System

Abstract

A system is provided.SOLUTION: A system comprising: means for digitizing and accumulating physical data of an athlete; means for acquiring daily physical data and transmitting the data to a server; means for analyzing the transmitted data and suggesting a training menu; means for presenting a training menu to a user; means for selecting optimal ingredients for nutritional support and creating a recipe; means for presenting a recipe and an ingredient list to a user; and means for collecting and analyzing mental data and providing mental support.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] To optimize an athlete's performance, it is essential to accurately understand daily fluctuations in physical data and provide appropriate training programs, nutritional supplements, and mental care. However, individually collecting this data and taking appropriate measures is extremely time-consuming and requires specialized knowledge. Furthermore, no method has been established for centrally managing this data and providing it as a comprehensive condition management system. Therefore, an objective of the present invention is to provide a comprehensive system for efficiently and effectively managing an athlete's performance. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for quantifying and storing an athlete's physical data, a means for acquiring daily physical data and transmitting it to a server, a means for analyzing the transmitted data and proposing a training menu, and a means for presenting the training menu to the user. It also includes a means for selecting optimal ingredients for nutritional supplementation and creating a recipe, a means for presenting the recipe and ingredient list to the user, and a means for collecting and analyzing mental data and providing mental support. This system centrally manages an athlete's basic data and daily fluctuation data, enabling performance optimization.

[0006] "Basic data" refers to the athlete's basic physical information, including height, weight, muscle mass, cardiopulmonary function, etc.

[0007] "Fluctuation data" refers to data that indicates daily fluctuations in an athlete's physical condition, and includes body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, amount of sleep, urinary and fecal status, and the like.

[0008] The "server" is a central computer system that collects, stores, and analyzes basic and fluctuation data on athletes, and provides training menus, nutritional supplements, and mental support based on that data.

[0009] A "terminal" is an individual device held by an athlete, which exchanges data with the server and presents information to the athlete.

[0010] A "training menu" is an athlete's exercise and workout program that is generated by the server and presented to the user via the terminal.

[0011] "Nutrition" means selecting appropriate ingredients and creating recipes based on the athlete's basic and fluctuation data, thereby ensuring that the athlete consumes the nutrients they need.

[0012] A "recipe" is a list of cooking instructions and ingredients that is created by the server and provided to the user via the terminal.

[0013] "Mental data" refers to information about an athlete's mental state, including stress levels and psychological fatigue.

[0014] "Mental support" refers to providing assistance to improve an athlete's mental state based on the mental data collected and analyzed by the server.

[0015] A "wearable device" is a device worn by an athlete that measures variable data such as body temperature, heart rate, and blood pressure. [Brief explanation of the drawings]

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

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

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a comprehensive condition management system for optimizing athletic performance. This system provides a variety of means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, and providing mental support.

[0038] Collecting and quantifying basic data

[0039] User

[0040] During initial setup, users enter basic data such as height, weight, muscle mass, and cardiopulmonary function into a dedicated app.

[0041] Terminal

[0042] The terminal transmits the basic data entered by the user to the server.

[0043] server

[0044] The server stores the received basic data in a database and automatically digitizes it for analysis.

[0045] Collection and analysis of daily fluctuation data

[0046] Terminal

[0047] The device automatically collects daily data from the user's wearable device, including body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep.

[0048] server

[0049] The server adds the received fluctuation data to a database and analyzes it to detect outliers and unusual trends.

[0050] Training menu suggestions

[0051] server

[0052] The server analyzes the basic and variability data and creates an appropriate training menu based on the analysis, for example, a menu including strength training, cardio exercises, stretching, etc.

[0053] Terminal

[0054] The terminal notifies the user of the generated training menu and presents the detailed contents.

[0055] Nutritious ingredient selection and recipe provision

[0056] server

[0057] The server calculates the required nutrients based on the user's basic data and training data, and creates an ingredient list based on the results.

[0058] Terminal

[0059] The terminal presents the user with a list of ingredients and recipes provided by the server. For example, it provides ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions for using those ingredients.

[0060] Mental support

[0061] User

[0062] The user periodically answers questions about their mental state and transmits the data to the server via their terminal.

[0063] server

[0064] The server analyzes the received mental data to detect stress levels and psychological fatigue, and based on the analysis results, suggests relaxation methods or counseling as needed.

[0065] Terminal

[0066] The device notifies the user of mental support suggestions provided by the server, such as suggestions for meditation or yoga, or counseling appointments.

[0067] Specific examples

[0068] Collection of basic data

[0069] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[0070] The device sends this data to the server, which stores it in a database.

[0071] Collecting fluctuation data

[0072] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[0073] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[0074] Training menu suggestions

[0075] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[0076] The terminal displays this training menu to the user and explains the details.

[0077] Nutritional supplementation

[0078] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[0079] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[0080] Mental support

[0081] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[0082] The server analyzes stress levels and suggests yoga as a way to relax.

[0083] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[0084] This system will enable the centralized management of athletes' basic data and daily fluctuation data, enabling comprehensive condition management, which will optimize athletes' performance and provide efficient and effective training and care.

[0085] The processing flow will be explained below.

[0086] Step 1:

[0087] User

[0088] The user launches the app and enters basic data such as their height, weight, muscle mass, and cardiopulmonary function as the initial setting.

[0089] Step 2:

[0090] Terminal

[0091] The terminal transmits the basic data entered by the user to the server.

[0092] Step 3:

[0093] server

[0094] The server stores the received basic data in a database and automatically digitizes it for analysis.

[0095] Step 4:

[0096] Terminal

[0097] The device automatically collects daily fluctuation data (body temperature, blood pressure, blood sugar level, heart rate variability, etc.) from the user's wearable device.

[0098] Step 5:

[0099] Terminal

[0100] The terminal transmits daily fluctuation data to the server.

[0101] Step 6:

[0102] server

[0103] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[0104] Step 7:

[0105] server

[0106] The server analyzes the basic data and the variation data and generates an appropriate training menu.

[0107] Step 8:

[0108] Terminal

[0109] The terminal notifies the user of the training menu from the server and presents the detailed contents.

[0110] Step 9:

[0111] server

[0112] The server calculates the required nutrients based on the user's basic data and training data.

[0113] Step 10:

[0114] server

[0115] The server creates an optimal ingredient list and recipe based on the calculation results.

[0116] Step 11:

[0117] Terminal

[0118] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[0119] Step 12:

[0120] User

[0121] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[0122] Step 13:

[0123] server

[0124] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[0125] Step 14:

[0126] server

[0127] Based on the analysis results, the server suggests relaxation methods and counseling.

[0128] Step 15:

[0129] Terminal

[0130] The terminal notifies the user of suggestions for mental support from the server and provides specific relaxation techniques and counseling appointment information.

[0131] This system allows athletes to efficiently manage their basic data and daily fluctuation data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[0132] Example 1

[0133] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0134] In order to optimize athletes' performance and efficiently manage their overall condition, it is important to centrally manage physical data, fluctuation data, and mental data and provide appropriate training, nutritional support, and mental support in real time. However, conventional systems that manage these data separately have issues with data consistency and immediacy, making it difficult to provide optimized support for individual athletes.

[0135] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0136] In this invention, the server includes means for quantifying and storing the athlete's basic data, means for acquiring daily fluctuation data and sending it to the server, means for analyzing outliers and unusual trends based on the acquired data, means for generating a training menu based on the analysis results, means for notifying the user of the generated training menu, means for calculating necessary nutrients, selecting optimal ingredients, and creating a recipe, means for presenting the recipe and ingredient list to the user, and means for collecting and analyzing mental data and providing mental support, thereby optimizing the athlete's performance and enabling comprehensive condition management.

[0137] An "athlete" is someone who possesses high skill and physical strength in competitive sports.

[0138] "Basic data" refers to data that quantifies basic information about an athlete's body, such as height, weight, muscle mass, and cardiopulmonary function.

[0139] "Variation data" refers to information about daily changes in an athlete's body, such as body temperature, blood pressure, blood sugar levels, heart rate variability, fatigue, and amount of sleep.

[0140] "Server" refers to a computer system that receives, stores, analyzes, etc. data.

[0141] "Terminal" refers to a device used by a user to input or refer to data, including, for example, a smartphone or tablet.

[0142] "Digitization" refers to the process of converting data collected from users into a numerical format that is easy to analyze.

[0143] "Storage" refers to storing collected data for a long period of time.

[0144] An "outlier" is a data value that falls outside the normal range in a statistical analysis.

[0145] "Anomalous trends" refers to trends in data where clear patterns or anomalies can be identified in data analysis.

[0146] A "training menu" refers to a series of exercises and plans proposed to improve an athlete's physical strength and maintain their condition.

[0147] "Nutrients" refers to proteins, vitamins, minerals, and other components necessary for athletes' health and performance.

[0148] "Ingredients List" refers to a list of foods or ingredients needed to prepare a suggested recipe.

[0149] A "recipe" is a document that details the steps for preparing a dish using specific ingredients.

[0150] "Mental data" refers to information about an athlete's mental state, such as stress levels and psychological fatigue.

[0151] "Mental support" refers to assistance to maintain and improve the mental health of athletes. Specifically, this includes suggestions for relaxation techniques and counseling.

[0152] This invention is a comprehensive condition management system for optimizing athletic performance. This system provides a variety of means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, and providing mental support.

[0153] Collecting and quantifying basic data

[0154] User

[0155] During initial setup, users use a dedicated app to enter basic data such as height, weight, muscle mass, and cardiopulmonary function.

[0156] Terminal

[0157] The terminal sends the basic data entered by the user to the server, where the data is encrypted to ensure security.

[0158] server

[0159] The server stores the received basic data in a database and automatically converts it into numerical values ​​for analysis. For example, it statistically analyzes the QRS complex wave parameters of cardiopulmonary function to check for deviations from standard values.

[0160] Collection and analysis of daily fluctuation data

[0161] Terminal

[0162] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and sleep amount from the user's wearable device (e.g., smartwatch). The collected data is temporarily stored in the device in real time.

[0163] Terminal

[0164] The device periodically (e.g., every 10 minutes) transmits the temporarily stored data to the server, where data integrity and encryption are performed.

[0165] server

[0166] The server adds the received fluctuation data to a database and performs statistical analysis. If an abnormal value is detected, an alert is sent to the terminal to notify the user.

[0167] Training menu suggestions

[0168] server

[0169] The server generates a training plan that is optimal for the user's physical condition based on the basic and fluctuation data. For example, it creates a menu of strength training (20 minutes), cardio (30 minutes), and stretching (15 minutes).

[0170] Terminal

[0171] The device will then notify the user of the generated training menu and display the details within the app, including video links and text descriptions for each exercise.

[0172] Nutritious ingredient selection and recipe provision

[0173] server

[0174] The server calculates the amount of nutrients (e.g., protein, vitamins) needed based on the user's basic and training data. For example, it creates an ingredient list such as "200g chicken breast, 100g broccoli, 200g brown rice."

[0175] server

[0176] The server generates a recipe that includes cooking methods for each ingredient, such as suggesting "grilled chicken breast."

[0177] Terminal

[0178] The device receives the ingredients list and recipe from the server and displays it in the app, notifying the user. The recipe includes detailed information such as cooking time, steps, and necessary equipment.

[0179] Mental support

[0180] User

[0181] Users periodically answer in-app surveys to record their current stress levels and psychological state, for example, by entering "My stress level has been high recently."

[0182] Terminal

[0183] The terminal sends the mental data entered by the user to the server, where the data is checked for integrity and encrypted during transmission.

[0184] server

[0185] The server analyzes the received mental data to detect stress levels and psychological fatigue, and based on the analysis results, generates suggestions for relaxation methods (e.g., yoga) and counseling appointments.

[0186] Terminal

[0187] The device notifies the user of the mental support suggestions received from the server and displays detailed instructions and a link to book a counseling appointment within the app.

[0188] Specific examples

[0189] Collection of basic data

[0190] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[0191] The device sends this data to the server, which stores it in a database.

[0192] Collecting fluctuation data

[0193] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[0194] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[0195] Training menu suggestions

[0196] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[0197] The terminal displays this training menu to the user and explains the details.

[0198] Nutritional supplementation

[0199] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[0200] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[0201] Mental support

[0202] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[0203] The server analyzes stress levels and suggests yoga as a way to relax.

[0204] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[0205] Example prompts for generative AI models

[0206] "Please enter the athlete's basic data (height 175cm, weight 70kg, muscle mass 50kg) and generate a training menu based on one week's heart rate data. Also, please provide a list of ingredients and recipes for proper nutrition."

[0207] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0208] Step 1:

[0209] Input: The user launches a dedicated app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function.

[0210] How it works: A user fills out a form in the app and enters data such as height 175cm, weight 70kg, muscle mass 50kg, etc. The app performs a consistency check to ensure there are no input errors.

[0211] Output: The correct formatted basic data is saved to the device.

[0212] Step 2:

[0213] Input: The terminal receives basic data entered by the user.

[0214] How it works: The device encrypts the underlying data it receives and sends it to the server using a secure communication protocol.

[0215] Output: The encrypted underlying data is sent to the server.

[0216] Step 3:

[0217] Input: The server receives the basic data sent from the terminal.

[0218] How it works: The server decrypts the data and stores it in a database. It also analyzes and quantifies the data. For example, it analyzes the QRS complex wave of cardiopulmonary function and calculates the difference from the standard value.

[0219] Output: The quantified basic data is saved in a database.

[0220] Step 4:

[0221] Input: The device collects daily fluctuation data (body temperature, blood pressure, blood glucose level, heart rate variability, fatigue, and amount of sleep) from the user's wearable device.

[0222] How it works: The device acquires data collected from the wearable device in real time and temporarily stores it.

[0223] Output: The temporarily saved fluctuation data is saved on the terminal.

[0224] Step 5:

[0225] Input: The terminal periodically sends the temporarily stored fluctuation data to the server.

[0226] How it works: The device encrypts the collected data and sends it to the server periodically (e.g., every 10 minutes).

[0227] Output: The encrypted variable data is sent to the server.

[0228] Step 6:

[0229] Input: The server receives the variable data sent from the terminal.

[0230] How it works: The server decrypts the data, stores it in a database, and analyzes it. If an abnormal value is detected, an alert is generated and sent to the device. For example, if blood sugar levels suddenly rise, the user is immediately notified of this information.

[0231] Output: Analysis results and alert information are stored on the server and sent to the terminal as needed.

[0232] Step 7:

[0233] Input: The server generates a training menu based on basic data and variation data.

[0234] How it works: Based on data analysis, the server automatically generates optimal training menus, including strength training, cardio, and stretching.

[0235] Output: The generated training menu is saved in the database.

[0236] Step 8:

[0237] Input: The terminal receives the training menu generated from the server.

[0238] How it works: The device will notify the user of a workout menu and display detailed instructions within the app, including video links and text descriptions for each exercise.

[0239] Output: The training menu notified to the user.

[0240] Step 9:

[0241] Input: The server calculates the required nutrients based on the user's basic and training data.

[0242] How it works: The server calculates the necessary nutrients, such as protein and vitamins, and creates an ingredient list based on that (e.g., 200g chicken breast, 100g broccoli, 200g brown rice). It then generates a recipe based on this list.

[0243] Output: Ingredient list and recipe based on calculated nutrients.

[0244] Step 10:

[0245] Input: The device receives the ingredient list and recipe provided by the server.

[0246] How it works: The device displays the received ingredient list and recipe in the app and notifies the user, with the recipe including details such as cooking time, steps, and required equipment.

[0247] Output: The recipe and ingredient list provided to the user.

[0248] Step 11:

[0249] Input: The user inputs mental data into the app.

[0250] How it works: Users fill out an in-app survey to input their stress levels and psychological state. For example, they might enter, "My stress level has been high recently."

[0251] Output: The input mental data is saved on the device.

[0252] Step 12:

[0253] Input: The terminal receives mental data input from the user.

[0254] How it works: The device encrypts the received mental data and sends it to the server using a secure communication protocol.

[0255] Output: The encrypted mental data is sent to the server.

[0256] Step 13:

[0257] Input: The server receives the mental data sent from the terminal.

[0258] How it works: The server decrypts and stores the mental data in a database. It also analyzes the data to detect stress levels and psychological fatigue. If necessary, it generates suggestions for relaxation methods (e.g., yoga) or counseling.

[0259] Output: The analysis results and recommendations are stored in a database and sent to a terminal if necessary.

[0260] Step 14:

[0261] Input: The device receives the analysis results and suggestions sent from the server.

[0262] How it works: The device displays the analysis results and recommendations it receives within the app and notifies the user, including relaxation techniques and a link to book a consultation.

[0263] Output: The user is notified of relaxation techniques and a link to book a consultation.

[0264] (Application example 1)

[0265] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0266] Comprehensive condition management is necessary to maximize the performance of athletes and machines. However, current systems have the problem of not being able to centrally collect and analyze physical and machine data, propose training and maintenance, or manage nutrition and consumable parts. There is a need for a system that can manage all of this data in an integrated manner and provide optimal support efficiently and effectively.

[0267] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0268] In this invention, the server includes means for quantifying and storing the athlete's physical data, means for acquiring daily physical data and transmitting it to the server, means for analyzing the transmitted data and proposing a training menu, means for presenting the training menu to the user, means for selecting optimal ingredients for nutritional supplementation and creating recipes, means for presenting recipes and ingredient lists to the user, means for collecting and analyzing mental data and providing mental support, means for collecting basic machine data and daily fluctuation data and proposing a maintenance schedule based thereon, means for generating and presenting machine operation optimization parameters, means for creating a consumable parts list and providing it to the user, and means for detecting abnormalities and issuing corresponding alerts. This allows for centralized management of both athlete training and nutritional management and machine maintenance and operation optimization, enabling efficient and effective support.

[0269] An "athlete" is an individual or group who trains with the goal of achieving high performance in a sport or competition.

[0270] "Physical data" refers to specific numerical values ​​representing an athlete's physical information, such as height, weight, muscle mass, and cardiopulmonary function.

[0271] A "server" is a computer system that stores, analyzes, and provides data over a network.

[0272] "Machinery" refers to equipment such as robots and automated devices used in production activities in factories, etc.

[0273] "Fluctuating data" is information that changes daily, such as body temperature, blood pressure, heart rate, and blood sugar level, which show different values ​​in real time.

[0274] A "training menu" is a plan that lists the specific exercises and practices that an athlete should perform.

[0275] "User" refers to a person or individual who uses this condition management system.

[0276] "Nutrition" refers to ensuring that athletes properly consume the ingredients and food they need to maintain their physical condition and improve their performance.

[0277] A "food list" is a list of ingredients selected based on a specific purpose or training menu.

[0278] "Mental data" is information used to examine an athlete's stress level and psychological state.

[0279] "Mental support" refers to providing advice and care to maintain and improve an athlete's mental health.

[0280] "Basic data" refers to data that forms the basis of operation, such as the machine model, installation date, and initial setting information at the start of use.

[0281] A "maintenance schedule" is a plan for carrying out appropriate maintenance and inspection of machinery.

[0282] "Operational optimization parameters" are settings and adjustments that allow a machine to operate efficiently and effectively.

[0283] A "consumable parts list" is a list of parts that need to be replaced or replenished periodically during machine operation.

[0284] "Abnormal" refers to a state in which a machine deviates from its normal operation and indicates that a breakdown or malfunction has occurred.

[0285] An "alert" is a warning or notification issued when an abnormality or unusual situation is detected.

[0286] A specific system configuration and processing procedure for carrying out the present invention will be described. This system is a comprehensive condition management system for optimizing the performance of athletes and factory robots, and includes the following means:

[0287] First, the user enters the athlete's physical data and basic machine data into a dedicated application. This application runs on a smartphone, tablet, or desktop PC and is designed to make data entry and transmission easy. During initial setup, basic data such as height, weight, muscle mass, cardiopulmonary function, robot model, and installation date are entered.

[0288] Next, the device (smartphone, tablet, PC, etc.) sends the basic data entered by the user to a server. This server is a computer system installed on a network that has the function of storing, analyzing, and providing data. Specifically, it uses a database (e.g., PostgreSQL) to store the basic data and digitize it for analysis.

[0289] Daily fluctuation data is collected through wearable devices and various sensors. This includes body temperature, blood pressure, blood sugar levels, heart rate variability, fatigue level, and amount of sleep. This data is also sent to the server via the device. The server adds the received fluctuation data to a database and performs analysis.

[0290] The transmitted data is analyzed using data analysis algorithms running on the server. For example, machine learning models written in Python are used to detect abnormal values ​​and specific trends in heart rate and blood pressure. The robot's vibration and temperature data are also analyzed to detect abnormalities.

[0291] Based on the analysis, the server creates training and maintenance schedules: training for athletes includes strength training, cardio exercises, and stretching, while maintenance for robots includes oil checks and filter changes.

[0292] The server also selects ingredients for nutritional support and provides recipes. The server calculates the nutritional needs of the user based on their basic and training data, and creates an ingredient list. For example, it presents ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions.

[0293] Additionally, collecting and analyzing mental data is also important. Users periodically answer questions about their mental state and send the data to a server via their device. The server analyzes stress levels and psychological fatigue and provides appropriate mental support, such as suggestions for yoga or counseling.

[0294] For example, when collecting basic data, the user launches the app and enters data such as height 175 cm, weight 70 kg, and muscle mass 50 kg. The wearable device also sends heart rate data to the terminal at 8:00 a.m. every day, and the server records an average heart rate of 72 BPM. As a maintenance schedule suggestion, the next maintenance is scheduled for November 10, 2023, and the tasks include an oil check and filter change.

[0295] Example prompt sentence:

[0296] "The ABB IRB 6700 robot was installed on January 1, 2023. Please collect the basic data and store it in the database."

[0297] "Based on today's inspection results, the robot has been running for 16 hours and one anomaly has been detected. Please send this data to the server."

[0298] "The next maintenance is scheduled for November 10, 2023. Tasks will include an oil check and filter change."

[0299] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0300] Step 1:

[0301] During the initial setup, the user enters basic data about the athlete and the machine into a dedicated app. Specifically, information such as height, weight, muscle mass, cardiopulmonary function, robot model, and installation date is entered, and the data is saved on the device. This input data is converted into numerical values ​​as basic data.

[0302] Input: Basic data on athletes and machines

[0303] Data processing: digitization of basic data

[0304] Output: The digitized basic data is saved on the device.

[0305] Step 2:

[0306] The device sends the stored basic data to the server, which receives it and stores it in a database. The server then manages the basic data as quantified information for analysis.

[0307] Input: Basic data stored on the device

[0308] Data processing: data transmission and storage

[0309] Output: The digitized basic data is saved in the server database.

[0310] Step 3:

[0311] To collect daily fluctuation data, the device acquires data such as body temperature, blood pressure, blood sugar level, heart rate variability, fatigue level, and amount of sleep from wearable devices and sensors, and periodically transmits this daily fluctuation data to a server.

[0312] Input: Variable data from wearable devices and sensors

[0313] Data Processing: Data Acquisition and Transmission

[0314] Output: Daily fluctuation data is sent to the server

[0315] Step 4:

[0316] The server adds the received fluctuation data to a database and performs analysis. Specifically, it uses a Python machine learning model to detect outliers and unusual trends. This allows for early detection of abnormalities in, for example, heart rate or blood pressure.

[0317] Input: Daily fluctuation data

[0318] Data processing: detecting outliers and unusual trends

[0319] Output: Analysis results of specific trends and outliers

[0320] Step 5:

[0321] The server analyzes baseline and fluctuation data and creates optimal training and maintenance schedules based on that data, which could include strength training and cardio exercises for athletes, or oil checks and filter changes for robots.

[0322] Input: Fundamental data and daily fluctuation data

[0323] Data processing: Creation of training menus and maintenance schedules

[0324] Output: Training menu and maintenance schedule

[0325] Step 6:

[0326] The device notifies the user of the training menu and maintenance schedule received from the server and presents detailed information, allowing the user to understand specifically what kind of training and maintenance they should perform.

[0327] Input: Training menu and maintenance schedule

[0328] Data Processing: Notification and Details

[0329] Output: Detailed presentation of training menu and maintenance schedule

[0330] Step 7:

[0331] The server calculates the nutritional needs of the user based on their basic and training data, and then creates an ingredient list and recipe based on that. For example, it presents ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions.

[0332] Input: Base data and training data

[0333] Data processing: Nutrition calculations and generating ingredient lists and recipes

[0334] Output: Ingredient list and recipe

[0335] Step 8:

[0336] The terminal presents the user with a list of ingredients and recipes provided by the server, allowing the user to specifically understand which ingredients to select and how to cook them to replenish their nutrients.

[0337] Input: Ingredient list and recipe

[0338] Data Processing: Notification and Details

[0339] Output: Ingredient list and recipe details

[0340] Step 9:

[0341] Users periodically answer questions about their mental state and send the data, including their stress level and psychological fatigue level, to a server via their device.

[0342] Input: Answers to questions about mental state

[0343] Data processing: Data transmission

[0344] Output: Sending mental data

[0345] Step 10:

[0346] The server analyzes the received mental data, assesses stress levels and psychological fatigue, and provides appropriate mental support, such as yoga suggestions or counseling appointments, if necessary.

[0347] Input: Mental data

[0348] Data processing: Evaluation of stress levels and psychological fatigue

[0349] Output: Mental support suggestions

[0350] Step 11:

[0351] The device notifies the user of suggestions for mental support provided by the server, such as suggestions for meditation or yoga, or information on booking counseling appointments.

[0352] Input: Mental support suggestions

[0353] Data Processing: Notification and Details

[0354] Output: Detailed mental support information

[0355] Step 12:

[0356] The server collects basic machine data and daily fluctuation data, and based on that data, proposes a maintenance schedule, including the next maintenance date and specific work content (oil check, filter change, etc.).

[0357] Input: Fundamental data and daily fluctuation data

[0358] Data processing: Maintenance schedule generation

[0359] Output: Maintenance Schedule

[0360] Step 13:

[0361] The server generates and presents machine operation optimization parameters to the user, providing settings for efficient and effective machine operation.

[0362] Input: Fundamental and variable data

[0363] Data processing: Generation of operational optimization parameters

[0364] Output: Operational optimization parameters

[0365] Step 14:

[0366] The server creates a list of consumable parts and provides it to the user, making it easy to arrange for the necessary consumable parts.

[0367] Input: Fundamental and variable data

[0368] Data processing: Generating a list of consumable parts

[0369] Output: Consumable parts list

[0370] Step 15:

[0371] The server detects abnormalities and issues an alert accordingly, allowing users to quickly detect abnormalities and take appropriate measures.

[0372] Input: Daily fluctuation data

[0373] Data processing: Anomaly detection and alerting

[0374] Output: Alert

[0375] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0376] This invention is a comprehensive condition management system for optimizing athletic performance, and aims to provide more advanced mental support by combining it with an emotion engine that recognizes the user's emotions. This system provides various means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, providing mental support, and collecting and analyzing emotional data using the emotion engine.

[0377] Collecting and quantifying basic data

[0378] User

[0379] The user launches the app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function as the initial settings.

[0380] Terminal

[0381] The terminal transmits the basic data entered by the user to the server.

[0382] server

[0383] The server stores the received basic data in a database and automatically digitizes it for analysis.

[0384] Collection and analysis of daily fluctuation data

[0385] Terminal

[0386] The device automatically collects daily data from the user's wearable device, including body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep.

[0387] server

[0388] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[0389] Training menu suggestions

[0390] server

[0391] The server analyzes the basic and variability data and creates an appropriate training menu based on the analysis, for example, a menu including strength training, cardio exercises, stretching, etc.

[0392] Terminal

[0393] The terminal notifies the user of the generated training menu and presents the detailed contents.

[0394] Nutritious ingredient selection and recipe provision

[0395] server

[0396] The server calculates the required nutrients based on the user's basic data and training data, and creates an ingredient list based on the results.

[0397] Terminal

[0398] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[0399] Mental support

[0400] User

[0401] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[0402] server

[0403] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[0404] Terminal

[0405] The terminal notifies the user of the mental support suggestions provided by the server, and provides specific relaxation techniques and counseling reservation information.

[0406] Emotional data collection and analysis using an emotion engine

[0407] Terminal

[0408] Devices equipped with an emotion engine use technologies such as facial and voice recognition to collect user emotion data in real time.

[0409] server

[0410] The server receives the emotional data sent from the emotion engine and analyzes it in combination with mental data. For example, if the user is feeling stressed, it can make adjustments such as suggesting that the training intensity be reduced.

[0411] Specific examples

[0412] Collection of basic data

[0413] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[0414] The device sends this data to the server, which stores it in a database.

[0415] Collecting fluctuation data

[0416] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[0417] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[0418] Training menu suggestions

[0419] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[0420] The terminal displays this training menu to the user and explains the details.

[0421] Nutritional supplementation

[0422] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[0423] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[0424] Mental support

[0425] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[0426] The server analyzes stress levels and suggests yoga as a way to relax.

[0427] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[0428] Emotion Engine

[0429] The device uses facial recognition technology to collect emotional data from the user in real time.

[0430] The server analyzes the emotional data and adjusts flexible training and mental support when the user feels stressed.

[0431] The device will notify the user of the analysis results and suggest relaxation techniques or counseling to reduce stress.

[0432] This system allows athletes to efficiently manage their basic data, daily fluctuation data, and emotional data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[0433] The processing flow will be explained below.

[0434] Step 1:

[0435] User

[0436] The user launches the app and enters basic data such as their height, weight, muscle mass, and cardiopulmonary function as the initial setting.

[0437] Step 2:

[0438] Terminal

[0439] The terminal transmits the basic data entered by the user to the server.

[0440] Step 3:

[0441] server

[0442] The server stores the received basic data in a database and automatically digitizes it for analysis.

[0443] Step 4:

[0444] Terminal

[0445] The device automatically collects daily fluctuation data (body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, amount of sleep, etc.) from the user's wearable device.

[0446] Step 5:

[0447] Terminal

[0448] The terminal transmits the acquired daily fluctuation data to the server.

[0449] Step 6:

[0450] server

[0451] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[0452] Step 7:

[0453] server

[0454] The server analyzes the basic data and the variation data and generates an appropriate training menu based on the analysis.

[0455] Step 8:

[0456] Terminal

[0457] The terminal notifies the user of the training menu from the server and presents the detailed contents.

[0458] Step 9:

[0459] server

[0460] The server calculates nutrient needs based on the user's baseline and training data.

[0461] Step 10:

[0462] server

[0463] The server creates an optimal ingredient list and recipe based on the calculation results.

[0464] Step 11:

[0465] Terminal

[0466] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[0467] Step 12:

[0468] User

[0469] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[0470] Step 13:

[0471] server

[0472] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[0473] Step 14:

[0474] server

[0475] Based on the analysis results, the server suggests relaxation methods and counseling.

[0476] Step 15:

[0477] Terminal

[0478] The terminal notifies the user of the mental support suggestions provided by the server, and provides specific relaxation techniques and counseling reservation information.

[0479] Step 16:

[0480] Terminal

[0481] The device uses facial and voice recognition technology to collect emotional data in real time.

[0482] Step 17:

[0483] Terminal

[0484] The terminal transmits the collected emotion data to the server.

[0485] Step 18:

[0486] server

[0487] The server receives the emotion data sent from the emotion engine and analyzes it in combination with the mental data.

[0488] Step 19:

[0489] server

[0490] The server updates the training menu and mental support content based on the emotional and mental data, and suggests reducing the training intensity when the user feels stressed.

[0491] Step 20:

[0492] Terminal

[0493] The device notifies the user of the analysis results and suggests relaxation techniques and counseling to reduce stress.

[0494] This system allows athletes to efficiently manage their basic data, daily fluctuation data, and emotional data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[0495] Example 2

[0496] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0497] Conventional athlete condition management systems quantify physical data and suggest training menus, but do not provide comprehensive support that takes mental and emotional data into account. This makes it difficult to appropriately detect and deal with stress and psychological fatigue when athletes experience it. Furthermore, training intensity is not adjusted based on emotional fluctuations. As a result, there is an issue where athletes' performance cannot be fully optimized.

[0498] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0499] In this invention, the server includes means for quantifying and storing the athlete's physical data, means for acquiring daily physical data and transmitting it to the server, means for analyzing the transmitted data and proposing a training menu, means for presenting the training menu to the user, means for selecting optimal ingredients for nutritional supplementation and creating recipes, means for presenting recipes and ingredient lists to the user, means for collecting and analyzing mental data and providing mental support, means for collecting and analyzing emotional data, and means for adjusting training intensity based on the emotional and mental data. This enables comprehensive condition management that takes into account the athlete's mental state and emotions.

[0500] An "athlete" is an individual who possesses significant physical ability or skill and who engages in specialized sports or physical activities.

[0501] "Physical data" refers to information that quantifies an athlete's physical condition, such as their height, weight, muscle mass, body fat percentage, and cardiopulmonary function.

[0502] "Variation data" refers to data that indicates the daily physical condition, and includes body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, amount of sleep, and the like.

[0503] A "server" is a computer system that collects, analyzes, stores, and distributes data.

[0504] A "training menu" is a specific exercise plan aimed at improving an athlete's performance, and includes strength training, cardio exercises, stretching, etc.

[0505] "Nutrition" refers to the act of providing athletes with the balanced nutrients they need, and is achieved through the provision of ingredients and recipes.

[0506] "Mental data" refers to data that indicates an athlete's psychological state and stress level.

[0507] "Emotional data" is data that shows an athlete's emotional state in real time and is collected using facial and voice recognition technology.

[0508] The "emotion engine" is a system engine that uses facial and voice recognition technology to collect and analyze emotional data from users.

[0509] "Training intensity" refers to the load and difficulty of exercise in a training menu, and is adjusted based on the athlete's fitness and physical condition.

[0510] This invention is a comprehensive condition management system aimed at optimizing athletes' performance. This system collects basic data, collects and analyzes daily fluctuation data, proposes training menus, selects ingredients for nutritional supplementation and provides recipes, provides mental support, and collects and analyzes emotional data using an emotion engine.

[0511] Collecting and quantifying basic data

[0512] The user starts the app and enters basic information such as height, weight, muscle mass, and cardiopulmonary function as the initial setting. This data is sent to the server via the device. The server stores the received basic data in a database and converts it into numerical values ​​for future analysis.

[0513] For example, a user inputs his / her height of 175 cm, weight of 70 kg, and muscle mass of 50 kg, and the terminal sends this data to the server, where it is stored in a database.

[0514] Collection and analysis of daily fluctuation data

[0515] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep from the user's wearable device. This data is then sent from the device to a server. The server adds the received fluctuation data to a database and compares it with past data for analysis.

[0516] For example, a wearable device sends heart rate data to a terminal at 8:00 a.m. every morning, and the terminal transfers the data to a server. The server analyzes the data and records an average heart rate of 72 BPM.

[0517] Training menu suggestions

[0518] The server analyzes the basic and variability data and generates an appropriate training menu. The generated training menu is sent to the user via the device. For example, it may include specific exercise plans such as strength training, stretching, and jogging.

[0519] As a specific example, the server generates a menu of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging, and the terminal displays the menu to the user.

[0520] Nutritious ingredient selection and recipe provision

[0521] The server calculates the necessary nutrients based on the training data and basic data, and creates an appropriate food list, which is presented to the user via their device and includes specific recipes.

[0522] As a specific example, the server creates an ingredient list for protein intake: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice, and the terminal presents the list and a recipe for grilled chicken breast to the user.

[0523] Mental support

[0524] Users periodically answer questions about their mental state and send the data from their device to a server. The server analyzes the received mental data to detect stress levels and psychological fatigue. Based on the server's analysis results, the system provides appropriate mental support to the user.

[0525] For example, a user might enter "I've been feeling stressed lately" and send that data to a server. The server then analyzes the stress level and suggests yoga as a relaxation method. The device then notifies the user of the suggestion and provides yoga instructions and video links.

[0526] Emotional data collection and analysis using an emotion engine

[0527] The device uses an emotion engine to collect the user's emotional data in real time. This emotional data is acquired using facial and voice recognition technology and sent from the device to a server. The server then analyzes the emotional data in combination with mental data and responds flexibly, such as adjusting training intensity when the user is feeling stressed.

[0528] For example, the device recognizes the user's face and determines their emotional state as "stressed." The server analyzes the data and makes a "suggestion to reduce training intensity." The device then notifies the user of the suggestion and provides specific relaxation methods and training adjustments.

[0529] Prompt Sentence Examples

[0530] "Please describe the specific procedures for a comprehensive condition management system to optimize athlete performance. Please include details regarding user emotion recognition."

[0531] This system allows athletes to manage their overall condition, optimizing their performance and maintaining their health.

[0532] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0533] Step 1: Enter and submit basic data

[0534] The user launches the app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function. The device sends the basic data entered by the user to the server. The server stores the received basic data in a database. This data is quantified for analysis. The input is the user's physical information, and the output is the basic data stored in the database.

[0535] Step 2: Collect and send daily fluctuation data

[0536] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep from the user's wearable device. This data is sent from the device to a server, which adds the received fluctuation data to a database. The server then compares it with past data and performs analysis. The input is the fluctuation data from the wearable device, and the output is the analyzed data and the stored results.

[0537] Step 3: Create a training menu

[0538] The server analyzes the basic data and variation data and generates an appropriate training menu based on the analysis. For example, a menu including strength training, stretching, jogging, etc. is created. The terminal notifies the user of the generated training menu. The input is the basic data and variation data, and the output is the generated training menu.

[0539] Step 4: Nutritious ingredients selection and recipe provision

[0540] The server calculates the necessary nutrients based on the training data and basic data and creates an appropriate ingredient list. The terminal presents the ingredient list and recipe created by the server to the user. The input is the training data and basic data, and the output is the created ingredient list and recipe.

[0541] Step 5: Collect and analyze mental data

[0542] Users periodically answer questions about their mental state and send the data to a server via their device. The server analyzes the received mental data to detect stress levels and psychological fatigue. Based on the results, the server proposes appropriate mental support to the user. The input is the answers to the questions about their mental state, and the output is the analysis results and suggestions for mental support.

[0543] Step 6: Collect and analyze emotion data

[0544] The device uses an emotion engine to collect the user's emotional data in real time. For example, it can acquire emotional data using facial or voice recognition technology. The device then sends the acquired emotional data to a server, which then combines it with mental data for analysis. Based on the analysis results, adjustments to training intensity and additional mental support are suggested. The input is emotional data, and the output is the analysis results and suggestions for adjusting training intensity.

[0545] Specifically, when a user notifies the server that they are feeling stressed, the server analyzes the data and provides suggestions for reducing training intensity or providing instructions on relaxation techniques. The server then notifies the user of the suggestions via their device.

[0546] (Application example 2)

[0547] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0548] Conventional systems aimed at optimizing athlete performance only target physical and mental data, and do not comprehensively address operational efficiency or maintenance management. Furthermore, they do not provide appropriate instructions that take into account the emotional data of managers. This can lead to reduced efficiency, particularly in the operation of factory robots, and can lead to missed maintenance timing. Therefore, a new system is needed to optimize the operational efficiency and maintenance of factory robots, similar to athlete performance.

[0549] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for quantifying and storing the athlete's physical data, means for acquiring daily physical data and transmitting it to the server, means for analyzing the transmitted data and proposing a training menu, means for presenting the training menu to the user, means for selecting optimal ingredients for nutritional supplementation and creating a recipe, means for presenting the recipe and ingredient list to the user, means for collecting and analyzing mental data and providing mental support, means for collecting and analyzing basic robot data and daily fluctuation data, means for generating and presenting an optimal work menu, means for proposing a maintenance schedule based on the robot's status, and means for collecting and analyzing user emotional data and providing flexible instructions to the robot based on the data. This improves the operating efficiency of factory robots, enables appropriate maintenance, and enables flexible operation that takes into account the emotional state of the manager.

[0550] "Athlete's physical data" refers to data that quantifies physiological indicators such as an athlete's height, weight, muscle mass, and cardiopulmonary function.

[0551] "Daily physical data" refers to data on fluctuations that athletes collect on a daily basis, such as body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, and amount of sleep.

[0552] A "server" is a central computer that stores data, performs analysis, and generates various offers.

[0553] A "training menu" is an exercise program created based on the athlete's basic data and daily physical data.

[0554] "User" refers to the entity that uses the system to input data or receive suggestions, such as an athlete or administrator.

[0555] "Nutrition" refers to the selection of ingredients and dietary methods to meet an athlete's nutritional needs.

[0556] An "ingredient list" is a list of ingredients selected to meet nutritional needs.

[0557] A "recipe" is a written or illustrated guide to cooking steps for athletes to replenish their nutrition.

[0558] "Mental data" refers to information such as an athlete's stress level, psychological fatigue, and emotional state.

[0559] "Mental support" refers to interventions to support athletes' mental health, such as relaxation techniques and counseling suggestions.

[0560] "Basic robot data" refers to the robot's basic performance indicators, such as quantified data on working speed and lifting force.

[0561] "Daily fluctuation data" refers to fluctuation data such as temperature, operating time, motor temperature, and error frequency that the robot collects on a daily basis.

[0562] A "task menu" is a business program created based on the robot's basic data and daily fluctuation data.

[0563] A "maintenance schedule" is a maintenance management plan proposed based on the condition of the robot.

[0564] "Emotional data" is data collected in real time about the emotional state of the manager.

[0565] "Flexible instructions" are appropriate work instructions given to a robot based on collected emotional data.

[0566] This invention is a comprehensive condition management system for optimizing athlete performance, and is particularly applicable to the operational efficiency and maintenance management of factory robots. The system includes the collection and quantification of basic data, the collection and analysis of daily fluctuation data, the proposal of work menus, the planning of maintenance schedules, and flexible instructions based on the user's emotional data.

[0567] Collecting and quantifying basic data

[0568] User

[0569] The user starts the application and inputs the robot's basic performance data (working speed, lifting force, etc.) as initial settings.

[0570] Terminal

[0571] The terminal transmits the basic data entered by the user to the server.

[0572] server

[0573] The server stores the received basic data in a database and automatically digitizes it for analysis.

[0574] Collection and analysis of daily fluctuation data

[0575] Terminal

[0576] The device automatically collects daily fluctuation data from sensors, such as temperature, operating time, motor temperature, and error frequency.

[0577] server

[0578] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[0579] Work menu suggestions

[0580] server

[0581] The server analyzes the basic data and the variable data and creates an appropriate operation menu based on the analysis, such as a menu for assembling parts, painting, loading pallets, etc.

[0582] Terminal

[0583] The terminal notifies the user of the generated operation menu and presents the detailed contents.

[0584] Maintenance schedule planning

[0585] server

[0586] The server proposes an optimal maintenance schedule based on the robot's basic data and daily fluctuation data.

[0587] Terminal

[0588] The terminal notifies the user of the maintenance schedule provided by the server and urges the user to carry out appropriate maintenance.

[0589] Emotion data collection and analysis

[0590] Terminal

[0591] The device equipped with the emotion engine uses technologies such as facial recognition and voice recognition of the user (administrator) to collect emotional data of the user in real time.

[0592] server

[0593] The server receives the emotion data sent from the emotion engine and uses it to adjust the work menu and maintenance schedule, for example, by providing instructions that allow flexible responses even when the user is feeling stressed.

[0594] Hardware and software used

[0595] Hardware

[0596] Sensors (temperature sensors, error detection sensors)

[0597] Factory robots (general robotic arms)

[0598] Computer server (for database and analysis)

[0599] software

[0600] Python (data collection and analysis)

[0601] Database management system (MySQL)

[0602] Systems with emotion engines (e.g., Face++)

[0603] Specific examples

[0604] For example, when a user introduces a new robot, they input its basic performance data into the application. The terminal sends this data to the server, which stores it in a database. Data from temperature sensors, operating hours, error frequency, and other daily fluctuation data are also sent from the terminal to the server, which analyzes them. The server then uses this data to propose work menus and create maintenance schedules. The emotion engine collects the manager's emotional data in real time, and flexible instructions are given based on that data.

[0605] Prompt Sentence Examples

[0606] "Collect today's data on the temperature sensors and motor temperatures of factory robots, compare it with the average values ​​for the past seven days, and report any abnormalities."

[0607] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0608] Step 1:

[0609] The user starts the application and inputs the robot's basic performance data (working speed, lifting force, etc.) as the initial setting. This collects basic data about the robot. Specific examples of data input by the user include a working speed of 10 meters per second and a lifting force of 100 kilograms. This data will be the basic information needed for subsequent analysis and proposals.

[0610] Step 2:

[0611] The terminal sends the basic data entered by the user to the server. The terminal converts the entered data into an appropriate format and sends it to the server. The input data includes various performance indicators such as work speed and lifting force. The basic data is sent as output to the server and stored in a database.

[0612] Step 3:

[0613] The server stores the received basic data in a database and automatically digitizes it for analysis. The server uses a database management system (e.g., MySQL) to store and manage the data. The stored data is used in subsequent analysis steps.

[0614] Step 4:

[0615] The terminal automatically obtains daily fluctuation data from the sensors, such as temperature, operating time, motor temperature, and error frequency. For example, the input from the temperature sensor may be 25 degrees, the motor temperature may be 60 degrees, the operating time may be 8 hours, and the error frequency may be 3. This data is important for understanding the operational status of the robot in real time.

[0616] Step 5:

[0617] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data. By comparing it with past data stored in the database, outliers and trends can be detected. For example, an abnormal temperature can be detected by comparing the average temperature with data from the past seven days.

[0618] Step 6:

[0619] The server analyzes basic and variable data and creates an appropriate work menu based on that. An example of a work menu might be two hours for assembling parts, 30 minutes for painting, and one hour for loading onto a pallet. The server automatically generates the optimal work menu using a generative AI model and various algorithms.

[0620] Step 7:

[0621] The terminal notifies the user of the generated work menu and presents detailed content. The terminal displays the work menu received from the server to the user in an appropriate format. A specific prompt sentence might be, "Today's work menu is 2 hours of part assembly, 30 minutes of painting, and 1 hour of pallet loading."

[0622] Step 8:

[0623] The server proposes an optimal maintenance schedule based on the robot's basic data and daily fluctuation data. For example, it can suggest preventive maintenance when the motor temperature is high or the error frequency increases. A specific maintenance schedule is generated as an output.

[0624] Step 9:

[0625] The terminal notifies the user of the maintenance schedule provided by the server and encourages them to carry out the appropriate maintenance. The terminal displays the details of the maintenance schedule in an easy-to-understand manner to the user and supports the implementation. Specifically, the terminal notifies the user that "the next maintenance should be carried out tomorrow at 3:00 PM."

[0626] Step 10:

[0627] The device is equipped with an emotion engine that uses facial and voice recognition technology to collect real-time emotion data from the user (administrator). For example, if the user is feeling stressed, that data is collected.

[0628] Step 11:

[0629] The server receives the emotion data sent from the emotion engine and uses it to adjust the work menu and maintenance schedule. Specifically, if the user is feeling stressed, the server may adjust the work load, for example, by reducing the workload.

[0630] Through these processing steps, it becomes possible to optimize the performance of athletes and the operational efficiency and maintenance management of factory robots.

[0631] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0632] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0633] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0634] [Second embodiment]

[0635] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0636] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0637] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0638] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0639] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0640] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0641] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0642] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0643] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0644] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0645] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0646] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0647] This invention is a comprehensive condition management system for optimizing athletic performance. This system provides a variety of means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, and providing mental support.

[0648] Collecting and quantifying basic data

[0649] User

[0650] During initial setup, users enter basic data such as height, weight, muscle mass, and cardiopulmonary function into a dedicated app.

[0651] Terminal

[0652] The terminal transmits the basic data entered by the user to the server.

[0653] server

[0654] The server stores the received basic data in a database and automatically digitizes it for analysis.

[0655] Collection and analysis of daily fluctuation data

[0656] Terminal

[0657] The device automatically collects daily data from the user's wearable device, including body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep.

[0658] server

[0659] The server adds the received fluctuation data to a database and analyzes it to detect outliers and unusual trends.

[0660] Training menu suggestions

[0661] server

[0662] The server analyzes the basic and variability data and creates an appropriate training menu based on the analysis, for example, a menu including strength training, cardio exercises, stretching, etc.

[0663] Terminal

[0664] The terminal notifies the user of the generated training menu and presents the detailed contents.

[0665] Nutritious ingredient selection and recipe provision

[0666] server

[0667] The server calculates the required nutrients based on the user's basic data and training data, and creates an ingredient list based on the results.

[0668] Terminal

[0669] The terminal presents the user with a list of ingredients and recipes provided by the server. For example, it provides ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions for using those ingredients.

[0670] Mental support

[0671] User

[0672] The user periodically answers questions about their mental state and transmits the data to the server via their terminal.

[0673] server

[0674] The server analyzes the received mental data to detect stress levels and psychological fatigue, and based on the analysis results, suggests relaxation methods or counseling as needed.

[0675] Terminal

[0676] The device notifies the user of mental support suggestions provided by the server, such as suggestions for meditation or yoga, or counseling appointments.

[0677] Specific examples

[0678] Collection of basic data

[0679] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[0680] The device sends this data to the server, which stores it in a database.

[0681] Collecting fluctuation data

[0682] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[0683] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[0684] Training menu suggestions

[0685] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[0686] The terminal displays this training menu to the user and explains the details.

[0687] Nutritional supplementation

[0688] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[0689] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[0690] Mental support

[0691] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[0692] The server analyzes stress levels and suggests yoga as a way to relax.

[0693] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[0694] This system will enable the centralized management of athletes' basic data and daily fluctuation data, enabling comprehensive condition management, which will optimize athletes' performance and provide efficient and effective training and care.

[0695] The processing flow will be explained below.

[0696] Step 1:

[0697] User

[0698] The user launches the app and enters basic data such as their height, weight, muscle mass, and cardiopulmonary function as the initial setting.

[0699] Step 2:

[0700] Terminal

[0701] The terminal transmits the basic data entered by the user to the server.

[0702] Step 3:

[0703] server

[0704] The server stores the received basic data in a database and automatically digitizes it for analysis.

[0705] Step 4:

[0706] Terminal

[0707] The device automatically collects daily fluctuation data (body temperature, blood pressure, blood sugar level, heart rate variability, etc.) from the user's wearable device.

[0708] Step 5:

[0709] Terminal

[0710] The terminal transmits daily fluctuation data to the server.

[0711] Step 6:

[0712] server

[0713] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[0714] Step 7:

[0715] server

[0716] The server analyzes the basic data and the variation data and generates an appropriate training menu.

[0717] Step 8:

[0718] Terminal

[0719] The terminal notifies the user of the training menu from the server and presents the detailed contents.

[0720] Step 9:

[0721] server

[0722] The server calculates the required nutrients based on the user's basic data and training data.

[0723] Step 10:

[0724] server

[0725] The server creates an optimal ingredient list and recipe based on the calculation results.

[0726] Step 11:

[0727] Terminal

[0728] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[0729] Step 12:

[0730] User

[0731] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[0732] Step 13:

[0733] server

[0734] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[0735] Step 14:

[0736] server

[0737] Based on the analysis results, the server suggests relaxation methods and counseling.

[0738] Step 15:

[0739] Terminal

[0740] The terminal notifies the user of suggestions for mental support from the server and provides specific relaxation techniques and counseling appointment information.

[0741] This system allows athletes to efficiently manage their basic data and daily fluctuation data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[0742] Example 1

[0743] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0744] In order to optimize athletes' performance and efficiently manage their overall condition, it is important to centrally manage physical data, fluctuation data, and mental data and provide appropriate training, nutritional support, and mental support in real time. However, conventional systems that manage these data separately have issues with data consistency and immediacy, making it difficult to provide optimized support for individual athletes.

[0745] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0746] In this invention, the server includes means for quantifying and storing the athlete's basic data, means for acquiring daily fluctuation data and sending it to the server, means for analyzing outliers and unusual trends based on the acquired data, means for generating a training menu based on the analysis results, means for notifying the user of the generated training menu, means for calculating necessary nutrients, selecting optimal ingredients, and creating a recipe, means for presenting the recipe and ingredient list to the user, and means for collecting and analyzing mental data and providing mental support, thereby optimizing the athlete's performance and enabling comprehensive condition management.

[0747] An "athlete" is someone who possesses high skill and physical strength in competitive sports.

[0748] "Basic data" refers to data that quantifies basic information about an athlete's body, such as height, weight, muscle mass, and cardiopulmonary function.

[0749] "Variation data" refers to information about daily changes in an athlete's body, such as body temperature, blood pressure, blood sugar levels, heart rate variability, fatigue, and amount of sleep.

[0750] "Server" refers to a computer system that receives, stores, analyzes, etc. data.

[0751] "Terminal" refers to a device used by a user to input or refer to data, including, for example, a smartphone or tablet.

[0752] "Digitization" refers to the process of converting data collected from users into a numerical format that is easy to analyze.

[0753] "Storage" refers to storing collected data for a long period of time.

[0754] An "outlier" is a data value that falls outside the normal range in a statistical analysis.

[0755] "Anomalous trends" refers to trends in data where clear patterns or anomalies can be identified in data analysis.

[0756] A "training menu" refers to a series of exercises and plans proposed to improve an athlete's physical strength and maintain their condition.

[0757] "Nutrients" refers to proteins, vitamins, minerals, and other components necessary for athletes' health and performance.

[0758] "Ingredients List" refers to a list of foods or ingredients needed to prepare a suggested recipe.

[0759] A "recipe" is a document that details the steps for preparing a dish using specific ingredients.

[0760] "Mental data" refers to information about an athlete's mental state, such as stress levels and psychological fatigue.

[0761] "Mental support" refers to assistance to maintain and improve the mental health of athletes. Specifically, this includes suggestions for relaxation techniques and counseling.

[0762] This invention is a comprehensive condition management system for optimizing athletic performance. This system provides a variety of means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, and providing mental support.

[0763] Collecting and quantifying basic data

[0764] User

[0765] During initial setup, users use a dedicated app to enter basic data such as height, weight, muscle mass, and cardiopulmonary function.

[0766] Terminal

[0767] The terminal sends the basic data entered by the user to the server, where the data is encrypted to ensure security.

[0768] server

[0769] The server stores the received basic data in a database and automatically converts it into numerical values ​​for analysis. For example, it statistically analyzes the QRS complex wave parameters of cardiopulmonary function to check for deviations from standard values.

[0770] Collection and analysis of daily fluctuation data

[0771] Terminal

[0772] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and sleep amount from the user's wearable device (e.g., smartwatch). The collected data is temporarily stored in the device in real time.

[0773] Terminal

[0774] The device periodically (e.g., every 10 minutes) transmits the temporarily stored data to the server, where data integrity and encryption are performed.

[0775] server

[0776] The server adds the received fluctuation data to a database and performs statistical analysis. If an abnormal value is detected, an alert is sent to the terminal to notify the user.

[0777] Training menu suggestions

[0778] server

[0779] The server generates a training plan that is optimal for the user's physical condition based on the basic and fluctuation data. For example, it creates a menu of strength training (20 minutes), cardio (30 minutes), and stretching (15 minutes).

[0780] Terminal

[0781] The device will then notify the user of the generated training menu and display the details within the app, including video links and text descriptions for each exercise.

[0782] Nutritious ingredient selection and recipe provision

[0783] server

[0784] The server calculates the amount of nutrients (e.g., protein, vitamins) needed based on the user's basic and training data. For example, it creates an ingredient list such as "200g chicken breast, 100g broccoli, 200g brown rice."

[0785] server

[0786] The server generates a recipe that includes cooking methods for each ingredient, such as suggesting "grilled chicken breast."

[0787] Terminal

[0788] The device receives the ingredients list and recipe from the server and displays it in the app, notifying the user. The recipe includes detailed information such as cooking time, steps, and necessary equipment.

[0789] Mental support

[0790] User

[0791] Users periodically answer in-app surveys to record their current stress levels and psychological state, for example, by entering "My stress level has been high recently."

[0792] Terminal

[0793] The terminal sends the mental data entered by the user to the server, where the data is checked for integrity and encrypted during transmission.

[0794] server

[0795] The server analyzes the received mental data to detect stress levels and psychological fatigue, and based on the analysis results, generates suggestions for relaxation methods (e.g., yoga) and counseling appointments.

[0796] Terminal

[0797] The device notifies the user of the mental support suggestions received from the server and displays detailed instructions and a link to book a counseling appointment within the app.

[0798] Specific examples

[0799] Collection of basic data

[0800] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[0801] The device sends this data to the server, which stores it in a database.

[0802] Collecting fluctuation data

[0803] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[0804] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[0805] Training menu suggestions

[0806] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[0807] The terminal displays this training menu to the user and explains the details.

[0808] Nutritional supplementation

[0809] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[0810] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[0811] Mental support

[0812] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[0813] The server analyzes stress levels and suggests yoga as a way to relax.

[0814] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[0815] Example prompts for generative AI models

[0816] "Please enter the athlete's basic data (height 175cm, weight 70kg, muscle mass 50kg) and generate a training menu based on one week's heart rate data. Also, please provide a list of ingredients and recipes for proper nutrition."

[0817] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0818] Step 1:

[0819] Input: The user launches a dedicated app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function.

[0820] How it works: A user fills out a form in the app and enters data such as height 175cm, weight 70kg, muscle mass 50kg, etc. The app performs a consistency check to ensure there are no input errors.

[0821] Output: The correct formatted basic data is saved to the device.

[0822] Step 2:

[0823] Input: The terminal receives basic data entered by the user.

[0824] How it works: The device encrypts the underlying data it receives and sends it to the server using a secure communication protocol.

[0825] Output: The encrypted underlying data is sent to the server.

[0826] Step 3:

[0827] Input: The server receives the basic data sent from the terminal.

[0828] How it works: The server decrypts the data and stores it in a database. It also analyzes and quantifies the data. For example, it analyzes the QRS complex wave of cardiopulmonary function and calculates the difference from the standard value.

[0829] Output: The quantified basic data is saved in a database.

[0830] Step 4:

[0831] Input: The device collects daily fluctuation data (body temperature, blood pressure, blood glucose level, heart rate variability, fatigue, and amount of sleep) from the user's wearable device.

[0832] How it works: The device acquires data collected from the wearable device in real time and temporarily stores it.

[0833] Output: The temporarily saved fluctuation data is saved on the terminal.

[0834] Step 5:

[0835] Input: The terminal periodically sends the temporarily stored fluctuation data to the server.

[0836] How it works: The device encrypts the collected data and sends it to the server periodically (e.g., every 10 minutes).

[0837] Output: The encrypted variable data is sent to the server.

[0838] Step 6:

[0839] Input: The server receives the variable data sent from the terminal.

[0840] How it works: The server decrypts the data, stores it in a database, and analyzes it. If an abnormal value is detected, an alert is generated and sent to the device. For example, if blood sugar levels suddenly rise, the user is immediately notified of this information.

[0841] Output: Analysis results and alert information are stored on the server and sent to the terminal as needed.

[0842] Step 7:

[0843] Input: The server generates a training menu based on basic data and variation data.

[0844] How it works: Based on data analysis, the server automatically generates optimal training menus, including strength training, cardio, and stretching.

[0845] Output: The generated training menu is saved in the database.

[0846] Step 8:

[0847] Input: The terminal receives the training menu generated from the server.

[0848] How it works: The device will notify the user of a workout menu and display detailed instructions within the app, including video links and text descriptions for each exercise.

[0849] Output: The training menu notified to the user.

[0850] Step 9:

[0851] Input: The server calculates the required nutrients based on the user's basic and training data.

[0852] How it works: The server calculates the necessary nutrients, such as protein and vitamins, and creates an ingredient list based on that (e.g., 200g chicken breast, 100g broccoli, 200g brown rice). It then generates a recipe based on this list.

[0853] Output: Ingredient list and recipe based on calculated nutrients.

[0854] Step 10:

[0855] Input: The device receives the ingredient list and recipe provided by the server.

[0856] How it works: The device displays the received ingredient list and recipe in the app and notifies the user, with the recipe including details such as cooking time, steps, and required equipment.

[0857] Output: The recipe and ingredient list provided to the user.

[0858] Step 11:

[0859] Input: The user inputs mental data into the app.

[0860] How it works: Users fill out an in-app survey to input their stress levels and psychological state. For example, they might enter, "My stress level has been high recently."

[0861] Output: The input mental data is saved on the device.

[0862] Step 12:

[0863] Input: The terminal receives mental data input from the user.

[0864] How it works: The device encrypts the received mental data and sends it to the server using a secure communication protocol.

[0865] Output: The encrypted mental data is sent to the server.

[0866] Step 13:

[0867] Input: The server receives the mental data sent from the terminal.

[0868] How it works: The server decrypts and stores the mental data in a database. It also analyzes the data to detect stress levels and psychological fatigue. If necessary, it generates suggestions for relaxation methods (e.g., yoga) or counseling.

[0869] Output: The analysis results and recommendations are stored in a database and sent to a terminal if necessary.

[0870] Step 14:

[0871] Input: The device receives the analysis results and suggestions sent from the server.

[0872] How it works: The device displays the analysis results and recommendations it receives within the app and notifies the user, including relaxation techniques and a link to book a consultation.

[0873] Output: The user is notified of relaxation techniques and a link to book a consultation.

[0874] (Application example 1)

[0875] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0876] Comprehensive condition management is necessary to maximize the performance of athletes and machines. However, current systems have the problem of not being able to centrally collect and analyze physical and machine data, propose training and maintenance, or manage nutrition and consumable parts. There is a need for a system that can manage all of this data in an integrated manner and provide optimal support efficiently and effectively.

[0877] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0878] In this invention, the server includes means for quantifying and storing the athlete's physical data, means for acquiring daily physical data and transmitting it to the server, means for analyzing the transmitted data and proposing a training menu, means for presenting the training menu to the user, means for selecting optimal ingredients for nutritional supplementation and creating recipes, means for presenting recipes and ingredient lists to the user, means for collecting and analyzing mental data and providing mental support, means for collecting basic machine data and daily fluctuation data and proposing a maintenance schedule based thereon, means for generating and presenting machine operation optimization parameters, means for creating a consumable parts list and providing it to the user, and means for detecting abnormalities and issuing corresponding alerts. This allows for centralized management of both athlete training and nutritional management and machine maintenance and operation optimization, enabling efficient and effective support.

[0879] An "athlete" is an individual or group who trains with the goal of achieving high performance in a sport or competition.

[0880] "Physical data" refers to specific numerical values ​​representing an athlete's physical information, such as height, weight, muscle mass, and cardiopulmonary function.

[0881] A "server" is a computer system that stores, analyzes, and provides data over a network.

[0882] "Machinery" refers to equipment such as robots and automated devices used in production activities in factories, etc.

[0883] "Fluctuating data" is information that changes daily, such as body temperature, blood pressure, heart rate, and blood sugar level, which show different values ​​in real time.

[0884] A "training menu" is a plan that lists the specific exercises and practices that an athlete should perform.

[0885] "User" refers to a person or individual who uses this condition management system.

[0886] "Nutrition" refers to ensuring that athletes properly consume the ingredients and food they need to maintain their physical condition and improve their performance.

[0887] A "food list" is a list of ingredients selected based on a specific purpose or training menu.

[0888] "Mental data" is information used to examine an athlete's stress level and psychological state.

[0889] "Mental support" refers to providing advice and care to maintain and improve an athlete's mental health.

[0890] "Basic data" refers to data that forms the basis of operation, such as the machine model, installation date, and initial setting information at the start of use.

[0891] A "maintenance schedule" is a plan for carrying out appropriate maintenance and inspection of machinery.

[0892] "Operational optimization parameters" are settings and adjustments that allow a machine to operate efficiently and effectively.

[0893] A "consumable parts list" is a list of parts that need to be replaced or replenished periodically during machine operation.

[0894] "Abnormal" refers to a state in which a machine deviates from its normal operation and indicates that a breakdown or malfunction has occurred.

[0895] An "alert" is a warning or notification issued when an abnormality or unusual situation is detected.

[0896] A specific system configuration and processing procedure for carrying out the present invention will be described. This system is a comprehensive condition management system for optimizing the performance of athletes and factory robots, and includes the following means:

[0897] First, the user enters the athlete's physical data and basic machine data into a dedicated application. This application runs on a smartphone, tablet, or desktop PC and is designed to make data entry and transmission easy. During initial setup, basic data such as height, weight, muscle mass, cardiopulmonary function, robot model, and installation date are entered.

[0898] Next, the device (smartphone, tablet, PC, etc.) sends the basic data entered by the user to a server. This server is a computer system installed on a network that has the function of storing, analyzing, and providing data. Specifically, it uses a database (e.g., PostgreSQL) to store the basic data and digitize it for analysis.

[0899] Daily fluctuation data is collected through wearable devices and various sensors. This includes body temperature, blood pressure, blood sugar levels, heart rate variability, fatigue level, and amount of sleep. This data is also sent to the server via the device. The server adds the received fluctuation data to a database and performs analysis.

[0900] The transmitted data is analyzed using data analysis algorithms running on the server. For example, machine learning models written in Python are used to detect abnormal values ​​and specific trends in heart rate and blood pressure. The robot's vibration and temperature data are also analyzed to detect abnormalities.

[0901] Based on the analysis, the server creates training and maintenance schedules: training for athletes includes strength training, cardio exercises, and stretching, while maintenance for robots includes oil checks and filter changes.

[0902] The server also selects ingredients for nutritional support and provides recipes. The server calculates the nutritional needs of the user based on their basic and training data, and creates an ingredient list. For example, it presents ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions.

[0903] Additionally, collecting and analyzing mental data is also important. Users periodically answer questions about their mental state and send the data to a server via their device. The server analyzes stress levels and psychological fatigue and provides appropriate mental support, such as suggestions for yoga or counseling.

[0904] For example, when collecting basic data, the user launches the app and enters data such as height 175 cm, weight 70 kg, and muscle mass 50 kg. The wearable device also sends heart rate data to the terminal at 8:00 a.m. every day, and the server records an average heart rate of 72 BPM. As a maintenance schedule suggestion, the next maintenance is scheduled for November 10, 2023, and the tasks include an oil check and filter change.

[0905] Example prompt sentence:

[0906] "The ABB IRB 6700 robot was installed on January 1, 2023. Please collect the basic data and store it in the database."

[0907] "Based on today's inspection results, the robot has been running for 16 hours and one anomaly has been detected. Please send this data to the server."

[0908] "The next maintenance is scheduled for November 10, 2023. Tasks will include an oil check and filter change."

[0909] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0910] Step 1:

[0911] During the initial setup, the user enters basic data about the athlete and the machine into a dedicated app. Specifically, information such as height, weight, muscle mass, cardiopulmonary function, robot model, and installation date is entered, and the data is saved on the device. This input data is converted into numerical values ​​as basic data.

[0912] Input: Basic data on athletes and machines

[0913] Data processing: digitization of basic data

[0914] Output: The digitized basic data is saved on the device.

[0915] Step 2:

[0916] The device sends the stored basic data to the server, which receives it and stores it in a database. The server then manages the basic data as quantified information for analysis.

[0917] Input: Basic data stored on the device

[0918] Data processing: data transmission and storage

[0919] Output: The digitized basic data is saved in the server database.

[0920] Step 3:

[0921] To collect daily fluctuation data, the device acquires data such as body temperature, blood pressure, blood sugar level, heart rate variability, fatigue level, and amount of sleep from wearable devices and sensors, and periodically transmits this daily fluctuation data to a server.

[0922] Input: Variable data from wearable devices and sensors

[0923] Data Processing: Data Acquisition and Transmission

[0924] Output: Daily fluctuation data is sent to the server

[0925] Step 4:

[0926] The server adds the received fluctuation data to a database and performs analysis. Specifically, it uses a Python machine learning model to detect outliers and unusual trends. This allows for early detection of abnormalities in, for example, heart rate or blood pressure.

[0927] Input: Daily fluctuation data

[0928] Data processing: detecting outliers and unusual trends

[0929] Output: Analysis results of specific trends and outliers

[0930] Step 5:

[0931] The server analyzes baseline and fluctuation data and creates optimal training and maintenance schedules based on that data, which could include strength training and cardio exercises for athletes, or oil checks and filter changes for robots.

[0932] Input: Fundamental data and daily fluctuation data

[0933] Data processing: Creation of training menus and maintenance schedules

[0934] Output: Training menu and maintenance schedule

[0935] Step 6:

[0936] The device notifies the user of the training menu and maintenance schedule received from the server and presents detailed information, allowing the user to understand specifically what kind of training and maintenance they should perform.

[0937] Input: Training menu and maintenance schedule

[0938] Data Processing: Notification and Details

[0939] Output: Detailed presentation of training menu and maintenance schedule

[0940] Step 7:

[0941] The server calculates the nutritional needs of the user based on their basic and training data, and then creates an ingredient list and recipe based on that. For example, it presents ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions.

[0942] Input: Base data and training data

[0943] Data processing: Nutrition calculations and generating ingredient lists and recipes

[0944] Output: Ingredient list and recipe

[0945] Step 8:

[0946] The terminal presents the user with a list of ingredients and recipes provided by the server, allowing the user to specifically understand which ingredients to select and how to cook them to replenish their nutrients.

[0947] Input: Ingredient list and recipe

[0948] Data Processing: Notification and Details

[0949] Output: Ingredient list and recipe details

[0950] Step 9:

[0951] Users periodically answer questions about their mental state and send the data, including their stress level and psychological fatigue level, to a server via their device.

[0952] Input: Answers to questions about mental state

[0953] Data processing: Data transmission

[0954] Output: Sending mental data

[0955] Step 10:

[0956] The server analyzes the received mental data, assesses stress levels and psychological fatigue, and provides appropriate mental support, such as yoga suggestions or counseling appointments, if necessary.

[0957] Input: Mental data

[0958] Data processing: Evaluation of stress levels and psychological fatigue

[0959] Output: Mental support suggestions

[0960] Step 11:

[0961] The device notifies the user of suggestions for mental support provided by the server, such as suggestions for meditation or yoga, or information on booking counseling appointments.

[0962] Input: Mental support suggestions

[0963] Data Processing: Notification and Details

[0964] Output: Detailed mental support information

[0965] Step 12:

[0966] The server collects basic machine data and daily fluctuation data, and based on that data, proposes a maintenance schedule, including the next maintenance date and specific work content (oil check, filter change, etc.).

[0967] Input: Fundamental data and daily fluctuation data

[0968] Data processing: Maintenance schedule generation

[0969] Output: Maintenance Schedule

[0970] Step 13:

[0971] The server generates and presents machine operation optimization parameters to the user, providing settings for efficient and effective machine operation.

[0972] Input: Fundamental and variable data

[0973] Data processing: Generation of operational optimization parameters

[0974] Output: Operational optimization parameters

[0975] Step 14:

[0976] The server creates a list of consumable parts and provides it to the user, making it easy to arrange for the necessary consumable parts.

[0977] Input: Fundamental and variable data

[0978] Data processing: Generating a list of consumable parts

[0979] Output: Consumable parts list

[0980] Step 15:

[0981] The server detects abnormalities and issues an alert accordingly, allowing users to quickly detect abnormalities and take appropriate measures.

[0982] Input: Daily fluctuation data

[0983] Data processing: Anomaly detection and alerting

[0984] Output: Alert

[0985] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0986] This invention is a comprehensive condition management system for optimizing athletic performance, and aims to provide more advanced mental support by combining it with an emotion engine that recognizes the user's emotions. This system provides various means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, providing mental support, and collecting and analyzing emotional data using the emotion engine.

[0987] Collecting and quantifying basic data

[0988] User

[0989] The user launches the app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function as the initial settings.

[0990] Terminal

[0991] The terminal transmits the basic data entered by the user to the server.

[0992] server

[0993] The server stores the received basic data in a database and automatically digitizes it for analysis.

[0994] Collection and analysis of daily fluctuation data

[0995] Terminal

[0996] The device automatically collects daily data from the user's wearable device, including body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep.

[0997] server

[0998] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[0999] Training menu suggestions

[1000] server

[1001] The server analyzes the basic and variability data and creates an appropriate training menu based on the analysis, for example, a menu including strength training, cardio exercises, stretching, etc.

[1002] Terminal

[1003] The terminal notifies the user of the generated training menu and presents the detailed contents.

[1004] Nutritious ingredient selection and recipe provision

[1005] server

[1006] The server calculates the required nutrients based on the user's basic data and training data, and creates an ingredient list based on the results.

[1007] Terminal

[1008] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[1009] Mental support

[1010] User

[1011] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[1012] server

[1013] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[1014] Terminal

[1015] The terminal notifies the user of the mental support suggestions provided by the server, and provides specific relaxation techniques and counseling reservation information.

[1016] Emotional data collection and analysis using an emotion engine

[1017] Terminal

[1018] Devices equipped with an emotion engine use technologies such as facial and voice recognition to collect user emotion data in real time.

[1019] server

[1020] The server receives the emotional data sent from the emotion engine and analyzes it in combination with mental data. For example, if the user is feeling stressed, it can make adjustments such as suggesting that the training intensity be reduced.

[1021] Specific examples

[1022] Collection of basic data

[1023] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[1024] The device sends this data to the server, which stores it in a database.

[1025] Collecting fluctuation data

[1026] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[1027] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[1028] Training menu suggestions

[1029] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[1030] The terminal displays this training menu to the user and explains the details.

[1031] Nutritional supplementation

[1032] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[1033] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[1034] Mental support

[1035] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[1036] The server analyzes stress levels and suggests yoga as a way to relax.

[1037] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[1038] Emotion Engine

[1039] The device uses facial recognition technology to collect emotional data from the user in real time.

[1040] The server analyzes the emotional data and adjusts flexible training and mental support when the user feels stressed.

[1041] The device will notify the user of the analysis results and suggest relaxation techniques or counseling to reduce stress.

[1042] This system allows athletes to efficiently manage their basic data, daily fluctuation data, and emotional data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[1043] The processing flow will be explained below.

[1044] Step 1:

[1045] User

[1046] The user launches the app and enters basic data such as their height, weight, muscle mass, and cardiopulmonary function as the initial setting.

[1047] Step 2:

[1048] Terminal

[1049] The terminal transmits the basic data entered by the user to the server.

[1050] Step 3:

[1051] server

[1052] The server stores the received basic data in a database and automatically digitizes it for analysis.

[1053] Step 4:

[1054] Terminal

[1055] The device automatically collects daily fluctuation data (body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, amount of sleep, etc.) from the user's wearable device.

[1056] Step 5:

[1057] Terminal

[1058] The terminal transmits the acquired daily fluctuation data to the server.

[1059] Step 6:

[1060] server

[1061] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[1062] Step 7:

[1063] server

[1064] The server analyzes the basic data and the variation data and generates an appropriate training menu based on the analysis.

[1065] Step 8:

[1066] Terminal

[1067] The terminal notifies the user of the training menu from the server and presents the detailed contents.

[1068] Step 9:

[1069] server

[1070] The server calculates nutrient needs based on the user's baseline and training data.

[1071] Step 10:

[1072] server

[1073] The server creates an optimal ingredient list and recipe based on the calculation results.

[1074] Step 11:

[1075] Terminal

[1076] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[1077] Step 12:

[1078] User

[1079] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[1080] Step 13:

[1081] server

[1082] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[1083] Step 14:

[1084] server

[1085] Based on the analysis results, the server suggests relaxation methods and counseling.

[1086] Step 15:

[1087] Terminal

[1088] The terminal notifies the user of the mental support suggestions provided by the server, and provides specific relaxation techniques and counseling reservation information.

[1089] Step 16:

[1090] Terminal

[1091] The device uses facial and voice recognition technology to collect emotional data in real time.

[1092] Step 17:

[1093] Terminal

[1094] The terminal transmits the collected emotion data to the server.

[1095] Step 18:

[1096] server

[1097] The server receives the emotion data sent from the emotion engine and analyzes it in combination with the mental data.

[1098] Step 19:

[1099] server

[1100] The server updates the training menu and mental support content based on the emotional and mental data, and suggests reducing the training intensity when the user feels stressed.

[1101] Step 20:

[1102] Terminal

[1103] The device notifies the user of the analysis results and suggests relaxation techniques and counseling to reduce stress.

[1104] This system allows athletes to efficiently manage their basic data, daily fluctuation data, and emotional data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[1105] Example 2

[1106] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1107] Conventional athlete condition management systems quantify physical data and suggest training menus, but do not provide comprehensive support that takes mental and emotional data into account. This makes it difficult to appropriately detect and deal with stress and psychological fatigue when athletes experience it. Furthermore, training intensity is not adjusted based on emotional fluctuations. As a result, there is an issue where athletes' performance cannot be fully optimized.

[1108] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1109] In this invention, the server includes means for quantifying and storing the athlete's physical data, means for acquiring daily physical data and transmitting it to the server, means for analyzing the transmitted data and proposing a training menu, means for presenting the training menu to the user, means for selecting optimal ingredients for nutritional supplementation and creating recipes, means for presenting recipes and ingredient lists to the user, means for collecting and analyzing mental data and providing mental support, means for collecting and analyzing emotional data, and means for adjusting training intensity based on the emotional and mental data. This enables comprehensive condition management that takes into account the athlete's mental state and emotions.

[1110] An "athlete" is an individual who possesses significant physical ability or skill and who engages in specialized sports or physical activities.

[1111] "Physical data" refers to information that quantifies an athlete's physical condition, such as their height, weight, muscle mass, body fat percentage, and cardiopulmonary function.

[1112] "Variation data" refers to data that indicates the daily physical condition, and includes body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, amount of sleep, and the like.

[1113] A "server" is a computer system that collects, analyzes, stores, and distributes data.

[1114] A "training menu" is a specific exercise plan aimed at improving an athlete's performance, and includes strength training, cardio exercises, stretching, etc.

[1115] "Nutrition" refers to the act of providing athletes with the balanced nutrients they need, and is achieved through the provision of ingredients and recipes.

[1116] "Mental data" refers to data that indicates an athlete's psychological state and stress level.

[1117] "Emotional data" is data that shows an athlete's emotional state in real time and is collected using facial and voice recognition technology.

[1118] The "emotion engine" is a system engine that uses facial and voice recognition technology to collect and analyze emotional data from users.

[1119] "Training intensity" refers to the load and difficulty of exercise in a training menu, and is adjusted based on the athlete's fitness and physical condition.

[1120] This invention is a comprehensive condition management system aimed at optimizing athletes' performance. This system collects basic data, collects and analyzes daily fluctuation data, proposes training menus, selects ingredients for nutritional supplementation and provides recipes, provides mental support, and collects and analyzes emotional data using an emotion engine.

[1121] Collecting and quantifying basic data

[1122] The user starts the app and enters basic information such as height, weight, muscle mass, and cardiopulmonary function as the initial setting. This data is sent to the server via the device. The server stores the received basic data in a database and converts it into numerical values ​​for future analysis.

[1123] For example, a user inputs his / her height of 175 cm, weight of 70 kg, and muscle mass of 50 kg, and the terminal sends this data to the server, where it is stored in a database.

[1124] Collection and analysis of daily fluctuation data

[1125] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep from the user's wearable device. This data is then sent from the device to a server. The server adds the received fluctuation data to a database and compares it with past data for analysis.

[1126] For example, a wearable device sends heart rate data to a terminal at 8:00 a.m. every morning, and the terminal transfers the data to a server. The server analyzes the data and records an average heart rate of 72 BPM.

[1127] Training menu suggestions

[1128] The server analyzes the basic and variability data and generates an appropriate training menu. The generated training menu is sent to the user via the device. For example, it may include specific exercise plans such as strength training, stretching, and jogging.

[1129] As a specific example, the server generates a menu of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging, and the terminal displays the menu to the user.

[1130] Nutritious ingredient selection and recipe provision

[1131] The server calculates the necessary nutrients based on the training data and basic data, and creates an appropriate food list, which is presented to the user via their device and includes specific recipes.

[1132] As a specific example, the server creates an ingredient list for protein intake: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice, and the terminal presents the list and a recipe for grilled chicken breast to the user.

[1133] Mental support

[1134] Users periodically answer questions about their mental state and send the data from their device to a server. The server analyzes the received mental data to detect stress levels and psychological fatigue. Based on the server's analysis results, the system provides appropriate mental support to the user.

[1135] For example, a user might enter "I've been feeling stressed lately" and send that data to a server. The server then analyzes the stress level and suggests yoga as a relaxation method. The device then notifies the user of the suggestion and provides yoga instructions and video links.

[1136] Emotional data collection and analysis using an emotion engine

[1137] The device uses an emotion engine to collect the user's emotional data in real time. This emotional data is acquired using facial and voice recognition technology and sent from the device to a server. The server then analyzes the emotional data in combination with mental data and responds flexibly, such as adjusting training intensity when the user is feeling stressed.

[1138] For example, the device recognizes the user's face and determines their emotional state as "stressed." The server analyzes the data and makes a "suggestion to reduce training intensity." The device then notifies the user of the suggestion and provides specific relaxation methods and training adjustments.

[1139] Prompt Sentence Examples

[1140] "Please describe the specific procedures for a comprehensive condition management system to optimize athlete performance. Please include details regarding user emotion recognition."

[1141] This system allows athletes to manage their overall condition, optimizing their performance and maintaining their health.

[1142] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1143] Step 1: Enter and submit basic data

[1144] The user launches the app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function. The device sends the basic data entered by the user to the server. The server stores the received basic data in a database. This data is quantified for analysis. The input is the user's physical information, and the output is the basic data stored in the database.

[1145] Step 2: Collect and send daily fluctuation data

[1146] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep from the user's wearable device. This data is sent from the device to a server, which adds the received fluctuation data to a database. The server then compares it with past data and performs analysis. The input is the fluctuation data from the wearable device, and the output is the analyzed data and the stored results.

[1147] Step 3: Create a training menu

[1148] The server analyzes the basic data and variation data and generates an appropriate training menu based on the analysis. For example, a menu including strength training, stretching, jogging, etc. is created. The terminal notifies the user of the generated training menu. The input is the basic data and variation data, and the output is the generated training menu.

[1149] Step 4: Nutritious ingredients selection and recipe provision

[1150] The server calculates the necessary nutrients based on the training data and basic data and creates an appropriate ingredient list. The terminal presents the ingredient list and recipe created by the server to the user. The input is the training data and basic data, and the output is the created ingredient list and recipe.

[1151] Step 5: Collect and analyze mental data

[1152] Users periodically answer questions about their mental state and send the data to a server via their device. The server analyzes the received mental data to detect stress levels and psychological fatigue. Based on the results, the server proposes appropriate mental support to the user. The input is the answers to the questions about their mental state, and the output is the analysis results and suggestions for mental support.

[1153] Step 6: Collect and analyze emotion data

[1154] The device uses an emotion engine to collect the user's emotional data in real time. For example, it can acquire emotional data using facial or voice recognition technology. The device then sends the acquired emotional data to a server, which then combines it with mental data for analysis. Based on the analysis results, adjustments to training intensity and additional mental support are suggested. The input is emotional data, and the output is the analysis results and suggestions for adjusting training intensity.

[1155] Specifically, when a user notifies the server that they are feeling stressed, the server analyzes the data and provides suggestions for reducing training intensity or providing instructions on relaxation techniques. The server then notifies the user of the suggestions via their device.

[1156] (Application example 2)

[1157] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1158] Conventional systems aimed at optimizing athlete performance only target physical and mental data, and do not comprehensively address operational efficiency or maintenance management. Furthermore, they do not provide appropriate instructions that take into account the emotional data of managers. This can lead to reduced efficiency, particularly in the operation of factory robots, and can lead to missed maintenance timing. Therefore, a new system is needed to optimize the operational efficiency and maintenance of factory robots, similar to athlete performance.

[1159] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for quantifying and storing the athlete's physical data, means for acquiring daily physical data and transmitting it to the server, means for analyzing the transmitted data and proposing a training menu, means for presenting the training menu to the user, means for selecting optimal ingredients for nutritional supplementation and creating a recipe, means for presenting the recipe and ingredient list to the user, means for collecting and analyzing mental data and providing mental support, means for collecting and analyzing basic robot data and daily fluctuation data, means for generating and presenting an optimal work menu, means for proposing a maintenance schedule based on the robot's status, and means for collecting and analyzing user emotional data and providing flexible instructions to the robot based on the data. This improves the operating efficiency of factory robots, enables appropriate maintenance, and enables flexible operation that takes into account the emotional state of the manager.

[1160] "Athlete's physical data" refers to data that quantifies physiological indicators such as an athlete's height, weight, muscle mass, and cardiopulmonary function.

[1161] "Daily physical data" refers to data on fluctuations that athletes collect on a daily basis, such as body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, and amount of sleep.

[1162] A "server" is a central computer that stores data, performs analysis, and generates various offers.

[1163] A "training menu" is an exercise program created based on the athlete's basic data and daily physical data.

[1164] "User" refers to the entity that uses the system to input data or receive suggestions, such as an athlete or administrator.

[1165] "Nutrition" refers to the selection of ingredients and dietary methods to meet an athlete's nutritional needs.

[1166] An "ingredient list" is a list of ingredients selected to meet nutritional needs.

[1167] A "recipe" is a written or illustrated guide to cooking steps for athletes to replenish their nutrition.

[1168] "Mental data" refers to information such as an athlete's stress level, psychological fatigue, and emotional state.

[1169] "Mental support" refers to interventions to support athletes' mental health, such as relaxation techniques and counseling suggestions.

[1170] "Basic robot data" refers to the robot's basic performance indicators, such as quantified data on working speed and lifting force.

[1171] "Daily fluctuation data" refers to fluctuation data such as temperature, operating time, motor temperature, and error frequency that the robot collects on a daily basis.

[1172] A "task menu" is a business program created based on the robot's basic data and daily fluctuation data.

[1173] A "maintenance schedule" is a maintenance management plan proposed based on the condition of the robot.

[1174] "Emotional data" is data collected in real time about the emotional state of the manager.

[1175] "Flexible instructions" are appropriate work instructions given to a robot based on collected emotional data.

[1176] This invention is a comprehensive condition management system for optimizing athlete performance, and is particularly applicable to the operational efficiency and maintenance management of factory robots. The system includes the collection and quantification of basic data, the collection and analysis of daily fluctuation data, the proposal of work menus, the planning of maintenance schedules, and flexible instructions based on the user's emotional data.

[1177] Collecting and quantifying basic data

[1178] User

[1179] The user starts the application and inputs the robot's basic performance data (working speed, lifting force, etc.) as initial settings.

[1180] Terminal

[1181] The terminal transmits the basic data entered by the user to the server.

[1182] server

[1183] The server stores the received basic data in a database and automatically digitizes it for analysis.

[1184] Collection and analysis of daily fluctuation data

[1185] Terminal

[1186] The device automatically collects daily fluctuation data from sensors, such as temperature, operating time, motor temperature, and error frequency.

[1187] server

[1188] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[1189] Work menu suggestions

[1190] server

[1191] The server analyzes the basic data and the variable data and creates an appropriate operation menu based on the analysis, such as a menu for assembling parts, painting, loading pallets, etc.

[1192] Terminal

[1193] The terminal notifies the user of the generated operation menu and presents the detailed contents.

[1194] Maintenance schedule planning

[1195] server

[1196] The server proposes an optimal maintenance schedule based on the robot's basic data and daily fluctuation data.

[1197] Terminal

[1198] The terminal notifies the user of the maintenance schedule provided by the server and urges the user to carry out appropriate maintenance.

[1199] Emotion data collection and analysis

[1200] Terminal

[1201] The device equipped with the emotion engine uses technologies such as facial recognition and voice recognition of the user (administrator) to collect emotional data of the user in real time.

[1202] server

[1203] The server receives the emotion data sent from the emotion engine and uses it to adjust the work menu and maintenance schedule, for example, by providing instructions that allow flexible responses even when the user is feeling stressed.

[1204] Hardware and software used

[1205] Hardware

[1206] Sensors (temperature sensors, error detection sensors)

[1207] Factory robots (general robotic arms)

[1208] Computer server (for database and analysis)

[1209] software

[1210] Python (data collection and analysis)

[1211] Database management system (MySQL)

[1212] Systems with emotion engines (e.g., Face++)

[1213] Specific examples

[1214] For example, when a user introduces a new robot, they input its basic performance data into the application. The terminal sends this data to the server, which stores it in a database. Data from temperature sensors, operating hours, error frequency, and other daily fluctuation data are also sent from the terminal to the server, which analyzes them. The server then uses this data to propose work menus and create maintenance schedules. The emotion engine collects the manager's emotional data in real time, and flexible instructions are given based on that data.

[1215] Prompt Sentence Examples

[1216] "Collect today's data on the temperature sensors and motor temperatures of factory robots, compare it with the average values ​​for the past seven days, and report any abnormalities."

[1217] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1218] Step 1:

[1219] The user starts the application and inputs the robot's basic performance data (working speed, lifting force, etc.) as the initial setting. This collects basic data about the robot. Specific examples of data input by the user include a working speed of 10 meters per second and a lifting force of 100 kilograms. This data will be the basic information needed for subsequent analysis and proposals.

[1220] Step 2:

[1221] The terminal sends the basic data entered by the user to the server. The terminal converts the entered data into an appropriate format and sends it to the server. The input data includes various performance indicators such as work speed and lifting force. The basic data is sent as output to the server and stored in a database.

[1222] Step 3:

[1223] The server stores the received basic data in a database and automatically digitizes it for analysis. The server uses a database management system (e.g., MySQL) to store and manage the data. The stored data is used in subsequent analysis steps.

[1224] Step 4:

[1225] The terminal automatically obtains daily fluctuation data from the sensors, such as temperature, operating time, motor temperature, and error frequency. For example, the input from the temperature sensor may be 25 degrees, the motor temperature may be 60 degrees, the operating time may be 8 hours, and the error frequency may be 3. This data is important for understanding the operational status of the robot in real time.

[1226] Step 5:

[1227] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data. By comparing it with past data stored in the database, outliers and trends can be detected. For example, an abnormal temperature can be detected by comparing the average temperature with data from the past seven days.

[1228] Step 6:

[1229] The server analyzes basic and variable data and creates an appropriate work menu based on that. An example of a work menu might be two hours for assembling parts, 30 minutes for painting, and one hour for loading onto a pallet. The server automatically generates the optimal work menu using a generative AI model and various algorithms.

[1230] Step 7:

[1231] The terminal notifies the user of the generated work menu and presents detailed content. The terminal displays the work menu received from the server to the user in an appropriate format. A specific prompt sentence might be, "Today's work menu is 2 hours of part assembly, 30 minutes of painting, and 1 hour of pallet loading."

[1232] Step 8:

[1233] The server proposes an optimal maintenance schedule based on the robot's basic data and daily fluctuation data. For example, it can suggest preventive maintenance when the motor temperature is high or the error frequency increases. A specific maintenance schedule is generated as an output.

[1234] Step 9:

[1235] The terminal notifies the user of the maintenance schedule provided by the server and encourages them to carry out the appropriate maintenance. The terminal displays the details of the maintenance schedule in an easy-to-understand manner to the user and supports the implementation. Specifically, the terminal notifies the user that "the next maintenance should be carried out tomorrow at 3:00 PM."

[1236] Step 10:

[1237] The device is equipped with an emotion engine that uses facial and voice recognition technology to collect real-time emotion data from the user (administrator). For example, if the user is feeling stressed, that data is collected.

[1238] Step 11:

[1239] The server receives the emotion data sent from the emotion engine and uses it to adjust the work menu and maintenance schedule. Specifically, if the user is feeling stressed, the server may adjust the work load, for example, by reducing the workload.

[1240] Through these processing steps, it becomes possible to optimize the performance of athletes and the operational efficiency and maintenance management of factory robots.

[1241] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1242] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1243] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1244] [Third embodiment]

[1245] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1246] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1247] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1248] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1249] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1250] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1251] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1252] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1253] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1254] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1255] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1256] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1257] This invention is a comprehensive condition management system for optimizing athletic performance. This system provides a variety of means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, and providing mental support.

[1258] Collecting and quantifying basic data

[1259] User

[1260] During initial setup, users enter basic data such as height, weight, muscle mass, and cardiopulmonary function into a dedicated app.

[1261] Terminal

[1262] The terminal transmits the basic data entered by the user to the server.

[1263] server

[1264] The server stores the received basic data in a database and automatically digitizes it for analysis.

[1265] Collection and analysis of daily fluctuation data

[1266] Terminal

[1267] The device automatically collects daily data from the user's wearable device, including body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep.

[1268] server

[1269] The server adds the received fluctuation data to a database and analyzes it to detect outliers and unusual trends.

[1270] Training menu suggestions

[1271] server

[1272] The server analyzes the basic and variability data and creates an appropriate training menu based on the analysis, for example, a menu including strength training, cardio exercises, stretching, etc.

[1273] Terminal

[1274] The terminal notifies the user of the generated training menu and presents the detailed contents.

[1275] Nutritious ingredient selection and recipe provision

[1276] server

[1277] The server calculates the required nutrients based on the user's basic data and training data, and creates an ingredient list based on the results.

[1278] Terminal

[1279] The terminal presents the user with a list of ingredients and recipes provided by the server. For example, it provides ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions for using those ingredients.

[1280] Mental support

[1281] User

[1282] The user periodically answers questions about their mental state and transmits the data to the server via their terminal.

[1283] server

[1284] The server analyzes the received mental data to detect stress levels and psychological fatigue, and based on the analysis results, suggests relaxation methods or counseling as needed.

[1285] Terminal

[1286] The device notifies the user of mental support suggestions provided by the server, such as suggestions for meditation or yoga, or counseling appointments.

[1287] Specific examples

[1288] Collection of basic data

[1289] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[1290] The device sends this data to the server, which stores it in a database.

[1291] Collecting fluctuation data

[1292] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[1293] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[1294] Training menu suggestions

[1295] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[1296] The terminal displays this training menu to the user and explains the details.

[1297] Nutritional supplementation

[1298] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[1299] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[1300] Mental support

[1301] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[1302] The server analyzes stress levels and suggests yoga as a way to relax.

[1303] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[1304] This system will enable the centralized management of athletes' basic data and daily fluctuation data, enabling comprehensive condition management, which will optimize athletes' performance and provide efficient and effective training and care.

[1305] The processing flow will be explained below.

[1306] Step 1:

[1307] User

[1308] The user launches the app and enters basic data such as their height, weight, muscle mass, and cardiopulmonary function as the initial setting.

[1309] Step 2:

[1310] Terminal

[1311] The terminal transmits the basic data entered by the user to the server.

[1312] Step 3:

[1313] server

[1314] The server stores the received basic data in a database and automatically digitizes it for analysis.

[1315] Step 4:

[1316] Terminal

[1317] The device automatically collects daily fluctuation data (body temperature, blood pressure, blood sugar level, heart rate variability, etc.) from the user's wearable device.

[1318] Step 5:

[1319] Terminal

[1320] The terminal transmits daily fluctuation data to the server.

[1321] Step 6:

[1322] server

[1323] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[1324] Step 7:

[1325] server

[1326] The server analyzes the basic data and the variation data and generates an appropriate training menu.

[1327] Step 8:

[1328] Terminal

[1329] The terminal notifies the user of the training menu from the server and presents the detailed contents.

[1330] Step 9:

[1331] server

[1332] The server calculates the required nutrients based on the user's basic data and training data.

[1333] Step 10:

[1334] server

[1335] The server creates an optimal ingredient list and recipe based on the calculation results.

[1336] Step 11:

[1337] Terminal

[1338] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[1339] Step 12:

[1340] User

[1341] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[1342] Step 13:

[1343] server

[1344] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[1345] Step 14:

[1346] server

[1347] Based on the analysis results, the server suggests relaxation methods and counseling.

[1348] Step 15:

[1349] Terminal

[1350] The terminal notifies the user of suggestions for mental support from the server and provides specific relaxation techniques and counseling appointment information.

[1351] This system allows athletes to efficiently manage their basic data and daily fluctuation data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[1352] Example 1

[1353] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1354] In order to optimize athletes' performance and efficiently manage their overall condition, it is important to centrally manage physical data, fluctuation data, and mental data and provide appropriate training, nutritional support, and mental support in real time. However, conventional systems that manage these data separately have issues with data consistency and immediacy, making it difficult to provide optimized support for individual athletes.

[1355] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1356] In this invention, the server includes means for quantifying and storing the athlete's basic data, means for acquiring daily fluctuation data and sending it to the server, means for analyzing outliers and unusual trends based on the acquired data, means for generating a training menu based on the analysis results, means for notifying the user of the generated training menu, means for calculating necessary nutrients, selecting optimal ingredients, and creating a recipe, means for presenting the recipe and ingredient list to the user, and means for collecting and analyzing mental data and providing mental support, thereby optimizing the athlete's performance and enabling comprehensive condition management.

[1357] An "athlete" is someone who possesses high skill and physical strength in competitive sports.

[1358] "Basic data" refers to data that quantifies basic information about an athlete's body, such as height, weight, muscle mass, and cardiopulmonary function.

[1359] "Variation data" refers to information about daily changes in an athlete's body, such as body temperature, blood pressure, blood sugar levels, heart rate variability, fatigue, and amount of sleep.

[1360] "Server" refers to a computer system that receives, stores, analyzes, etc. data.

[1361] "Terminal" refers to a device used by a user to input or refer to data, including, for example, a smartphone or tablet.

[1362] "Digitization" refers to the process of converting data collected from users into a numerical format that is easy to analyze.

[1363] "Storage" refers to storing collected data for a long period of time.

[1364] An "outlier" is a data value that falls outside the normal range in a statistical analysis.

[1365] "Anomalous trends" refers to trends in data where clear patterns or anomalies can be identified in data analysis.

[1366] A "training menu" refers to a series of exercises and plans proposed to improve an athlete's physical strength and maintain their condition.

[1367] "Nutrients" refers to proteins, vitamins, minerals, and other components necessary for athletes' health and performance.

[1368] "Ingredients List" refers to a list of foods or ingredients needed to prepare a suggested recipe.

[1369] A "recipe" is a document that details the steps for preparing a dish using specific ingredients.

[1370] "Mental data" refers to information about an athlete's mental state, such as stress levels and psychological fatigue.

[1371] "Mental support" refers to assistance to maintain and improve the mental health of athletes. Specifically, this includes suggestions for relaxation techniques and counseling.

[1372] This invention is a comprehensive condition management system for optimizing athletic performance. This system provides a variety of means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, and providing mental support.

[1373] Collecting and quantifying basic data

[1374] User

[1375] During initial setup, users use a dedicated app to enter basic data such as height, weight, muscle mass, and cardiopulmonary function.

[1376] Terminal

[1377] The terminal sends the basic data entered by the user to the server, where the data is encrypted to ensure security.

[1378] server

[1379] The server stores the received basic data in a database and automatically converts it into numerical values ​​for analysis. For example, it statistically analyzes the QRS complex wave parameters of cardiopulmonary function to check for deviations from standard values.

[1380] Collection and analysis of daily fluctuation data

[1381] Terminal

[1382] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and sleep amount from the user's wearable device (e.g., smartwatch). The collected data is temporarily stored in the device in real time.

[1383] Terminal

[1384] The device periodically (e.g., every 10 minutes) transmits the temporarily stored data to the server, where data integrity and encryption are performed.

[1385] server

[1386] The server adds the received fluctuation data to a database and performs statistical analysis. If an abnormal value is detected, an alert is sent to the terminal to notify the user.

[1387] Training menu suggestions

[1388] server

[1389] The server generates a training plan that is optimal for the user's physical condition based on the basic and fluctuation data. For example, it creates a menu of strength training (20 minutes), cardio (30 minutes), and stretching (15 minutes).

[1390] Terminal

[1391] The device will then notify the user of the generated training menu and display the details within the app, including video links and text descriptions for each exercise.

[1392] Nutritious ingredient selection and recipe provision

[1393] server

[1394] The server calculates the amount of nutrients (e.g., protein, vitamins) needed based on the user's basic and training data. For example, it creates an ingredient list such as "200g chicken breast, 100g broccoli, 200g brown rice."

[1395] server

[1396] The server generates a recipe that includes cooking methods for each ingredient, such as suggesting "grilled chicken breast."

[1397] Terminal

[1398] The device receives the ingredients list and recipe from the server and displays it in the app, notifying the user. The recipe includes detailed information such as cooking time, steps, and necessary equipment.

[1399] Mental support

[1400] User

[1401] Users periodically answer in-app surveys to record their current stress levels and psychological state, for example, by entering "My stress level has been high recently."

[1402] Terminal

[1403] The terminal sends the mental data entered by the user to the server, where the data is checked for integrity and encrypted during transmission.

[1404] server

[1405] The server analyzes the received mental data to detect stress levels and psychological fatigue, and based on the analysis results, generates suggestions for relaxation methods (e.g., yoga) and counseling appointments.

[1406] Terminal

[1407] The device notifies the user of the mental support suggestions received from the server and displays detailed instructions and a link to book a counseling appointment within the app.

[1408] Specific examples

[1409] Collection of basic data

[1410] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[1411] The device sends this data to the server, which stores it in a database.

[1412] Collecting fluctuation data

[1413] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[1414] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[1415] Training menu suggestions

[1416] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[1417] The terminal displays this training menu to the user and explains the details.

[1418] Nutritional supplementation

[1419] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[1420] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[1421] Mental support

[1422] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[1423] The server analyzes stress levels and suggests yoga as a way to relax.

[1424] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[1425] Example prompts for generative AI models

[1426] "Please enter the athlete's basic data (height 175cm, weight 70kg, muscle mass 50kg) and generate a training menu based on one week's heart rate data. Also, please provide a list of ingredients and recipes for proper nutrition."

[1427] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1428] Step 1:

[1429] Input: The user launches a dedicated app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function.

[1430] How it works: A user fills out a form in the app and enters data such as height 175cm, weight 70kg, muscle mass 50kg, etc. The app performs a consistency check to ensure there are no input errors.

[1431] Output: The correct formatted basic data is saved to the device.

[1432] Step 2:

[1433] Input: The terminal receives basic data entered by the user.

[1434] How it works: The device encrypts the underlying data it receives and sends it to the server using a secure communication protocol.

[1435] Output: The encrypted underlying data is sent to the server.

[1436] Step 3:

[1437] Input: The server receives the basic data sent from the terminal.

[1438] How it works: The server decrypts the data and stores it in a database. It also analyzes and quantifies the data. For example, it analyzes the QRS complex wave of cardiopulmonary function and calculates the difference from the standard value.

[1439] Output: The quantified basic data is saved in a database.

[1440] Step 4:

[1441] Input: The device collects daily fluctuation data (body temperature, blood pressure, blood glucose level, heart rate variability, fatigue, and amount of sleep) from the user's wearable device.

[1442] How it works: The device acquires data collected from the wearable device in real time and temporarily stores it.

[1443] Output: The temporarily saved fluctuation data is saved on the terminal.

[1444] Step 5:

[1445] Input: The terminal periodically sends the temporarily stored fluctuation data to the server.

[1446] How it works: The device encrypts the collected data and sends it to the server periodically (e.g., every 10 minutes).

[1447] Output: The encrypted variable data is sent to the server.

[1448] Step 6:

[1449] Input: The server receives the variable data sent from the terminal.

[1450] How it works: The server decrypts the data, stores it in a database, and analyzes it. If an abnormal value is detected, an alert is generated and sent to the device. For example, if blood sugar levels suddenly rise, the user is immediately notified of this information.

[1451] Output: Analysis results and alert information are stored on the server and sent to the terminal as needed.

[1452] Step 7:

[1453] Input: The server generates a training menu based on basic data and variation data.

[1454] How it works: Based on data analysis, the server automatically generates optimal training menus, including strength training, cardio, and stretching.

[1455] Output: The generated training menu is saved in the database.

[1456] Step 8:

[1457] Input: The terminal receives the training menu generated from the server.

[1458] How it works: The device will notify the user of a workout menu and display detailed instructions within the app, including video links and text descriptions for each exercise.

[1459] Output: The training menu notified to the user.

[1460] Step 9:

[1461] Input: The server calculates the required nutrients based on the user's basic and training data.

[1462] How it works: The server calculates the necessary nutrients, such as protein and vitamins, and creates an ingredient list based on that (e.g., 200g chicken breast, 100g broccoli, 200g brown rice). It then generates a recipe based on this list.

[1463] Output: Ingredient list and recipe based on calculated nutrients.

[1464] Step 10:

[1465] Input: The device receives the ingredient list and recipe provided by the server.

[1466] How it works: The device displays the received ingredient list and recipe in the app and notifies the user, with the recipe including details such as cooking time, steps, and required equipment.

[1467] Output: The recipe and ingredient list provided to the user.

[1468] Step 11:

[1469] Input: The user inputs mental data into the app.

[1470] How it works: Users fill out an in-app survey to input their stress levels and psychological state. For example, they might enter, "My stress level has been high recently."

[1471] Output: The input mental data is saved on the device.

[1472] Step 12:

[1473] Input: The terminal receives mental data input from the user.

[1474] How it works: The device encrypts the received mental data and sends it to the server using a secure communication protocol.

[1475] Output: The encrypted mental data is sent to the server.

[1476] Step 13:

[1477] Input: The server receives the mental data sent from the terminal.

[1478] How it works: The server decrypts and stores the mental data in a database. It also analyzes the data to detect stress levels and psychological fatigue. If necessary, it generates suggestions for relaxation methods (e.g., yoga) or counseling.

[1479] Output: The analysis results and recommendations are stored in a database and sent to a terminal if necessary.

[1480] Step 14:

[1481] Input: The device receives the analysis results and suggestions sent from the server.

[1482] How it works: The device displays the analysis results and recommendations it receives within the app and notifies the user, including relaxation techniques and a link to book a consultation.

[1483] Output: The user is notified of relaxation techniques and a link to book a consultation.

[1484] (Application example 1)

[1485] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1486] Comprehensive condition management is necessary to maximize the performance of athletes and machines. However, current systems have the problem of not being able to centrally collect and analyze physical and machine data, propose training and maintenance, or manage nutrition and consumable parts. There is a need for a system that can manage all of this data in an integrated manner and provide optimal support efficiently and effectively.

[1487] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1488] In this invention, the server includes means for quantifying and storing the athlete's physical data, means for acquiring daily physical data and transmitting it to the server, means for analyzing the transmitted data and proposing a training menu, means for presenting the training menu to the user, means for selecting optimal ingredients for nutritional supplementation and creating recipes, means for presenting recipes and ingredient lists to the user, means for collecting and analyzing mental data and providing mental support, means for collecting basic machine data and daily fluctuation data and proposing a maintenance schedule based thereon, means for generating and presenting machine operation optimization parameters, means for creating a consumable parts list and providing it to the user, and means for detecting abnormalities and issuing corresponding alerts. This allows for centralized management of both athlete training and nutritional management and machine maintenance and operation optimization, enabling efficient and effective support.

[1489] An "athlete" is an individual or group who trains with the goal of achieving high performance in a sport or competition.

[1490] "Physical data" refers to specific numerical values ​​representing an athlete's physical information, such as height, weight, muscle mass, and cardiopulmonary function.

[1491] A "server" is a computer system that stores, analyzes, and provides data over a network.

[1492] "Machinery" refers to equipment such as robots and automated devices used in production activities in factories, etc.

[1493] "Fluctuating data" is information that changes daily, such as body temperature, blood pressure, heart rate, and blood sugar level, which show different values ​​in real time.

[1494] A "training menu" is a plan that lists the specific exercises and practices that an athlete should perform.

[1495] "User" refers to a person or individual who uses this condition management system.

[1496] "Nutrition" refers to ensuring that athletes properly consume the ingredients and food they need to maintain their physical condition and improve their performance.

[1497] A "food list" is a list of ingredients selected based on a specific purpose or training menu.

[1498] "Mental data" is information used to examine an athlete's stress level and psychological state.

[1499] "Mental support" refers to providing advice and care to maintain and improve an athlete's mental health.

[1500] "Basic data" refers to data that forms the basis of operation, such as the machine model, installation date, and initial setting information at the start of use.

[1501] A "maintenance schedule" is a plan for carrying out appropriate maintenance and inspection of machinery.

[1502] "Operational optimization parameters" are settings and adjustments that allow a machine to operate efficiently and effectively.

[1503] A "consumable parts list" is a list of parts that need to be replaced or replenished periodically during machine operation.

[1504] "Abnormal" refers to a state in which a machine deviates from its normal operation and indicates that a breakdown or malfunction has occurred.

[1505] An "alert" is a warning or notification issued when an abnormality or unusual situation is detected.

[1506] A specific system configuration and processing procedure for carrying out the present invention will be described. This system is a comprehensive condition management system for optimizing the performance of athletes and factory robots, and includes the following means:

[1507] First, the user enters the athlete's physical data and basic machine data into a dedicated application. This application runs on a smartphone, tablet, or desktop PC and is designed to make data entry and transmission easy. During initial setup, basic data such as height, weight, muscle mass, cardiopulmonary function, robot model, and installation date are entered.

[1508] Next, the device (smartphone, tablet, PC, etc.) sends the basic data entered by the user to a server. This server is a computer system installed on a network that has the function of storing, analyzing, and providing data. Specifically, it uses a database (e.g., PostgreSQL) to store the basic data and digitize it for analysis.

[1509] Daily fluctuation data is collected through wearable devices and various sensors. This includes body temperature, blood pressure, blood sugar levels, heart rate variability, fatigue level, and amount of sleep. This data is also sent to the server via the device. The server adds the received fluctuation data to a database and performs analysis.

[1510] The transmitted data is analyzed using data analysis algorithms running on the server. For example, machine learning models written in Python are used to detect abnormal values ​​and specific trends in heart rate and blood pressure. The robot's vibration and temperature data are also analyzed to detect abnormalities.

[1511] Based on the analysis, the server creates training and maintenance schedules: training for athletes includes strength training, cardio exercises, and stretching, while maintenance for robots includes oil checks and filter changes.

[1512] The server also selects ingredients for nutritional support and provides recipes. The server calculates the nutritional needs of the user based on their basic and training data, and creates an ingredient list. For example, it presents ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions.

[1513] Additionally, collecting and analyzing mental data is also important. Users periodically answer questions about their mental state and send the data to a server via their device. The server analyzes stress levels and psychological fatigue and provides appropriate mental support, such as suggestions for yoga or counseling.

[1514] For example, when collecting basic data, the user launches the app and enters data such as height 175 cm, weight 70 kg, and muscle mass 50 kg. The wearable device also sends heart rate data to the terminal at 8:00 a.m. every day, and the server records an average heart rate of 72 BPM. As a maintenance schedule suggestion, the next maintenance is scheduled for November 10, 2023, and the tasks include an oil check and filter change.

[1515] Example prompt sentence:

[1516] "The ABB IRB 6700 robot was installed on January 1, 2023. Please collect the basic data and store it in the database."

[1517] "Based on today's inspection results, the robot has been running for 16 hours and one anomaly has been detected. Please send this data to the server."

[1518] "The next maintenance is scheduled for November 10, 2023. Tasks will include an oil check and filter change."

[1519] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1520] Step 1:

[1521] During the initial setup, the user enters basic data about the athlete and the machine into a dedicated app. Specifically, information such as height, weight, muscle mass, cardiopulmonary function, robot model, and installation date is entered, and the data is saved on the device. This input data is converted into numerical values ​​as basic data.

[1522] Input: Basic data on athletes and machines

[1523] Data processing: digitization of basic data

[1524] Output: The digitized basic data is saved on the device.

[1525] Step 2:

[1526] The device sends the stored basic data to the server, which receives it and stores it in a database. The server then manages the basic data as quantified information for analysis.

[1527] Input: Basic data stored on the device

[1528] Data processing: data transmission and storage

[1529] Output: The digitized basic data is saved in the server database.

[1530] Step 3:

[1531] To collect daily fluctuation data, the device acquires data such as body temperature, blood pressure, blood sugar level, heart rate variability, fatigue level, and amount of sleep from wearable devices and sensors, and periodically transmits this daily fluctuation data to a server.

[1532] Input: Variable data from wearable devices and sensors

[1533] Data Processing: Data Acquisition and Transmission

[1534] Output: Daily fluctuation data is sent to the server

[1535] Step 4:

[1536] The server adds the received fluctuation data to a database and performs analysis. Specifically, it uses a Python machine learning model to detect outliers and unusual trends. This allows for early detection of abnormalities in, for example, heart rate or blood pressure.

[1537] Input: Daily fluctuation data

[1538] Data processing: detecting outliers and unusual trends

[1539] Output: Analysis results of specific trends and outliers

[1540] Step 5:

[1541] The server analyzes baseline and fluctuation data and creates optimal training and maintenance schedules based on that data, which could include strength training and cardio exercises for athletes, or oil checks and filter changes for robots.

[1542] Input: Fundamental data and daily fluctuation data

[1543] Data processing: Creation of training menus and maintenance schedules

[1544] Output: Training menu and maintenance schedule

[1545] Step 6:

[1546] The device notifies the user of the training menu and maintenance schedule received from the server and presents detailed information, allowing the user to understand specifically what kind of training and maintenance they should perform.

[1547] Input: Training menu and maintenance schedule

[1548] Data Processing: Notification and Details

[1549] Output: Detailed presentation of training menu and maintenance schedule

[1550] Step 7:

[1551] The server calculates the nutritional needs of the user based on their basic and training data, and then creates an ingredient list and recipe based on that. For example, it presents ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions.

[1552] Input: Base data and training data

[1553] Data processing: Nutrition calculations and generating ingredient lists and recipes

[1554] Output: Ingredient list and recipe

[1555] Step 8:

[1556] The terminal presents the user with a list of ingredients and recipes provided by the server, allowing the user to specifically understand which ingredients to select and how to cook them to replenish their nutrients.

[1557] Input: Ingredient list and recipe

[1558] Data Processing: Notification and Details

[1559] Output: Ingredient list and recipe details

[1560] Step 9:

[1561] Users periodically answer questions about their mental state and send the data, including their stress level and psychological fatigue level, to a server via their device.

[1562] Input: Answers to questions about mental state

[1563] Data processing: Data transmission

[1564] Output: Sending mental data

[1565] Step 10:

[1566] The server analyzes the received mental data, assesses stress levels and psychological fatigue, and provides appropriate mental support, such as yoga suggestions or counseling appointments, if necessary.

[1567] Input: Mental data

[1568] Data processing: Evaluation of stress levels and psychological fatigue

[1569] Output: Mental support suggestions

[1570] Step 11:

[1571] The device notifies the user of suggestions for mental support provided by the server, such as suggestions for meditation or yoga, or information on booking counseling appointments.

[1572] Input: Mental support suggestions

[1573] Data Processing: Notification and Details

[1574] Output: Detailed mental support information

[1575] Step 12:

[1576] The server collects basic machine data and daily fluctuation data, and based on that data, proposes a maintenance schedule, including the next maintenance date and specific work content (oil check, filter change, etc.).

[1577] Input: Fundamental data and daily fluctuation data

[1578] Data processing: Maintenance schedule generation

[1579] Output: Maintenance Schedule

[1580] Step 13:

[1581] The server generates and presents machine operation optimization parameters to the user, providing settings for efficient and effective machine operation.

[1582] Input: Fundamental and variable data

[1583] Data processing: Generation of operational optimization parameters

[1584] Output: Operational optimization parameters

[1585] Step 14:

[1586] The server creates a list of consumable parts and provides it to the user, making it easy to arrange for the necessary consumable parts.

[1587] Input: Fundamental and variable data

[1588] Data processing: Generating a list of consumable parts

[1589] Output: Consumable parts list

[1590] Step 15:

[1591] The server detects abnormalities and issues an alert accordingly, allowing users to quickly detect abnormalities and take appropriate measures.

[1592] Input: Daily fluctuation data

[1593] Data processing: Anomaly detection and alerting

[1594] Output: Alert

[1595] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1596] This invention is a comprehensive condition management system for optimizing athletic performance, and aims to provide more advanced mental support by combining it with an emotion engine that recognizes the user's emotions. This system provides various means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, providing mental support, and collecting and analyzing emotional data using the emotion engine.

[1597] Collecting and quantifying basic data

[1598] User

[1599] The user launches the app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function as the initial settings.

[1600] Terminal

[1601] The terminal transmits the basic data entered by the user to the server.

[1602] server

[1603] The server stores the received basic data in a database and automatically digitizes it for analysis.

[1604] Collection and analysis of daily fluctuation data

[1605] Terminal

[1606] The device automatically collects daily data from the user's wearable device, including body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep.

[1607] server

[1608] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[1609] Training menu suggestions

[1610] server

[1611] The server analyzes the basic and variability data and creates an appropriate training menu based on the analysis, for example, a menu including strength training, cardio exercises, stretching, etc.

[1612] Terminal

[1613] The terminal notifies the user of the generated training menu and presents the detailed contents.

[1614] Nutritious ingredient selection and recipe provision

[1615] server

[1616] The server calculates the required nutrients based on the user's basic data and training data, and creates an ingredient list based on the results.

[1617] Terminal

[1618] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[1619] Mental support

[1620] User

[1621] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[1622] server

[1623] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[1624] Terminal

[1625] The terminal notifies the user of the mental support suggestions provided by the server, and provides specific relaxation techniques and counseling reservation information.

[1626] Emotional data collection and analysis using an emotion engine

[1627] Terminal

[1628] Devices equipped with an emotion engine use technologies such as facial and voice recognition to collect user emotion data in real time.

[1629] server

[1630] The server receives the emotional data sent from the emotion engine and analyzes it in combination with mental data. For example, if the user is feeling stressed, it can make adjustments such as suggesting that the training intensity be reduced.

[1631] Specific examples

[1632] Collection of basic data

[1633] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[1634] The device sends this data to the server, which stores it in a database.

[1635] Collecting fluctuation data

[1636] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[1637] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[1638] Training menu suggestions

[1639] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[1640] The terminal displays this training menu to the user and explains the details.

[1641] Nutritional supplementation

[1642] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[1643] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[1644] Mental support

[1645] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[1646] The server analyzes stress levels and suggests yoga as a way to relax.

[1647] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[1648] Emotion Engine

[1649] The device uses facial recognition technology to collect emotional data from the user in real time.

[1650] The server analyzes the emotional data and adjusts flexible training and mental support when the user feels stressed.

[1651] The device will notify the user of the analysis results and suggest relaxation techniques or counseling to reduce stress.

[1652] This system allows athletes to efficiently manage their basic data, daily fluctuation data, and emotional data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[1653] The processing flow will be explained below.

[1654] Step 1:

[1655] User

[1656] The user launches the app and enters basic data such as their height, weight, muscle mass, and cardiopulmonary function as the initial setting.

[1657] Step 2:

[1658] Terminal

[1659] The terminal transmits the basic data entered by the user to the server.

[1660] Step 3:

[1661] server

[1662] The server stores the received basic data in a database and automatically digitizes it for analysis.

[1663] Step 4:

[1664] Terminal

[1665] The device automatically collects daily fluctuation data (body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, amount of sleep, etc.) from the user's wearable device.

[1666] Step 5:

[1667] Terminal

[1668] The terminal transmits the acquired daily fluctuation data to the server.

[1669] Step 6:

[1670] server

[1671] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[1672] Step 7:

[1673] server

[1674] The server analyzes the basic data and the variation data and generates an appropriate training menu based on the analysis.

[1675] Step 8:

[1676] Terminal

[1677] The terminal notifies the user of the training menu from the server and presents the detailed contents.

[1678] Step 9:

[1679] server

[1680] The server calculates nutrient needs based on the user's baseline and training data.

[1681] Step 10:

[1682] server

[1683] The server creates an optimal ingredient list and recipe based on the calculation results.

[1684] Step 11:

[1685] Terminal

[1686] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[1687] Step 12:

[1688] User

[1689] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[1690] Step 13:

[1691] server

[1692] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[1693] Step 14:

[1694] server

[1695] Based on the analysis results, the server suggests relaxation methods and counseling.

[1696] Step 15:

[1697] Terminal

[1698] The terminal notifies the user of the mental support suggestions provided by the server, and provides specific relaxation techniques and counseling reservation information.

[1699] Step 16:

[1700] Terminal

[1701] The device uses facial and voice recognition technology to collect emotional data in real time.

[1702] Step 17:

[1703] Terminal

[1704] The terminal transmits the collected emotion data to the server.

[1705] Step 18:

[1706] server

[1707] The server receives the emotion data sent from the emotion engine and analyzes it in combination with the mental data.

[1708] Step 19:

[1709] server

[1710] The server updates the training menu and mental support content based on the emotional and mental data, and suggests reducing the training intensity when the user feels stressed.

[1711] Step 20:

[1712] Terminal

[1713] The device notifies the user of the analysis results and suggests relaxation techniques and counseling to reduce stress.

[1714] This system allows athletes to efficiently manage their basic data, daily fluctuation data, and emotional data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[1715] Example 2

[1716] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1717] Conventional athlete condition management systems quantify physical data and suggest training menus, but do not provide comprehensive support that takes mental and emotional data into account. This makes it difficult to appropriately detect and deal with stress and psychological fatigue when athletes experience it. Furthermore, training intensity is not adjusted based on emotional fluctuations. As a result, there is an issue where athletes' performance cannot be fully optimized.

[1718] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1719] In this invention, the server includes means for quantifying and storing the athlete's physical data, means for acquiring daily physical data and transmitting it to the server, means for analyzing the transmitted data and proposing a training menu, means for presenting the training menu to the user, means for selecting optimal ingredients for nutritional supplementation and creating recipes, means for presenting recipes and ingredient lists to the user, means for collecting and analyzing mental data and providing mental support, means for collecting and analyzing emotional data, and means for adjusting training intensity based on the emotional and mental data. This enables comprehensive condition management that takes into account the athlete's mental state and emotions.

[1720] An "athlete" is an individual who possesses significant physical ability or skill and who engages in specialized sports or physical activities.

[1721] "Physical data" refers to information that quantifies an athlete's physical condition, such as their height, weight, muscle mass, body fat percentage, and cardiopulmonary function.

[1722] "Variation data" refers to data that indicates the daily physical condition, and includes body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, amount of sleep, and the like.

[1723] A "server" is a computer system that collects, analyzes, stores, and distributes data.

[1724] A "training menu" is a specific exercise plan aimed at improving an athlete's performance, and includes strength training, cardio exercises, stretching, etc.

[1725] "Nutrition" refers to the act of providing athletes with the balanced nutrients they need, and is achieved through the provision of ingredients and recipes.

[1726] "Mental data" refers to data that indicates an athlete's psychological state and stress level.

[1727] "Emotional data" is data that shows an athlete's emotional state in real time and is collected using facial and voice recognition technology.

[1728] The "emotion engine" is a system engine that uses facial and voice recognition technology to collect and analyze emotional data from users.

[1729] "Training intensity" refers to the load and difficulty of exercise in a training menu, and is adjusted based on the athlete's fitness and physical condition.

[1730] This invention is a comprehensive condition management system aimed at optimizing athletes' performance. This system collects basic data, collects and analyzes daily fluctuation data, proposes training menus, selects ingredients for nutritional supplementation and provides recipes, provides mental support, and collects and analyzes emotional data using an emotion engine.

[1731] Collecting and quantifying basic data

[1732] The user starts the app and enters basic information such as height, weight, muscle mass, and cardiopulmonary function as the initial setting. This data is sent to the server via the device. The server stores the received basic data in a database and converts it into numerical values ​​for future analysis.

[1733] For example, a user inputs his / her height of 175 cm, weight of 70 kg, and muscle mass of 50 kg, and the terminal sends this data to the server, where it is stored in a database.

[1734] Collection and analysis of daily fluctuation data

[1735] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep from the user's wearable device. This data is then sent from the device to a server. The server adds the received fluctuation data to a database and compares it with past data for analysis.

[1736] For example, a wearable device sends heart rate data to a terminal at 8:00 a.m. every morning, and the terminal transfers the data to a server. The server analyzes the data and records an average heart rate of 72 BPM.

[1737] Training menu suggestions

[1738] The server analyzes the basic and variability data and generates an appropriate training menu. The generated training menu is sent to the user via the device. For example, it may include specific exercise plans such as strength training, stretching, and jogging.

[1739] As a specific example, the server generates a menu of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging, and the terminal displays the menu to the user.

[1740] Nutritious ingredient selection and recipe provision

[1741] The server calculates the necessary nutrients based on the training data and basic data, and creates an appropriate food list, which is presented to the user via their device and includes specific recipes.

[1742] As a specific example, the server creates an ingredient list for protein intake: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice, and the terminal presents the list and a recipe for grilled chicken breast to the user.

[1743] Mental support

[1744] Users periodically answer questions about their mental state and send the data from their device to a server. The server analyzes the received mental data to detect stress levels and psychological fatigue. Based on the server's analysis results, the system provides appropriate mental support to the user.

[1745] For example, a user might enter "I've been feeling stressed lately" and send that data to a server. The server then analyzes the stress level and suggests yoga as a relaxation method. The device then notifies the user of the suggestion and provides yoga instructions and video links.

[1746] Emotional data collection and analysis using an emotion engine

[1747] The device uses an emotion engine to collect the user's emotional data in real time. This emotional data is acquired using facial and voice recognition technology and sent from the device to a server. The server then analyzes the emotional data in combination with mental data and responds flexibly, such as adjusting training intensity when the user is feeling stressed.

[1748] For example, the device recognizes the user's face and determines their emotional state as "stressed." The server analyzes the data and makes a "suggestion to reduce training intensity." The device then notifies the user of the suggestion and provides specific relaxation methods and training adjustments.

[1749] Prompt Sentence Examples

[1750] "Please describe the specific procedures for a comprehensive condition management system to optimize athlete performance. Please include details regarding user emotion recognition."

[1751] This system allows athletes to manage their overall condition, optimizing their performance and maintaining their health.

[1752] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1753] Step 1: Enter and submit basic data

[1754] The user launches the app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function. The device sends the basic data entered by the user to the server. The server stores the received basic data in a database. This data is quantified for analysis. The input is the user's physical information, and the output is the basic data stored in the database.

[1755] Step 2: Collect and send daily fluctuation data

[1756] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep from the user's wearable device. This data is sent from the device to a server, which adds the received fluctuation data to a database. The server then compares it with past data and performs analysis. The input is the fluctuation data from the wearable device, and the output is the analyzed data and the stored results.

[1757] Step 3: Create a training menu

[1758] The server analyzes the basic data and variation data and generates an appropriate training menu based on the analysis. For example, a menu including strength training, stretching, jogging, etc. is created. The terminal notifies the user of the generated training menu. The input is the basic data and variation data, and the output is the generated training menu.

[1759] Step 4: Nutritious ingredients selection and recipe provision

[1760] The server calculates the necessary nutrients based on the training data and basic data and creates an appropriate ingredient list. The terminal presents the ingredient list and recipe created by the server to the user. The input is the training data and basic data, and the output is the created ingredient list and recipe.

[1761] Step 5: Collect and analyze mental data

[1762] Users periodically answer questions about their mental state and send the data to a server via their device. The server analyzes the received mental data to detect stress levels and psychological fatigue. Based on the results, the server proposes appropriate mental support to the user. The input is the answers to the questions about their mental state, and the output is the analysis results and suggestions for mental support.

[1763] Step 6: Collect and analyze emotion data

[1764] The device uses an emotion engine to collect the user's emotional data in real time. For example, it can acquire emotional data using facial or voice recognition technology. The device then sends the acquired emotional data to a server, which then combines it with mental data for analysis. Based on the analysis results, adjustments to training intensity and additional mental support are suggested. The input is emotional data, and the output is the analysis results and suggestions for adjusting training intensity.

[1765] Specifically, when a user notifies the server that they are feeling stressed, the server analyzes the data and provides suggestions for reducing training intensity or providing instructions on relaxation techniques. The server then notifies the user of the suggestions via their device.

[1766] (Application example 2)

[1767] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1768] Conventional systems aimed at optimizing athlete performance only target physical and mental data, and do not comprehensively address operational efficiency or maintenance management. Furthermore, they do not provide appropriate instructions that take into account the emotional data of managers. This can lead to reduced efficiency, particularly in the operation of factory robots, and can lead to missed maintenance timing. Therefore, a new system is needed to optimize the operational efficiency and maintenance of factory robots, similar to athlete performance.

[1769] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for quantifying and storing the athlete's physical data, means for acquiring daily physical data and transmitting it to the server, means for analyzing the transmitted data and proposing a training menu, means for presenting the training menu to the user, means for selecting optimal ingredients for nutritional supplementation and creating a recipe, means for presenting the recipe and ingredient list to the user, means for collecting and analyzing mental data and providing mental support, means for collecting and analyzing basic robot data and daily fluctuation data, means for generating and presenting an optimal work menu, means for proposing a maintenance schedule based on the robot's status, and means for collecting and analyzing user emotional data and providing flexible instructions to the robot based on the data. This improves the operating efficiency of factory robots, enables appropriate maintenance, and enables flexible operation that takes into account the emotional state of the manager.

[1770] "Athlete's physical data" refers to data that quantifies physiological indicators such as an athlete's height, weight, muscle mass, and cardiopulmonary function.

[1771] "Daily physical data" refers to data on fluctuations that athletes collect on a daily basis, such as body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, and amount of sleep.

[1772] A "server" is a central computer that stores data, performs analysis, and generates various offers.

[1773] A "training menu" is an exercise program created based on the athlete's basic data and daily physical data.

[1774] "User" refers to the entity that uses the system to input data or receive suggestions, such as an athlete or administrator.

[1775] "Nutrition" refers to the selection of ingredients and dietary methods to meet an athlete's nutritional needs.

[1776] An "ingredient list" is a list of ingredients selected to meet nutritional needs.

[1777] A "recipe" is a written or illustrated guide to cooking steps for athletes to replenish their nutrition.

[1778] "Mental data" refers to information such as an athlete's stress level, psychological fatigue, and emotional state.

[1779] "Mental support" refers to interventions to support athletes' mental health, such as relaxation techniques and counseling suggestions.

[1780] "Basic robot data" refers to the robot's basic performance indicators, such as quantified data on working speed and lifting force.

[1781] "Daily fluctuation data" refers to fluctuation data such as temperature, operating time, motor temperature, and error frequency that the robot collects on a daily basis.

[1782] A "task menu" is a business program created based on the robot's basic data and daily fluctuation data.

[1783] A "maintenance schedule" is a maintenance management plan proposed based on the condition of the robot.

[1784] "Emotional data" is data collected in real time about the emotional state of the manager.

[1785] "Flexible instructions" are appropriate work instructions given to a robot based on collected emotional data.

[1786] This invention is a comprehensive condition management system for optimizing athlete performance, and is particularly applicable to the operational efficiency and maintenance management of factory robots. The system includes the collection and quantification of basic data, the collection and analysis of daily fluctuation data, the proposal of work menus, the planning of maintenance schedules, and flexible instructions based on the user's emotional data.

[1787] Collecting and quantifying basic data

[1788] User

[1789] The user starts the application and inputs the robot's basic performance data (working speed, lifting force, etc.) as initial settings.

[1790] Terminal

[1791] The terminal transmits the basic data entered by the user to the server.

[1792] server

[1793] The server stores the received basic data in a database and automatically digitizes it for analysis.

[1794] Collection and analysis of daily fluctuation data

[1795] Terminal

[1796] The device automatically collects daily fluctuation data from sensors, such as temperature, operating time, motor temperature, and error frequency.

[1797] server

[1798] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[1799] Work menu suggestions

[1800] server

[1801] The server analyzes the basic data and the variable data and creates an appropriate operation menu based on the analysis, such as a menu for assembling parts, painting, loading pallets, etc.

[1802] Terminal

[1803] The terminal notifies the user of the generated operation menu and presents the detailed contents.

[1804] Maintenance schedule planning

[1805] server

[1806] The server proposes an optimal maintenance schedule based on the robot's basic data and daily fluctuation data.

[1807] Terminal

[1808] The terminal notifies the user of the maintenance schedule provided by the server and urges the user to carry out appropriate maintenance.

[1809] Emotion data collection and analysis

[1810] Terminal

[1811] The device equipped with the emotion engine uses technologies such as facial recognition and voice recognition of the user (administrator) to collect emotional data of the user in real time.

[1812] server

[1813] The server receives the emotion data sent from the emotion engine and uses it to adjust the work menu and maintenance schedule, for example, by providing instructions that allow flexible responses even when the user is feeling stressed.

[1814] Hardware and software used

[1815] Hardware

[1816] Sensors (temperature sensors, error detection sensors)

[1817] Factory robots (general robotic arms)

[1818] Computer server (for database and analysis)

[1819] software

[1820] Python (data collection and analysis)

[1821] Database management system (MySQL)

[1822] Systems with emotion engines (e.g., Face++)

[1823] Specific examples

[1824] For example, when a user introduces a new robot, they input its basic performance data into the application. The terminal sends this data to the server, which stores it in a database. Data from temperature sensors, operating hours, error frequency, and other daily fluctuation data are also sent from the terminal to the server, which analyzes them. The server then uses this data to propose work menus and create maintenance schedules. The emotion engine collects the manager's emotional data in real time, and flexible instructions are given based on that data.

[1825] Prompt Sentence Examples

[1826] "Collect today's data on the temperature sensors and motor temperatures of factory robots, compare it with the average values ​​for the past seven days, and report any abnormalities."

[1827] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1828] Step 1:

[1829] The user starts the application and inputs the robot's basic performance data (working speed, lifting force, etc.) as the initial setting. This collects basic data about the robot. Specific examples of data input by the user include a working speed of 10 meters per second and a lifting force of 100 kilograms. This data will be the basic information needed for subsequent analysis and proposals.

[1830] Step 2:

[1831] The terminal sends the basic data entered by the user to the server. The terminal converts the entered data into an appropriate format and sends it to the server. The input data includes various performance indicators such as work speed and lifting force. The basic data is sent as output to the server and stored in a database.

[1832] Step 3:

[1833] The server stores the received basic data in a database and automatically digitizes it for analysis. The server uses a database management system (e.g., MySQL) to store and manage the data. The stored data is used in subsequent analysis steps.

[1834] Step 4:

[1835] The terminal automatically obtains daily fluctuation data from the sensors, such as temperature, operating time, motor temperature, and error frequency. For example, the input from the temperature sensor may be 25 degrees, the motor temperature may be 60 degrees, the operating time may be 8 hours, and the error frequency may be 3. This data is important for understanding the operational status of the robot in real time.

[1836] Step 5:

[1837] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data. By comparing it with past data stored in the database, outliers and trends can be detected. For example, an abnormal temperature can be detected by comparing the average temperature with data from the past seven days.

[1838] Step 6:

[1839] The server analyzes basic and variable data and creates an appropriate work menu based on that. An example of a work menu might be two hours for assembling parts, 30 minutes for painting, and one hour for loading onto a pallet. The server automatically generates the optimal work menu using a generative AI model and various algorithms.

[1840] Step 7:

[1841] The terminal notifies the user of the generated work menu and presents detailed content. The terminal displays the work menu received from the server to the user in an appropriate format. A specific prompt sentence might be, "Today's work menu is 2 hours of part assembly, 30 minutes of painting, and 1 hour of pallet loading."

[1842] Step 8:

[1843] The server proposes an optimal maintenance schedule based on the robot's basic data and daily fluctuation data. For example, it can suggest preventive maintenance when the motor temperature is high or the error frequency increases. A specific maintenance schedule is generated as an output.

[1844] Step 9:

[1845] The terminal notifies the user of the maintenance schedule provided by the server and encourages them to carry out the appropriate maintenance. The terminal displays the details of the maintenance schedule in an easy-to-understand manner to the user and supports the implementation. Specifically, the terminal notifies the user that "the next maintenance should be carried out tomorrow at 3:00 PM."

[1846] Step 10:

[1847] The device is equipped with an emotion engine that uses facial and voice recognition technology to collect real-time emotion data from the user (administrator). For example, if the user is feeling stressed, that data is collected.

[1848] Step 11:

[1849] The server receives the emotion data sent from the emotion engine and uses it to adjust the work menu and maintenance schedule. Specifically, if the user is feeling stressed, the server may adjust the work load, for example, by reducing the workload.

[1850] Through these processing steps, it becomes possible to optimize the performance of athletes and the operational efficiency and maintenance management of factory robots.

[1851] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1852] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1853] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1854] [Fourth embodiment]

[1855] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1856] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1857] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1858] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1859] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1860] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1861] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1862] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1863] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1864] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1865] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1866] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1867] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1868] This invention is a comprehensive condition management system for optimizing athletic performance. This system provides a variety of means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, and providing mental support.

[1869] Collecting and quantifying basic data

[1870] User

[1871] During initial setup, users enter basic data such as height, weight, muscle mass, and cardiopulmonary function into a dedicated app.

[1872] Terminal

[1873] The terminal transmits the basic data entered by the user to the server.

[1874] server

[1875] The server stores the received basic data in a database and automatically digitizes it for analysis.

[1876] Collection and analysis of daily fluctuation data

[1877] Terminal

[1878] The device automatically collects daily data from the user's wearable device, including body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep.

[1879] server

[1880] The server adds the received fluctuation data to a database and analyzes it to detect outliers and unusual trends.

[1881] Training menu suggestions

[1882] server

[1883] The server analyzes the basic and variability data and creates an appropriate training menu based on the analysis, for example, a menu including strength training, cardio exercises, stretching, etc.

[1884] Terminal

[1885] The terminal notifies the user of the generated training menu and presents the detailed contents.

[1886] Nutritious ingredient selection and recipe provision

[1887] server

[1888] The server calculates the required nutrients based on the user's basic data and training data, and creates an ingredient list based on the results.

[1889] Terminal

[1890] The terminal presents the user with a list of ingredients and recipes provided by the server. For example, it provides ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions for using those ingredients.

[1891] Mental support

[1892] User

[1893] The user periodically answers questions about their mental state and transmits the data to the server via their terminal.

[1894] server

[1895] The server analyzes the received mental data to detect stress levels and psychological fatigue, and based on the analysis results, suggests relaxation methods or counseling as needed.

[1896] Terminal

[1897] The device notifies the user of mental support suggestions provided by the server, such as suggestions for meditation or yoga, or counseling appointments.

[1898] Specific examples

[1899] Collection of basic data

[1900] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[1901] The device sends this data to the server, which stores it in a database.

[1902] Collecting fluctuation data

[1903] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[1904] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[1905] Training menu suggestions

[1906] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[1907] The terminal displays this training menu to the user and explains the details.

[1908] Nutritional supplementation

[1909] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[1910] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[1911] Mental support

[1912] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[1913] The server analyzes stress levels and suggests yoga as a way to relax.

[1914] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[1915] This system will enable the centralized management of athletes' basic data and daily fluctuation data, enabling comprehensive condition management, which will optimize athletes' performance and provide efficient and effective training and care.

[1916] The processing flow will be explained below.

[1917] Step 1:

[1918] User

[1919] The user launches the app and enters basic data such as their height, weight, muscle mass, and cardiopulmonary function as the initial setting.

[1920] Step 2:

[1921] Terminal

[1922] The terminal transmits the basic data entered by the user to the server.

[1923] Step 3:

[1924] server

[1925] The server stores the received basic data in a database and automatically digitizes it for analysis.

[1926] Step 4:

[1927] Terminal

[1928] The device automatically collects daily fluctuation data (body temperature, blood pressure, blood sugar level, heart rate variability, etc.) from the user's wearable device.

[1929] Step 5:

[1930] Terminal

[1931] The terminal transmits daily fluctuation data to the server.

[1932] Step 6:

[1933] server

[1934] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[1935] Step 7:

[1936] server

[1937] The server analyzes the basic data and the variation data and generates an appropriate training menu.

[1938] Step 8:

[1939] Terminal

[1940] The terminal notifies the user of the training menu from the server and presents the detailed contents.

[1941] Step 9:

[1942] server

[1943] The server calculates the required nutrients based on the user's basic data and training data.

[1944] Step 10:

[1945] server

[1946] The server creates an optimal ingredient list and recipe based on the calculation results.

[1947] Step 11:

[1948] Terminal

[1949] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[1950] Step 12:

[1951] User

[1952] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[1953] Step 13:

[1954] server

[1955] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[1956] Step 14:

[1957] server

[1958] Based on the analysis results, the server suggests relaxation methods and counseling.

[1959] Step 15:

[1960] Terminal

[1961] The terminal notifies the user of suggestions for mental support from the server and provides specific relaxation techniques and counseling appointment information.

[1962] This system allows athletes to efficiently manage their basic data and daily fluctuation data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[1963] Example 1

[1964] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1965] In order to optimize athletes' performance and efficiently manage their overall condition, it is important to centrally manage physical data, fluctuation data, and mental data and provide appropriate training, nutritional support, and mental support in real time. However, conventional systems that manage these data separately have issues with data consistency and immediacy, making it difficult to provide optimized support for individual athletes.

[1966] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1967] In this invention, the server includes means for quantifying and storing the athlete's basic data, means for acquiring daily fluctuation data and sending it to the server, means for analyzing outliers and unusual trends based on the acquired data, means for generating a training menu based on the analysis results, means for notifying the user of the generated training menu, means for calculating necessary nutrients, selecting optimal ingredients, and creating a recipe, means for presenting the recipe and ingredient list to the user, and means for collecting and analyzing mental data and providing mental support, thereby optimizing the athlete's performance and enabling comprehensive condition management.

[1968] An "athlete" is someone who possesses high skill and physical strength in competitive sports.

[1969] "Basic data" refers to data that quantifies basic information about an athlete's body, such as height, weight, muscle mass, and cardiopulmonary function.

[1970] "Variation data" refers to information about daily changes in an athlete's body, such as body temperature, blood pressure, blood sugar levels, heart rate variability, fatigue, and amount of sleep.

[1971] "Server" refers to a computer system that receives, stores, analyzes, etc. data.

[1972] "Terminal" refers to a device used by a user to input or refer to data, including, for example, a smartphone or tablet.

[1973] "Digitization" refers to the process of converting data collected from users into a numerical format that is easy to analyze.

[1974] "Storage" refers to storing collected data for a long period of time.

[1975] An "outlier" is a data value that falls outside the normal range in a statistical analysis.

[1976] "Anomalous trends" refers to trends in data where clear patterns or anomalies can be identified in data analysis.

[1977] A "training menu" refers to a series of exercises and plans proposed to improve an athlete's physical strength and maintain their condition.

[1978] "Nutrients" refers to proteins, vitamins, minerals, and other components necessary for athletes' health and performance.

[1979] "Ingredients List" refers to a list of foods or ingredients needed to prepare a suggested recipe.

[1980] A "recipe" is a document that details the steps for preparing a dish using specific ingredients.

[1981] "Mental data" refers to information about an athlete's mental state, such as stress levels and psychological fatigue.

[1982] "Mental support" refers to assistance to maintain and improve the mental health of athletes. Specifically, this includes suggestions for relaxation techniques and counseling.

[1983] This invention is a comprehensive condition management system for optimizing athletic performance. This system provides a variety of means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, and providing mental support.

[1984] Collecting and quantifying basic data

[1985] User

[1986] During initial setup, users use a dedicated app to enter basic data such as height, weight, muscle mass, and cardiopulmonary function.

[1987] Terminal

[1988] The terminal sends the basic data entered by the user to the server, where the data is encrypted to ensure security.

[1989] server

[1990] The server stores the received basic data in a database and automatically converts it into numerical values ​​for analysis. For example, it statistically analyzes the QRS complex wave parameters of cardiopulmonary function to check for deviations from standard values.

[1991] Collection and analysis of daily fluctuation data

[1992] Terminal

[1993] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and sleep amount from the user's wearable device (e.g., smartwatch). The collected data is temporarily stored in the device in real time.

[1994] Terminal

[1995] The device periodically (e.g., every 10 minutes) transmits the temporarily stored data to the server, where data integrity and encryption are performed.

[1996] server

[1997] The server adds the received fluctuation data to a database and performs statistical analysis. If an abnormal value is detected, an alert is sent to the terminal to notify the user.

[1998] Training menu suggestions

[1999] server

[2000] The server generates a training plan that is optimal for the user's physical condition based on the basic and fluctuation data. For example, it creates a menu of strength training (20 minutes), cardio (30 minutes), and stretching (15 minutes).

[2001] Terminal

[2002] The device will then notify the user of the generated training menu and display the details within the app, including video links and text descriptions for each exercise.

[2003] Nutritious ingredient selection and recipe provision

[2004] server

[2005] The server calculates the amount of nutrients (e.g., protein, vitamins) needed based on the user's basic and training data. For example, it creates an ingredient list such as "200g chicken breast, 100g broccoli, 200g brown rice."

[2006] server

[2007] The server generates a recipe that includes cooking methods for each ingredient, such as suggesting "grilled chicken breast."

[2008] Terminal

[2009] The device receives the ingredients list and recipe from the server and displays it in the app, notifying the user. The recipe includes detailed information such as cooking time, steps, and necessary equipment.

[2010] Mental support

[2011] User

[2012] Users periodically answer in-app surveys to record their current stress levels and psychological state, for example, by entering "My stress level has been high recently."

[2013] Terminal

[2014] The terminal sends the mental data entered by the user to the server, where the data is checked for integrity and encrypted during transmission.

[2015] server

[2016] The server analyzes the received mental data to detect stress levels and psychological fatigue, and based on the analysis results, generates suggestions for relaxation methods (e.g., yoga) and counseling appointments.

[2017] Terminal

[2018] The device notifies the user of the mental support suggestions received from the server and displays detailed instructions and a link to book a counseling appointment within the app.

[2019] Specific examples

[2020] Collection of basic data

[2021] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[2022] The device sends this data to the server, which stores it in a database.

[2023] Collecting fluctuation data

[2024] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[2025] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[2026] Training menu suggestions

[2027] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[2028] The terminal displays this training menu to the user and explains the details.

[2029] Nutritional supplementation

[2030] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[2031] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[2032] Mental support

[2033] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[2034] The server analyzes stress levels and suggests yoga as a way to relax.

[2035] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[2036] Example prompts for generative AI models

[2037] "Please enter the athlete's basic data (height 175cm, weight 70kg, muscle mass 50kg) and generate a training menu based on one week's heart rate data. Also, please provide a list of ingredients and recipes for proper nutrition."

[2038] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2039] Step 1:

[2040] Input: The user launches a dedicated app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function.

[2041] How it works: A user fills out a form in the app and enters data such as height 175cm, weight 70kg, muscle mass 50kg, etc. The app performs a consistency check to ensure there are no input errors.

[2042] Output: The correct formatted basic data is saved to the device.

[2043] Step 2:

[2044] Input: The terminal receives basic data entered by the user.

[2045] How it works: The device encrypts the underlying data it receives and sends it to the server using a secure communication protocol.

[2046] Output: The encrypted underlying data is sent to the server.

[2047] Step 3:

[2048] Input: The server receives the basic data sent from the terminal.

[2049] How it works: The server decrypts the data and stores it in a database. It also analyzes and quantifies the data. For example, it analyzes the QRS complex wave of cardiopulmonary function and calculates the difference from the standard value.

[2050] Output: The quantified basic data is saved in a database.

[2051] Step 4:

[2052] Input: The device collects daily fluctuation data (body temperature, blood pressure, blood glucose level, heart rate variability, fatigue, and amount of sleep) from the user's wearable device.

[2053] How it works: The device acquires data collected from the wearable device in real time and temporarily stores it.

[2054] Output: The temporarily saved fluctuation data is saved on the terminal.

[2055] Step 5:

[2056] Input: The terminal periodically sends the temporarily stored fluctuation data to the server.

[2057] How it works: The device encrypts the collected data and sends it to the server periodically (e.g., every 10 minutes).

[2058] Output: The encrypted variable data is sent to the server.

[2059] Step 6:

[2060] Input: The server receives the variable data sent from the terminal.

[2061] How it works: The server decrypts the data, stores it in a database, and analyzes it. If an abnormal value is detected, an alert is generated and sent to the device. For example, if blood sugar levels suddenly rise, the user is immediately notified of this information.

[2062] Output: Analysis results and alert information are stored on the server and sent to the terminal as needed.

[2063] Step 7:

[2064] Input: The server generates a training menu based on basic data and variation data.

[2065] How it works: Based on data analysis, the server automatically generates optimal training menus, including strength training, cardio, and stretching.

[2066] Output: The generated training menu is saved in the database.

[2067] Step 8:

[2068] Input: The terminal receives the training menu generated from the server.

[2069] How it works: The device will notify the user of a workout menu and display detailed instructions within the app, including video links and text descriptions for each exercise.

[2070] Output: The training menu notified to the user.

[2071] Step 9:

[2072] Input: The server calculates the required nutrients based on the user's basic and training data.

[2073] How it works: The server calculates the necessary nutrients, such as protein and vitamins, and creates an ingredient list based on that (e.g., 200g chicken breast, 100g broccoli, 200g brown rice). It then generates a recipe based on this list.

[2074] Output: Ingredient list and recipe based on calculated nutrients.

[2075] Step 10:

[2076] Input: The device receives the ingredient list and recipe provided by the server.

[2077] How it works: The device displays the received ingredient list and recipe in the app and notifies the user, with the recipe including details such as cooking time, steps, and required equipment.

[2078] Output: The recipe and ingredient list provided to the user.

[2079] Step 11:

[2080] Input: The user inputs mental data into the app.

[2081] How it works: Users fill out an in-app survey to input their stress levels and psychological state. For example, they might enter, "My stress level has been high recently."

[2082] Output: The input mental data is saved on the device.

[2083] Step 12:

[2084] Input: The terminal receives mental data input from the user.

[2085] How it works: The device encrypts the received mental data and sends it to the server using a secure communication protocol.

[2086] Output: The encrypted mental data is sent to the server.

[2087] Step 13:

[2088] Input: The server receives the mental data sent from the terminal.

[2089] How it works: The server decrypts and stores the mental data in a database. It also analyzes the data to detect stress levels and psychological fatigue. If necessary, it generates suggestions for relaxation methods (e.g., yoga) or counseling.

[2090] Output: The analysis results and recommendations are stored in a database and sent to a terminal if necessary.

[2091] Step 14:

[2092] Input: The device receives the analysis results and suggestions sent from the server.

[2093] How it works: The device displays the analysis results and recommendations it receives within the app and notifies the user, including relaxation techniques and a link to book a consultation.

[2094] Output: The user is notified of relaxation techniques and a link to book a consultation.

[2095] (Application example 1)

[2096] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2097] Comprehensive condition management is necessary to maximize the performance of athletes and machines. However, current systems have the problem of not being able to centrally collect and analyze physical and machine data, propose training and maintenance, or manage nutrition and consumable parts. There is a need for a system that can manage all of this data in an integrated manner and provide optimal support efficiently and effectively.

[2098] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2099] In this invention, the server includes means for quantifying and storing the athlete's physical data, means for acquiring daily physical data and transmitting it to the server, means for analyzing the transmitted data and proposing a training menu, means for presenting the training menu to the user, means for selecting optimal ingredients for nutritional supplementation and creating recipes, means for presenting recipes and ingredient lists to the user, means for collecting and analyzing mental data and providing mental support, means for collecting basic machine data and daily fluctuation data and proposing a maintenance schedule based thereon, means for generating and presenting machine operation optimization parameters, means for creating a consumable parts list and providing it to the user, and means for detecting abnormalities and issuing corresponding alerts. This allows for centralized management of both athlete training and nutritional management and machine maintenance and operation optimization, enabling efficient and effective support.

[2100] An "athlete" is an individual or group who trains with the goal of achieving high performance in a sport or competition.

[2101] "Physical data" refers to specific numerical values ​​representing an athlete's physical information, such as height, weight, muscle mass, and cardiopulmonary function.

[2102] A "server" is a computer system that stores, analyzes, and provides data over a network.

[2103] "Machinery" refers to equipment such as robots and automated devices used in production activities in factories, etc.

[2104] "Fluctuating data" is information that changes daily, such as body temperature, blood pressure, heart rate, and blood sugar level, which show different values ​​in real time.

[2105] A "training menu" is a plan that lists the specific exercises and practices that an athlete should perform.

[2106] "User" refers to a person or individual who uses this condition management system.

[2107] "Nutrition" refers to ensuring that athletes properly consume the ingredients and food they need to maintain their physical condition and improve their performance.

[2108] A "food list" is a list of ingredients selected based on a specific purpose or training menu.

[2109] "Mental data" is information used to examine an athlete's stress level and psychological state.

[2110] "Mental support" refers to providing advice and care to maintain and improve an athlete's mental health.

[2111] "Basic data" refers to data that forms the basis of operation, such as the machine model, installation date, and initial setting information at the start of use.

[2112] A "maintenance schedule" is a plan for carrying out appropriate maintenance and inspection of machinery.

[2113] "Operational optimization parameters" are settings and adjustments that allow a machine to operate efficiently and effectively.

[2114] A "consumable parts list" is a list of parts that need to be replaced or replenished periodically during machine operation.

[2115] "Abnormal" refers to a state in which a machine deviates from its normal operation and indicates that a breakdown or malfunction has occurred.

[2116] An "alert" is a warning or notification issued when an abnormality or unusual situation is detected.

[2117] A specific system configuration and processing procedure for carrying out the present invention will be described. This system is a comprehensive condition management system for optimizing the performance of athletes and factory robots, and includes the following means:

[2118] First, the user enters the athlete's physical data and basic machine data into a dedicated application. This application runs on a smartphone, tablet, or desktop PC and is designed to make data entry and transmission easy. During initial setup, basic data such as height, weight, muscle mass, cardiopulmonary function, robot model, and installation date are entered.

[2119] Next, the device (smartphone, tablet, PC, etc.) sends the basic data entered by the user to a server. This server is a computer system installed on a network that has the function of storing, analyzing, and providing data. Specifically, it uses a database (e.g., PostgreSQL) to store the basic data and digitize it for analysis.

[2120] Daily fluctuation data is collected through wearable devices and various sensors. This includes body temperature, blood pressure, blood sugar levels, heart rate variability, fatigue level, and amount of sleep. This data is also sent to the server via the device. The server adds the received fluctuation data to a database and performs analysis.

[2121] The transmitted data is analyzed using data analysis algorithms running on the server. For example, machine learning models written in Python are used to detect abnormal values ​​and specific trends in heart rate and blood pressure. The robot's vibration and temperature data are also analyzed to detect abnormalities.

[2122] Based on the analysis, the server creates training and maintenance schedules: training for athletes includes strength training, cardio exercises, and stretching, while maintenance for robots includes oil checks and filter changes.

[2123] The server also selects ingredients for nutritional support and provides recipes. The server calculates the nutritional needs of the user based on their basic and training data, and creates an ingredient list. For example, it presents ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions.

[2124] Additionally, collecting and analyzing mental data is also important. Users periodically answer questions about their mental state and send the data to a server via their device. The server analyzes stress levels and psychological fatigue and provides appropriate mental support, such as suggestions for yoga or counseling.

[2125] For example, when collecting basic data, the user launches the app and enters data such as height 175 cm, weight 70 kg, and muscle mass 50 kg. The wearable device also sends heart rate data to the terminal at 8:00 a.m. every day, and the server records an average heart rate of 72 BPM. As a maintenance schedule suggestion, the next maintenance is scheduled for November 10, 2023, and the tasks include an oil check and filter change.

[2126] Example prompt sentence:

[2127] "The ABB IRB 6700 robot was installed on January 1, 2023. Please collect the basic data and store it in the database."

[2128] "Based on today's inspection results, the robot has been running for 16 hours and one anomaly has been detected. Please send this data to the server."

[2129] "The next maintenance is scheduled for November 10, 2023. Tasks will include an oil check and filter change."

[2130] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2131] Step 1:

[2132] During the initial setup, the user enters basic data about the athlete and the machine into a dedicated app. Specifically, information such as height, weight, muscle mass, cardiopulmonary function, robot model, and installation date is entered, and the data is saved on the device. This input data is converted into numerical values ​​as basic data.

[2133] Input: Basic data on athletes and machines

[2134] Data processing: digitization of basic data

[2135] Output: The digitized basic data is saved on the device.

[2136] Step 2:

[2137] The device sends the stored basic data to the server, which receives it and stores it in a database. The server then manages the basic data as quantified information for analysis.

[2138] Input: Basic data stored on the device

[2139] Data processing: data transmission and storage

[2140] Output: The digitized basic data is saved in the server database.

[2141] Step 3:

[2142] To collect daily fluctuation data, the device acquires data such as body temperature, blood pressure, blood sugar level, heart rate variability, fatigue level, and amount of sleep from wearable devices and sensors, and periodically transmits this daily fluctuation data to a server.

[2143] Input: Variable data from wearable devices and sensors

[2144] Data Processing: Data Acquisition and Transmission

[2145] Output: Daily fluctuation data is sent to the server

[2146] Step 4:

[2147] The server adds the received fluctuation data to a database and performs analysis. Specifically, it uses a Python machine learning model to detect outliers and unusual trends. This allows for early detection of abnormalities in, for example, heart rate or blood pressure.

[2148] Input: Daily fluctuation data

[2149] Data processing: detecting outliers and unusual trends

[2150] Output: Analysis results of specific trends and outliers

[2151] Step 5:

[2152] The server analyzes baseline and fluctuation data and creates optimal training and maintenance schedules based on that data, which could include strength training and cardio exercises for athletes, or oil checks and filter changes for robots.

[2153] Input: Fundamental data and daily fluctuation data

[2154] Data processing: Creation of training menus and maintenance schedules

[2155] Output: Training menu and maintenance schedule

[2156] Step 6:

[2157] The device notifies the user of the training menu and maintenance schedule received from the server and presents detailed information, allowing the user to understand specifically what kind of training and maintenance they should perform.

[2158] Input: Training menu and maintenance schedule

[2159] Data Processing: Notification and Details

[2160] Output: Detailed presentation of training menu and maintenance schedule

[2161] Step 7:

[2162] The server calculates the nutritional needs of the user based on their basic and training data, and then creates an ingredient list and recipe based on that. For example, it presents ingredients such as chicken breast, broccoli, and brown rice, along with cooking instructions.

[2163] Input: Base data and training data

[2164] Data processing: Nutrition calculations and generating ingredient lists and recipes

[2165] Output: Ingredient list and recipe

[2166] Step 8:

[2167] The terminal presents the user with a list of ingredients and recipes provided by the server, allowing the user to specifically understand which ingredients to select and how to cook them to replenish their nutrients.

[2168] Input: Ingredient list and recipe

[2169] Data Processing: Notification and Details

[2170] Output: Ingredient list and recipe details

[2171] Step 9:

[2172] Users periodically answer questions about their mental state and send the data, including their stress level and psychological fatigue level, to a server via their device.

[2173] Input: Answers to questions about mental state

[2174] Data processing: Data transmission

[2175] Output: Sending mental data

[2176] Step 10:

[2177] The server analyzes the received mental data, assesses stress levels and psychological fatigue, and provides appropriate mental support, such as yoga suggestions or counseling appointments, if necessary.

[2178] Input: Mental data

[2179] Data processing: Evaluation of stress levels and psychological fatigue

[2180] Output: Mental support suggestions

[2181] Step 11:

[2182] The device notifies the user of suggestions for mental support provided by the server, such as suggestions for meditation or yoga, or information on booking counseling appointments.

[2183] Input: Mental support suggestions

[2184] Data Processing: Notification and Details

[2185] Output: Detailed mental support information

[2186] Step 12:

[2187] The server collects basic machine data and daily fluctuation data, and based on that data, proposes a maintenance schedule, including the next maintenance date and specific work content (oil check, filter change, etc.).

[2188] Input: Fundamental data and daily fluctuation data

[2189] Data processing: Maintenance schedule generation

[2190] Output: Maintenance Schedule

[2191] Step 13:

[2192] The server generates and presents machine operation optimization parameters to the user, providing settings for efficient and effective machine operation.

[2193] Input: Fundamental and variable data

[2194] Data processing: Generation of operational optimization parameters

[2195] Output: Operational optimization parameters

[2196] Step 14:

[2197] The server creates a list of consumable parts and provides it to the user, making it easy to arrange for the necessary consumable parts.

[2198] Input: Fundamental and variable data

[2199] Data processing: Generating a list of consumable parts

[2200] Output: Consumable parts list

[2201] Step 15:

[2202] The server detects abnormalities and issues an alert accordingly, allowing users to quickly detect abnormalities and take appropriate measures.

[2203] Input: Daily fluctuation data

[2204] Data processing: Anomaly detection and alerting

[2205] Output: Alert

[2206] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2207] This invention is a comprehensive condition management system for optimizing athletic performance, and aims to provide more advanced mental support by combining it with an emotion engine that recognizes the user's emotions. This system provides various means for collecting and quantifying basic data, collecting and analyzing daily fluctuation data, proposing training menus, selecting ingredients for nutritional supplementation and providing recipes, providing mental support, and collecting and analyzing emotional data using the emotion engine.

[2208] Collecting and quantifying basic data

[2209] User

[2210] The user launches the app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function as the initial settings.

[2211] Terminal

[2212] The terminal transmits the basic data entered by the user to the server.

[2213] server

[2214] The server stores the received basic data in a database and automatically digitizes it for analysis.

[2215] Collection and analysis of daily fluctuation data

[2216] Terminal

[2217] The device automatically collects daily data from the user's wearable device, including body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep.

[2218] server

[2219] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[2220] Training menu suggestions

[2221] server

[2222] The server analyzes the basic and variability data and creates an appropriate training menu based on the analysis, for example, a menu including strength training, cardio exercises, stretching, etc.

[2223] Terminal

[2224] The terminal notifies the user of the generated training menu and presents the detailed contents.

[2225] Nutritious ingredient selection and recipe provision

[2226] server

[2227] The server calculates the required nutrients based on the user's basic data and training data, and creates an ingredient list based on the results.

[2228] Terminal

[2229] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[2230] Mental support

[2231] User

[2232] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[2233] server

[2234] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[2235] Terminal

[2236] The terminal notifies the user of the mental support suggestions provided by the server, and provides specific relaxation techniques and counseling reservation information.

[2237] Emotional data collection and analysis using an emotion engine

[2238] Terminal

[2239] Devices equipped with an emotion engine use technologies such as facial and voice recognition to collect user emotion data in real time.

[2240] server

[2241] The server receives the emotional data sent from the emotion engine and analyzes it in combination with mental data. For example, if the user is feeling stressed, it can make adjustments such as suggesting that the training intensity be reduced.

[2242] Specific examples

[2243] Collection of basic data

[2244] Users launch the app and enter data such as height 175cm, weight 70kg, and muscle mass 50kg.

[2245] The device sends this data to the server, which stores it in a database.

[2246] Collecting fluctuation data

[2247] The wearable device sends heart rate data to the terminal every day at 8:00 a.m.

[2248] The device transfers the data to a server, which analyzes it and records an average heart rate of 72 BPM.

[2249] Training menu suggestions

[2250] Based on past data, the server creates a menu consisting of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging.

[2251] The terminal displays this training menu to the user and explains the details.

[2252] Nutritional supplementation

[2253] The server calculates the amount of protein needed and creates an ingredient list based on that: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice.

[2254] The device will present this list to the user along with recipes, such as how to grill chicken breasts.

[2255] Mental support

[2256] The user enters "I've been feeling stressed lately" into the app and the data is sent to the server.

[2257] The server analyzes stress levels and suggests yoga as a way to relax.

[2258] The device will notify the user of yoga suggestions and provide yoga instructions and video links.

[2259] Emotion Engine

[2260] The device uses facial recognition technology to collect emotional data from the user in real time.

[2261] The server analyzes the emotional data and adjusts flexible training and mental support when the user feels stressed.

[2262] The device will notify the user of the analysis results and suggest relaxation techniques or counseling to reduce stress.

[2263] This system allows athletes to efficiently manage their basic data, daily fluctuation data, and emotional data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[2264] The processing flow will be explained below.

[2265] Step 1:

[2266] User

[2267] The user launches the app and enters basic data such as their height, weight, muscle mass, and cardiopulmonary function as the initial setting.

[2268] Step 2:

[2269] Terminal

[2270] The terminal transmits the basic data entered by the user to the server.

[2271] Step 3:

[2272] server

[2273] The server stores the received basic data in a database and automatically digitizes it for analysis.

[2274] Step 4:

[2275] Terminal

[2276] The device automatically collects daily fluctuation data (body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, amount of sleep, etc.) from the user's wearable device.

[2277] Step 5:

[2278] Terminal

[2279] The terminal transmits the acquired daily fluctuation data to the server.

[2280] Step 6:

[2281] server

[2282] The server adds the received fluctuation data to a database and analyzes it while comparing it with past data.

[2283] Step 7:

[2284] server

[2285] The server analyzes the basic data and the variation data and generates an appropriate training menu based on the analysis.

[2286] Step 8:

[2287] Terminal

[2288] The terminal notifies the user of the training menu from the server and presents the detailed contents.

[2289] Step 9:

[2290] server

[2291] The server calculates nutrient needs based on the user's baseline and training data.

[2292] Step 10:

[2293] server

[2294] The server creates an optimal ingredient list and recipe based on the calculation results.

[2295] Step 11:

[2296] Terminal

[2297] The terminal presents the user with a list of ingredients and recipes provided by the server, encouraging them to take in appropriate nutrition.

[2298] Step 12:

[2299] User

[2300] The user periodically answers questions about his / her mental state and transmits the data to the server via the terminal.

[2301] Step 13:

[2302] server

[2303] The server analyzes the received mental data and detects stress levels and psychological fatigue.

[2304] Step 14:

[2305] server

[2306] Based on the analysis results, the server suggests relaxation methods and counseling.

[2307] Step 15:

[2308] Terminal

[2309] The terminal notifies the user of the mental support suggestions provided by the server, and provides specific relaxation techniques and counseling reservation information.

[2310] Step 16:

[2311] Terminal

[2312] The device uses facial and voice recognition technology to collect emotional data in real time.

[2313] Step 17:

[2314] Terminal

[2315] The terminal transmits the collected emotion data to the server.

[2316] Step 18:

[2317] server

[2318] The server receives the emotion data sent from the emotion engine and analyzes it in combination with the mental data.

[2319] Step 19:

[2320] server

[2321] The server updates the training menu and mental support content based on the emotional and mental data, and suggests reducing the training intensity when the user feels stressed.

[2322] Step 20:

[2323] Terminal

[2324] The device notifies the user of the analysis results and suggests relaxation techniques and counseling to reduce stress.

[2325] This system allows athletes to efficiently manage their basic data, daily fluctuation data, and emotional data, enabling comprehensive condition management, thereby optimizing performance and maintaining health.

[2326] Example 2

[2327] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2328] Conventional athlete condition management systems quantify physical data and suggest training menus, but do not provide comprehensive support that takes mental and emotional data into account. This makes it difficult to appropriately detect and deal with stress and psychological fatigue when athletes experience it. Furthermore, training intensity is not adjusted based on emotional fluctuations. As a result, there is an issue where athletes' performance cannot be fully optimized.

[2329] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2330] In this invention, the server includes means for quantifying and storing the athlete's physical data, means for acquiring daily physical data and transmitting it to the server, means for analyzing the transmitted data and proposing a training menu, means for presenting the training menu to the user, means for selecting optimal ingredients for nutritional supplementation and creating recipes, means for presenting recipes and ingredient lists to the user, means for collecting and analyzing mental data and providing mental support, means for collecting and analyzing emotional data, and means for adjusting training intensity based on the emotional and mental data. This enables comprehensive condition management that takes into account the athlete's mental state and emotions.

[2331] An "athlete" is an individual who possesses significant physical ability or skill and who engages in specialized sports or physical activities.

[2332] "Physical data" refers to information that quantifies an athlete's physical condition, such as their height, weight, muscle mass, body fat percentage, and cardiopulmonary function.

[2333] "Variation data" refers to data that indicates the daily physical condition, and includes body temperature, blood pressure, blood sugar level, heart rate variability, fatigue, amount of sleep, and the like.

[2334] A "server" is a computer system that collects, analyzes, stores, and distributes data.

[2335] A "training menu" is a specific exercise plan aimed at improving an athlete's performance, and includes strength training, cardio exercises, stretching, etc.

[2336] "Nutrition" refers to the act of providing athletes with the balanced nutrients they need, and is achieved through the provision of ingredients and recipes.

[2337] "Mental data" refers to data that indicates an athlete's psychological state and stress level.

[2338] "Emotional data" is data that shows an athlete's emotional state in real time and is collected using facial and voice recognition technology.

[2339] The "emotion engine" is a system engine that uses facial and voice recognition technology to collect and analyze emotional data from users.

[2340] "Training intensity" refers to the load and difficulty of exercise in a training menu, and is adjusted based on the athlete's fitness and physical condition.

[2341] This invention is a comprehensive condition management system aimed at optimizing athletes' performance. This system collects basic data, collects and analyzes daily fluctuation data, proposes training menus, selects ingredients for nutritional supplementation and provides recipes, provides mental support, and collects and analyzes emotional data using an emotion engine.

[2342] Collecting and quantifying basic data

[2343] The user starts the app and enters basic information such as height, weight, muscle mass, and cardiopulmonary function as the initial setting. This data is sent to the server via the device. The server stores the received basic data in a database and converts it into numerical values ​​for future analysis.

[2344] For example, a user inputs his / her height of 175 cm, weight of 70 kg, and muscle mass of 50 kg, and the terminal sends this data to the server, where it is stored in a database.

[2345] Collection and analysis of daily fluctuation data

[2346] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep from the user's wearable device. This data is then sent from the device to a server. The server adds the received fluctuation data to a database and compares it with past data for analysis.

[2347] For example, a wearable device sends heart rate data to a terminal at 8:00 a.m. every morning, and the terminal transfers the data to a server. The server analyzes the data and records an average heart rate of 72 BPM.

[2348] Training menu suggestions

[2349] The server analyzes the basic and variability data and generates an appropriate training menu. The generated training menu is sent to the user via the device. For example, it may include specific exercise plans such as strength training, stretching, and jogging.

[2350] As a specific example, the server generates a menu of 20 minutes of strength training, 15 minutes of stretching, and 30 minutes of jogging, and the terminal displays the menu to the user.

[2351] Nutritious ingredient selection and recipe provision

[2352] The server calculates the necessary nutrients based on the training data and basic data, and creates an appropriate food list, which is presented to the user via their device and includes specific recipes.

[2353] As a specific example, the server creates an ingredient list for protein intake: 200 grams of chicken breast, 100 grams of broccoli, and 200 grams of brown rice, and the terminal presents the list and a recipe for grilled chicken breast to the user.

[2354] Mental support

[2355] Users periodically answer questions about their mental state and send the data from their device to a server. The server analyzes the received mental data to detect stress levels and psychological fatigue. Based on the server's analysis results, the system provides appropriate mental support to the user.

[2356] For example, a user might enter "I've been feeling stressed lately" and send that data to a server. The server then analyzes the stress level and suggests yoga as a relaxation method. The device then notifies the user of the suggestion and provides yoga instructions and video links.

[2357] Emotional data collection and analysis using an emotion engine

[2358] The device uses an emotion engine to collect the user's emotional data in real time. This emotional data is acquired using facial and voice recognition technology and sent from the device to a server. The server then analyzes the emotional data in combination with mental data and responds flexibly, such as adjusting training intensity when the user is feeling stressed.

[2359] For example, the device recognizes the user's face and determines their emotional state as "stressed." The server analyzes the data and makes a "suggestion to reduce training intensity." The device then notifies the user of the suggestion and provides specific relaxation methods and training adjustments.

[2360] Prompt Sentence Examples

[2361] "Please describe the specific procedures for a comprehensive condition management system to optimize athlete performance. Please include details regarding user emotion recognition."

[2362] This system allows athletes to manage their overall condition, optimizing their performance and maintaining their health.

[2363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2364] Step 1: Enter and submit basic data

[2365] The user launches the app and enters basic data such as height, weight, muscle mass, and cardiopulmonary function. The device sends the basic data entered by the user to the server. The server stores the received basic data in a database. This data is quantified for analysis. The input is the user's physical information, and the output is the basic data stored in the database.

[2366] Step 2: Collect and send daily fluctuation data

[2367] The device automatically collects daily data on fluctuations such as body temperature, blood pressure, blood sugar, heart rate variability, fatigue, and amount of sleep from the user's wearable device. This data is sent from the device to a server, which adds the received fluctuation data to a database. The server then compares it with past data and performs analysis. The input is the fluctuation data from the wearable device, and the output is the analyzed data and the stored results.

[2368] Step 3: Create a training menu

[2369] The server analyzes the basic data and variation data and generates an appropriate training menu based on the analysis. For example, a menu including strength training, stretching, jogging, etc. is created. The terminal notifies the user of the generated training menu. The input is the basic data and variation data, and the output is the generated training menu.

[2370] Step 4: Nutritious ingredients selection and recipe provision

[2371] The server calculates the necessary nutrients based on the training data and basic data and creates an appropriate ingredient list. The terminal presents the ingredient list and recipe created by the server to the user. The input is the training data and basic data, and the output is the created ingredient list and recipe.

[2372] Step 5: Collect and analyze mental data

[2373] Users periodically answer questions about their mental state and send the data to a server via their device. The server analyzes the received mental data to detect stress levels and psychological fatigue. Based on the results, the server proposes appropriate mental support to the user. The input is the answers to the questions about their mental state, and the output is the analysis results and suggestions for mental support.

[2374] Step 6: Collect and analyze emotion data

[2375] The device uses an emotion engine to collect the user's emotional data in real time. For example, it can acquire emotional data using facial or voice recognition technology. The device then sends the acquired emotional data to a server, which then combines it with mental data for analysis. Based on the analysis results, adjustments to training intensity and additional mental support are suggested. The input is emotional data, and the output is the analysis results and suggestions for adjusting training intensity.

[2376] Specifically, when a user notifies the server that they are feeling stressed, the server analyzes the data and provides suggestions for reducing training intensity or providing instructions on relaxation techniques. The serv...

Claims

1. A means to quantify and store athletes' physical data, A means for acquiring daily physical data and transmitting it to a server; A means for analyzing the transmitted data and proposing training menus; means for presenting a training menu to a user; A means to select the best ingredients for nutrition and create recipes; means for presenting a recipe and ingredient list to a user; A system including means for collecting and analyzing mental data and providing mental support.

2. 2. The system according to claim 1, wherein the training menu is proposed based on basic data and daily physical data.

3. 2. The system according to claim 1, wherein the selection of ingredients for nutritional supplementation and the creation of recipes are performed based on basic data and training data.

4. 2. The system according to claim 1, wherein mental data is collected periodically and analyzed for stress trends.

5. 5. The system according to claim 4, wherein the mental support provided to the user includes suggestions based on the analysis results, such as specific relaxation techniques and counseling appointment information.

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