphone. As the user expresses their emotions through the device during training, this data is sent to the server. The server uses adjustment mechanisms to analyze the emotional data and dynamically adjust the training plan. This analysis allows for adjustments to the plan to increase motivation and take fatigue into consideration.

[0891] Step 5:

[0892] The server evaluates past health history and generated training plans, and uses predictive tools to forecast future health risks. This forecast involves analysis to assess the impact of specific actions on health. The forecast results are used to notify the user of the risks and issue warnings.

[0893] Step 6:

[0894] Users input feedback on the effectiveness of their training and areas for improvement into their device. This feedback information is sent to the server and stored in a database. The server uses corrective measures to incorporate this information into subsequent training plans, thereby ensuring continuous improvement.

[0895] (Application Example 2)

[0896] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0897] In recent years, with the increasing diversity of individual lifestyles and health conditions, there has been a growing demand for individually optimized methods of maintaining health. However, conventional health maintenance systems only provide general guidelines and have struggled to offer dynamic, personalized plans tailored to individual growth stages and health conditions. Furthermore, they lacked mechanisms to adjust activity plans to take into account changes in emotional states. A system is needed to address these challenges and support health maintenance efficiently and effectively.

[0898] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0899] In this invention, the server includes a determination means for receiving physical information and determining the growth stage; an analysis means for analyzing movement information and identifying technical problems; a generation means for automatically generating individually optimized health maintenance plans based on the analysis results; a prediction means for analyzing past health status history and activity plans and predicting health risks; and an adjustment means for determining health-related biometric information and emotional states and dynamically adjusting the activity plan. This enables efficient health maintenance according to the growth stage and emotional state of each individual user.

[0900] "Physical information" is a general term for health-related data such as the user's heart rate, steps taken, and body fat percentage.

[0901] "Growth stage" is a concept that refers to the stages that indicate the process of a user's physical and age-related development.

[0902] "Determination means" refers to the process or technology used to identify and determine the growth stage based on the input information.

[0903] "Movement information" refers to data related to the user's exercise and activity patterns, and specifically includes steps taken, distance traveled, posture, etc.

[0904] "Analysis methods" refer to the processes and techniques used to analyze acquired data and identify technical problems and areas for improvement.

[0905] "Generation method" refers to the process or technology for automatically creating individually optimized health maintenance plans based on analysis results.

[0906] A "health maintenance plan" refers to a plan that outlines a series of activities and dietary patterns recommended for users to maintain or improve their health.

[0907] "Health history" refers to a collection of records and data related to past health conditions.

[0908] An "activity plan" is a set of elements that make up the guidelines and schedules for a user's daily activities.

[0909] "Health risk" refers to the possibility or circumstances under which a user's health may be harmed, and it is an evaluation criterion aimed at detecting such risks proactively.

[0910] "Predictive methods" refer to processes and technologies used to foresee future health risks based on past data and trends.

[0911] "Adjustment methods" refer to processes and techniques for dynamically changing activity plans, taking into account the user's current health and emotional state.

[0912] "Emotional state" is a concept that describes a user's current psychological and emotional condition, which can sometimes affect their health.

[0913] "Biometric information" is a general term for data related to the user's physical responses and condition, such as heart rate and body temperature.

[0914] This invention is a system for supporting health maintenance, and it has a configuration that dynamically generates an individualized health maintenance plan from the user's physical information, movement information, and emotional state.

[0915] The system includes a server and user terminals. The server is equipped with hardware for determining physical information and growth stage, and software for analysis and generation. Specifically, it processes data using the Python data analysis libraries Pandas and NumPy, and performs sentiment analysis using TensorFlow. Furthermore, it uses machine learning algorithms based on historical data to predict health risks.

[0916] Users use their smartphones to input or automatically collect various data from their daily lives (e.g., heart rate, steps taken). The collected data is sent to a server and stored in a database.

[0917] The user's emotional state is captured through the device's camera and microphone, and emotion analysis is performed on the server. Based on the analysis results, a specific health maintenance plan is individually generated and provided to the user. The generating AI model takes into account the user's current lifestyle and goals, and recommends various health maintenance activities.

[0918] For example, when a user is monitoring their daily walks, the app might send a notification such as, "Your step count has increased, so we recommend adding a new stretching routine as your next step."

[0919] An example of a prompt for a generative AI model might be: "Analyze what health changes the user needs based on their heart rate and emotional state during recent activities."

[0920] In this way, the system can provide personalized and continuous health support to individual users.

[0921] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0922] Step 1:

[0923] The user's device collects physical information such as heart rate, steps taken, and body fat percentage. This includes data from sensors built into the device and from wearable devices. It receives physical information as input and sends it to the server.

[0924] Step 2:

[0925] The server uses the received physical information to determine the growth stage. It analyzes the data using Pandas and identifies the appropriate growth stage based on the user's age and physical data. The server then generates the identified growth stage information as output.

[0926] Step 3:

[0927] The user's device uses its camera and microphone to collect data on the user's facial expressions and voice. It receives facial expression information as input and sends it to the server.

[0928] Step 4:

[0929] The server uses collected facial and voice information to perform emotion analysis using TensorFlow. It takes the data as input to determine the user's emotional state and generates this as output.

[0930] Step 5:

[0931] The server analyzes operational information to identify technical problems and areas where the user needs assistance. It performs data calculations using NumPy and outputs specific technical challenges.

[0932] Step 6:

[0933] The server automatically generates an individually optimized health maintenance plan using an AI model based on the determined growth stage, emotional state, and technical challenges. This results in the output of the most suitable health maintenance plan for the user.

[0934] Step 7:

[0935] The server analyzes past health history and generated plans to predict health risks. It uses machine learning algorithms to build a predictive model and obtains a health risk assessment as output.

[0936] Step 8:

[0937] The generated health maintenance plan and health risk assessment are notified to the user's device. The device then presents these results to the user and allows them to adjust their activity plan as needed.

[0938] This sequence of steps allows the system to provide dynamic support tailored to the user's health condition.

[0939] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0940] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0941] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0942] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0943] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0944] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0945] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0946] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0947] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0948] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0949] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0950] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0951] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0952] 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.

[0953] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0954] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0955] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0956] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0957] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0958] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0959] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0960] The following is further disclosed regarding the embodiments described above.

[0961] (Claim 1)

[0962] A determination means that receives physical data and determines the growth stage,

[0963] An analytical method for analyzing motion data and identifying form and technical issues,

[0964] A generation means that automatically generates individually optimized training plans based on analysis results,

[0965] A predictive method that analyzes past injury history and training plans to predict injury risk,

[0966] A notification method that informs users of risk mitigation measures based on prediction results,

[0967] A system that includes this.

[0968] (Claim 2)

[0969] The system according to claim 1, further comprising a means for receiving feedback from the user based on the generated training plan and modifying the training plan.

[0970] (Claim 3)

[0971] The system according to claim 1, further comprising a data storage means for storing training plans, growth stage information, and injury risk information in a central database and using the analysis results for learning.

[0972] "Example 1"

[0973] (Claim 1)

[0974] A means for receiving information about the body and determining the developmental stage,

[0975] An analytical means for analyzing motion information and identifying form and technical issues,

[0976] A generation means that automatically generates individually optimized motion plans based on analysis results,

[0977] A predictive method that analyzes information on past injuries and exercise plans to predict the likelihood of injury,

[0978] A notification method that informs users of risk mitigation measures based on prediction results,

[0979] A communication means for sending and receiving information from the user's terminal,

[0980] A system that includes this.

[0981] (Claim 2)

[0982] The system according to claim 1, further comprising a means for receiving feedback from the user based on the generated exercise plan and modifying the exercise plan.

[0983] (Claim 3)

[0984] The system according to claim 1, further comprising an information storage means for storing exercise plans, developmental stage information, and injury possibility information in a central database and using the analysis results for learning.

[0985] "Application Example 1"

[0986] (Claim 1)

[0987] A determination means that receives physical information and determines the stage of growth,

[0988] An analytical means for analyzing motion information and identifying issues related to shape and skills,

[0989] A generation means that automatically generates individually optimized training plans based on analysis results,

[0990] A predictive method that analyzes past injury history and training plans to predict health risks,

[0991] A notification method that informs users of risk mitigation measures based on prediction results,

[0992] A feedback provision method that provides real-time feedback based on physical information and analysis results,

[0993] A system that includes this.

[0994] (Claim 2)

[0995] The system according to claim 1, further comprising adjustment means for receiving feedback from the user based on the generated training plan and modifying the training plan.

[0996] (Claim 3)

[0997] The system according to claim 1, further comprising a data storage means for storing training plans, growth stage information, and health risk information in a centralized data storage and utilizing the analysis results for learning.

[0998] "Example 2 of combining an emotion engine"

[0999] (Claim 1)

[1000] A determination means that receives physical information and determines the stage of growth,

[1001] An analytical means for analyzing operational information and identifying technical problems,

[1002] A generation means that automatically generates individually optimized motion plans based on analysis results,

[1003] A predictive tool that analyzes past health history and exercise plans to predict health risks,

[1004] A notification system that informs users of risk reduction measures based on prediction results,

[1005] An adjustment mechanism that acquires emotional data and dynamically adjusts the exercise plan according to motivation and fatigue levels,

[1006] A system that includes this.

[1007] (Claim 2)

[1008] The system according to claim 1, further comprising a modification means for receiving feedback from the user based on the generated exercise plan and modifying the exercise plan.

[1009] (Claim 3)

[1010] The system according to claim 1, further comprising an information storage means for recording exercise plans, growth stage information, and health risk information in a central information storage unit and using the results of the analysis for learning.

[1011] "Application example 2 when combining with an emotional engine"

[1012] (Claim 1)

[1013] A determination means that receives physical information and determines the stage of growth,

[1014] An analytical means for analyzing operational information and identifying technical problems,

[1015] A generation means that automatically generates individually optimized health maintenance plans based on analysis results,

[1016] A predictive tool that analyzes past health history and activity plans to predict health risks,

[1017] A notification mechanism that informs users of risk mitigation measures based on prediction results,

[1018] A means of adjusting the activity plan dynamically based on health-related biometric information and emotional state,

[1019] A system that includes this.

[1020] (Claim 2)

[1021] The system according to claim 1, further comprising means for receiving feedback from the user based on the generated health maintenance plan and modifying the activity plan.

[1022] (Claim 3)

[1023] The system according to claim 1, further comprising information storage means for storing health maintenance plans, growth stage information, and health risk information in a central information infrastructure and using the results of analysis for learning. [Explanation of symbols]

[1024] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A determination means that receives physical information and determines the stage of growth, An analytical means for analyzing motion information and identifying issues related to shape and skills, A generation means that automatically generates individually optimized training plans based on analysis results, A predictive method that analyzes past injury history and training plans to predict health risks, A notification method that informs users of risk mitigation measures based on prediction results, A feedback provision method that provides real-time feedback based on physical information and analysis results, A system that includes this.

2. The system according to claim 1, further comprising adjustment means for receiving feedback from the user based on the generated training plan and modifying the training plan.

3. The system according to claim 1, further comprising a data storage means for storing training plans, growth stage information, and health risk information in a centralized data storage and utilizing the analysis results for learning.

Images