system

Abstract

We provide the system. [Solution] A means of obtaining user physical condition information from a device for collecting activity data, A data analysis means for processing the aforementioned physical condition information, A program generation means that generates an optimal exercise program for the user based on the information processed by the data analysis means, A means of presenting the generated exercise program to the user, A system that includes a feedback mechanism to provide real-time feedback on the user's exercise activities.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including 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] In recent years, it has become difficult for modern people with busy lives to find an effective exercise program suitable for their individual physical conditions and lifestyles and to continuously engage in it. Also, due to the difficulty of self-management, it is difficult to grasp the progress of exercise and maintain motivation, and as a result, the problem is that exercise goals cannot be achieved.

Means for Solving the Problems

[0005] The present invention solves the above problem by providing a system that generates an optimal exercise program for the user by acquiring physical condition information from a device that collects user activity data and processing it with data analysis means. This exercise program is presented to the user and provides real-time feedback, effectively supporting the user's exercise activities. Furthermore, it promotes continuous exercise by providing information that improves motivation according to the progress of the exercise program. In addition, by providing communication means between the device and the server, the transmission and reception of activity data are carried out smoothly.

[0006] "Activity data" refers to numerical information that indicates a user's physical activity, and examples include steps taken, heart rate, calories burned, and exercise time.

[0007] A "device" is a device worn on a user's body that has the function of collecting activity data.

[0008] "Physical condition information" refers to data that indicates the user's health status and athletic ability, and is derived from activity data.

[0009] "Data analysis means" refers to processes and technologies for analyzing collected physical condition information and evaluating the user's health status and exercise progress.

[0010] An "exercise program" is a plan or set of instructions that proposes exercises suitable for the user, and includes the type, intensity, and frequency of exercise.

[0011] "Program generation means" refers to a method or technique for designing and creating an optimized exercise program based on analyzed data.

[0012] "Feedback means" refers to processes or devices that provide users with information, evaluations, and advice regarding their exercise.

[0013] "Communication methods" refer to the technologies and methods necessary to send and receive data between a device and a server. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine.

Embodiments for Implementing the Invention

[0015] 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.

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

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a 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, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides an innovative system for optimizing a user's exercise activities. This system collects physical condition information via an activity device worn by the user and generates and presents an optimal exercise program based on this information. The system enables efficient data communication between the server, terminal, and user's device, providing real-time feedback on exercise information and supporting the user's continued exercise.

[0036] First, the user uses the activity device in their daily life to collect activity data such as steps taken, heart rate, and calories burned. This device has the function of transmitting the collected data to the terminal. The terminal receives the data from the device and sends it to the server.

[0037] The server processes the received data using a dedicated data analysis system. This system analyzes the user's health status, past exercise history, and goals to evaluate the user's exercise capacity and physical condition. Based on this evaluation, the server generates an optimal exercise program for the user. The exercise program includes the type, intensity, and frequency of exercise, and is designed to support the user in achieving their goals.

[0038] The generated exercise program is sent back to the terminal and presented to the user. By performing exercises based on this program, the user can effectively continue their training. The server also provides real-time feedback to the user during and after exercise through various feedback mechanisms. This feedback includes information on exercise progress, areas for improvement, and motivational tips.

[0039] For example, if a user's goal is weight loss, the activity device collects daily walking data. The server analyzes this data and generates a five-day-a-week walking program for the user, specifying the intensity needed to maintain an appropriate heart rate. By following this program and receiving feedback from the device, the user can consistently achieve their exercise goals. In this way, the present invention enables improved health management and exercise effectiveness for the user.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device collects activity data such as heart rate, steps taken, and calories burned in real time from the activity device worn by the user. This data is stored on the device at regular intervals.

[0043] Step 2:

[0044] The device transmits collected activity data to the server using a communication method. The data is typically transferred to the server via Bluetooth or Wi-Fi, and appropriate protocols are configured to ensure stable communication.

[0045] Step 3:

[0046] The server processes the received activity data using data analysis tools. Here, it evaluates the user's current exercise ability and physical condition, while also comparing it with the user's past training history and health information.

[0047] Step 4:

[0048] Based on the data analysis results, the server generates a personalized exercise program for each user. This includes the type of exercise, recommended intensity, frequency, and specific training content.

[0049] Step 5:

[0050] The server sends the generated exercise program to the terminal. The program is customized based on the user's behavioral patterns and preferences.

[0051] Step 6:

[0052] The user follows the exercise program displayed on the device and begins training based on the instructions. During the exercise, the device continuously monitors the user's progress.

[0053] Step 7:

[0054] The server sends real-time feedback to the device based on data collected during exercise, providing users with information. This includes advice on adjusting exercise intensity and areas for improvement.

[0055] Step 8:

[0056] Users review the feedback provided after training to help them improve their next session. The server displays encouraging messages and evaluations based on their performance that day on their device.

[0057] (Example 1)

[0058] 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."

[0059] In modern society, efficiently managing the health and improving the effectiveness of exercise for individual users has become a crucial challenge. Existing systems require considerable effort and time to accurately collect user physical condition information and continuously provide appropriate exercise programs, making it difficult to maintain user motivation. Therefore, there is a need for a system that generates optimal exercise programs tailored to each user's individual goals and provides real-time feedback that matches the user's activity progress.

[0060] 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.

[0061] In this invention, the server includes means for acquiring user physical condition information from a measuring device for collecting activity data, information analysis means for processing the physical condition information, and program construction means for generating an optimal exercise program for the user based on the information processed by the information analysis means. This enables the user to continuously achieve their exercise goals by automatically generating an exercise program optimized for each individual user and providing appropriate feedback in real time.

[0062] "Activity data" refers to information about a user's physical activity, including data such as steps taken, heart rate, and calories burned, which indicates the user's activity level.

[0063] A "measuring device" is a device used to collect user activity data, and includes wearable devices equipped with accelerometers and heart rate sensors.

[0064] "Physical condition information" refers to data that indicates the user's health status and exercise level, and includes information such as heart rate and calories burned obtained from activity data.

[0065] "Information analysis means" refers to a means of processing collected physical condition information and performing analysis based on the user's exercise history and goals.

[0066] "Program construction means" refers to a means for generating an optimal exercise program for the user based on the analysis results obtained by the information analysis means.

[0067] An "evaluation tool" is a means of providing real-time information about a user's exercise activities and providing feedback on their progress and areas for improvement.

[0068] A "generative AI model" is a model based on artificial intelligence technology used to optimize a user's exercise program.

[0069] "Communication means" refers to means for efficient information transmission between measuring devices, terminals, and central processing units.

[0070] This system is designed to optimize users' exercise activities and support individual health management. Its main components consist of measuring devices, terminals, and a server, and these elements communicate with each other to provide users with optimal exercise programs and feedback.

[0071] Users wear a measuring device during their daily lives, which collects activity data. This device includes an accelerometer and a heart rate sensor, and continuously records data such as steps taken, heart rate, and calories burned. The measuring device has the ability to transmit data to a terminal via Bluetooth or Wi-Fi.

[0072] The terminal organizes the data received from the measuring device and sends it to the server. The server analyzes the data using data analysis software such as Python. Generative AI models are used in the analysis to create an optimal exercise program that takes into account various data such as the user's past exercise history and health status.

[0073] The generated exercise program includes exercise type, intensity, and frequency, and is designed to help users achieve their health goals. The exercise program is sent to the device and presented to the user visually. The server also provides appropriate feedback in real time during and after exercise to help improve user motivation and performance.

[0074] For example, if a user aims to manage their weight, this system provides a five-day-a-week walking program. During this program, feedback is provided that includes information to help maintain an appropriate heart rate.

[0075] An example of a prompt to a generating AI model is: "Create a weekly exercise plan to help the user achieve their weight loss goal. Indicate the optimal exercise intensity for walking, taking heart rate into consideration." This allows the system to provide a program customized to the user's specific needs.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users collect activity data by wearing the measuring device while going about their daily lives. During this time, the accelerometer and heart rate sensor work to acquire data on steps taken, heart rate, and calories burned. This data is temporarily stored in the measuring device's memory. The input data is information about the user's movements, while the output data is the activity data stored in the measuring device.

[0079] Step 2:

[0080] The terminal receives activity data from the measuring device via Bluetooth or Wi-Fi. During this process, data format conversion may occur, and pre-processing is performed to make the data easier for the server to process. The input is the activity data transmitted from the measuring device, and the output is the pre-processed data.

[0081] Step 3:

[0082] The server performs information analysis using activity data received from the terminal. Data analysis software such as Python is used to clean, filter, and transform the data before analysis is performed by a generative AI model. This analysis provides a detailed assessment based on the user's current health status and exercise history. The input is pre-processed activity data, and the output is the analyzed information.

[0083] Step 4:

[0084] The server generates an exercise program optimized for each individual user based on the analysis results. The generating AI model determines the type, intensity, and frequency of exercise according to the user's goals and status. Prompt statements are used to generate the program; for example, the model is given the command, "Create a weekly exercise plan to help the user achieve their weight loss goal." The input is the analyzed information, and the output is the exercise program.

[0085] Step 5:

[0086] The device receives the generated exercise program and presents it visually to the user. During the exercise, it provides feedback on progress and real-time achievement, and after the exercise, it provides an overall evaluation. The input is the generated exercise program, and the output is exercise instructions and feedback information for the user.

[0087] (Application Example 1)

[0088] 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."

[0089] Modern users often struggle to obtain optimal exercise plans tailored to their individual physical condition and fitness goals. Furthermore, they often lack real-time, accurate guidance during exercise, resulting in insufficient feedback to maintain motivation. To address these issues, a system is needed that provides personalized exercise plans and supports users' continuous health improvement through real-time guidance and feedback.

[0090] 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.

[0091] In this invention, the server includes means for acquiring user physical condition information from a device for collecting activity information, data analysis means for processing the physical condition information, program generation means for generating an optimal exercise plan for the user based on the information processed by the data analysis means, means for presenting the generated exercise plan to the user, means for acquiring and presenting an exercise plan from the server based on the user's exercise data, and instruction means for providing real-time guidance based on biological information during exercise. This enables the provision of an exercise program optimized for each individual user, and improves training and motivation through real-time guidance and feedback during exercise.

[0092] "Activity information" refers to all data related to the user's physical condition, and specifically includes steps taken, heart rate, calories burned, etc.

[0093] "Device" refers to a device that, when worn by a user, has the function of collecting information about the user's physical condition.

[0094] "Physical condition information" refers to data about the user's physiological and motor state, and this information is obtained through activity data.

[0095] "Data analysis means" refers to a system or process that has the function of processing collected physical condition information and extracting meaningful information from that data.

[0096] An "exercise plan" refers to a program that determines the optimal type, intensity, and frequency of exercise for the user, based on information about the user's physical condition.

[0097] "Program generation means" refers to a system or process for automatically generating an optimal exercise plan based on data analysis results.

[0098] "Means of presentation" refers to a device or process for providing the generated motor plan to the user visually or audibly.

[0099] "Instructional methods" refer to systems or processes for providing real-time instructions and advice to users during exercise.

[0100] "Feedback" refers to information provided regarding evaluations and areas for improvement concerning a user's exercise activities and progress.

[0101] "Motivation enhancement" refers to information and methods that increase users' desire to exercise and encourage them to continue exercising.

[0102] The system for implementing this invention uses a terminal that collects physical condition information from a device worn by the user and transmits it to a server. The server performs data analysis based on the received information and generates an exercise plan optimized for the user. The generated exercise plan is transmitted to the terminal and presented to the user.

[0103] The server forms the core of this system and is equipped with data analysis tools for efficiently analyzing activity information. These analysis tools utilize programming languages ​​such as Python and AI models to extract meaningful information from the collected data. Specifically, they collect data such as the user's steps, heart rate, and calories burned, and process this data within the system.

[0104] The server also uses a program generation mechanism to provide users with the generated exercise plan. This mechanism customizes the type and intensity of exercise individually, creating a plan that is realistically achievable for the user. Furthermore, it includes a guidance mechanism that provides real-time instruction, offering immediate instructions and advice based on biometric information during the user's exercise.

[0105] The terminal uses communication technologies such as Bluetooth and Wi-Fi to transfer data between the device and the server. Users receive exercise plans via their smartphones or other devices and train based on the information displayed on the screen. Feedback allows users to check their exercise progress and areas for improvement in a timely manner, and information to boost motivation is also provided as needed.

[0106] As a concrete example, consider a user attempting a 10km running program. The user begins exercising based on a plan presented on the device: "30 minutes of jogging, 5 minutes of interval training." Feedback from the device monitors heart rate and exercise intensity, and notifies the user of appropriate rest periods, aiming for sustained health improvement.

[0107] Examples of input prompts for a generative AI model:

[0108] "Please describe the design of a smartphone application that provides personalized exercise programs based on running data for fitness club members. The application should include features that analyze user data such as heart rate, calories burned, and steps taken, and provide real-time training guidance."

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The terminal acquires activity information from the device. It receives data such as steps, heart rate, and calories burned collected by the device as input. By transferring data in real time from the device via Bluetooth technology, the terminal understands the user's current physical condition. It then prepares to send this data to the server as output.

[0112] Step 2:

[0113] The terminal sends activity information to the server. Using the activity information obtained in step 1 as input, the data is transferred to the server via Wi-Fi or a mobile network. At this time, efficient communication is achieved by formatting the data. As output, data in a format usable by the server is sent.

[0114] Step 3:

[0115] The server processes the received activity information using data analysis tools. It accepts activity information sent from the terminal as input and performs data analysis using Python or AI models. Specifically, it refers to past exercise history and health status to evaluate changes in the data. It generates analysis results as output and proceeds to the next processing stage.

[0116] Step 4:

[0117] The server generates an optimal exercise plan for the user based on the analysis results. The analysis results obtained in step 3 are used as input. The generating AI model is used to plan the type, intensity, and frequency of exercise that matches the user's fitness goals. The server then prepares to provide the generated exercise plan to the user's device as output.

[0118] Step 5:

[0119] The terminal receives the exercise plan from the server and presents it to the user. It receives the exercise plan from the server as input and displays the plan details on the terminal's display. Specific operations include a function to display the daily training menu on the user interface. The plan is presented as output in a format that the user can visually confirm.

[0120] Step 6:

[0121] Users perform exercises while receiving feedback and guidance from their device. They refer to the exercise plan and guidance displayed on the device in real time as input. By performing exercises based on advice derived from biometric data, training is achieved according to the plan. The user's exercise completion rate and progress are recorded as output.

[0122] 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.

[0123] This invention is a system for providing a more effective training program by considering the user's emotional state in addition to optimizing the user's exercise. This system integrates an activity device and an emotion engine to collect, analyze, and adapt the user's physical and emotional data.

[0124] Users wear activity devices that collect activity data such as heart rate, steps taken, and calories burned through their daily exercise. Furthermore, an emotion engine analyzes the user's voice tone, facial expressions, and skin electrical activity to assess their emotional state. This emotional data is then transmitted to the server along with the activity data.

[0125] The server processes the received activity and emotional data using data analysis tools. This analysis comprehensively evaluates the user's physical and emotional state and generates an optimal exercise program tailored to the user's current physical ability and mood. For example, if the user's emotional state indicates stress, the server can recommend exercises with relaxing effects.

[0126] The generated exercise program is sent from the server to the user's terminal and presented to the user. The user performs the exercise according to this program, and feedback is displayed on the terminal as it is provided. The feedback system provides real-time feedback during and after the exercise. This includes advice to improve motivation, taking into account the user's emotional state, and instructions to maintain a balance between body and emotion.

[0127] As a concrete example, consider a case where User B is exercising with the goal of reducing stress. The activity device collects daily walking data, and the emotion engine senses User B's stress level through voice recognition. The server analyzes this data and generates a program that suggests three yoga sessions per week to User B, including deep breathing exercises to reduce stress. User B performs the exercise based on this program and receives feedback to maximize the relaxation effect. In this way, the present invention improves exercise efficiency and health management by providing support that takes into account both the user's physical and emotional state.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The device acquires activity data such as heart rate, steps taken, and calories burned from the user's activity device. Simultaneously, it uses an emotion engine to analyze the user's voice and facial expressions and collect data to evaluate their emotional state.

[0131] Step 2:

[0132] The device sends the collected activity data and emotion data to the server as a single package. This transmission is performed periodically using a communication method.

[0133] Step 3:

[0134] The server stores the received data in a database and then processes it using data analysis tools. Both activity data and emotional data are analyzed to comprehensively evaluate the user's physical and emotional state.

[0135] Step 4:

[0136] The server generates an optimal exercise program for the user based on the analysis results. It adjusts the program content according to the user's emotional state, selecting, for example, relaxation exercises or exercises that have a positive effect on emotions.

[0137] Step 5:

[0138] The server sends the generated exercise program to the user's terminal. The program includes customized information such as the type and intensity of exercise and the recommended frequency.

[0139] Step 6:

[0140] The user checks the exercise program displayed on the device and begins exercising accordingly. During the exercise, the device continuously monitors the user's progress and emotional state.

[0141] Step 7:

[0142] The server provides real-time feedback to the device regarding the exercise being performed, thereby supporting the user's progress. The feedback includes advice based on emotional state to help maintain motivation.

[0143] Step 8:

[0144] Users review the feedback and evaluation provided after their workout. This feedback helps them plan their next training session, taking into account new analysis results from the emotion engine.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] Traditional exercise programs often design programs based solely on the user's physical data, making it difficult to provide programs that take the user's emotional state into consideration. This resulted in the inability to provide optimal exercise programs tailored to the user's emotional state, leading to the problem of exercise not being fully realized.

[0148] 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.

[0149] In this invention, the server includes means for acquiring information from a device for collecting user activity data and emotional data; data analysis means for processing the information and comprehensively evaluating the user's physical and emotional state; and program generation means for generating an optimal exercise program tailored to the user's emotional state based on the analyzed information. This makes it possible to provide an exercise program that responds not only to the user's physical state but also to their emotional state.

[0150] "Activity data" refers to data that indicates the user's physical condition and exercise performance, and includes steps taken, heart rate, calories burned, etc.

[0151] "Emotional data" refers to data that indicates a user's mental or emotional state, and is obtained through methods such as tone of voice, facial expressions, and skin electrical activity.

[0152] "Data analysis methods" refer to techniques for comprehensively processing collected activity data and emotional data to evaluate the user's current physical and emotional state.

[0153] "Program generation means" refers to a function or device that creates an optimal exercise program for the user based on analyzed data, taking into account the user's physical and emotional state.

[0154] A "feedback mechanism" is a method or system that provides users with real-time feedback during and after exercise to maximize the effectiveness of exercise and improve motivation.

[0155] "Communication means" refers to methods and systems for sending and receiving data between a device and an information processing device, enabling accurate synchronization of activity data and emotional data.

[0156] This system provides a program to comprehensively improve the user's physical and emotional state. This section details the components of the system: the activity device worn by the user, the emotion engine, the data processing server, and the terminal that handles the user interface.

[0157] Users collect data on their daily exercise by wearing a dedicated activity device. This device can acquire physical data such as heart rate, steps taken, and calories burned. In parallel, an emotion engine analyzes the user's emotional state through voice tone, facial expressions, and skin electrical activity. This data forms the basis for a detailed understanding of the user's daily activities.

[0158] The server plays a central role in processing user activity and emotional data received from the terminal. Here, data analysis tools are used to comprehensively evaluate the user's physical and emotional state. Using the analysis results, a program generation tool generates an exercise program optimized for each individual user. This exercise program takes into account not only the physical aspects but also the user's emotional state.

[0159] The device presents the user with exercise programs transmitted from the server. It also provides real-time feedback during and after the activity, playing a crucial role in maintaining and improving user motivation. Because the feedback includes advice based on the user's emotional state, users can continue exercising more effectively.

[0160] As a concrete example, consider a case where a user is exercising to reduce stress. This system processes walking data acquired by the user's activity device and stress level data analyzed by an emotion engine on a server. As a result, the server recommends three yoga sessions per week and generates a program that incorporates deep breathing exercises for relaxation. This exercise program is presented to the user via the device, and the user can perform the exercise while receiving feedback.

[0161] An example of a prompt for a generative AI model would be: "Generate a description of a system that optimizes exercise programs by considering the user's emotional state. This system integrates and analyzes physical and emotional data to suggest an appropriate exercise program."

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The user wears an activity device that collects activity data such as steps, heart rate, and calories burned. Simultaneously, an emotion engine begins analysis, acquiring emotional data such as voice tone, facial expressions, and skin electrical activity. This data serves as input to understand the user's physical and emotional state. The collected data is sent to the user's device, preparing it for the next processing step.

[0165] Step 2:

[0166] The terminal transmits acquired activity and emotional data to the server in real time. The server, upon receiving the input data, analyzes this information using data analysis tools. This analysis comprehensively evaluates the user's physical and emotional state, and outputs basic data for creating appropriate programs tailored to the user's motor skills and emotions.

[0167] Step 3:

[0168] The server generates an exercise program optimized for the user based on the analyzed data. Using the program generation mechanism, it selects exercises and relaxation methods appropriate to the user's physical and emotional state. The optimal exercise program output at this stage includes details such as the frequency, type, and duration of the exercise.

[0169] Step 4:

[0170] The generated exercise program is sent from the server to the terminal. The terminal presents the received program to the user, allowing them to start exercising immediately. The user performs the exercise according to this program, and the terminal monitors the progress in real time.

[0171] Step 5:

[0172] During and after exercise, the device provides feedback to the user. Using this feedback mechanism, it outputs advice to improve motivation, taking into account the user's emotional state, as well as instructions based on their level of achievement. This allows the user to effectively execute their exercise program and supports their health management.

[0173] (Application Example 2)

[0174] 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".

[0175] The problem that this invention aims to solve is to improve the work efficiency of workers in work environments such as factories, while also reducing stress by taking into account their emotional state. Conventional work management systems only consider data related to physical load, and tend to overlook the emotional aspect, resulting in problems such as decreased work efficiency and increased mental stress.

[0176] 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.

[0177] In this invention, the server includes means for acquiring user physical condition information from a device for collecting activity data, data analysis means for processing the physical condition information, and program generation means for generating an optimal work program for the user based on the information processed by the data analysis means. This makes it possible to propose an optimal work procedure that comprehensively considers the user's physical and emotional aspects.

[0178] "Activity data" refers to information about the user's physical movements, including distance traveled and weight lifted during work.

[0179] "Device" refers to equipment used to collect and transmit user activity data, and includes packaged hardware such as wearable devices and sensors.

[0180] "Physical condition information" refers to information related to the user's physical health and activity, such as heart rate, blood pressure, and calories burned.

[0181] "Data analysis means" refers to software or hardware used to process and analyze collected physical condition information, and involves the use of algorithms and AI models.

[0182] A "program generation means" is a device that has the function of generating optimal work procedures and activity guidelines for the user based on data analysis information.

[0183] A "feedback system" is a system that transmits information to the user in real time and provides appropriate guidance and suggestions for improvement during or after work.

[0184] A "stress reduction measure" is a system that takes into account the emotional state of workers and adjusts work procedures or gives instructions for rest.

[0185] The system for realizing this invention aims to improve work efficiency and the user's health by comprehensively monitoring the user's physical and emotional state and generating an optimal work program.

[0186] The system primarily consists of a wearable device worn by the user, a server that analyzes the data, and a terminal that provides information to the user. The wearable device collects activity data such as heart rate and distance traveled as physical status information, and incorporates sensors to simultaneously assess emotional state. This data is transmitted to the server via Bluetooth or Wi-Fi.

[0187] The server uses a Raspberry Pi 4 and a data analysis tool programmed in Python to analyze the user's physical and emotional state. It also utilizes Google Cloud's Natural Language API to analyze emotions from voice input. Based on the results of this analysis, an optimal work program is generated that takes into account the user's current physical limitations and emotional stress levels.

[0188] The generated work program and feedback information are delivered in real time to the user's device, such as a smartphone. This includes instructions for adjusting specific work procedures and recommendations for breaks. The feedback also includes simple stretching exercises aimed at relaxation and advice to reduce psychological stress.

[0189] As a concrete example, in a factory worker performing assembly work, an activity device monitors heart rate variability and body movement. If an increase in stress is detected, the server provides feedback to slow down the work pace and suggests a 5-minute stretching break. During this process, the following prompt sentences can be input to the generating AI model.

[0190] For example, "During factory work, the workers' heart rates increased, and emotional analysis indicated high levels of stress. Please propose an optimal work program to alleviate physical and emotional stress before the next shift."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The user puts on a wearable device and begins working. The wearable device collects the user's heart rate, distance traveled, and other physical parameters in real time. This data is transmitted to a server via Bluetooth communication. The input is the user's physical activity data, and the output is the raw data sent to the server.

[0194] Step 2:

[0195] A data analysis engine programmed in Python processes the physical activity data received by the server. The analysis engine calculates workload and fatigue levels from the data fluctuations. The input is the raw data sent in step 1, and the output is the analyzed workload and fatigue level values.

[0196] Step 3:

[0197] The server uses Google Cloud's Natural Language API to analyze emotional data from the user's voice. An emotional analysis model identifies the user's emotional state (such as stress level) from their voice tone. The input is the user's voice data, and the output is an evaluation of their emotional state.

[0198] Step 4:

[0199] The server integrates activity and emotional data and uses an AI model to generate an optimal work program for the user. The generating AI model uses prompts to determine specific actions that optimize work efficiency and emotional balance. The input is the analyzed workload, fatigue level, and emotional state; the output is the specific work program.

[0200] Step 5:

[0201] The server sends the generated work program to the user's terminal. The user's terminal displays the work program and guides the user through the next steps and any necessary adjustments. The input is the work program generated in step 4, and the output is the instructions displayed on the user's terminal.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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".

[0218] This invention provides an innovative system for optimizing a user's exercise activities. This system collects physical condition information via an activity device worn by the user and generates and presents an optimal exercise program based on this information. The system enables efficient data communication between the server, terminal, and user's device, providing real-time feedback on exercise information and supporting the user's continued exercise.

[0219] First, the user uses the activity device in their daily life to collect activity data such as steps taken, heart rate, and calories burned. This device has the function of transmitting the collected data to the terminal. The terminal receives the data from the device and sends it to the server.

[0220] The server processes the received data using a dedicated data analysis system. This system analyzes the user's health status, past exercise history, and goals to evaluate the user's exercise capacity and physical condition. Based on this evaluation, the server generates an optimal exercise program for the user. The exercise program includes the type, intensity, and frequency of exercise, and is designed to support the user in achieving their goals.

[0221] The generated exercise program is sent back to the terminal and presented to the user. By performing exercises based on this program, the user can effectively continue their training. The server also provides real-time feedback to the user during and after exercise through various feedback mechanisms. This feedback includes information on exercise progress, areas for improvement, and motivational tips.

[0222] For example, if a user's goal is weight loss, the activity device collects daily walking data. The server analyzes this data and generates a five-day-a-week walking program for the user, specifying the intensity needed to maintain an appropriate heart rate. By following this program and receiving feedback from the device, the user can consistently achieve their exercise goals. In this way, the present invention enables improved health management and exercise effectiveness for the user.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The device collects activity data such as heart rate, steps taken, and calories burned in real time from the activity device worn by the user. This data is stored on the device at regular intervals.

[0226] Step 2:

[0227] The device transmits collected activity data to the server using a communication method. The data is typically transferred to the server via Bluetooth or Wi-Fi, and appropriate protocols are configured to ensure stable communication.

[0228] Step 3:

[0229] The server processes the received activity data using data analysis tools. Here, it evaluates the user's current exercise ability and physical condition, while also comparing it with the user's past training history and health information.

[0230] Step 4:

[0231] Based on the data analysis results, the server generates a personalized exercise program for each user. This includes the type of exercise, recommended intensity, frequency, and specific training content.

[0232] Step 5:

[0233] The server sends the generated exercise program to the terminal. The program is customized based on the user's behavioral patterns and preferences.

[0234] Step 6:

[0235] The user follows the exercise program displayed on the device and begins training based on the instructions. During the exercise, the device continuously monitors the user's progress.

[0236] Step 7:

[0237] The server sends real-time feedback to the device based on data collected during exercise, providing users with information. This includes advice on adjusting exercise intensity and areas for improvement.

[0238] Step 8:

[0239] Users review the feedback provided after training to help them improve their next session. The server displays encouraging messages and evaluations based on their performance that day on their device.

[0240] (Example 1)

[0241] 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."

[0242] In modern society, efficiently managing the health and improving the effectiveness of exercise for individual users has become a crucial challenge. Existing systems require considerable effort and time to accurately collect user physical condition information and continuously provide appropriate exercise programs, making it difficult to maintain user motivation. Therefore, there is a need for a system that generates optimal exercise programs tailored to each user's individual goals and provides real-time feedback that matches the user's activity progress.

[0243] 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.

[0244] In this invention, the server includes means for acquiring user physical condition information from a measuring device for collecting activity data, information analysis means for processing the physical condition information, and program construction means for generating an optimal exercise program for the user based on the information processed by the information analysis means. This enables the user to continuously achieve their exercise goals by automatically generating an exercise program optimized for each individual user and providing appropriate feedback in real time.

[0245] "Activity data" refers to information about a user's physical activity, including data such as steps taken, heart rate, and calories burned, which indicates the user's activity level.

[0246] A "measuring device" is a device used to collect user activity data, and includes wearable devices equipped with accelerometers and heart rate sensors.

[0247] "Physical condition information" refers to data that indicates the user's health status and exercise level, and includes information such as heart rate and calories burned obtained from activity data.

[0248] "Information analysis means" refers to a means of processing collected physical condition information and performing analysis based on the user's exercise history and goals.

[0249] "Program construction means" refers to a means for generating an optimal exercise program for the user based on the analysis results obtained by the information analysis means.

[0250] An "evaluation tool" is a means of providing real-time information about a user's exercise activities and providing feedback on their progress and areas for improvement.

[0251] A "generative AI model" is a model based on artificial intelligence technology used to optimize a user's exercise program.

[0252] "Communication means" refers to means for efficient information transmission between measuring devices, terminals, and central processing units.

[0253] This system is designed to optimize users' exercise activities and support individual health management. Its main components consist of measuring devices, terminals, and a server, and these elements communicate with each other to provide users with optimal exercise programs and feedback.

[0254] Users wear a measuring device during their daily lives, which collects activity data. This device includes an accelerometer and a heart rate sensor, and continuously records data such as steps taken, heart rate, and calories burned. The measuring device has the ability to transmit data to a terminal via Bluetooth or Wi-Fi.

[0255] The terminal organizes the data received from the measuring device and sends it to the server. The server analyzes the data using data analysis software such as Python. Generative AI models are used in the analysis to create an optimal exercise program that takes into account various data such as the user's past exercise history and health status.

[0256] The generated exercise program includes exercise type, intensity, and frequency, and is designed to help users achieve their health goals. The exercise program is sent to the device and presented to the user visually. The server also provides appropriate feedback in real time during and after exercise to help improve user motivation and performance.

[0257] For example, if a user aims to manage their weight, this system provides a five-day-a-week walking program. During this program, feedback is provided that includes information to help maintain an appropriate heart rate.

[0258] An example of a prompt to a generating AI model is: "Create a weekly exercise plan to help the user achieve their weight loss goal. Indicate the optimal exercise intensity for walking, taking heart rate into consideration." This allows the system to provide a program customized to the user's specific needs.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] Users collect activity data by wearing the measuring device while going about their daily lives. During this time, the accelerometer and heart rate sensor work to acquire data on steps taken, heart rate, and calories burned. This data is temporarily stored in the measuring device's memory. The input data is information about the user's movements, while the output data is the activity data stored in the measuring device.

[0262] Step 2:

[0263] The terminal receives activity data from the measuring device via Bluetooth or Wi-Fi. During this process, data format conversion may occur, and pre-processing is performed to make the data easier for the server to process. The input is the activity data transmitted from the measuring device, and the output is the pre-processed data.

[0264] Step 3:

[0265] The server performs information analysis using activity data received from the terminal. Data analysis software such as Python is used to clean, filter, and transform the data before analysis is performed by a generative AI model. This analysis provides a detailed assessment based on the user's current health status and exercise history. The input is pre-processed activity data, and the output is the analyzed information.

[0266] Step 4:

[0267] The server generates an exercise program optimized for each individual user based on the analysis results. The generating AI model determines the type, intensity, and frequency of exercise according to the user's goals and status. Prompt statements are used to generate the program; for example, the model is given the command, "Create a weekly exercise plan to help the user achieve their weight loss goal." The input is the analyzed information, and the output is the exercise program.

[0268] Step 5:

[0269] The device receives the generated exercise program and presents it visually to the user. During the exercise, it provides feedback on progress and real-time achievement, and after the exercise, it provides an overall evaluation. The input is the generated exercise program, and the output is exercise instructions and feedback information for the user.

[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] Modern users often struggle to obtain optimal exercise plans tailored to their individual physical condition and fitness goals. Furthermore, they often lack real-time, accurate guidance during exercise, resulting in insufficient feedback to maintain motivation. To address these issues, a system is needed that provides personalized exercise plans and supports users' continuous health improvement through real-time guidance and feedback.

[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 acquiring user physical condition information from a device for collecting activity information, data analysis means for processing the physical condition information, program generation means for generating an optimal exercise plan for the user based on the information processed by the data analysis means, means for presenting the generated exercise plan to the user, means for acquiring and presenting an exercise plan from the server based on the user's exercise data, and instruction means for providing real-time guidance based on biological information during exercise. This enables the provision of an exercise program optimized for each individual user, and improves training and motivation through real-time guidance and feedback during exercise.

[0275] "Activity information" refers to all data related to the user's physical condition, and specifically includes steps taken, heart rate, calories burned, etc.

[0276] "Device" refers to a device that, when worn by a user, has the function of collecting information about the user's physical condition.

[0277] "Physical condition information" refers to data about the user's physiological and motor state, and this information is obtained through activity data.

[0278] "Data analysis means" refers to a system or process that has the function of processing collected physical condition information and extracting meaningful information from that data.

[0279] An "exercise plan" refers to a program that determines the optimal type, intensity, and frequency of exercise for the user, based on information about the user's physical condition.

[0280] "Program generation means" refers to a system or process for automatically generating an optimal exercise plan based on data analysis results.

[0281] "Means of presentation" refers to a device or process for providing the generated motor plan to the user visually or audibly.

[0282] The "guidance means" refers to a system or process for giving instructions and advice in real time during the user's exercise.

[0283] "Feedback" refers to information on evaluations and improvement points provided regarding the user's exercise activities and progress.

[0284] "Motivation enhancement" refers to information and means for enhancing the user's exercise motivation and promoting continuous exercise activities.

[0285] The system for implementing this invention uses a terminal that collects physical condition information from a device worn by the user and transmits it to a server. The server performs data analysis based on the received information and generates an optimized exercise plan for the user. The generated exercise plan is transmitted to the terminal and presented to the user.

[0286] The server forms the core of this system and has data analysis means for efficiently analyzing activity information. This analysis means utilizes programming languages such as Python and AI models to extract meaningful information from the collected data. Specifically, it collects data such as the user's number of steps, heart rate, and calories burned, and processes them within the system.

[0287] Also, the server uses program generation means to provide the generated exercise plan to the user. This is for customizing the type and intensity of exercise individually and formulating a plan that the user can realistically achieve. Furthermore, it also includes guidance means for providing real-time guidance, which provides immediate instructions and advice based on biometric information during the user's exercise.

[0288] The terminal uses communication technologies such as Bluetooth and Wi-Fi to transfer data between the device and the server. The user receives the exercise plan through a terminal such as a smartphone and conducts training based on the information presented on the display. Through the feedback means, the progress of the exercise and improvement points can be checked in a timely manner, and information for motivation enhancement is also provided at any time.

[0289] As a concrete example, consider a user attempting a 10km running program. The user begins exercising based on a plan presented on the device: "30 minutes of jogging, 5 minutes of interval training." Feedback from the device monitors heart rate and exercise intensity, and notifies the user of appropriate rest periods, aiming for sustained health improvement.

[0290] Examples of input prompts for a generative AI model:

[0291] "Please describe the design of a smartphone application that provides personalized exercise programs based on running data for fitness club members. The application should include features that analyze user data such as heart rate, calories burned, and steps taken, and provide real-time training guidance."

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The terminal acquires activity information from the device. It receives data such as steps, heart rate, and calories burned collected by the device as input. By transferring data in real time from the device via Bluetooth technology, the terminal understands the user's current physical condition. It then prepares to send this data to the server as output.

[0295] Step 2:

[0296] The terminal sends activity information to the server. Using the activity information obtained in step 1 as input, the data is transferred to the server via Wi-Fi or a mobile network. At this time, efficient communication is achieved by formatting the data. As output, data in a format usable by the server is sent.

[0297] Step 3:

[0298] The server processes the received activity information using data analysis tools. It accepts activity information sent from the terminal as input and performs data analysis using Python or AI models. Specifically, it refers to past exercise history and health status to evaluate changes in the data. It generates analysis results as output and proceeds to the next processing stage.

[0299] Step 4:

[0300] The server generates an optimal exercise plan for the user based on the analysis results. The analysis results obtained in step 3 are used as input. The generating AI model is used to plan the type, intensity, and frequency of exercise that matches the user's fitness goals. The server then prepares to provide the generated exercise plan to the user's device as output.

[0301] Step 5:

[0302] The terminal receives the exercise plan from the server and presents it to the user. It receives the exercise plan from the server as input and displays the plan details on the terminal's display. Specific operations include a function to display the daily training menu on the user interface. The plan is presented as output in a format that the user can visually confirm.

[0303] Step 6:

[0304] Users perform exercises while receiving feedback and guidance from their device. They refer to the exercise plan and guidance displayed on the device in real time as input. By performing exercises based on advice derived from biometric data, training is achieved according to the plan. The user's exercise completion rate and progress are recorded as output.

[0305] 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.

[0306] In addition to optimizing the user's exercise, the present invention is a system for providing a more effective training program by considering the user's emotional state. This system integrates an activity device and an emotion engine to collect, analyze, and adapt the user's physical and emotional data.

[0307] The user wears an activity device and collects activity data such as heart rate, number of steps, and calories burned through daily exercise. Furthermore, the emotion engine analyzes the user's voice tone, facial expression, skin electrical activity, etc. to evaluate the emotional state. As a result, the emotional data is sent to the server together with the activity data.

[0308] The server processes the received activity data and emotional data by data analysis means. In this analysis, the user's physical state and emotional state are comprehensively evaluated, and an optimal exercise program is generated according to the user's current exercise ability and mood. For example, when the user's emotional state shows stress, an exercise with a relaxation effect can be recommended.

[0309] The generated exercise program is sent from the server to the user's terminal and presented to the user. The user performs exercise according to this program, and feedback provided at any time is displayed on the terminal. The feedback means provides real-time feedback during and after exercise. Here, advice for improving motivation considering the user's emotional state and instructions for maintaining the balance between the body and emotion are included.

[0310] As a concrete example, consider a case where User B is exercising with the goal of reducing stress. The activity device collects daily walking data, and the emotion engine senses User B's stress level through voice recognition. The server analyzes this data and generates a program that suggests three yoga sessions per week to User B, including deep breathing exercises to reduce stress. User B performs the exercise based on this program and receives feedback to maximize the relaxation effect. In this way, the present invention improves exercise efficiency and health management by providing support that takes into account both the user's physical and emotional state.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The device acquires activity data such as heart rate, steps taken, and calories burned from the user's activity device. Simultaneously, it uses an emotion engine to analyze the user's voice and facial expressions and collect data to evaluate their emotional state.

[0314] Step 2:

[0315] The device sends the collected activity data and emotion data to the server as a single package. This transmission is performed periodically using a communication method.

[0316] Step 3:

[0317] The server stores the received data in a database and then processes it using data analysis tools. Both activity data and emotional data are analyzed to comprehensively evaluate the user's physical and emotional state.

[0318] Step 4:

[0319] The server generates an optimal exercise program for the user based on the analysis results. It adjusts the program content according to the user's emotional state, selecting, for example, relaxation exercises or exercises that have a positive effect on emotions.

[0320] Step 5:

[0321] The server sends the generated exercise program to the user's terminal. The program includes customized information such as the type and intensity of exercise and the recommended frequency.

[0322] Step 6:

[0323] The user checks the exercise program displayed on the device and begins exercising accordingly. During the exercise, the device continuously monitors the user's progress and emotional state.

[0324] Step 7:

[0325] The server provides real-time feedback to the device regarding the exercise being performed, thereby supporting the user's progress. The feedback includes advice based on emotional state to help maintain motivation.

[0326] Step 8:

[0327] Users review the feedback and evaluation provided after their workout. This feedback helps them plan their next training session, taking into account new analysis results from the emotion engine.

[0328] (Example 2)

[0329] 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".

[0330] Traditional exercise programs often design programs based solely on the user's physical data, making it difficult to provide programs that take the user's emotional state into consideration. This resulted in the inability to provide optimal exercise programs tailored to the user's emotional state, leading to the problem of exercise not being fully realized.

[0331] 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.

[0332] In this invention, the server includes means for acquiring information from a device for collecting user activity data and emotional data; data analysis means for processing the information and comprehensively evaluating the user's physical and emotional state; and program generation means for generating an optimal exercise program tailored to the user's emotional state based on the analyzed information. This makes it possible to provide an exercise program that responds not only to the user's physical state but also to their emotional state.

[0333] "Activity data" refers to data that indicates the user's physical condition and exercise performance, and includes steps taken, heart rate, calories burned, etc.

[0334] "Emotional data" refers to data that indicates a user's mental or emotional state, and is obtained through methods such as tone of voice, facial expressions, and skin electrical activity.

[0335] "Data analysis methods" refer to techniques for comprehensively processing collected activity data and emotional data to evaluate the user's current physical and emotional state.

[0336] "Program generation means" refers to a function or device that creates an optimal exercise program for the user based on analyzed data, taking into account the user's physical and emotional state.

[0337] A "feedback mechanism" is a method or system that provides users with real-time feedback during and after exercise to maximize the effectiveness of exercise and improve motivation.

[0338] "Communication means" refers to methods and systems for sending and receiving data between a device and an information processing device, enabling accurate synchronization of activity data and emotional data.

[0339] This system provides a program to comprehensively improve the user's physical and emotional state. This section details the components of the system: the activity device worn by the user, the emotion engine, the data processing server, and the terminal that handles the user interface.

[0340] Users collect data on their daily exercise by wearing a dedicated activity device. This device can acquire physical data such as heart rate, steps taken, and calories burned. In parallel, an emotion engine analyzes the user's emotional state through voice tone, facial expressions, and skin electrical activity. This data forms the basis for a detailed understanding of the user's daily activities.

[0341] The server plays a central role in processing user activity and emotional data received from the terminal. Here, data analysis tools are used to comprehensively evaluate the user's physical and emotional state. Using the analysis results, a program generation tool generates an exercise program optimized for each individual user. This exercise program takes into account not only the physical aspects but also the user's emotional state.

[0342] The device presents the user with exercise programs transmitted from the server. It also provides real-time feedback during and after the activity, playing a crucial role in maintaining and improving user motivation. Because the feedback includes advice based on the user's emotional state, users can continue exercising more effectively.

[0343] As a concrete example, consider a case where a user is exercising to reduce stress. This system processes walking data acquired by the user's activity device and stress level data analyzed by an emotion engine on a server. As a result, the server recommends three yoga sessions per week and generates a program that incorporates deep breathing exercises for relaxation. This exercise program is presented to the user via the device, and the user can perform the exercise while receiving feedback.

[0344] An example of a prompt for a generative AI model would be: "Generate a description of a system that optimizes exercise programs by considering the user's emotional state. This system integrates and analyzes physical and emotional data to suggest an appropriate exercise program."

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The user wears an activity device that collects activity data such as steps, heart rate, and calories burned. Simultaneously, an emotion engine begins analysis, acquiring emotional data such as voice tone, facial expressions, and skin electrical activity. This data serves as input to understand the user's physical and emotional state. The collected data is sent to the user's device, preparing it for the next processing step.

[0348] Step 2:

[0349] The terminal transmits acquired activity and emotional data to the server in real time. The server, upon receiving the input data, analyzes this information using data analysis tools. This analysis comprehensively evaluates the user's physical and emotional state, and outputs basic data for creating appropriate programs tailored to the user's motor skills and emotions.

[0350] Step 3:

[0351] The server generates an exercise program optimized for the user based on the analyzed data. Using the program generation mechanism, it selects exercises and relaxation methods appropriate to the user's physical and emotional state. The optimal exercise program output at this stage includes details such as the frequency, type, and duration of the exercise.

[0352] Step 4:

[0353] The generated exercise program is sent from the server to the terminal. The terminal presents the received program to the user, allowing them to start exercising immediately. The user performs the exercise according to this program, and the terminal monitors the progress in real time.

[0354] Step 5:

[0355] During and after exercise, the device provides feedback to the user. Using this feedback mechanism, it outputs advice to improve motivation, taking into account the user's emotional state, as well as instructions based on their level of achievement. This allows the user to effectively execute their exercise program and supports their health management.

[0356] (Application Example 2)

[0357] 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 will be referred to as the "terminal."

[0358] The problem that this invention aims to solve is to improve the work efficiency of workers in work environments such as factories, while also reducing stress by taking into account their emotional state. Conventional work management systems only consider data related to physical load, and tend to overlook the emotional aspect, resulting in problems such as decreased work efficiency and increased mental stress.

[0359] 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.

[0360] In this invention, the server includes means for acquiring user physical condition information from a device for collecting activity data, data analysis means for processing the physical condition information, and program generation means for generating an optimal work program for the user based on the information processed by the data analysis means. This makes it possible to propose an optimal work procedure that comprehensively considers the user's physical and emotional aspects.

[0361] "Activity data" refers to information about the user's physical movements, including distance traveled and weight lifted during work.

[0362] "Device" refers to equipment used to collect and transmit user activity data, and includes packaged hardware such as wearable devices and sensors.

[0363] "Physical condition information" refers to information related to the user's physical health and activity, such as heart rate, blood pressure, and calories burned.

[0364] "Data analysis means" refers to software or hardware used to process and analyze collected physical condition information, and involves the use of algorithms and AI models.

[0365] A "program generation means" is a device that has the function of generating optimal work procedures and activity guidelines for the user based on data analysis information.

[0366] A "feedback system" is a system that transmits information to the user in real time and provides appropriate guidance and suggestions for improvement during or after work.

[0367] A "stress reduction measure" is a system that takes into account the emotional state of workers and adjusts work procedures or gives instructions for rest.

[0368] The system for realizing this invention aims to improve work efficiency and the user's health by comprehensively monitoring the user's physical and emotional state and generating an optimal work program.

[0369] The system primarily consists of a wearable device worn by the user, a server that analyzes the data, and a terminal that provides information to the user. The wearable device collects activity data such as heart rate and distance traveled as physical status information, and incorporates sensors to simultaneously assess emotional state. This data is transmitted to the server via Bluetooth or Wi-Fi.

[0370] The server uses a Raspberry Pi 4 and a data analysis tool programmed in Python to analyze the user's physical and emotional state. It also utilizes Google Cloud's Natural Language API to analyze emotions from voice input. Based on the results of this analysis, an optimal work program is generated that takes into account the user's current physical limitations and emotional stress levels.

[0371] The generated work program and feedback information are delivered in real time to the user's device, such as a smartphone. This includes instructions for adjusting specific work procedures and recommendations for breaks. The feedback also includes simple stretching exercises aimed at relaxation and advice to reduce psychological stress.

[0372] As a concrete example, in a factory worker performing assembly work, an activity device monitors heart rate variability and body movement. If an increase in stress is detected, the server provides feedback to slow down the work pace and suggests a 5-minute stretching break. During this process, the following prompt sentences can be input to the generating AI model.

[0373] For example, "During factory work, the workers' heart rates increased, and emotional analysis indicated high levels of stress. Please propose an optimal work program to alleviate physical and emotional stress before the next shift."

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The user puts on a wearable device and begins working. The wearable device collects the user's heart rate, distance traveled, and other physical parameters in real time. This data is transmitted to a server via Bluetooth communication. The input is the user's physical activity data, and the output is the raw data sent to the server.

[0377] Step 2:

[0378] A data analysis engine programmed in Python processes the physical activity data received by the server. The analysis engine calculates workload and fatigue levels from the data fluctuations. The input is the raw data sent in step 1, and the output is the analyzed workload and fatigue level values.

[0379] Step 3:

[0380] The server uses Google Cloud's Natural Language API to analyze emotional data from the user's voice. An emotional analysis model identifies the user's emotional state (such as stress level) from their voice tone. The input is the user's voice data, and the output is an evaluation of their emotional state.

[0381] Step 4:

[0382] The server integrates activity and emotional data and uses an AI model to generate an optimal work program for the user. The generating AI model uses prompts to determine specific actions that optimize work efficiency and emotional balance. The input is the analyzed workload, fatigue level, and emotional state; the output is the specific work program.

[0383] Step 5:

[0384] The server sends the generated work program to the user's terminal. The user's terminal displays the work program and guides the user through the next steps and any necessary adjustments. The input is the work program generated in step 4, and the output is the instructions displayed on the user's terminal.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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".

[0401] This invention provides an innovative system for optimizing a user's exercise activities. This system collects physical condition information via an activity device worn by the user and generates and presents an optimal exercise program based on this information. The system enables efficient data communication between the server, terminal, and user's device, providing real-time feedback on exercise information and supporting the user's continued exercise.

[0402] First, the user uses the activity device in their daily life to collect activity data such as steps taken, heart rate, and calories burned. This device has the function of transmitting the collected data to the terminal. The terminal receives the data from the device and sends it to the server.

[0403] The server processes the received data using a dedicated data analysis system. This system analyzes the user's health status, past exercise history, and goals to evaluate the user's exercise capacity and physical condition. Based on this evaluation, the server generates an optimal exercise program for the user. The exercise program includes the type, intensity, and frequency of exercise, and is designed to support the user in achieving their goals.

[0404] The generated exercise program is sent back to the terminal and presented to the user. By performing exercises based on this program, the user can effectively continue their training. The server also provides real-time feedback to the user during and after exercise through various feedback mechanisms. This feedback includes information on exercise progress, areas for improvement, and motivational tips.

[0405] For example, if a user's goal is weight loss, the activity device collects daily walking data. The server analyzes this data and generates a five-day-a-week walking program for the user, specifying the intensity needed to maintain an appropriate heart rate. By following this program and receiving feedback from the device, the user can consistently achieve their exercise goals. In this way, the present invention enables improved health management and exercise effectiveness for the user.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] The device collects activity data such as heart rate, steps taken, and calories burned in real time from the activity device worn by the user. This data is stored on the device at regular intervals.

[0409] Step 2:

[0410] The device transmits collected activity data to the server using a communication method. The data is typically transferred to the server via Bluetooth or Wi-Fi, and appropriate protocols are configured to ensure stable communication.

[0411] Step 3:

[0412] The server processes the received activity data using data analysis tools. Here, it evaluates the user's current exercise ability and physical condition, while also comparing it with the user's past training history and health information.

[0413] Step 4:

[0414] Based on the data analysis results, the server generates a personalized exercise program for each user. This includes the type of exercise, recommended intensity, frequency, and specific training content.

[0415] Step 5:

[0416] The server sends the generated exercise program to the terminal. The program is customized based on the user's behavioral patterns and preferences.

[0417] Step 6:

[0418] The user follows the exercise program displayed on the device and begins training based on the instructions. During the exercise, the device continuously monitors the user's progress.

[0419] Step 7:

[0420] The server sends real-time feedback to the device based on data collected during exercise, providing users with information. This includes advice on adjusting exercise intensity and areas for improvement.

[0421] Step 8:

[0422] Users review the feedback provided after training to help them improve their next session. The server displays encouraging messages and evaluations based on their performance that day on their device.

[0423] (Example 1)

[0424] 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."

[0425] In modern society, efficiently managing the health and improving the effectiveness of exercise for individual users has become a crucial challenge. Existing systems require considerable effort and time to accurately collect user physical condition information and continuously provide appropriate exercise programs, making it difficult to maintain user motivation. Therefore, there is a need for a system that generates optimal exercise programs tailored to each user's individual goals and provides real-time feedback that matches the user's activity progress.

[0426] 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.

[0427] In this invention, the server includes means for acquiring user physical condition information from a measuring device for collecting activity data, information analysis means for processing the physical condition information, and program construction means for generating an optimal exercise program for the user based on the information processed by the information analysis means. This enables the user to continuously achieve their exercise goals by automatically generating an exercise program optimized for each individual user and providing appropriate feedback in real time.

[0428] "Activity data" refers to information about a user's physical activity, including data such as steps taken, heart rate, and calories burned, which indicates the user's activity level.

[0429] A "measuring device" is a device used to collect user activity data, and includes wearable devices equipped with accelerometers and heart rate sensors.

[0430] "Physical condition information" refers to data that indicates the user's health status and exercise level, and includes information such as heart rate and calories burned obtained from activity data.

[0431] "Information analysis means" refers to a means of processing collected physical condition information and performing analysis based on the user's exercise history and goals.

[0432] "Program construction means" refers to a means for generating an optimal exercise program for the user based on the analysis results obtained by the information analysis means.

[0433] An "evaluation tool" is a means of providing real-time information about a user's exercise activities and providing feedback on their progress and areas for improvement.

[0434] A "generative AI model" is a model based on artificial intelligence technology used to optimize a user's exercise program.

[0435] "Communication means" refers to means for efficient information transmission between measuring devices, terminals, and central processing units.

[0436] This system is designed to optimize users' exercise activities and support individual health management. Its main components consist of measuring devices, terminals, and a server, and these elements communicate with each other to provide users with optimal exercise programs and feedback.

[0437] Users wear a measuring device during their daily lives, which collects activity data. This device includes an accelerometer and a heart rate sensor, and continuously records data such as steps taken, heart rate, and calories burned. The measuring device has the ability to transmit data to a terminal via Bluetooth or Wi-Fi.

[0438] The terminal organizes the data received from the measuring device and sends it to the server. The server analyzes the data using data analysis software such as Python. Generative AI models are used in the analysis to create an optimal exercise program that takes into account various data such as the user's past exercise history and health status.

[0439] The generated exercise program includes exercise type, intensity, and frequency, and is designed to help users achieve their health goals. The exercise program is sent to the device and presented to the user visually. The server also provides appropriate feedback in real time during and after exercise to help improve user motivation and performance.

[0440] For example, if a user aims to manage their weight, this system provides a five-day-a-week walking program. During this program, feedback is provided that includes information to help maintain an appropriate heart rate.

[0441] An example of a prompt to a generating AI model is: "Create a weekly exercise plan to help the user achieve their weight loss goal. Indicate the optimal exercise intensity for walking, taking heart rate into consideration." This allows the system to provide a program customized to the user's specific needs.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] Users collect activity data by wearing the measuring device while going about their daily lives. During this time, the accelerometer and heart rate sensor work to acquire data on steps taken, heart rate, and calories burned. This data is temporarily stored in the measuring device's memory. The input data is information about the user's movements, while the output data is the activity data stored in the measuring device.

[0445] Step 2:

[0446] The terminal receives activity data from the measuring device via Bluetooth or Wi-Fi. During this process, data format conversion may occur, and pre-processing is performed to make the data easier for the server to process. The input is the activity data transmitted from the measuring device, and the output is the pre-processed data.

[0447] Step 3:

[0448] The server performs information analysis using activity data received from the terminal. Data analysis software such as Python is used to clean, filter, and transform the data before analysis is performed by a generative AI model. This analysis provides a detailed assessment based on the user's current health status and exercise history. The input is pre-processed activity data, and the output is the analyzed information.

[0449] Step 4:

[0450] The server generates an exercise program optimized for each individual user based on the analysis results. The generating AI model determines the type, intensity, and frequency of exercise according to the user's goals and status. Prompt statements are used to generate the program; for example, the model is given the command, "Create a weekly exercise plan to help the user achieve their weight loss goal." The input is the analyzed information, and the output is the exercise program.

[0451] Step 5:

[0452] The device receives the generated exercise program and presents it visually to the user. During the exercise, it provides feedback on progress and real-time achievement, and after the exercise, it provides an overall evaluation. The input is the generated exercise program, and the output is exercise instructions and feedback information for the user.

[0453] (Application Example 1)

[0454] 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."

[0455] Modern users often struggle to obtain optimal exercise plans tailored to their individual physical condition and fitness goals. Furthermore, they often lack real-time, accurate guidance during exercise, resulting in insufficient feedback to maintain motivation. To address these issues, a system is needed that provides personalized exercise plans and supports users' continuous health improvement through real-time guidance and feedback.

[0456] 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.

[0457] In this invention, the server includes means for acquiring user physical condition information from a device for collecting activity information, data analysis means for processing the physical condition information, program generation means for generating an optimal exercise plan for the user based on the information processed by the data analysis means, means for presenting the generated exercise plan to the user, means for acquiring and presenting an exercise plan from the server based on the user's exercise data, and instruction means for providing real-time guidance based on biological information during exercise. This enables the provision of an exercise program optimized for each individual user, and improves training and motivation through real-time guidance and feedback during exercise.

[0458] "Activity information" refers to all data related to the user's physical condition, and specifically includes steps taken, heart rate, calories burned, etc.

[0459] "Device" refers to a device that, when worn by a user, has the function of collecting information about the user's physical condition.

[0460] "Physical condition information" refers to data about the user's physiological and motor state, and this information is obtained through activity data.

[0461] "Data analysis means" refers to a system or process that has the function of processing collected physical condition information and extracting meaningful information from that data.

[0462] An "exercise plan" refers to a program that determines the optimal type, intensity, and frequency of exercise for the user, based on information about the user's physical condition.

[0463] "Program generation means" refers to a system or process for automatically generating an optimal exercise plan based on data analysis results.

[0464] "Means of presentation" refers to a device or process for providing the generated motor plan to the user visually or audibly.

[0465] "Instructional methods" refer to systems or processes for providing real-time instructions and advice to users during exercise.

[0466] "Feedback" refers to information provided regarding evaluations and areas for improvement concerning a user's exercise activities and progress.

[0467] "Motivation enhancement" refers to information and methods that increase users' desire to exercise and encourage them to continue exercising.

[0468] The system for implementing this invention uses a terminal that collects physical condition information from a device worn by the user and transmits it to a server. The server performs data analysis based on the received information and generates an exercise plan optimized for the user. The generated exercise plan is transmitted to the terminal and presented to the user.

[0469] The server forms the core of this system and is equipped with data analysis tools for efficiently analyzing activity information. These analysis tools utilize programming languages ​​such as Python and AI models to extract meaningful information from the collected data. Specifically, they collect data such as the user's steps, heart rate, and calories burned, and process this data within the system.

[0470] The server also uses a program generation mechanism to provide users with the generated exercise plan. This mechanism customizes the type and intensity of exercise individually, creating a plan that is realistically achievable for the user. Furthermore, it includes a guidance mechanism that provides real-time instruction, offering immediate instructions and advice based on biometric information during the user's exercise.

[0471] The terminal uses communication technologies such as Bluetooth and Wi-Fi to transfer data between the device and the server. Users receive exercise plans via their smartphones or other devices and train based on the information displayed on the screen. Feedback allows users to check their exercise progress and areas for improvement in a timely manner, and information to boost motivation is also provided as needed.

[0472] As a concrete example, consider a user attempting a 10km running program. The user begins exercising based on a plan presented on the device: "30 minutes of jogging, 5 minutes of interval training." Feedback from the device monitors heart rate and exercise intensity, and notifies the user of appropriate rest periods, aiming for sustained health improvement.

[0473] Examples of input prompts for a generative AI model:

[0474] "Please describe the design of a smartphone application that provides personalized exercise programs based on running data for fitness club members. The application should include features that analyze user data such as heart rate, calories burned, and steps taken, and provide real-time training guidance."

[0475] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0476] Step 1:

[0477] The terminal acquires activity information from the device. It receives data such as steps, heart rate, and calories burned collected by the device as input. By transferring data in real time from the device via Bluetooth technology, the terminal understands the user's current physical condition. It then prepares to send this data to the server as output.

[0478] Step 2:

[0479] The terminal sends activity information to the server. Using the activity information obtained in step 1 as input, the data is transferred to the server via Wi-Fi or a mobile network. At this time, efficient communication is achieved by formatting the data. As output, data in a format usable by the server is sent.

[0480] Step 3:

[0481] The server processes the received activity information using data analysis tools. It accepts activity information sent from the terminal as input and performs data analysis using Python or AI models. Specifically, it refers to past exercise history and health status to evaluate changes in the data. It generates analysis results as output and proceeds to the next processing stage.

[0482] Step 4:

[0483] The server generates an optimal exercise plan for the user based on the analysis results. The analysis results obtained in step 3 are used as input. The generating AI model is used to plan the type, intensity, and frequency of exercise that matches the user's fitness goals. The server then prepares to provide the generated exercise plan to the user's device as output.

[0484] Step 5:

[0485] The terminal receives the exercise plan from the server and presents it to the user. It receives the exercise plan from the server as input and displays the plan details on the terminal's display. Specific operations include a function to display the daily training menu on the user interface. The plan is presented as output in a format that the user can visually confirm.

[0486] Step 6:

[0487] Users perform exercises while receiving feedback and guidance from their device. They refer to the exercise plan and guidance displayed on the device in real time as input. By performing exercises based on advice derived from biometric data, training is achieved according to the plan. The user's exercise completion rate and progress are recorded as output.

[0488] 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.

[0489] This invention is a system for providing a more effective training program by considering the user's emotional state in addition to optimizing the user's exercise. This system integrates an activity device and an emotion engine to collect, analyze, and adapt the user's physical and emotional data.

[0490] Users wear activity devices that collect activity data such as heart rate, steps taken, and calories burned through their daily exercise. Furthermore, an emotion engine analyzes the user's voice tone, facial expressions, and skin electrical activity to assess their emotional state. This emotional data is then transmitted to the server along with the activity data.

[0491] The server processes the received activity and emotional data using data analysis tools. This analysis comprehensively evaluates the user's physical and emotional state and generates an optimal exercise program tailored to the user's current physical ability and mood. For example, if the user's emotional state indicates stress, the server can recommend exercises with relaxing effects.

[0492] The generated exercise program is sent from the server to the user's terminal and presented to the user. The user performs the exercise according to this program, and feedback is displayed on the terminal as it is provided. The feedback system provides real-time feedback during and after the exercise. This includes advice to improve motivation, taking into account the user's emotional state, and instructions to maintain a balance between body and emotion.

[0493] As a concrete example, consider a case where User B is exercising with the goal of reducing stress. The activity device collects daily walking data, and the emotion engine senses User B's stress level through voice recognition. The server analyzes this data and generates a program that suggests three yoga sessions per week to User B, including deep breathing exercises to reduce stress. User B performs the exercise based on this program and receives feedback to maximize the relaxation effect. In this way, the present invention improves exercise efficiency and health management by providing support that takes into account both the user's physical and emotional state.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The device acquires activity data such as heart rate, steps taken, and calories burned from the user's activity device. Simultaneously, it uses an emotion engine to analyze the user's voice and facial expressions and collect data to evaluate their emotional state.

[0497] Step 2:

[0498] The device sends the collected activity data and emotion data to the server as a single package. This transmission is performed periodically using a communication method.

[0499] Step 3:

[0500] The server stores the received data in a database and then processes it using data analysis tools. Both activity data and emotional data are analyzed to comprehensively evaluate the user's physical and emotional state.

[0501] Step 4:

[0502] The server generates an optimal exercise program for the user based on the analysis results. It adjusts the program content according to the user's emotional state, selecting, for example, relaxation exercises or exercises that have a positive effect on emotions.

[0503] Step 5:

[0504] The server sends the generated exercise program to the user's terminal. The program includes customized information such as the type and intensity of exercise and the recommended frequency.

[0505] Step 6:

[0506] The user checks the exercise program displayed on the device and begins exercising accordingly. During the exercise, the device continuously monitors the user's progress and emotional state.

[0507] Step 7:

[0508] The server provides real-time feedback to the device regarding the exercise being performed, thereby supporting the user's progress. The feedback includes advice based on emotional state to help maintain motivation.

[0509] Step 8:

[0510] Users review the feedback and evaluation provided after their workout. This feedback helps them plan their next training session, taking into account new analysis results from the emotion engine.

[0511] (Example 2)

[0512] 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."

[0513] Traditional exercise programs often design programs based solely on the user's physical data, making it difficult to provide programs that take the user's emotional state into consideration. This resulted in the inability to provide optimal exercise programs tailored to the user's emotional state, leading to the problem of exercise not being fully realized.

[0514] 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.

[0515] In this invention, the server includes means for acquiring information from a device for collecting user activity data and emotional data; data analysis means for processing the information and comprehensively evaluating the user's physical and emotional state; and program generation means for generating an optimal exercise program tailored to the user's emotional state based on the analyzed information. This makes it possible to provide an exercise program that responds not only to the user's physical state but also to their emotional state.

[0516] "Activity data" refers to data that indicates the user's physical condition and exercise performance, and includes steps taken, heart rate, calories burned, etc.

[0517] "Emotional data" refers to data that indicates a user's mental or emotional state, and is obtained through methods such as tone of voice, facial expressions, and skin electrical activity.

[0518] "Data analysis methods" refer to techniques for comprehensively processing collected activity data and emotional data to evaluate the user's current physical and emotional state.

[0519] "Program generation means" refers to a function or device that creates an optimal exercise program for the user based on analyzed data, taking into account the user's physical and emotional state.

[0520] A "feedback mechanism" is a method or system that provides users with real-time feedback during and after exercise to maximize the effectiveness of exercise and improve motivation.

[0521] "Communication means" refers to methods and systems for sending and receiving data between a device and an information processing device, enabling accurate synchronization of activity data and emotional data.

[0522] This system provides a program to comprehensively improve the user's physical and emotional state. This section details the components of the system: the activity device worn by the user, the emotion engine, the data processing server, and the terminal that handles the user interface.

[0523] Users collect data on their daily exercise by wearing a dedicated activity device. This device can acquire physical data such as heart rate, steps taken, and calories burned. In parallel, an emotion engine analyzes the user's emotional state through voice tone, facial expressions, and skin electrical activity. This data forms the basis for a detailed understanding of the user's daily activities.

[0524] The server plays a central role in processing user activity and emotional data received from the terminal. Here, data analysis tools are used to comprehensively evaluate the user's physical and emotional state. Using the analysis results, a program generation tool generates an exercise program optimized for each individual user. This exercise program takes into account not only the physical aspects but also the user's emotional state.

[0525] The device presents the user with exercise programs transmitted from the server. It also provides real-time feedback during and after the activity, playing a crucial role in maintaining and improving user motivation. Because the feedback includes advice based on the user's emotional state, users can continue exercising more effectively.

[0526] As a concrete example, consider a case where a user is exercising to reduce stress. This system processes walking data acquired by the user's activity device and stress level data analyzed by an emotion engine on a server. As a result, the server recommends three yoga sessions per week and generates a program that incorporates deep breathing exercises for relaxation. This exercise program is presented to the user via the device, and the user can perform the exercise while receiving feedback.

[0527] An example of a prompt for a generative AI model would be: "Generate a description of a system that optimizes exercise programs by considering the user's emotional state. This system integrates and analyzes physical and emotional data to suggest an appropriate exercise program."

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] The user wears an activity device that collects activity data such as steps, heart rate, and calories burned. Simultaneously, an emotion engine begins analysis, acquiring emotional data such as voice tone, facial expressions, and skin electrical activity. This data serves as input to understand the user's physical and emotional state. The collected data is sent to the user's device, preparing it for the next processing step.

[0531] Step 2:

[0532] The terminal transmits acquired activity and emotional data to the server in real time. The server, upon receiving the input data, analyzes this information using data analysis tools. This analysis comprehensively evaluates the user's physical and emotional state, and outputs basic data for creating appropriate programs tailored to the user's motor skills and emotions.

[0533] Step 3:

[0534] The server generates an exercise program optimized for the user based on the analyzed data. Using the program generation mechanism, it selects exercises and relaxation methods appropriate to the user's physical and emotional state. The optimal exercise program output at this stage includes details such as the frequency, type, and duration of the exercise.

[0535] Step 4:

[0536] The generated exercise program is sent from the server to the terminal. The terminal presents the received program to the user, allowing them to start exercising immediately. The user performs the exercise according to this program, and the terminal monitors the progress in real time.

[0537] Step 5:

[0538] During and after exercise, the device provides feedback to the user. Using this feedback mechanism, it outputs advice to improve motivation, taking into account the user's emotional state, as well as instructions based on their level of achievement. This allows the user to effectively execute their exercise program and supports their health management.

[0539] (Application Example 2)

[0540] 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."

[0541] The problem that this invention aims to solve is to improve the work efficiency of workers in work environments such as factories, while also reducing stress by taking into account their emotional state. Conventional work management systems only consider data related to physical load, and tend to overlook the emotional aspect, resulting in problems such as decreased work efficiency and increased mental stress.

[0542] 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.

[0543] In this invention, the server includes means for acquiring user physical condition information from a device for collecting activity data, data analysis means for processing the physical condition information, and program generation means for generating an optimal work program for the user based on the information processed by the data analysis means. This makes it possible to propose an optimal work procedure that comprehensively considers the user's physical and emotional aspects.

[0544] "Activity data" refers to information about the user's physical movements, including distance traveled and weight lifted during work.

[0545] "Device" refers to equipment used to collect and transmit user activity data, and includes packaged hardware such as wearable devices and sensors.

[0546] "Physical condition information" refers to information related to the user's physical health and activity, such as heart rate, blood pressure, and calories burned.

[0547] "Data analysis means" refers to software or hardware used to process and analyze collected physical condition information, and involves the use of algorithms and AI models.

[0548] A "program generation means" is a device that has the function of generating optimal work procedures and activity guidelines for the user based on data analysis information.

[0549] A "feedback system" is a system that transmits information to the user in real time and provides appropriate guidance and suggestions for improvement during or after work.

[0550] A "stress reduction measure" is a system that takes into account the emotional state of workers and adjusts work procedures or gives instructions for rest.

[0551] The system for realizing this invention aims to improve work efficiency and the user's health by comprehensively monitoring the user's physical and emotional state and generating an optimal work program.

[0552] The system primarily consists of a wearable device worn by the user, a server that analyzes the data, and a terminal that provides information to the user. The wearable device collects activity data such as heart rate and distance traveled as physical status information, and incorporates sensors to simultaneously assess emotional state. This data is transmitted to the server via Bluetooth or Wi-Fi.

[0553] The server uses a Raspberry Pi 4 and a data analysis tool programmed in Python to analyze the user's physical and emotional state. It also utilizes Google Cloud's Natural Language API to analyze emotions from voice input. Based on the results of this analysis, an optimal work program is generated that takes into account the user's current physical limitations and emotional stress levels.

[0554] The generated work program and feedback information are delivered in real time to the user's device, such as a smartphone. This includes instructions for adjusting specific work procedures and recommendations for breaks. The feedback also includes simple stretching exercises aimed at relaxation and advice to reduce psychological stress.

[0555] As a concrete example, in a factory worker performing assembly work, an activity device monitors heart rate variability and body movement. If an increase in stress is detected, the server provides feedback to slow down the work pace and suggests a 5-minute stretching break. During this process, the following prompt sentences can be input to the generating AI model.

[0556] For example, "During factory work, the workers' heart rates increased, and emotional analysis indicated high levels of stress. Please propose an optimal work program to alleviate physical and emotional stress before the next shift."

[0557] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0558] Step 1:

[0559] The user puts on a wearable device and begins working. The wearable device collects the user's heart rate, distance traveled, and other physical parameters in real time. This data is transmitted to a server via Bluetooth communication. The input is the user's physical activity data, and the output is the raw data sent to the server.

[0560] Step 2:

[0561] A data analysis engine programmed in Python processes the physical activity data received by the server. The analysis engine calculates workload and fatigue levels from the data fluctuations. The input is the raw data sent in step 1, and the output is the analyzed workload and fatigue level values.

[0562] Step 3:

[0563] The server uses Google Cloud's Natural Language API to analyze emotional data from the user's voice. An emotional analysis model identifies the user's emotional state (such as stress level) from their voice tone. The input is the user's voice data, and the output is an evaluation of their emotional state.

[0564] Step 4:

[0565] The server integrates activity and emotional data and uses an AI model to generate an optimal work program for the user. The generating AI model uses prompts to determine specific actions that optimize work efficiency and emotional balance. The input is the analyzed workload, fatigue level, and emotional state; the output is the specific work program.

[0566] Step 5:

[0567] The server sends the generated work program to the user's terminal. The user's terminal displays the work program and guides the user through the next steps and any necessary adjustments. The input is the work program generated in step 4, and the output is the instructions displayed on the user's terminal.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] [Fourth Embodiment]

[0572] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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".

[0585] This invention provides an innovative system for optimizing a user's exercise activities. This system collects physical condition information via an activity device worn by the user and generates and presents an optimal exercise program based on this information. The system enables efficient data communication between the server, terminal, and user's device, providing real-time feedback on exercise information and supporting the user's continued exercise.

[0586] First, the user uses the activity device in their daily life to collect activity data such as steps taken, heart rate, and calories burned. This device has the function of transmitting the collected data to the terminal. The terminal receives the data from the device and sends it to the server.

[0587] The server processes the received data using a dedicated data analysis system. This system analyzes the user's health status, past exercise history, and goals to evaluate the user's exercise capacity and physical condition. Based on this evaluation, the server generates an optimal exercise program for the user. The exercise program includes the type, intensity, and frequency of exercise, and is designed to support the user in achieving their goals.

[0588] The generated exercise program is sent back to the terminal and presented to the user. By performing exercises based on this program, the user can effectively continue their training. The server also provides real-time feedback to the user during and after exercise through various feedback mechanisms. This feedback includes information on exercise progress, areas for improvement, and motivational tips.

[0589] For example, if a user's goal is weight loss, the activity device collects daily walking data. The server analyzes this data and generates a five-day-a-week walking program for the user, specifying the intensity needed to maintain an appropriate heart rate. By following this program and receiving feedback from the device, the user can consistently achieve their exercise goals. In this way, the present invention enables improved health management and exercise effectiveness for the user.

[0590] The following describes the processing flow.

[0591] Step 1:

[0592] The device collects activity data such as heart rate, steps taken, and calories burned in real time from the activity device worn by the user. This data is stored on the device at regular intervals.

[0593] Step 2:

[0594] The device transmits collected activity data to the server using a communication method. The data is typically transferred to the server via Bluetooth or Wi-Fi, and appropriate protocols are configured to ensure stable communication.

[0595] Step 3:

[0596] The server processes the received activity data using data analysis tools. Here, it evaluates the user's current exercise ability and physical condition, while also comparing it with the user's past training history and health information.

[0597] Step 4:

[0598] Based on the data analysis results, the server generates a personalized exercise program for each user. This includes the type of exercise, recommended intensity, frequency, and specific training content.

[0599] Step 5:

[0600] The server sends the generated exercise program to the terminal. The program is customized based on the user's behavioral patterns and preferences.

[0601] Step 6:

[0602] The user follows the exercise program displayed on the device and begins training based on the instructions. During the exercise, the device continuously monitors the user's progress.

[0603] Step 7:

[0604] The server sends real-time feedback to the device based on data collected during exercise, providing users with information. This includes advice on adjusting exercise intensity and areas for improvement.

[0605] Step 8:

[0606] Users review the feedback provided after training to help them improve their next session. The server displays encouraging messages and evaluations based on their performance that day on their device.

[0607] (Example 1)

[0608] 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".

[0609] In modern society, efficiently managing the health and improving the effectiveness of exercise for individual users has become a crucial challenge. Existing systems require considerable effort and time to accurately collect user physical condition information and continuously provide appropriate exercise programs, making it difficult to maintain user motivation. Therefore, there is a need for a system that generates optimal exercise programs tailored to each user's individual goals and provides real-time feedback that matches the user's activity progress.

[0610] 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.

[0611] In this invention, the server includes means for acquiring user physical condition information from a measuring device for collecting activity data, information analysis means for processing the physical condition information, and program construction means for generating an optimal exercise program for the user based on the information processed by the information analysis means. This enables the user to continuously achieve their exercise goals by automatically generating an exercise program optimized for each individual user and providing appropriate feedback in real time.

[0612] "Activity data" refers to information about a user's physical activity, including data such as steps taken, heart rate, and calories burned, which indicates the user's activity level.

[0613] A "measuring device" is a device used to collect user activity data, and includes wearable devices equipped with accelerometers and heart rate sensors.

[0614] "Physical condition information" refers to data that indicates the user's health status and exercise level, and includes information such as heart rate and calories burned obtained from activity data.

[0615] "Information analysis means" refers to a means of processing collected physical condition information and performing analysis based on the user's exercise history and goals.

[0616] "Program construction means" refers to a means for generating an optimal exercise program for the user based on the analysis results obtained by the information analysis means.

[0617] An "evaluation tool" is a means of providing real-time information about a user's exercise activities and providing feedback on their progress and areas for improvement.

[0618] A "generative AI model" is a model based on artificial intelligence technology used to optimize a user's exercise program.

[0619] "Communication means" refers to means for efficient information transmission between measuring devices, terminals, and central processing units.

[0620] This system is designed to optimize users' exercise activities and support individual health management. Its main components consist of measuring devices, terminals, and a server, and these elements communicate with each other to provide users with optimal exercise programs and feedback.

[0621] Users wear a measuring device during their daily lives, which collects activity data. This device includes an accelerometer and a heart rate sensor, and continuously records data such as steps taken, heart rate, and calories burned. The measuring device has the ability to transmit data to a terminal via Bluetooth or Wi-Fi.

[0622] The terminal organizes the data received from the measuring device and sends it to the server. The server analyzes the data using data analysis software such as Python. Generative AI models are used in the analysis to create an optimal exercise program that takes into account various data such as the user's past exercise history and health status.

[0623] The generated exercise program includes exercise type, intensity, and frequency, and is designed to help users achieve their health goals. The exercise program is sent to the device and presented to the user visually. The server also provides appropriate feedback in real time during and after exercise to help improve user motivation and performance.

[0624] For example, if a user aims to manage their weight, this system provides a five-day-a-week walking program. During this program, feedback is provided that includes information to help maintain an appropriate heart rate.

[0625] An example of a prompt to a generating AI model is: "Create a weekly exercise plan to help the user achieve their weight loss goal. Indicate the optimal exercise intensity for walking, taking heart rate into consideration." This allows the system to provide a program customized to the user's specific needs.

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] Users collect activity data by wearing the measuring device while going about their daily lives. During this time, the accelerometer and heart rate sensor work to acquire data on steps taken, heart rate, and calories burned. This data is temporarily stored in the measuring device's memory. The input data is information about the user's movements, while the output data is the activity data stored in the measuring device.

[0629] Step 2:

[0630] The terminal receives activity data from the measuring device via Bluetooth or Wi-Fi. During this process, data format conversion may occur, and pre-processing is performed to make the data easier for the server to process. The input is the activity data transmitted from the measuring device, and the output is the pre-processed data.

[0631] Step 3:

[0632] The server performs information analysis using activity data received from the terminal. Data analysis software such as Python is used to clean, filter, and transform the data before analysis is performed by a generative AI model. This analysis provides a detailed assessment based on the user's current health status and exercise history. The input is pre-processed activity data, and the output is the analyzed information.

[0633] Step 4:

[0634] The server generates an exercise program optimized for each individual user based on the analysis results. The generating AI model determines the type, intensity, and frequency of exercise according to the user's goals and status. Prompt statements are used to generate the program; for example, the model is given the command, "Create a weekly exercise plan to help the user achieve their weight loss goal." The input is the analyzed information, and the output is the exercise program.

[0635] Step 5:

[0636] The device receives the generated exercise program and presents it visually to the user. During the exercise, it provides feedback on progress and real-time achievement, and after the exercise, it provides an overall evaluation. The input is the generated exercise program, and the output is exercise instructions and feedback information for the user.

[0637] (Application Example 1)

[0638] 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".

[0639] Modern users often struggle to obtain optimal exercise plans tailored to their individual physical condition and fitness goals. Furthermore, they often lack real-time, accurate guidance during exercise, resulting in insufficient feedback to maintain motivation. To address these issues, a system is needed that provides personalized exercise plans and supports users' continuous health improvement through real-time guidance and feedback.

[0640] 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.

[0641] In this invention, the server includes means for acquiring user physical condition information from a device for collecting activity information, data analysis means for processing the physical condition information, program generation means for generating an optimal exercise plan for the user based on the information processed by the data analysis means, means for presenting the generated exercise plan to the user, means for acquiring and presenting an exercise plan from the server based on the user's exercise data, and instruction means for providing real-time guidance based on biological information during exercise. This enables the provision of an exercise program optimized for each individual user, and improves training and motivation through real-time guidance and feedback during exercise.

[0642] "Activity information" refers to all data related to the user's physical condition, and specifically includes steps taken, heart rate, calories burned, etc.

[0643] "Device" refers to a device that, when worn by a user, has the function of collecting information about the user's physical condition.

[0644] "Physical condition information" refers to data about the user's physiological and motor state, and this information is obtained through activity data.

[0645] "Data analysis means" refers to a system or process that has the function of processing collected physical condition information and extracting meaningful information from that data.

[0646] An "exercise plan" refers to a program that determines the optimal type, intensity, and frequency of exercise for the user, based on information about the user's physical condition.

[0647] "Program generation means" refers to a system or process for automatically generating an optimal exercise plan based on data analysis results.

[0648] "Means of presentation" refers to a device or process for providing the generated motor plan to the user visually or audibly.

[0649] "Instructional methods" refer to systems or processes for providing real-time instructions and advice to users during exercise.

[0650] "Feedback" refers to information provided regarding evaluations and areas for improvement concerning a user's exercise activities and progress.

[0651] "Motivation enhancement" refers to information and methods that increase users' desire to exercise and encourage them to continue exercising.

[0652] The system for implementing this invention uses a terminal that collects physical condition information from a device worn by the user and transmits it to a server. The server performs data analysis based on the received information and generates an exercise plan optimized for the user. The generated exercise plan is transmitted to the terminal and presented to the user.

[0653] The server forms the core of this system and is equipped with data analysis tools for efficiently analyzing activity information. These analysis tools utilize programming languages ​​such as Python and AI models to extract meaningful information from the collected data. Specifically, they collect data such as the user's steps, heart rate, and calories burned, and process this data within the system.

[0654] The server also uses a program generation mechanism to provide users with the generated exercise plan. This mechanism customizes the type and intensity of exercise individually, creating a plan that is realistically achievable for the user. Furthermore, it includes a guidance mechanism that provides real-time instruction, offering immediate instructions and advice based on biometric information during the user's exercise.

[0655] The terminal uses communication technologies such as Bluetooth and Wi-Fi to transfer data between the device and the server. Users receive exercise plans via their smartphones or other devices and train based on the information displayed on the screen. Feedback allows users to check their exercise progress and areas for improvement in a timely manner, and information to boost motivation is also provided as needed.

[0656] As a concrete example, consider a user attempting a 10km running program. The user begins exercising based on a plan presented on the device: "30 minutes of jogging, 5 minutes of interval training." Feedback from the device monitors heart rate and exercise intensity, and notifies the user of appropriate rest periods, aiming for sustained health improvement.

[0657] Examples of input prompts for a generative AI model:

[0658] "Please describe the design of a smartphone application that provides personalized exercise programs based on running data for fitness club members. The application should include features that analyze user data such as heart rate, calories burned, and steps taken, and provide real-time training guidance."

[0659] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0660] Step 1:

[0661] The terminal acquires activity information from the device. It receives data such as steps, heart rate, and calories burned collected by the device as input. By transferring data in real time from the device via Bluetooth technology, the terminal understands the user's current physical condition. It then prepares to send this data to the server as output.

[0662] Step 2:

[0663] The terminal sends activity information to the server. Using the activity information obtained in step 1 as input, the data is transferred to the server via Wi-Fi or a mobile network. At this time, efficient communication is achieved by formatting the data. As output, data in a format usable by the server is sent.

[0664] Step 3:

[0665] The server processes the received activity information using data analysis tools. It accepts activity information sent from the terminal as input and performs data analysis using Python or AI models. Specifically, it refers to past exercise history and health status to evaluate changes in the data. It generates analysis results as output and proceeds to the next processing stage.

[0666] Step 4:

[0667] The server generates an optimal exercise plan for the user based on the analysis results. The analysis results obtained in step 3 are used as input. The generating AI model is used to plan the type, intensity, and frequency of exercise that matches the user's fitness goals. The server then prepares to provide the generated exercise plan to the user's device as output.

[0668] Step 5:

[0669] The terminal receives the exercise plan from the server and presents it to the user. It receives the exercise plan from the server as input and displays the plan details on the terminal's display. Specific operations include a function to display the daily training menu on the user interface. The plan is presented as output in a format that the user can visually confirm.

[0670] Step 6:

[0671] Users perform exercises while receiving feedback and guidance from their device. They refer to the exercise plan and guidance displayed on the device in real time as input. By performing exercises based on advice derived from biometric data, training is achieved according to the plan. The user's exercise completion rate and progress are recorded as output.

[0672] 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.

[0673] This invention is a system for providing a more effective training program by considering the user's emotional state in addition to optimizing the user's exercise. This system integrates an activity device and an emotion engine to collect, analyze, and adapt the user's physical and emotional data.

[0674] Users wear activity devices that collect activity data such as heart rate, steps taken, and calories burned through their daily exercise. Furthermore, an emotion engine analyzes the user's voice tone, facial expressions, and skin electrical activity to assess their emotional state. This emotional data is then transmitted to the server along with the activity data.

[0675] The server processes the received activity and emotional data using data analysis tools. This analysis comprehensively evaluates the user's physical and emotional state and generates an optimal exercise program tailored to the user's current physical ability and mood. For example, if the user's emotional state indicates stress, the server can recommend exercises with relaxing effects.

[0676] The generated exercise program is sent from the server to the user's terminal and presented to the user. The user performs the exercise according to this program, and feedback is displayed on the terminal as it is provided. The feedback system provides real-time feedback during and after the exercise. This includes advice to improve motivation, taking into account the user's emotional state, and instructions to maintain a balance between body and emotion.

[0677] As a concrete example, consider a case where User B is exercising with the goal of reducing stress. The activity device collects daily walking data, and the emotion engine senses User B's stress level through voice recognition. The server analyzes this data and generates a program that suggests three yoga sessions per week to User B, including deep breathing exercises to reduce stress. User B performs the exercise based on this program and receives feedback to maximize the relaxation effect. In this way, the present invention improves exercise efficiency and health management by providing support that takes into account both the user's physical and emotional state.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The device acquires activity data such as heart rate, steps taken, and calories burned from the user's activity device. Simultaneously, it uses an emotion engine to analyze the user's voice and facial expressions and collect data to evaluate their emotional state.

[0681] Step 2:

[0682] The device sends the collected activity data and emotion data to the server as a single package. This transmission is performed periodically using a communication method.

[0683] Step 3:

[0684] The server stores the received data in a database and then processes it using data analysis tools. Both activity data and emotional data are analyzed to comprehensively evaluate the user's physical and emotional state.

[0685] Step 4:

[0686] The server generates an optimal exercise program for the user based on the analysis results. It adjusts the program content according to the user's emotional state, selecting, for example, relaxation exercises or exercises that have a positive effect on emotions.

[0687] Step 5:

[0688] The server sends the generated exercise program to the user's terminal. The program includes customized information such as the type and intensity of exercise and the recommended frequency.

[0689] Step 6:

[0690] The user checks the exercise program displayed on the device and begins exercising accordingly. During the exercise, the device continuously monitors the user's progress and emotional state.

[0691] Step 7:

[0692] The server provides real-time feedback to the device regarding the exercise being performed, thereby supporting the user's progress. The feedback includes advice based on emotional state to help maintain motivation.

[0693] Step 8:

[0694] Users review the feedback and evaluation provided after their workout. This feedback helps them plan their next training session, taking into account new analysis results from the emotion engine.

[0695] (Example 2)

[0696] 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".

[0697] Traditional exercise programs often design programs based solely on the user's physical data, making it difficult to provide programs that take the user's emotional state into consideration. This resulted in the inability to provide optimal exercise programs tailored to the user's emotional state, leading to the problem of exercise not being fully realized.

[0698] 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.

[0699] In this invention, the server includes means for acquiring information from a device for collecting user activity data and emotional data; data analysis means for processing the information and comprehensively evaluating the user's physical and emotional state; and program generation means for generating an optimal exercise program tailored to the user's emotional state based on the analyzed information. This makes it possible to provide an exercise program that responds not only to the user's physical state but also to their emotional state.

[0700] "Activity data" refers to data that indicates the user's physical condition and exercise performance, and includes steps taken, heart rate, calories burned, etc.

[0701] "Emotional data" refers to data that indicates a user's mental or emotional state, and is obtained through methods such as tone of voice, facial expressions, and skin electrical activity.

[0702] "Data analysis methods" refer to techniques for comprehensively processing collected activity data and emotional data to evaluate the user's current physical and emotional state.

[0703] "Program generation means" refers to a function or device that creates an optimal exercise program for the user based on analyzed data, taking into account the user's physical and emotional state.

[0704] A "feedback mechanism" is a method or system that provides users with real-time feedback during and after exercise to maximize the effectiveness of exercise and improve motivation.

[0705] "Communication means" refers to methods and systems for sending and receiving data between a device and an information processing device, enabling accurate synchronization of activity data and emotional data.

[0706] This system provides a program to comprehensively improve the user's physical and emotional state. This section details the components of the system: the activity device worn by the user, the emotion engine, the data processing server, and the terminal that handles the user interface.

[0707] Users collect data on their daily exercise by wearing a dedicated activity device. This device can acquire physical data such as heart rate, steps taken, and calories burned. In parallel, an emotion engine analyzes the user's emotional state through voice tone, facial expressions, and skin electrical activity. This data forms the basis for a detailed understanding of the user's daily activities.

[0708] The server plays a central role in processing user activity and emotional data received from the terminal. Here, data analysis tools are used to comprehensively evaluate the user's physical and emotional state. Using the analysis results, a program generation tool generates an exercise program optimized for each individual user. This exercise program takes into account not only the physical aspects but also the user's emotional state.

[0709] The device presents the user with exercise programs transmitted from the server. It also provides real-time feedback during and after the activity, playing a crucial role in maintaining and improving user motivation. Because the feedback includes advice based on the user's emotional state, users can continue exercising more effectively.

[0710] As a concrete example, consider a case where a user is exercising to reduce stress. This system processes walking data acquired by the user's activity device and stress level data analyzed by an emotion engine on a server. As a result, the server recommends three yoga sessions per week and generates a program that incorporates deep breathing exercises for relaxation. This exercise program is presented to the user via the device, and the user can perform the exercise while receiving feedback.

[0711] An example of a prompt for a generative AI model would be: "Generate a description of a system that optimizes exercise programs by considering the user's emotional state. This system integrates and analyzes physical and emotional data to suggest an appropriate exercise program."

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] The user wears an activity device that collects activity data such as steps, heart rate, and calories burned. Simultaneously, an emotion engine begins analysis, acquiring emotional data such as voice tone, facial expressions, and skin electrical activity. This data serves as input to understand the user's physical and emotional state. The collected data is sent to the user's device, preparing it for the next processing step.

[0715] Step 2:

[0716] The terminal transmits acquired activity and emotional data to the server in real time. The server, upon receiving the input data, analyzes this information using data analysis tools. This analysis comprehensively evaluates the user's physical and emotional state, and outputs basic data for creating appropriate programs tailored to the user's motor skills and emotions.

[0717] Step 3:

[0718] The server generates an exercise program optimized for the user based on the analyzed data. Using the program generation mechanism, it selects exercises and relaxation methods appropriate to the user's physical and emotional state. The optimal exercise program output at this stage includes details such as the frequency, type, and duration of the exercise.

[0719] Step 4:

[0720] The generated exercise program is sent from the server to the terminal. The terminal presents the received program to the user, allowing them to start exercising immediately. The user performs the exercise according to this program, and the terminal monitors the progress in real time.

[0721] Step 5:

[0722] During and after exercise, the device provides feedback to the user. Using this feedback mechanism, it outputs advice to improve motivation, taking into account the user's emotional state, as well as instructions based on their level of achievement. This allows the user to effectively execute their exercise program and supports their health management.

[0723] (Application Example 2)

[0724] 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".

[0725] The problem that this invention aims to solve is to improve the work efficiency of workers in work environments such as factories, while also reducing stress by taking into account their emotional state. Conventional work management systems only consider data related to physical load, and tend to overlook the emotional aspect, resulting in problems such as decreased work efficiency and increased mental stress.

[0726] 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.

[0727] In this invention, the server includes means for acquiring user physical condition information from a device for collecting activity data, data analysis means for processing the physical condition information, and program generation means for generating an optimal work program for the user based on the information processed by the data analysis means. This makes it possible to propose an optimal work procedure that comprehensively considers the user's physical and emotional aspects.

[0728] "Activity data" refers to information about the user's physical movements, including distance traveled and weight lifted during work.

[0729] "Device" refers to equipment used to collect and transmit user activity data, and includes packaged hardware such as wearable devices and sensors.

[0730] "Physical condition information" refers to information related to the user's physical health and activity, such as heart rate, blood pressure, and calories burned.

[0731] "Data analysis means" refers to software or hardware used to process and analyze collected physical condition information, and involves the use of algorithms and AI models.

[0732] A "program generation means" is a device that has the function of generating optimal work procedures and activity guidelines for the user based on data analysis information.

[0733] A "feedback system" is a system that transmits information to the user in real time and provides appropriate guidance and suggestions for improvement during or after work.

[0734] A "stress reduction measure" is a system that takes into account the emotional state of workers and adjusts work procedures or gives instructions for rest.

[0735] The system for realizing this invention aims to improve work efficiency and the user's health by comprehensively monitoring the user's physical and emotional state and generating an optimal work program.

[0736] The system primarily consists of a wearable device worn by the user, a server that analyzes the data, and a terminal that provides information to the user. The wearable device collects activity data such as heart rate and distance traveled as physical status information, and incorporates sensors to simultaneously assess emotional state. This data is transmitted to the server via Bluetooth or Wi-Fi.

[0737] The server uses a Raspberry Pi 4 and a data analysis tool programmed in Python to analyze the user's physical and emotional state. It also utilizes Google Cloud's Natural Language API to analyze emotions from voice input. Based on the results of this analysis, an optimal work program is generated that takes into account the user's current physical limitations and emotional stress levels.

[0738] The generated work program and feedback information are delivered in real time to the user's device, such as a smartphone. This includes instructions for adjusting specific work procedures and recommendations for breaks. The feedback also includes simple stretching exercises aimed at relaxation and advice to reduce psychological stress.

[0739] As a concrete example, in a factory worker performing assembly work, an activity device monitors heart rate variability and body movement. If an increase in stress is detected, the server provides feedback to slow down the work pace and suggests a 5-minute stretching break. During this process, the following prompt sentences can be input to the generating AI model.

[0740] For example, "During factory work, the workers' heart rates increased, and emotional analysis indicated high levels of stress. Please propose an optimal work program to alleviate physical and emotional stress before the next shift."

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The user puts on a wearable device and begins working. The wearable device collects the user's heart rate, distance traveled, and other physical parameters in real time. This data is transmitted to a server via Bluetooth communication. The input is the user's physical activity data, and the output is the raw data sent to the server.

[0744] Step 2:

[0745] A data analysis engine programmed in Python processes the physical activity data received by the server. The analysis engine calculates workload and fatigue levels from the data fluctuations. The input is the raw data sent in step 1, and the output is the analyzed workload and fatigue level values.

[0746] Step 3:

[0747] The server uses Google Cloud's Natural Language API to analyze emotional data from the user's voice. An emotional analysis model identifies the user's emotional state (such as stress level) from their voice tone. The input is the user's voice data, and the output is an evaluation of their emotional state.

[0748] Step 4:

[0749] The server integrates activity and emotional data and uses an AI model to generate an optimal work program for the user. The generating AI model uses prompts to determine specific actions that optimize work efficiency and emotional balance. The input is the analyzed workload, fatigue level, and emotional state; the output is the specific work program.

[0750] Step 5:

[0751] The server sends the generated work program to the user's terminal. The user's terminal displays the work program and guides the user through the next steps and any necessary adjustments. The input is the work program generated in step 4, and the output is the instructions displayed on the user's terminal.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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."

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] The following is further disclosed regarding the embodiments described above.

[0774] (Claim 1)

[0775] A means of obtaining user physical condition information from a device for collecting activity data,

[0776] A data analysis means for processing the aforementioned physical condition information,

[0777] A program generation means that generates an optimal exercise program for the user based on the information processed by the data analysis means,

[0778] A means of presenting the generated exercise program to the user,

[0779] A system that includes a feedback mechanism to provide real-time feedback on the user's exercise activities.

[0780] (Claim 2)

[0781] The system according to claim 1, further comprising means for providing information to improve the user's motivation in accordance with the user's progress in their exercise program.

[0782] (Claim 3)

[0783] The system according to claim 1, comprising communication means for communicating between a device and a server and for transmitting and receiving user activity data.

[0784] "Example 1"

[0785] (Claim 1)

[0786] A means of obtaining information about the user's physical condition from a measuring device for collecting activity data,

[0787] Information analysis means for processing the aforementioned physical condition information,

[0788] A program construction means that generates an optimal exercise program for the user based on the information processed by the information analysis means,

[0789] A means of presenting the generated exercise program to the user,

[0790] An evaluation method that provides real-time assessment of users' exercise activities,

[0791] A means of optimizing exercise programs using generative AI models to support users in achieving their goals,

[0792] A system comprising communication means for efficient information transmission between the aforementioned measuring device, terminal, and central processing unit.

[0793] (Claim 2)

[0794] The system according to claim 1, further comprising means for providing information to improve motivation in accordance with the progress of the user's exercise program.

[0795] (Claim 3)

[0796] The system according to claim 1, further comprising means for providing users with information on areas for improvement and achievements during and after exercise in order to enhance evaluation.

[0797] "Application Example 1"

[0798] (Claim 1)

[0799] A means for acquiring user physical condition information from a device for collecting activity information,

[0800] A data analysis means for processing the aforementioned physical condition information,

[0801] A program generation means that generates an optimal exercise plan for the user based on the information processed by the data analysis means,

[0802] A means of presenting the generated exercise plan to the user,

[0803] A feedback system that provides real-time feedback on the user's exercise activities,

[0804] A means of obtaining and presenting an exercise plan from a server based on the user's exercise data,

[0805] A system that includes a coaching method that provides real-time guidance based on biological information during exercise.

[0806] (Claim 2)

[0807] The system according to claim 1, further comprising means for providing information to improve the user's motivation in accordance with the progress of the user's exercise plan.

[0808] (Claim 3)

[0809] The system according to claim 1, comprising communication means for communicating between a device and a data server, and for transmitting and receiving user activity information.

[0810] "Example 2 of combining an emotion engine"

[0811] (Claim 1)

[0812] A means of obtaining information from a device for collecting user activity data and emotional data,

[0813] A data analysis means for processing the aforementioned information and comprehensively evaluating the user's physical and emotional state,

[0814] A program generation means that generates an optimal exercise program tailored to the user's emotional state based on the analyzed information,

[0815] A means of presenting a generated exercise program to the user and having them perform the exercise based on it,

[0816] A system that provides real-time feedback to users during and after exercise, including feedback mechanisms to improve motivation according to their emotional state.

[0817] (Claim 2)

[0818] The system according to claim 1, which provides information to maintain relaxation effects and emotional balance in accordance with the progress of an exercise program, based on the user's emotional data.

[0819] (Claim 3)

[0820] The system according to claim 1, comprising communication means for communicating, transmitting, and receiving user activity data and emotion data between a device and an information processing device.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] A means for obtaining information about the user's physical condition from a device for collecting activity data,

[0824] A data analysis means for processing the aforementioned physical condition information,

[0825] A program generation means that generates an optimal work program for the user based on the information processed by the data analysis means,

[0826] A means of presenting the generated work program to the user,

[0827] A feedback mechanism that provides real-time feedback regarding the user's work activities,

[0828] A system including means for adjusting work procedures to reduce stress based on the aforementioned feedback.

[0829] (Claim 2)

[0830] The system according to claim 1, further comprising means for providing information to improve motivation in accordance with the progress of the user's work program.

[0831] (Claim 3)

[0832] The system according to claim 1, comprising communication means for communicating between a device and an information processing device, and for transmitting and receiving user activity data. [Explanation of Symbols]

[0833] 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 means of obtaining user physical condition information from a device for collecting activity data, A data analysis means for processing the aforementioned physical condition information, A program generation means that generates an optimal exercise program for the user based on the information processed by the data analysis means, A means of presenting the generated exercise program to the user, A system that includes a feedback mechanism to provide real-time feedback on the user's exercise activities.

2. The system according to claim 1, further comprising means for providing information to improve the user's motivation in accordance with the user's progress in their exercise program.

3. The system according to claim 1, comprising communication means for communicating between a device and a server and for transmitting and receiving user activity data.

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