system

Abstract

We provide the system. [Solution] A device that collects operational information in real time, A computer that receives and analyzes information transmitted from the aforementioned device, Means for providing instructions generated by the computer to the user via the device, A computational algorithm that generates data to improve the user's activity patterns based on the analyzed information, An information processing device equipped with a function to record the user's actions and provide feedback based on those records, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When performing running or other exercises, it is difficult to provide appropriate guidance to individual users. Without a dedicated coach, users cannot accurately grasp their own exercise patterns and physical conditions and select appropriate training methods. As a result, not only is it impossible to expect an improvement in performance, but the risk of injury due to inappropriate exercise forms also increases. The purpose of this invention is to solve these problems by analyzing users' exercise data in real time and providing accurate advice.

Means for Solving the Problems

[0005] This invention provides a system that collects and analyzes motion data in real time using a device worn by the user. The data acquired from the device is transmitted to a server, which analyzes the data. Based on this analysis, information for providing appropriate guidance to the user is generated and provided to the user via the device. Furthermore, the server provides a more refined training environment by considering environmental conditions such as weather data. As a result, the user can improve their athletic performance and reduce the risk of injury.

[0006] "Motion data" refers to information about the user's body movements, including measurements such as pace, heart rate, and acceleration.

[0007] A "terminal" is a device worn by a user that collects operational data in real time and transmits it to a server.

[0008] A "server" is a computer system that receives and analyzes data sent from a terminal.

[0009] "Instructions" refer to advice and guidance provided by the server based on its analysis to improve the user's movement patterns.

[0010] An "algorithm" is a series of computational methods used to analyze a user's movement patterns based on motion data and generate necessary information.

[0011] A "user" is an individual who wears a device and uses the system.

[0012] "Weather data" refers to meteorological information obtained from external resources, including data such as temperature, wind speed, and precipitation. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

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

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention comprises a device worn by the user, a server that receives and analyzes data from the device, and a system for providing feedback to the user. This allows the user to improve their exercise in real time and perform appropriate training.

[0035] First, a device is attached to the user's body and collects the user's movement data in real time. The device senses data such as pace, heart rate, and acceleration, and stores this data at regular intervals. The collected data is immediately transmitted to a server using wireless communication technology.

[0036] The server performs detailed analysis based on the received data. The server incorporates advanced algorithms to quickly analyze the user's exercise patterns, heart rate changes, and acceleration patterns. The server also retrieves weather data from an external weather API and incorporates it into the analysis results, generating information useful for improving training courses and form.

[0037] Based on the analyzed data, the server generates real-time instructions tailored to the user's condition. These instructions include suggestions for improving form, adjusting exercise intensity, and timing rest periods. Music suitable for the user's heart rate and pace is also selected and provided as part of the training.

[0038] The generated instructions are sent to the user via the terminal. The terminal immediately notifies the user and, for example, displays advice on improving exercise on the display. This allows the user to monitor their exercise status in real time during training and make necessary adjustments.

[0039] For example, if the pace is too fast and the heart rate is higher than normal, the server will detect this and provide the user with instructions via the device to "slow down and stabilize your heart rate." Furthermore, if the heart rate is stable, upbeat music will play to encourage the user, making the training more enjoyable.

[0040] In this way, by using this system, users can optimize their exercise and effectively improve their fitness while simultaneously reducing the risk of injury.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device collects user activity data. Using sensors, the device acquires information such as pace, heart rate, and acceleration in real time and temporarily stores the data in its internal memory.

[0044] Step 2:

[0045] The terminal sends the data it collects to the server at regular intervals. The terminal uses wireless communication to send this data to the server in packet format.

[0046] Step 3:

[0047] The server receives data sent from the terminal. After receiving the data, the server converts it into an appropriate data format for analysis.

[0048] Step 4:

[0049] The server analyzes the received data. An algorithm within the server evaluates heart rate patterns and acceleration changes to diagnose the user's exercise style and physical condition.

[0050] Step 5:

[0051] The server retrieves weather data. The server accesses external weather services and incorporates the current weather and forecast into the data analysis.

[0052] Step 6:

[0053] The server generates instructions for the user based on the analysis results. The server creates specific advice, such as form improvement, training intensity adjustments, and music suggestions.

[0054] Step 7:

[0055] The server sends the generated instructions and music to the terminal. The server immediately sends this information to the terminal for the user interface.

[0056] Step 8:

[0057] The device notifies the user of instructions. The device supports training by displaying advice on its screen and playing selected music.

[0058] Step 9:

[0059] The user adjusts their exercise based on advice from their device. They follow the instructions to correct their form and pace, resulting in more efficient training.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] Traditional exercise support systems have faced challenges in providing real-time feedback and personalized advice. A lack of information regarding appropriate exercise intensity and form improvements can reduce exercise efficiency and increase the risk of injury. Furthermore, the lack of training guidance that takes external environmental factors into account results in insufficient comprehensive exercise support.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for analyzing information from a measuring device that acquires physical information during exercise in real time, means for using a calculation method to generate advice for improving an individual's exercise habits, means for reflecting external environmental information in the analysis results, and means for generating music guidance and providing accompaniment suitable for the individual's training. As a result, the user can receive real-time optimized exercise guidance and improve their fitness efficiently and safely.

[0065] A "measuring device" is a device that collects a user's physical information in real time during exercise and acquires it as data.

[0066] A "calculation unit" is a device that receives information transferred from a measuring device, performs complex analysis, and generates useful feedback for the user.

[0067] "Environmental information" refers to data about external conditions that affect the user's movement, such as external weather and location information.

[0068] A "calculation method" refers to a set of algorithms and processes that generate advice for improving an individual's exercise habits based on the acquired data.

[0069] "Music guidance" refers to playlists or selections of music curated to support user training and improve motivation.

[0070] This invention consists of a terminal worn by the user, a server that receives and analyzes data from the terminal, and a system for providing feedback to the user. The user can wear the terminal on their body and collect data in real time during exercise. The terminal uses internally mounted heart rate sensors, accelerometers, GPS modules, etc., to collect data such as pace, heart rate, distance traveled, and acceleration.

[0071] The device transmits collected data to the server using wireless communication technology (e.g., Bluetooth or Wi-Fi). The server analyzes the received data using a dedicated algorithm. This algorithm evaluates the user's exercise style, heart rate changes, and acceleration patterns, and generates exercise improvement suggestions tailored to the user. The server also obtains environmental information via an external weather API and incorporates it into the analysis results to provide more accurate guidance.

[0072] The generated exercise instructions and music guidance are provided to the user through a terminal. The terminal is equipped with a display and audio output device, and notifies the user of appropriate feedback in real time. For example, if the server detects that the heart rate is rising due to excessive exercise, it generates a message such as "Please slow down" and notifies the user through the terminal. In addition, music appropriate to the exercise situation is played to increase the user's motivation.

[0073] As a concrete example, the server analyzes the user's heart rate data and suggests adjustments to rest times and exercise intensity as needed. Music guidance is generated based on the individual's exercise pace, improving training efficiency. An example of a prompt message could be written in the format of, "Design a program that analyzes the user's exercise data and provides optimal exercise support information in real time. Specifically, I would like information on a system that suggests exercise instructions and music suitable for the user based on heart rate and pace data." In this way, the present invention enables users to exercise more safely and effectively, maximizing their fitness benefits.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The device collects real-time physical information from the user during exercise while they are wearing it. It receives data from sensors such as a heart rate sensor, accelerometer, and GPS module as input. It then performs the specific operation of storing multiple data points acquired from these sensors in its internal memory.

[0077] Step 2:

[0078] The terminal organizes and packages the accumulated data at regular intervals and transmits it to the server using wireless communication technology (Bluetooth or Wi-Fi). The input is a collection of raw data, and the output uses a protocol to securely transmit that data wirelessly.

[0079] Step 3:

[0080] The server receives data sent from the terminal and executes a dedicated analysis algorithm. Based on the input data, it processes the data to analyze exercise patterns, heart rate changes, and acceleration patterns, and then outputs exercise improvement information tailored to the user. Specifically, the algorithm analyzes the trends in the data and generates the analysis results.

[0081] Step 4:

[0082] The server obtains environmental information using an external weather API. It takes weather data based on the current geographic location as input and integrates it with the previously obtained analysis results. This generates output that produces environmentally conscious exercise instructions.

[0083] Step 5:

[0084] The server creates specific exercise instructions and music recommendations for the user based on the analyzed results. Inputs include the analysis results, weather data, and the user's individual profile information. It then selects exercise instructions and music suitable for the user and converts them into an operation file as output.

[0085] Step 6:

[0086] The server sends the generated notification information to the terminal. The outputted instructions and music guidance content are delivered to the terminal. Specifically, data packets are sent from the server to the terminal.

[0087] Step 7:

[0088] The device notifies the user in real time of received exercise instructions and music guidance. It receives feedback data as input, displays instructions visually on the display, and outputs music through the earphones. Specifically, it provides immediate feedback to the user through real-time display and audio output.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] In recent years, with increasing public interest in exercise and health management, there has been a growing demand for technology that can analyze users' movements in real time and provide appropriate feedback. However, currently, analysis that takes into account individual physical conditions and external environments is insufficient, making it difficult to provide personalized advice. This leads to challenges such as users being unable to train efficiently and an increased risk of injury from strenuous exercise.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means for using a device that collects operational information in real time, means for using a computer that receives and analyzes information transmitted from the device, and means for comprehensively analyzing the user's physical data and environmental data. This makes it possible to provide real-time advice based on the user's individual condition.

[0094] "Motion information" refers to data related to the user's body movements, which is collected and analyzed in real time.

[0095] A "device" is a device worn by a user and used to collect motion information.

[0096] A "computer" is a computer system that analyzes information received from a device and performs data processing to provide appropriate feedback to the user.

[0097] "Means" refers to the methods or techniques incorporated into a system to achieve a specific function.

[0098] A "computational algorithm" is a set of procedures and processing methods used to generate data based on analyzed information in order to improve the user's activity patterns.

[0099] An "information processing device" is part of a system that has the function of providing appropriate feedback based on recorded user actions.

[0100] "Health data" refers to information related to the user's physiological state, representing data about the body's functions and condition.

[0101] "Environmental data" refers to information about weather and surrounding conditions related to the location and time of the user's activities.

[0102] The system implementing this invention aims to provide appropriate feedback to the user by collecting and analyzing operational information in real time. The user wears a specific device on their body, which collects operational information and health data. This data is transmitted to a computer via wireless communication.

[0103] The computer efficiently analyzes received motion information by acquiring data from multiple sensors and cameras and generating situation-specific feedback. For analysis, it uses machine learning frameworks such as TENSORFLOW® to process data and build models. Furthermore, the computer also acquires environmental data and incorporates it into the instructions it provides. These instructions include specific advice for improving form and adjusting condition during activities.

[0104] As a concrete example, suppose a user is jogging and the device uses an accelerometer to record their running pace. This data is transmitted in real time to a computer, which compares the user's pace and heart rate to generate advice on maintaining an appropriate exercise intensity.

[0105] Furthermore, it is possible to generate feedback messages based on the analysis results using a generative AI model. An example of a prompt message would be: "Analyze the user's movement data while jogging and generate suggestions for efficient form improvement. For example, please provide simple advice such as relaxing your shoulders." Based on this prompt message, the system has a structure that can instantly provide easy-to-understand feedback to the user.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The terminal collects motion information. The user wears the device, which measures acceleration, pace, and heart rate in real time through sensors. The input at this time is the user's physical movement, and the output is the collected motion information. The terminal temporarily stores this data in preparation for the next transmission.

[0109] Step 2:

[0110] The terminal sends the collected data to the server. Using wireless communication, the terminal sends all measured operational information to the server. In this process, operational information is input as data packets from the terminal and output as received data on the server side.

[0111] Step 3:

[0112] The server analyzes the data. The server uses machine learning frameworks such as TensorFlow to analyze the received motion information. The input here is motion information from the terminal, and the output is the analyzed movement patterns and recommended training guidance. Through this analysis, the server evaluates the user's exercise efficiency and generates feedback for improving their form.

[0113] Step 4:

[0114] The server collects environmental data and incorporates it into the analysis results. The server obtains current environmental data from an external weather API and integrates it into the analysis results. The input to this process is weather data, and the output is environment-aware exercise advice. The server takes temperature, humidity, etc., into consideration and modifies the user's exercise plan.

[0115] Step 5:

[0116] The server uses a generative AI model to generate optimal feedback. Based on the previously analyzed data and environmental information, the server sends prompts to the generative AI model to obtain the optimal feedback for the user. The input for this step is the analyzed data and prompt text, and the output is the feedback text.

[0117] Step 6:

[0118] The server sends feedback to the terminal, which then presents it to the user. The generated feedback is sent to the terminal and provided to the user visually or audibly. The input is feedback data from the server, and the output is notifications and advice to the user. The terminal immediately notifies the user of the information via its display or speaker.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention provides a system that acquires a user's biometric and emotional data in real time and supports the improvement of their movement patterns based on that data. The system consists of a terminal that collects motion data and emotional data, a server that analyzes the data and generates instructions, and a feedback function that provides those instructions to the user.

[0121] The device is worn on the user's body and collects motion data in real time. It also features an emotion engine that recognizes emotions through the user's voice, facial expressions, or physiological indicators. This device immediately transmits the acquired motion and emotion data to a server.

[0122] The server continuously receives and analyzes data sent from the terminal. The server's algorithm comprehensively evaluates data such as the user's heart rate, pace, and emotional state. Emotional data is used to understand the user's current psychological state and analyze how their emotions affect their exercise style.

[0123] Based on the analysis results, the server generates the most effective instructions for the user. For example, if the user is feeling stressed, it might suggest breathing exercises to help them relax. It might also recommend music tailored to their emotional state, all designed to maximize the effectiveness of the training.

[0124] The generated instructions and music are provided to the user via the device. The device provides notifications and plays music through its display and audio output. This allows the user to continue exercising while receiving instructions optimized for their current mental state and exercise status.

[0125] For example, if a user wants to continue training quickly, but the emotion engine detects impatience, the server might analyze this and send a message to the device saying, "Don't rush, maintain your pace." The device might also play calming or focus-enhancing music to help improve the user's performance.

[0126] Thus, by introducing an emotion engine, the present invention can enhance the user's overall training experience and provide support that is more appropriate to their mental and physical state.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The device collects the user's biometric and emotional data in real time. The device uses an accelerometer and heart rate sensor to acquire motion data and operates an emotion engine that recognizes emotions from the user's voice and facial expression data.

[0130] Step 2:

[0131] The device sends collected biometric and emotional data to the server. The device wirelessly transmits this data to the server at regular intervals.

[0132] Step 3:

[0133] The server analyzes the data received from the terminal. The server analyzes the user's behavioral and emotional data to evaluate the user's state (e.g., fatigue level and motivation level).

[0134] Step 4:

[0135] The server generates instructions and music based on the analysis results. The server generates exercise advice based on the user's psychological state and selects appropriate music.

[0136] Step 5:

[0137] The server sends generated instructions and music to the terminal. The server immediately sends this feedback back to the terminal.

[0138] Step 6:

[0139] The device notifies the user of instructions and plays music. The device displays advice via its screen and plays the selected music through speakers or headphones.

[0140] Step 7:

[0141] The user adjusts their exercise according to the advice from their device. Based on the notified instructions, the user adjusts the pace and form of their exercise to maximize the training effect.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0144] In modern life, people often experience stress and anxiety, making it difficult to train efficiently and healthily. Furthermore, there is a lack of systems that provide not only exercise instruction but also feedback tailored to the user's psychological state. Therefore, there is a need for a system that can analyze physiological and psychological data in real time and provide users with appropriate exercise guidance and psychological support.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for receiving and analyzing motion data and emotional data, means for generating information suitable for the user's exercise style and psychological state based on the analysis results, and means for providing the information to the user through a feedback device. This enables the user to continue safe and effective training based on instructions optimized for their psychological and physical state at any given time.

[0147] A "measuring device" is a device that has the function of collecting motion data and emotion data in real time and transmitting it to a server.

[0148] A "calculation unit" is a device that analyzes data received from measuring devices and has the function of comprehensively evaluating heart rate, exercise pace, and emotional state.

[0149] A "feedback device" is a device that conveys instructions generated by a computing unit to the user, and has the function of providing information through voice or visuals.

[0150] A "generative model" is an algorithm that generates information tailored to the user's exercise style and psychological state based on analyzed data.

[0151] "Environmental data" refers to data about external factors that affect the user's training and psychological state, such as weather, temperature, and humidity.

[0152] This invention is a system that collects a user's physiological and emotional data in real time and supports the improvement of exercise patterns based on that data. The system consists of a measuring device, a computing device, and a feedback device. The measuring device uses a wearable device (e.g., a smartwatch) that is attached to the user's body and acquires physiological data such as heart rate and exercise pace. This measuring device should preferably be equipped with a microphone and a camera to recognize emotional states through voice and facial expressions. The collected data is transmitted to the computing device via wireless communication.

[0153] The server functions as a computing device, including a high-performance processor and large memory capacity to analyze data collected in real time. It utilizes machine learning libraries such as TensorFlow and Scikit-learn for rapid and accurate data processing. This analysis allows the server to comprehensively assess the user's heart rate, exercise pace, and emotional state. However, beyond data analysis, it also uses generative AI models based on the analysis results to generate appropriate feedback, providing optimal exercise instructions and psychological support for individual users.

[0154] Feedback information, such as generated instructions and recommended music, is provided to the user via a feedback device. Information is communicated to the user in real time through text messages and voice notifications displayed on the device. This allows the user to continue training safely and effectively while receiving instructions optimized for their psychological state and exercise condition.

[0155] For example, if a user experiences extreme tension during training, the server analyzes this data and sends a message to the feedback device saying, "Take slow, deep breaths to relax." It can also select and play music that promotes relaxation. This allows users to train efficiently while reducing tension.

[0156] Examples of prompts for a generative AI model include: "Design an algorithm that considers the user's current emotional state and provides optimal exercise instructions and music. Consider how to maximize the user's psychology and exercise performance."

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1: Data Collection

[0159] The device uses a wearable device attached to the user's body to collect motion and emotional data in real time. Specifically, it measures exercise intensity and pace using an accelerometer and records heart rate using a heart rate sensor. It also uses a microphone and camera on the device to analyze voice and facial expressions and recognize emotional states. Inputs include physiological and emotional data obtained from biometric sensors and the emotion recognition system. Outputs are digital data ready to be sent to the server. This information is transmitted to the server in real time using the data transmission function.

[0160] Step 2: Data Analysis

[0161] The server receives data sent from the terminal. For analysis, it uses data science libraries built in, for example, Python or R. The input is heart rate, exercise pace, and emotional state data sent from the terminal. Based on this data, the server uses statistical analysis and machine learning models to generate output that identifies the user's psychological and physical state. Specifically, it uses deep learning tools such as TensorFlow to evaluate the interrelationships between data and identify factors that influence the user's current psychological and physical state.

[0162] Step 3: Instruction Generation

[0163] The server generates optimal exercise instructions for the user based on the analysis results. The input is the evaluation results of the psychological and physical state obtained from data analysis. The output is exercise instructions and psychological support information created using a generative AI model. For example, if it is found that the user is in a high-stress state, the server will create instructions recommending deep breathing exercises for relaxation. It can also select and play appropriate music. This process uses an algorithm to select the most appropriate feedback strategy from a large amount of data.

[0164] Step 4: Provide feedback

[0165] The device provides the user with instructions sent from the server. These can be displayed as text messages on the device's screen or directly provided as voice notifications. Input consists of exercise instructions and music selection information received from the server. Output is real-time feedback provided to the user through visual and auditory means. This allows the user to receive training optimized for their psychological and physical state, maximizing its effectiveness.

[0166] (Application Example 2)

[0167] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0168] While the demand for health management and fitness is increasing in modern society, there is a problem in finding training methods and relaxation techniques that are optimized for each individual's physical and mental state. In particular, there is a need for methods that analyze movement data and emotional states in real time and suggest appropriate exercises and relaxation techniques based on that analysis.

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

[0170] In this invention, the server includes a device for collecting operational data in real time, a computer for receiving and analyzing data transmitted from the device, a function for providing instructions generated by the computer to a human via the device, and a computation means for generating musical information according to the human's emotional state. This enables customized fitness and relaxation support tailored to the user's health condition and emotions.

[0171] "Motion data" refers to information about the user's body movements, including elements such as speed, direction, and timing of the movement.

[0172] A "device" is a piece of equipment that collects operational data and transmits it to a server, and is worn on the user's body.

[0173] A "computer" is a computer system that receives and analyzes data transmitted from a device.

[0174] "Instructions" refer to the content of exercise guidance and behavioral suggestions for the user, generated based on data analyzed by the computer.

[0175] "Computational means" refers to algorithms and programs that provide the function of analyzing data within a computer and generating information for a specific purpose.

[0176] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and respiratory rate.

[0177] "Emotional state" refers to information that indicates the user's mental state, and is indicated by factors such as voice tone, facial expressions, and heart rate variability.

[0178] "Music information" refers to data of music that is generated according to the user's emotional state and played to enhance the effects of relaxation or exercise.

[0179] The system for carrying out this invention comprises a device worn by the user, a server which is a computer system, and a computing means for providing feedback to the user. The device collects motion data and biometric information in real time and transmits it to the server. The device is equipped with high-precision sensors that accurately grasp the user's movement and physical condition.

[0180] The server has computational capabilities to analyze the received data and generates information to improve the user's movement patterns based on the collected data. AI algorithms are used for this analysis, enabling real-time data processing. The instructions generated from the analysis results are provided to the user as individually optimized feedback.

[0181] Instructions provided to the user are communicated through the device's display or audio output. These instructions include exercise adjustments, relaxation techniques tailored to emotional state, or musical information. This allows users to receive optimal support in their training and daily lives, according to their physical and emotional needs.

[0182] For example, if a user experiences stress while practicing yoga, the device detects this through fluctuations in heart rate. The server generates relaxation-promoting music and breathing instructions, which are then provided to the user through the device. This dynamic feedback not only optimizes the user's performance but also contributes to maintaining their physical and mental well-being.

[0183] The generative AI model allows for further customization. An example of a prompt message would be, "Stress was detected while the user was doing yoga. Please generate relaxation suggestions," enabling feedback tailored to the user's state.

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The device collects user movement data and biometric information in real time. Inputs include information obtained from the user's body, such as heart rate, body temperature, and movement speed and direction. Sensors detect this information and input it into the system as movement data. Outputs are datasets representing the user's physical and exercise states.

[0187] Step 2:

[0188] The terminal sends the collected data to the server. The data is transferred over the network using an appropriate communication protocol. The input is the dataset obtained in step 1, and the output is the total data package received by the server.

[0189] Step 3:

[0190] The server analyzes the data it receives. The input consists of biometric and motion data transmitted from the terminal. The server uses an AI algorithm to process the data and evaluate the user's current movement and emotional state. The output consists of analysis results to optimize the user's movement style and feedback commands tailored to their emotional state.

[0191] Step 4:

[0192] The server generates instructions and music information based on the analysis results. The input is the analysis results from step 3. The generating AI model is used to create specific exercise instructions and relaxation-promoting music information tailored to the user's state. The output is a set of instructions and a music list provided to the user.

[0193] Step 5:

[0194] The server sends the generated instructions and music information to the terminal. The input is the instruction set and music list generated in step 4. The output is the feedback data received by the terminal.

[0195] Step 6:

[0196] The terminal provides the user with the instructions it receives. Input consists of a set of instructions and music information from the server. The terminal communicates these to the user through its display and audio output. Output is music playback for exercise guidance or relaxation for the user.

[0197] Step 7:

[0198] The user acts according to the provided instructions, performing exercise and relaxation. The input is the specific instructions provided by the device. The user's actions update the motion data and biometric information, initiating the next feedback cycle. The output is the user's improved exercise performance and state of relaxation.

[0199] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0200] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0206] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0208] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0210] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0212] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0215] This invention comprises a device worn by the user, a server that receives and analyzes data from the device, and a system for providing feedback to the user. This allows the user to improve their exercise in real time and perform appropriate training.

[0216] First, a device is attached to the user's body and collects the user's movement data in real time. The device senses data such as pace, heart rate, and acceleration, and stores this data at regular intervals. The collected data is immediately transmitted to a server using wireless communication technology.

[0217] The server performs detailed analysis based on the received data. The server incorporates advanced algorithms to quickly analyze the user's exercise patterns, heart rate changes, and acceleration patterns. The server also retrieves weather data from an external weather API and incorporates it into the analysis results, generating information useful for improving training courses and form.

[0218] Based on the analyzed data, the server generates real-time instructions tailored to the user's condition. These instructions include suggestions for improving form, adjusting exercise intensity, and timing rest periods. Music suitable for the user's heart rate and pace is also selected and provided as part of the training.

[0219] The generated instructions are sent to the user via the terminal. The terminal immediately notifies the user and, for example, displays advice on improving exercise on the display. This allows the user to monitor their exercise status in real time during training and make necessary adjustments.

[0220] For example, if the pace is too fast and the heart rate is higher than normal, the server will detect this and provide the user with instructions via the device to "slow down and stabilize your heart rate." Furthermore, if the heart rate is stable, upbeat music will play to encourage the user, making the training more enjoyable.

[0221] In this way, by using this system, users can optimize their exercise and effectively improve their fitness while simultaneously reducing the risk of injury.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The device collects user activity data. Using sensors, the device acquires information such as pace, heart rate, and acceleration in real time and temporarily stores the data in its internal memory.

[0225] Step 2:

[0226] The terminal sends the data it collects to the server at regular intervals. The terminal uses wireless communication to send this data to the server in packet format.

[0227] Step 3:

[0228] The server receives data sent from the terminal. After receiving the data, the server converts it into an appropriate data format for analysis.

[0229] Step 4:

[0230] The server analyzes the received data. An algorithm within the server evaluates heart rate patterns and acceleration changes to diagnose the user's exercise style and physical condition.

[0231] Step 5:

[0232] The server retrieves weather data. The server accesses external weather services and incorporates the current weather and forecast into the data analysis.

[0233] Step 6:

[0234] The server generates instructions for the user based on the analysis results. The server creates specific advice, such as form improvement, training intensity adjustments, and music suggestions.

[0235] Step 7:

[0236] The server sends the generated instructions and music to the terminal. The server immediately sends this information to the terminal for the user interface.

[0237] Step 8:

[0238] The device notifies the user of instructions. The device supports training by displaying advice on its screen and playing selected music.

[0239] Step 9:

[0240] The user adjusts their exercise based on advice from their device. They follow the instructions to correct their form and pace, resulting in more efficient training.

[0241] (Example 1)

[0242] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0243] Traditional exercise support systems have faced challenges in providing real-time feedback and personalized advice. A lack of information regarding appropriate exercise intensity and form improvements can reduce exercise efficiency and increase the risk of injury. Furthermore, the lack of training guidance that takes external environmental factors into account results in insufficient comprehensive exercise support.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] In this invention, the server includes means for analyzing information from a measuring device that acquires physical information during exercise in real time, means for using a calculation method to generate advice for improving an individual's exercise habits, means for reflecting external environmental information in the analysis results, and means for generating music guidance and providing accompaniment suitable for the individual's training. As a result, the user can receive real-time optimized exercise guidance and improve their fitness efficiently and safely.

[0246] A "measuring device" is a device that collects a user's physical information in real time during exercise and acquires it as data.

[0247] A "calculation unit" is a device that receives information transferred from a measuring device, performs complex analysis, and generates useful feedback for the user.

[0248] "Environmental information" refers to data about external conditions that affect the user's movement, such as external weather and location information.

[0249] A "calculation method" refers to a set of algorithms and processes that generate advice for improving an individual's exercise habits based on the acquired data.

[0250] "Music guidance" refers to playlists or selections of music curated to support user training and improve motivation.

[0251] This invention consists of a terminal worn by the user, a server that receives and analyzes data from the terminal, and a system for providing feedback to the user. The user can wear the terminal on their body and collect data in real time during exercise. The terminal uses internally mounted heart rate sensors, accelerometers, GPS modules, etc., to collect data such as pace, heart rate, distance traveled, and acceleration.

[0252] The device transmits collected data to the server using wireless communication technology (e.g., Bluetooth or Wi-Fi). The server analyzes the received data using a dedicated algorithm. This algorithm evaluates the user's exercise style, heart rate changes, and acceleration patterns, and generates exercise improvement suggestions tailored to the user. The server also obtains environmental information via an external weather API and incorporates it into the analysis results to provide more accurate guidance.

[0253] The generated exercise instructions and music guidance are provided to the user through a terminal. The terminal is equipped with a display and audio output device, and notifies the user of appropriate feedback in real time. For example, if the server detects that the heart rate is rising due to excessive exercise, it generates a message such as "Please slow down" and notifies the user through the terminal. In addition, music appropriate to the exercise situation is played to increase the user's motivation.

[0254] As a concrete example, the server analyzes the user's heart rate data and suggests adjustments to rest times and exercise intensity as needed. Music guidance is generated based on the individual's exercise pace, improving training efficiency. An example of a prompt message could be written in the format of, "Design a program that analyzes the user's exercise data and provides optimal exercise support information in real time. Specifically, I would like information on a system that suggests exercise instructions and music suitable for the user based on heart rate and pace data." In this way, the present invention enables users to exercise more safely and effectively, maximizing their fitness benefits.

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The device collects real-time physical information from the user during exercise while they are wearing it. It receives data from sensors such as a heart rate sensor, accelerometer, and GPS module as input. It then performs the specific operation of storing multiple data points acquired from these sensors in its internal memory.

[0258] Step 2:

[0259] The terminal organizes and packages the accumulated data at regular intervals and transmits it to the server using wireless communication technology (Bluetooth or Wi-Fi). The input is a collection of raw data, and the output uses a protocol to securely transmit that data wirelessly.

[0260] Step 3:

[0261] The server receives data sent from the terminal and executes a dedicated analysis algorithm. Based on the input data, it processes the data to analyze exercise patterns, heart rate changes, and acceleration patterns, and then outputs exercise improvement information tailored to the user. Specifically, the algorithm analyzes the trends in the data and generates the analysis results.

[0262] Step 4:

[0263] The server obtains environmental information using an external weather API. It takes weather data based on the current geographic location as input and integrates it with the previously obtained analysis results. This generates output that produces environmentally conscious exercise instructions.

[0264] Step 5:

[0265] The server creates specific exercise instructions and music recommendations for the user based on the analyzed results. Inputs include the analysis results, weather data, and the user's individual profile information. It then selects exercise instructions and music suitable for the user and converts them into an operation file as output.

[0266] Step 6:

[0267] The server sends the generated notification information to the terminal. The outputted instructions and music guidance content are delivered to the terminal. Specifically, data packets are sent from the server to the terminal.

[0268] Step 7:

[0269] The device notifies the user in real time of received exercise instructions and music guidance. It receives feedback data as input, displays instructions visually on the display, and outputs music through the earphones. Specifically, it provides immediate feedback to the user through real-time display and audio output.

[0270] (Application Example 1)

[0271] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] In recent years, with increasing public interest in exercise and health management, there has been a growing demand for technology that can analyze users' movements in real time and provide appropriate feedback. However, currently, analysis that takes into account individual physical conditions and external environments is insufficient, making it difficult to provide personalized advice. This leads to challenges such as users being unable to train efficiently and an increased risk of injury from strenuous exercise.

[0273] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0274] In this invention, the server includes means for using a device that collects operational information in real time, means for using a computer that receives and analyzes information transmitted from the device, and means for comprehensively analyzing the user's physical data and environmental data. This makes it possible to provide real-time advice based on the user's individual condition.

[0275] "Motion information" refers to data related to the user's body movements, which is collected and analyzed in real time.

[0276] A "device" is a device worn by a user and used to collect motion information.

[0277] A "computer" is a computer system that analyzes information received from a device and performs data processing to provide appropriate feedback to the user.

[0278] A "means" refers to a method or technology incorporated to realize a specific function by a system.

[0279] A "computing algorithm" is a procedure or processing method for generating data based on the analyzed information in order to improve the user's activity pattern.

[0280] An "information processing device" is a part of a system equipped with the function of providing appropriate feedback based on the recorded actions of the user.

[0281] "Health data" is information related to the user's physiological state and is data representing the functions and states of the body.

[0282] "Environmental data" is information related to the weather and surrounding conditions related to the location and time where the user is active.

[0283] The system for implementing the present invention aims to provide appropriate feedback to the user by collecting and analyzing operation information in real time. The user wears a specific device on the body, and that device collects operation information and health data. These data are transmitted to the computer via wireless communication.

[0284] The computer takes in data from multiple sensors and cameras to efficiently analyze the received operation information and generates feedback according to the situation. For the analysis, a machine learning framework such as TensorFlow is used to perform data processing and model construction. Furthermore, the computer also acquires environmental data and reflects it in the provided instructions. These instructions include specific advice for improving the form and adjusting the condition during activities.

[0285] As a specific example, assume that while the user is jogging, the device uses an acceleration sensor to record the running pace. This data is transmitted to the computer in real time, and the computer compares the user's pace with their heart rate and generates advice to maintain an appropriate exercise intensity.

[0286] It is also possible to generate a feedback text based on the analysis results using a generated AI model. An example of the prompt text here is: "Analyze the motion data of the user while jogging and generate an efficient form improvement plan. For example, please provide simple advice such as relaxing the shoulder muscles." It has a structure that can provide the user with instant and easy-to-understand feedback based on this prompt text.

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The terminal collects operation information. The user wears the device, and the device measures acceleration, pace, and heart rate in real time through the sensor. The input at this time is the user's body movement, and the output is the collected operation information. The terminal temporarily stores this data in preparation for the next transmission.

[0290] Step 2:

[0291] The terminal transmits the collected data to the server. Using wireless communication, the terminal transmits all the measured operation information to the server. In this process, the operation information is input as a data packet from the terminal and output as the received data on the server side.

[0292] Step 3:

[0293] The server analyzes the data. The server uses machine learning frameworks such as TensorFlow to analyze the received motion information. The input here is motion information from the terminal, and the output is the analyzed movement patterns and recommended training guidance. Through this analysis, the server evaluates the user's exercise efficiency and generates feedback for improving their form.

[0294] Step 4:

[0295] The server collects environmental data and incorporates it into the analysis results. The server obtains current environmental data from an external weather API and integrates it into the analysis results. The input to this process is weather data, and the output is environment-aware exercise advice. The server takes temperature, humidity, etc., into consideration and modifies the user's exercise plan.

[0296] Step 5:

[0297] The server uses a generative AI model to generate optimal feedback. Based on the previously analyzed data and environmental information, the server sends prompts to the generative AI model to obtain the optimal feedback for the user. The input for this step is the analyzed data and prompt text, and the output is the feedback text.

[0298] Step 6:

[0299] The server sends feedback to the terminal, which then presents it to the user. The generated feedback is sent to the terminal and provided to the user visually or audibly. The input is feedback data from the server, and the output is notifications and advice to the user. The terminal immediately notifies the user of the information via its display or speaker.

[0300] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0301] This invention provides a system that acquires a user's biometric and emotional data in real time and supports the improvement of their movement patterns based on that data. The system consists of a terminal that collects motion data and emotional data, a server that analyzes the data and generates instructions, and a feedback function that provides those instructions to the user.

[0302] The device is worn on the user's body and collects motion data in real time. It also features an emotion engine that recognizes emotions through the user's voice, facial expressions, or physiological indicators. This device immediately transmits the acquired motion and emotion data to a server.

[0303] The server continuously receives and analyzes data sent from the terminal. The server's algorithm comprehensively evaluates data such as the user's heart rate, pace, and emotional state. Emotional data is used to understand the user's current psychological state and analyze how their emotions affect their exercise style.

[0304] Based on the analysis results, the server generates the most effective instructions for the user. For example, if the user is feeling stressed, it might suggest breathing exercises to help them relax. It might also recommend music tailored to their emotional state, all designed to maximize the effectiveness of the training.

[0305] The generated instructions and music are provided to the user via the device. The device provides notifications and plays music through its display and audio output. This allows the user to continue exercising while receiving instructions optimized for their current mental state and exercise status.

[0306] For example, if a user wants to continue training quickly, but the emotion engine detects impatience, the server might analyze this and send a message to the device saying, "Don't rush, maintain your pace." The device might also play calming or focus-enhancing music to help improve the user's performance.

[0307] Thus, by introducing the emotion engine, the present invention can enhance the overall training experience of the user and provide more suitable support according to the mental and physical states.

[0308] The following describes the process flow.

[0309] Step 1:

[0310] The terminal collects the user's biological data and emotion data in real time. The terminal uses an acceleration sensor and a heart rate sensor to obtain motion data, and operates an emotion engine that recognizes emotions from the user's voice and facial expression data.

[0311] Step 2:

[0312] The terminal transmits the biological data and emotion data collected to the server. The terminal wirelessly transmits these data to the server at regular intervals.

[0313] Step 3:

[0314] The server analyzes the data received from the terminal. The server analyzes the user's motion data and emotion data to evaluate the user's state (e.g., fatigue state and motivation level).

[0315] Step 4:

[0316] The server generates instructions and music based on the analysis results. The server generates exercise advice based on the user's psychological state and selects appropriate music.

[0317] Step 5:

[0318] The server transmits the instructions and music generated to the terminal. The server immediately sends back this feedback to the terminal.

[0319] Step 6:

[0320] The device notifies the user of instructions and plays music. The device displays advice via its screen and plays the selected music through speakers or headphones.

[0321] Step 7:

[0322] The user adjusts their exercise according to the advice from their device. Based on the notified instructions, the user adjusts the pace and form of their exercise to maximize the training effect.

[0323] (Example 2)

[0324] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0325] In modern life, people often experience stress and anxiety, making it difficult to train efficiently and healthily. Furthermore, there is a lack of systems that provide not only exercise instruction but also feedback tailored to the user's psychological state. Therefore, there is a need for a system that can analyze physiological and psychological data in real time and provide users with appropriate exercise guidance and psychological support.

[0326] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0327] In this invention, the server includes means for receiving and analyzing motion data and emotional data, means for generating information suitable for the user's exercise style and psychological state based on the analysis results, and means for providing the information to the user through a feedback device. This enables the user to continue safe and effective training based on instructions optimized for their psychological and physical state at any given time.

[0328] A "measuring device" is a device that has the function of collecting motion data and emotion data in real time and transmitting it to a server.

[0329] A "calculation unit" is a device that analyzes data received from measuring devices and has the function of comprehensively evaluating heart rate, exercise pace, and emotional state.

[0330] A "feedback device" is a device that conveys instructions generated by a computing unit to the user, and has the function of providing information through voice or visuals.

[0331] A "generative model" is an algorithm that generates information tailored to the user's exercise style and psychological state based on analyzed data.

[0332] "Environmental data" refers to data about external factors that affect the user's training and psychological state, such as weather, temperature, and humidity.

[0333] This invention is a system that collects a user's physiological and emotional data in real time and supports the improvement of exercise patterns based on that data. The system consists of a measuring device, a computing device, and a feedback device. The measuring device uses a wearable device (e.g., a smartwatch) that is attached to the user's body and acquires physiological data such as heart rate and exercise pace. This measuring device should preferably be equipped with a microphone and a camera to recognize emotional states through voice and facial expressions. The collected data is transmitted to the computing device via wireless communication.

[0334] The server functions as a computing device, including a high-performance processor and large memory capacity to analyze data collected in real time. It utilizes machine learning libraries such as TensorFlow and Scikit-learn for rapid and accurate data processing. This analysis allows the server to comprehensively assess the user's heart rate, exercise pace, and emotional state. However, beyond data analysis, it also uses generative AI models based on the analysis results to generate appropriate feedback, providing optimal exercise instructions and psychological support for individual users.

[0335] Feedback information, such as generated instructions and recommended music, is provided to the user via a feedback device. Information is communicated to the user in real time through text messages and voice notifications displayed on the device. This allows the user to continue training safely and effectively while receiving instructions optimized for their psychological state and exercise condition.

[0336] For example, if a user experiences extreme tension during training, the server analyzes this data and sends a message to the feedback device saying, "Take slow, deep breaths to relax." It can also select and play music that promotes relaxation. This allows users to train efficiently while reducing tension.

[0337] Examples of prompts for a generative AI model include: "Design an algorithm that considers the user's current emotional state and provides optimal exercise instructions and music. Consider how to maximize the user's psychology and exercise performance."

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1: Data Collection

[0340] The device uses a wearable device attached to the user's body to collect motion and emotional data in real time. Specifically, it measures exercise intensity and pace using an accelerometer and records heart rate using a heart rate sensor. It also uses a microphone and camera on the device to analyze voice and facial expressions and recognize emotional states. Inputs include physiological and emotional data obtained from biometric sensors and the emotion recognition system. Outputs are digital data ready to be sent to the server. This information is transmitted to the server in real time using the data transmission function.

[0341] Step 2: Data Analysis

[0342] The server receives data sent from the terminal. For analysis, it uses data science libraries built in, for example, Python or R. The input is heart rate, exercise pace, and emotional state data sent from the terminal. Based on this data, the server uses statistical analysis and machine learning models to generate output that identifies the user's psychological and physical state. Specifically, it uses deep learning tools such as TensorFlow to evaluate the interrelationships between data and identify factors that influence the user's current psychological and physical state.

[0343] Step 3: Instruction Generation

[0344] The server generates optimal exercise instructions for the user based on the analysis results. The input is the evaluation results of the psychological and physical state obtained from data analysis. The output is exercise instructions and psychological support information created using a generative AI model. For example, if it is found that the user is in a high-stress state, the server will create instructions recommending deep breathing exercises for relaxation. It can also select and play appropriate music. This process uses an algorithm to select the most appropriate feedback strategy from a large amount of data.

[0345] Step 4: Provide feedback

[0346] The device provides the user with instructions sent from the server. These can be displayed as text messages on the device's screen or directly provided as voice notifications. Input consists of exercise instructions and music selection information received from the server. Output is real-time feedback provided to the user through visual and auditory means. This allows the user to receive training optimized for their psychological and physical state, maximizing its effectiveness.

[0347] (Application Example 2)

[0348] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0349] While the demand for health management and fitness is increasing in modern society, there is a problem in finding training methods and relaxation techniques that are optimized for each individual's physical and mental state. In particular, there is a need for methods that analyze movement data and emotional states in real time and suggest appropriate exercises and relaxation techniques based on that analysis.

[0350] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0351] In this invention, the server includes a device for collecting operational data in real time, a computer for receiving and analyzing data transmitted from the device, a function for providing instructions generated by the computer to a human via the device, and a computation means for generating musical information according to the human's emotional state. This enables customized fitness and relaxation support tailored to the user's health condition and emotions.

[0352] "Motion data" refers to information about the user's body movements, including elements such as speed, direction, and timing of the movement.

[0353] A "device" is a piece of equipment that collects operational data and transmits it to a server, and is worn on the user's body.

[0354] A "computer" is a computer system that receives and analyzes data transmitted from a device.

[0355] "Instructions" refer to the content of exercise guidance and behavioral suggestions for the user, generated based on data analyzed by the computer.

[0356] "Computational means" refers to algorithms and programs that provide the function of analyzing data within a computer and generating information for a specific purpose.

[0357] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and respiratory rate.

[0358] "Emotional state" refers to information that indicates the user's mental state, and is indicated by factors such as voice tone, facial expressions, and heart rate variability.

[0359] "Music information" refers to data of music that is generated according to the user's emotional state and played to enhance the effects of relaxation or exercise.

[0360] The system for carrying out this invention comprises a device worn by the user, a server which is a computer system, and a computing means for providing feedback to the user. The device collects motion data and biometric information in real time and transmits it to the server. The device is equipped with high-precision sensors that accurately grasp the user's movement and physical condition.

[0361] The server has computational capabilities to analyze the received data and generates information to improve the user's movement patterns based on the collected data. AI algorithms are used for this analysis, enabling real-time data processing. The instructions generated from the analysis results are provided to the user as individually optimized feedback.

[0362] Instructions provided to the user are communicated through the device's display or audio output. These instructions include exercise adjustments, relaxation techniques tailored to emotional state, or musical information. This allows users to receive optimal support in their training and daily lives, according to their physical and emotional needs.

[0363] For example, if a user experiences stress while practicing yoga, the device detects this through fluctuations in heart rate. The server generates relaxation-promoting music and breathing instructions, which are then provided to the user through the device. This dynamic feedback not only optimizes the user's performance but also contributes to maintaining their physical and mental well-being.

[0364] The generative AI model allows for further customization. An example of a prompt message would be, "Stress was detected while the user was doing yoga. Please generate relaxation suggestions," enabling feedback tailored to the user's state.

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The device collects user movement data and biometric information in real time. Inputs include information obtained from the user's body, such as heart rate, body temperature, and movement speed and direction. Sensors detect this information and input it into the system as movement data. Outputs are datasets representing the user's physical and exercise states.

[0368] Step 2:

[0369] The terminal sends the collected data to the server. The data is transferred over the network using an appropriate communication protocol. The input is the dataset obtained in step 1, and the output is the total data package received by the server.

[0370] Step 3:

[0371] The server analyzes the data it receives. The input consists of biometric and motion data transmitted from the terminal. The server uses an AI algorithm to process the data and evaluate the user's current movement and emotional state. The output consists of analysis results to optimize the user's movement style and feedback commands tailored to their emotional state.

[0372] Step 4:

[0373] The server generates instructions and music information based on the analysis results. The input is the analysis results from step 3. The generating AI model is used to create specific exercise instructions and relaxation-promoting music information tailored to the user's state. The output is a set of instructions and a music list provided to the user.

[0374] Step 5:

[0375] The server sends the generated instructions and music information to the terminal. The input is the instruction set and music list generated in step 4. The output is the feedback data received by the terminal.

[0376] Step 6:

[0377] The terminal provides the user with the instructions it receives. Input consists of a set of instructions and music information from the server. The terminal communicates these to the user through its display and audio output. Output is music playback for exercise guidance or relaxation for the user.

[0378] Step 7:

[0379] The user acts according to the provided instructions, performing exercise and relaxation. The input is the specific instructions provided by the device. The user's actions update the motion data and biometric information, initiating the next feedback cycle. The output is the user's improved exercise performance and state of relaxation.

[0380] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0381] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0382] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0386] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0387] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0388] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0389] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0390] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0391] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0392] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0393] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0394] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0395] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0396] This invention comprises a device worn by the user, a server that receives and analyzes data from the device, and a system for providing feedback to the user. This allows the user to improve their exercise in real time and perform appropriate training.

[0397] First, a device is attached to the user's body and collects the user's movement data in real time. The device senses data such as pace, heart rate, and acceleration, and stores this data at regular intervals. The collected data is immediately transmitted to a server using wireless communication technology.

[0398] The server performs detailed analysis based on the received data. The server incorporates advanced algorithms to quickly analyze the user's exercise patterns, heart rate changes, and acceleration patterns. The server also retrieves weather data from an external weather API and incorporates it into the analysis results, generating information useful for improving training courses and form.

[0399] Based on the analyzed data, the server generates real-time instructions tailored to the user's condition. These instructions include suggestions for improving form, adjusting exercise intensity, and timing rest periods. Music suitable for the user's heart rate and pace is also selected and provided as part of the training.

[0400] The generated instructions are sent to the user via the terminal. The terminal immediately notifies the user and, for example, displays advice on improving exercise on the display. This allows the user to monitor their exercise status in real time during training and make necessary adjustments.

[0401] For example, if the pace is too fast and the heart rate is higher than normal, the server will detect this and provide the user with instructions via the device to "slow down and stabilize your heart rate." Furthermore, if the heart rate is stable, upbeat music will play to encourage the user, making the training more enjoyable.

[0402] In this way, by using this system, users can optimize their exercise and effectively improve their fitness while simultaneously reducing the risk of injury.

[0403] The following describes the processing flow.

[0404] Step 1:

[0405] The device collects user activity data. Using sensors, the device acquires information such as pace, heart rate, and acceleration in real time and temporarily stores the data in its internal memory.

[0406] Step 2:

[0407] The terminal sends the data it collects to the server at regular intervals. The terminal uses wireless communication to send this data to the server in packet format.

[0408] Step 3:

[0409] The server receives data sent from the terminal. After receiving the data, the server converts it into an appropriate data format for analysis.

[0410] Step 4:

[0411] The server analyzes the received data. An algorithm within the server evaluates heart rate patterns and acceleration changes to diagnose the user's exercise style and physical condition.

[0412] Step 5:

[0413] The server retrieves weather data. The server accesses external weather services and incorporates the current weather and forecast into the data analysis.

[0414] Step 6:

[0415] The server generates instructions for the user based on the analysis results. The server creates specific advice, such as form improvement, training intensity adjustments, and music suggestions.

[0416] Step 7:

[0417] The server sends the generated instructions and music to the terminal. The server immediately sends this information to the terminal for the user interface.

[0418] Step 8:

[0419] The device notifies the user of instructions. The device supports training by displaying advice on its screen and playing selected music.

[0420] Step 9:

[0421] The user adjusts their exercise based on advice from their device. They follow the instructions to correct their form and pace, resulting in more efficient training.

[0422] (Example 1)

[0423] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0424] Traditional exercise support systems have faced challenges in providing real-time feedback and personalized advice. A lack of information regarding appropriate exercise intensity and form improvements can reduce exercise efficiency and increase the risk of injury. Furthermore, the lack of training guidance that takes external environmental factors into account results in insufficient comprehensive exercise support.

[0425] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0426] In this invention, the server includes means for analyzing information from a measuring device that acquires physical information during exercise in real time, means for using a calculation method to generate advice for improving an individual's exercise habits, means for reflecting external environmental information in the analysis results, and means for generating music guidance and providing accompaniment suitable for the individual's training. As a result, the user can receive real-time optimized exercise guidance and improve their fitness efficiently and safely.

[0427] A "measuring device" is a device that collects a user's physical information in real time during exercise and acquires it as data.

[0428] A "calculation unit" is a device that receives information transferred from a measuring device, performs complex analysis, and generates useful feedback for the user.

[0429] "Environmental information" refers to data about external conditions that affect the user's movement, such as external weather and location information.

[0430] A "calculation method" refers to a set of algorithms and processes that generate advice for improving an individual's exercise habits based on the acquired data.

[0431] "Music guidance" refers to playlists or selections of music curated to support user training and improve motivation.

[0432] This invention consists of a terminal worn by the user, a server that receives and analyzes data from the terminal, and a system for providing feedback to the user. The user can wear the terminal on their body and collect data in real time during exercise. The terminal uses internally mounted heart rate sensors, accelerometers, GPS modules, etc., to collect data such as pace, heart rate, distance traveled, and acceleration.

[0433] The device transmits collected data to the server using wireless communication technology (e.g., Bluetooth or Wi-Fi). The server analyzes the received data using a dedicated algorithm. This algorithm evaluates the user's exercise style, heart rate changes, and acceleration patterns, and generates exercise improvement suggestions tailored to the user. The server also obtains environmental information via an external weather API and incorporates it into the analysis results to provide more accurate guidance.

[0434] The generated exercise instructions and music guidance are provided to the user through a terminal. The terminal is equipped with a display and audio output device, and notifies the user of appropriate feedback in real time. For example, if the server detects that the heart rate is rising due to excessive exercise, it generates a message such as "Please slow down" and notifies the user through the terminal. In addition, music appropriate to the exercise situation is played to increase the user's motivation.

[0435] As a concrete example, the server analyzes the user's heart rate data and suggests adjustments to rest times and exercise intensity as needed. Music guidance is generated based on the individual's exercise pace, improving training efficiency. An example of a prompt message could be written in the format of, "Design a program that analyzes the user's exercise data and provides optimal exercise support information in real time. Specifically, I would like information on a system that suggests exercise instructions and music suitable for the user based on heart rate and pace data." In this way, the present invention enables users to exercise more safely and effectively, maximizing their fitness benefits.

[0436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0437] Step 1:

[0438] The device collects real-time physical information from the user during exercise while they are wearing it. It receives data from sensors such as a heart rate sensor, accelerometer, and GPS module as input. It then performs the specific operation of storing multiple data points acquired from these sensors in its internal memory.

[0439] Step 2:

[0440] The terminal organizes and packages the accumulated data at regular intervals and transmits it to the server using wireless communication technology (Bluetooth or Wi-Fi). The input is a collection of raw data, and the output uses a protocol to securely transmit that data wirelessly.

[0441] Step 3:

[0442] The server receives data sent from the terminal and executes a dedicated analysis algorithm. Based on the input data, it processes the data to analyze exercise patterns, heart rate changes, and acceleration patterns, and then outputs exercise improvement information tailored to the user. Specifically, the algorithm analyzes the trends in the data and generates the analysis results.

[0443] Step 4:

[0444] The server obtains environmental information using an external weather API. It takes weather data based on the current geographic location as input and integrates it with the previously obtained analysis results. This generates output that produces environmentally conscious exercise instructions.

[0445] Step 5:

[0446] The server creates specific exercise instructions and music recommendations for the user based on the analyzed results. Inputs include the analysis results, weather data, and the user's individual profile information. It then selects exercise instructions and music suitable for the user and converts them into an operation file as output.

[0447] Step 6:

[0448] The server sends the generated notification information to the terminal. The outputted instructions and music guidance content are delivered to the terminal. Specifically, data packets are sent from the server to the terminal.

[0449] Step 7:

[0450] The device notifies the user in real time of received exercise instructions and music guidance. It receives feedback data as input, displays instructions visually on the display, and outputs music through the earphones. Specifically, it provides immediate feedback to the user through real-time display and audio output.

[0451] (Application Example 1)

[0452] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0453] In recent years, with increasing public interest in exercise and health management, there has been a growing demand for technology that can analyze users' movements in real time and provide appropriate feedback. However, currently, analysis that takes into account individual physical conditions and external environments is insufficient, making it difficult to provide personalized advice. This leads to challenges such as users being unable to train efficiently and an increased risk of injury from strenuous exercise.

[0454] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0455] In this invention, the server includes means for using a device that collects operational information in real time, means for using a computer that receives and analyzes information transmitted from the device, and means for comprehensively analyzing the user's physical data and environmental data. This makes it possible to provide real-time advice based on the user's individual condition.

[0456] "Motion information" refers to data related to the user's body movements, which is collected and analyzed in real time.

[0457] A "device" is a device worn by a user and used to collect motion information.

[0458] A "computer" is a computer system that analyzes information received from a device and performs data processing to provide appropriate feedback to the user.

[0459] "Means" refers to the methods or techniques incorporated into a system to achieve a specific function.

[0460] A "computational algorithm" is a set of procedures and processing methods used to generate data based on analyzed information in order to improve the user's activity patterns.

[0461] An "information processing device" is part of a system that has the function of providing appropriate feedback based on recorded user actions.

[0462] "Health data" refers to information related to the user's physiological state, representing data about the body's functions and condition.

[0463] "Environmental data" refers to information about weather and surrounding conditions related to the location and time of the user's activities.

[0464] The system implementing this invention aims to provide appropriate feedback to the user by collecting and analyzing operational information in real time. The user wears a specific device on their body, which collects operational information and health data. This data is transmitted to a computer via wireless communication.

[0465] The computer efficiently analyzes received motion information by acquiring data from multiple sensors and cameras and generating situation-appropriate feedback. For analysis, it uses machine learning frameworks such as TensorFlow to process the data and build models. Furthermore, the computer also acquires environmental data and incorporates it into the instructions it provides. These instructions include specific advice for improving form and adjusting condition during activities.

[0466] As a concrete example, suppose a user is jogging and the device uses an accelerometer to record their running pace. This data is transmitted in real time to a computer, which compares the user's pace and heart rate to generate advice on maintaining an appropriate exercise intensity.

[0467] Furthermore, it is possible to generate feedback messages based on the analysis results using a generative AI model. An example of a prompt message would be: "Analyze the user's movement data while jogging and generate suggestions for efficient form improvement. For example, please provide simple advice such as relaxing your shoulders." Based on this prompt message, the system has a structure that can instantly provide easy-to-understand feedback to the user.

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The terminal collects motion information. The user wears the device, which measures acceleration, pace, and heart rate in real time through sensors. The input at this time is the user's physical movement, and the output is the collected motion information. The terminal temporarily stores this data in preparation for the next transmission.

[0471] Step 2:

[0472] The terminal sends the collected data to the server. Using wireless communication, the terminal sends all measured operational information to the server. In this process, operational information is input as data packets from the terminal and output as received data on the server side.

[0473] Step 3:

[0474] The server analyzes the data. The server uses machine learning frameworks such as TensorFlow to analyze the received motion information. The input here is motion information from the terminal, and the output is the analyzed movement patterns and recommended training guidance. Through this analysis, the server evaluates the user's exercise efficiency and generates feedback for improving their form.

[0475] Step 4:

[0476] The server collects environmental data and incorporates it into the analysis results. The server obtains current environmental data from an external weather API and integrates it into the analysis results. The input to this process is weather data, and the output is environment-aware exercise advice. The server takes temperature, humidity, etc., into consideration and modifies the user's exercise plan.

[0477] Step 5:

[0478] The server uses a generative AI model to generate optimal feedback. Based on the previously analyzed data and environmental information, the server sends prompts to the generative AI model to obtain the optimal feedback for the user. The input for this step is the analyzed data and prompt text, and the output is the feedback text.

[0479] Step 6:

[0480] The server sends feedback to the terminal, which then presents it to the user. The generated feedback is sent to the terminal and provided to the user visually or audibly. The input is feedback data from the server, and the output is notifications and advice to the user. The terminal immediately notifies the user of the information via its display or speaker.

[0481] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0482] This invention provides a system that acquires a user's biometric and emotional data in real time and supports the improvement of their movement patterns based on that data. The system consists of a terminal that collects motion data and emotional data, a server that analyzes the data and generates instructions, and a feedback function that provides those instructions to the user.

[0483] The device is worn on the user's body and collects motion data in real time. It also features an emotion engine that recognizes emotions through the user's voice, facial expressions, or physiological indicators. This device immediately transmits the acquired motion and emotion data to a server.

[0484] The server continuously receives and analyzes data sent from the terminal. The server's algorithm comprehensively evaluates data such as the user's heart rate, pace, and emotional state. Emotional data is used to understand the user's current psychological state and analyze how their emotions affect their exercise style.

[0485] Based on the analysis results, the server generates the most effective instructions for the user. For example, if the user is feeling stressed, it might suggest breathing exercises to help them relax. It might also recommend music tailored to their emotional state, all designed to maximize the effectiveness of the training.

[0486] The generated instructions and music are provided to the user via the device. The device provides notifications and plays music through its display and audio output. This allows the user to continue exercising while receiving instructions optimized for their current mental state and exercise status.

[0487] For example, if a user wants to continue training quickly, but the emotion engine detects impatience, the server might analyze this and send a message to the device saying, "Don't rush, maintain your pace." The device might also play calming or focus-enhancing music to help improve the user's performance.

[0488] Thus, by introducing an emotion engine, the present invention can enhance the user's overall training experience and provide support that is more appropriate to their mental and physical state.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] The device collects the user's biometric and emotional data in real time. The device uses an accelerometer and heart rate sensor to acquire motion data and operates an emotion engine that recognizes emotions from the user's voice and facial expression data.

[0492] Step 2:

[0493] The device sends collected biometric and emotional data to the server. The device wirelessly transmits this data to the server at regular intervals.

[0494] Step 3:

[0495] The server analyzes the data received from the terminal. The server analyzes the user's behavioral and emotional data to evaluate the user's state (e.g., fatigue level and motivation level).

[0496] Step 4:

[0497] The server generates instructions and music based on the analysis results. The server generates exercise advice based on the user's psychological state and selects appropriate music.

[0498] Step 5:

[0499] The server sends generated instructions and music to the terminal. The server immediately sends this feedback back to the terminal.

[0500] Step 6:

[0501] The device notifies the user of instructions and plays music. The device displays advice via its screen and plays the selected music through speakers or headphones.

[0502] Step 7:

[0503] The user adjusts their exercise according to the advice from their device. Based on the notified instructions, the user adjusts the pace and form of their exercise to maximize the training effect.

[0504] (Example 2)

[0505] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0506] In modern life, people often experience stress and anxiety, making it difficult to train efficiently and healthily. Furthermore, there is a lack of systems that provide not only exercise instruction but also feedback tailored to the user's psychological state. Therefore, there is a need for a system that can analyze physiological and psychological data in real time and provide users with appropriate exercise guidance and psychological support.

[0507] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0508] In this invention, the server includes means for receiving and analyzing motion data and emotional data, means for generating information suitable for the user's exercise style and psychological state based on the analysis results, and means for providing the information to the user through a feedback device. This enables the user to continue safe and effective training based on instructions optimized for their psychological and physical state at any given time.

[0509] A "measuring device" is a device that has the function of collecting motion data and emotion data in real time and transmitting it to a server.

[0510] A "calculation unit" is a device that analyzes data received from measuring devices and has the function of comprehensively evaluating heart rate, exercise pace, and emotional state.

[0511] A "feedback device" is a device that conveys instructions generated by a computing unit to the user, and has the function of providing information through voice or visuals.

[0512] A "generative model" is an algorithm that generates information tailored to the user's exercise style and psychological state based on analyzed data.

[0513] "Environmental data" refers to data about external factors that affect the user's training and psychological state, such as weather, temperature, and humidity.

[0514] This invention is a system that collects a user's physiological and emotional data in real time and supports the improvement of exercise patterns based on that data. The system consists of a measuring device, a computing device, and a feedback device. The measuring device uses a wearable device (e.g., a smartwatch) that is attached to the user's body and acquires physiological data such as heart rate and exercise pace. This measuring device should preferably be equipped with a microphone and a camera to recognize emotional states through voice and facial expressions. The collected data is transmitted to the computing device via wireless communication.

[0515] The server functions as a computing device, including a high-performance processor and large memory capacity to analyze data collected in real time. It utilizes machine learning libraries such as TensorFlow and Scikit-learn for rapid and accurate data processing. This analysis allows the server to comprehensively assess the user's heart rate, exercise pace, and emotional state. However, beyond data analysis, it also uses generative AI models based on the analysis results to generate appropriate feedback, providing optimal exercise instructions and psychological support for individual users.

[0516] Feedback information, such as generated instructions and recommended music, is provided to the user via a feedback device. Information is communicated to the user in real time through text messages and voice notifications displayed on the device. This allows the user to continue training safely and effectively while receiving instructions optimized for their psychological state and exercise condition.

[0517] For example, if a user experiences extreme tension during training, the server analyzes this data and sends a message to the feedback device saying, "Take slow, deep breaths to relax." It can also select and play music that promotes relaxation. This allows users to train efficiently while reducing tension.

[0518] Examples of prompts for a generative AI model include: "Design an algorithm that considers the user's current emotional state and provides optimal exercise instructions and music. Consider how to maximize the user's psychology and exercise performance."

[0519] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0520] Step 1: Data Collection

[0521] The device uses a wearable device attached to the user's body to collect motion and emotional data in real time. Specifically, it measures exercise intensity and pace using an accelerometer and records heart rate using a heart rate sensor. It also uses a microphone and camera on the device to analyze voice and facial expressions and recognize emotional states. Inputs include physiological and emotional data obtained from biometric sensors and the emotion recognition system. Outputs are digital data ready to be sent to the server. This information is transmitted to the server in real time using the data transmission function.

[0522] Step 2: Data Analysis

[0523] The server receives data sent from the terminal. For analysis, it uses data science libraries built in, for example, Python or R. The input is heart rate, exercise pace, and emotional state data sent from the terminal. Based on this data, the server uses statistical analysis and machine learning models to generate output that identifies the user's psychological and physical state. Specifically, it uses deep learning tools such as TensorFlow to evaluate the interrelationships between data and identify factors that influence the user's current psychological and physical state.

[0524] Step 3: Instruction Generation

[0525] The server generates optimal exercise instructions for the user based on the analysis results. The input is the evaluation results of the psychological and physical state obtained from data analysis. The output is exercise instructions and psychological support information created using a generative AI model. For example, if it is found that the user is in a high-stress state, the server will create instructions recommending deep breathing exercises for relaxation. It can also select and play appropriate music. This process uses an algorithm to select the most appropriate feedback strategy from a large amount of data.

[0526] Step 4: Provide feedback

[0527] The device provides the user with instructions sent from the server. These can be displayed as text messages on the device's screen or directly provided as voice notifications. Input consists of exercise instructions and music selection information received from the server. Output is real-time feedback provided to the user through visual and auditory means. This allows the user to receive training optimized for their psychological and physical state, maximizing its effectiveness.

[0528] (Application Example 2)

[0529] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0530] While the demand for health management and fitness is increasing in modern society, there is a problem in finding training methods and relaxation techniques that are optimized for each individual's physical and mental state. In particular, there is a need for methods that analyze movement data and emotional states in real time and suggest appropriate exercises and relaxation techniques based on that analysis.

[0531] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0532] In this invention, the server includes a device for collecting operational data in real time, a computer for receiving and analyzing data transmitted from the device, a function for providing instructions generated by the computer to a human via the device, and a computation means for generating musical information according to the human's emotional state. This enables customized fitness and relaxation support tailored to the user's health condition and emotions.

[0533] "Motion data" refers to information about the user's body movements, including elements such as speed, direction, and timing of the movement.

[0534] A "device" is a piece of equipment that collects operational data and transmits it to a server, and is worn on the user's body.

[0535] A "computer" is a computer system that receives and analyzes data transmitted from a device.

[0536] "Instructions" refer to the content of exercise guidance and behavioral suggestions for the user, generated based on data analyzed by the computer.

[0537] "Computational means" refers to algorithms and programs that provide the function of analyzing data within a computer and generating information for a specific purpose.

[0538] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and respiratory rate.

[0539] "Emotional state" refers to information that indicates the user's mental state, and is indicated by factors such as voice tone, facial expressions, and heart rate variability.

[0540] "Music information" refers to data of music that is generated according to the user's emotional state and played to enhance the effects of relaxation or exercise.

[0541] The system for carrying out this invention comprises a device worn by the user, a server which is a computer system, and a computing means for providing feedback to the user. The device collects motion data and biometric information in real time and transmits it to the server. The device is equipped with high-precision sensors that accurately grasp the user's movement and physical condition.

[0542] The server has computational capabilities to analyze the received data and generates information to improve the user's movement patterns based on the collected data. AI algorithms are used for this analysis, enabling real-time data processing. The instructions generated from the analysis results are provided to the user as individually optimized feedback.

[0543] Instructions provided to the user are communicated through the device's display or audio output. These instructions include exercise adjustments, relaxation techniques tailored to emotional state, or musical information. This allows users to receive optimal support in their training and daily lives, according to their physical and emotional needs.

[0544] For example, if a user experiences stress while practicing yoga, the device detects this through fluctuations in heart rate. The server generates relaxation-promoting music and breathing instructions, which are then provided to the user through the device. This dynamic feedback not only optimizes the user's performance but also contributes to maintaining their physical and mental well-being.

[0545] The generative AI model allows for further customization. An example of a prompt message would be, "Stress was detected while the user was doing yoga. Please generate relaxation suggestions," enabling feedback tailored to the user's state.

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] The device collects user movement data and biometric information in real time. Inputs include information obtained from the user's body, such as heart rate, body temperature, and movement speed and direction. Sensors detect this information and input it into the system as movement data. Outputs are datasets representing the user's physical and exercise states.

[0549] Step 2:

[0550] The terminal sends the collected data to the server. The data is transferred over the network using an appropriate communication protocol. The input is the dataset obtained in step 1, and the output is the total data package received by the server.

[0551] Step 3:

[0552] The server analyzes the data it receives. The input consists of biometric and motion data transmitted from the terminal. The server uses an AI algorithm to process the data and evaluate the user's current movement and emotional state. The output consists of analysis results to optimize the user's movement style and feedback commands tailored to their emotional state.

[0553] Step 4:

[0554] The server generates instructions and music information based on the analysis results. The input is the analysis results from step 3. The generating AI model is used to create specific exercise instructions and relaxation-promoting music information tailored to the user's state. The output is a set of instructions and a music list provided to the user.

[0555] Step 5:

[0556] The server sends the generated instructions and music information to the terminal. The input is the instruction set and music list generated in step 4. The output is the feedback data received by the terminal.

[0557] Step 6:

[0558] The terminal provides the user with the instructions it receives. Input consists of a set of instructions and music information from the server. The terminal communicates these to the user through its display and audio output. Output is music playback for exercise guidance or relaxation for the user.

[0559] Step 7:

[0560] The user acts according to the provided instructions, performing exercise and relaxation. The input is the specific instructions provided by the device. The user's actions update the motion data and biometric information, initiating the next feedback cycle. The output is the user's improved exercise performance and state of relaxation.

[0561] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0562] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0563] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0564] [Fourth Embodiment]

[0565] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0566] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0567] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0568] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0569] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0570] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0571] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0572] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0573] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0574] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0575] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0576] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0577] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0578] This invention comprises a device worn by the user, a server that receives and analyzes data from the device, and a system for providing feedback to the user. This allows the user to improve their exercise in real time and perform appropriate training.

[0579] First, a device is attached to the user's body and collects the user's movement data in real time. The device senses data such as pace, heart rate, and acceleration, and stores this data at regular intervals. The collected data is immediately transmitted to a server using wireless communication technology.

[0580] The server performs detailed analysis based on the received data. The server incorporates advanced algorithms to quickly analyze the user's exercise patterns, heart rate changes, and acceleration patterns. The server also retrieves weather data from an external weather API and incorporates it into the analysis results, generating information useful for improving training courses and form.

[0581] Based on the analyzed data, the server generates real-time instructions tailored to the user's condition. These instructions include suggestions for improving form, adjusting exercise intensity, and timing rest periods. Music suitable for the user's heart rate and pace is also selected and provided as part of the training.

[0582] The generated instructions are sent to the user via the terminal. The terminal immediately notifies the user and, for example, displays advice on improving exercise on the display. This allows the user to monitor their exercise status in real time during training and make necessary adjustments.

[0583] For example, if the pace is too fast and the heart rate is higher than normal, the server will detect this and provide the user with instructions via the device to "slow down and stabilize your heart rate." Furthermore, if the heart rate is stable, upbeat music will play to encourage the user, making the training more enjoyable.

[0584] In this way, by using this system, users can optimize their exercise and effectively improve their fitness while simultaneously reducing the risk of injury.

[0585] The following describes the processing flow.

[0586] Step 1:

[0587] The device collects user activity data. Using sensors, the device acquires information such as pace, heart rate, and acceleration in real time and temporarily stores the data in its internal memory.

[0588] Step 2:

[0589] The terminal sends the data it collects to the server at regular intervals. The terminal uses wireless communication to send this data to the server in packet format.

[0590] Step 3:

[0591] The server receives data sent from the terminal. After receiving the data, the server converts it into an appropriate data format for analysis.

[0592] Step 4:

[0593] The server analyzes the received data. An algorithm within the server evaluates heart rate patterns and acceleration changes to diagnose the user's exercise style and physical condition.

[0594] Step 5:

[0595] The server retrieves weather data. The server accesses external weather services and incorporates the current weather and forecast into the data analysis.

[0596] Step 6:

[0597] The server generates instructions for the user based on the analysis results. The server creates specific advice, such as form improvement, training intensity adjustments, and music suggestions.

[0598] Step 7:

[0599] The server sends the generated instructions and music to the terminal. The server immediately sends this information to the terminal for the user interface.

[0600] Step 8:

[0601] The device notifies the user of instructions. The device supports training by displaying advice on its screen and playing selected music.

[0602] Step 9:

[0603] The user adjusts their exercise based on advice from their device. They follow the instructions to correct their form and pace, resulting in more efficient training.

[0604] (Example 1)

[0605] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0606] Traditional exercise support systems have faced challenges in providing real-time feedback and personalized advice. A lack of information regarding appropriate exercise intensity and form improvements can reduce exercise efficiency and increase the risk of injury. Furthermore, the lack of training guidance that takes external environmental factors into account results in insufficient comprehensive exercise support.

[0607] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0608] In this invention, the server includes means for analyzing information from a measuring device that acquires physical information during exercise in real time, means for using a calculation method to generate advice for improving an individual's exercise habits, means for reflecting external environmental information in the analysis results, and means for generating music guidance and providing accompaniment suitable for the individual's training. As a result, the user can receive real-time optimized exercise guidance and improve their fitness efficiently and safely.

[0609] A "measuring device" is a device that collects a user's physical information in real time during exercise and acquires it as data.

[0610] A "calculation unit" is a device that receives information transferred from a measuring device, performs complex analysis, and generates useful feedback for the user.

[0611] "Environmental information" refers to data about external conditions that affect the user's movement, such as external weather and location information.

[0612] A "calculation method" refers to a set of algorithms and processes that generate advice for improving an individual's exercise habits based on the acquired data.

[0613] "Music guidance" refers to playlists or selections of music curated to support user training and improve motivation.

[0614] This invention consists of a terminal worn by the user, a server that receives and analyzes data from the terminal, and a system for providing feedback to the user. The user can wear the terminal on their body and collect data in real time during exercise. The terminal uses internally mounted heart rate sensors, accelerometers, GPS modules, etc., to collect data such as pace, heart rate, distance traveled, and acceleration.

[0615] The device transmits collected data to the server using wireless communication technology (e.g., Bluetooth or Wi-Fi). The server analyzes the received data using a dedicated algorithm. This algorithm evaluates the user's exercise style, heart rate changes, and acceleration patterns, and generates exercise improvement suggestions tailored to the user. The server also obtains environmental information via an external weather API and incorporates it into the analysis results to provide more accurate guidance.

[0616] The generated exercise instructions and music guidance are provided to the user through a terminal. The terminal is equipped with a display and audio output device, and notifies the user of appropriate feedback in real time. For example, if the server detects that the heart rate is rising due to excessive exercise, it generates a message such as "Please slow down" and notifies the user through the terminal. In addition, music appropriate to the exercise situation is played to increase the user's motivation.

[0617] As a concrete example, the server analyzes the user's heart rate data and suggests adjustments to rest times and exercise intensity as needed. Music guidance is generated based on the individual's exercise pace, improving training efficiency. An example of a prompt message could be written in the format of, "Design a program that analyzes the user's exercise data and provides optimal exercise support information in real time. Specifically, I would like information on a system that suggests exercise instructions and music suitable for the user based on heart rate and pace data." In this way, the present invention enables users to exercise more safely and effectively, maximizing their fitness benefits.

[0618] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0619] Step 1:

[0620] The device collects real-time physical information from the user during exercise while they are wearing it. It receives data from sensors such as a heart rate sensor, accelerometer, and GPS module as input. It then performs the specific operation of storing multiple data points acquired from these sensors in its internal memory.

[0621] Step 2:

[0622] The terminal organizes and packages the accumulated data at regular intervals and transmits it to the server using wireless communication technology (Bluetooth or Wi-Fi). The input is a collection of raw data, and the output uses a protocol to securely transmit that data wirelessly.

[0623] Step 3:

[0624] The server receives data sent from the terminal and executes a dedicated analysis algorithm. Based on the input data, it processes the data to analyze exercise patterns, heart rate changes, and acceleration patterns, and then outputs exercise improvement information tailored to the user. Specifically, the algorithm analyzes the trends in the data and generates the analysis results.

[0625] Step 4:

[0626] The server obtains environmental information using an external weather API. It takes weather data based on the current geographic location as input and integrates it with the previously obtained analysis results. This generates output that produces environmentally conscious exercise instructions.

[0627] Step 5:

[0628] The server creates specific exercise instructions and music recommendations for the user based on the analyzed results. Inputs include the analysis results, weather data, and the user's individual profile information. It then selects exercise instructions and music suitable for the user and converts them into an operation file as output.

[0629] Step 6:

[0630] The server sends the generated notification information to the terminal. The outputted instructions and music guidance content are delivered to the terminal. Specifically, data packets are sent from the server to the terminal.

[0631] Step 7:

[0632] The device notifies the user in real time of received exercise instructions and music guidance. It receives feedback data as input, displays instructions visually on the display, and outputs music through the earphones. Specifically, it provides immediate feedback to the user through real-time display and audio output.

[0633] (Application Example 1)

[0634] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] In recent years, with increasing public interest in exercise and health management, there has been a growing demand for technology that can analyze users' movements in real time and provide appropriate feedback. However, currently, analysis that takes into account individual physical conditions and external environments is insufficient, making it difficult to provide personalized advice. This leads to challenges such as users being unable to train efficiently and an increased risk of injury from strenuous exercise.

[0636] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0637] In this invention, the server includes means for using a device that collects operational information in real time, means for using a computer that receives and analyzes information transmitted from the device, and means for comprehensively analyzing the user's physical data and environmental data. This makes it possible to provide real-time advice based on the user's individual condition.

[0638] "Motion information" refers to data related to the user's body movements, which is collected and analyzed in real time.

[0639] A "device" is a device worn by a user and used to collect motion information.

[0640] A "computer" is a computer system that analyzes information received from a device and performs data processing to provide appropriate feedback to the user.

[0641] "Means" refers to the methods or techniques incorporated into a system to achieve a specific function.

[0642] A "computational algorithm" is a set of procedures and processing methods used to generate data based on analyzed information in order to improve the user's activity patterns.

[0643] An "information processing device" is part of a system that has the function of providing appropriate feedback based on recorded user actions.

[0644] "Health data" refers to information related to the user's physiological state, representing data about the body's functions and condition.

[0645] "Environmental data" refers to information about weather and surrounding conditions related to the location and time of the user's activities.

[0646] The system implementing this invention aims to provide appropriate feedback to the user by collecting and analyzing operational information in real time. The user wears a specific device on their body, which collects operational information and health data. This data is transmitted to a computer via wireless communication.

[0647] The computer efficiently analyzes received motion information by acquiring data from multiple sensors and cameras and generating situation-appropriate feedback. For analysis, it uses machine learning frameworks such as TensorFlow to process the data and build models. Furthermore, the computer also acquires environmental data and incorporates it into the instructions it provides. These instructions include specific advice for improving form and adjusting condition during activities.

[0648] As a concrete example, suppose a user is jogging and the device uses an accelerometer to record their running pace. This data is transmitted in real time to a computer, which compares the user's pace and heart rate to generate advice on maintaining an appropriate exercise intensity.

[0649] Furthermore, it is possible to generate feedback messages based on the analysis results using a generative AI model. An example of a prompt message would be: "Analyze the user's movement data while jogging and generate suggestions for efficient form improvement. For example, please provide simple advice such as relaxing your shoulders." Based on this prompt message, the system has a structure that can instantly provide easy-to-understand feedback to the user.

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The terminal collects motion information. The user wears the device, which measures acceleration, pace, and heart rate in real time through sensors. The input at this time is the user's physical movement, and the output is the collected motion information. The terminal temporarily stores this data in preparation for the next transmission.

[0653] Step 2:

[0654] The terminal sends the collected data to the server. Using wireless communication, the terminal sends all measured operational information to the server. In this process, operational information is input as data packets from the terminal and output as received data on the server side.

[0655] Step 3:

[0656] The server analyzes the data. The server uses machine learning frameworks such as TensorFlow to analyze the received motion information. The input here is motion information from the terminal, and the output is the analyzed movement patterns and recommended training guidance. Through this analysis, the server evaluates the user's exercise efficiency and generates feedback for improving their form.

[0657] Step 4:

[0658] The server collects environmental data and incorporates it into the analysis results. The server obtains current environmental data from an external weather API and integrates it into the analysis results. The input to this process is weather data, and the output is environment-aware exercise advice. The server takes temperature, humidity, etc., into consideration and modifies the user's exercise plan.

[0659] Step 5:

[0660] The server uses a generative AI model to generate optimal feedback. Based on the previously analyzed data and environmental information, the server sends prompts to the generative AI model to obtain the optimal feedback for the user. The input for this step is the analyzed data and prompt text, and the output is the feedback text.

[0661] Step 6:

[0662] The server sends feedback to the terminal, which then presents it to the user. The generated feedback is sent to the terminal and provided to the user visually or audibly. The input is feedback data from the server, and the output is notifications and advice to the user. The terminal immediately notifies the user of the information via its display or speaker.

[0663] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0664] This invention provides a system that acquires a user's biometric and emotional data in real time and supports the improvement of their movement patterns based on that data. The system consists of a terminal that collects motion data and emotional data, a server that analyzes the data and generates instructions, and a feedback function that provides those instructions to the user.

[0665] The device is worn on the user's body and collects motion data in real time. It also features an emotion engine that recognizes emotions through the user's voice, facial expressions, or physiological indicators. This device immediately transmits the acquired motion and emotion data to a server.

[0666] The server continuously receives and analyzes data sent from the terminal. The server's algorithm comprehensively evaluates data such as the user's heart rate, pace, and emotional state. Emotional data is used to understand the user's current psychological state and analyze how their emotions affect their exercise style.

[0667] Based on the analysis results, the server generates the most effective instructions for the user. For example, if the user is feeling stressed, it might suggest breathing exercises to help them relax. It might also recommend music tailored to their emotional state, all designed to maximize the effectiveness of the training.

[0668] The generated instructions and music are provided to the user via the device. The device provides notifications and plays music through its display and audio output. This allows the user to continue exercising while receiving instructions optimized for their current mental state and exercise status.

[0669] For example, if a user wants to continue training quickly, but the emotion engine detects impatience, the server might analyze this and send a message to the device saying, "Don't rush, maintain your pace." The device might also play calming or focus-enhancing music to help improve the user's performance.

[0670] Thus, by introducing an emotion engine, the present invention can enhance the user's overall training experience and provide support that is more appropriate to their mental and physical state.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] The device collects the user's biometric and emotional data in real time. The device uses an accelerometer and heart rate sensor to acquire motion data and operates an emotion engine that recognizes emotions from the user's voice and facial expression data.

[0674] Step 2:

[0675] The device sends collected biometric and emotional data to the server. The device wirelessly transmits this data to the server at regular intervals.

[0676] Step 3:

[0677] The server analyzes the data received from the terminal. The server analyzes the user's behavioral and emotional data to evaluate the user's state (e.g., fatigue level and motivation level).

[0678] Step 4:

[0679] The server generates instructions and music based on the analysis results. The server generates exercise advice based on the user's psychological state and selects appropriate music.

[0680] Step 5:

[0681] The server sends generated instructions and music to the terminal. The server immediately sends this feedback back to the terminal.

[0682] Step 6:

[0683] The device notifies the user of instructions and plays music. The device displays advice via its screen and plays the selected music through speakers or headphones.

[0684] Step 7:

[0685] The user adjusts their exercise according to the advice from their device. Based on the notified instructions, the user adjusts the pace and form of their exercise to maximize the training effect.

[0686] (Example 2)

[0687] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0688] In modern life, people often experience stress and anxiety, making it difficult to train efficiently and healthily. Furthermore, there is a lack of systems that provide not only exercise instruction but also feedback tailored to the user's psychological state. Therefore, there is a need for a system that can analyze physiological and psychological data in real time and provide users with appropriate exercise guidance and psychological support.

[0689] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0690] In this invention, the server includes means for receiving and analyzing motion data and emotional data, means for generating information suitable for the user's exercise style and psychological state based on the analysis results, and means for providing the information to the user through a feedback device. This enables the user to continue safe and effective training based on instructions optimized for their psychological and physical state at any given time.

[0691] A "measuring device" is a device that has the function of collecting motion data and emotion data in real time and transmitting it to a server.

[0692] A "calculation unit" is a device that analyzes data received from measuring devices and has the function of comprehensively evaluating heart rate, exercise pace, and emotional state.

[0693] A "feedback device" is a device that conveys instructions generated by a computing unit to the user, and has the function of providing information through voice or visuals.

[0694] A "generative model" is an algorithm that generates information tailored to the user's exercise style and psychological state based on analyzed data.

[0695] "Environmental data" refers to data about external factors that affect the user's training and psychological state, such as weather, temperature, and humidity.

[0696] This invention is a system that collects a user's physiological and emotional data in real time and supports the improvement of exercise patterns based on that data. The system consists of a measuring device, a computing device, and a feedback device. The measuring device uses a wearable device (e.g., a smartwatch) that is attached to the user's body and acquires physiological data such as heart rate and exercise pace. This measuring device should preferably be equipped with a microphone and a camera to recognize emotional states through voice and facial expressions. The collected data is transmitted to the computing device via wireless communication.

[0697] The server functions as a computing device, including a high-performance processor and large memory capacity to analyze data collected in real time. It utilizes machine learning libraries such as TensorFlow and Scikit-learn for rapid and accurate data processing. This analysis allows the server to comprehensively assess the user's heart rate, exercise pace, and emotional state. However, beyond data analysis, it also uses generative AI models based on the analysis results to generate appropriate feedback, providing optimal exercise instructions and psychological support for individual users.

[0698] Feedback information, such as generated instructions and recommended music, is provided to the user via a feedback device. Information is communicated to the user in real time through text messages and voice notifications displayed on the device. This allows the user to continue training safely and effectively while receiving instructions optimized for their psychological state and exercise condition.

[0699] For example, if a user experiences extreme tension during training, the server analyzes this data and sends a message to the feedback device saying, "Take slow, deep breaths to relax." It can also select and play music that promotes relaxation. This allows users to train efficiently while reducing tension.

[0700] Examples of prompts for a generative AI model include: "Design an algorithm that considers the user's current emotional state and provides optimal exercise instructions and music. Consider how to maximize the user's psychology and exercise performance."

[0701] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0702] Step 1: Data Collection

[0703] The device uses a wearable device attached to the user's body to collect motion and emotional data in real time. Specifically, it measures exercise intensity and pace using an accelerometer and records heart rate using a heart rate sensor. It also uses a microphone and camera on the device to analyze voice and facial expressions and recognize emotional states. Inputs include physiological and emotional data obtained from biometric sensors and the emotion recognition system. Outputs are digital data ready to be sent to the server. This information is transmitted to the server in real time using the data transmission function.

[0704] Step 2: Data Analysis

[0705] The server receives data sent from the terminal. For analysis, it uses data science libraries built in, for example, Python or R. The input is heart rate, exercise pace, and emotional state data sent from the terminal. Based on this data, the server uses statistical analysis and machine learning models to generate output that identifies the user's psychological and physical state. Specifically, it uses deep learning tools such as TensorFlow to evaluate the interrelationships between data and identify factors that influence the user's current psychological and physical state.

[0706] Step 3: Instruction Generation

[0707] The server generates optimal exercise instructions for the user based on the analysis results. The input is the evaluation results of the psychological and physical state obtained from data analysis. The output is exercise instructions and psychological support information created using a generative AI model. For example, if it is found that the user is in a high-stress state, the server will create instructions recommending deep breathing exercises for relaxation. It can also select and play appropriate music. This process uses an algorithm to select the most appropriate feedback strategy from a large amount of data.

[0708] Step 4: Provide feedback

[0709] The device provides the user with instructions sent from the server. These can be displayed as text messages on the device's screen or directly provided as voice notifications. Input consists of exercise instructions and music selection information received from the server. Output is real-time feedback provided to the user through visual and auditory means. This allows the user to receive training optimized for their psychological and physical state, maximizing its effectiveness.

[0710] (Application Example 2)

[0711] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] While the demand for health management and fitness is increasing in modern society, there is a problem in finding training methods and relaxation techniques that are optimized for each individual's physical and mental state. In particular, there is a need for methods that analyze movement data and emotional states in real time and suggest appropriate exercises and relaxation techniques based on that analysis.

[0713] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0714] In this invention, the server includes a device for collecting operational data in real time, a computer for receiving and analyzing data transmitted from the device, a function for providing instructions generated by the computer to a human via the device, and a computation means for generating musical information according to the human's emotional state. This enables customized fitness and relaxation support tailored to the user's health condition and emotions.

[0715] "Motion data" refers to information about the user's body movements, including elements such as speed, direction, and timing of the movement.

[0716] A "device" is a piece of equipment that collects operational data and transmits it to a server, and is worn on the user's body.

[0717] A "computer" is a computer system that receives and analyzes data transmitted from a device.

[0718] "Instructions" refer to the content of exercise guidance and behavioral suggestions for the user, generated based on data analyzed by the computer.

[0719] "Computational means" refers to algorithms and programs that provide the function of analyzing data within a computer and generating information for a specific purpose.

[0720] "Biometric information" refers to data that indicates the user's physical condition, such as heart rate, body temperature, and respiratory rate.

[0721] "Emotional state" refers to information that indicates the user's mental state, and is indicated by factors such as voice tone, facial expressions, and heart rate variability.

[0722] "Music information" refers to data of music that is generated according to the user's emotional state and played to enhance the effects of relaxation or exercise.

[0723] The system for carrying out this invention comprises a device worn by the user, a server which is a computer system, and a computing means for providing feedback to the user. The device collects motion data and biometric information in real time and transmits it to the server. The device is equipped with high-precision sensors that accurately grasp the user's movement and physical condition.

[0724] The server has computational capabilities to analyze the received data and generates information to improve the user's movement patterns based on the collected data. AI algorithms are used for this analysis, enabling real-time data processing. The instructions generated from the analysis results are provided to the user as individually optimized feedback.

[0725] Instructions provided to the user are communicated through the device's display or audio output. These instructions include exercise adjustments, relaxation techniques tailored to emotional state, or musical information. This allows users to receive optimal support in their training and daily lives, according to their physical and emotional needs.

[0726] For example, if a user experiences stress while practicing yoga, the device detects this through fluctuations in heart rate. The server generates relaxation-promoting music and breathing instructions, which are then provided to the user through the device. This dynamic feedback not only optimizes the user's performance but also contributes to maintaining their physical and mental well-being.

[0727] The generative AI model allows for further customization. An example of a prompt message would be, "Stress was detected while the user was doing yoga. Please generate relaxation suggestions," enabling feedback tailored to the user's state.

[0728] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0729] Step 1:

[0730] The device collects user movement data and biometric information in real time. Inputs include information obtained from the user's body, such as heart rate, body temperature, and movement speed and direction. Sensors detect this information and input it into the system as movement data. Outputs are datasets representing the user's physical and exercise states.

[0731] Step 2:

[0732] The terminal sends the collected data to the server. The data is transferred over the network using an appropriate communication protocol. The input is the dataset obtained in step 1, and the output is the total data package received by the server.

[0733] Step 3:

[0734] The server analyzes the data it receives. The input consists of biometric and motion data transmitted from the terminal. The server uses an AI algorithm to process the data and evaluate the user's current movement and emotional state. The output consists of analysis results to optimize the user's movement style and feedback commands tailored to their emotional state.

[0735] Step 4:

[0736] The server generates instructions and music information based on the analysis results. The input is the analysis results from step 3. The generating AI model is used to create specific exercise instructions and relaxation-promoting music information tailored to the user's state. The output is a set of instructions and a music list provided to the user.

[0737] Step 5:

[0738] The server sends the generated instructions and music information to the terminal. The input is the instruction set and music list generated in step 4. The output is the feedback data received by the terminal.

[0739] Step 6:

[0740] The terminal provides the user with the instructions it receives. Input consists of a set of instructions and music information from the server. The terminal communicates these to the user through its display and audio output. Output is music playback for exercise guidance or relaxation for the user.

[0741] Step 7:

[0742] The user acts according to the provided instructions, performing exercise and relaxation. The input is the specific instructions provided by the device. The user's actions update the motion data and biometric information, initiating the next feedback cycle. The output is the user's improved exercise performance and state of relaxation.

[0743] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0744] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0745] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0746] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0747] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0748] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0749] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0750] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0751] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0752] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0753] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0754] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0755] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0756] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0757] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0758] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0759] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0760] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0761] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0762] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0763] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0764] The following is further disclosed regarding the embodiments described above.

[0765] (Claim 1)

[0766] A terminal that collects motion data in real time,

[0767] A server that receives and analyzes data transmitted from the aforementioned terminal,

[0768] The server provides instructions to the user via the terminal,

[0769] An algorithm that generates information to improve the user's movement pattern based on the analyzed data,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, wherein the terminal has a function to collect the user's biometric data and transmit it to the server.

[0773] (Claim 3)

[0774] The system according to claim 1, wherein the server has a function to acquire weather data and reflect it in the instructions.

[0775] "Example 1"

[0776] (Claim 1)

[0777] A measuring device that acquires physical information during exercise in real time,

[0778] A computing device that receives and analyzes information transferred from the aforementioned measuring device,

[0779] A means of providing guidance created by the aforementioned computing device to an individual through the aforementioned measuring device,

[0780] A calculation method for generating advice to improve an individual's exercise habits based on the analyzed information,

[0781] A means for incorporating environmental information collected from external sources into the analysis results,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, wherein the measuring device has a function to acquire personal physical information and transfer it to the computing device.

[0785] (Claim 3)

[0786] The system according to claim 1, wherein the computing device has a function to generate musical guidance and provide accompaniment suitable for individual training.

[0787] "Application Example 1"

[0788] (Claim 1)

[0789] A device that collects operational information in real time,

[0790] A computer that receives and analyzes information transmitted from the aforementioned device,

[0791] Means for providing instructions generated by the computer to the user via the device,

[0792] A computational algorithm that generates data to improve the user's activity patterns based on the analyzed information,

[0793] An information processing device equipped with a function to record the user's actions and provide feedback based on those records,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, wherein the device has a function to collect user health data and transmit it to the computer.

[0797] (Claim 3)

[0798] The system according to claim 1, further comprising means for the computer to acquire environmental data and reflect it in the instructions to optimize the user's actions.

[0799] "Example 2 of combining an emotion engine"

[0800] (Claim 1)

[0801] A measuring device that collects motion data and emotion data in real time,

[0802] A computing device that receives data transmitted from the aforementioned measuring device and comprehensively analyzes heart rate, exercise pace, and emotional state,

[0803] A feedback device that provides instructions generated by the aforementioned computing device to the user via the aforementioned measuring device,

[0804] A generative model that generates information suitable for the user's exercise style and psychological state based on the analyzed data,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, wherein the measuring device has a function to collect the user's physiological data and transmit it to the computing device.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein the computing device has a function to acquire environmental data and reflect it in the instructions.

[0810] "Application example 2 when combining with an emotional engine"

[0811] (Claim 1)

[0812] A device that collects operational data in real time,

[0813] A computer that receives and analyzes data transmitted from the aforementioned device,

[0814] The function of providing instructions generated by the computer to a human via the device,

[0815] A calculation means for generating information to improve human movement patterns based on the analyzed data,

[0816] A computational means for generating musical information according to a person's emotional state,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the device has a function to collect human biological information and transmit it to the computer.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising a function that acquires weather information and reflects it in the instructions. [Explanation of Symbols]

[0822] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device that collects operational information in real time, A computer that receives and analyzes information transmitted from the aforementioned device, Means for providing instructions generated by the computer to the user via the device, A computational algorithm that generates data to improve the user's activity patterns based on the analyzed information, An information processing device equipped with a function to record the user's actions and provide feedback based on those records, A system that includes this.

2. The system according to claim 1, wherein the device has a function to collect user health data and transmit it to the computer.

3. The system according to claim 1, further comprising means for the computer to acquire environmental data and reflect it in the instructions to optimize the user's actions.

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