system

Abstract

We provide the system. [Solution] A means for inputting individual body shape and constitution data, A means for referencing a database of athletes with similar characteristics in the past using the input data, and processing the generated data, A means for generating an exercise training menu optimized for the user based on the processed data, A means for displaying the generated exercise training menu and related products to the user, A means for collecting feedback after performing exercises based on the aforementioned training menu and using it to improve the system, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 society, it is difficult to find an optimal exercise program according to an individual's physical characteristics and exercise experience. Also, many people desire to improve in sports or exercise, but they have the problem of being unable to receive individually optimized guidance and not knowing effective training methods. Furthermore, since appropriate advice cannot be obtained regarding the selection and purchase of equipment necessary for training, there is also the problem that it is difficult to build an efficient exercise habit.

Means for Solving the Problems

[0005] This invention uses means to input individual body shape and constitution data, compares this data with a database of past athletes with similar characteristics, and performs analysis and processing. Based on the processed data, it generates an exercise training menu optimized for the user. The generated menu is displayed on the terminal, and options for purchasing related products are also provided. Furthermore, after the exercise is performed according to the training menu, feedback from the user is collected and used to improve the system, enabling personalized and continuous exercise guidance.

[0006] "Individual body shape and constitution data" refers to information related to the user's individual physical characteristics and health status, such as height, weight, age, and exercise experience.

[0007] "Input method" refers to an interface or device for collecting user physical data and exercise-related information in digital format and transferring it to a processing system.

[0008] A "database" refers to a systematic collection of information used to store, search, compare, and analyze data on athletes with similar characteristics from the past.

[0009] "Generated data" refers to the analysis results output by comparing the entered personal data with information in the database.

[0010] An "exercise training menu" refers to a training plan that incorporates individually optimized exercise content and schedules based on the user's physical characteristics and feedback.

[0011] "Feedback" refers to information provided by users about the results and experiences of their training, which is used to improve and adjust the system.

[0012] A "system" refers to a collection of hardware and software components that input and process data, generate training menus, display and collect feedback, and provide personalized exercise instruction. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention provides a function for delivering personalized exercise guidance tailored to individual users. To that end, it includes the following system configuration and operating procedures.

[0035] First, the device acquires body shape and physical characteristics data from the user. This data includes the user's height, weight, age, past exercise experience, and health status. To collect this information, the device provides a form for the user to fill out via a smartphone or tablet application.

[0036] Next, this data is sent to a server. The server compares the received data with a database containing information on athletes with similar characteristics from the past. It then uses generative AI to generate more detailed analysis results.

[0037] Based on this analysis, the server generates an exercise training program optimized for the user. This program includes appropriate training content, exercise frequency, number of repetitions, and equipment to be used.

[0038] The generated exercise training menu is sent back to the device and displayed on the user's interface. Through this interface, the user can review the training menu and receive further explanations, including videos and images, as needed. The device also provides purchase links for related products that the user may be interested in.

[0039] After completing a training session, users submit their results and feedback through a feedback form. This feedback is saved to a cloud server via their device. The server analyzes this feedback data to help structure future training menus and improve the overall system.

[0040] For example, if a user who wants to incorporate jogging into their daily routine provides information through the app, the server generates a recommended plan that includes a suitable exercise pace, distance, and frequency for that user, and displays it on their device. The user can then use this plan as a guide to carry out their daily exercise.

[0041] This invention makes it possible for everyone to receive appropriate exercise guidance and find an exercise approach that suits their health condition and fitness goals.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user launches the smartphone app and enters their body shape and physical characteristics data on the new registration screen. This includes height, weight, age, and past exercise experience. Once the data is entered, the user presses the submit button to move the data to the next step.

[0045] Step 2:

[0046] The terminal validates the data entered by the user, checking whether the data format is correct. If there is an input error, it prompts the user to correct it, and if there are no problems, it encrypts the data and sends it to the cloud server.

[0047] Step 3:

[0048] The server receives data sent from the terminal and stores it in a database. Then, it starts an analysis using the generated AI. The AI ​​compares this data with data from past athletes with similar physiques and characteristics, and generates analysis results.

[0049] Step 4:

[0050] The server generates an exercise training program optimized for the user. This program includes recommended training content, frequency, number of repetitions, and equipment to be used.

[0051] Step 5:

[0052] The server sends the generated exercise training menu to the terminal.

[0053] Step 6:

[0054] The device displays the received training menu on its user interface. It provides detailed explanations using videos and images to help users easily understand the training process. It also displays purchase links for related products.

[0055] Step 7:

[0056] Users perform exercises according to the provided training. After completing the training, they submit feedback on their results and impressions via the application.

[0057] Step 8:

[0058] The device receives feedback and sends it to the cloud server.

[0059] Step 9:

[0060] The server analyzes the feedback and uses the data to improve the AI ​​model. It updates individual training menus as needed and incorporates them into the next training cycle.

[0061] (Example 1)

[0062] 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."

[0063] In today's world, providing personalized exercise guidance tailored to each individual is a challenging task. In particular, there is a need for an efficient system that can propose optimized exercise programs for users with varying physical characteristics and exercise histories. A uniform teaching method makes it difficult to improve motivation and promote effective health, and fails to meet the diverse needs of users.

[0064] 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.

[0065] In this invention, the server includes a device for acquiring individual bio-shape and physical data, a device for using the acquired data to reference an information source that stores past individual data with similar characteristics, and for analyzing the data using a generating AI model, and a device for generating an exercise guidance plan optimized for the user based on the analyzed data. This realizes a system that efficiently provides individual exercise guidance plans to each user, enabling the improvement of users' health and motivation.

[0066] "Biometric data" refers to information that indicates the physical characteristics of a user, and includes data such as basic body measurements like height and weight.

[0067] "Physical data" refers to a wide range of information related to the user's body, such as their health status and past exercise history.

[0068] An "information source" refers to a database that stores and makes available historical data and other similar data.

[0069] A "generative AI model" is an artificial intelligence technology used to create optimized exercise instruction plans based on user data.

[0070] An "exercise instruction plan" refers to a plan that proposes personalized exercise menus and training content based on the user's characteristics.

[0071] "Related supplies" refers to tools, equipment, or products that are recommended for use in an exercise instruction plan or that can be helpful as supplementary aids.

[0072] This invention is a system for providing optimized exercise guidance to individual users. The system uses a terminal, a server, and a generative AI model to create a personalized exercise plan.

[0073] The device acquires biometric shape data and physical data from the user. This data is collected through applications on mobile devices such as smartphones and tablets. The device has an application implemented using a software framework called React Native, which enables real-time collection of user input.

[0074] The information entered by the user is sent to the server via the terminal. Based on the received data, the server refers to past records with similar characteristics and performs analysis using a generative AI model. The server uses artificial intelligence technologies such as TENSORFLOW® to generate an optimal exercise guidance plan for the user.

[0075] The exercise plan generated by the server is sent back to the terminal and displayed on the user interface. The user can then use this as a guide to perform their exercise. The terminal also has the ability to provide detailed explanations using videos and diagrams.

[0076] For example, if a user wants to incorporate jogging into their daily routine, they can input their height, weight, age, and past exercise experience through the app. The server then provides an exercise plan that includes the optimal pace, distance, and frequency. The user can then follow this plan to exercise daily.

[0077] The following is an example of a prompt: "Generate a personalized jogging plan based on the user's height, weight, age, and past exercise experience."

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The device acquires biometric and physical data from the user. The user enters information such as height, weight, age, and exercise experience using the application's form. The entered data is temporarily stored in the device's memory.

[0081] Step 2:

[0082] The terminal converts the acquired data into packets and sends them to the server via a secure connection. The HTTPS protocol is used for this transmission. The server receives the data packets generated by the terminal as output.

[0083] Step 3:

[0084] The server compares the received user data with past records in the database. This comparison uses a database search algorithm to quickly find records with similar characteristics. It references the user data as input and retrieves similar records from the database.

[0085] Step 4:

[0086] The server uses a generative AI model to generate an exercise instruction plan based on the user's characteristics. This AI model uses machine learning algorithms to suggest the optimal plan based on information learned from past data. The exercise plan is output based on the data input to the AI ​​model.

[0087] Step 5:

[0088] The server converts the generated exercise instruction plan into JSON format and sends it to the terminal. The terminal receives the JSON data output by the server. This data contains important information for use in the next step.

[0089] Step 6:

[0090] The device parses the received JSON data and displays it in the user interface. Using the React Native framework, the plan details are presented in a visually easy-to-understand format. Through this interface, users can obtain detailed exercise instructions.

[0091] (Application Example 1)

[0092] 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."

[0093] The challenges lie in providing exercise guidance tailored to individual physical conditions and addressing the lack of real-time movement evaluation and feedback. Typical training plans at fitness gyms are uniform and not optimized for individual users, often resulting in ineffective exercise. Furthermore, the lack of objective means to evaluate one's own movements during training makes it difficult to perform exercises with proper form.

[0094] 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.

[0095] In this invention, the server includes means for inputting individual body shape and constitution data; means for referencing a database of past athletes with similar characteristics using the input data and processing the generated data; means for generating an exercise training menu optimized for the user based on the processed data; means for displaying the generated exercise training menu and related products to the user; means for collecting feedback after the exercise is performed based on the training menu and using it to improve the system; and means for evaluating the user's movements in real time in cooperation with a smart device and providing points for improvement in exercise. This enables personalized exercise guidance and real-time movement evaluation that is suitable for each individual user.

[0096] "Individual body shape and constitution data" refers to information that indicates the physical characteristics unique to each user, such as height, weight, age, and exercise experience.

[0097] A "database" is a collection of information that has been accumulated about athletes with similar characteristics in the past, and it serves as a standard for analysis and comparison based on the user's physical characteristics.

[0098] "Means for processing generated data" refers to methods that use physical data obtained from users to refer to a database and perform calculations and operations to generate an exercise training menu optimized for the user.

[0099] An "exercise training menu" is a plan that specifically outlines the type of exercise, frequency, number of repetitions, and equipment to be used, tailored to each individual user.

[0100] A "smart device" is a device used to evaluate a user's movements in real time. It has the function of detecting the user's exercise form and posture and indicating areas for improvement as needed.

[0101] "Real-time evaluation" is a process that analyzes a user's movements immediately while they are performing an exercise, providing feedback on accuracy and areas for improvement.

[0102] The system implementing this invention generates an individually optimized exercise training menu based on the user's body shape and physical condition data.

[0103] The device uses a smartphone or tablet as a means of collecting the user's body shape and physical characteristics data, and provides a form to obtain information such as height, weight, age, and exercise experience from the user. This data is transmitted from the device to the server.

[0104] The server uses the received data to compare it with a database containing information on athletes with similar characteristics from the past. It utilizes a generative AI model using Python and TensorFlow to perform detailed analysis. Based on the analysis, it generates an exercise training menu tailored to the user. The generated menu includes recommendations for specific exercises, frequency, repetitions, and equipment to be used. Furthermore, it can integrate with smart devices to evaluate the user's movements in real time during training and provide feedback on areas for improvement.

[0105] The generated exercise training menu is sent to the device and displayed through the user interface. The user can review the menu through this interface and receive detailed video and image explanations as needed. The device also provides purchase links for related products to support the user's training.

[0106] As a concrete example, consider a 35-year-old beginner runner using this system. This user inputs their data, and a generative AI model suggests a suitable jogging plan. This plan would include details such as "3 kilometers per day, 3 times a week." Furthermore, a smart device monitors the user's posture during training and provides suggestions for improvement to ensure correct form. An example of a prompt for the generative AI model would be:

[0107] Height: 180cm

[0108] Weight: 75kg

[0109] Age: 28

[0110] Goal: Strengthening

[0111] Please generate a plan.

[0112] In this way, users can receive personalized exercise guidance and perform exercises that match their own health condition and fitness goals.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The terminal displays a form on each device for the user to input body shape and physical characteristics data. The user enters information such as height, weight, age, and exercise experience. This data is temporarily stored in the terminal's local database. The entered data is then prepared to be sent to the server.

[0116] Step 2:

[0117] User input data is sent from the terminal to the server. The server receives this data and compares it with data of past athletes with similar characteristics recorded in the database. The server then launches a generative AI model using Python and TensorFlow to perform statistical analysis based on the input data. The output of the analysis includes an initial draft of an exercise training menu optimized for the user.

[0118] Step 3:

[0119] The server generates an exercise training menu tailored to the user based on the analysis results of the generated AI model. The generated menu includes detailed suggestions regarding the type of exercise, frequency, number of repetitions, and equipment to be used. The generated exercise training menu and information on appropriate related products are sent to the terminal.

[0120] Step 4:

[0121] The device displays the received exercise training menu on the user interface. Users can review and evaluate the provided training plan. Furthermore, they can gain a deeper understanding of the detailed training content through videos and images, enabling them to accurately grasp the exercise methods.

[0122] Step 5:

[0123] As the user performs training, the smart device monitors the user's movements in real time, evaluating their posture and providing feedback. The smart device uses cameras and sensors to detect the user's form and points out areas for improvement as needed. This information is continuously transmitted to the terminal and provided to the user as feedback.

[0124] Step 6:

[0125] After completing a training session, users input their thoughts and results into the system via a feedback form. The device sends this feedback to a server, which analyzes the feedback data to improve the next exercise training menu and enhance the service. By using a generative AI model, the feedback data is utilized to generate the next prompt, resulting in more personalized content.

[0126] 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.

[0127] This invention is a system for recognizing the user's emotions, in addition to individual body shape and constitution data, and reflecting these in the exercise training menu. Embodiments of this invention will be described below.

[0128] First, the device collects body shape and physical characteristics data from the user. This includes information such as height, weight, age, and past exercise experience. The user enters this information via a smartphone app, and the data is sent to the cloud in an encrypted format.

[0129] Next, the server receives this data and performs an analysis based on a database of athletes with similar characteristics from the past. The generative AI processes this data and generates an exercise training menu that is best suited to the user's profile.

[0130] The server also runs an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and other information through sensors and microphones to understand the user's mental state and emotions. Based on this information, the generated training menu, its timing, and expression methods are dynamically adjusted.

[0131] The generated exercise training menu is sent to the device and displayed on the user's interface. Detailed video explanations and images are provided, allowing the user to visually understand the specific exercise methods. The device is also designed to consider the user's current emotional state and display appropriate encouragement and warnings.

[0132] After completing an exercise, users provide feedback through the application. This includes their emotions and physical sensations during the exercise, as well as their level of achievement. The feedback information is then sent back from the device to the server, where an AI model continuously uses it to improve the training program.

[0133] For example, if a user is feeling fatigued or stressed while jogging, the emotion engine will detect this state, and the device will provide voice advice such as, "Try slowing down," or "You're almost at your next goal." This allows the user to maintain motivation while continuing their training.

[0134] Thus, the present invention enables more effective and personalized exercise instruction that takes into account physical characteristics and emotional aspects.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user launches a smartphone app and opens a form to enter their body shape and physical characteristics data. This form includes fields such as height, weight, age, and past exercise experience.

[0138] Step 2:

[0139] The terminal validates the entered data in real time, ensuring that all required fields are entered correctly. Once validation is complete, the data is encrypted and sent to the cloud server.

[0140] Step 3:

[0141] The server stores the received data in a database and analyzes it using a generative AI. The analysis includes comparing the data with that of athletes with similar physical characteristics from the past, and based on this, an optimized exercise training menu is generated.

[0142] Step 4:

[0143] The device activates an emotion engine for emotion recognition. This engine uses the device's built-in camera and microphone to monitor the user's facial expressions and voice tone, and analyzes the user's emotional state in real time.

[0144] Step 5:

[0145] The server takes into account the user's emotions as recognized by the emotion engine and adjusts the previously generated exercise training menu as needed. This includes fine-tuning the difficulty of the training and adding encouraging messages.

[0146] Step 6:

[0147] The device displays the final exercise training menu to the user. The menu includes videos and images to make the details easier to understand, and also displays messages and advice based on the user's current emotional state.

[0148] Step 7:

[0149] The user performs exercises according to the given training menu. During the exercise, the emotion engine continuously monitors the user's emotions and provides additional instructions and encouragement from the device as needed.

[0150] Step 8:

[0151] After completing an exercise session, users input feedback about the training through the app. This feedback includes their emotional state, physical sensations, and opinions on the suitability of the workout plan.

[0152] Step 9:

[0153] The device collects feedback and forwards it to the server.

[0154] Step 10:

[0155] The server incorporates the feedback data into the continuous improvement of the AI ​​model, and as needed, enhances the accuracy of subsequent exercise training menus.

[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] Modern exercise instruction often fails to adequately consider individual physical characteristics and emotional states, resulting in the inability to provide optimal training for each user. Furthermore, the lack of mechanisms for immediate feedback on users' emotions during training makes effective motivation difficult. Additionally, feedback information is often not properly utilized, hindering improvements to training programs.

[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 inputting individual body shape and characteristic data, means for identifying the user's emotions using voice and video and dynamically adjusting the training plan based on those emotions, and means for collecting positive feedback after exercise is performed based on the training plan and using it to improve the knowledge system. This enables personalized exercise guidance that meets the physical and emotional needs of each user.

[0161] "Individual body shape and characteristic data" refers to information that represents the user's physical characteristics, such as height, weight, age, and activity experience.

[0162] A "sports professional" refers to someone who specializes in sports, and their data is used to design training plans for others with similar characteristics.

[0163] "Data records" refer to databases containing accumulated past information and performance data, which form the basis for information analysis.

[0164] An "exercise training plan" refers to a program of exercise instruction required by the user, and is an optimized menu built using a generative AI model.

[0165] "Identifying emotions" is a process that uses audio and video to analyze the user's emotional state and dynamically adjusts the training plan based on that analysis.

[0166] "Positive feedback" refers to the feedback information received from users after they have completed an exercise program, and particularly positive feedback plays an important role in improving the system.

[0167] A "knowledge system" refers to a collection of information used to improve the accuracy of exercise training plans, based on collected data and feedback.

[0168] In this invention, a specific process is executed sequentially to provide an exercise training plan that takes into account the physical characteristics and emotional state of each individual user. First, the terminal collects body shape and characteristic data from the user. Specifically, using a dedicated application installed on a smartphone, the user inputs their height, weight, age, and past activity experience. This data is encrypted by the application and securely transmitted to the cloud.

[0169] Next, the server analyzes the data received in the cloud. The generative AI model used here is Python-based and utilizes historical data records of athletes with similar characteristics. This allows for the generation of exercise training plans tailored to each user. By applying this model, the type and intensity of exercise are individually optimized.

[0170] Furthermore, the server analyzes audio and video data acquired from the device to identify the user's emotions. This data, acquired by devices equipped with cameras and microphones, is processed through an emotion engine to determine the user's emotional state. OpenCV libraries and speech recognition APIs are used to perform facial expression analysis and voice tone analysis.

[0171] Based on the above, the server dynamically adjusts the exercise training plan as needed, and the adjusted plan is sent to the terminal. The user's smartphone displays video explanations and image guides using HTML5 and CSS3. Furthermore, the terminal uses a TTS (Text-to-Speech) engine to provide real-time voice feedback. Specifically, it generates voice instructions such as "Let's slow down" or "Almost there."

[0172] After exercise, users send feedback through the application. This feedback includes emotions, fatigue levels, and satisfaction during training, and is used for analysis on the server. The AI ​​model utilizes this feedback to improve the quality of future exercise training plans.

[0173] As a concrete example, an example of a prompt sentence to be input to the generating AI model is, "Consider the user's current emotional state and generate advice that provides appropriate encouragement." Based on this prompt, the AI ​​provides an appropriate exercise plan and feedback that meets the user's needs.

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

[0175] Step 1:

[0176] The device collects body shape and characteristic data from the user. Inputs include information entered by the user into the application, specifically height, weight, age, and activity level. This data is formatted within the application and sent to the cloud in an encrypted format as output.

[0177] Step 2:

[0178] The server decrypts encrypted data received from the cloud and analyzes the input data. The generative AI model used here compares this data with past data records of athletes with similar characteristics, and processes the data to generate the most suitable exercise training plan for each user. The output is an optimized exercise training plan for each user.

[0179] Step 3:

[0180] The server receives audio and video data transmitted from the terminal as input to identify the user's emotions. Specifically, this data is acquired through the camera and microphone. Using an emotion engine, facial expression analysis is performed using speech recognition APIs and OpenCV libraries to determine the emotional state. The output is data that includes the user's emotional state.

[0181] Step 4:

[0182] The server dynamically adjusts the generated exercise training plan based on the emotional state data obtained in step 3. The inputs are the exercise training plan and the emotional state data. Using these, the server adjusts the intensity and content of the plan, and outputs an updated exercise training plan.

[0183] Step 5:

[0184] The server sends a customized exercise training plan to the terminal. The terminal receives this and uses it as input on the user's device. Using HTML5 and CSS3, detailed video explanations and image guides are output and visualized. This information serves as a guide for the user as they perform their training.

[0185] Step 6:

[0186] During exercise, the device provides voice feedback to the user. Input consists of real-time data from the server and information based on the user's progress. Using a Text-to-Speech (TTS) engine, it outputs specific voice instructions such as "Let's slow down" or "Almost there."

[0187] Step 7:

[0188] After exercise, users input feedback through the application. This feedback includes emotions during exercise, fatigue levels, satisfaction levels, etc. The input feedback information is sent back to the server and analyzed by an AI model. As output, improvement measures are suggested that will be reflected in future exercise training plans.

[0189] (Application Example 2)

[0190] 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".

[0191] Traditional fitness systems often fail to provide exercise programs tailored to individual users, instead offering mechanical training programs without considering the user's emotional state. As a result, the exercise effects are not maximized, and user motivation does not improve.

[0192] 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.

[0193] In this invention, the server includes means for inputting individual body shape and constitution information, means for referencing a set of information on past athletes with similar characteristics using the input information and processing the generated information, and means for analyzing the user's emotional state using sensors and a microphone and dynamically adjusting the exercise training procedure based on the mental state. This makes it possible to provide an optimal exercise training menu based on each user's individual body information and emotional state.

[0194] "Body shape and constitution information" refers to data such as the user's height, weight, age, and past exercise experience.

[0195] "Emotional state" refers to information that indicates the user's mental state based on their facial expressions and tone of voice.

[0196] An "exercise participant" is a person who possesses information about exercise and has past experience in exercise.

[0197] An "information set" is a collection of data gathered under specific conditions, and is a database that includes information about the physical characteristics and constitution of past athletes.

[0198] An "exercise training procedure" is a schedule or set of instructions and steps for exercise that are generated based on the user's physical characteristics and emotional state.

[0199] "Sensors and microphones" are electronic devices used to acquire a user's biometric information and voice.

[0200] "Dynamic adjustment" means that the system changes and adapts in real time according to the user's state.

[0201] The system for carrying out this invention utilizes the user's body shape and physical characteristics information to provide exercise training procedures that take into account their emotional state. The system mainly consists of two main elements: a server and a terminal.

[0202] The server is located in the cloud and processes body shape and physical characteristics information sent by the user. The information is transmitted from the terminal in an encrypted state, ensuring its security. The server references a database of past exercise data and uses a generative AI model to generate optimal exercise training procedures. In this process, the generative AI model operates based on prompts derived from the user's information.

[0203] The device consists of a smartphone or other mobile device that receives user input information. Furthermore, the device uses its camera and microphone to analyze the user's emotional state in real time. The device dynamically displays exercise training procedures and encouraging messages according to the user's emotional state.

[0204] Specifically, the system utilizes devices that analyze facial expressions and voice tone using cameras and microphones. Furthermore, Firebase and Google AI Platform are used to power cloud databases and generative AI models. This allows users to receive information in real time and undergo appropriate training.

[0205] For example, suppose a user is using a training machine. The device's camera monitors the user's movements and immediately points out any incorrect form. Furthermore, if the user is fatigued, an encouraging message is displayed. Specifically, voice navigation such as "You're almost at your next goal" can also be added.

[0206] Examples of prompts for a generative AI model include the following:

[0207] "Height 175cm, weight 70kg, 30-year-old male, emotional state: slightly fatigued. Please generate the following training menu and words of encouragement."

[0208] This system enables personalized exercise training tailored to each individual's physical condition and emotional state, thereby improving the user's fitness experience.

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The server receives body shape and physical characteristics information transmitted from the user's device. This information includes height, weight, age, and exercise experience, and is transmitted in an encrypted format. This data is decrypted on the server and prepared for the next processing.

[0212] Step 2:

[0213] The device uses its camera and microphone to collect the user's emotional state in real time. The user's facial expressions and voice tone are used as input, and data is acquired by sensors. This emotional data is initially processed on the device before being sent to a server.

[0214] Step 3:

[0215] The server uses the received body shape and constitution information, along with emotional data, to provide prompt messages to the generating AI model. These prompt messages are generated based on detailed information corresponding to the user's current state. For example, they might say, "Height 175cm, weight 70kg, 30-year-old male, emotional state: slightly fatigued."

[0216] Step 4:

[0217] The server uses a generative AI model to generate the optimal exercise training procedure for the user from a given prompt. Based on the input data, it searches for similar situations by referring to a set of information on past exercisers and generates a procedure based on the results. The output is a customized exercise training procedure.

[0218] Step 5:

[0219] The server delivers the generated exercise training instructions to the terminal. The terminal displays these instructions to the user and provides visual and audio navigation at the necessary times. Specifically, the steps of the procedure may be displayed on the screen, and encouraging messages may be played in audio.

[0220] Step 6:

[0221] After completing an exercise training session, users input their experience and feelings into a feedback form on their device. The device then sends this feedback data to a server, which an AI model uses to improve future training menus.

[0222] Step 7:

[0223] The server stores the received feedback data and continuously learns from it. This enables the provision of more accurate services that reflect the user's training effectiveness and emotional feedback.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] [Second Embodiment]

[0228] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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".

[0240] This invention provides a function for delivering personalized exercise guidance tailored to individual users. To that end, it includes the following system configuration and operating procedures.

[0241] First, the device acquires body shape and physical characteristics data from the user. This data includes the user's height, weight, age, past exercise experience, and health status. To collect this information, the device provides a form for the user to fill out via a smartphone or tablet application.

[0242] Next, this data is sent to a server. The server compares the received data with a database containing information on athletes with similar characteristics from the past. It then uses generative AI to generate more detailed analysis results.

[0243] Based on this analysis, the server generates an exercise training program optimized for the user. This program includes appropriate training content, exercise frequency, number of repetitions, and equipment to be used.

[0244] The generated exercise training menu is sent back to the device and displayed on the user's interface. Through this interface, the user can review the training menu and receive further explanations, including videos and images, as needed. The device also provides purchase links for related products that the user may be interested in.

[0245] After completing a training session, users submit their results and feedback through a feedback form. This feedback is saved to a cloud server via their device. The server analyzes this feedback data to help structure future training menus and improve the overall system.

[0246] For example, if a user who wants to incorporate jogging into their daily routine provides information through the app, the server generates a recommended plan that includes a suitable exercise pace, distance, and frequency for that user, and displays it on their device. The user can then use this plan as a guide to carry out their daily exercise.

[0247] This invention makes it possible for everyone to receive appropriate exercise guidance and find an exercise approach that suits their health condition and fitness goals.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The user launches the smartphone app and enters their body shape and physical characteristics data on the new registration screen. This includes height, weight, age, and past exercise experience. Once the data is entered, the user presses the submit button to move the data to the next step.

[0251] Step 2:

[0252] The terminal validates the data entered by the user, checking whether the data format is correct. If there is an input error, it prompts the user to correct it, and if there are no problems, it encrypts the data and sends it to the cloud server.

[0253] Step 3:

[0254] The server receives data sent from the terminal and stores it in a database. Then, it starts an analysis using the generated AI. The AI ​​compares this data with data from past athletes with similar physiques and characteristics, and generates analysis results.

[0255] Step 4:

[0256] The server generates an exercise training program optimized for the user. This program includes recommended training content, frequency, number of repetitions, and equipment to be used.

[0257] Step 5:

[0258] The server sends the generated exercise training menu to the terminal.

[0259] Step 6:

[0260] The device displays the received training menu on its user interface. It provides detailed explanations using videos and images to help users easily understand the training process. It also displays purchase links for related products.

[0261] Step 7:

[0262] Users perform exercises according to the provided training. After completing the training, they submit feedback on their results and impressions via the application.

[0263] Step 8:

[0264] The device receives feedback and sends it to the cloud server.

[0265] Step 9:

[0266] The server analyzes the feedback and uses the data to improve the AI ​​model. It updates individual training menus as needed and incorporates them into the next training cycle.

[0267] (Example 1)

[0268] 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."

[0269] In today's world, providing personalized exercise guidance tailored to each individual is a challenging task. In particular, there is a need for an efficient system that can propose optimized exercise programs for users with varying physical characteristics and exercise histories. A uniform teaching method makes it difficult to improve motivation and promote effective health, and fails to meet the diverse needs of users.

[0270] 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.

[0271] In this invention, the server includes a device for acquiring individual bio-shape and physical data, a device for using the acquired data to reference an information source that stores past individual data with similar characteristics, and for analyzing the data using a generating AI model, and a device for generating an exercise guidance plan optimized for the user based on the analyzed data. This realizes a system that efficiently provides individual exercise guidance plans to each user, enabling the improvement of users' health and motivation.

[0272] "Biometric data" refers to information that indicates the physical characteristics of a user, and includes data such as basic body measurements like height and weight.

[0273] "Physical data" refers to a wide range of information related to the user's body, such as their health status and past exercise history.

[0274] An "information source" refers to a database that stores and makes available historical data and other similar data.

[0275] A "generative AI model" is an artificial intelligence technology used to create optimized exercise instruction plans based on user data.

[0276] An "exercise instruction plan" refers to a plan that proposes personalized exercise menus and training content based on the user's characteristics.

[0277] "Related supplies" refers to tools, equipment, or products that are recommended for use in an exercise instruction plan or that can be helpful as supplementary aids.

[0278] This invention is a system for providing optimized exercise guidance to individual users. The system uses a terminal, a server, and a generative AI model to create a personalized exercise plan.

[0279] The terminal acquires biometric shape data and body data from the user. This data is collected via an application on a portable information terminal such as a smartphone or tablet. An application using a software framework called React Native is implemented on the terminal, and it is possible to collect input from the user in real time.

[0280] The information input by the user is sent by the terminal to the server. Based on the received data, the server refers to records with similar past characteristics and performs analysis using a generative AI model. The server uses artificial intelligence technologies such as TensorFlow to generate an optimal exercise guidance plan for the user.

[0281] The exercise plan generated by the server is sent back to the terminal and displayed on the user interface. The user can carry out exercises referring to this. Also, the terminal has a function of providing detailed explanations by video and illustrations.

[0282] As a specific example, if the user wants to incorporate jogging into their daily routine, by inputting their height, weight, age, and past exercise experience through the app, the server provides an exercise plan including the optimal exercise pace, distance, and frequency. The user can proceed with their daily exercise according to this plan.

[0283] The following is an example of a prompt sentence: "Please generate an individually optimized jogging plan based on the user's height, weight, age, and past exercise experience."

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The terminal acquires biometric shape data and body data from the user. The user uses the application form to input information such as height, weight, age, and exercise experience. The input data is temporarily stored in the terminal's memory.

[0287] Step 2:

[0288] The terminal converts the acquired data into packets and sends them to the server through a secure connection. The HTTPS protocol is used for this transmission. The server receives the data packets generated by the terminal as output.

[0289] Step 3:

[0290] The server compares the received user data with the past database. For this comparison, a database search algorithm is used to quickly find records with similar characteristics. Refer to the user data as input and retrieve similar records from the database.

[0291] Step 4:

[0292] The server uses a generated AI model to generate an exercise guidance plan based on the user's characteristics. This AI model uses machine learning algorithms to propose an optimal plan based on the information learned from past data. An exercise plan is output based on the data input into the AI model.

[0293] Step 5:

[0294] The server converts the generated exercise guidance plan into JSON format and sends it to the terminal. The terminal receives the JSON data output by the server. This data becomes important information for use in the next step.

[0295] Step 6:

[0296] The device parses the received JSON data and displays it in the user interface. Using the React Native framework, the plan details are presented in a visually easy-to-understand format. Through this interface, users can obtain detailed exercise instructions.

[0297] (Application Example 1)

[0298] 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."

[0299] The challenges lie in providing exercise guidance tailored to individual physical conditions and addressing the lack of real-time movement evaluation and feedback. Typical training plans at fitness gyms are uniform and not optimized for individual users, often resulting in ineffective exercise. Furthermore, the lack of objective means to evaluate one's own movements during training makes it difficult to perform exercises with proper form.

[0300] 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.

[0301] In this invention, the server includes means for inputting individual body shape and constitution data; means for referencing a database of past athletes with similar characteristics using the input data and processing the generated data; means for generating an exercise training menu optimized for the user based on the processed data; means for displaying the generated exercise training menu and related products to the user; means for collecting feedback after the exercise is performed based on the training menu and using it to improve the system; and means for evaluating the user's movements in real time in cooperation with a smart device and providing points for improvement in exercise. This enables personalized exercise guidance and real-time movement evaluation that is suitable for each individual user.

[0302] "Individual body shape and physical constitution data" refers to information indicating physical characteristics unique to individual users, such as height, weight, age, and exercise experience.

[0303] A "database" is a collection of information in which information on past athletes with similar characteristics is accumulated, and it serves as a basis for analysis and comparison based on the physical characteristics of users.

[0304] "Means for processing the generated data" is a method of referring to a database using the physical data obtained from a user and performing calculations and operations for generating an exercise training menu optimized for the user.

[0305] An "exercise training menu" is a plan that specifically shows the exercise content, exercise frequency, number of times, equipment to be used, etc. suitable for individual users.

[0306] A "smart device" is a device used to evaluate the actions of a user in real time, having a function to detect the form and posture of the user's exercise and show points for improvement as necessary.

[0307] "Real-time evaluation" is a process of immediately analyzing the actions on the spot and providing feedback on accuracy and points for improvement while the user is performing an exercise.

[0308] The system for implementing this invention generates an individually optimized exercise training menu based on the body shape and physical constitution data of the user.

[0309] As a means for collecting the body shape and physical constitution data of the user, the terminal uses a smartphone or a tablet and provides a form for obtaining information such as height, weight, age, and exercise experience from the user. These data are transmitted from the terminal to the server.

[0310] The server uses the received data to compare it with a database containing information on athletes with similar characteristics from the past. It utilizes a generative AI model using Python and TensorFlow to perform detailed analysis. Based on the analysis, it generates an exercise training menu tailored to the user. The generated menu includes recommendations for specific exercises, frequency, repetitions, and equipment to be used. Furthermore, it can integrate with smart devices to evaluate the user's movements in real time during training and provide feedback on areas for improvement.

[0311] The generated exercise training menu is sent to the device and displayed through the user interface. The user can review the menu through this interface and receive detailed video and image explanations as needed. The device also provides purchase links for related products to support the user's training.

[0312] As a concrete example, consider a 35-year-old beginner runner using this system. This user inputs their data, and a generative AI model suggests a suitable jogging plan. This plan would include details such as "3 kilometers per day, 3 times a week." Furthermore, a smart device monitors the user's posture during training and provides suggestions for improvement to ensure correct form. An example of a prompt for the generative AI model would be:

[0313] Height: 180cm

[0314] Weight: 75kg

[0315] Age: 28

[0316] Goal: Strengthening

[0317] Please generate a plan.

[0318] In this way, users can receive personalized exercise guidance and perform exercises that match their own health condition and fitness goals.

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The terminal displays a form on each device for the user to input body shape and physical characteristics data. The user enters information such as height, weight, age, and exercise experience. This data is temporarily stored in the terminal's local database. The entered data is then prepared to be sent to the server.

[0322] Step 2:

[0323] User input data is sent from the terminal to the server. The server receives this data and compares it with data of past athletes with similar characteristics recorded in the database. The server then launches a generative AI model using Python and TensorFlow to perform statistical analysis based on the input data. The output of the analysis includes an initial draft of an exercise training menu optimized for the user.

[0324] Step 3:

[0325] The server generates an exercise training menu tailored to the user based on the analysis results of the generated AI model. The generated menu includes detailed suggestions regarding the type of exercise, frequency, number of repetitions, and equipment to be used. The generated exercise training menu and information on appropriate related products are sent to the terminal.

[0326] Step 4:

[0327] The device displays the received exercise training menu on the user interface. Users can review and evaluate the provided training plan. Furthermore, they can gain a deeper understanding of the detailed training content through videos and images, enabling them to accurately grasp the exercise methods.

[0328] Step 5:

[0329] As the user performs training, the smart device monitors the user's movements in real time, evaluating their posture and providing feedback. The smart device uses cameras and sensors to detect the user's form and points out areas for improvement as needed. This information is continuously transmitted to the terminal and provided to the user as feedback.

[0330] Step 6:

[0331] After completing a training session, users input their thoughts and results into the system via a feedback form. The device sends this feedback to a server, which analyzes the feedback data to improve the next exercise training menu and enhance the service. By using a generative AI model, the feedback data is utilized to generate the next prompt, resulting in more personalized content.

[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 for recognizing the user's emotions, in addition to individual body shape and constitution data, and reflecting these in the exercise training menu. Embodiments of this invention will be described below.

[0334] First, the device collects body shape and physical characteristics data from the user. This includes information such as height, weight, age, and past exercise experience. The user enters this information via a smartphone app, and the data is sent to the cloud in an encrypted format.

[0335] Next, the server receives this data and performs an analysis based on a database of athletes with similar characteristics from the past. The generative AI processes this data and generates an exercise training menu that is best suited to the user's profile.

[0336] The server also runs an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and other information through sensors and microphones to understand the user's mental state and emotions. Based on this information, the generated training menu, its timing, and expression methods are dynamically adjusted.

[0337] The generated exercise training menu is sent to the device and displayed on the user's interface. Detailed video explanations and images are provided, allowing the user to visually understand the specific exercise methods. The device is also designed to consider the user's current emotional state and display appropriate encouragement and warnings.

[0338] After completing an exercise, users provide feedback through the application. This includes their emotions and physical sensations during the exercise, as well as their level of achievement. The feedback information is then sent back from the device to the server, where an AI model continuously uses it to improve the training program.

[0339] For example, if a user is feeling fatigued or stressed while jogging, the emotion engine will detect this state, and the device will provide voice advice such as, "Try slowing down," or "You're almost at your next goal." This allows the user to maintain motivation while continuing their training.

[0340] Thus, the present invention enables more effective and personalized exercise instruction that takes into account physical characteristics and emotional aspects.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The user launches a smartphone app and opens a form to enter their body shape and physical characteristics data. This form includes fields such as height, weight, age, and past exercise experience.

[0344] Step 2:

[0345] The terminal validates the entered data in real time, ensuring that all required fields are entered correctly. Once validation is complete, the data is encrypted and sent to the cloud server.

[0346] Step 3:

[0347] The server stores the received data in a database and analyzes it using a generative AI. The analysis includes comparing the data with that of athletes with similar physical characteristics from the past, and based on this, an optimized exercise training menu is generated.

[0348] Step 4:

[0349] The device activates an emotion engine for emotion recognition. This engine uses the device's built-in camera and microphone to monitor the user's facial expressions and voice tone, and analyzes the user's emotional state in real time.

[0350] Step 5:

[0351] The server takes into account the user's emotions as recognized by the emotion engine and adjusts the previously generated exercise training menu as needed. This includes fine-tuning the difficulty of the training and adding encouraging messages.

[0352] Step 6:

[0353] The device displays the final exercise training menu to the user. The menu includes videos and images to make the details easier to understand, and also displays messages and advice based on the user's current emotional state.

[0354] Step 7:

[0355] The user performs exercises according to the given training menu. During the exercise, the emotion engine continuously monitors the user's emotions and provides additional instructions and encouragement from the device as needed.

[0356] Step 8:

[0357] After completing an exercise session, users input feedback about the training through the app. This feedback includes their emotional state, physical sensations, and opinions on the suitability of the workout plan.

[0358] Step 9:

[0359] The device collects feedback and forwards it to the server.

[0360] Step 10:

[0361] The server incorporates the feedback data into the continuous improvement of the AI ​​model, and as needed, enhances the accuracy of subsequent exercise training menus.

[0362] (Example 2)

[0363] 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".

[0364] Modern exercise instruction often fails to adequately consider individual physical characteristics and emotional states, resulting in the inability to provide optimal training for each user. Furthermore, the lack of mechanisms for immediate feedback on users' emotions during training makes effective motivation difficult. Additionally, feedback information is often not properly utilized, hindering improvements to training programs.

[0365] 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.

[0366] In this invention, the server includes means for inputting individual body shape and characteristic data, means for identifying the user's emotions using voice and video and dynamically adjusting the training plan based on those emotions, and means for collecting positive feedback after exercise is performed based on the training plan and using it to improve the knowledge system. This enables personalized exercise guidance that meets the physical and emotional needs of each user.

[0367] "Individual body shape and characteristic data" refers to information that represents the user's physical characteristics, such as height, weight, age, and activity experience.

[0368] A "sports professional" refers to someone who specializes in sports, and their data is used to design training plans for others with similar characteristics.

[0369] "Data records" refer to databases containing accumulated past information and performance data, which form the basis for information analysis.

[0370] An "exercise training plan" refers to a program of exercise instruction required by the user, and is an optimized menu built using a generative AI model.

[0371] "Identifying emotions" is a process that uses audio and video to analyze the user's emotional state and dynamically adjusts the training plan based on that analysis.

[0372] "Positive feedback" refers to the feedback information received from users after they have completed an exercise program, and particularly positive feedback plays an important role in improving the system.

[0373] A "knowledge system" refers to a collection of information used to improve the accuracy of exercise training plans, based on collected data and feedback.

[0374] In this invention, a specific process is executed sequentially to provide an exercise training plan that takes into account the physical characteristics and emotional state of each individual user. First, the terminal collects body shape and characteristic data from the user. Specifically, using a dedicated application installed on a smartphone, the user inputs their height, weight, age, and past activity experience. This data is encrypted by the application and securely transmitted to the cloud.

[0375] Next, the server analyzes the data received in the cloud. The generative AI model used here is Python-based and utilizes historical data records of athletes with similar characteristics. This allows for the generation of exercise training plans tailored to each user. By applying this model, the type and intensity of exercise are individually optimized.

[0376] Furthermore, the server analyzes audio and video data acquired from the device to identify the user's emotions. This data, acquired by devices equipped with cameras and microphones, is processed through an emotion engine to determine the user's emotional state. OpenCV libraries and speech recognition APIs are used to perform facial expression analysis and voice tone analysis.

[0377] Based on the above, the server dynamically adjusts the exercise training plan as needed, and the adjusted plan is sent to the terminal. The user's smartphone displays video explanations and image guides using HTML5 and CSS3. Furthermore, the terminal uses a TTS (Text-to-Speech) engine to provide real-time voice feedback. Specifically, it generates voice instructions such as "Let's slow down" or "Almost there."

[0378] After exercise, users send feedback through the application. This feedback includes emotions, fatigue levels, and satisfaction during training, and is used for analysis on the server. The AI ​​model utilizes this feedback to improve the quality of future exercise training plans.

[0379] As a concrete example, an example of a prompt sentence to be input to the generating AI model is, "Consider the user's current emotional state and generate advice that provides appropriate encouragement." Based on this prompt, the AI ​​provides an appropriate exercise plan and feedback that meets the user's needs.

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] The device collects body shape and characteristic data from the user. Inputs include information entered by the user into the application, specifically height, weight, age, and activity level. This data is formatted within the application and sent to the cloud in an encrypted format as output.

[0383] Step 2:

[0384] The server decrypts encrypted data received from the cloud and analyzes the input data. The generative AI model used here compares this data with past data records of athletes with similar characteristics, and processes the data to generate the most suitable exercise training plan for each user. The output is an optimized exercise training plan for each user.

[0385] Step 3:

[0386] The server receives audio and video data transmitted from the terminal as input to identify the user's emotions. Specifically, this data is acquired through the camera and microphone. Using an emotion engine, facial expression analysis is performed using speech recognition APIs and OpenCV libraries to determine the emotional state. The output is data that includes the user's emotional state.

[0387] Step 4:

[0388] The server dynamically adjusts the generated exercise training plan based on the emotional state data obtained in step 3. The inputs are the exercise training plan and the emotional state data. Using these, the server adjusts the intensity and content of the plan, and outputs an updated exercise training plan.

[0389] Step 5:

[0390] The server sends a customized exercise training plan to the terminal. The terminal receives this and uses it as input on the user's device. Using HTML5 and CSS3, detailed video explanations and image guides are output and visualized. This information serves as a guide for the user as they perform their training.

[0391] Step 6:

[0392] During exercise, the device provides voice feedback to the user. Input consists of real-time data from the server and information based on the user's progress. Using a Text-to-Speech (TTS) engine, it outputs specific voice instructions such as "Let's slow down" or "Almost there."

[0393] Step 7:

[0394] After exercise, users input feedback through the application. This feedback includes emotions during exercise, fatigue levels, satisfaction levels, etc. The input feedback information is sent back to the server and analyzed by an AI model. As output, improvement measures are suggested that will be reflected in future exercise training plans.

[0395] (Application Example 2)

[0396] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0397] Traditional fitness systems often fail to provide exercise programs tailored to individual users, instead offering mechanical training programs without considering the user's emotional state. As a result, the exercise effects are not maximized, and user motivation does not improve.

[0398] 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.

[0399] In this invention, the server includes means for inputting individual body shape and constitution information, means for referencing a set of information on past athletes with similar characteristics using the input information and processing the generated information, and means for analyzing the user's emotional state using sensors and a microphone and dynamically adjusting the exercise training procedure based on the mental state. This makes it possible to provide an optimal exercise training menu based on each user's individual body information and emotional state.

[0400] "Body shape and constitution information" refers to data such as the user's height, weight, age, and past exercise experience.

[0401] "Emotional state" refers to information that indicates the user's mental state based on their facial expressions and tone of voice.

[0402] An "exercise participant" is a person who possesses information about exercise and has past experience in exercise.

[0403] An "information set" is a collection of data gathered under specific conditions, and is a database that includes information about the physical characteristics and constitution of past athletes.

[0404] An "exercise training procedure" is a schedule or set of instructions and steps for exercise that are generated based on the user's physical characteristics and emotional state.

[0405] "Sensors and microphones" are electronic devices used to acquire a user's biometric information and voice.

[0406] "Dynamic adjustment" means that the system changes and adapts in real time according to the user's state.

[0407] The system for carrying out this invention utilizes the user's body shape and physical characteristics information to provide exercise training procedures that take into account their emotional state. The system mainly consists of two main elements: a server and a terminal.

[0408] The server is located in the cloud and processes body shape and physical characteristics information sent by the user. The information is transmitted from the terminal in an encrypted state, ensuring its security. The server references a database of past exercise data and uses a generative AI model to generate optimal exercise training procedures. In this process, the generative AI model operates based on prompts derived from the user's information.

[0409] The device consists of a smartphone or other mobile device that receives user input information. Furthermore, the device uses its camera and microphone to analyze the user's emotional state in real time. The device dynamically displays exercise training procedures and encouraging messages according to the user's emotional state.

[0410] Specific equipment includes cameras and microphones to analyze facial expressions and voice tone. Firebase and Google AI platforms are used to run cloud databases and generative AI models. This allows users to receive information in real time and receive appropriate training.

[0411] For example, suppose a user is using a training machine. The device's camera monitors the user's movements and immediately points out any incorrect form. Furthermore, if the user is fatigued, an encouraging message is displayed. Specifically, voice navigation such as "You're almost at your next goal" can also be added.

[0412] Examples of prompts for a generative AI model include the following:

[0413] "Height 175cm, weight 70kg, 30-year-old male, emotional state: slightly fatigued. Please generate the following training menu and words of encouragement."

[0414] This system enables personalized exercise training tailored to each individual's physical condition and emotional state, thereby improving the user's fitness experience.

[0415] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0416] Step 1:

[0417] The server receives body shape and physical characteristics information transmitted from the user's device. This information includes height, weight, age, and exercise experience, and is transmitted in an encrypted format. This data is decrypted on the server and prepared for the next processing.

[0418] Step 2:

[0419] The device uses its camera and microphone to collect the user's emotional state in real time. The user's facial expressions and voice tone are used as input, and data is acquired by sensors. This emotional data is initially processed on the device before being sent to a server.

[0420] Step 3:

[0421] The server uses the received body shape and constitution information, along with emotional data, to provide prompt messages to the generating AI model. These prompt messages are generated based on detailed information corresponding to the user's current state. For example, they might say, "Height 175cm, weight 70kg, 30-year-old male, emotional state: slightly fatigued."

[0422] Step 4:

[0423] The server uses a generative AI model to generate the optimal exercise training procedure for the user from a given prompt. Based on the input data, it searches for similar situations by referring to a set of information on past exercisers and generates a procedure based on the results. The output is a customized exercise training procedure.

[0424] Step 5:

[0425] The server delivers the generated exercise training instructions to the terminal. The terminal displays these instructions to the user and provides visual and audio navigation at the necessary times. Specifically, the steps of the procedure may be displayed on the screen, and encouraging messages may be played in audio.

[0426] Step 6:

[0427] After completing an exercise training session, users input their experience and feelings into a feedback form on their device. The device then sends this feedback data to a server, which an AI model uses to improve future training menus.

[0428] Step 7:

[0429] The server stores the received feedback data and continuously learns from it. This enables the provision of more accurate services that reflect the user's training effectiveness and emotional feedback.

[0430] 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.

[0431] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0432] 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.

[0433] [Third Embodiment]

[0434] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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".

[0446] This invention provides a function for delivering personalized exercise guidance tailored to individual users. To that end, it includes the following system configuration and operating procedures.

[0447] First, the device acquires body shape and physical characteristics data from the user. This data includes the user's height, weight, age, past exercise experience, and health status. To collect this information, the device provides a form for the user to fill out via a smartphone or tablet application.

[0448] Next, this data is sent to a server. The server compares the received data with a database containing information on athletes with similar characteristics from the past. It then uses generative AI to generate more detailed analysis results.

[0449] Based on this analysis, the server generates an exercise training program optimized for the user. This program includes appropriate training content, exercise frequency, number of repetitions, and equipment to be used.

[0450] The generated exercise training menu is sent back to the device and displayed on the user's interface. Through this interface, the user can review the training menu and receive further explanations, including videos and images, as needed. The device also provides purchase links for related products that the user may be interested in.

[0451] After completing a training session, users submit their results and feedback through a feedback form. This feedback is saved to a cloud server via their device. The server analyzes this feedback data to help structure future training menus and improve the overall system.

[0452] For example, if a user who wants to incorporate jogging into their daily routine provides information through the app, the server generates a recommended plan that includes a suitable exercise pace, distance, and frequency for that user, and displays it on their device. The user can then use this plan as a guide to carry out their daily exercise.

[0453] This invention makes it possible for everyone to receive appropriate exercise guidance and find an exercise approach that suits their health condition and fitness goals.

[0454] The following describes the processing flow.

[0455] Step 1:

[0456] The user launches the smartphone app and enters their body shape and physical characteristics data on the new registration screen. This includes height, weight, age, and past exercise experience. Once the data is entered, the user presses the submit button to move the data to the next step.

[0457] Step 2:

[0458] The terminal validates the data entered by the user, checking whether the data format is correct. If there is an input error, it prompts the user to correct it, and if there are no problems, it encrypts the data and sends it to the cloud server.

[0459] Step 3:

[0460] The server receives data sent from the terminal and stores it in a database. Then, it starts an analysis using the generated AI. The AI ​​compares this data with data from past athletes with similar physiques and characteristics, and generates analysis results.

[0461] Step 4:

[0462] The server generates an exercise training program optimized for the user. This program includes recommended training content, frequency, number of repetitions, and equipment to be used.

[0463] Step 5:

[0464] The server sends the generated exercise training menu to the terminal.

[0465] Step 6:

[0466] The device displays the received training menu on its user interface. It provides detailed explanations using videos and images to help users easily understand the training process. It also displays purchase links for related products.

[0467] Step 7:

[0468] Users perform exercises according to the provided training. After completing the training, they submit feedback on their results and impressions via the application.

[0469] Step 8:

[0470] The device receives feedback and sends it to the cloud server.

[0471] Step 9:

[0472] The server analyzes the feedback and uses the data to improve the AI ​​model. It updates individual training menus as needed and incorporates them into the next training cycle.

[0473] (Example 1)

[0474] 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."

[0475] In today's world, providing personalized exercise guidance tailored to each individual is a challenging task. In particular, there is a need for an efficient system that can propose optimized exercise programs for users with varying physical characteristics and exercise histories. A uniform teaching method makes it difficult to improve motivation and promote effective health, and fails to meet the diverse needs of users.

[0476] 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.

[0477] In this invention, the server includes a device for acquiring individual bio-shape and physical data, a device for using the acquired data to reference an information source that stores past individual data with similar characteristics, and for analyzing the data using a generating AI model, and a device for generating an exercise guidance plan optimized for the user based on the analyzed data. This realizes a system that efficiently provides individual exercise guidance plans to each user, enabling the improvement of users' health and motivation.

[0478] "Biometric data" refers to information that indicates the physical characteristics of a user, and includes data such as basic body measurements like height and weight.

[0479] "Physical data" refers to a wide range of information related to the user's body, such as their health status and past exercise history.

[0480] An "information source" refers to a database that stores and makes available historical data and other similar data.

[0481] A "generative AI model" is an artificial intelligence technology used to create optimized exercise instruction plans based on user data.

[0482] An "exercise instruction plan" refers to a plan that proposes personalized exercise menus and training content based on the user's characteristics.

[0483] "Related supplies" refers to tools, equipment, or products that are recommended for use in an exercise instruction plan or that can be helpful as supplementary aids.

[0484] This invention is a system for providing optimized exercise guidance to individual users. The system uses a terminal, a server, and a generative AI model to create a personalized exercise plan.

[0485] The device acquires biometric shape data and physical data from the user. This data is collected through applications on mobile devices such as smartphones and tablets. The device has an application implemented using a software framework called React Native, which enables real-time collection of user input.

[0486] The information entered by the user is sent to the server via the device. Based on the received data, the server refers to past records with similar characteristics and performs analysis using a generative AI model. The server uses artificial intelligence technologies such as TensorFlow to generate an optimal exercise guidance plan for the user.

[0487] The exercise plan generated by the server is sent back to the terminal and displayed on the user interface. The user can then use this as a guide to perform their exercise. The terminal also has the ability to provide detailed explanations using videos and diagrams.

[0488] For example, if a user wants to incorporate jogging into their daily routine, they can input their height, weight, age, and past exercise experience through the app. The server then provides an exercise plan that includes the optimal pace, distance, and frequency. The user can then follow this plan to exercise daily.

[0489] The following is an example of a prompt: "Generate a personalized jogging plan based on the user's height, weight, age, and past exercise experience."

[0490] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0491] Step 1:

[0492] The device acquires biometric and physical data from the user. The user enters information such as height, weight, age, and exercise experience using the application's form. The entered data is temporarily stored in the device's memory.

[0493] Step 2:

[0494] The terminal converts the acquired data into packets and sends them to the server via a secure connection. The HTTPS protocol is used for this transmission. The server receives the data packets generated by the terminal as output.

[0495] Step 3:

[0496] The server compares the received user data with past records in the database. This comparison uses a database search algorithm to quickly find records with similar characteristics. It references the user data as input and retrieves similar records from the database.

[0497] Step 4:

[0498] The server uses a generative AI model to generate an exercise instruction plan based on the user's characteristics. This AI model uses machine learning algorithms to suggest the optimal plan based on information learned from past data. The exercise plan is output based on the data input to the AI ​​model.

[0499] Step 5:

[0500] The server converts the generated exercise instruction plan into JSON format and sends it to the terminal. The terminal receives the JSON data output by the server. This data contains important information for use in the next step.

[0501] Step 6:

[0502] The device parses the received JSON data and displays it in the user interface. Using the React Native framework, the plan details are presented in a visually easy-to-understand format. Through this interface, users can obtain detailed exercise instructions.

[0503] (Application Example 1)

[0504] 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."

[0505] The challenges lie in providing exercise guidance tailored to individual physical conditions and addressing the lack of real-time movement evaluation and feedback. Typical training plans at fitness gyms are uniform and not optimized for individual users, often resulting in ineffective exercise. Furthermore, the lack of objective means to evaluate one's own movements during training makes it difficult to perform exercises with proper form.

[0506] 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.

[0507] In this invention, the server includes means for inputting individual body shape and constitution data; means for referencing a database of past athletes with similar characteristics using the input data and processing the generated data; means for generating an exercise training menu optimized for the user based on the processed data; means for displaying the generated exercise training menu and related products to the user; means for collecting feedback after the exercise is performed based on the training menu and using it to improve the system; and means for evaluating the user's movements in real time in cooperation with a smart device and providing points for improvement in exercise. This enables personalized exercise guidance and real-time movement evaluation that is suitable for each individual user.

[0508] "Individual body shape and constitution data" refers to information that indicates the physical characteristics unique to each user, such as height, weight, age, and exercise experience.

[0509] A "database" is a collection of information that has been accumulated about athletes with similar characteristics in the past, and it serves as a standard for analysis and comparison based on the user's physical characteristics.

[0510] "Means for processing generated data" refers to methods that use physical data obtained from users to refer to a database and perform calculations and operations to generate an exercise training menu optimized for the user.

[0511] An "exercise training menu" is a plan that specifically outlines the type of exercise, frequency, number of repetitions, and equipment to be used, tailored to each individual user.

[0512] A "smart device" is a device used to evaluate a user's movements in real time. It has the function of detecting the user's exercise form and posture and indicating areas for improvement as needed.

[0513] "Real-time evaluation" is a process that analyzes a user's movements immediately while they are performing an exercise, providing feedback on accuracy and areas for improvement.

[0514] The system implementing this invention generates an individually optimized exercise training menu based on the user's body shape and physical condition data.

[0515] The device uses a smartphone or tablet as a means of collecting the user's body shape and physical characteristics data, and provides a form to obtain information such as height, weight, age, and exercise experience from the user. This data is transmitted from the device to the server.

[0516] The server uses the received data to compare it with a database containing information on athletes with similar characteristics from the past. It utilizes a generative AI model using Python and TensorFlow to perform detailed analysis. Based on the analysis, it generates an exercise training menu tailored to the user. The generated menu includes recommendations for specific exercises, frequency, repetitions, and equipment to be used. Furthermore, it can integrate with smart devices to evaluate the user's movements in real time during training and provide feedback on areas for improvement.

[0517] The generated exercise training menu is sent to the device and displayed through the user interface. The user can review the menu through this interface and receive detailed video and image explanations as needed. The device also provides purchase links for related products to support the user's training.

[0518] As a concrete example, consider a 35-year-old beginner runner using this system. This user inputs their data, and a generative AI model suggests a suitable jogging plan. This plan would include details such as "3 kilometers per day, 3 times a week." Furthermore, a smart device monitors the user's posture during training and provides suggestions for improvement to ensure correct form. An example of a prompt for the generative AI model would be:

[0519] Height: 180cm

[0520] Weight: 75kg

[0521] Age: 28

[0522] Goal: Strengthening

[0523] Please generate a plan.

[0524] In this way, users can receive personalized exercise guidance and perform exercises that match their own health condition and fitness goals.

[0525] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0526] Step 1:

[0527] The terminal displays a form on each device for the user to input body shape and physical characteristics data. The user enters information such as height, weight, age, and exercise experience. This data is temporarily stored in the terminal's local database. The entered data is then prepared to be sent to the server.

[0528] Step 2:

[0529] User input data is sent from the terminal to the server. The server receives this data and compares it with data of past athletes with similar characteristics recorded in the database. The server then launches a generative AI model using Python and TensorFlow to perform statistical analysis based on the input data. The output of the analysis includes an initial draft of an exercise training menu optimized for the user.

[0530] Step 3:

[0531] The server generates an exercise training menu tailored to the user based on the analysis results of the generated AI model. The generated menu includes detailed suggestions regarding the type of exercise, frequency, number of repetitions, and equipment to be used. The generated exercise training menu and information on appropriate related products are sent to the terminal.

[0532] Step 4:

[0533] The device displays the received exercise training menu on the user interface. Users can review and evaluate the provided training plan. Furthermore, they can gain a deeper understanding of the detailed training content through videos and images, enabling them to accurately grasp the exercise methods.

[0534] Step 5:

[0535] As the user performs training, the smart device monitors the user's movements in real time, evaluating their posture and providing feedback. The smart device uses cameras and sensors to detect the user's form and points out areas for improvement as needed. This information is continuously transmitted to the terminal and provided to the user as feedback.

[0536] Step 6:

[0537] After completing a training session, users input their thoughts and results into the system via a feedback form. The device sends this feedback to a server, which analyzes the feedback data to improve the next exercise training menu and enhance the service. By using a generative AI model, the feedback data is utilized to generate the next prompt, resulting in more personalized content.

[0538] 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.

[0539] This invention is a system for recognizing the user's emotions, in addition to individual body shape and constitution data, and reflecting these in the exercise training menu. Embodiments of this invention will be described below.

[0540] First, the device collects body shape and physical characteristics data from the user. This includes information such as height, weight, age, and past exercise experience. The user enters this information via a smartphone app, and the data is sent to the cloud in an encrypted format.

[0541] Next, the server receives this data and performs an analysis based on a database of athletes with similar characteristics from the past. The generative AI processes this data and generates an exercise training menu that is best suited to the user's profile.

[0542] The server also runs an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and other information through sensors and microphones to understand the user's mental state and emotions. Based on this information, the generated training menu, its timing, and expression methods are dynamically adjusted.

[0543] The generated exercise training menu is sent to the device and displayed on the user's interface. Detailed video explanations and images are provided, allowing the user to visually understand the specific exercise methods. The device is also designed to consider the user's current emotional state and display appropriate encouragement and warnings.

[0544] After completing an exercise, users provide feedback through the application. This includes their emotions and physical sensations during the exercise, as well as their level of achievement. The feedback information is then sent back from the device to the server, where an AI model continuously uses it to improve the training program.

[0545] For example, if a user is feeling fatigued or stressed while jogging, the emotion engine will detect this state, and the device will provide voice advice such as, "Try slowing down," or "You're almost at your next goal." This allows the user to maintain motivation while continuing their training.

[0546] Thus, the present invention enables more effective and personalized exercise instruction that takes into account physical characteristics and emotional aspects.

[0547] The following describes the processing flow.

[0548] Step 1:

[0549] The user launches a smartphone app and opens a form to enter their body shape and physical characteristics data. This form includes fields such as height, weight, age, and past exercise experience.

[0550] Step 2:

[0551] The terminal validates the entered data in real time, ensuring that all required fields are entered correctly. Once validation is complete, the data is encrypted and sent to the cloud server.

[0552] Step 3:

[0553] The server stores the received data in a database and analyzes it using a generative AI. The analysis includes comparing the data with that of athletes with similar physical characteristics from the past, and based on this, an optimized exercise training menu is generated.

[0554] Step 4:

[0555] The device activates an emotion engine for emotion recognition. This engine uses the device's built-in camera and microphone to monitor the user's facial expressions and voice tone, and analyzes the user's emotional state in real time.

[0556] Step 5:

[0557] The server takes into account the user's emotions as recognized by the emotion engine and adjusts the previously generated exercise training menu as needed. This includes fine-tuning the difficulty of the training and adding encouraging messages.

[0558] Step 6:

[0559] The device displays the final exercise training menu to the user. The menu includes videos and images to make the details easier to understand, and also displays messages and advice based on the user's current emotional state.

[0560] Step 7:

[0561] The user performs exercises according to the given training menu. During the exercise, the emotion engine continuously monitors the user's emotions and provides additional instructions and encouragement from the device as needed.

[0562] Step 8:

[0563] After completing an exercise session, users input feedback about the training through the app. This feedback includes their emotional state, physical sensations, and opinions on the suitability of the workout plan.

[0564] Step 9:

[0565] The device collects feedback and forwards it to the server.

[0566] Step 10:

[0567] The server incorporates the feedback data into the continuous improvement of the AI ​​model, and as needed, enhances the accuracy of subsequent exercise training menus.

[0568] (Example 2)

[0569] 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."

[0570] Modern exercise instruction often fails to adequately consider individual physical characteristics and emotional states, resulting in the inability to provide optimal training for each user. Furthermore, the lack of mechanisms for immediate feedback on users' emotions during training makes effective motivation difficult. Additionally, feedback information is often not properly utilized, hindering improvements to training programs.

[0571] 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.

[0572] In this invention, the server includes means for inputting individual body shape and characteristic data, means for identifying the user's emotions using voice and video and dynamically adjusting the training plan based on those emotions, and means for collecting positive feedback after exercise is performed based on the training plan and using it to improve the knowledge system. This enables personalized exercise guidance that meets the physical and emotional needs of each user.

[0573] "Individual body shape and characteristic data" refers to information that represents the user's physical characteristics, such as height, weight, age, and activity experience.

[0574] A "sports professional" refers to someone who specializes in sports, and their data is used to design training plans for others with similar characteristics.

[0575] "Data records" refer to databases containing accumulated past information and performance data, which form the basis for information analysis.

[0576] An "exercise training plan" refers to a program of exercise instruction required by the user, and is an optimized menu built using a generative AI model.

[0577] "Identifying emotions" is a process that uses audio and video to analyze the user's emotional state and dynamically adjusts the training plan based on that analysis.

[0578] "Positive feedback" refers to the feedback information received from users after they have completed an exercise program, and particularly positive feedback plays an important role in improving the system.

[0579] A "knowledge system" refers to a collection of information used to improve the accuracy of exercise training plans, based on collected data and feedback.

[0580] In this invention, a specific process is executed sequentially to provide an exercise training plan that takes into account the physical characteristics and emotional state of each individual user. First, the terminal collects body shape and characteristic data from the user. Specifically, using a dedicated application installed on a smartphone, the user inputs their height, weight, age, and past activity experience. This data is encrypted by the application and securely transmitted to the cloud.

[0581] Next, the server analyzes the data received in the cloud. The generative AI model used here is Python-based and utilizes historical data records of athletes with similar characteristics. This allows for the generation of exercise training plans tailored to each user. By applying this model, the type and intensity of exercise are individually optimized.

[0582] Furthermore, the server analyzes audio and video data acquired from the device to identify the user's emotions. This data, acquired by devices equipped with cameras and microphones, is processed through an emotion engine to determine the user's emotional state. OpenCV libraries and speech recognition APIs are used to perform facial expression analysis and voice tone analysis.

[0583] Based on the above, the server dynamically adjusts the exercise training plan as needed, and the adjusted plan is sent to the terminal. The user's smartphone displays video explanations and image guides using HTML5 and CSS3. Furthermore, the terminal uses a TTS (Text-to-Speech) engine to provide real-time voice feedback. Specifically, it generates voice instructions such as "Let's slow down" or "Almost there."

[0584] After exercise, users send feedback through the application. This feedback includes emotions, fatigue levels, and satisfaction during training, and is used for analysis on the server. The AI ​​model utilizes this feedback to improve the quality of future exercise training plans.

[0585] As a concrete example, an example of a prompt sentence to be input to the generating AI model is, "Consider the user's current emotional state and generate advice that provides appropriate encouragement." Based on this prompt, the AI ​​provides an appropriate exercise plan and feedback that meets the user's needs.

[0586] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0587] Step 1:

[0588] The device collects body shape and characteristic data from the user. Inputs include information entered by the user into the application, specifically height, weight, age, and activity level. This data is formatted within the application and sent to the cloud in an encrypted format as output.

[0589] Step 2:

[0590] The server decrypts encrypted data received from the cloud and analyzes the input data. The generative AI model used here compares this data with past data records of athletes with similar characteristics, and processes the data to generate the most suitable exercise training plan for each user. The output is an optimized exercise training plan for each user.

[0591] Step 3:

[0592] The server receives audio and video data transmitted from the terminal as input to identify the user's emotions. Specifically, this data is acquired through the camera and microphone. Using an emotion engine, facial expression analysis is performed using speech recognition APIs and OpenCV libraries to determine the emotional state. The output is data that includes the user's emotional state.

[0593] Step 4:

[0594] The server dynamically adjusts the generated exercise training plan based on the emotional state data obtained in step 3. The inputs are the exercise training plan and the emotional state data. Using these, the server adjusts the intensity and content of the plan, and outputs an updated exercise training plan.

[0595] Step 5:

[0596] The server sends a customized exercise training plan to the terminal. The terminal receives this and uses it as input on the user's device. Using HTML5 and CSS3, detailed video explanations and image guides are output and visualized. This information serves as a guide for the user as they perform their training.

[0597] Step 6:

[0598] During exercise, the device provides voice feedback to the user. Input consists of real-time data from the server and information based on the user's progress. Using a Text-to-Speech (TTS) engine, it outputs specific voice instructions such as "Let's slow down" or "Almost there."

[0599] Step 7:

[0600] After exercise, users input feedback through the application. This feedback includes emotions during exercise, fatigue levels, satisfaction levels, etc. The input feedback information is sent back to the server and analyzed by an AI model. As output, improvement measures are suggested that will be reflected in future exercise training plans.

[0601] (Application Example 2)

[0602] 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."

[0603] Traditional fitness systems often fail to provide exercise programs tailored to individual users, instead offering mechanical training programs without considering the user's emotional state. As a result, the exercise effects are not maximized, and user motivation does not improve.

[0604] 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.

[0605] In this invention, the server includes means for inputting individual body shape and constitution information, means for referencing a set of information on past athletes with similar characteristics using the input information and processing the generated information, and means for analyzing the user's emotional state using sensors and a microphone and dynamically adjusting the exercise training procedure based on the mental state. This makes it possible to provide an optimal exercise training menu based on each user's individual body information and emotional state.

[0606] "Body shape and constitution information" refers to data such as the user's height, weight, age, and past exercise experience.

[0607] "Emotional state" refers to information that indicates the user's mental state based on their facial expressions and tone of voice.

[0608] An "exercise participant" is a person who possesses information about exercise and has past experience in exercise.

[0609] An "information set" is a collection of data gathered under specific conditions, and is a database that includes information about the physical characteristics and constitution of past athletes.

[0610] An "exercise training procedure" is a schedule or set of instructions and steps for exercise that are generated based on the user's physical characteristics and emotional state.

[0611] "Sensors and microphones" are electronic devices used to acquire a user's biometric information and voice.

[0612] "Dynamic adjustment" means that the system changes and adapts in real time according to the user's state.

[0613] The system for carrying out this invention utilizes the user's body shape and physical characteristics information to provide exercise training procedures that take into account their emotional state. The system mainly consists of two main elements: a server and a terminal.

[0614] The server is located in the cloud and processes body shape and physical characteristics information sent by the user. The information is transmitted from the terminal in an encrypted state, ensuring its security. The server references a database of past exercise data and uses a generative AI model to generate optimal exercise training procedures. In this process, the generative AI model operates based on prompts derived from the user's information.

[0615] The device consists of a smartphone or other mobile device that receives user input information. Furthermore, the device uses its camera and microphone to analyze the user's emotional state in real time. The device dynamically displays exercise training procedures and encouraging messages according to the user's emotional state.

[0616] Specific equipment includes cameras and microphones to analyze facial expressions and voice tone. Firebase and Google AI platforms are used to run cloud databases and generative AI models. This allows users to receive information in real time and receive appropriate training.

[0617] For example, suppose a user is using a training machine. The device's camera monitors the user's movements and immediately points out any incorrect form. Furthermore, if the user is fatigued, an encouraging message is displayed. Specifically, voice navigation such as "You're almost at your next goal" can also be added.

[0618] Examples of prompts for a generative AI model include the following:

[0619] "Height 175cm, weight 70kg, 30-year-old male, emotional state: slightly fatigued. Please generate the following training menu and words of encouragement."

[0620] This system enables personalized exercise training tailored to each individual's physical condition and emotional state, thereby improving the user's fitness experience.

[0621] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0622] Step 1:

[0623] The server receives body shape and physical characteristics information transmitted from the user's device. This information includes height, weight, age, and exercise experience, and is transmitted in an encrypted format. This data is decrypted on the server and prepared for the next processing.

[0624] Step 2:

[0625] The device uses its camera and microphone to collect the user's emotional state in real time. The user's facial expressions and voice tone are used as input, and data is acquired by sensors. This emotional data is initially processed on the device before being sent to a server.

[0626] Step 3:

[0627] The server uses the received body shape and constitution information, along with emotional data, to provide prompt messages to the generating AI model. These prompt messages are generated based on detailed information corresponding to the user's current state. For example, they might say, "Height 175cm, weight 70kg, 30-year-old male, emotional state: slightly fatigued."

[0628] Step 4:

[0629] The server uses a generative AI model to generate the optimal exercise training procedure for the user from a given prompt. Based on the input data, it searches for similar situations by referring to a set of information on past exercisers and generates a procedure based on the results. The output is a customized exercise training procedure.

[0630] Step 5:

[0631] The server delivers the generated exercise training instructions to the terminal. The terminal displays these instructions to the user and provides visual and audio navigation at the necessary times. Specifically, the steps of the procedure may be displayed on the screen, and encouraging messages may be played in audio.

[0632] Step 6:

[0633] After completing an exercise training session, users input their experience and feelings into a feedback form on their device. The device then sends this feedback data to a server, which an AI model uses to improve future training menus.

[0634] Step 7:

[0635] The server stores the received feedback data and continuously learns from it. This enables the provision of more accurate services that reflect the user's training effectiveness and emotional feedback.

[0636] 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.

[0637] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0638] 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.

[0639] [Fourth Embodiment]

[0640] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0641] 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.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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).

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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".

[0653] This invention provides a function for delivering personalized exercise guidance tailored to individual users. To that end, it includes the following system configuration and operating procedures.

[0654] First, the device acquires body shape and physical characteristics data from the user. This data includes the user's height, weight, age, past exercise experience, and health status. To collect this information, the device provides a form for the user to fill out via a smartphone or tablet application.

[0655] Next, this data is sent to a server. The server compares the received data with a database containing information on athletes with similar characteristics from the past. It then uses generative AI to generate more detailed analysis results.

[0656] Based on this analysis, the server generates an exercise training program optimized for the user. This program includes appropriate training content, exercise frequency, number of repetitions, and equipment to be used.

[0657] The generated exercise training menu is sent back to the device and displayed on the user's interface. Through this interface, the user can review the training menu and receive further explanations, including videos and images, as needed. The device also provides purchase links for related products that the user may be interested in.

[0658] After completing a training session, users submit their results and feedback through a feedback form. This feedback is saved to a cloud server via their device. The server analyzes this feedback data to help structure future training menus and improve the overall system.

[0659] For example, if a user who wants to incorporate jogging into their daily routine provides information through the app, the server generates a recommended plan that includes a suitable exercise pace, distance, and frequency for that user, and displays it on their device. The user can then use this plan as a guide to carry out their daily exercise.

[0660] This invention makes it possible for everyone to receive appropriate exercise guidance and find an exercise approach that suits their health condition and fitness goals.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] The user launches the smartphone app and enters their body shape and physical characteristics data on the new registration screen. This includes height, weight, age, and past exercise experience. Once the data is entered, the user presses the submit button to move the data to the next step.

[0664] Step 2:

[0665] The terminal validates the data entered by the user, checking whether the data format is correct. If there is an input error, it prompts the user to correct it, and if there are no problems, it encrypts the data and sends it to the cloud server.

[0666] Step 3:

[0667] The server receives data sent from the terminal and stores it in a database. Then, it starts an analysis using the generated AI. The AI ​​compares this data with data from past athletes with similar physiques and characteristics, and generates analysis results.

[0668] Step 4:

[0669] The server generates an exercise training program optimized for the user. This program includes recommended training content, frequency, number of repetitions, and equipment to be used.

[0670] Step 5:

[0671] The server sends the generated exercise training menu to the terminal.

[0672] Step 6:

[0673] The device displays the received training menu on its user interface. It provides detailed explanations using videos and images to help users easily understand the training process. It also displays purchase links for related products.

[0674] Step 7:

[0675] Users perform exercises according to the provided training. After completing the training, they submit feedback on their results and impressions via the application.

[0676] Step 8:

[0677] The device receives feedback and sends it to the cloud server.

[0678] Step 9:

[0679] The server analyzes the feedback and uses the data to improve the AI ​​model. It updates individual training menus as needed and incorporates them into the next training cycle.

[0680] (Example 1)

[0681] 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".

[0682] In today's world, providing personalized exercise guidance tailored to each individual is a challenging task. In particular, there is a need for an efficient system that can propose optimized exercise programs for users with varying physical characteristics and exercise histories. A uniform teaching method makes it difficult to improve motivation and promote effective health, and fails to meet the diverse needs of users.

[0683] 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.

[0684] In this invention, the server includes a device for acquiring individual bio-shape and physical data, a device for using the acquired data to reference an information source that stores past individual data with similar characteristics, and for analyzing the data using a generating AI model, and a device for generating an exercise guidance plan optimized for the user based on the analyzed data. This realizes a system that efficiently provides individual exercise guidance plans to each user, enabling the improvement of users' health and motivation.

[0685] "Biometric data" refers to information that indicates the physical characteristics of a user, and includes data such as basic body measurements like height and weight.

[0686] "Physical data" refers to a wide range of information related to the user's body, such as their health status and past exercise history.

[0687] An "information source" refers to a database that stores and makes available historical data and other similar data.

[0688] A "generative AI model" is an artificial intelligence technology used to create optimized exercise instruction plans based on user data.

[0689] An "exercise instruction plan" refers to a plan that proposes personalized exercise menus and training content based on the user's characteristics.

[0690] "Related supplies" refers to tools, equipment, or products that are recommended for use in an exercise instruction plan or that can be helpful as supplementary aids.

[0691] This invention is a system for providing optimized exercise guidance to individual users. The system uses a terminal, a server, and a generative AI model to create a personalized exercise plan.

[0692] The device acquires biometric shape data and physical data from the user. This data is collected through applications on mobile devices such as smartphones and tablets. The device has an application implemented using a software framework called React Native, which enables real-time collection of user input.

[0693] The information entered by the user is sent to the server via the device. Based on the received data, the server refers to past records with similar characteristics and performs analysis using a generative AI model. The server uses artificial intelligence technologies such as TensorFlow to generate an optimal exercise guidance plan for the user.

[0694] The exercise plan generated by the server is sent back to the terminal and displayed on the user interface. The user can then use this as a guide to perform their exercise. The terminal also has the ability to provide detailed explanations using videos and diagrams.

[0695] For example, if a user wants to incorporate jogging into their daily routine, they can input their height, weight, age, and past exercise experience through the app. The server then provides an exercise plan that includes the optimal pace, distance, and frequency. The user can then follow this plan to exercise daily.

[0696] The following is an example of a prompt: "Generate a personalized jogging plan based on the user's height, weight, age, and past exercise experience."

[0697] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0698] Step 1:

[0699] The device acquires biometric and physical data from the user. The user enters information such as height, weight, age, and exercise experience using the application's form. The entered data is temporarily stored in the device's memory.

[0700] Step 2:

[0701] The terminal converts the acquired data into packets and sends them to the server via a secure connection. The HTTPS protocol is used for this transmission. The server receives the data packets generated by the terminal as output.

[0702] Step 3:

[0703] The server compares the received user data with past records in the database. This comparison uses a database search algorithm to quickly find records with similar characteristics. It references the user data as input and retrieves similar records from the database.

[0704] Step 4:

[0705] The server uses a generative AI model to generate an exercise instruction plan based on the user's characteristics. This AI model uses machine learning algorithms to suggest the optimal plan based on information learned from past data. The exercise plan is output based on the data input to the AI ​​model.

[0706] Step 5:

[0707] The server converts the generated exercise instruction plan into JSON format and sends it to the terminal. The terminal receives the JSON data output by the server. This data contains important information for use in the next step.

[0708] Step 6:

[0709] The device parses the received JSON data and displays it in the user interface. Using the React Native framework, the plan details are presented in a visually easy-to-understand format. Through this interface, users can obtain detailed exercise instructions.

[0710] (Application Example 1)

[0711] 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".

[0712] The challenges lie in providing exercise guidance tailored to individual physical conditions and addressing the lack of real-time movement evaluation and feedback. Typical training plans at fitness gyms are uniform and not optimized for individual users, often resulting in ineffective exercise. Furthermore, the lack of objective means to evaluate one's own movements during training makes it difficult to perform exercises with proper form.

[0713] 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.

[0714] In this invention, the server includes means for inputting individual body shape and constitution data; means for referencing a database of past athletes with similar characteristics using the input data and processing the generated data; means for generating an exercise training menu optimized for the user based on the processed data; means for displaying the generated exercise training menu and related products to the user; means for collecting feedback after the exercise is performed based on the training menu and using it to improve the system; and means for evaluating the user's movements in real time in cooperation with a smart device and providing points for improvement in exercise. This enables personalized exercise guidance and real-time movement evaluation that is suitable for each individual user.

[0715] "Individual body shape and constitution data" refers to information that indicates the physical characteristics unique to each user, such as height, weight, age, and exercise experience.

[0716] A "database" is a collection of information that has been accumulated about athletes with similar characteristics in the past, and it serves as a standard for analysis and comparison based on the user's physical characteristics.

[0717] "Means for processing generated data" refers to methods that use physical data obtained from users to refer to a database and perform calculations and operations to generate an exercise training menu optimized for the user.

[0718] An "exercise training menu" is a plan that specifically outlines the type of exercise, frequency, number of repetitions, and equipment to be used, tailored to each individual user.

[0719] A "smart device" is a device used to evaluate a user's movements in real time. It has the function of detecting the user's exercise form and posture and indicating areas for improvement as needed.

[0720] "Real-time evaluation" is a process that analyzes a user's movements immediately while they are performing an exercise, providing feedback on accuracy and areas for improvement.

[0721] The system implementing this invention generates an individually optimized exercise training menu based on the user's body shape and physical condition data.

[0722] The device uses a smartphone or tablet as a means of collecting the user's body shape and physical characteristics data, and provides a form to obtain information such as height, weight, age, and exercise experience from the user. This data is transmitted from the device to the server.

[0723] The server uses the received data to compare it with a database containing information on athletes with similar characteristics from the past. It utilizes a generative AI model using Python and TensorFlow to perform detailed analysis. Based on the analysis, it generates an exercise training menu tailored to the user. The generated menu includes recommendations for specific exercises, frequency, repetitions, and equipment to be used. Furthermore, it can integrate with smart devices to evaluate the user's movements in real time during training and provide feedback on areas for improvement.

[0724] The generated exercise training menu is sent to the device and displayed through the user interface. The user can review the menu through this interface and receive detailed video and image explanations as needed. The device also provides purchase links for related products to support the user's training.

[0725] As a concrete example, consider a 35-year-old beginner runner using this system. This user inputs their data, and a generative AI model suggests a suitable jogging plan. This plan would include details such as "3 kilometers per day, 3 times a week." Furthermore, a smart device monitors the user's posture during training and provides suggestions for improvement to ensure correct form. An example of a prompt for the generative AI model would be:

[0726] Height: 180cm

[0727] Weight: 75kg

[0728] Age: 28

[0729] Goal: Strengthening

[0730] Please generate a plan.

[0731] In this way, users can receive personalized exercise guidance and perform exercises that match their own health condition and fitness goals.

[0732] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0733] Step 1:

[0734] The terminal displays a form on each device for the user to input body shape and physical characteristics data. The user enters information such as height, weight, age, and exercise experience. This data is temporarily stored in the terminal's local database. The entered data is then prepared to be sent to the server.

[0735] Step 2:

[0736] User input data is sent from the terminal to the server. The server receives this data and compares it with data of past athletes with similar characteristics recorded in the database. The server then launches a generative AI model using Python and TensorFlow to perform statistical analysis based on the input data. The output of the analysis includes an initial draft of an exercise training menu optimized for the user.

[0737] Step 3:

[0738] The server generates an exercise training menu tailored to the user based on the analysis results of the generated AI model. The generated menu includes detailed suggestions regarding the type of exercise, frequency, number of repetitions, and equipment to be used. The generated exercise training menu and information on appropriate related products are sent to the terminal.

[0739] Step 4:

[0740] The device displays the received exercise training menu on the user interface. Users can review and evaluate the provided training plan. Furthermore, they can gain a deeper understanding of the detailed training content through videos and images, enabling them to accurately grasp the exercise methods.

[0741] Step 5:

[0742] As the user performs training, the smart device monitors the user's movements in real time, evaluating their posture and providing feedback. The smart device uses cameras and sensors to detect the user's form and points out areas for improvement as needed. This information is continuously transmitted to the terminal and provided to the user as feedback.

[0743] Step 6:

[0744] After completing a training session, users input their thoughts and results into the system via a feedback form. The device sends this feedback to a server, which analyzes the feedback data to improve the next exercise training menu and enhance the service. By using a generative AI model, the feedback data is utilized to generate the next prompt, resulting in more personalized content.

[0745] 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.

[0746] This invention is a system for recognizing the user's emotions, in addition to individual body shape and constitution data, and reflecting these in the exercise training menu. Embodiments of this invention will be described below.

[0747] First, the device collects body shape and physical characteristics data from the user. This includes information such as height, weight, age, and past exercise experience. The user enters this information via a smartphone app, and the data is sent to the cloud in an encrypted format.

[0748] Next, the server receives this data and performs an analysis based on a database of athletes with similar characteristics from the past. The generative AI processes this data and generates an exercise training menu that is best suited to the user's profile.

[0749] The server also runs an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and other information through sensors and microphones to understand the user's mental state and emotions. Based on this information, the generated training menu, its timing, and expression methods are dynamically adjusted.

[0750] The generated exercise training menu is sent to the device and displayed on the user's interface. Detailed video explanations and images are provided, allowing the user to visually understand the specific exercise methods. The device is also designed to consider the user's current emotional state and display appropriate encouragement and warnings.

[0751] After completing an exercise, users provide feedback through the application. This includes their emotions and physical sensations during the exercise, as well as their level of achievement. The feedback information is then sent back from the device to the server, where an AI model continuously uses it to improve the training program.

[0752] For example, if a user is feeling fatigued or stressed while jogging, the emotion engine will detect this state, and the device will provide voice advice such as, "Try slowing down," or "You're almost at your next goal." This allows the user to maintain motivation while continuing their training.

[0753] Thus, the present invention enables more effective and personalized exercise instruction that takes into account physical characteristics and emotional aspects.

[0754] The following describes the processing flow.

[0755] Step 1:

[0756] The user launches a smartphone app and opens a form to enter their body shape and physical characteristics data. This form includes fields such as height, weight, age, and past exercise experience.

[0757] Step 2:

[0758] The terminal validates the entered data in real time, ensuring that all required fields are entered correctly. Once validation is complete, the data is encrypted and sent to the cloud server.

[0759] Step 3:

[0760] The server stores the received data in a database and analyzes it using a generative AI. The analysis includes comparing the data with that of athletes with similar physical characteristics from the past, and based on this, an optimized exercise training menu is generated.

[0761] Step 4:

[0762] The device activates an emotion engine for emotion recognition. This engine uses the device's built-in camera and microphone to monitor the user's facial expressions and voice tone, and analyzes the user's emotional state in real time.

[0763] Step 5:

[0764] The server takes into account the user's emotions as recognized by the emotion engine and adjusts the previously generated exercise training menu as needed. This includes fine-tuning the difficulty of the training and adding encouraging messages.

[0765] Step 6:

[0766] The device displays the final exercise training menu to the user. The menu includes videos and images to make the details easier to understand, and also displays messages and advice based on the user's current emotional state.

[0767] Step 7:

[0768] The user performs exercises according to the given training menu. During the exercise, the emotion engine continuously monitors the user's emotions and provides additional instructions and encouragement from the device as needed.

[0769] Step 8:

[0770] After completing an exercise session, users input feedback about the training through the app. This feedback includes their emotional state, physical sensations, and opinions on the suitability of the workout plan.

[0771] Step 9:

[0772] The device collects feedback and forwards it to the server.

[0773] Step 10:

[0774] The server incorporates the feedback data into the continuous improvement of the AI ​​model, and as needed, enhances the accuracy of subsequent exercise training menus.

[0775] (Example 2)

[0776] 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".

[0777] Modern exercise instruction often fails to adequately consider individual physical characteristics and emotional states, resulting in the inability to provide optimal training for each user. Furthermore, the lack of mechanisms for immediate feedback on users' emotions during training makes effective motivation difficult. Additionally, feedback information is often not properly utilized, hindering improvements to training programs.

[0778] 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.

[0779] In this invention, the server includes means for inputting individual body shape and characteristic data, means for identifying the user's emotions using voice and video and dynamically adjusting the training plan based on those emotions, and means for collecting positive feedback after exercise is performed based on the training plan and using it to improve the knowledge system. This enables personalized exercise guidance that meets the physical and emotional needs of each user.

[0780] "Individual body shape and characteristic data" refers to information that represents the user's physical characteristics, such as height, weight, age, and activity experience.

[0781] A "sports professional" refers to someone who specializes in sports, and their data is used to design training plans for others with similar characteristics.

[0782] "Data records" refer to databases containing accumulated past information and performance data, which form the basis for information analysis.

[0783] An "exercise training plan" refers to a program of exercise instruction required by the user, and is an optimized menu built using a generative AI model.

[0784] "Identifying emotions" is a process that uses audio and video to analyze the user's emotional state and dynamically adjusts the training plan based on that analysis.

[0785] "Positive feedback" refers to the feedback information received from users after they have completed an exercise program, and particularly positive feedback plays an important role in improving the system.

[0786] A "knowledge system" refers to a collection of information used to improve the accuracy of exercise training plans, based on collected data and feedback.

[0787] In this invention, a specific process is executed sequentially to provide an exercise training plan that takes into account the physical characteristics and emotional state of each individual user. First, the terminal collects body shape and characteristic data from the user. Specifically, using a dedicated application installed on a smartphone, the user inputs their height, weight, age, and past activity experience. This data is encrypted by the application and securely transmitted to the cloud.

[0788] Next, the server analyzes the data received in the cloud. The generative AI model used here is Python-based and utilizes historical data records of athletes with similar characteristics. This allows for the generation of exercise training plans tailored to each user. By applying this model, the type and intensity of exercise are individually optimized.

[0789] Furthermore, the server analyzes audio and video data acquired from the device to identify the user's emotions. This data, acquired by devices equipped with cameras and microphones, is processed through an emotion engine to determine the user's emotional state. OpenCV libraries and speech recognition APIs are used to perform facial expression analysis and voice tone analysis.

[0790] Based on the above, the server dynamically adjusts the exercise training plan as needed, and the adjusted plan is sent to the terminal. The user's smartphone displays video explanations and image guides using HTML5 and CSS3. Furthermore, the terminal uses a TTS (Text-to-Speech) engine to provide real-time voice feedback. Specifically, it generates voice instructions such as "Let's slow down" or "Almost there."

[0791] After exercise, users send feedback through the application. This feedback includes emotions, fatigue levels, and satisfaction during training, and is used for analysis on the server. The AI ​​model utilizes this feedback to improve the quality of future exercise training plans.

[0792] As a concrete example, an example of a prompt sentence to be input to the generating AI model is, "Consider the user's current emotional state and generate advice that provides appropriate encouragement." Based on this prompt, the AI ​​provides an appropriate exercise plan and feedback that meets the user's needs.

[0793] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0794] Step 1:

[0795] The device collects body shape and characteristic data from the user. Inputs include information entered by the user into the application, specifically height, weight, age, and activity level. This data is formatted within the application and sent to the cloud in an encrypted format as output.

[0796] Step 2:

[0797] The server decrypts encrypted data received from the cloud and analyzes the input data. The generative AI model used here compares this data with past data records of athletes with similar characteristics, and processes the data to generate the most suitable exercise training plan for each user. The output is an optimized exercise training plan for each user.

[0798] Step 3:

[0799] The server receives audio and video data transmitted from the terminal as input to identify the user's emotions. Specifically, this data is acquired through the camera and microphone. Using an emotion engine, facial expression analysis is performed using speech recognition APIs and OpenCV libraries to determine the emotional state. The output is data that includes the user's emotional state.

[0800] Step 4:

[0801] The server dynamically adjusts the generated exercise training plan based on the emotional state data obtained in step 3. The inputs are the exercise training plan and the emotional state data. Using these, the server adjusts the intensity and content of the plan, and outputs an updated exercise training plan.

[0802] Step 5:

[0803] The server sends a customized exercise training plan to the terminal. The terminal receives this and uses it as input on the user's device. Using HTML5 and CSS3, detailed video explanations and image guides are output and visualized. This information serves as a guide for the user as they perform their training.

[0804] Step 6:

[0805] During exercise, the device provides voice feedback to the user. Input consists of real-time data from the server and information based on the user's progress. Using a Text-to-Speech (TTS) engine, it outputs specific voice instructions such as "Let's slow down" or "Almost there."

[0806] Step 7:

[0807] After exercise, users input feedback through the application. This feedback includes emotions during exercise, fatigue levels, satisfaction levels, etc. The input feedback information is sent back to the server and analyzed by an AI model. As output, improvement measures are suggested that will be reflected in future exercise training plans.

[0808] (Application Example 2)

[0809] 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".

[0810] Traditional fitness systems often fail to provide exercise programs tailored to individual users, instead offering mechanical training programs without considering the user's emotional state. As a result, the exercise effects are not maximized, and user motivation does not improve.

[0811] 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.

[0812] In this invention, the server includes means for inputting individual body shape and constitution information, means for referencing a set of information on past athletes with similar characteristics using the input information and processing the generated information, and means for analyzing the user's emotional state using sensors and a microphone and dynamically adjusting the exercise training procedure based on the mental state. This makes it possible to provide an optimal exercise training menu based on each user's individual body information and emotional state.

[0813] "Body shape and constitution information" refers to data such as the user's height, weight, age, and past exercise experience.

[0814] "Emotional state" refers to information that indicates the user's mental state based on their facial expressions and tone of voice.

[0815] An "exercise participant" is a person who possesses information about exercise and has past experience in exercise.

[0816] An "information set" is a collection of data gathered under specific conditions, and is a database that includes information about the physical characteristics and constitution of past athletes.

[0817] An "exercise training procedure" is a schedule or set of instructions and steps for exercise that are generated based on the user's physical characteristics and emotional state.

[0818] "Sensors and microphones" are electronic devices used to acquire a user's biometric information and voice.

[0819] "Dynamic adjustment" means that the system changes and adapts in real time according to the user's state.

[0820] The system for carrying out this invention utilizes the user's body shape and physical characteristics information to provide exercise training procedures that take into account their emotional state. The system mainly consists of two main elements: a server and a terminal.

[0821] The server is located in the cloud and processes body shape and physical characteristics information sent by the user. The information is transmitted from the terminal in an encrypted state, ensuring its security. The server references a database of past exercise data and uses a generative AI model to generate optimal exercise training procedures. In this process, the generative AI model operates based on prompts derived from the user's information.

[0822] The device consists of a smartphone or other mobile device that receives user input information. Furthermore, the device uses its camera and microphone to analyze the user's emotional state in real time. The device dynamically displays exercise training procedures and encouraging messages according to the user's emotional state.

[0823] Specific equipment includes cameras and microphones to analyze facial expressions and voice tone. Firebase and Google AI platforms are used to run cloud databases and generative AI models. This allows users to receive information in real time and receive appropriate training.

[0824] For example, suppose a user is using a training machine. The device's camera monitors the user's movements and immediately points out any incorrect form. Furthermore, if the user is fatigued, an encouraging message is displayed. Specifically, voice navigation such as "You're almost at your next goal" can also be added.

[0825] Examples of prompts for a generative AI model include the following:

[0826] "Height 175cm, weight 70kg, 30-year-old male, emotional state: slightly fatigued. Please generate the following training menu and words of encouragement."

[0827] This system enables personalized exercise training tailored to each individual's physical condition and emotional state, thereby improving the user's fitness experience.

[0828] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0829] Step 1:

[0830] The server receives body shape and physical characteristics information transmitted from the user's device. This information includes height, weight, age, and exercise experience, and is transmitted in an encrypted format. This data is decrypted on the server and prepared for the next processing.

[0831] Step 2:

[0832] The device uses its camera and microphone to collect the user's emotional state in real time. The user's facial expressions and voice tone are used as input, and data is acquired by sensors. This emotional data is initially processed on the device before being sent to a server.

[0833] Step 3:

[0834] The server uses the received body shape and constitution information, along with emotional data, to provide prompt messages to the generating AI model. These prompt messages are generated based on detailed information corresponding to the user's current state. For example, they might say, "Height 175cm, weight 70kg, 30-year-old male, emotional state: slightly fatigued."

[0835] Step 4:

[0836] The server uses a generative AI model to generate the optimal exercise training procedure for the user from a given prompt. Based on the input data, it searches for similar situations by referring to a set of information on past exercisers and generates a procedure based on the results. The output is a customized exercise training procedure.

[0837] Step 5:

[0838] The server delivers the generated exercise training instructions to the terminal. The terminal displays these instructions to the user and provides visual and audio navigation at the necessary times. Specifically, the steps of the procedure may be displayed on the screen, and encouraging messages may be played in audio.

[0839] Step 6:

[0840] After completing an exercise training session, users input their experience and feelings into a feedback form on their device. The device then sends this feedback data to a server, which an AI model uses to improve future training menus.

[0841] Step 7:

[0842] The server stores the received feedback data and continuously learns from it. This enables the provision of more accurate services that reflect the user's training effectiveness and emotional feedback.

[0843] 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.

[0844] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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."

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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 as being incorporated by reference.

[0864] The following is further disclosed regarding the embodiments described above.

[0865] (Claim 1)

[0866] A means for inputting individual body shape and constitution data,

[0867] A means for referencing a database of athletes with similar characteristics in the past using the input data, and processing the generated data,

[0868] A means for generating an exercise training menu optimized for the user based on the processed data,

[0869] A means for displaying the generated exercise training menu and related products to the user,

[0870] A means for collecting feedback after performing exercises based on the aforementioned training menu and using it to improve the system,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, wherein the aforementioned body shape and constitution data includes height, weight, age, and exercise experience.

[0874] (Claim 3)

[0875] The system according to claim 1, which provides a detailed explanation of the generated exercise training menu using videos and images.

[0876] "Example 1"

[0877] (Claim 1)

[0878] A device for acquiring individual biological shape and body data,

[0879] A device that uses the acquired data to reference an information source that has accumulated individual data with similar characteristics from the past, and analyzes the data using a generative AI model,

[0880] Based on the analyzed data, a device generates an exercise instruction plan optimized for the user,

[0881] A device that displays the generated exercise instruction plan and related items to the user,

[0882] A device for collecting responses after performing exercises based on the aforementioned exercise instruction plan and using the data to improve the system,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, wherein the biological shape and physical data include height, weight, age, and exercise history.

[0886] (Claim 3)

[0887] The system according to claim 1, which provides a detailed explanation of the generated exercise instruction plan using video and diagrams.

[0888] "Application Example 1"

[0889] (Claim 1)

[0890] A means for inputting individual body shape and constitution data,

[0891] A means for referencing a database of athletes with similar characteristics in the past using the input data, and processing the generated data,

[0892] A means for generating an exercise training menu optimized for the user based on the processed data,

[0893] A means for displaying the generated exercise training menu and related products to the user,

[0894] A means for collecting feedback after performing exercises based on the aforementioned training menu and using it to improve the system,

[0895] A means of evaluating the user's movements in real time in conjunction with a smart device and providing suggestions for improving exercise,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, wherein the aforementioned body shape and constitution data includes height, weight, age, and exercise experience.

[0899] (Claim 3)

[0900] The system according to claim 1, which provides a detailed explanation of the generated exercise training menu using videos and images.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] A means for inputting individual body shape and characteristic data,

[0904] A means for processing the generated information by referring to data records of athletes with similar characteristics in the past using the input data,

[0905] A means for generating an exercise training plan optimized for the user based on the processed information,

[0906] A means for displaying the generated exercise training plan and related support information to the user,

[0907] A means for identifying the user's emotions using audio and video, and dynamically adjusting the training plan based on the identified emotions,

[0908] A means of collecting positive feedback after the implementation of exercises based on the aforementioned training plan and using it to improve the knowledge system,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, wherein the body shape and characteristic data includes height, weight, age, and activity experience.

[0912] (Claim 3)

[0913] The system according to claim 1, which provides a detailed explanation of the generated exercise training plan using videos and images.

[0914] "Application example 2 when combining with an emotional engine"

[0915] (Claim 1)

[0916] A means for inputting individual body shape and constitution information,

[0917] A means for processing the generated information by referring to a set of information on past athletes with similar characteristics using the input information,

[0918] A means for generating an exercise training procedure optimized for the user based on the processed information,

[0919] A means for displaying the generated exercise training procedures and related products to the user,

[0920] A means for analyzing the user's emotional state using sensors and microphones, and dynamically adjusting the exercise training procedure based on that mental state,

[0921] A means for collecting feedback after performing exercises based on the aforementioned training procedure and using it to improve the system,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, wherein the aforementioned body shape and constitution information includes height, weight, age, and past exercise experience.

[0925] (Claim 3)

[0926] The system according to claim 1, which provides a detailed explanation of the generated exercise training procedure using videos and images, and displays encouragement and warnings that take into account the user's emotional state. [Explanation of symbols]

[0927] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting individual body shape and constitution data, A means for referencing a database of athletes with similar characteristics in the past using the input data, and processing the generated data, A means for generating an exercise training menu optimized for the user based on the processed data, A means for displaying the generated exercise training menu and related products to the user, A means for collecting feedback after performing exercises based on the aforementioned training menu and using it to improve the system, A system that includes this.

2. The system according to claim 1, wherein the aforementioned body shape and constitution data includes height, weight, age, and exercise experience.

3. The system according to claim 1, which provides a detailed explanation of the generated exercise training menu using videos and images.

Images