system

Abstract

We provide the system. [Solution] Means for obtaining the user's physical information, A means for analyzing the user's motor characteristics based on the acquired information, A means for generating an exercise plan suitable for the user based on the analysis results, A means of presenting the generated exercise plan to the user, A means of monitoring the user's movements during exercise and providing feedback, A means of evaluating user progress and dynamically updating the exercise plan, A system that includes means for recommending products necessary for exercise to users.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, many people are seeking individually optimized exercise plans, but it is a difficult task to provide an exercise menu that precisely matches the physical characteristics of each individual. Also, it is difficult to continuously monitor the progress and status of individual users and dynamically adjust the exercise plan. Under such circumstances, a system that enables users to perform efficient and effective training has not yet been sufficiently provided. Furthermore, there is also a lack of an integrated solution that appropriately recommends and supports the purchase of items necessary for training.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following configuration: It acquires the user's physical information based on images or videos, and analyzes the user's movement characteristics in detail based on that information. Then, it automatically generates an optimal exercise plan for the user using the results of that analysis. The generated exercise plan is presented to the user, and the system is equipped with a mechanism to continuously monitor the user's movements during exercise and provide immediate feedback. Furthermore, it evaluates the user's progress and dynamically updates the exercise plan as needed to meet individual needs. In addition, the system is equipped with a function to individually recommend necessary training products and support their purchase.

[0006] A "user" refers to an individual who uses the system to receive an exercise plan.

[0007] "Physical information" refers to information that includes not only basic information such as the user's height, weight, and body fat percentage, but also data on muscle structure and posture obtained from images and videos.

[0008] "Motor characteristics" refer to characteristics related to athletic performance, such as the user's physical features, athletic ability, posture, and muscle structure.

[0009] An "exercise plan" refers to a training program optimized for each individual user, generated based on the user's characteristics.

[0010] "Feedback" refers to information that provides real-time instructions and suggestions for improvement regarding the user's movements during exercise.

[0011] "Dynamic updating" refers to the process of automatically changing or adjusting the exercise plan in response to changes in the user's progress and physical condition.

[0012] "Recommending a product" refers to the act of suggesting the most suitable items to support a user's training based on their exercise plan.

[0013] A "system" refers to a technical entity that combines a set of technical means and functions for acquiring physical information, analyzing movement characteristics, generating exercise plans, and providing feedback to a user. [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out 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 numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

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

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[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] The system of this invention is an integrated platform that provides an optimized exercise plan for each individual user and manages its progress and feedback in real time. The following describes how this system is implemented.

[0036] First, users log into the system and enter their physical information through a dedicated app. This information includes basic data such as height, weight, and body fat percentage, and by uploading images and videos, more detailed data on body structure is also collected.

[0037] Next, the server uses an AI analysis engine to analyze the user's exercise characteristics based on the received data. This analysis clarifies the user's current fitness level, muscle characteristics, and postural habits. This allows for the generation of a customized exercise plan tailored to each individual user.

[0038] The generated exercise plan is sent to the terminal based on the server information and presented to the user. The presented plan includes specific details such as daily exercise content, number of sets, and rest times, allowing the user to perform the exercises at their own pace. Detailed guidance is supplemented with videos and text to support proper form during training.

[0039] During training, the device uses sensors to monitor the user's movements in real time and communicates with a server to provide immediate feedback. This helps users exercise effectively and correct any form errors instantly.

[0040] Furthermore, the server regularly collects user progress data and evaluates achievements and areas for improvement. Based on this feedback, the exercise plan is dynamically updated, ensuring that an optimized exercise program is always provided.

[0041] Furthermore, the system recommends necessary training products and supplementary items to the user and assists with the purchase process via the terminal. In this way, the system of the present invention provides comprehensive and personalized support to help users achieve their fitness goals.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users launch a dedicated app and input basic data such as height, weight, and body fat percentage. They also upload images and videos to capture detailed information about their bodies.

[0045] Step 2:

[0046] The terminal receives the input data and, if necessary, uses sensors to obtain detailed information about the user's posture and movements. This allows more accurate physical information to be sent to the server.

[0047] Step 3:

[0048] The server analyzes the received information and uses an AI engine to evaluate the user's movement characteristics. Specifically, it identifies the user's characteristics by considering factors such as muscle balance, postural features, and past exercise history.

[0049] Step 4:

[0050] The server uses the analysis results to generate an optimal exercise plan for each individual user. This includes the type of exercise, number of sets, repetitions, and rest time. It also references past success stories to develop a plan that aligns with the user's goals.

[0051] Step 5:

[0052] The device presents the user with a generated exercise plan. The plan includes detailed instructions and video guides for each exercise, and the user begins training according to it.

[0053] Step 6:

[0054] During training, the device monitors the user's movements using built-in sensors. If the movements do not conform to the plan or the form is incorrect, it provides immediate feedback.

[0055] Step 7:

[0056] The server periodically collects training data and evaluates the user's progress. It analyzes the results and areas for improvement, and dynamically updates the exercise plan as needed.

[0057] Step 8:

[0058] The device suggests exercise equipment and items to the user, provides links to online stores, and supports a smooth purchasing experience.

[0059] (Example 1)

[0060] 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."

[0061] In modern society, providing personalized exercise plans and managing their progress is crucial. However, many fitness programs are standardized, making it difficult to offer optimal guidance tailored to individual body structures, fitness levels, and goals. Furthermore, users need to accurately track their progress and update their plans appropriately. Choosing the right exercise equipment is also a complex process.

[0062] 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.

[0063] In this invention, the server includes means for acquiring information from the user, means for analyzing the user's activity characteristics based on the acquired information, and means for formulating an activity plan optimized for the user based on the analysis results. This makes it possible to provide a customized exercise plan for each individual user and to continuously follow up on its effectiveness and progress.

[0064] A "user" refers to an individual who uses this system to receive a personalized exercise plan.

[0065] "Information" refers to various types of data used to develop exercise plans, such as the user's physical data, activity characteristics, and past activity data.

[0066] "Activity characteristics" refer to individual exercise-related characteristics that are considered when formulating an exercise plan, such as the user's athletic ability, muscle characteristics, and postural tendencies.

[0067] "Analysis" refers to the process of evaluating activity characteristics based on information provided by the user and performing calculations and interpretations to derive the optimal exercise plan.

[0068] An "activity plan" refers to a program provided to individual users that includes specific exercise guidelines such as the type of exercise, the number of sets, and rest times.

[0069] "Feedback" refers to suggestions and advice provided to users during or after their exercise, with the aim of correcting or improving their movements.

[0070] "Progress" is an indicator that shows how close a user is getting to their goal through activities based on their exercise plan.

[0071] "Items" refer to tools and equipment that are useful when a user exercises, and are intended to support efficient training.

[0072] A "generative AI model" refers to an artificial intelligence algorithm used to generate individual movement plans based on user information.

[0073] This system is a comprehensive platform that provides personalized exercise plans for each user and manages their progress and feedback in real time. The following hardware and software are used to implement this system.

[0074] Users first use a device with a dedicated application installed. This device is a mobile information terminal such as a smartphone or tablet, which allows users to log in to the system and enter their profile information. Physical information input includes basic data such as height, weight, and body fat percentage, and users can also upload images and videos to obtain more detailed physical structure data.

[0075] The server receives information transmitted from the terminal and uses an AI analysis engine to analyze the user's activity characteristics. This analysis uses machine learning algorithms to evaluate the user's motor skills and postural characteristics from physical information. Based on the analysis results, the generative AI model develops an optimal exercise plan for each user. This plan includes the type of exercise, the number of sets, and rest times.

[0076] The formulated exercise plan is sent from the server to the terminal and presented to the user through the application. The presented exercise plan includes video and text-based guides, which the user uses as a reference while training.

[0077] The device uses built-in sensors to monitor user behavior in real time. The collected data is shared with a server and immediately provides feedback to the user. This feedback is communicated to the user as voice assistant or on-screen instructions to help correct their behavior.

[0078] Furthermore, the server periodically evaluates the user's progress and updates the exercise plan based on the results. This dynamic update ensures that the user is always provided with the most suitable exercise program.

[0079] Examples of prompt messages include: "Prompt the user to enter their current height, weight, and body fat percentage, and upload a full-body photo," and "Configure the device to monitor the user's movements and provide feedback if any movements are inaccurate."

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user opens a dedicated app and logs in. The information they enter includes physical data such as height, weight, and body fat percentage. They can also upload images and videos. The device collects this data and securely transmits the information entered in the form and the uploaded media to the server.

[0083] Step 2:

[0084] The server receives information from the terminal. The input data includes the user's physical data, images, and videos. The server first verifies the basic data to confirm its accuracy. Then, it activates the AI ​​analysis engine and analyzes the user's activity characteristics based on the data. This analysis uses machine learning algorithms to evaluate the user's motor skills, posture, and muscle characteristics. The output of the analysis is a characteristic evaluation result based on the user profile.

[0085] Step 3:

[0086] Based on the analysis results from the AI ​​analysis engine, the server uses a generated AI model to formulate an activity plan optimized for the user. The input is the analysis results, and the output is a program containing the specific details of the exercise plan. This program includes detailed instructions such as individual exercises, number of sets, and rest times.

[0087] Step 4:

[0088] The formulated activity plan is transmitted to the terminal by the server. Based on the received information, the terminal presents the exercise plan to the user. The plan is presented to the user through the application as a video or text guide. The user uses this as a reference and begins training according to the instructions.

[0089] Step 5:

[0090] While the user is training, the device monitors the user's movements in real time using its built-in sensors. The input is movement data from the sensors, which is sent to the server. The data is analyzed in conjunction with the server to determine if the training is being performed with proper form. As a result, the system can provide the user with real-time feedback and encourage them to correct their movements.

[0091] Step 6:

[0092] After the training session ends, the server collects the user's progress data and dynamically updates the next exercise plan based on that data. The input is past training data, and the output is the updated exercise program. This updated program is then sent back to the terminal and presented to the user in preparation for the next training session.

[0093] (Application Example 1)

[0094] 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."

[0095] In modern life, designing an exercise plan optimized for individual needs and effectively performing exercise requires specialized knowledge and guidance. However, providing personalized fitness support to individual users and dynamically updating plans as they progress is a technically challenging task that requires significant resources.

[0096] 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.

[0097] In this invention, the server includes means for acquiring the user's physical information, means for analyzing the user's movement characteristics, means for generating an exercise plan suitable for the user, means for presenting the plan to the user, and means for evaluating the user's movements in real time and providing immediate corrective instructions visually or audibly. This enables fitness support that is optimized for each individual user and provides real-time feedback.

[0098] A "user" is an individual who utilizes the system and receives an optimized exercise plan based on their physical information and motor characteristics.

[0099] "Physical information" refers to data related to the user's physical characteristics, such as basic data like height, weight, and body fat percentage, as well as images and videos of their posture.

[0100] "Motor characteristics" refer to characteristic information that indicates the user's physical fitness level, muscle characteristics, and postural habits, and these are clarified through analysis.

[0101] An "exercise plan" is a customized fitness program tailored to the user, including details such as the type of exercise, the number of sets, and rest times.

[0102] "Feedback" refers to the immediate instructions and evaluations a user receives while performing an exercise, providing clues to correct errors in their movements.

[0103] "Dynamic updating" refers to the process of modifying the exercise plan based on the user's progress data, ensuring that it is always up-to-date.

[0104] "Real-time evaluation" is a process that instantly analyzes the user's movement and provides the results immediately.

[0105] "Providing feedback visually or audibly" refers to a means of conveying feedback to the user in the form of images or sounds, and plays a role in improving the quality of movement.

[0106] This invention is a system for providing fitness support optimized for individual users. This system is broadly composed of three components: a server, terminals (including robots), and the user.

[0107] First, the user logs into the system via a terminal and enters their physical information. This information includes the user's height, weight, body fat percentage, and images / videos of their posture, and is sent to the server. The server receives this physical information and uses an AI analysis engine to analyze the user's movement characteristics. This analysis uses analysis software such as TENSORFLOW® and OpenCV to quantitatively evaluate the user's fitness level, muscle characteristics, and postural habits.

[0108] Based on the analysis results, the server generates an exercise plan optimized for the user. This exercise plan is a program that includes specific exercises, the number of sets, and rest times, and is sent from the server to the terminal. During the user's exercise, the robot terminal uses its built-in camera and sensors to monitor the user's movements in real time. The robot captures the movements and sends the data to the server. Based on this, the server dynamically generates feedback and provides real-time instructions to the user via voice or visual means through the terminal.

[0109] For example, if the server determines that the user's knee position is not correct while performing a squat, the robot will give specific voice instructions such as, "Move your knees a little further forward." In this way, the user can exercise effectively.

[0110] An example of a prompt would be: "Describe a process in which a fitness robot analyzes the user's squat form in real time and gives voice instructions for the optimal posture."

[0111] This invention makes it possible to provide an optimal training environment that combines the provision of fitness plans tailored to individual exercise needs with real-time feedback.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user logs into the terminal and enters their physical information. This information includes height, weight, body fat percentage, and images / videos of their posture. This data is prepared for analysis on the server.

[0115] Step 2:

[0116] The terminal sends the entered physical information to the server. The server receives this information and passes the data to the AI ​​analysis engine. Here, basic preprocessing is performed, including data standardization and necessary feature extraction.

[0117] Step 3:

[0118] The server utilizes an AI analysis engine to analyze the user's movement characteristics. This analysis uses a TensorFlow model to identify the user's fitness level, muscle characteristics, and postural habits. The analysis results are generated as output.

[0119] Step 4:

[0120] Based on the analysis results, the server generates an exercise plan optimized for the user. This plan includes details such as exercise content, number of sets, and rest times, and the generated plan is sent to the terminal. The output is the exercise plan optimized for the user.

[0121] Step 5:

[0122] The user receives an exercise plan via a terminal and begins exercising accordingly. The terminal, including the robot, begins monitoring the user's movements in real time using its built-in cameras and sensors.

[0123] Step 6:

[0124] The terminal captures user behavior data and sends it to the server. The server receives this data and performs analysis for real-time feedback. Specifically, this includes evaluating the behavior and generating form correction instructions as needed.

[0125] Step 7:

[0126] The server sends the generated feedback to the terminal, which then provides instructions to the user visually or audibly. This process allows the user to immediately correct their actions. The output consists of specific correction instructions.

[0127] Step 8:

[0128] When listening feedback is provided, the server accumulates the user's progress data and dynamically updates the exercise plan. The final result is a continuously optimized exercise plan.

[0129] 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.

[0130] This invention is a system that combines the user's physical and emotional information to provide an individually optimized exercise plan and manages progress in real time. By incorporating an emotion engine, this system provides feedback and adjusts the exercise plan in accordance with the user's emotional state.

[0131] First, users access a dedicated app and input basic physical information. By uploading images and videos during this process, more detailed physical information can be obtained. This includes real-time posture and movement analysis using sensors.

[0132] Next, the server analyzes the collected information. The AI ​​engine analyzes movement characteristics based on the user's physical information, and the emotion engine evaluates the user's emotional state using facial recognition technology. This emotional information is then used to generate a more individually tailored movement plan.

[0133] The generated exercise plan takes into account the user's physical characteristics and emotional state, and the server transmits it to the terminal for presentation. The plan includes exercise content and sequence tailored to the user's mood and motivation. Furthermore, during the execution of the plan, the user receives appropriate instructions and encouraging messages in response to changes in their emotions.

[0134] During training, the device not only monitors the user's movements using sensors but also detects emotional changes by analyzing facial expressions and voice tone in conjunction with an emotion engine. This information is used to provide feedback on the exercise plan, and the plan is adjusted as needed.

[0135] The server periodically collects user feedback and emotional data, and evaluates it based on exercise progress and emotional stability. This allows for the provision of exercise plans that aim not only to improve the user's physical health in the long term but also to enhance their psychological satisfaction.

[0136] For example, users who show tension or anxiety in the early stages of training are suggested to use relaxation-promoting exercises or relaxing music. Conversely, if they show high motivation, they are switched to a more challenging exercise plan. In this way, the present invention is a system that integrates physical and psychological requirements to provide exercise support that responds precisely to individual needs.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] Users use a dedicated app to input physical information such as height, weight, and types of exercise they have experienced. At the same time, they use a camera to capture images and video data of their posture and facial expressions.

[0140] Step 2:

[0141] The device analyzes the collected data and, along with the user's basic physical information, infers their emotional state from their facial expressions. This data is then transferred to a server.

[0142] Step 3:

[0143] The server analyzes the received physical and emotional data and uses an AI engine to evaluate the user's motor characteristics. Simultaneously, the emotional engine detects the user's mood and motivational state.

[0144] Step 4:

[0145] Based on these analysis results, the server generates an optimized exercise plan for the user. The exercise plan includes content tailored to the type, duration, and order of exercises, as well as the user's emotional state.

[0146] Step 5:

[0147] The server sends the generated exercise plan to the terminal, which then presents this plan to the user. Motivational messages tailored to the user's emotional state are also displayed at this point.

[0148] Step 6:

[0149] The user begins exercising according to the provided plan. During the exercise, sensors on the device monitor the user's movements and facial expressions in real time, and transmit this information to the server.

[0150] Step 7:

[0151] The server analyzes movement and emotional data in real time and generates feedback based on the user's emotional changes and exercise achievement. If necessary, it adjusts parts of the exercise plan on the fly.

[0152] Step 8:

[0153] The device provides the user with feedback and real-time instructions, offering specific advice to correct behavior and improve emotions.

[0154] Step 9:

[0155] After an exercise session is completed, the server integrates all the data and uses it to adjust the long-term exercise plan. It also analyzes emotional trends to prepare for subsequent sessions.

[0156] (Example 2)

[0157] 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".

[0158] In modern times, providing exercise plans optimized for individual users, thereby improving both physical health and psychological satisfaction, is challenging. Because standardized exercise programs are primarily offered, there is a lack of detailed and flexible feedback and plan adjustments that take into account individual physical information and emotional states. This invention aims to provide an exercise planning system that comprehensively meets the physical and emotional needs of such users.

[0159] 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.

[0160] In this invention, the server includes means for acquiring the user's physical and emotional information, means for analyzing the user's motor characteristics and emotional state based on the acquired information, and means for generating an exercise plan suitable for the user based on the analysis results and making adjustments according to the emotional state. This enables the provision of an exercise plan optimized for each individual user, as well as real-time feedback and adjustments.

[0161] "User physical information" refers to data related to the user's physiological and physical attributes, such as height, weight, body type, posture, and past exercise history.

[0162] "Emotional information" refers to data that indicates the user's psychological state, obtained from things like facial expressions and tone of voice.

[0163] "Motor characteristics" refer to features related to a user's athletic ability, such as motor skills, flexibility, muscle strength, and endurance.

[0164] An "exercise plan" is a comprehensive program that includes the type, sequence, and duration of exercises tailored to the user.

[0165] "Feedback" refers to information and advice provided to users during or after exercise regarding their exercise performance and progress.

[0166] "Real-time" refers to a state where data is processed instantly and the results are reflected to the user immediately.

[0167] An "algorithm that references successful cases" is a computational method that analyzes past effective exercise strategies and plans and applies them to new exercise plans.

[0168] The embodiments for carrying out the present invention are described below. This system combines the user's physical and emotional information to provide an individually optimized exercise plan and manages the progress of the exercise and the stability of emotions.

[0169] Users first input basic physical information using a dedicated app. This information includes physiological data such as height, weight, and age. Furthermore, users can upload images and videos. This allows for detailed physical information to be obtained using image processing software, and real-time posture and motion analysis is also performed via sensors.

[0170] The server receives and analyzes physical and emotional information transmitted by the user. The analysis utilizes an AI engine and an emotion engine. The AI ​​engine analyzes movement characteristics based on the user's physical information, while the emotion engine evaluates emotional states using facial recognition technology. These engines are used to generate an optimal movement plan for the user.

[0171] The device receives the generated exercise plan and presents it to the user. The plan includes the content and sequence of exercises and is adjusted according to the user's emotional state. During training, the device uses sensors to monitor the user's movements and detect changes in the user's emotions. This enables real-time feedback and dynamic adjustments to the plan.

[0172] For example, users who show anxiety in the early stages of training are offered relaxation-promoting exercises and the use of relaxing music. On the other hand, users who show high motivation are provided with a more challenging exercise plan.

[0173] An example of a prompt to the generative AI model used in this system is, "Please suggest a training method to alleviate the user's anxiety." In this way, the present invention comprehensively supports the diverse physical and psychological needs of users and provides a highly satisfying exercise experience.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] Users input basic physical information such as height, weight, age, and gender through a dedicated app. The device receives this data and sends it to the server as a base dataset used by the program. This establishes the basic information about the user's physical characteristics.

[0177] Step 2:

[0178] The device receives images and videos sent by the user as data for analysis. Using image processing technology, it extracts the user's body shape, posture, and movement patterns, and sends this as detailed physical information to the server. The data obtained from the images functions as additional data necessary for physical assessment.

[0179] Step 3:

[0180] The server integrates the user's basic and detailed physical information, and uses this information to analyze their movement characteristics with an AI engine. The dataset created in the previous step is used as input data, and the analysis results output characteristics such as the user's motor skills and flexibility.

[0181] Step 4:

[0182] The server uses the user's facial image to perform facial recognition with an emotion engine and evaluate the user's emotional state. This process takes images and voice tones as input and outputs emotional patterns and psychological states. This information is used to optimize the motor plan from an emotional perspective.

[0183] Step 5:

[0184] The server generates an exercise plan tailored to the user's physical characteristics and emotional state, based on evaluation results from the AI ​​engine and the emotion engine. In this process, a computational model processes the input data and outputs an exercise plan that includes customized exercise content and sequence.

[0185] Step 6:

[0186] The generated exercise plan is sent from the server to the terminal. The terminal visually presents the plan to the user and explains its contents using a voice assistant as needed. The user then performs the training based on this plan.

[0187] Step 7:

[0188] During training, the device uses sensors to monitor the user's movements and emotional changes in real time. Sensor data and the user's facial expressions are used as input, and the plan is adjusted as needed based on the feedback received as output. Advice is provided that responds immediately to the user's exercise performance and emotional changes.

[0189] Step 8:

[0190] The server periodically collects user feedback and exercise performance, and evaluates long-term progress and emotional stability. This allows for the regeneration of a new exercise plan aimed at improving health and enhancing psychological satisfaction.

[0191] (Application Example 2)

[0192] 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".

[0193] The challenge is to provide a system that not only offers personalized exercise plans but also flexibly responds to the user's emotional state, providing real-time feedback and dynamically adjusting the exercise plan.

[0194] 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.

[0195] In this invention, the server includes means for acquiring the user's physical characteristics, means for analyzing the emotional state through facial recognition, and means for generating an exercise plan and providing real-time feedback. This makes it possible to provide an individually optimized exercise plan based on the user's physical and emotional state, and to dynamically adjust the plan in response to changes in emotions during exercise.

[0196] "User physical characteristics" refer to information that indicates the physical characteristics and abilities of individual users, and are data collected through sensors and cameras.

[0197] "Emotional state" refers to the user's current emotions and mood, and is information evaluated using facial recognition technology and voice analysis.

[0198] An "exercise plan" is a plan that takes full advantage of the user's physical characteristics and emotional state, and includes the content and sequence of exercises that are individually optimized for each user.

[0199] "Feedback" refers to the information given to a user during exercise, including areas for improvement in movement and encouragement and instructions based on changes in emotions.

[0200] "Dynamic updating" means adjusting and optimizing the exercise plan in real time in response to changes in the user's progress and emotional data.

[0201] "Facial recognition" is a technology that uses a camera to analyze a user's facial expressions and evaluate their emotional state.

[0202] An embodiment of this invention includes a system comprising a user, a server, and a terminal. First, the user initiates access to the system and acquires physical characteristics through sensors and cameras. The server aggregates this information and analyzes the physical characteristics. The server also analyzes the user's emotional state using facial recognition technology.

[0203] The AI ​​engine generates an individually optimized exercise plan based on the user's physical characteristics and emotional state. Meanwhile, the device presents the generated exercise plan to the user and suggests exercises adjusted according to the user's mood and motivation. During exercise, the device monitors the user's movements and facial expressions, and provides feedback in conjunction with the emotion engine. The server monitors the user's progress and dynamically updates the exercise plan based on emotional data.

[0204] For example, if a user needs to relax, the system will suggest a yoga session or play relaxing music. Conversely, if the user is highly motivated, it will recommend more challenging exercises.

[0205] Examples of prompts for the generative AI model include: "What kind of exercise and music would be effective if the user is feeling stressed?" or "Suggest an optimal exercise plan for a highly motivated user."

[0206] This embodiment of the invention allows users to receive a real-time, customized fitness plan, enabling them to efficiently manage their physical and mental health.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The user accesses the system and inputs their physical characteristics through sensors and cameras. The terminal then collects the user's morphological data and transmits it to the server. This data includes the user's body shape, movements, and real-time pose information.

[0210] Step 2:

[0211] Based on this information, the server uses an AI engine to perform data analysis. The server extracts the user's movement characteristics and generates gesture and movement characteristic data. The output movement characteristic data is then used to generate a movement plan.

[0212] Step 3:

[0213] Simultaneously, the server analyzes facial images acquired through the camera using an expression recognition algorithm to obtain emotional state data. This analysis determines the user's current emotional state (e.g., happiness, anxiety, excitement) and prepares feedback based on this.

[0214] Step 4:

[0215] The server uses an AI engine to generate an optimal exercise plan based on collected physical characteristics data and emotional state data. The generated exercise plan includes exercise content and sequence tailored to the user's characteristics.

[0216] Step 5:

[0217] The generated exercise plan is sent to the device, which presents it to the user visually and audibly. The device dynamically adjusts the suggested exercises according to the user's mood and motivation.

[0218] Step 6:

[0219] During exercise, the device uses sensors to monitor the user's movements and emotions. It analyzes changes in movements and facial expressions and provides this information to the user as feedback. This feedback includes words to boost motivation and instructions for improving form.

[0220] Step 7:

[0221] After an exercise session, the server evaluates the user's progress and generates data to inform the next exercise plan. The evaluation analyzes the completeness of the exercise and the progression of emotions to optimize the next session. The generated feedback data is used to plan the next session.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] The system of this invention is an integrated platform that provides an optimized exercise plan for each individual user and manages its progress and feedback in real time. The following describes how this system is implemented.

[0239] First, users log into the system and enter their physical information through a dedicated app. This information includes basic data such as height, weight, and body fat percentage, and by uploading images and videos, more detailed data on body structure is also collected.

[0240] Next, the server uses an AI analysis engine to analyze the user's exercise characteristics based on the received data. This analysis clarifies the user's current fitness level, muscle characteristics, and postural habits. This allows for the generation of a customized exercise plan tailored to each individual user.

[0241] The generated exercise plan is sent to the terminal based on the server information and presented to the user. The presented plan includes specific details such as daily exercise content, number of sets, and rest times, allowing the user to perform the exercises at their own pace. Detailed guidance is supplemented with videos and text to support proper form during training.

[0242] During training, the device uses sensors to monitor the user's movements in real time and communicates with a server to provide immediate feedback. This helps users exercise effectively and correct any form errors immediately.

[0243] Furthermore, the server regularly collects user progress data and evaluates achievements and areas for improvement. Based on this feedback, the exercise plan is dynamically updated, ensuring that an optimized exercise program is always provided.

[0244] Furthermore, the system recommends necessary training products and supplementary items to the user and assists with the purchase process via the terminal. In this way, the system of the present invention provides comprehensive and personalized support to help users achieve their fitness goals.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] Users launch a dedicated app and input basic data such as height, weight, and body fat percentage. They also upload images and videos to capture detailed information about their bodies.

[0248] Step 2:

[0249] The terminal receives the input data and, if necessary, uses sensors to obtain detailed information about the user's posture and movements. This allows more accurate physical information to be sent to the server.

[0250] Step 3:

[0251] The server analyzes the received information and uses an AI engine to evaluate the user's motor characteristics. Specifically, it identifies the user's characteristics by considering factors such as muscle balance, postural features, and past exercise history.

[0252] Step 4:

[0253] The server uses the analysis results to generate an optimal exercise plan for each individual user. This includes the type of exercise, number of sets, repetitions, and rest time. It also references past success stories to develop a plan that aligns with the user's goals.

[0254] Step 5:

[0255] The device presents the user with a generated exercise plan. The plan includes detailed instructions and video guides for each exercise, and the user begins training according to it.

[0256] Step 6:

[0257] During training, the device monitors the user's movements using built-in sensors. If the movements do not conform to the plan or the form is incorrect, it provides immediate feedback.

[0258] Step 7:

[0259] The server periodically collects training data and evaluates the user's progress. It analyzes the results and areas for improvement, and dynamically updates the exercise plan as needed.

[0260] Step 8:

[0261] The device suggests exercise equipment and items to the user, provides links to online stores, and supports a smooth purchasing experience.

[0262] (Example 1)

[0263] 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."

[0264] In modern society, providing personalized exercise plans and managing their progress is crucial. However, many fitness programs are standardized, making it difficult to offer optimal guidance tailored to individual body structures, fitness levels, and goals. Furthermore, users need to accurately track their progress and update their plans appropriately. Choosing the right exercise equipment can also be complicated.

[0265] 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.

[0266] In this invention, the server includes means for acquiring information from the user, means for analyzing the user's activity characteristics based on the acquired information, and means for formulating an activity plan optimized for the user based on the analysis results. This makes it possible to provide a customized exercise plan for each individual user and to continuously follow up on its effectiveness and progress.

[0267] A "user" refers to an individual who uses this system to receive a personalized exercise plan.

[0268] "Information" refers to various types of data used to develop exercise plans, such as the user's physical data, activity characteristics, and past activity data.

[0269] "Activity characteristics" refer to individual exercise-related characteristics that are considered when formulating an exercise plan, such as the user's athletic ability, muscle characteristics, and postural tendencies.

[0270] "Analysis" refers to the process of evaluating activity characteristics based on information provided by the user and performing calculations and interpretations to derive the optimal exercise plan.

[0271] An "activity plan" refers to a program provided to individual users that includes specific exercise guidelines such as the type of exercise, the number of sets, and rest times.

[0272] "Feedback" refers to suggestions and advice provided to users during or after their exercise, with the aim of correcting or improving their movements.

[0273] "Progress" is an indicator that shows how close a user is getting to their goal through activities based on their exercise plan.

[0274] "Items" refer to tools and equipment that are useful when a user exercises, and are intended to support efficient training.

[0275] A "generative AI model" refers to an artificial intelligence algorithm used to generate individual movement plans based on user information.

[0276] This system is a comprehensive platform that provides personalized exercise plans for each user and manages their progress and feedback in real time. The following hardware and software are used to implement this system.

[0277] Users first use a device with a dedicated application installed. This device is a mobile information terminal such as a smartphone or tablet, which allows users to log in to the system and enter their profile information. Physical information input includes basic data such as height, weight, and body fat percentage, and users can also upload images and videos to obtain more detailed physical structure data.

[0278] The server receives information transmitted from the terminal and uses an AI analysis engine to analyze the user's activity characteristics. This analysis uses machine learning algorithms to evaluate the user's motor skills and postural characteristics from physical information. Based on the analysis results, the generative AI model develops an optimal exercise plan for each user. This plan includes the type of exercise, the number of sets, and rest times.

[0279] The formulated exercise plan is sent from the server to the terminal and presented to the user through the application. The presented exercise plan includes video and text-based guides, which the user uses as a reference while training.

[0280] The device uses built-in sensors to monitor user behavior in real time. The collected data is shared with a server and immediately provides feedback to the user. This feedback is communicated to the user as voice assistant or on-screen instructions to help correct their behavior.

[0281] Furthermore, the server periodically evaluates the user's progress and updates the exercise plan based on the results. This dynamic update ensures that the user is always provided with the most suitable exercise program.

[0282] Examples of prompt messages include: "Prompt the user to enter their current height, weight, and body fat percentage, and upload a full-body photo," and "Configure the device to monitor the user's movements and provide feedback if any movements are inaccurate."

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The user opens the dedicated app and logs in. The information to be input is physical data such as height, weight, and body fat percentage. Also, images and videos can be uploaded. The terminal collects these data and securely transmits the information entered in the form and the uploaded media to the server.

[0286] Step 2:

[0287] The server receives the information received from the terminal. The input data includes the user's physical data, images, and videos. The server first verifies the basic data and checks its accuracy. Then, it activates the AI analysis engine and analyzes the user's activity characteristics based on the data. In this analysis, machine learning algorithms are used to evaluate the user's exercise ability, posture, and muscle characteristics. The output of the analysis is the characteristic evaluation result based on the user profile.

[0288] Step 3:

[0289] Based on the analysis result by the AI analysis engine, the server formulates an activity plan optimized for the user using the generated AI model. The input is the analysis result, and the output is a program including the specific content of the exercise plan. This program includes detailed instructions such as individual exercises, number of sets, and rest times.

[0290] Step 4:

[0291] The formulated activity plan is transmitted by the server to the terminal. Based on the received information, the terminal presents the exercise plan to the user. The presentation method is displayed to the user as a video or text guide through the application. The user refers to it and starts training according to the instructions.

[0292] Step 5:

[0293] While the user is training, the device monitors the user's movements in real time using its built-in sensors. The input is movement data from the sensors, which is sent to the server. The data is analyzed in conjunction with the server to determine if the training is being performed with proper form. As a result, the system can provide the user with real-time feedback and encourage them to correct their movements.

[0294] Step 6:

[0295] After the training session ends, the server collects the user's progress data and dynamically updates the next exercise plan based on that data. The input is past training data, and the output is the updated exercise program. This updated program is then sent back to the terminal and presented to the user in preparation for the next training session.

[0296] (Application Example 1)

[0297] 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."

[0298] In modern life, designing an exercise plan optimized for individual needs and effectively performing exercise requires specialized knowledge and guidance. However, providing personalized fitness support to individual users and dynamically updating plans as they progress is a technically challenging task that requires significant resources.

[0299] 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.

[0300] In this invention, the server includes means for acquiring the user's physical information, means for analyzing the user's exercise characteristics, means for generating an exercise plan suitable for the user, means for presenting it to the user, and means for evaluating the user's actions in real time and providing an immediate correction instruction visually or audibly. This enables fitness support optimized for individual users and providing real-time feedback.

[0301] A "user" is an individual who uses the system and is the one who receives an exercise plan optimized based on physical information and exercise characteristics.

[0302] "Physical information" refers to data related to the user's physical characteristics, such as basic data like the user's height, weight, body fat percentage, and images / videos of the posture.

[0303] "Exercise characteristics" refers to characteristic information indicating the user's physical strength level, muscle characteristics, and posture habits, which are clarified through analysis.

[0304] An "exercise plan" is a customized fitness program suitable for the user, including details of the exercises, number of sets, rest times, etc.

[0305] "Feedback" is an immediate instruction or evaluation received by the user when performing an exercise, and is information that serves as a clue for correcting movement errors.

[0306] "Dynamically update" is a process of modifying the exercise plan based on the user's progress data to always keep it up-to-date.

[0307] "Evaluating in real time" is a process of immediately analyzing the user's exercise movements and instantaneously providing the results.

[0308] "Providing visually or audibly" is a means of conveying feedback to the user as video or sound, and has the role of improving the quality of the exercise.

[0309] This invention is a system for providing fitness support optimized for individual users. This system is broadly composed of three components: a server, terminals (including robots), and the user.

[0310] First, the user logs into the system via their device and enters their physical information. This information includes the user's height, weight, body fat percentage, and images / videos of their posture, and is sent to the server. The server receives this physical information and uses an AI analysis engine to analyze the user's movement characteristics. This analysis uses analysis software such as TensorFlow and OpenCV to quantitatively evaluate the user's fitness level, muscle characteristics, and postural habits.

[0311] Based on the analysis results, the server generates an exercise plan optimized for the user. This exercise plan is a program that includes specific exercises, the number of sets, and rest times, and is sent from the server to the terminal. During the user's exercise, the robot terminal uses its built-in camera and sensors to monitor the user's movements in real time. The robot captures the movements and sends the data to the server. Based on this, the server dynamically generates feedback and provides real-time instructions to the user via voice or visual means through the terminal.

[0312] For example, if the server determines that the user's knee position is not correct while performing a squat, the robot will give specific voice instructions such as, "Move your knees a little further forward." In this way, the user can exercise effectively.

[0313] An example of a prompt would be: "Describe a process in which a fitness robot analyzes the user's squat form in real time and gives voice instructions for the optimal posture."

[0314] This invention makes it possible to provide an optimal training environment that combines the provision of fitness plans tailored to individual exercise needs with real-time feedback.

[0315] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0316] Step 1:

[0317] The user logs into the terminal and enters their physical information. This information includes height, weight, body fat percentage, and images / videos of their posture. This data is then prepared for analysis on the server.

[0318] Step 2:

[0319] The terminal sends the entered physical information to the server. The server receives this information and passes the data to the AI ​​analysis engine. Here, basic preprocessing is performed, including data standardization and necessary feature extraction.

[0320] Step 3:

[0321] The server utilizes an AI analysis engine to analyze the user's movement characteristics. This analysis uses a TensorFlow model to identify the user's fitness level, muscle characteristics, and postural habits. The analysis results are generated as output.

[0322] Step 4:

[0323] Based on the analysis results, the server generates an exercise plan optimized for the user. This plan includes details such as exercise content, number of sets, and rest times, and the generated plan is sent to the terminal. The output is the exercise plan optimized for the user.

[0324] Step 5:

[0325] The user receives an exercise plan via a terminal and begins exercising accordingly. The terminal, including the robot, begins monitoring the user's movements in real time using its built-in cameras and sensors.

[0326] Step 6:

[0327] The terminal captures user behavior data and sends it to the server. The server receives this data and performs analysis for real-time feedback. Specifically, this includes evaluating the behavior and generating form correction instructions as needed.

[0328] Step 7:

[0329] The server sends the generated feedback to the terminal, which then provides instructions to the user visually or audibly. This process allows the user to immediately correct their actions. The output consists of specific correction instructions.

[0330] Step 8:

[0331] When listening feedback is provided, the server accumulates the user's progress data and dynamically updates the exercise plan. The final result is a continuously optimized exercise plan.

[0332] 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.

[0333] This invention is a system that combines the user's physical and emotional information to provide an individually optimized exercise plan and manages progress in real time. By incorporating an emotion engine, this system provides feedback and adjusts the exercise plan in accordance with the user's emotional state.

[0334] First, users access a dedicated app and input basic physical information. By uploading images and videos during this process, more detailed physical information can be obtained. This includes real-time posture and movement analysis using sensors.

[0335] Next, the server analyzes the collected information. The AI ​​engine analyzes movement characteristics based on the user's physical information, and the emotion engine evaluates the user's emotional state using facial recognition technology. This emotional information is then used to generate a more personalized movement plan.

[0336] The generated exercise plan takes into account the user's physical characteristics and emotional state, and the server transmits it to the terminal for presentation. The plan includes exercise content and sequence tailored to the user's mood and motivation. Furthermore, during the execution of the plan, the user receives appropriate instructions and encouraging messages in response to changes in their emotions.

[0337] During training, the device not only monitors the user's movements using sensors but also detects emotional changes by analyzing facial expressions and voice tone in conjunction with an emotion engine. This information is used to provide feedback on the exercise plan, and the plan is adjusted as needed.

[0338] The server periodically collects user feedback and emotional data, and evaluates it based on exercise progress and emotional stability. This allows for the provision of exercise plans that aim not only to improve the user's physical health in the long term but also to enhance their psychological satisfaction.

[0339] For example, users who show tension or anxiety in the early stages of training are suggested to use relaxation-promoting exercises or relaxing music. Conversely, if they show high motivation, they are switched to a more challenging exercise plan. In this way, the present invention is a system that integrates physical and psychological requirements to provide exercise support that responds precisely to individual needs.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] Users use a dedicated app to input physical information such as height, weight, and types of exercise they have experienced. At the same time, they use a camera to capture images and video data of their posture and facial expressions.

[0343] Step 2:

[0344] The device analyzes the collected data and, along with the user's basic physical information, infers their emotional state from their facial expressions. This data is then transferred to a server.

[0345] Step 3:

[0346] The server analyzes the received physical and emotional data and uses an AI engine to evaluate the user's motor characteristics. Simultaneously, the emotional engine detects the user's mood and motivational state.

[0347] Step 4:

[0348] Based on these analysis results, the server generates an optimized exercise plan for the user. The exercise plan includes content tailored to the type, duration, and order of exercises, as well as the user's emotional state.

[0349] Step 5:

[0350] The server sends the generated exercise plan to the terminal, which then presents this plan to the user. Motivational messages tailored to the user's emotional state are also displayed at this point.

[0351] Step 6:

[0352] The user begins exercising according to the provided plan. During the exercise, sensors on the device monitor the user's movements and facial expressions in real time, and transmit this information to the server.

[0353] Step 7:

[0354] The server analyzes movement and emotional data in real time and generates feedback based on the user's emotional changes and exercise achievement. If necessary, it adjusts parts of the exercise plan on the fly.

[0355] Step 8:

[0356] The device provides the user with feedback and real-time instructions, offering specific advice to correct behavior and improve emotions.

[0357] Step 9:

[0358] After an exercise session is completed, the server integrates all the data and uses it to adjust the long-term exercise plan. It also analyzes emotional trends to prepare for subsequent sessions.

[0359] (Example 2)

[0360] 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".

[0361] In modern times, providing exercise plans optimized for individual users, thereby improving both physical health and psychological satisfaction, is challenging. Because standardized exercise programs are primarily offered, there is a lack of detailed and flexible feedback and plan adjustments that take into account individual physical information and emotional states. This invention aims to provide an exercise planning system that comprehensively meets the physical and emotional needs of such users.

[0362] 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.

[0363] In this invention, the server includes means for acquiring the user's physical and emotional information, means for analyzing the user's motor characteristics and emotional state based on the acquired information, and means for generating an exercise plan suitable for the user based on the analysis results and making adjustments according to the emotional state. This enables the provision of an exercise plan optimized for each individual user, as well as real-time feedback and adjustments.

[0364] "User physical information" refers to data related to the user's physiological and physical attributes, such as height, weight, body type, posture, and past exercise history.

[0365] "Emotional information" refers to data that indicates the user's psychological state, obtained from things like facial expressions and tone of voice.

[0366] "Motor characteristics" refer to features related to a user's athletic ability, such as motor skills, flexibility, muscle strength, and endurance.

[0367] An "exercise plan" is a comprehensive program that includes the type, sequence, and duration of exercises tailored to the user.

[0368] "Feedback" refers to information and advice provided to users during or after exercise regarding their exercise performance and progress.

[0369] "Real-time" refers to a state where data is processed instantly and the results are reflected to the user immediately.

[0370] An "algorithm that references successful cases" is a computational method that analyzes past effective exercise strategies and plans and applies them to new exercise plans.

[0371] The embodiments for carrying out the present invention are described below. This system combines the user's physical and emotional information to provide an individually optimized exercise plan and manages the progress of the exercise and the stability of emotions.

[0372] Users first input basic physical information using a dedicated app. This information includes physiological data such as height, weight, and age. Furthermore, users can upload images and videos. This allows for detailed physical information to be obtained using image processing software, and real-time posture and motion analysis is also performed via sensors.

[0373] The server receives and analyzes physical and emotional information transmitted by the user. The analysis utilizes an AI engine and an emotion engine. The AI ​​engine analyzes movement characteristics based on the user's physical information, while the emotion engine evaluates emotional states using facial recognition technology. These engines are used to generate an optimal movement plan for the user.

[0374] The device receives the generated exercise plan and presents it to the user. The plan includes the content and sequence of exercises and is adjusted according to the user's emotional state. During training, the device uses sensors to monitor the user's movements and detect changes in the user's emotions. This enables real-time feedback and dynamic adjustments to the plan.

[0375] For example, users who show anxiety in the early stages of training are offered relaxation-promoting exercises and the use of relaxing music. On the other hand, users who show high motivation are provided with a more challenging exercise plan.

[0376] An example of a prompt to the generative AI model used in this system is, "Please suggest a training method to alleviate the user's anxiety." In this way, the present invention comprehensively supports the diverse physical and psychological needs of users and provides a highly satisfying exercise experience.

[0377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0378] Step 1:

[0379] Users input basic physical information such as height, weight, age, and gender through a dedicated app. The device receives this data and sends it to the server as a base dataset used by the program. This establishes the basic information about the user's physical characteristics.

[0380] Step 2:

[0381] The terminal receives images and videos sent by the user as data for analysis. Using image processing technology, it extracts the user's body shape, posture, and movement patterns, and sends this as detailed physical information to the server. The data obtained from the images functions as additional data necessary for physical assessment.

[0382] Step 3:

[0383] The server integrates the user's basic and detailed physical information, and uses this information to analyze their movement characteristics with an AI engine. The dataset created in the previous step is used as input data, and the analysis results output characteristics such as the user's motor skills and flexibility.

[0384] Step 4:

[0385] The server uses the user's facial image to perform facial recognition with an emotion engine and evaluate the user's emotional state. This process takes images and voice tones as input and outputs emotional patterns and psychological states. This information is used to optimize the motor plan from an emotional perspective.

[0386] Step 5:

[0387] The server generates an exercise plan tailored to the user's physical characteristics and emotional state, based on evaluation results from the AI ​​engine and the emotion engine. In this process, a computational model processes the input data and outputs an exercise plan that includes customized exercise content and sequence.

[0388] Step 6:

[0389] The generated exercise plan is sent from the server to the terminal. The terminal visually presents the plan to the user and explains its contents using a voice assistant as needed. The user then performs the training based on this plan.

[0390] Step 7:

[0391] During training, the device uses sensors to monitor the user's movements and emotional changes in real time. Sensor data and the user's facial expressions are used as input, and the plan is adjusted as needed based on the feedback received as output. Advice is provided that responds immediately to the user's exercise performance and emotional changes.

[0392] Step 8:

[0393] The server periodically collects user feedback and exercise performance, and evaluates long-term progress and emotional stability. This allows for the regeneration of a new exercise plan aimed at improving health and enhancing psychological satisfaction.

[0394] (Application Example 2)

[0395] 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."

[0396] The challenge is to provide a system that not only offers personalized exercise plans but also flexibly responds to the user's emotional state, providing real-time feedback and dynamically adjusting the exercise plan.

[0397] 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.

[0398] In this invention, the server includes means for acquiring the user's physical characteristics, means for analyzing the emotional state through facial recognition, and means for generating an exercise plan and providing real-time feedback. This makes it possible to provide an individually optimized exercise plan based on the user's physical and emotional state, and to dynamically adjust the plan in response to changes in emotions during exercise.

[0399] "User physical characteristics" refer to information that indicates the physical characteristics and abilities of individual users, and are data collected through sensors and cameras.

[0400] "Emotional state" refers to the user's current emotions and mood, and is information evaluated using facial recognition technology and voice analysis.

[0401] An "exercise plan" is a plan that takes full advantage of the user's physical characteristics and emotional state, and includes the content and sequence of exercises that are individually optimized for each user.

[0402] "Feedback" refers to information given to users during exercise, including areas for improvement in movement and encouragement and instructions based on changes in their emotions.

[0403] "Dynamic updating" means adjusting and optimizing the exercise plan in real time in response to changes in the user's progress and emotional data.

[0404] "Facial recognition" is a technology that uses a camera to analyze a user's facial expressions and evaluate their emotional state.

[0405] An embodiment of this invention includes a system comprising a user, a server, and a terminal. First, the user initiates access to the system and acquires physical characteristics through sensors and cameras. The server aggregates this information and analyzes the physical characteristics. The server also analyzes the user's emotional state using facial recognition technology.

[0406] The AI ​​engine generates an individually optimized exercise plan based on the user's physical characteristics and emotional state. Meanwhile, the device presents the generated exercise plan to the user and suggests exercises adjusted according to the user's mood and motivation. During exercise, the device monitors the user's movements and facial expressions, and provides feedback in conjunction with the emotion engine. The server monitors the user's progress and dynamically updates the exercise plan based on emotional data.

[0407] For example, if a user needs to relax, the system will suggest a yoga session or play relaxing music. Conversely, if the user is highly motivated, it will recommend more challenging exercises.

[0408] Examples of prompts for the generative AI model include: "What kind of exercise and music would be effective if the user is feeling stressed?" or "Suggest an optimal exercise plan for a highly motivated user."

[0409] This embodiment of the invention allows users to receive a real-time, customized fitness plan, enabling them to efficiently manage their physical and mental health.

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] The user accesses the system and inputs their physical characteristics through sensors and cameras. The terminal then collects the user's morphological data and transmits it to the server. This data includes the user's body shape, movements, and real-time pose information.

[0413] Step 2:

[0414] Based on this information, the server uses an AI engine to perform data analysis. The server extracts the user's movement characteristics and generates gesture and movement characteristic data. The output movement characteristic data is then used to generate a movement plan.

[0415] Step 3:

[0416] Simultaneously, the server analyzes the facial images acquired through the camera using an expression recognition algorithm to obtain emotional state data. This analysis determines the user's current emotional state (e.g., happiness, anxiety, excitement) and prepares feedback based on this.

[0417] Step 4:

[0418] The server uses an AI engine to generate an optimal exercise plan based on collected physical characteristics data and emotional state data. The generated exercise plan includes exercise content and sequence tailored to the user's characteristics.

[0419] Step 5:

[0420] The generated exercise plan is sent to the device, which presents it to the user visually and audibly. The device dynamically adjusts the suggested exercises according to the user's mood and motivation.

[0421] Step 6:

[0422] During exercise, the device uses sensors to monitor the user's movements and emotions. It analyzes changes in movements and facial expressions and provides this information to the user as feedback. This feedback includes words to boost motivation and instructions for improving form.

[0423] Step 7:

[0424] After an exercise session, the server evaluates the user's progress and generates data to inform the next exercise plan. The evaluation analyzes the completeness of the exercise and the progression of emotions to optimize the next session. The generated feedback data is used to plan the next session.

[0425] 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.

[0426] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0427] 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.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] The system of this invention is an integrated platform that provides an optimized exercise plan for each individual user and manages its progress and feedback in real time. The following describes how this system is implemented.

[0442] First, users log into the system and enter their physical information through a dedicated app. This information includes basic data such as height, weight, and body fat percentage, and by uploading images and videos, more detailed data on body structure is also collected.

[0443] Next, the server uses an AI analysis engine to analyze the user's exercise characteristics based on the received data. This analysis clarifies the user's current fitness level, muscle characteristics, and postural habits. This allows for the generation of a customized exercise plan tailored to each individual user.

[0444] The generated exercise plan is sent to the terminal based on the server information and presented to the user. The presented plan includes specific details such as daily exercise content, number of sets, and rest times, allowing the user to perform the exercises at their own pace. Detailed guidance is supplemented with videos and text to support proper form during training.

[0445] During training, the device uses sensors to monitor the user's movements in real time and communicates with a server to provide immediate feedback. This helps users exercise effectively and correct any form errors immediately.

[0446] Furthermore, the server regularly collects user progress data and evaluates achievements and areas for improvement. Based on this feedback, the exercise plan is dynamically updated, ensuring that an optimized exercise program is always provided.

[0447] Furthermore, the system recommends necessary training products and supplementary items to the user and assists with the purchase process via the terminal. In this way, the system of the present invention provides comprehensive and personalized support to help users achieve their fitness goals.

[0448] The following describes the processing flow.

[0449] Step 1:

[0450] Users launch a dedicated app and input basic data such as height, weight, and body fat percentage. They also upload images and videos to capture detailed information about their bodies.

[0451] Step 2:

[0452] The terminal receives the input data and, if necessary, uses sensors to obtain detailed information about the user's posture and movements. This allows more accurate physical information to be sent to the server.

[0453] Step 3:

[0454] The server analyzes the received information and uses an AI engine to evaluate the user's motor characteristics. Specifically, it identifies the user's characteristics by considering factors such as muscle balance, postural features, and past exercise history.

[0455] Step 4:

[0456] The server uses the analysis results to generate an optimal exercise plan for each individual user. This includes the type of exercise, number of sets, repetitions, and rest time. It also references past success stories to develop a plan that aligns with the user's goals.

[0457] Step 5:

[0458] The device presents the user with a generated exercise plan. The plan includes detailed instructions and video guides for each exercise, and the user begins training according to it.

[0459] Step 6:

[0460] During training, the device monitors the user's movements using built-in sensors. If the movements do not conform to the plan or the form is incorrect, it provides immediate feedback.

[0461] Step 7:

[0462] The server periodically collects training data and evaluates the user's progress. It analyzes the results and areas for improvement, and dynamically updates the exercise plan as needed.

[0463] Step 8:

[0464] The device suggests exercise equipment and items to the user, provides links to online stores, and supports a smooth purchasing experience.

[0465] (Example 1)

[0466] 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."

[0467] In modern society, providing personalized exercise plans and managing their progress is crucial. However, many fitness programs are standardized, making it difficult to offer optimal guidance tailored to individual body structures, fitness levels, and goals. Furthermore, users need to accurately track their progress and update their plans appropriately. Choosing the right exercise equipment can also be complicated.

[0468] 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.

[0469] In this invention, the server includes means for acquiring information from the user, means for analyzing the user's activity characteristics based on the acquired information, and means for formulating an activity plan optimized for the user based on the analysis results. This makes it possible to provide a customized exercise plan for each individual user and to continuously follow up on its effectiveness and progress.

[0470] A "user" refers to an individual who uses this system to receive a personalized exercise plan.

[0471] "Information" refers to various types of data used to develop exercise plans, such as the user's physical data, activity characteristics, and past activity data.

[0472] "Activity characteristics" refer to individual exercise-related characteristics that are considered when formulating an exercise plan, such as the user's athletic ability, muscle characteristics, and postural tendencies.

[0473] "Analysis" refers to the process of evaluating activity characteristics based on information provided by the user and performing calculations and interpretations to derive the optimal exercise plan.

[0474] An "activity plan" refers to a program provided to individual users that includes specific exercise guidelines such as the type of exercise, the number of sets, and rest times.

[0475] "Feedback" refers to suggestions and advice provided to users during or after their exercise, with the aim of correcting or improving their movements.

[0476] "Progress" is an indicator that shows how close a user is getting to their goal through activities based on their exercise plan.

[0477] "Items" refer to tools and equipment that are useful when a user exercises, and are intended to support efficient training.

[0478] A "generative AI model" refers to an artificial intelligence algorithm used to generate individual movement plans based on user information.

[0479] This system is a comprehensive platform that provides personalized exercise plans for each user and manages their progress and feedback in real time. The following hardware and software are used to implement this system.

[0480] Users first use a device with a dedicated application installed. This device is a mobile information terminal such as a smartphone or tablet, which allows users to log in to the system and enter their profile information. Physical information input includes basic data such as height, weight, and body fat percentage, and users can also upload images and videos to obtain more detailed physical structure data.

[0481] The server receives information transmitted from the terminal and uses an AI analysis engine to analyze the user's activity characteristics. This analysis uses machine learning algorithms to evaluate the user's motor skills and postural characteristics from physical information. Based on the analysis results, the generative AI model develops an optimal exercise plan for each user. This plan includes the type of exercise, the number of sets, and rest times.

[0482] The formulated exercise plan is sent from the server to the terminal and presented to the user through the application. The presented exercise plan includes video and text-based guides, which the user uses as a reference while training.

[0483] The device uses built-in sensors to monitor user behavior in real time. The collected data is shared with a server and immediately provides feedback to the user. This feedback is communicated to the user as voice assistant or on-screen instructions to help correct their behavior.

[0484] Furthermore, the server periodically evaluates the user's progress and updates the exercise plan based on the results. This dynamic update ensures that the user is always provided with the most suitable exercise program.

[0485] Examples of prompt messages include: "Prompt the user to enter their current height, weight, and body fat percentage, and upload a full-body photo," and "Configure the device to monitor the user's movements and provide feedback if any movements are inaccurate."

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The user opens a dedicated app and logs in. The information they enter includes physical data such as height, weight, and body fat percentage. They can also upload images and videos. The device collects this data and securely transmits the information entered in the form and the uploaded media to the server.

[0489] Step 2:

[0490] The server receives information from the terminal. The input data includes the user's physical data, images, and videos. The server first verifies the basic data to confirm its accuracy. Then, it activates the AI ​​analysis engine and analyzes the user's activity characteristics based on the data. This analysis uses machine learning algorithms to evaluate the user's motor skills, posture, and muscle characteristics. The output of the analysis is a characteristic evaluation result based on the user profile.

[0491] Step 3:

[0492] Based on the analysis results from the AI ​​analysis engine, the server uses a generated AI model to formulate an activity plan optimized for the user. The input is the analysis results, and the output is a program containing the specific details of the exercise plan. This program includes detailed instructions such as individual exercises, number of sets, and rest times.

[0493] Step 4:

[0494] The formulated activity plan is transmitted to the terminal by the server. Based on the received information, the terminal presents the exercise plan to the user. The plan is presented to the user through the application as a video or text guide. The user uses this as a reference and begins training according to the instructions.

[0495] Step 5:

[0496] While the user is training, the device monitors the user's movements in real time using its built-in sensors. The input is movement data from the sensors, which is sent to the server. The data is analyzed in conjunction with the server to determine if the training is being performed with proper form. As a result, the system can provide the user with real-time feedback and encourage them to correct their movements.

[0497] Step 6:

[0498] After the training session ends, the server collects the user's progress data and dynamically updates the next exercise plan based on that data. The input is past training data, and the output is the updated exercise program. This updated program is then sent back to the terminal and presented to the user in preparation for the next training session.

[0499] (Application Example 1)

[0500] 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."

[0501] In modern life, designing an exercise plan optimized for individual needs and effectively performing exercise requires specialized knowledge and guidance. However, providing personalized fitness support to individual users and dynamically updating plans as they progress is a technically challenging task that requires significant resources.

[0502] 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.

[0503] In this invention, the server includes means for acquiring the user's physical information, means for analyzing the user's movement characteristics, means for generating an exercise plan suitable for the user, means for presenting the plan to the user, and means for evaluating the user's movements in real time and providing immediate corrective instructions visually or audibly. This enables fitness support that is optimized for each individual user and provides real-time feedback.

[0504] A "user" is an individual who utilizes the system and receives an optimized exercise plan based on their physical information and motor characteristics.

[0505] "Physical information" refers to data related to the user's physical characteristics, such as basic data like height, weight, and body fat percentage, as well as images and videos of their posture.

[0506] "Motor characteristics" refer to characteristic information that indicates the user's physical fitness level, muscle characteristics, and postural habits, and these are clarified through analysis.

[0507] An "exercise plan" is a customized fitness program tailored to the user, including details such as the type of exercise, the number of sets, and rest times.

[0508] "Feedback" refers to the immediate instructions and evaluations a user receives while performing an exercise, providing clues to correct errors in their movements.

[0509] "Dynamic updating" refers to the process of modifying the exercise plan based on the user's progress data, ensuring that it is always up-to-date.

[0510] "Real-time evaluation" is a process that instantly analyzes the user's movement and provides the results immediately.

[0511] "Providing feedback visually or audibly" refers to a means of conveying feedback to the user in the form of images or sounds, and plays a role in improving the quality of movement.

[0512] This invention is a system for providing fitness support optimized for individual users. This system is broadly composed of three components: a server, terminals (including robots), and the user.

[0513] First, the user logs into the system via their device and enters their physical information. This information includes the user's height, weight, body fat percentage, and images / videos of their posture, and is sent to the server. The server receives this physical information and uses an AI analysis engine to analyze the user's movement characteristics. This analysis uses analysis software such as TensorFlow and OpenCV to quantitatively evaluate the user's fitness level, muscle characteristics, and postural habits.

[0514] Based on the analysis results, the server generates an exercise plan optimized for the user. This exercise plan is a program that includes specific exercises, the number of sets, and rest times, and is sent from the server to the terminal. During the user's exercise, the robot terminal uses its built-in camera and sensors to monitor the user's movements in real time. The robot captures the movements and sends the data to the server. Based on this, the server dynamically generates feedback and provides real-time instructions to the user via voice or visual means through the terminal.

[0515] For example, if the server determines that the user's knee position is not correct while performing a squat, the robot will give specific voice instructions such as, "Move your knees a little further forward." In this way, the user can exercise effectively.

[0516] An example of a prompt would be: "Describe a process in which a fitness robot analyzes the user's squat form in real time and gives voice instructions for the optimal posture."

[0517] This invention makes it possible to provide an optimal training environment that combines the provision of fitness plans tailored to individual exercise needs with real-time feedback.

[0518] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0519] Step 1:

[0520] The user logs into the terminal and enters their physical information. This information includes height, weight, body fat percentage, and images / videos of their posture. This data is then prepared for analysis on the server.

[0521] Step 2:

[0522] The terminal sends the entered physical information to the server. The server receives this information and passes the data to the AI ​​analysis engine. Here, basic preprocessing is performed, including data standardization and necessary feature extraction.

[0523] Step 3:

[0524] The server utilizes an AI analysis engine to analyze the user's movement characteristics. This analysis uses a TensorFlow model to identify the user's fitness level, muscle characteristics, and postural habits. The analysis results are generated as output.

[0525] Step 4:

[0526] Based on the analysis results, the server generates an exercise plan optimized for the user. This plan includes details such as exercise content, number of sets, and rest times, and the generated plan is sent to the terminal. The output is the exercise plan optimized for the user.

[0527] Step 5:

[0528] The user receives an exercise plan via a terminal and begins exercising accordingly. The terminal, including the robot, begins monitoring the user's movements in real time using its built-in cameras and sensors.

[0529] Step 6:

[0530] The terminal captures user behavior data and sends it to the server. The server receives this data and performs analysis for real-time feedback. Specifically, this includes evaluating the behavior and generating form correction instructions as needed.

[0531] Step 7:

[0532] The server sends the generated feedback to the terminal, which then provides instructions to the user visually or audibly. This process allows the user to immediately correct their actions. The output consists of specific correction instructions.

[0533] Step 8:

[0534] When listening feedback is provided, the server accumulates the user's progress data and dynamically updates the exercise plan. The final result is a continuously optimized exercise plan.

[0535] 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.

[0536] This invention is a system that combines the user's physical and emotional information to provide an individually optimized exercise plan and manages progress in real time. By incorporating an emotion engine, this system provides feedback and adjusts the exercise plan in accordance with the user's emotional state.

[0537] First, users access a dedicated app and input basic physical information. By uploading images and videos during this process, more detailed physical information can be obtained. This includes real-time posture and movement analysis using sensors.

[0538] Next, the server analyzes the collected information. The AI ​​engine analyzes movement characteristics based on the user's physical information, and the emotion engine evaluates the user's emotional state using facial recognition technology. This emotional information is then used to generate a more personalized movement plan.

[0539] The generated exercise plan takes into account the user's physical characteristics and emotional state, and the server transmits it to the terminal for presentation. The plan includes exercise content and sequence tailored to the user's mood and motivation. Furthermore, during the execution of the plan, the user receives appropriate instructions and encouraging messages in response to changes in their emotions.

[0540] During training, the device not only monitors the user's movements using sensors but also detects emotional changes by analyzing facial expressions and voice tone in conjunction with an emotion engine. This information is used to provide feedback on the exercise plan, and the plan is adjusted as needed.

[0541] The server periodically collects user feedback and emotional data, and evaluates it based on exercise progress and emotional stability. This allows for the provision of exercise plans that aim not only to improve the user's physical health in the long term but also to enhance their psychological satisfaction.

[0542] For example, users who show tension or anxiety in the early stages of training are suggested to use relaxation-promoting exercises or relaxing music. Conversely, if they show high motivation, they are switched to a more challenging exercise plan. In this way, the present invention is a system that integrates physical and psychological requirements to provide exercise support that responds precisely to individual needs.

[0543] The following describes the processing flow.

[0544] Step 1:

[0545] Users use a dedicated app to input physical information such as height, weight, and types of exercise they have experienced. At the same time, they use a camera to capture images and video data of their posture and facial expressions.

[0546] Step 2:

[0547] The device analyzes the collected data and, along with the user's basic physical information, infers their emotional state from their facial expressions. This data is then transferred to a server.

[0548] Step 3:

[0549] The server analyzes the received physical and emotional data and uses an AI engine to evaluate the user's motor characteristics. Simultaneously, the emotional engine detects the user's mood and motivational state.

[0550] Step 4:

[0551] Based on these analysis results, the server generates an optimized exercise plan for the user. The exercise plan includes content tailored to the type, duration, and order of exercises, as well as the user's emotional state.

[0552] Step 5:

[0553] The server sends the generated exercise plan to the terminal, which then presents this plan to the user. Motivational messages tailored to the user's emotional state are also displayed at this point.

[0554] Step 6:

[0555] The user begins exercising according to the provided plan. During the exercise, sensors on the device monitor the user's movements and facial expressions in real time, and transmit this information to the server.

[0556] Step 7:

[0557] The server analyzes movement and emotional data in real time and generates feedback based on the user's emotional changes and exercise achievement. If necessary, it adjusts parts of the exercise plan on the fly.

[0558] Step 8:

[0559] The device provides the user with feedback and real-time instructions, offering specific advice to correct behavior and improve emotions.

[0560] Step 9:

[0561] After an exercise session is completed, the server integrates all the data and uses it to adjust the long-term exercise plan. It also analyzes emotional trends to prepare for subsequent sessions.

[0562] (Example 2)

[0563] 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."

[0564] In modern times, providing exercise plans optimized for individual users, thereby improving both physical health and psychological satisfaction, is challenging. Because standardized exercise programs are primarily offered, there is a lack of detailed and flexible feedback and plan adjustments that take into account individual physical information and emotional states. This invention aims to provide an exercise planning system that comprehensively meets the physical and emotional needs of such users.

[0565] 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.

[0566] In this invention, the server includes means for acquiring the user's physical and emotional information, means for analyzing the user's motor characteristics and emotional state based on the acquired information, and means for generating an exercise plan suitable for the user based on the analysis results and making adjustments according to the emotional state. This enables the provision of an exercise plan optimized for each individual user, as well as real-time feedback and adjustments.

[0567] "User physical information" refers to data related to the user's physiological and physical attributes, such as height, weight, body type, posture, and past exercise history.

[0568] "Emotional information" refers to data that indicates the user's psychological state, obtained from things like facial expressions and tone of voice.

[0569] "Motor characteristics" refer to features related to a user's athletic ability, such as motor skills, flexibility, muscle strength, and endurance.

[0570] An "exercise plan" is a comprehensive program that includes the type, sequence, and duration of exercises tailored to the user.

[0571] "Feedback" refers to information and advice provided to users during or after exercise regarding their exercise performance and progress.

[0572] "Real-time" refers to a state where data is processed instantly and the results are reflected to the user immediately.

[0573] An "algorithm that references successful cases" is a computational method that analyzes past effective exercise strategies and plans and applies them to new exercise plans.

[0574] The embodiments for carrying out the present invention are described below. This system combines the user's physical and emotional information to provide an individually optimized exercise plan and manages the progress of the exercise and the stability of emotions.

[0575] Users first input basic physical information using a dedicated app. This information includes physiological data such as height, weight, and age. Furthermore, users can upload images and videos. This allows for detailed physical information to be obtained using image processing software, and real-time posture and motion analysis is also performed via sensors.

[0576] The server receives and analyzes physical and emotional information transmitted by the user. The analysis utilizes an AI engine and an emotion engine. The AI ​​engine analyzes movement characteristics based on the user's physical information, while the emotion engine evaluates emotional states using facial recognition technology. These engines are used to generate an optimal movement plan for the user.

[0577] The device receives the generated exercise plan and presents it to the user. The plan includes the content and sequence of exercises and is adjusted according to the user's emotional state. During training, the device uses sensors to monitor the user's movements and detect changes in the user's emotions. This enables real-time feedback and dynamic adjustments to the plan.

[0578] For example, users who show anxiety in the early stages of training are offered relaxation-promoting exercises and the use of relaxing music. On the other hand, users who show high motivation are provided with a more challenging exercise plan.

[0579] An example of a prompt to the generative AI model used in this system is, "Please suggest a training method to alleviate the user's anxiety." In this way, the present invention comprehensively supports the diverse physical and psychological needs of users and provides a highly satisfying exercise experience.

[0580] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0581] Step 1:

[0582] Users input basic physical information such as height, weight, age, and gender through a dedicated app. The device receives this data and sends it to the server as a base dataset used by the program. This establishes the basic information about the user's physical characteristics.

[0583] Step 2:

[0584] The terminal receives images and videos sent by the user as data for analysis. Using image processing technology, it extracts the user's body shape, posture, and movement patterns, and sends this as detailed physical information to the server. The data obtained from the images functions as additional data necessary for physical assessment.

[0585] Step 3:

[0586] The server integrates the user's basic and detailed physical information, and uses this information to analyze their movement characteristics with an AI engine. The dataset created in the previous step is used as input data, and the analysis results output characteristics such as the user's motor skills and flexibility.

[0587] Step 4:

[0588] The server uses the user's facial image to perform facial recognition with an emotion engine and evaluate the user's emotional state. This process takes images and voice tones as input and outputs emotional patterns and psychological states. This information is used to optimize the motor plan from an emotional perspective.

[0589] Step 5:

[0590] The server generates an exercise plan tailored to the user's physical characteristics and emotional state, based on evaluation results from the AI ​​engine and the emotion engine. In this process, a computational model processes the input data and outputs an exercise plan that includes customized exercise content and sequence.

[0591] Step 6:

[0592] The generated exercise plan is sent from the server to the terminal. The terminal visually presents the plan to the user and explains its contents using a voice assistant as needed. The user then performs the training based on this plan.

[0593] Step 7:

[0594] During training, the device uses sensors to monitor the user's movements and emotional changes in real time. Sensor data and the user's facial expressions are used as input, and the plan is adjusted as needed based on the feedback received as output. Advice is provided that responds immediately to the user's exercise performance and emotional changes.

[0595] Step 8:

[0596] The server periodically collects user feedback and exercise performance, and evaluates long-term progress and emotional stability. This allows for the regeneration of a new exercise plan aimed at improving health and enhancing psychological satisfaction.

[0597] (Application Example 2)

[0598] 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."

[0599] The challenge is to provide a system that not only offers personalized exercise plans but also flexibly responds to the user's emotional state, providing real-time feedback and dynamically adjusting the exercise plan.

[0600] 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.

[0601] In this invention, the server includes means for acquiring the user's physical characteristics, means for analyzing the emotional state through facial recognition, and means for generating an exercise plan and providing real-time feedback. This makes it possible to provide an individually optimized exercise plan based on the user's physical and emotional state, and to dynamically adjust the plan in response to changes in emotions during exercise.

[0602] "User physical characteristics" refer to information that indicates the physical characteristics and abilities of individual users, and are data collected through sensors and cameras.

[0603] "Emotional state" refers to the user's current emotions and mood, and is information evaluated using facial recognition technology and voice analysis.

[0604] An "exercise plan" is a plan that takes full advantage of the user's physical characteristics and emotional state, and includes the content and sequence of exercises that are individually optimized for each user.

[0605] "Feedback" refers to information given to users during exercise, including areas for improvement in movement and encouragement and instructions based on changes in their emotions.

[0606] "Dynamic updating" means adjusting and optimizing the exercise plan in real time in response to changes in the user's progress and emotional data.

[0607] "Facial recognition" is a technology that uses a camera to analyze a user's facial expressions and evaluate their emotional state.

[0608] An embodiment of this invention includes a system comprising a user, a server, and a terminal. First, the user initiates access to the system and acquires physical characteristics through sensors and cameras. The server aggregates this information and analyzes the physical characteristics. The server also analyzes the user's emotional state using facial recognition technology.

[0609] The AI ​​engine generates an individually optimized exercise plan based on the user's physical characteristics and emotional state. Meanwhile, the device presents the generated exercise plan to the user and suggests exercises adjusted according to the user's mood and motivation. During exercise, the device monitors the user's movements and facial expressions, and provides feedback in conjunction with the emotion engine. The server monitors the user's progress and dynamically updates the exercise plan based on emotional data.

[0610] For example, if a user needs to relax, the system will suggest a yoga session or play relaxing music. Conversely, if the user is highly motivated, it will recommend more challenging exercises.

[0611] Examples of prompts for the generative AI model include: "What kind of exercise and music would be effective if the user is feeling stressed?" or "Suggest an optimal exercise plan for a highly motivated user."

[0612] This embodiment of the invention allows users to receive a real-time, customized fitness plan, enabling them to efficiently manage their physical and mental health.

[0613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0614] Step 1:

[0615] The user accesses the system and inputs their physical characteristics through sensors and cameras. The terminal then collects the user's morphological data and transmits it to the server. This data includes the user's body shape, movements, and real-time pose information.

[0616] Step 2:

[0617] Based on this information, the server uses an AI engine to perform data analysis. The server extracts the user's movement characteristics and generates gesture and movement characteristic data. The output movement characteristic data is then used to generate a movement plan.

[0618] Step 3:

[0619] Simultaneously, the server analyzes the facial images acquired through the camera using an expression recognition algorithm to obtain emotional state data. This analysis determines the user's current emotional state (e.g., happiness, anxiety, excitement) and prepares feedback based on this.

[0620] Step 4:

[0621] The server uses an AI engine to generate an optimal exercise plan based on collected physical characteristics data and emotional state data. The generated exercise plan includes exercise content and sequence tailored to the user's characteristics.

[0622] Step 5:

[0623] The generated exercise plan is sent to the device, which presents it to the user visually and audibly. The device dynamically adjusts the suggested exercises according to the user's mood and motivation.

[0624] Step 6:

[0625] During exercise, the device uses sensors to monitor the user's movements and emotions. It analyzes changes in movements and facial expressions and provides this information to the user as feedback. This feedback includes words to boost motivation and instructions for improving form.

[0626] Step 7:

[0627] After an exercise session, the server evaluates the user's progress and generates data to inform the next exercise plan. The evaluation analyzes the completeness of the exercise and the progression of emotions to optimize the next session. The generated feedback data is used to plan the next session.

[0628] 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.

[0629] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0630] 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.

[0631] [Fourth Embodiment]

[0632] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0633] 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.

[0634] 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).

[0635] 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.

[0636] 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.

[0637] 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).

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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".

[0645] The system of this invention is an integrated platform that provides an optimized exercise plan for each individual user and manages its progress and feedback in real time. The following describes how this system is implemented.

[0646] First, users log into the system and enter their physical information through a dedicated app. This information includes basic data such as height, weight, and body fat percentage, and by uploading images and videos, more detailed data on body structure is also collected.

[0647] Next, the server uses an AI analysis engine to analyze the user's exercise characteristics based on the received data. This analysis clarifies the user's current fitness level, muscle characteristics, and postural habits. This allows for the generation of a customized exercise plan tailored to each individual user.

[0648] The generated exercise plan is sent to the terminal based on the server information and presented to the user. The presented plan includes specific details such as daily exercise content, number of sets, and rest times, allowing the user to perform the exercises at their own pace. Detailed guidance is supplemented with videos and text to support proper form during training.

[0649] During training, the device uses sensors to monitor the user's movements in real time and communicates with a server to provide immediate feedback. This helps users exercise effectively and correct any form errors immediately.

[0650] Furthermore, the server regularly collects user progress data and evaluates achievements and areas for improvement. Based on this feedback, the exercise plan is dynamically updated, ensuring that an optimized exercise program is always provided.

[0651] Furthermore, the system recommends necessary training products and supplementary items to the user and assists with the purchase process via the terminal. In this way, the system of the present invention provides comprehensive and personalized support to help users achieve their fitness goals.

[0652] The following describes the processing flow.

[0653] Step 1:

[0654] Users launch a dedicated app and input basic data such as height, weight, and body fat percentage. They also upload images and videos to capture detailed information about their bodies.

[0655] Step 2:

[0656] The terminal receives the input data and, if necessary, uses sensors to obtain detailed information about the user's posture and movements. This allows more accurate physical information to be sent to the server.

[0657] Step 3:

[0658] The server analyzes the received information and uses an AI engine to evaluate the user's motor characteristics. Specifically, it identifies the user's characteristics by considering factors such as muscle balance, postural features, and past exercise history.

[0659] Step 4:

[0660] The server uses the analysis results to generate an optimal exercise plan for each individual user. This includes the type of exercise, number of sets, repetitions, and rest time. It also references past success stories to develop a plan that aligns with the user's goals.

[0661] Step 5:

[0662] The device presents the user with a generated exercise plan. The plan includes detailed instructions and video guides for each exercise, and the user begins training according to it.

[0663] Step 6:

[0664] During training, the device monitors the user's movements using built-in sensors. If the movements do not conform to the plan or the form is incorrect, it provides immediate feedback.

[0665] Step 7:

[0666] The server periodically collects training data and evaluates the user's progress. It analyzes the results and areas for improvement, and dynamically updates the exercise plan as needed.

[0667] Step 8:

[0668] The device suggests exercise equipment and items to the user, provides links to online stores, and supports a smooth purchasing experience.

[0669] (Example 1)

[0670] 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".

[0671] In modern society, providing personalized exercise plans and managing their progress is crucial. However, many fitness programs are standardized, making it difficult to offer optimal guidance tailored to individual body structures, fitness levels, and goals. Furthermore, users need to accurately track their progress and update their plans appropriately. Choosing the right exercise equipment can also be complicated.

[0672] 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.

[0673] In this invention, the server includes means for acquiring information from the user, means for analyzing the user's activity characteristics based on the acquired information, and means for formulating an activity plan optimized for the user based on the analysis results. This makes it possible to provide a customized exercise plan for each individual user and to continuously follow up on its effectiveness and progress.

[0674] A "user" refers to an individual who uses this system to receive a personalized exercise plan.

[0675] "Information" refers to various types of data used to develop exercise plans, such as the user's physical data, activity characteristics, and past activity data.

[0676] "Activity characteristics" refer to individual exercise-related characteristics that are considered when formulating an exercise plan, such as the user's athletic ability, muscle characteristics, and postural tendencies.

[0677] "Analysis" refers to the process of evaluating activity characteristics based on information provided by the user and performing calculations and interpretations to derive the optimal exercise plan.

[0678] An "activity plan" refers to a program provided to individual users that includes specific exercise guidelines such as the type of exercise, the number of sets, and rest times.

[0679] "Feedback" refers to suggestions and advice provided to users during or after their exercise, with the aim of correcting or improving their movements.

[0680] "Progress" is an indicator that shows how close a user is getting to their goal through activities based on their exercise plan.

[0681] "Items" refer to tools and equipment that are useful when a user exercises, and are intended to support efficient training.

[0682] A "generative AI model" refers to an artificial intelligence algorithm used to generate individual movement plans based on user information.

[0683] This system is a comprehensive platform that provides personalized exercise plans for each user and manages their progress and feedback in real time. The following hardware and software are used to implement this system.

[0684] Users first use a device with a dedicated application installed. This device is a mobile information terminal such as a smartphone or tablet, which allows users to log in to the system and enter their profile information. Physical information input includes basic data such as height, weight, and body fat percentage, and users can also upload images and videos to obtain more detailed physical structure data.

[0685] The server receives information transmitted from the terminal and uses an AI analysis engine to analyze the user's activity characteristics. This analysis uses machine learning algorithms to evaluate the user's motor skills and postural characteristics from physical information. Based on the analysis results, the generative AI model develops an optimal exercise plan for each user. This plan includes the type of exercise, the number of sets, and rest times.

[0686] The formulated exercise plan is sent from the server to the terminal and presented to the user through the application. The presented exercise plan includes video and text-based guides, which the user uses as a reference while training.

[0687] The device uses built-in sensors to monitor user behavior in real time. The collected data is shared with a server and immediately provides feedback to the user. This feedback is communicated to the user as voice assistant or on-screen instructions to help correct their behavior.

[0688] Furthermore, the server periodically evaluates the user's progress and updates the exercise plan based on the results. This dynamic update ensures that the user is always provided with the most suitable exercise program.

[0689] Examples of prompt messages include: "Prompt the user to enter their current height, weight, and body fat percentage, and upload a full-body photo," and "Configure the device to monitor the user's movements and provide feedback if any movements are inaccurate."

[0690] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0691] Step 1:

[0692] The user opens a dedicated app and logs in. The information they enter includes physical data such as height, weight, and body fat percentage. They can also upload images and videos. The device collects this data and securely transmits the information entered in the form and the uploaded media to the server.

[0693] Step 2:

[0694] The server receives information from the terminal. The input data includes the user's physical data, images, and videos. The server first verifies the basic data to confirm its accuracy. Then, it activates the AI ​​analysis engine and analyzes the user's activity characteristics based on the data. This analysis uses machine learning algorithms to evaluate the user's motor skills, posture, and muscle characteristics. The output of the analysis is a characteristic evaluation result based on the user profile.

[0695] Step 3:

[0696] Based on the analysis results from the AI ​​analysis engine, the server uses a generated AI model to formulate an activity plan optimized for the user. The input is the analysis results, and the output is a program containing the specific details of the exercise plan. This program includes detailed instructions such as individual exercises, number of sets, and rest times.

[0697] Step 4:

[0698] The formulated activity plan is transmitted to the terminal by the server. Based on the received information, the terminal presents the exercise plan to the user. The plan is presented to the user through the application as a video or text guide. The user uses this as a reference and begins training according to the instructions.

[0699] Step 5:

[0700] While the user is training, the device monitors the user's movements in real time using its built-in sensors. The input is movement data from the sensors, which is sent to the server. The data is analyzed in conjunction with the server to determine if the training is being performed with proper form. As a result, the system can provide the user with real-time feedback and encourage them to correct their movements.

[0701] Step 6:

[0702] After the training session ends, the server collects the user's progress data and dynamically updates the next exercise plan based on that data. The input is past training data, and the output is the updated exercise program. This updated program is then sent back to the terminal and presented to the user in preparation for the next training session.

[0703] (Application Example 1)

[0704] 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".

[0705] In modern life, designing an exercise plan optimized for individual needs and effectively performing exercise requires specialized knowledge and guidance. However, providing personalized fitness support to individual users and dynamically updating plans as they progress is a technically challenging task that requires significant resources.

[0706] 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.

[0707] In this invention, the server includes means for acquiring the user's physical information, means for analyzing the user's movement characteristics, means for generating an exercise plan suitable for the user, means for presenting the plan to the user, and means for evaluating the user's movements in real time and providing immediate corrective instructions visually or audibly. This enables fitness support that is optimized for each individual user and provides real-time feedback.

[0708] A "user" is an individual who utilizes the system and receives an optimized exercise plan based on their physical information and motor characteristics.

[0709] "Physical information" refers to data related to the user's physical characteristics, such as basic data like height, weight, and body fat percentage, as well as images and videos of their posture.

[0710] "Motor characteristics" refer to characteristic information that indicates the user's physical fitness level, muscle characteristics, and postural habits, and these are clarified through analysis.

[0711] An "exercise plan" is a customized fitness program tailored to the user, including details such as the type of exercise, the number of sets, and rest times.

[0712] "Feedback" refers to the immediate instructions and evaluations a user receives while performing an exercise, providing clues to correct errors in their movements.

[0713] "Dynamic updating" refers to the process of modifying the exercise plan based on the user's progress data, ensuring that it is always up-to-date.

[0714] "Real-time evaluation" is a process that instantly analyzes the user's movement and provides the results immediately.

[0715] "Providing feedback visually or audibly" refers to a means of conveying feedback to the user in the form of images or sounds, and plays a role in improving the quality of movement.

[0716] This invention is a system for providing fitness support optimized for individual users. This system is broadly composed of three components: a server, terminals (including robots), and the user.

[0717] First, the user logs into the system via their device and enters their physical information. This information includes the user's height, weight, body fat percentage, and images / videos of their posture, and is sent to the server. The server receives this physical information and uses an AI analysis engine to analyze the user's movement characteristics. This analysis uses analysis software such as TensorFlow and OpenCV to quantitatively evaluate the user's fitness level, muscle characteristics, and postural habits.

[0718] Based on the analysis results, the server generates an exercise plan optimized for the user. This exercise plan is a program that includes specific exercises, the number of sets, and rest times, and is sent from the server to the terminal. During the user's exercise, the robot terminal uses its built-in camera and sensors to monitor the user's movements in real time. The robot captures the movements and sends the data to the server. Based on this, the server dynamically generates feedback and provides real-time instructions to the user via voice or visual means through the terminal.

[0719] For example, if the server determines that the user's knee position is not correct while performing a squat, the robot will give specific voice instructions such as, "Move your knees a little further forward." In this way, the user can exercise effectively.

[0720] An example of a prompt would be: "Describe a process in which a fitness robot analyzes the user's squat form in real time and gives voice instructions for the optimal posture."

[0721] This invention makes it possible to provide an optimal training environment that combines the provision of fitness plans tailored to individual exercise needs with real-time feedback.

[0722] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0723] Step 1:

[0724] The user logs into the terminal and enters their physical information. This information includes height, weight, body fat percentage, and images / videos of their posture. This data is then prepared for analysis on the server.

[0725] Step 2:

[0726] The terminal sends the entered physical information to the server. The server receives this information and passes the data to the AI ​​analysis engine. Here, basic preprocessing is performed, including data standardization and necessary feature extraction.

[0727] Step 3:

[0728] The server utilizes an AI analysis engine to analyze the user's movement characteristics. This analysis uses a TensorFlow model to identify the user's fitness level, muscle characteristics, and postural habits. The analysis results are generated as output.

[0729] Step 4:

[0730] Based on the analysis results, the server generates an exercise plan optimized for the user. This plan includes details such as exercise content, number of sets, and rest times, and the generated plan is sent to the terminal. The output is the exercise plan optimized for the user.

[0731] Step 5:

[0732] The user receives an exercise plan via a terminal and begins exercising accordingly. The terminal, including the robot, begins monitoring the user's movements in real time using its built-in cameras and sensors.

[0733] Step 6:

[0734] The terminal captures user behavior data and sends it to the server. The server receives this data and performs analysis for real-time feedback. Specifically, this includes evaluating the behavior and generating form correction instructions as needed.

[0735] Step 7:

[0736] The server sends the generated feedback to the terminal, which then provides instructions to the user visually or audibly. This process allows the user to immediately correct their actions. The output consists of specific correction instructions.

[0737] Step 8:

[0738] When listening feedback is provided, the server accumulates the user's progress data and dynamically updates the exercise plan. The final result is a continuously optimized exercise plan.

[0739] 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.

[0740] This invention is a system that combines the user's physical and emotional information to provide an individually optimized exercise plan and manages progress in real time. By incorporating an emotion engine, this system provides feedback and adjusts the exercise plan in accordance with the user's emotional state.

[0741] First, users access a dedicated app and input basic physical information. By uploading images and videos during this process, more detailed physical information can be obtained. This includes real-time posture and movement analysis using sensors.

[0742] Next, the server analyzes the collected information. The AI ​​engine analyzes movement characteristics based on the user's physical information, and the emotion engine evaluates the user's emotional state using facial recognition technology. This emotional information is then used to generate a more personalized movement plan.

[0743] The generated exercise plan takes into account the user's physical characteristics and emotional state, and the server transmits it to the terminal for presentation. The plan includes exercise content and sequence tailored to the user's mood and motivation. Furthermore, during the execution of the plan, the user receives appropriate instructions and encouraging messages in response to changes in their emotions.

[0744] During training, the device not only monitors the user's movements using sensors but also detects emotional changes by analyzing facial expressions and voice tone in conjunction with an emotion engine. This information is used to provide feedback on the exercise plan, and the plan is adjusted as needed.

[0745] The server periodically collects user feedback and emotional data, and evaluates it based on exercise progress and emotional stability. This allows for the provision of exercise plans that aim not only to improve the user's physical health in the long term but also to enhance their psychological satisfaction.

[0746] For example, users who show tension or anxiety in the early stages of training are suggested to use relaxation-promoting exercises or relaxing music. Conversely, if they show high motivation, they are switched to a more challenging exercise plan. In this way, the present invention is a system that integrates physical and psychological requirements to provide exercise support that responds precisely to individual needs.

[0747] The following describes the processing flow.

[0748] Step 1:

[0749] Users use a dedicated app to input physical information such as height, weight, and types of exercise they have experienced. At the same time, they use a camera to capture images and video data of their posture and facial expressions.

[0750] Step 2:

[0751] The device analyzes the collected data and, along with the user's basic physical information, infers their emotional state from their facial expressions. This data is then transferred to a server.

[0752] Step 3:

[0753] The server analyzes the received physical and emotional data and uses an AI engine to evaluate the user's motor characteristics. Simultaneously, the emotional engine detects the user's mood and motivational state.

[0754] Step 4:

[0755] Based on these analysis results, the server generates an optimized exercise plan for the user. The exercise plan includes content tailored to the type, duration, and order of exercises, as well as the user's emotional state.

[0756] Step 5:

[0757] The server sends the generated exercise plan to the terminal, which then presents this plan to the user. Motivational messages tailored to the user's emotional state are also displayed at this point.

[0758] Step 6:

[0759] The user begins exercising according to the provided plan. During the exercise, sensors on the device monitor the user's movements and facial expressions in real time, and transmit this information to the server.

[0760] Step 7:

[0761] The server analyzes movement and emotional data in real time and generates feedback based on the user's emotional changes and exercise achievement. If necessary, it adjusts parts of the exercise plan on the fly.

[0762] Step 8:

[0763] The device provides the user with feedback and real-time instructions, offering specific advice to correct behavior and improve emotions.

[0764] Step 9:

[0765] After an exercise session is completed, the server integrates all the data and uses it to adjust the long-term exercise plan. It also analyzes emotional trends to prepare for subsequent sessions.

[0766] (Example 2)

[0767] 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".

[0768] In modern times, providing exercise plans optimized for individual users, thereby improving both physical health and psychological satisfaction, is challenging. Because standardized exercise programs are primarily offered, there is a lack of detailed and flexible feedback and plan adjustments that take into account individual physical information and emotional states. This invention aims to provide an exercise planning system that comprehensively meets the physical and emotional needs of such users.

[0769] 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.

[0770] In this invention, the server includes means for acquiring the user's physical and emotional information, means for analyzing the user's motor characteristics and emotional state based on the acquired information, and means for generating an exercise plan suitable for the user based on the analysis results and making adjustments according to the emotional state. This enables the provision of an exercise plan optimized for each individual user, as well as real-time feedback and adjustments.

[0771] "User physical information" refers to data related to the user's physiological and physical attributes, such as height, weight, body type, posture, and past exercise history.

[0772] "Emotional information" refers to data that indicates the user's psychological state, obtained from things like facial expressions and tone of voice.

[0773] "Motor characteristics" refer to features related to a user's athletic ability, such as motor skills, flexibility, muscle strength, and endurance.

[0774] An "exercise plan" is a comprehensive program that includes the type, sequence, and duration of exercises tailored to the user.

[0775] "Feedback" refers to information and advice provided to users during or after exercise regarding their exercise performance and progress.

[0776] "Real-time" refers to a state where data is processed instantly and the results are reflected to the user immediately.

[0777] An "algorithm that references successful cases" is a computational method that analyzes past effective exercise strategies and plans and applies them to new exercise plans.

[0778] The embodiments for carrying out the present invention are described below. This system combines the user's physical and emotional information to provide an individually optimized exercise plan and manages the progress of the exercise and the stability of emotions.

[0779] Users first input basic physical information using a dedicated app. This information includes physiological data such as height, weight, and age. Furthermore, users can upload images and videos. This allows for detailed physical information to be obtained using image processing software, and real-time posture and motion analysis is also performed via sensors.

[0780] The server receives and analyzes physical and emotional information transmitted by the user. The analysis utilizes an AI engine and an emotion engine. The AI ​​engine analyzes movement characteristics based on the user's physical information, while the emotion engine evaluates emotional states using facial recognition technology. These engines are used to generate an optimal movement plan for the user.

[0781] The device receives the generated exercise plan and presents it to the user. The plan includes the content and sequence of exercises and is adjusted according to the user's emotional state. During training, the device uses sensors to monitor the user's movements and detect changes in the user's emotions. This enables real-time feedback and dynamic adjustments to the plan.

[0782] For example, users who show anxiety in the early stages of training are offered relaxation-promoting exercises and the use of relaxing music. On the other hand, users who show high motivation are provided with a more challenging exercise plan.

[0783] An example of a prompt to the generative AI model used in this system is, "Please suggest a training method to alleviate the user's anxiety." In this way, the present invention comprehensively supports the diverse physical and psychological needs of users and provides a highly satisfying exercise experience.

[0784] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0785] Step 1:

[0786] Users input basic physical information such as height, weight, age, and gender through a dedicated app. The device receives this data and sends it to the server as a base dataset used by the program. This establishes the basic information about the user's physical characteristics.

[0787] Step 2:

[0788] The terminal receives images and videos sent by the user as data for analysis. Using image processing technology, it extracts the user's body shape, posture, and movement patterns, and sends this as detailed physical information to the server. The data obtained from the images functions as additional data necessary for physical assessment.

[0789] Step 3:

[0790] The server integrates the user's basic and detailed physical information, and uses this information to analyze their movement characteristics with an AI engine. The dataset created in the previous step is used as input data, and the analysis results output characteristics such as the user's motor skills and flexibility.

[0791] Step 4:

[0792] The server uses the user's facial image to perform facial recognition with an emotion engine and evaluate the user's emotional state. This process takes images and voice tones as input and outputs emotional patterns and psychological states. This information is used to optimize the motor plan from an emotional perspective.

[0793] Step 5:

[0794] The server generates an exercise plan tailored to the user's physical characteristics and emotional state, based on evaluation results from the AI ​​engine and the emotion engine. In this process, a computational model processes the input data and outputs an exercise plan that includes customized exercise content and sequence.

[0795] Step 6:

[0796] The generated exercise plan is sent from the server to the terminal. The terminal visually presents the plan to the user and explains its contents using a voice assistant as needed. The user then performs the training based on this plan.

[0797] Step 7:

[0798] During training, the device uses sensors to monitor the user's movements and emotional changes in real time. Sensor data and the user's facial expressions are used as input, and the plan is adjusted as needed based on the feedback received as output. Advice is provided that responds immediately to the user's exercise performance and emotional changes.

[0799] Step 8:

[0800] The server periodically collects user feedback and exercise performance, and evaluates long-term progress and emotional stability. This allows for the regeneration of a new exercise plan aimed at improving health and enhancing psychological satisfaction.

[0801] (Application Example 2)

[0802] 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".

[0803] The challenge is to provide a system that not only offers personalized exercise plans but also flexibly responds to the user's emotional state, providing real-time feedback and dynamically adjusting the exercise plan.

[0804] 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.

[0805] In this invention, the server includes means for acquiring the user's physical characteristics, means for analyzing the emotional state through facial recognition, and means for generating an exercise plan and providing real-time feedback. This makes it possible to provide an individually optimized exercise plan based on the user's physical and emotional state, and to dynamically adjust the plan in response to changes in emotions during exercise.

[0806] "User physical characteristics" refer to information that indicates the physical characteristics and abilities of individual users, and are data collected through sensors and cameras.

[0807] "Emotional state" refers to the user's current emotions and mood, and is information evaluated using facial recognition technology and voice analysis.

[0808] An "exercise plan" is a plan that takes full advantage of the user's physical characteristics and emotional state, and includes the content and sequence of exercises that are individually optimized for each user.

[0809] "Feedback" refers to information given to users during exercise, including areas for improvement in movement and encouragement and instructions based on changes in their emotions.

[0810] "Dynamic updating" means adjusting and optimizing the exercise plan in real time in response to changes in the user's progress and emotional data.

[0811] "Facial recognition" is a technology that uses a camera to analyze a user's facial expressions and evaluate their emotional state.

[0812] An embodiment of this invention includes a system comprising a user, a server, and a terminal. First, the user initiates access to the system and acquires physical characteristics through sensors and cameras. The server aggregates this information and analyzes the physical characteristics. The server also analyzes the user's emotional state using facial recognition technology.

[0813] The AI ​​engine generates an individually optimized exercise plan based on the user's physical characteristics and emotional state. Meanwhile, the device presents the generated exercise plan to the user and suggests exercises adjusted according to the user's mood and motivation. During exercise, the device monitors the user's movements and facial expressions, and provides feedback in conjunction with the emotion engine. The server monitors the user's progress and dynamically updates the exercise plan based on emotional data.

[0814] For example, if a user needs to relax, the system will suggest a yoga session or play relaxing music. Conversely, if the user is highly motivated, it will recommend more challenging exercises.

[0815] Examples of prompts for the generative AI model include: "What kind of exercise and music would be effective if the user is feeling stressed?" or "Suggest an optimal exercise plan for a highly motivated user."

[0816] This embodiment of the invention allows users to receive a real-time, customized fitness plan, enabling them to efficiently manage their physical and mental health.

[0817] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0818] Step 1:

[0819] The user accesses the system and inputs their physical characteristics through sensors and cameras. The terminal then collects the user's morphological data and transmits it to the server. This data includes the user's body shape, movements, and real-time pose information.

[0820] Step 2:

[0821] Based on this information, the server uses an AI engine to perform data analysis. The server extracts the user's movement characteristics and generates gesture and movement characteristic data. The output movement characteristic data is then used to generate a movement plan.

[0822] Step 3:

[0823] Simultaneously, the server analyzes the facial images acquired through the camera using an expression recognition algorithm to obtain emotional state data. This analysis determines the user's current emotional state (e.g., happiness, anxiety, excitement) and prepares feedback based on this.

[0824] Step 4:

[0825] The server uses an AI engine to generate an optimal exercise plan based on collected physical characteristics data and emotional state data. The generated exercise plan includes exercise content and sequence tailored to the user's characteristics.

[0826] Step 5:

[0827] The generated exercise plan is sent to the device, which presents it to the user visually and audibly. The device dynamically adjusts the suggested exercises according to the user's mood and motivation.

[0828] Step 6:

[0829] During exercise, the device uses sensors to monitor the user's movements and emotions. It analyzes changes in movements and facial expressions and provides this information to the user as feedback. This feedback includes words to boost motivation and instructions for improving form.

[0830] Step 7:

[0831] After an exercise session, the server evaluates the user's progress and generates data to inform the next exercise plan. The evaluation analyzes the completeness of the exercise and the progression of emotions to optimize the next session. The generated feedback data is used to plan the next session.

[0832] 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.

[0833] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0834] 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.

[0835] 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.

[0836] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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."

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] The following is further disclosed regarding the embodiments described above.

[0854] (Claim 1)

[0855] Means for obtaining the user's physical information,

[0856] A means for analyzing the user's motor characteristics based on the acquired information,

[0857] A means for generating an exercise plan suitable for the user based on the analysis results,

[0858] A means of presenting the generated exercise plan to the user,

[0859] A means of monitoring the user's movements during exercise and providing feedback,

[0860] A means of evaluating user progress and dynamically updating the exercise plan,

[0861] A system that includes means for recommending products necessary for exercise to users.

[0862] (Claim 2)

[0863] The system according to claim 1, which acquires a user's physical information based on images or videos.

[0864] (Claim 3)

[0865] The system according to claim 1, which uses an algorithm that references past successful cases in generating an exercise plan for a user.

[0866] "Example 1"

[0867] (Claim 1)

[0868] Means of obtaining information from users,

[0869] A means for analyzing user activity characteristics based on acquired information,

[0870] A means of formulating an activity plan optimized for the user based on the analysis results,

[0871] A means of providing the formulated activity plan to the user,

[0872] A means of monitoring the actions of active users and providing immediate feedback,

[0873] A means to periodically evaluate user progress and dynamically update activity plans,

[0874] A means of recommending items necessary for the activity to the user,

[0875] A means of using algorithms to formulate an action plan by referring to past success stories,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, which acquires a user's physical information based on video data.

[0879] (Claim 3)

[0880] The system according to claim 1, which uses a generative AI model to formulate a user activity plan.

[0881] "Application Example 1"

[0882] (Claim 1)

[0883] Means for obtaining the user's physical information,

[0884] A means for analyzing the user's motor characteristics based on the acquired information,

[0885] A means for generating an exercise plan suitable for the user based on the analysis results,

[0886] A means of presenting the generated exercise plan to the user,

[0887] A means of monitoring the user's movements during exercise and providing feedback,

[0888] A means of evaluating user progress and dynamically updating the exercise plan,

[0889] A means of recommending products necessary for exercise to users,

[0890] A system that includes means for evaluating user actions in real time and providing immediate corrective instructions visually or audibly.

[0891] (Claim 2)

[0892] The system according to claim 1, which acquires a user's physical information based on images or videos.

[0893] (Claim 3)

[0894] The system according to claim 1, which uses an algorithm that references past successful cases in generating an exercise plan for a user.

[0895] "Example 2 of combining an emotion engine"

[0896] (Claim 1)

[0897] Means for acquiring the user's physical and emotional information,

[0898] A means for analyzing the user's motor characteristics and emotional state based on the acquired information,

[0899] A means to generate an exercise plan suitable for the user based on the analysis results and to make adjustments according to the emotional state,

[0900] A means of presenting a generated exercise plan to the user and providing feedback in response to emotional changes,

[0901] A means for monitoring the user's movements and emotional changes during exercise and dynamically updating the exercise plan in real time,

[0902] A system that includes means for evaluating a user's progress and psychological state, and providing an exercise plan that improves their health and psychological satisfaction.

[0903] (Claim 2)

[0904] The system according to claim 1, which acquires a user's physical and emotional information based on images or videos and evaluates their emotional state using facial recognition technology.

[0905] (Claim 3)

[0906] The system according to claim 1, which uses an algorithm that references past success stories and emotional information in generating an exercise plan for a user.

[0907] "Application example 2 when combining with an emotional engine"

[0908] (Claim 1)

[0909] Means for acquiring the user's physical characteristics,

[0910] A means for analyzing the user's motor characteristics based on the acquired information,

[0911] A means for generating an exercise plan suitable for the user based on the analysis results and the user's emotional state,

[0912] A method for presenting a generated exercise plan to the user and suggesting exercises tailored to their mood and motivation,

[0913] A means of monitoring the user's movements during exercise, analyzing changes in their emotions, and providing feedback,

[0914] A means to evaluate user progress and emotional data, dynamically update the exercise plan, and adjust it in real time.

[0915] A system that includes a means of recommending products necessary for exercise to users.

[0916] (Claim 2)

[0917] The system according to claim 1, which acquires the user's physical characteristics and emotional state based on images or videos and evaluates them by facial recognition.

[0918] (Claim 3)

[0919] The system according to claim 1, which uses an algorithm that references past successes and the user's emotional state in generating an exercise plan for the user. [Explanation of symbols]

[0920] 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. Means for obtaining the user's physical information, A means for analyzing the user's motor characteristics based on the acquired information, A means for generating an exercise plan suitable for the user based on the analysis results, A means of presenting the generated exercise plan to the user, A means of monitoring the user's movements during exercise and providing feedback, A means of evaluating user progress and dynamically updating the exercise plan, A system that includes means for recommending products necessary for exercise to users.

2. The system according to claim 1, which acquires a user's physical information based on images or videos.

3. The system according to claim 1, which uses an algorithm that references past successful cases in generating an exercise plan for a user.

Images