system

Abstract

We provide the system. [Solution] A device for measuring the user's biometric data, A device that receives information transmitted from a device, A server equipped with a computing device that analyzes received information and generates a motion plan, A means of displaying the generated exercise plan to the user, A means of sending user feedback to a server for analysis, Means of providing individualized exercise guidance to residents in an urban environment, 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 persona chatbot control method 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional way of going to a fitness gym, there is a problem that it is difficult for an individual to continuously manage their health because of the large time and economic burden, and many people feel resistance to training in front of others. To solve this problem, there is a need for a system that can be easily started, provides an optimal training plan according to each individual's situation, and protects privacy.

Means for Solving the Problems

[0005] This invention provides an artificial intelligence system that offers personalized training plans by linking a device that measures a user's physical data with a terminal and server that receive and analyze the data. This allows users to receive personalized exercise guidance from the comfort of their homes, enabling continuous health management without time or location constraints. Furthermore, the server continuously improves the generated plan based on user feedback, ensuring continuous and efficient health management.

[0006] A "user" refers to an individual who uses the system to manage their own health and receive training plans.

[0007] "Physical data" refers to information that quantifies the user's physical condition, such as weight, body fat percentage, and skeletal muscle percentage.

[0008] "Device" refers to hardware capable of measuring a user's physical data and transmitting it in digital format, such as a smart body composition analyzer.

[0009] A "terminal" refers to a device that processes data received from another device and communicates with a server, such as a smartphone or tablet.

[0010] A "server" refers to a computer system that receives and analyzes data sent from a terminal, and is used to generate training plans using artificial intelligence.

[0011] "Artificial intelligence" refers to machine learning algorithms that analyze collected data and generate training plans optimized for the user.

[0012] A "training plan" refers to the instructional content, including the type of exercise, repetitions, sets, and rest times, which are suggested based on the user's physical data and feedback.

[0013] "Feedback" refers to the comments and information about the user's physical condition provided after training, which is data that helps in creating the next training plan. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention realizes a system that provides users with personalized training plans using their own physical data when managing their health. The embodiments of this invention are configured with the user, terminal, and server as the core functions.

[0036] First, the user uses a dedicated smart body composition analyzer to acquire physical data such as their weight, body fat percentage, and skeletal muscle percentage. This data is then transmitted to the user's device via Bluetooth or Wi-Fi.

[0037] The terminal temporarily stores the user's biometric data that has been transmitted, and then performs a procedure to send it to the server. During this process, the integrity of the data is verified, and protocols are applied to ensure secure transmission.

[0038] The server stores data received from the terminal in a database and utilizes an artificial intelligence model to analyze it. Specifically, the analysis algorithm generates a training plan optimized for the user's current situation based on their past data and goals. This plan includes specific details such as the type of exercise, repetitions, sets, and rest time.

[0039] The generated training plan is sent from the server to the device, which then presents it to the user. The training content is provided along with videos and step-by-step guides that the user can complete at home, allowing the user to train at their own pace.

[0040] After completing a training session, users input feedback via their device. This feedback includes the difficulty level of the training, the degree of success, and any changes in their physical condition. The device sends this feedback to a server, which further analyzes the data to inform future training plans.

[0041] This system allows users to receive personalized fitness services at home without feeling self-conscious about training in front of others. This offers the advantage of enabling sustainable health management, overcoming time and financial constraints.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user steps onto a smart body composition scale, which collects body data such as weight, body fat percentage, and skeletal muscle percentage. This data is transmitted from the smart body composition scale to the device via Bluetooth or Wi-Fi.

[0045] Step 2:

[0046] The terminal temporarily stores the received data and verifies its integrity. Once verification is complete, it prepares to send the data to the server.

[0047] Step 3:

[0048] The device sends data that it has verified as authentic to the server using security protocols such as encryption technology. If the transmission is successful, the device notifies the user.

[0049] Step 4:

[0050] The server receives data sent from the terminal and stores it in the database. During this process, the data's integrity and consistency are checked again.

[0051] Step 5:

[0052] The server analyzes the latest user data stored in the database and uses an artificial intelligence model to generate a training plan optimized for the user's current state.

[0053] Step 6:

[0054] The generated training plan includes detailed information such as the type of exercise, repetitions, sets, and rest times, which the server then sends to the terminal.

[0055] Step 7:

[0056] The device displays the training plan received from the server to the user. Based on this, the user can begin training at home.

[0057] Step 8:

[0058] After the user completes the training, they input feedback via their device. This feedback includes the difficulty level and achievement of the training, as well as any changes in their physical condition.

[0059] Step 9:

[0060] The device collects user feedback and sends it to the server. This data is used to improve the next training plan.

[0061] Step 10:

[0062] The server analyzes the feedback and uses artificial intelligence to prepare for further optimizing the next training plan. Progress reports are also sent to the user.

[0063] (Example 1)

[0064] 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."

[0065] Existing health management systems struggle to provide personalized training plans based on each user's individual physical characteristics. Furthermore, they lack mechanisms to efficiently utilize user feedback and incorporate it into future training plans. Security must also be ensured during data transmission and storage.

[0066] 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.

[0067] In this invention, the server includes means for verifying the validity and integrity of received information, means for transmitting information using a secure communication path, and means equipped with artificial intelligence for analyzing stored information and generating a training plan. This enables the provision of an optimal training plan based on each user's physical characteristics, as well as the effective collection and utilization of related feedback.

[0068] "User" refers to an individual who uses this system for health management.

[0069] "Physical characteristics" refer to data about the user's body, including information such as mass, fat mass, and muscle mass.

[0070] "Equipment" refers to a device used to measure a user's physical characteristics.

[0071] "Information" refers to data based on the user's physical characteristics obtained from the device.

[0072] A "terminal" refers to a device that can receive and temporarily store information.

[0073] "Communication path" refers to a data transmission method used to securely send information from a terminal to a server.

[0074] A "computational device" refers to a device equipped with artificial intelligence that analyzes information and generates training plans.

[0075] A "training plan" refers to a plan that includes the classification of exercises, the number of repetitions, sets, and rest periods provided, based on the user's physical characteristics and goals.

[0076] "Artificial intelligence" refers to the algorithms used to analyze a user's physical characteristics and create a training plan.

[0077] "Feedback" refers to information about users' reactions and experiences collected after training has been conducted.

[0078] In this invention, the user uses equipment to measure physical characteristics for health management. Specifically, the user uses a smart body composition analyzer or similar device to measure physical characteristics such as mass, fat mass, and muscle mass. This measurement data is transmitted to the user's terminal via communication technologies such as Bluetooth or Wi-Fi. The terminal temporarily stores this information and transmits the data to a server using a secure communication path.

[0079] The server stores the received information in a database and analyzes the data using a generative AI model. The artificial intelligence algorithm on the server generates a customized training plan based on the user's physical characteristics and goals. This training plan includes exercise classification, repetitions, sets, and rest time. The terminal presents this generated training plan to the user, displaying videos and step-by-step guides related to the training plan.

[0080] After completing a training session, users provide feedback through their device. This feedback includes information about the difficulty and success of the training, as well as any changes in their physical condition. The device sends this feedback to a server, which uses it to improve future training plans.

[0081] As a concrete example, consider a user aiming to improve their jogging ability. The server generates a training plan based on the user's current weight and muscle mass, consisting of three 5km runs per week. The user then jogs at home according to this plan and provides feedback on their training experience and results afterward.

[0082] An example of a prompt to input into a generating AI model might be: "Please create a jogging plan for a man in his 30s. His current weight is 70kg and his body fat percentage is 22%. His goal is to reduce his body fat percentage to 20%. The pace should be manageable at home." In this way, personalized health management becomes possible.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user measures their physical characteristics using a smart body composition scale. Specifically, the user steps onto the scale to begin measurement, recording mass, fat mass, and muscle mass. These measurement results are automatically transmitted to a device via Bluetooth or Wi-Fi. The input is the user's physical characteristics, and the output is the measurement data.

[0086] Step 2:

[0087] The device temporarily stores the received physical characteristics. Specifically, a health management app installed on a smartphone or tablet retrieves the information and verifies the validity and consistency of the data. The input is data from a smart body composition scale, and the output is data whose consistency has been verified.

[0088] Step 3:

[0089] The device sends verified data to the server. A secure communication path such as HTTPS is used for this process. Specifically, the health management app uploads data to the server and displays a "Data transmission complete" notification. The input is verified data, and the output is the data sent to the server.

[0090] Step 4:

[0091] The server stores the data sent from the terminal in a database and analyzes it using a generated AI model. Specifically, the AI ​​algorithm on the server creates a training plan based on the user's past data and goals. The input is the transmitted physical characteristics data, and the output is a personalized training plan.

[0092] Step 5:

[0093] The server sends the generated training plan to the terminal. The terminal proposes the plan to the user and presents detailed information about the training content (videos and step-by-step guides). The input is the training plan from the server, and the output is the visual training guide on the terminal.

[0094] Step 6:

[0095] After completing a training session, the user inputs feedback on their response to the training via their device. Specifically, they input their responses to options such as difficulty level and success rate in a feedback form within the app. The input represents the user's training experience, and the output is feedback data.

[0096] Step 7:

[0097] The terminal sends user feedback to the server. The server analyzes the feedback and uses it as data to inform the next training plan. The input is user feedback data, and the output is the analysis results used for the next plan.

[0098] (Application Example 1)

[0099] 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."

[0100] In modern urban life, managing residents' health is a crucial issue, but many people find it difficult to implement personalized fitness plans amidst their busy daily lives. Furthermore, reluctance to exercise in public spaces is considered another factor hindering health maintenance. To address these challenges, it is necessary to provide health management support tailored to each individual resident in a safe and accessible environment.

[0101] 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.

[0102] In this invention, the server includes means for analyzing received information and generating an exercise plan, means for displaying the generated exercise plan to the user, and means for providing residents with personalized exercise guidance in an urban environment. This allows residents to obtain individually optimized exercise plans at various locations within the city and manage their health at their own pace while ensuring their privacy.

[0103] "User biometric data" refers to physical information such as weight, body fat percentage, and muscle mass percentage, which is used to understand an individual's health status.

[0104] A "measuring device" is an instrument used to acquire biological data, and it generates highly accurate data using digital technology.

[0105] A "device" is an electronic device used to receive, process, and communicate measured biological data.

[0106] A "server equipped with a computing device" is a computer system that has the function of analyzing received data using an analysis algorithm and generating a motion plan.

[0107] An "exercise plan" is a detailed fitness plan optimized based on individual biometric data, including the type of exercise, frequency, number of repetitions, and rest intervals.

[0108] A "personalized exercise guide" is support information that suggests exercise methods and locations suitable for each user's individual characteristics and environment.

[0109] "Residents" are defined as individuals who reside in a specific city or region and utilize health management services.

[0110] To realize this invention, the user uses a digital measuring device called a smart body composition scale. This device measures the user's biometric data such as weight, body fat percentage, and muscle mass percentage, and transmits this data to the user's smartphone (terminal) via Bluetooth or Wi-Fi. The terminal plays the role of securely and consistently transferring the data to the server.

[0111] The server includes computing devices for processing biometric data received from terminals, and this includes artificial intelligence models for database management and analysis. These AI models are built using machine learning platforms such as TENSORFLOW® and generate personalized exercise plans based on the received data. These plans include exercise type, frequency, number of repetitions, and rest intervals.

[0112] The generated exercise plan is sent back to the device and notified to the user via an application. This application provides visual guides and hints to help the user naturally incorporate the planned exercises into their daily routine.

[0113] For example, resident A in a city uses the system to receive a plan that combines daily walking with light strength training twice a week. This provides resident A with support that allows them to easily follow the plan at home or in a nearby park.

[0114] Furthermore, after the exercise session, feedback is received from the user and this information is also sent to the server. The system on the server incorporates this feedback information into its analysis and uses it to optimize the next exercise plan.

[0115] Example of a prompt:

[0116] "Optimize the next training plan based on the user's age, weight, body fat percentage, and feedback."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The user steps onto a smart body composition scale. This scale measures the user's biometric data, such as weight, body fat percentage, and muscle mass percentage, and transmits this data to the device via Bluetooth. The measured values ​​are converted into digital signals and input into the device.

[0120] Step 2:

[0121] The device temporarily stores biometric data received from the smart body composition scale in a secure format. Here, the integrity and completeness of the data are verified. The data format is converted, for example, to JSON format, in preparation for transmission to the server.

[0122] Step 3:

[0123] The terminal sends verified data to the server via a securely established protocol. During this process, the data is encrypted, and a secure connection to the server is established.

[0124] Step 4:

[0125] The server stores biometric data received from the terminal in a database. The database management systems used for storage are MySQL® and PostgreSQL, which handle the storage of numerical data and historical data management. This data is then used as input to prepare for analysis.

[0126] Step 5:

[0127] An AI model on the server generates an exercise plan based on historical data stored in the database and new input data. A data analysis algorithm is executed using the TensorFlow library. This analysis optimizes the plan according to the user's characteristics, determining the type, frequency, and number of repetitions of exercises.

[0128] Step 6:

[0129] The generated exercise plan is sent back from the server to the device. The device receives this information and presents it visually to the user. The plan displayed in the application includes specific exercise guides and links to reference videos.

[0130] Step 7:

[0131] After a user completes an exercise session, they input feedback about the activity through their device. This feedback includes information such as the difficulty level of the training, their sense of accomplishment, and any changes in their physical condition.

[0132] Step 8:

[0133] The terminal sends the input feedback to the server. The server uses this feedback data for the next analysis and incorporates it as feedback into the process of generating a more accurate motion plan.

[0134] This series of steps allows users to perform optimized exercises at their own pace, enabling continuous health management.

[0135] 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.

[0136] This invention relates to an AI personal trainer system incorporating emotion recognition capabilities, which provides a personalized training plan and motivational messages based on the user's physical and emotional data. This enables the provision of an appropriate fitness experience tailored to the user's psychological state.

[0137] The user first measures their body data using a dedicated smart body composition scale, and this information is transmitted to a terminal. This system also collects data for emotion recognition (voice and facial expression data) when the user inputs feedback into the terminal. An emotion engine analyzes the voice and facial expressions to identify the user's current emotional state.

[0138] The server analyzes the received physical and emotional data and generates a training plan based on it. The artificial intelligence adjusts the plan according to the user's emotional state, changing the difficulty and volume of the training as needed. In addition, motivational messages generated by the emotion engine are provided to enhance the user's motivation.

[0139] The generated training plan and motivational messages are presented to the user via the device. For example, if the user is feeling stressed, the server will suggest relaxing stretching exercises, while if they are full of energy, it will recommend more active training. In addition, positive messages such as "Let's do our best today!" generated by the emotion engine will be displayed on the user's screen.

[0140] Thus, the present invention provides a system that enables more personalized health management by taking into account both the physical and emotional aspects of the user. Through this system, it is possible to support the user's continuous fitness activities and ultimately promote their health.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users use a smart body composition scale to measure physical data such as weight, body fat percentage, and skeletal muscle percentage. This data is transmitted to the user's device via Bluetooth or Wi-Fi.

[0144] Step 2:

[0145] The device temporarily stores the received physical data and verifies its integrity. Once this verification is complete, it prepares to send the data to the server.

[0146] Step 3:

[0147] The device encrypts the body data collected from the user and sends it to the server.

[0148] Step 4:

[0149] The server receives the physical data transmitted from the terminal and stores it in the database. At this time, the integrity of the data is re-verified.

[0150] Step 5:

[0151] Before the user begins training, the device acquires the user's voice data and facial image, and the emotion engine analyzes this data to identify the user's emotional state.

[0152] Step 6:

[0153] The server uses artificial intelligence to generate a training plan optimized for the user based on the received physical and emotional data. The plan is then adjusted based on the emotions identified by the emotion engine.

[0154] Step 7:

[0155] The generated training plan and emotion-based motivational messages are sent from the server to the terminal.

[0156] Step 8:

[0157] The device displays the training plan and motivational messages received from the server to the user. The information is presented in a format that the user can immediately understand and act upon.

[0158] Step 9:

[0159] The user performs exercises based on the training plan provided. After the training, the user enters feedback on their feelings and physical condition into the device.

[0160] Step 10:

[0161] The device collects user feedback and sends it back to the server. This data is used to adjust the next training plan.

[0162] Step 11:

[0163] The server analyzes the feedback, updates the artificial intelligence model as needed, and prepares to optimize the next training plan.

[0164] (Example 2)

[0165] 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".

[0166] In modern society, the importance of individual health management is increasing, but general fitness plans often fail to take into account individual physical characteristics and emotional states, resulting in ineffective training. Furthermore, they often lack adequate motivation to maintain user engagement. Therefore, there is a need for a system that considers the user's physical and emotional state and provides personalized training plans and support.

[0167] 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.

[0168] In this invention, the server includes means for analyzing physical and emotional data to generate training plans and motivational messages, means for adjusting exercise content according to the user's emotional state, and means for receiving and analyzing feedback through a terminal. This enables personalized training plans and motivation based on the user's physical and emotional state.

[0169] "Body data" refers to information related to a user's body composition, including physiological measurements such as weight, body fat percentage, and muscle mass.

[0170] "Emotional data" refers to information indicating a user's psychological state, obtained through voice and image analysis, and is data used to identify emotions such as stress, joy, and fatigue.

[0171] A "terminal" refers to a device that receives data from users, analyzes audio and images, and transmits that information to a server.

[0172] A "server" is a central computing device that performs calculations and analyses to generate training plans based on physical and emotional data received from terminals.

[0173] A "training plan" refers to an exercise instruction plan generated by the server based on the user's physical and emotional state, and includes the type of exercise, intensity, number of repetitions, number of sets, rest time, etc.

[0174] "Motivational messages" are encouraging and guiding statements generated in response to the user's emotional state, and include content designed to promote fitness activities.

[0175] "Feedback" refers to user responses and opinions regarding the training plan, which are sent to the server via the device and used to generate the next plan.

[0176] This invention relates to an AI personal trainer system that integrates emotion recognition capabilities and provides personalized fitness plans based on the user's physical and emotional state.

[0177] The user uses a dedicated smart body composition scale to measure physical data such as weight, body fat percentage, and muscle mass. This information is transmitted to the user's device via Bluetooth or Wi-Fi. The device receives this data and also collects audio and image data if the user provides feedback via voice and facial expressions through the device. Voice recognition software and image analysis software are used in this process.

[0178] Data transmitted from the device is received and analyzed by the server. The server uses the received physical and emotional data to generate a personalized training plan using a generative AI model. This plan includes the type, intensity, repetitions, sets, and rest periods of exercise designed to improve the user's health, and is adjusted according to the user's emotional state. For example, if the user is stressed, relaxing activities are suggested, while high-intensity training is recommended if the user is energetic.

[0179] Furthermore, the server uses an emotion engine to generate motivational messages to encourage the user's fitness activities. These messages are displayed on the user's screen and have a positive impact on their daily fitness activities.

[0180] For example, if a user dictates to their device, "I'm a little tired today," the server analyzes this data and suggests a training plan that includes "light stretching while taking deep breaths," along with an encouraging message such as, "Relax and take care of yourself."

[0181] An example of a prompt would be: "Please suggest a training plan for when the user is feeling stressed. Also, please suggest an appropriate motivational message to display in that situation."

[0182] Through this system, users can receive personalized health management that takes their physical and emotional state into account, enabling them to promote their health through continuous fitness activities.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] The user measures their body data using a dedicated smart body composition scale. This data includes weight, body fat percentage, muscle mass, etc., and is transmitted to the device via Bluetooth or Wi-Fi. The device receives this direct data input and outputs digital data of the user's current physical condition.

[0186] Step 2:

[0187] The user provides feedback through voice and facial expressions via the device. The device uses its built-in microphone and camera to acquire voice and video data, which are stored as input data for emotion analysis. From this input, voice recognition software analyzes the tone and content of the voice, and an image analysis algorithm detects changes in facial expressions. As output, data indicating the user's emotional state is generated.

[0188] Step 3:

[0189] The device sends the physical data obtained in Step 1 and the emotional data generated in Step 2 to the server. The server receives this data, stores it in a database, and uses it as input data for analysis. Based on the input, a field-based algorithm constructs the user's health profile. The output is an analyzable dataset.

[0190] Step 4:

[0191] The server uses the physical and emotional data obtained from the analysis to run a generative AI model. This model generates a training plan tailored to the user's individual state. For example, if the user's stress level is determined to be high, it can recommend exercises that promote relaxation. The output is a training plan optimized for the user.

[0192] Step 5:

[0193] The server also uses an emotion engine to generate motivational messages that match the user's emotional state. These messages are designed to encourage a positive outlook on today's training and are displayed on the user's screen. As output, an encouraging message is constructed for the user.

[0194] Step 6:

[0195] The device displays the training plan generated in step 4 and the motivational message created in step 5 to the user. The user then performs the training based on this guidance. The output is the notification of the plan and message to the user.

[0196] Step 7:

[0197] The user completes the training and then inputs the results and feedback back into the device. The device receives this feedback and sends it to the server for generating the next plan. Based on the input, the feedback data is saved as data to support new analyses.

[0198] (Application Example 2)

[0199] 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".

[0200] In fitness programs, it is essential to provide personalized training plans that take into account not only the physical characteristics of each user but also their emotional state. However, conventional systems rely solely on the user's physical data and lack the flexibility to respond to emotional fluctuations. This makes it difficult to maintain consistent motivation, often resulting in ineffective training.

[0201] 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.

[0202] In this invention, the server includes an information processing device equipped with an intelligent engine that analyzes received data and emotion recognition data and generates a fitness plan; means for providing the generated fitness plan and motivational messages to the user; means for collecting voice and facial expression data to analyze the user's emotional state; and means for adjusting the fitness plan based on the emotional state. This makes it possible to provide a more effective and sustainable fitness plan tailored to the individual user's physical characteristics and emotional state.

[0203] A "device for measuring user's physical data" is a device that measures information about the user's body, such as weight, muscle mass, and body fat percentage.

[0204] An "information processing device" is a computer system that analyzes received data and processes the necessary information.

[0205] An "intelligent engine" is a device that includes algorithms for analyzing data and generating plans using artificial intelligence.

[0206] "Means of providing motivational messages" refers to methods of conveying encouraging or supportive messages to users, either visually or audibly, to help them continue their training.

[0207] "Means for collecting voice and facial expression data to analyze emotional states" refers to a mechanism that uses microphones and cameras to acquire voice and video data in order to understand the user's emotions.

[0208] "Methods for adjusting fitness plans based on emotional state" refer to algorithms that dynamically change exercise content and volume in response to analyzed emotional data.

[0209] The system for implementing this invention generates and provides a fitness plan based on the user's physical and emotional data. First, the user measures their physical data using a smart body composition scale, and this data is transmitted to a terminal. The terminal uses a microphone and camera to collect the user's voice and facial expression data and analyzes their emotional state.

[0210] The server analyzes this data using an intelligent engine and generates a personalized fitness plan. This plan also includes motivational messages tailored to the user's current emotional state. For example, if the server determines that the user is tired, it will recommend appropriate relaxation exercises and send an encouraging message to the user's device. The user's device can display this information on the screen and provide audio notifications.

[0211] As a specific scenario, suppose a user measures their body composition with a smart body composition scale before starting a workout, and then says to the device, "I'm feeling down today." In response, the intelligent engine generates a message suggesting light exercise along with, "Let's focus on relaxation today."

[0212] Furthermore, using a generative AI model, the following example prompt is input: "If the user's emotion is recognized as 'fatigue,' please provide a training plan and message best suited to that emotion. Specifically, generate relaxing exercises and words of encouragement." In this way, the system provides services that correspond to the user's state.

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The user uses a smart body composition scale to measure their body data. Data such as the user's weight, body fat percentage, and muscle mass are measured. This data is wirelessly transmitted to the user's device.

[0216] Step 2:

[0217] The device receives physical data and transfers it to a cloud server. The input here is the user's physical data, and the output is the data transfer to the server. No data processing is performed.

[0218] Step 3:

[0219] The system collects the user's voice and facial expressions using the microphone and camera built into the device. The input is voice and facial expression data, and the output is sending this data to a server.

[0220] Step 4:

[0221] The server uses the received audio and facial expression data to activate an emotion recognition engine and analyze the user's emotional state. The input is audio and facial expression data, and the output is the result of the emotional state determination. Audio analysis algorithms and facial expression recognition algorithms are used for data processing.

[0222] Step 5:

[0223] The server integrates physical data with analyzed emotional states and uses an intelligent engine to generate an optimal fitness plan. The input is physical data and emotional states, and the output is a suitable fitness plan. Data processing includes comparison with existing fitness information and plan generation using a generative AI model.

[0224] Step 6:

[0225] The server generates motivational messages based on the user's emotional state. The input is the emotional state, and the output is the motivational message. Natural language processing is performed based on the prompt to generate messages such as "Let's relax today."

[0226] Step 7:

[0227] The generated fitness plan and motivational messages are sent from the server to the user's device. The input is the data on the server, and the output is the display on the device. The device provides these to the user through a screen or audio output device.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] [Second Embodiment]

[0232] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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".

[0244] This invention realizes a system that provides users with personalized training plans using their own physical data when managing their health. The embodiments of this invention are configured with the user, terminal, and server as the core functions.

[0245] First, the user uses a dedicated smart body composition analyzer to acquire physical data such as their weight, body fat percentage, and skeletal muscle percentage. This data is then transmitted to the user's device via Bluetooth or Wi-Fi.

[0246] The terminal temporarily stores the user's biometric data that has been transmitted, and then performs a procedure to send it to the server. During this process, the integrity of the data is verified, and protocols are applied to ensure secure transmission.

[0247] The server stores data received from the terminal in a database and utilizes an artificial intelligence model to analyze it. Specifically, the analysis algorithm generates a training plan optimized for the user's current situation based on their past data and goals. This plan includes specific details such as the type of exercise, repetitions, sets, and rest time.

[0248] The generated training plan is sent from the server to the device, which then presents it to the user. The training content is provided along with videos and step-by-step guides that the user can complete at home, allowing the user to train at their own pace.

[0249] After completing a training session, users input feedback via their device. This feedback includes the difficulty level of the training, the degree of success, and any changes in their physical condition. The device sends this feedback to a server, which further analyzes the data to inform future training plans.

[0250] This system allows users to receive personalized fitness services at home without feeling self-conscious about training in front of others. This offers the advantage of enabling sustainable health management, overcoming time and financial constraints.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The user steps onto a smart body composition scale, which collects body data such as weight, body fat percentage, and skeletal muscle percentage. This data is transmitted from the smart body composition scale to the device via Bluetooth or Wi-Fi.

[0254] Step 2:

[0255] The terminal temporarily stores the received data and verifies its integrity. Once verification is complete, it prepares to send the data to the server.

[0256] Step 3:

[0257] The device sends data that it has verified as authentic to the server using security protocols such as encryption technology. If the transmission is successful, the device notifies the user.

[0258] Step 4:

[0259] The server receives data sent from the terminal and stores it in the database. During this process, the data's integrity and consistency are checked again.

[0260] Step 5:

[0261] The server analyzes the latest user data stored in the database and uses an artificial intelligence model to generate a training plan optimized for the user's current state.

[0262] Step 6:

[0263] The generated training plan includes detailed information such as the type of exercise, repetitions, sets, and rest times, which the server then sends to the terminal.

[0264] Step 7:

[0265] The device displays the training plan received from the server to the user. Based on this, the user can begin training at home.

[0266] Step 8:

[0267] After the user completes the training, they input feedback via their device. This feedback includes the difficulty level and achievement of the training, as well as any changes in their physical condition.

[0268] Step 9:

[0269] The device collects user feedback and sends it to the server. This data is used to improve the next training plan.

[0270] Step 10:

[0271] The server analyzes the feedback and uses artificial intelligence to prepare for further optimizing the next training plan. Progress reports are also sent to the user.

[0272] (Example 1)

[0273] 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."

[0274] Existing health management systems struggle to provide personalized training plans based on each user's individual physical characteristics. Furthermore, they lack mechanisms to efficiently utilize user feedback and incorporate it into future training plans. Security must also be ensured during data transmission and storage.

[0275] 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.

[0276] In this invention, the server includes means for verifying the validity and consistency of the received information, means for transmitting the information using a secure communication path, and means equipped with artificial intelligence for analyzing the stored information and generating a training plan. This enables the provision of an optimal training plan based on the physical characteristics of each user and the effective collection and utilization of related feedback.

[0277] The "user" refers to an individual who conducts health management using this system.

[0278] The "physical characteristics" are data related to the user's body and include information such as mass, fat mass, muscle mass, etc.

[0279] The "device" refers to an apparatus for measuring the user's physical characteristics.

[0280] The "information" refers to data based on the user's physical characteristics obtained from the device.

[0281] The "terminal" refers to a device capable of receiving information and temporarily storing it.

[0282] The "communication path" refers to a data transmission method for securely transmitting information from the terminal to the server.

[0283] The "computing device" refers to a device equipped with artificial intelligence for analyzing information and generating a training plan.

[0284] The "training plan" refers to a plan including the classification, number of repetitions, number of sets, and rest time of the exercises provided based on the user's physical characteristics and goals.

[0285] The "artificial intelligence" refers to an algorithm used to analyze the user's physical characteristics and create a training plan.

[0286] The "feedback" refers to information regarding the reactions and experiences collected from the user after the training is implemented.

[0287] In this invention, the user uses a device for measuring physical characteristics for health management. Specifically, the user uses a smart body composition analyzer or the like to measure physical characteristics such as mass, fat mass, and muscle mass. These measurement data are transmitted to the user's terminal via communication technologies such as Bluetooth or Wi-Fi. The terminal temporarily stores this information and transmits the data to the server using a secure communication path.

[0288] The server stores the received information in a database and analyzes the data using a generated AI model. The artificial intelligence algorithm on the server generates a customized training plan based on the user's physical characteristics and goals. This training plan includes the classification, number of repetitions, number of sets, and rest time of the exercise. The terminal has the role of presenting this generated training plan to the user and displays a video or step-by-step guide related to the training plan together.

[0289] After the training is completed, the user provides feedback through the terminal. This feedback includes information on the difficulty and success of the training and changes in physical condition. The terminal transmits this feedback to the server, and the server uses the feedback to improve the training plan for subsequent sessions.

[0290] As a specific example, consider the case where the user aims to improve their jogging ability. The server generates a training plan of running 5 km three times a week based on the user's current weight and muscle mass. According to this plan, the user jogs at home and provides feedback on the feeling and results of the training after completion.

[0291] An example of a prompt to input into a generating AI model might be: "Please create a jogging plan for a man in his 30s. His current weight is 70kg and his body fat percentage is 22%. His goal is to reduce his body fat percentage to 20%. The pace should be manageable at home." In this way, personalized health management becomes possible.

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] The user measures their physical characteristics using a smart body composition scale. Specifically, the user steps onto the scale to begin measurement, recording mass, fat mass, and muscle mass. These measurement results are automatically transmitted to a device via Bluetooth or Wi-Fi. The input is the user's physical characteristics, and the output is the measurement data.

[0295] Step 2:

[0296] The device temporarily stores the received physical characteristics. Specifically, a health management app installed on a smartphone or tablet retrieves the information and verifies the validity and consistency of the data. The input is data from a smart body composition scale, and the output is data whose consistency has been verified.

[0297] Step 3:

[0298] The device sends verified data to the server. A secure communication path such as HTTPS is used for this process. Specifically, the health management app uploads data to the server and displays a "Data transmission complete" notification. The input is verified data, and the output is the data sent to the server.

[0299] Step 4:

[0300] The server stores the data sent from the terminal in the database and analyzes it using the generative AI model. Specifically, the AI algorithm on the server creates a training plan based on the user's past data and goals. The input is the sent physical characteristic data, and the output is the individualized training plan.

[0301] Step 5:

[0302] The server sends the generated training plan to the terminal. The terminal proposes the plan to the user and presents detailed information about the training content (videos or step-by-step guides). The input is the training plan from the server, and the output is the visual training guide on the terminal.

[0303] Step 6:

[0304] After the training, the user inputs the training reaction as feedback on the terminal. As a specific operation, the user inputs in the options for evaluating the difficulty level and success degree in the feedback form on the app. The input is the user's training experience, and the output is the feedback data.

[0305] Step 7:

[0306] The terminal sends the user's feedback to the server. The server analyzes the feedback and uses it as data to be reflected in the next training plan. The input is the feedback data from the user, and the output is the analysis result to be utilized in the next plan.

[0307] (Application Example 1)

[0308] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0309] In modern urban life, managing residents' health is a crucial issue, but many people find it difficult to implement personalized fitness plans amidst their busy daily lives. Furthermore, reluctance to exercise in public spaces is considered another factor hindering health maintenance. To address these challenges, it is necessary to provide health management support tailored to each individual resident in a safe and accessible environment.

[0310] 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.

[0311] In this invention, the server includes means for analyzing received information and generating an exercise plan, means for displaying the generated exercise plan to the user, and means for providing residents with personalized exercise guidance in an urban environment. This allows residents to obtain individually optimized exercise plans at various locations within the city and manage their health at their own pace while ensuring their privacy.

[0312] "User biometric data" refers to physical information such as weight, body fat percentage, and muscle mass percentage, which is used to understand an individual's health status.

[0313] A "measuring device" is an instrument used to acquire biological data, and it generates highly accurate data using digital technology.

[0314] A "device" is an electronic device used to receive, process, and communicate measured biological data.

[0315] A "server equipped with a computing device" is a computer system that has the function of analyzing received data using an analysis algorithm and generating a motion plan.

[0316] An "exercise plan" is a detailed fitness plan optimized based on individual biometric data, including the type of exercise, frequency, number of repetitions, and rest intervals.

[0317] A "personalized exercise guide" is support information that suggests exercise methods and locations suitable for each user's individual characteristics and environment.

[0318] "Residents" are defined as individuals who reside in a specific city or region and utilize health management services.

[0319] To realize this invention, the user uses a digital measuring device called a smart body composition scale. This device measures the user's biometric data such as weight, body fat percentage, and muscle mass percentage, and transmits this data to the user's smartphone (terminal) via Bluetooth or Wi-Fi. The terminal plays the role of securely and consistently transferring the data to the server.

[0320] The server contains computing power for processing biometric data received from terminals, including an artificial intelligence model for database management and analysis. This AI model is built using machine learning platforms such as TensorFlow and generates a personalized exercise plan based on the received data. This plan includes the type of exercise, frequency, number of repetitions, and rest intervals.

[0321] The generated exercise plan is sent back to the device and notified to the user via an application. This application provides visual guides and hints to help the user naturally incorporate the planned exercises into their daily routine.

[0322] For example, resident A in a city uses the system to receive a plan that combines daily walking with light strength training twice a week. This provides resident A with support that allows them to easily follow the plan at home or in a nearby park.

[0323] Furthermore, after the exercise session, feedback is received from the user and this information is also sent to the server. The system on the server incorporates this feedback information into its analysis and uses it to optimize the next exercise plan.

[0324] Example of a prompt:

[0325] "Optimize the next training plan based on the user's age, weight, body fat percentage, and feedback."

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] The user steps onto a smart body composition scale. This scale measures the user's biometric data, such as weight, body fat percentage, and muscle mass percentage, and transmits this data to the device via Bluetooth. The measured values ​​are converted into digital signals and input into the device.

[0329] Step 2:

[0330] The device temporarily stores biometric data received from the smart body composition scale in a secure format. Here, the integrity and completeness of the data are verified. The data format is converted, for example, to JSON format, in preparation for transmission to the server.

[0331] Step 3:

[0332] The terminal sends verified data to the server via a securely established protocol. During this process, the data is encrypted, and a secure connection to the server is established.

[0333] Step 4:

[0334] The server stores biometric data received from the terminal in a database. The database management system used for storage is MySQL or PostgreSQL, which handles the storage of numerical data and historical data management. This data is then used as input to prepare for analysis.

[0335] Step 5:

[0336] An AI model on the server generates an exercise plan based on historical data stored in the database and new input data. A data analysis algorithm is executed using the TensorFlow library. This analysis optimizes the plan according to the user's characteristics, determining the type, frequency, and number of repetitions of exercises.

[0337] Step 6:

[0338] The generated exercise plan is sent back from the server to the device. The device receives this information and presents it visually to the user. The plan displayed in the application includes specific exercise guides and links to reference videos.

[0339] Step 7:

[0340] After a user completes an exercise session, they input feedback about the activity through their device. This feedback includes information such as the difficulty level of the training, their sense of accomplishment, and any changes in their physical condition.

[0341] Step 8:

[0342] The terminal sends the input feedback to the server. The server uses this feedback data for the next analysis and incorporates it as feedback into the process of generating a more accurate motion plan.

[0343] This series of steps allows users to perform optimized exercises at their own pace, enabling continuous health management.

[0344] 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.

[0345] This invention relates to an AI personal trainer system incorporating emotion recognition capabilities, which provides a personalized training plan and motivational messages based on the user's physical and emotional data. This enables the provision of an appropriate fitness experience tailored to the user's psychological state.

[0346] The user first measures their body data using a dedicated smart body composition scale, and this information is transmitted to a terminal. This system also collects data for emotion recognition (voice and facial expression data) when the user inputs feedback into the terminal. An emotion engine analyzes the voice and facial expressions to identify the user's current emotional state.

[0347] The server analyzes the received physical and emotional data and generates a training plan based on it. The artificial intelligence adjusts the plan according to the user's emotional state, changing the difficulty and volume of the training as needed. In addition, motivational messages generated by the emotion engine are provided to enhance the user's motivation.

[0348] The generated training plan and motivational messages are presented to the user via the device. For example, if the user is feeling stressed, the server will suggest relaxing stretching exercises, while if they are full of energy, it will recommend more active training. In addition, positive messages such as "Let's do our best today!" generated by the emotion engine will be displayed on the user's screen.

[0349] Thus, the present invention provides a system that enables more personalized health management by taking into account both the physical and emotional aspects of the user. Through this system, it is possible to support the user's continuous fitness activities and ultimately promote their health.

[0350] The following describes the processing flow.

[0351] Step 1:

[0352] Users use a smart body composition scale to measure physical data such as weight, body fat percentage, and skeletal muscle percentage. This data is transmitted to the user's device via Bluetooth or Wi-Fi.

[0353] Step 2:

[0354] The device temporarily stores the received physical data and verifies its integrity. Once this verification is complete, it prepares to send the data to the server.

[0355] Step 3:

[0356] The device encrypts the body data collected from the user and sends it to the server.

[0357] Step 4:

[0358] The server receives the physical data transmitted from the terminal and stores it in the database. At this time, the integrity of the data is re-verified.

[0359] Step 5:

[0360] Before the user begins training, the device acquires the user's voice data and facial image, and the emotion engine analyzes this data to identify the user's emotional state.

[0361] Step 6:

[0362] The server uses artificial intelligence to generate a training plan optimized for the user based on the received physical and emotional data. The plan is then adjusted based on the emotions identified by the emotion engine.

[0363] Step 7:

[0364] The generated training plan and emotion-based motivational messages are sent from the server to the terminal.

[0365] Step 8:

[0366] The device displays the training plan and motivational messages received from the server to the user. The information is presented in a format that the user can immediately understand and act upon.

[0367] Step 9:

[0368] The user performs exercises based on the training plan provided. After the training, the user enters feedback on their feelings and physical condition into the device.

[0369] Step 10:

[0370] The device collects user feedback and sends it back to the server. This data is used to adjust the next training plan.

[0371] Step 11:

[0372] The server analyzes the feedback, updates the artificial intelligence model as needed, and prepares to optimize the next training plan.

[0373] (Example 2)

[0374] 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".

[0375] In modern society, the importance of individual health management is increasing, but general fitness plans often fail to take into account individual physical characteristics and emotional states, resulting in ineffective training. Furthermore, they often lack adequate motivation to maintain user engagement. Therefore, there is a need for a system that considers the user's physical and emotional state and provides personalized training plans and support.

[0376] 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.

[0377] In this invention, the server includes means for analyzing physical and emotional data to generate training plans and motivational messages, means for adjusting exercise content according to the user's emotional state, and means for receiving and analyzing feedback through a terminal. This enables personalized training plans and motivation based on the user's physical and emotional state.

[0378] "Body data" refers to information related to a user's body composition, including physiological measurements such as weight, body fat percentage, and muscle mass.

[0379] "Emotional data" refers to information indicating a user's psychological state, obtained through voice and image analysis, and is data used to identify emotions such as stress, joy, and fatigue.

[0380] A "terminal" refers to a device that receives data from users, analyzes audio and images, and transmits that information to a server.

[0381] A "server" is a central computing device that performs calculations and analyses to generate training plans based on physical and emotional data received from terminals.

[0382] A "training plan" refers to an exercise instruction plan generated by the server based on the user's physical and emotional state, and includes the type of exercise, intensity, number of repetitions, number of sets, rest time, etc.

[0383] "Motivational messages" are encouraging and guiding statements generated in response to the user's emotional state, and include content designed to promote fitness activities.

[0384] "Feedback" refers to user responses and opinions regarding the training plan, which are sent to the server via the device and used to generate the next plan.

[0385] This invention relates to an AI personal trainer system that integrates emotion recognition capabilities and provides personalized fitness plans based on the user's physical and emotional state.

[0386] The user uses a dedicated smart body composition scale to measure physical data such as weight, body fat percentage, and muscle mass. This information is transmitted to the user's device via Bluetooth or Wi-Fi. The device receives this data and also collects audio and image data if the user provides feedback via voice and facial expressions through the device. Voice recognition software and image analysis software are used in this process.

[0387] Data transmitted from the device is received and analyzed by the server. The server uses the received physical and emotional data to generate a personalized training plan using a generative AI model. This plan includes the type, intensity, repetitions, sets, and rest periods of exercise designed to improve the user's health, and is adjusted according to the user's emotional state. For example, if the user is stressed, relaxing activities are suggested, while high-intensity training is recommended if the user is energetic.

[0388] Furthermore, the server uses an emotion engine to generate motivational messages to encourage the user's fitness activities. These messages are displayed on the user's screen and have a positive impact on their daily fitness activities.

[0389] For example, if a user dictates to their device, "I'm a little tired today," the server analyzes this data and suggests a training plan that includes "light stretching while taking deep breaths," along with an encouraging message such as, "Relax and take care of yourself."

[0390] An example of a prompt would be: "Please suggest a training plan for when the user is feeling stressed. Also, please suggest an appropriate motivational message to display in that situation."

[0391] Through this system, users can receive personalized health management that takes their physical and emotional state into account, enabling them to promote their health through continuous fitness activities.

[0392] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0393] Step 1:

[0394] The user measures their body data using a dedicated smart body composition scale. This data includes weight, body fat percentage, muscle mass, etc., and is transmitted to the device via Bluetooth or Wi-Fi. The device receives this direct data input and outputs digital data of the user's current physical condition.

[0395] Step 2:

[0396] The user provides feedback through voice and facial expressions via the device. The device uses its built-in microphone and camera to acquire voice and video data, which are stored as input data for emotion analysis. From this input, voice recognition software analyzes the tone and content of the voice, and an image analysis algorithm detects changes in facial expressions. As output, data indicating the user's emotional state is generated.

[0397] Step 3:

[0398] The device sends the physical data obtained in Step 1 and the emotional data generated in Step 2 to the server. The server receives this data, stores it in a database, and uses it as input data for analysis. Based on the input, a field-based algorithm constructs the user's health profile. The output is an analyzable dataset.

[0399] Step 4:

[0400] The server uses the physical and emotional data obtained from the analysis to run a generative AI model. This model generates a training plan tailored to the user's individual state. For example, if the user's stress level is determined to be high, it can recommend exercises that promote relaxation. The output is a training plan optimized for the user.

[0401] Step 5:

[0402] The server also uses an emotion engine to generate motivational messages that match the user's emotional state. These messages are designed to encourage a positive outlook on today's training and are displayed on the user's screen. As output, an encouraging message is constructed for the user.

[0403] Step 6:

[0404] The device displays the training plan generated in step 4 and the motivational message created in step 5 to the user. The user then performs the training based on this guidance. The output is the notification of the plan and message to the user.

[0405] Step 7:

[0406] The user completes the training and then inputs the results and feedback back into the device. The device receives this feedback and sends it to the server for generating the next plan. Based on the input, the feedback data is saved as data to support new analyses.

[0407] (Application Example 2)

[0408] 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".

[0409] In fitness programs, it is essential to provide personalized training plans that take into account not only the physical characteristics of each user but also their emotional state. However, conventional systems rely solely on the user's physical data and lack the flexibility to respond to emotional fluctuations. This makes it difficult to maintain consistent motivation, often resulting in ineffective training.

[0410] 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.

[0411] In this invention, the server includes an information processing device equipped with an intelligent engine that analyzes received data and emotion recognition data and generates a fitness plan; means for providing the generated fitness plan and motivational messages to the user; means for collecting voice and facial expression data to analyze the user's emotional state; and means for adjusting the fitness plan based on the emotional state. This makes it possible to provide a more effective and sustainable fitness plan tailored to the individual user's physical characteristics and emotional state.

[0412] A "device for measuring user's physical data" is a device that measures information about the user's body, such as weight, muscle mass, and body fat percentage.

[0413] An "information processing device" is a computer system that analyzes received data and processes the necessary information.

[0414] An "intelligent engine" is a device that includes algorithms for analyzing data and generating plans using artificial intelligence.

[0415] "Means of providing motivational messages" refers to methods of conveying encouraging or supportive messages to users, either visually or audibly, to help them continue their training.

[0416] "Means for collecting voice and facial expression data to analyze emotional states" refers to a mechanism that uses microphones and cameras to acquire voice and video data in order to understand the user's emotions.

[0417] "Methods for adjusting fitness plans based on emotional state" refer to algorithms that dynamically change exercise content and volume in response to analyzed emotional data.

[0418] The system for implementing this invention generates and provides a fitness plan based on the user's physical and emotional data. First, the user measures their physical data using a smart body composition scale, and this data is transmitted to a terminal. The terminal uses a microphone and camera to collect the user's voice and facial expression data and analyzes their emotional state.

[0419] The server analyzes this data using an intelligent engine and generates a personalized fitness plan. This plan also includes motivational messages tailored to the user's current emotional state. For example, if the server determines that the user is tired, it will recommend appropriate relaxation exercises and send an encouraging message to the user's device. The user's device can display this information on the screen and provide audio notifications.

[0420] As a specific scenario, suppose a user measures their body composition with a smart body composition scale before starting a workout, and then says to the device, "I'm feeling down today." In response, the intelligent engine generates a message suggesting light exercise along with, "Let's focus on relaxation today."

[0421] Furthermore, using a generative AI model, the following example prompt is input: "If the user's emotion is recognized as 'fatigue,' please provide a training plan and message best suited to that emotion. Specifically, generate relaxing exercises and words of encouragement." In this way, the system provides services that correspond to the user's state.

[0422] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0423] Step 1:

[0424] The user uses a smart body composition scale to measure their body data. Data such as the user's weight, body fat percentage, and muscle mass are measured. This data is wirelessly transmitted to the user's device.

[0425] Step 2:

[0426] The device receives physical data and transfers it to a cloud server. The input here is the user's physical data, and the output is the data transfer to the server. No data processing is performed.

[0427] Step 3:

[0428] The system collects the user's voice and facial expressions using the microphone and camera built into the device. The input is voice and facial expression data, and the output is sending this data to a server.

[0429] Step 4:

[0430] The server uses the received audio and facial expression data to activate an emotion recognition engine and analyze the user's emotional state. The input is audio and facial expression data, and the output is the result of the emotional state determination. Audio analysis algorithms and facial expression recognition algorithms are used for data processing.

[0431] Step 5:

[0432] The server integrates physical data with analyzed emotional states and uses an intelligent engine to generate an optimal fitness plan. The input is physical data and emotional states, and the output is a suitable fitness plan. Data processing includes comparison with existing fitness information and plan generation using a generative AI model.

[0433] Step 6:

[0434] The server generates motivational messages based on the user's emotional state. The input is the emotional state, and the output is the motivational message. Natural language processing is performed based on the prompt to generate messages such as "Let's relax today."

[0435] Step 7:

[0436] The generated fitness plan and motivational messages are sent from the server to the user's device. The input is the data on the server, and the output is the display on the device. The device provides these to the user through a screen or audio output device.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] [Third Embodiment]

[0441] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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".

[0453] This invention realizes a system that provides users with personalized training plans using their own physical data when managing their health. The embodiments of this invention are configured with the user, terminal, and server as the core functions.

[0454] First, the user uses a dedicated smart body composition analyzer to acquire physical data such as their weight, body fat percentage, and skeletal muscle percentage. This data is then transmitted to the user's device via Bluetooth or Wi-Fi.

[0455] The terminal temporarily stores the user's biometric data that has been transmitted, and then performs a procedure to send it to the server. During this process, the integrity of the data is verified, and protocols are applied to ensure secure transmission.

[0456] The server stores data received from the terminal in a database and utilizes an artificial intelligence model to analyze it. Specifically, the analysis algorithm generates a training plan optimized for the user's current situation based on their past data and goals. This plan includes specific details such as the type of exercise, repetitions, sets, and rest time.

[0457] The generated training plan is sent from the server to the device, which then presents it to the user. The training content is provided along with videos and step-by-step guides that the user can complete at home, allowing the user to train at their own pace.

[0458] After completing a training session, users input feedback via their device. This feedback includes the difficulty level of the training, the degree of success, and any changes in their physical condition. The device sends this feedback to a server, which further analyzes the data to inform future training plans.

[0459] This system allows users to receive personalized fitness services at home without feeling self-conscious about training in front of others. This offers the advantage of enabling sustainable health management, overcoming time and financial constraints.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] The user steps onto a smart body composition scale, which collects body data such as weight, body fat percentage, and skeletal muscle percentage. This data is transmitted from the smart body composition scale to the device via Bluetooth or Wi-Fi.

[0463] Step 2:

[0464] The terminal temporarily stores the received data and verifies its integrity. Once verification is complete, it prepares to send the data to the server.

[0465] Step 3:

[0466] The device sends data that it has verified as authentic to the server using security protocols such as encryption technology. If the transmission is successful, the device notifies the user.

[0467] Step 4:

[0468] The server receives data sent from the terminal and stores it in the database. During this process, the data's integrity and consistency are checked again.

[0469] Step 5:

[0470] The server analyzes the latest user data stored in the database and uses an artificial intelligence model to generate a training plan optimized for the user's current state.

[0471] Step 6:

[0472] The generated training plan includes detailed information such as the type of exercise, repetitions, sets, and rest times, which the server then sends to the terminal.

[0473] Step 7:

[0474] The device displays the training plan received from the server to the user. Based on this, the user can begin training at home.

[0475] Step 8:

[0476] After the user completes the training, they input feedback via their device. This feedback includes the difficulty level and achievement of the training, as well as any changes in their physical condition.

[0477] Step 9:

[0478] The device collects user feedback and sends it to the server. This data is used to improve the next training plan.

[0479] Step 10:

[0480] The server analyzes the feedback and uses artificial intelligence to prepare for further optimizing the next training plan. Progress reports are also sent to the user.

[0481] (Example 1)

[0482] 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."

[0483] Existing health management systems struggle to provide personalized training plans based on each user's individual physical characteristics. Furthermore, they lack mechanisms to efficiently utilize user feedback and incorporate it into future training plans. Security must also be ensured during data transmission and storage.

[0484] 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.

[0485] In this invention, the server includes means for verifying the validity and integrity of received information, means for transmitting information using a secure communication path, and means equipped with artificial intelligence for analyzing stored information and generating a training plan. This enables the provision of an optimal training plan based on each user's physical characteristics, as well as the effective collection and utilization of related feedback.

[0486] "User" refers to an individual who uses this system for health management.

[0487] "Physical characteristics" refer to data about the user's body, including information such as mass, fat mass, and muscle mass.

[0488] "Equipment" refers to a device used to measure a user's physical characteristics.

[0489] "Information" refers to data based on the user's physical characteristics obtained from the device.

[0490] A "terminal" refers to a device that can receive and temporarily store information.

[0491] "Communication path" refers to a data transmission method used to securely send information from a terminal to a server.

[0492] A "computational device" refers to a device equipped with artificial intelligence that analyzes information and generates training plans.

[0493] A "training plan" refers to a plan that includes the classification of exercises, the number of repetitions, sets, and rest periods provided, based on the user's physical characteristics and goals.

[0494] "Artificial intelligence" refers to the algorithms used to analyze a user's physical characteristics and create a training plan.

[0495] "Feedback" refers to information about users' reactions and experiences collected after training has been conducted.

[0496] In this invention, the user uses equipment to measure physical characteristics for health management. Specifically, the user uses a smart body composition analyzer or similar device to measure physical characteristics such as mass, fat mass, and muscle mass. This measurement data is transmitted to the user's terminal via communication technologies such as Bluetooth or Wi-Fi. The terminal temporarily stores this information and transmits the data to a server using a secure communication path.

[0497] The server stores the received information in a database and analyzes the data using a generative AI model. The artificial intelligence algorithm on the server generates a customized training plan based on the user's physical characteristics and goals. This training plan includes exercise classification, repetitions, sets, and rest time. The terminal presents this generated training plan to the user, displaying videos and step-by-step guides related to the training plan.

[0498] After completing a training session, users provide feedback through their device. This feedback includes information about the difficulty and success of the training, as well as any changes in their physical condition. The device sends this feedback to a server, which uses it to improve future training plans.

[0499] As a concrete example, consider a user aiming to improve their jogging ability. The server generates a training plan based on the user's current weight and muscle mass, consisting of three 5km runs per week. The user then jogs at home according to this plan and provides feedback on their training experience and results afterward.

[0500] An example of a prompt to input into a generating AI model might be: "Please create a jogging plan for a man in his 30s. His current weight is 70kg and his body fat percentage is 22%. His goal is to reduce his body fat percentage to 20%. The pace should be manageable at home." In this way, personalized health management becomes possible.

[0501] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0502] Step 1:

[0503] The user measures their physical characteristics using a smart body composition scale. Specifically, the user steps onto the scale to begin measurement, recording mass, fat mass, and muscle mass. These measurement results are automatically transmitted to a device via Bluetooth or Wi-Fi. The input is the user's physical characteristics, and the output is the measurement data.

[0504] Step 2:

[0505] The device temporarily stores the received physical characteristics. Specifically, a health management app installed on a smartphone or tablet retrieves the information and verifies the validity and consistency of the data. The input is data from a smart body composition scale, and the output is data whose consistency has been verified.

[0506] Step 3:

[0507] The device sends verified data to the server. A secure communication path such as HTTPS is used for this process. Specifically, the health management app uploads data to the server and displays a "Data transmission complete" notification. The input is verified data, and the output is the data sent to the server.

[0508] Step 4:

[0509] The server stores the data sent from the terminal in a database and analyzes it using a generated AI model. Specifically, the AI ​​algorithm on the server creates a training plan based on the user's past data and goals. The input is the transmitted physical characteristics data, and the output is a personalized training plan.

[0510] Step 5:

[0511] The server sends the generated training plan to the terminal. The terminal proposes the plan to the user and presents detailed information about the training content (videos and step-by-step guides). The input is the training plan from the server, and the output is the visual training guide on the terminal.

[0512] Step 6:

[0513] After completing a training session, the user inputs feedback on their response to the training via their device. Specifically, they input their responses to options such as difficulty level and success rate in a feedback form within the app. The input represents the user's training experience, and the output is feedback data.

[0514] Step 7:

[0515] The terminal sends user feedback to the server. The server analyzes the feedback and uses it as data to inform the next training plan. The input is user feedback data, and the output is the analysis results used for the next plan.

[0516] (Application Example 1)

[0517] 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."

[0518] In modern urban life, managing residents' health is a crucial issue, but many people find it difficult to implement personalized fitness plans amidst their busy daily lives. Furthermore, reluctance to exercise in public spaces is considered another factor hindering health maintenance. To address these challenges, it is necessary to provide health management support tailored to each individual resident in a safe and accessible environment.

[0519] 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.

[0520] In this invention, the server includes means for analyzing received information and generating an exercise plan, means for displaying the generated exercise plan to the user, and means for providing residents with personalized exercise guidance in an urban environment. This allows residents to obtain individually optimized exercise plans at various locations within the city and manage their health at their own pace while ensuring their privacy.

[0521] "User biometric data" refers to physical information such as weight, body fat percentage, and muscle mass percentage, which is used to understand an individual's health status.

[0522] A "measuring device" is an instrument used to acquire biological data, and it generates highly accurate data using digital technology.

[0523] A "device" is an electronic device used to receive, process, and communicate measured biological data.

[0524] A "server equipped with a computing device" is a computer system that has the function of analyzing received data using an analysis algorithm and generating a motion plan.

[0525] An "exercise plan" is a detailed fitness plan optimized based on individual biometric data, including the type of exercise, frequency, number of repetitions, and rest intervals.

[0526] A "personalized exercise guide" is support information that suggests exercise methods and locations suitable for each user's individual characteristics and environment.

[0527] "Residents" are defined as individuals who reside in a specific city or region and utilize health management services.

[0528] To realize this invention, the user uses a digital measuring device called a smart body composition scale. This device measures the user's biometric data such as weight, body fat percentage, and muscle mass percentage, and transmits this data to the user's smartphone (terminal) via Bluetooth or Wi-Fi. The terminal plays the role of securely and consistently transferring the data to the server.

[0529] The server contains computing power for processing biometric data received from terminals, including an artificial intelligence model for database management and analysis. This AI model is built using machine learning platforms such as TensorFlow and generates a personalized exercise plan based on the received data. This plan includes the type of exercise, frequency, number of repetitions, and rest intervals.

[0530] The generated exercise plan is sent back to the device and notified to the user via an application. This application provides visual guides and hints to help the user naturally incorporate the planned exercises into their daily routine.

[0531] For example, resident A in a city uses the system to receive a plan that combines daily walking with light strength training twice a week. This provides resident A with support that allows them to easily follow the plan at home or in a nearby park.

[0532] Furthermore, after the exercise session, feedback is received from the user and this information is also sent to the server. The system on the server incorporates this feedback information into its analysis and uses it to optimize the next exercise plan.

[0533] Example of a prompt:

[0534] "Optimize the next training plan based on the user's age, weight, body fat percentage, and feedback."

[0535] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0536] Step 1:

[0537] The user steps onto a smart body composition scale. This scale measures the user's biometric data, such as weight, body fat percentage, and muscle mass percentage, and transmits this data to the device via Bluetooth. The measured values ​​are converted into digital signals and input into the device.

[0538] Step 2:

[0539] The device temporarily stores biometric data received from the smart body composition scale in a secure format. Here, the integrity and completeness of the data are verified. The data format is converted, for example, to JSON format, in preparation for transmission to the server.

[0540] Step 3:

[0541] The terminal sends verified data to the server via a securely established protocol. During this process, the data is encrypted, and a secure connection to the server is established.

[0542] Step 4:

[0543] The server stores biometric data received from the terminal in a database. The database management system used for storage is MySQL or PostgreSQL, which handles the storage of numerical data and historical data management. This data is then used as input to prepare for analysis.

[0544] Step 5:

[0545] An AI model on the server generates an exercise plan based on historical data stored in the database and new input data. A data analysis algorithm is executed using the TensorFlow library. This analysis optimizes the plan according to the user's characteristics, determining the type, frequency, and number of repetitions of exercises.

[0546] Step 6:

[0547] The generated exercise plan is sent back from the server to the device. The device receives this information and presents it visually to the user. The plan displayed in the application includes specific exercise guides and links to reference videos.

[0548] Step 7:

[0549] After a user completes an exercise session, they input feedback about the activity through their device. This feedback includes information such as the difficulty level of the training, their sense of accomplishment, and any changes in their physical condition.

[0550] Step 8:

[0551] The terminal sends the input feedback to the server. The server uses this feedback data for the next analysis and incorporates it as feedback into the process of generating a more accurate motion plan.

[0552] This series of steps allows users to perform optimized exercises at their own pace, enabling continuous health management.

[0553] 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.

[0554] This invention relates to an AI personal trainer system incorporating emotion recognition capabilities, which provides a personalized training plan and motivational messages based on the user's physical and emotional data. This enables the provision of an appropriate fitness experience tailored to the user's psychological state.

[0555] The user first measures their body data using a dedicated smart body composition scale, and this information is transmitted to a terminal. This system also collects data for emotion recognition (voice and facial expression data) when the user inputs feedback into the terminal. An emotion engine analyzes the voice and facial expressions to identify the user's current emotional state.

[0556] The server analyzes the received physical and emotional data and generates a training plan based on it. The artificial intelligence adjusts the plan according to the user's emotional state, changing the difficulty and volume of the training as needed. In addition, motivational messages generated by the emotion engine are provided to enhance the user's motivation.

[0557] The generated training plan and motivational messages are presented to the user via the device. For example, if the user is feeling stressed, the server will suggest relaxing stretching exercises, while if they are full of energy, it will recommend more active training. In addition, positive messages such as "Let's do our best today!" generated by the emotion engine will be displayed on the user's screen.

[0558] Thus, the present invention provides a system that enables more personalized health management by taking into account both the physical and emotional aspects of the user. Through this system, it is possible to support the user's continuous fitness activities and ultimately promote their health.

[0559] The following describes the processing flow.

[0560] Step 1:

[0561] Users use a smart body composition scale to measure physical data such as weight, body fat percentage, and skeletal muscle percentage. This data is transmitted to the user's device via Bluetooth or Wi-Fi.

[0562] Step 2:

[0563] The device temporarily stores the received physical data and verifies its integrity. Once this verification is complete, it prepares to send the data to the server.

[0564] Step 3:

[0565] The device encrypts the body data collected from the user and sends it to the server.

[0566] Step 4:

[0567] The server receives the physical data transmitted from the terminal and stores it in the database. At this time, the integrity of the data is re-verified.

[0568] Step 5:

[0569] Before the user begins training, the device acquires the user's voice data and facial image, and the emotion engine analyzes this data to identify the user's emotional state.

[0570] Step 6:

[0571] The server uses artificial intelligence to generate a training plan optimized for the user based on the received physical and emotional data. The plan is then adjusted based on the emotions identified by the emotion engine.

[0572] Step 7:

[0573] The generated training plan and emotion-based motivational messages are sent from the server to the terminal.

[0574] Step 8:

[0575] The device displays the training plan and motivational messages received from the server to the user. The information is presented in a format that the user can immediately understand and act upon.

[0576] Step 9:

[0577] The user performs exercises based on the training plan provided. After the training, the user enters feedback on their feelings and physical condition into the device.

[0578] Step 10:

[0579] The device collects user feedback and sends it back to the server. This data is used to adjust the next training plan.

[0580] Step 11:

[0581] The server analyzes the feedback, updates the artificial intelligence model as needed, and prepares to optimize the next training plan.

[0582] (Example 2)

[0583] 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."

[0584] In modern society, the importance of individual health management is increasing, but general fitness plans often fail to take into account individual physical characteristics and emotional states, resulting in ineffective training. Furthermore, they often lack adequate motivation to maintain user engagement. Therefore, there is a need for a system that considers the user's physical and emotional state and provides personalized training plans and support.

[0585] 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.

[0586] In this invention, the server includes means for analyzing physical and emotional data to generate training plans and motivational messages, means for adjusting exercise content according to the user's emotional state, and means for receiving and analyzing feedback through a terminal. This enables personalized training plans and motivation based on the user's physical and emotional state.

[0587] "Body data" refers to information related to a user's body composition, including physiological measurements such as weight, body fat percentage, and muscle mass.

[0588] "Emotional data" refers to information indicating a user's psychological state, obtained through voice and image analysis, and is data used to identify emotions such as stress, joy, and fatigue.

[0589] A "terminal" refers to a device that receives data from users, analyzes audio and images, and transmits that information to a server.

[0590] A "server" is a central computing device that performs calculations and analyses to generate training plans based on physical and emotional data received from terminals.

[0591] A "training plan" refers to an exercise instruction plan generated by the server based on the user's physical and emotional state, and includes the type of exercise, intensity, number of repetitions, number of sets, rest time, etc.

[0592] "Motivational messages" are encouraging and guiding statements generated in response to the user's emotional state, and include content designed to promote fitness activities.

[0593] "Feedback" refers to user responses and opinions regarding the training plan, which are sent to the server via the device and used to generate the next plan.

[0594] This invention relates to an AI personal trainer system that integrates emotion recognition capabilities and provides personalized fitness plans based on the user's physical and emotional state.

[0595] The user uses a dedicated smart body composition scale to measure physical data such as weight, body fat percentage, and muscle mass. This information is transmitted to the user's device via Bluetooth or Wi-Fi. The device receives this data and also collects audio and image data if the user provides feedback via voice and facial expressions through the device. Voice recognition software and image analysis software are used in this process.

[0596] Data transmitted from the device is received and analyzed by the server. The server uses the received physical and emotional data to generate a personalized training plan using a generative AI model. This plan includes the type, intensity, repetitions, sets, and rest periods of exercise designed to improve the user's health, and is adjusted according to the user's emotional state. For example, if the user is stressed, relaxing activities are suggested, while high-intensity training is recommended if the user is energetic.

[0597] Furthermore, the server uses an emotion engine to generate motivational messages to encourage the user's fitness activities. These messages are displayed on the user's screen and have a positive impact on their daily fitness activities.

[0598] For example, if a user dictates to their device, "I'm a little tired today," the server analyzes this data and suggests a training plan that includes "light stretching while taking deep breaths," along with an encouraging message such as, "Relax and take care of yourself."

[0599] An example of a prompt would be: "Please suggest a training plan for when the user is feeling stressed. Also, please suggest an appropriate motivational message to display in that situation."

[0600] Through this system, users can receive personalized health management that takes their physical and emotional state into account, enabling them to promote their health through continuous fitness activities.

[0601] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0602] Step 1:

[0603] The user measures their body data using a dedicated smart body composition scale. This data includes weight, body fat percentage, muscle mass, etc., and is transmitted to the device via Bluetooth or Wi-Fi. The device receives this direct data input and outputs digital data of the user's current physical condition.

[0604] Step 2:

[0605] The user provides feedback through voice and facial expressions via the device. The device uses its built-in microphone and camera to acquire voice and video data, which are stored as input data for emotion analysis. From this input, voice recognition software analyzes the tone and content of the voice, and an image analysis algorithm detects changes in facial expressions. As output, data indicating the user's emotional state is generated.

[0606] Step 3:

[0607] The device sends the physical data obtained in Step 1 and the emotional data generated in Step 2 to the server. The server receives this data, stores it in a database, and uses it as input data for analysis. Based on the input, a field-based algorithm constructs the user's health profile. The output is an analyzable dataset.

[0608] Step 4:

[0609] The server uses the physical and emotional data obtained from the analysis to run a generative AI model. This model generates a training plan tailored to the user's individual state. For example, if the user's stress level is determined to be high, it can recommend exercises that promote relaxation. The output is a training plan optimized for the user.

[0610] Step 5:

[0611] The server also uses an emotion engine to generate motivational messages that match the user's emotional state. These messages are designed to encourage a positive outlook on today's training and are displayed on the user's screen. As output, an encouraging message is constructed for the user.

[0612] Step 6:

[0613] The device displays the training plan generated in step 4 and the motivational message created in step 5 to the user. The user then performs the training based on this guidance. The output is the notification of the plan and message to the user.

[0614] Step 7:

[0615] The user completes the training and then inputs the results and feedback back into the device. The device receives this feedback and sends it to the server for generating the next plan. Based on the input, the feedback data is saved as data to support new analyses.

[0616] (Application Example 2)

[0617] 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."

[0618] In fitness programs, it is essential to provide personalized training plans that take into account not only the physical characteristics of each user but also their emotional state. However, conventional systems rely solely on the user's physical data and lack the flexibility to respond to emotional fluctuations. This makes it difficult to maintain consistent motivation, often resulting in ineffective training.

[0619] 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.

[0620] In this invention, the server includes an information processing device equipped with an intelligent engine that analyzes received data and emotion recognition data and generates a fitness plan; means for providing the generated fitness plan and motivational messages to the user; means for collecting voice and facial expression data to analyze the user's emotional state; and means for adjusting the fitness plan based on the emotional state. This makes it possible to provide a more effective and sustainable fitness plan tailored to the individual user's physical characteristics and emotional state.

[0621] A "device for measuring user's physical data" is a device that measures information about the user's body, such as weight, muscle mass, and body fat percentage.

[0622] An "information processing device" is a computer system that analyzes received data and processes the necessary information.

[0623] An "intelligent engine" is a device that includes algorithms for analyzing data and generating plans using artificial intelligence.

[0624] "Means of providing motivational messages" refers to methods of conveying encouraging or supportive messages to users, either visually or audibly, to help them continue their training.

[0625] "Means for collecting voice and facial expression data to analyze emotional states" refers to a mechanism that uses microphones and cameras to acquire voice and video data in order to understand the user's emotions.

[0626] "Methods for adjusting fitness plans based on emotional state" refer to algorithms that dynamically change exercise content and volume in response to analyzed emotional data.

[0627] The system for implementing this invention generates and provides a fitness plan based on the user's physical and emotional data. First, the user measures their physical data using a smart body composition scale, and this data is transmitted to a terminal. The terminal uses a microphone and camera to collect the user's voice and facial expression data and analyzes their emotional state.

[0628] The server analyzes this data using an intelligent engine and generates a personalized fitness plan. This plan also includes motivational messages tailored to the user's current emotional state. For example, if the server determines that the user is tired, it will recommend appropriate relaxation exercises and send an encouraging message to the user's device. The user's device can display this information on the screen and provide audio notifications.

[0629] As a specific scenario, suppose a user measures their body composition with a smart body composition scale before starting a workout, and then says to the device, "I'm feeling down today." In response, the intelligent engine generates a message suggesting light exercise along with, "Let's focus on relaxation today."

[0630] Furthermore, using a generative AI model, the following example prompt is input: "If the user's emotion is recognized as 'fatigue,' please provide a training plan and message best suited to that emotion. Specifically, generate relaxing exercises and words of encouragement." In this way, the system provides services that correspond to the user's state.

[0631] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0632] Step 1:

[0633] The user uses a smart body composition scale to measure their body data. Data such as the user's weight, body fat percentage, and muscle mass are measured. This data is wirelessly transmitted to the user's device.

[0634] Step 2:

[0635] The device receives physical data and transfers it to a cloud server. The input here is the user's physical data, and the output is the data transfer to the server. No data processing is performed.

[0636] Step 3:

[0637] The system collects the user's voice and facial expressions using the microphone and camera built into the device. The input is voice and facial expression data, and the output is sending this data to a server.

[0638] Step 4:

[0639] The server uses the received audio and facial expression data to activate an emotion recognition engine and analyze the user's emotional state. The input is audio and facial expression data, and the output is the result of the emotional state determination. Audio analysis algorithms and facial expression recognition algorithms are used for data processing.

[0640] Step 5:

[0641] The server integrates physical data with analyzed emotional states and uses an intelligent engine to generate an optimal fitness plan. The input is physical data and emotional states, and the output is a suitable fitness plan. Data processing includes comparison with existing fitness information and plan generation using a generative AI model.

[0642] Step 6:

[0643] The server generates motivational messages based on the user's emotional state. The input is the emotional state, and the output is the motivational message. Natural language processing is performed based on the prompt to generate messages such as "Let's relax today."

[0644] Step 7:

[0645] The generated fitness plan and motivational messages are sent from the server to the user's device. The input is the data on the server, and the output is the display on the device. The device provides these to the user through a screen or audio output device.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] [Fourth Embodiment]

[0650] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0651] 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.

[0652] 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).

[0653] 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.

[0654] 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.

[0655] 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).

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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".

[0663] This invention realizes a system that provides users with personalized training plans using their own physical data when managing their health. The embodiments of this invention are configured with the user, terminal, and server as the core functions.

[0664] First, the user uses a dedicated smart body composition analyzer to acquire physical data such as their weight, body fat percentage, and skeletal muscle percentage. This data is then transmitted to the user's device via Bluetooth or Wi-Fi.

[0665] The terminal temporarily stores the user's biometric data that has been transmitted, and then performs a procedure to send it to the server. During this process, the integrity of the data is verified, and protocols are applied to ensure secure transmission.

[0666] The server stores data received from the terminal in a database and utilizes an artificial intelligence model to analyze it. Specifically, the analysis algorithm generates a training plan optimized for the user's current situation based on their past data and goals. This plan includes specific details such as the type of exercise, repetitions, sets, and rest time.

[0667] The generated training plan is sent from the server to the device, which then presents it to the user. The training content is provided along with videos and step-by-step guides that the user can complete at home, allowing the user to train at their own pace.

[0668] After completing a training session, users input feedback via their device. This feedback includes the difficulty level of the training, the degree of success, and any changes in their physical condition. The device sends this feedback to a server, which further analyzes the data to inform future training plans.

[0669] This system allows users to receive personalized fitness services at home without feeling self-conscious about training in front of others. This offers the advantage of enabling sustainable health management, overcoming time and financial constraints.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] The user steps onto a smart body composition scale, which collects body data such as weight, body fat percentage, and skeletal muscle percentage. This data is transmitted from the smart body composition scale to the device via Bluetooth or Wi-Fi.

[0673] Step 2:

[0674] The terminal temporarily stores the received data and verifies its integrity. Once verification is complete, it prepares to send the data to the server.

[0675] Step 3:

[0676] The device sends data that it has verified as authentic to the server using security protocols such as encryption technology. If the transmission is successful, the device notifies the user.

[0677] Step 4:

[0678] The server receives data sent from the terminal and stores it in the database. During this process, the data's integrity and consistency are checked again.

[0679] Step 5:

[0680] The server analyzes the latest user data stored in the database and uses an artificial intelligence model to generate a training plan optimized for the user's current state.

[0681] Step 6:

[0682] The generated training plan includes detailed information such as the type of exercise, repetitions, sets, and rest times, which the server then sends to the terminal.

[0683] Step 7:

[0684] The device displays the training plan received from the server to the user. Based on this, the user can begin training at home.

[0685] Step 8:

[0686] After the user completes the training, they input feedback via their device. This feedback includes the difficulty level and achievement of the training, as well as any changes in their physical condition.

[0687] Step 9:

[0688] The device collects user feedback and sends it to the server. This data is used to improve the next training plan.

[0689] Step 10:

[0690] The server analyzes the feedback and uses artificial intelligence to prepare for further optimizing the next training plan. Progress reports are also sent to the user.

[0691] (Example 1)

[0692] 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".

[0693] Existing health management systems struggle to provide personalized training plans based on each user's individual physical characteristics. Furthermore, they lack mechanisms to efficiently utilize user feedback and incorporate it into future training plans. Security must also be ensured during data transmission and storage.

[0694] 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.

[0695] In this invention, the server includes means for verifying the validity and integrity of received information, means for transmitting information using a secure communication path, and means equipped with artificial intelligence for analyzing stored information and generating a training plan. This enables the provision of an optimal training plan based on each user's physical characteristics, as well as the effective collection and utilization of related feedback.

[0696] "User" refers to an individual who uses this system for health management.

[0697] "Physical characteristics" refer to data about the user's body, including information such as mass, fat mass, and muscle mass.

[0698] "Equipment" refers to a device used to measure a user's physical characteristics.

[0699] "Information" refers to data based on the user's physical characteristics obtained from the device.

[0700] A "terminal" refers to a device that can receive and temporarily store information.

[0701] "Communication path" refers to a data transmission method used to securely send information from a terminal to a server.

[0702] A "computational device" refers to a device equipped with artificial intelligence that analyzes information and generates training plans.

[0703] A "training plan" refers to a plan that includes the classification of exercises, the number of repetitions, sets, and rest periods provided, based on the user's physical characteristics and goals.

[0704] "Artificial intelligence" refers to the algorithms used to analyze a user's physical characteristics and create a training plan.

[0705] "Feedback" refers to information about users' reactions and experiences collected after training has been conducted.

[0706] In this invention, the user uses equipment to measure physical characteristics for health management. Specifically, the user uses a smart body composition analyzer or similar device to measure physical characteristics such as mass, fat mass, and muscle mass. This measurement data is transmitted to the user's terminal via communication technologies such as Bluetooth or Wi-Fi. The terminal temporarily stores this information and transmits the data to a server using a secure communication path.

[0707] The server stores the received information in a database and analyzes the data using a generative AI model. The artificial intelligence algorithm on the server generates a customized training plan based on the user's physical characteristics and goals. This training plan includes exercise classification, repetitions, sets, and rest time. The terminal presents this generated training plan to the user, displaying videos and step-by-step guides related to the training plan.

[0708] After completing a training session, users provide feedback through their device. This feedback includes information about the difficulty and success of the training, as well as any changes in their physical condition. The device sends this feedback to a server, which uses it to improve future training plans.

[0709] As a concrete example, consider a user aiming to improve their jogging ability. The server generates a training plan based on the user's current weight and muscle mass, consisting of three 5km runs per week. The user then jogs at home according to this plan and provides feedback on their training experience and results afterward.

[0710] An example of a prompt to input into a generating AI model might be: "Please create a jogging plan for a man in his 30s. His current weight is 70kg and his body fat percentage is 22%. His goal is to reduce his body fat percentage to 20%. The pace should be manageable at home." In this way, personalized health management becomes possible.

[0711] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0712] Step 1:

[0713] The user measures their physical characteristics using a smart body composition scale. Specifically, the user steps onto the scale to begin measurement, recording mass, fat mass, and muscle mass. These measurement results are automatically transmitted to a device via Bluetooth or Wi-Fi. The input is the user's physical characteristics, and the output is the measurement data.

[0714] Step 2:

[0715] The device temporarily stores the received physical characteristics. Specifically, a health management app installed on a smartphone or tablet retrieves the information and verifies the validity and consistency of the data. The input is data from a smart body composition scale, and the output is data whose consistency has been verified.

[0716] Step 3:

[0717] The device sends verified data to the server. A secure communication path such as HTTPS is used for this process. Specifically, the health management app uploads data to the server and displays a "Data transmission complete" notification. The input is verified data, and the output is the data sent to the server.

[0718] Step 4:

[0719] The server stores the data sent from the terminal in a database and analyzes it using a generated AI model. Specifically, the AI ​​algorithm on the server creates a training plan based on the user's past data and goals. The input is the transmitted physical characteristics data, and the output is a personalized training plan.

[0720] Step 5:

[0721] The server sends the generated training plan to the terminal. The terminal proposes the plan to the user and presents detailed information about the training content (videos and step-by-step guides). The input is the training plan from the server, and the output is the visual training guide on the terminal.

[0722] Step 6:

[0723] After completing a training session, the user inputs feedback on their response to the training via their device. Specifically, they input their responses to options such as difficulty level and success rate in a feedback form within the app. The input represents the user's training experience, and the output is feedback data.

[0724] Step 7:

[0725] The terminal sends user feedback to the server. The server analyzes the feedback and uses it as data to inform the next training plan. The input is user feedback data, and the output is the analysis results used for the next plan.

[0726] (Application Example 1)

[0727] 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".

[0728] In modern urban life, managing residents' health is a crucial issue, but many people find it difficult to implement personalized fitness plans amidst their busy daily lives. Furthermore, reluctance to exercise in public spaces is considered another factor hindering health maintenance. To address these challenges, it is necessary to provide health management support tailored to each individual resident in a safe and accessible environment.

[0729] 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.

[0730] In this invention, the server includes means for analyzing received information and generating an exercise plan, means for displaying the generated exercise plan to the user, and means for providing residents with personalized exercise guidance in an urban environment. This allows residents to obtain individually optimized exercise plans at various locations within the city and manage their health at their own pace while ensuring their privacy.

[0731] "User biometric data" refers to physical information such as weight, body fat percentage, and muscle mass percentage, which is used to understand an individual's health status.

[0732] A "measuring device" is an instrument used to acquire biological data, and it generates highly accurate data using digital technology.

[0733] A "device" is an electronic device used to receive, process, and communicate measured biological data.

[0734] A "server equipped with a computing device" is a computer system that has the function of analyzing received data using an analysis algorithm and generating a motion plan.

[0735] An "exercise plan" is a detailed fitness plan optimized based on individual biometric data, including the type of exercise, frequency, number of repetitions, and rest intervals.

[0736] A "personalized exercise guide" is support information that suggests exercise methods and locations suitable for each user's individual characteristics and environment.

[0737] "Residents" are defined as individuals who reside in a specific city or region and utilize health management services.

[0738] To realize this invention, the user uses a digital measuring device called a smart body composition scale. This device measures the user's biometric data such as weight, body fat percentage, and muscle mass percentage, and transmits this data to the user's smartphone (terminal) via Bluetooth or Wi-Fi. The terminal plays the role of securely and consistently transferring the data to the server.

[0739] The server contains computing power for processing biometric data received from terminals, including an artificial intelligence model for database management and analysis. This AI model is built using machine learning platforms such as TensorFlow and generates a personalized exercise plan based on the received data. This plan includes the type of exercise, frequency, number of repetitions, and rest intervals.

[0740] The generated exercise plan is sent back to the device and notified to the user via an application. This application provides visual guides and hints to help the user naturally incorporate the planned exercises into their daily routine.

[0741] For example, resident A in a city uses the system to receive a plan that combines daily walking with light strength training twice a week. This provides resident A with support that allows them to easily follow the plan at home or in a nearby park.

[0742] Furthermore, after the exercise session, feedback is received from the user and this information is also sent to the server. The system on the server incorporates this feedback information into its analysis and uses it to optimize the next exercise plan.

[0743] Example of a prompt:

[0744] "Optimize the next training plan based on the user's age, weight, body fat percentage, and feedback."

[0745] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0746] Step 1:

[0747] The user steps onto a smart body composition scale. This scale measures the user's biometric data, such as weight, body fat percentage, and muscle mass percentage, and transmits this data to the device via Bluetooth. The measured values ​​are converted into digital signals and input into the device.

[0748] Step 2:

[0749] The device temporarily stores biometric data received from the smart body composition scale in a secure format. Here, the integrity and completeness of the data are verified. The data format is converted, for example, to JSON format, in preparation for transmission to the server.

[0750] Step 3:

[0751] The terminal sends verified data to the server via a securely established protocol. During this process, the data is encrypted, and a secure connection to the server is established.

[0752] Step 4:

[0753] The server stores biometric data received from the terminal in a database. The database management system used for storage is MySQL or PostgreSQL, which handles the storage of numerical data and historical data management. This data is then used as input to prepare for analysis.

[0754] Step 5:

[0755] An AI model on the server generates an exercise plan based on historical data stored in the database and new input data. A data analysis algorithm is executed using the TensorFlow library. This analysis optimizes the plan according to the user's characteristics, determining the type, frequency, and number of repetitions of exercises.

[0756] Step 6:

[0757] The generated exercise plan is sent back from the server to the device. The device receives this information and presents it visually to the user. The plan displayed in the application includes specific exercise guides and links to reference videos.

[0758] Step 7:

[0759] After a user completes an exercise session, they input feedback about the activity through their device. This feedback includes information such as the difficulty level of the training, their sense of accomplishment, and any changes in their physical condition.

[0760] Step 8:

[0761] The terminal sends the input feedback to the server. The server uses this feedback data for the next analysis and incorporates it as feedback into the process of generating a more accurate motion plan.

[0762] This series of steps allows users to perform optimized exercises at their own pace, enabling continuous health management.

[0763] 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.

[0764] This invention relates to an AI personal trainer system incorporating emotion recognition capabilities, which provides a personalized training plan and motivational messages based on the user's physical and emotional data. This enables the provision of an appropriate fitness experience tailored to the user's psychological state.

[0765] The user first measures their body data using a dedicated smart body composition scale, and this information is transmitted to a terminal. This system also collects data for emotion recognition (voice and facial expression data) when the user inputs feedback into the terminal. An emotion engine analyzes the voice and facial expressions to identify the user's current emotional state.

[0766] The server analyzes the received physical and emotional data and generates a training plan based on it. The artificial intelligence adjusts the plan according to the user's emotional state, changing the difficulty and volume of the training as needed. In addition, motivational messages generated by the emotion engine are provided to enhance the user's motivation.

[0767] The generated training plan and motivational messages are presented to the user via the device. For example, if the user is feeling stressed, the server will suggest relaxing stretching exercises, while if they are full of energy, it will recommend more active training. In addition, positive messages such as "Let's do our best today!" generated by the emotion engine will be displayed on the user's screen.

[0768] Thus, the present invention provides a system that enables more personalized health management by taking into account both the physical and emotional aspects of the user. Through this system, it is possible to support the user's continuous fitness activities and ultimately promote their health.

[0769] The following describes the processing flow.

[0770] Step 1:

[0771] Users use a smart body composition scale to measure physical data such as weight, body fat percentage, and skeletal muscle percentage. This data is transmitted to the user's device via Bluetooth or Wi-Fi.

[0772] Step 2:

[0773] The device temporarily stores the received physical data and verifies its integrity. Once this verification is complete, it prepares to send the data to the server.

[0774] Step 3:

[0775] The device encrypts the body data collected from the user and sends it to the server.

[0776] Step 4:

[0777] The server receives the physical data transmitted from the terminal and stores it in the database. At this time, the integrity of the data is re-verified.

[0778] Step 5:

[0779] Before the user begins training, the device acquires the user's voice data and facial image, and the emotion engine analyzes this data to identify the user's emotional state.

[0780] Step 6:

[0781] The server uses artificial intelligence to generate a training plan optimized for the user based on the received physical and emotional data. The plan is then adjusted based on the emotions identified by the emotion engine.

[0782] Step 7:

[0783] The generated training plan and emotion-based motivational messages are sent from the server to the terminal.

[0784] Step 8:

[0785] The device displays the training plan and motivational messages received from the server to the user. The information is presented in a format that the user can immediately understand and act upon.

[0786] Step 9:

[0787] The user performs exercises based on the training plan provided. After the training, the user enters feedback on their feelings and physical condition into the device.

[0788] Step 10:

[0789] The device collects user feedback and sends it back to the server. This data is used to adjust the next training plan.

[0790] Step 11:

[0791] The server analyzes the feedback, updates the artificial intelligence model as needed, and prepares to optimize the next training plan.

[0792] (Example 2)

[0793] 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".

[0794] In modern society, the importance of individual health management is increasing, but general fitness plans often fail to take into account individual physical characteristics and emotional states, resulting in ineffective training. Furthermore, they often lack adequate motivation to maintain user engagement. Therefore, there is a need for a system that considers the user's physical and emotional state and provides personalized training plans and support.

[0795] 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.

[0796] In this invention, the server includes means for analyzing physical and emotional data to generate training plans and motivational messages, means for adjusting exercise content according to the user's emotional state, and means for receiving and analyzing feedback through a terminal. This enables personalized training plans and motivation based on the user's physical and emotional state.

[0797] "Body data" refers to information related to a user's body composition, including physiological measurements such as weight, body fat percentage, and muscle mass.

[0798] "Emotional data" refers to information indicating a user's psychological state, obtained through voice and image analysis, and is data used to identify emotions such as stress, joy, and fatigue.

[0799] A "terminal" refers to a device that receives data from users, analyzes audio and images, and transmits that information to a server.

[0800] A "server" is a central computing device that performs calculations and analyses to generate training plans based on physical and emotional data received from terminals.

[0801] A "training plan" refers to an exercise instruction plan generated by the server based on the user's physical and emotional state, and includes the type of exercise, intensity, number of repetitions, number of sets, rest time, etc.

[0802] "Motivational messages" are encouraging and guiding statements generated in response to the user's emotional state, and include content designed to promote fitness activities.

[0803] "Feedback" refers to user responses and opinions regarding the training plan, which are sent to the server via the device and used to generate the next plan.

[0804] This invention relates to an AI personal trainer system that integrates emotion recognition capabilities and provides personalized fitness plans based on the user's physical and emotional state.

[0805] The user uses a dedicated smart body composition scale to measure physical data such as weight, body fat percentage, and muscle mass. This information is transmitted to the user's device via Bluetooth or Wi-Fi. The device receives this data and also collects audio and image data if the user provides feedback via voice and facial expressions through the device. Voice recognition software and image analysis software are used in this process.

[0806] Data transmitted from the device is received and analyzed by the server. The server uses the received physical and emotional data to generate a personalized training plan using a generative AI model. This plan includes the type, intensity, repetitions, sets, and rest periods of exercise designed to improve the user's health, and is adjusted according to the user's emotional state. For example, if the user is stressed, relaxing activities are suggested, while high-intensity training is recommended if the user is energetic.

[0807] Furthermore, the server uses an emotion engine to generate motivational messages to encourage the user's fitness activities. These messages are displayed on the user's screen and have a positive impact on their daily fitness activities.

[0808] For example, if a user dictates to their device, "I'm a little tired today," the server analyzes this data and suggests a training plan that includes "light stretching while taking deep breaths," along with an encouraging message such as, "Relax and take care of yourself."

[0809] An example of a prompt would be: "Please suggest a training plan for when the user is feeling stressed. Also, please suggest an appropriate motivational message to display in that situation."

[0810] Through this system, users can receive personalized health management that takes their physical and emotional state into account, enabling them to promote their health through continuous fitness activities.

[0811] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0812] Step 1:

[0813] The user measures their body data using a dedicated smart body composition scale. This data includes weight, body fat percentage, muscle mass, etc., and is transmitted to the device via Bluetooth or Wi-Fi. The device receives this direct data input and outputs digital data of the user's current physical condition.

[0814] Step 2:

[0815] The user provides feedback through voice and facial expressions via the device. The device uses its built-in microphone and camera to acquire voice and video data, which are stored as input data for emotion analysis. From this input, voice recognition software analyzes the tone and content of the voice, and an image analysis algorithm detects changes in facial expressions. As output, data indicating the user's emotional state is generated.

[0816] Step 3:

[0817] The device sends the physical data obtained in Step 1 and the emotional data generated in Step 2 to the server. The server receives this data, stores it in a database, and uses it as input data for analysis. Based on the input, a field-based algorithm constructs the user's health profile. The output is an analyzable dataset.

[0818] Step 4:

[0819] The server uses the physical and emotional data obtained from the analysis to run a generative AI model. This model generates a training plan tailored to the user's individual state. For example, if the user's stress level is determined to be high, it can recommend exercises that promote relaxation. The output is a training plan optimized for the user.

[0820] Step 5:

[0821] The server also uses an emotion engine to generate motivational messages that match the user's emotional state. These messages are designed to encourage a positive outlook on today's training and are displayed on the user's screen. As output, an encouraging message is constructed for the user.

[0822] Step 6:

[0823] The device displays the training plan generated in step 4 and the motivational message created in step 5 to the user. The user then performs the training based on this guidance. The output is the notification of the plan and message to the user.

[0824] Step 7:

[0825] The user completes the training and then inputs the results and feedback back into the device. The device receives this feedback and sends it to the server for generating the next plan. Based on the input, the feedback data is saved as data to support new analyses.

[0826] (Application Example 2)

[0827] 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".

[0828] In fitness programs, it is essential to provide personalized training plans that take into account not only the physical characteristics of each user but also their emotional state. However, conventional systems rely solely on the user's physical data and lack the flexibility to respond to emotional fluctuations. This makes it difficult to maintain consistent motivation, often resulting in ineffective training.

[0829] 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.

[0830] In this invention, the server includes an information processing device equipped with an intelligent engine that analyzes received data and emotion recognition data and generates a fitness plan; means for providing the generated fitness plan and motivational messages to the user; means for collecting voice and facial expression data to analyze the user's emotional state; and means for adjusting the fitness plan based on the emotional state. This makes it possible to provide a more effective and sustainable fitness plan tailored to the individual user's physical characteristics and emotional state.

[0831] A "device for measuring user's physical data" is a device that measures information about the user's body, such as weight, muscle mass, and body fat percentage.

[0832] An "information processing device" is a computer system that analyzes received data and processes the necessary information.

[0833] An "intelligent engine" is a device that includes algorithms for analyzing data and generating plans using artificial intelligence.

[0834] "Means of providing motivational messages" refers to methods of conveying encouraging or supportive messages to users, either visually or audibly, to help them continue their training.

[0835] "Means for collecting voice and facial expression data to analyze emotional states" refers to a mechanism that uses microphones and cameras to acquire voice and video data in order to understand the user's emotions.

[0836] "Methods for adjusting fitness plans based on emotional state" refer to algorithms that dynamically change exercise content and volume in response to analyzed emotional data.

[0837] The system for implementing this invention generates and provides a fitness plan based on the user's physical and emotional data. First, the user measures their physical data using a smart body composition scale, and this data is transmitted to a terminal. The terminal uses a microphone and camera to collect the user's voice and facial expression data and analyzes their emotional state.

[0838] The server analyzes this data using an intelligent engine and generates a personalized fitness plan. This plan also includes motivational messages tailored to the user's current emotional state. For example, if the server determines that the user is tired, it will recommend appropriate relaxation exercises and send an encouraging message to the user's device. The user's device can display this information on the screen and provide audio notifications.

[0839] As a specific scenario, suppose a user measures their body composition with a smart body composition scale before starting a workout, and then says to the device, "I'm feeling down today." In response, the intelligent engine generates a message suggesting light exercise along with, "Let's focus on relaxation today."

[0840] Furthermore, using a generative AI model, the following example prompt is input: "If the user's emotion is recognized as 'fatigue,' please provide a training plan and message best suited to that emotion. Specifically, generate relaxing exercises and words of encouragement." In this way, the system provides services that correspond to the user's state.

[0841] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0842] Step 1:

[0843] The user uses a smart body composition scale to measure their body data. Data such as the user's weight, body fat percentage, and muscle mass are measured. This data is wirelessly transmitted to the user's device.

[0844] Step 2:

[0845] The device receives physical data and transfers it to a cloud server. The input here is the user's physical data, and the output is the data transfer to the server. No data processing is performed.

[0846] Step 3:

[0847] The system collects the user's voice and facial expressions using the microphone and camera built into the device. The input is voice and facial expression data, and the output is sending this data to a server.

[0848] Step 4:

[0849] The server uses the received audio and facial expression data to activate an emotion recognition engine and analyze the user's emotional state. The input is audio and facial expression data, and the output is the result of the emotional state determination. Audio analysis algorithms and facial expression recognition algorithms are used for data processing.

[0850] Step 5:

[0851] The server integrates physical data with analyzed emotional states and uses an intelligent engine to generate an optimal fitness plan. The input is physical data and emotional states, and the output is a suitable fitness plan. Data processing includes comparison with existing fitness information and plan generation using a generative AI model.

[0852] Step 6:

[0853] The server generates motivational messages based on the user's emotional state. The input is the emotional state, and the output is the motivational message. Natural language processing is performed based on the prompt to generate messages such as "Let's relax today."

[0854] Step 7:

[0855] The generated fitness plan and motivational messages are sent from the server to the user's device. The input is the data on the server, and the output is the display on the device. The device provides these to the user through a screen or audio output device.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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."

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0877] The following is further disclosed regarding the embodiments described above.

[0878] (Claim 1)

[0879] A device for measuring the user's physical data,

[0880] A terminal that receives data transmitted from a device,

[0881] A server equipped with artificial intelligence that analyzes received data and generates a training plan,

[0882] A means of providing the generated training plan to the user,

[0883] A means of sending user feedback to a server and using it for analysis,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, wherein the physical data includes weight, body fat percentage, and skeletal muscle percentage.

[0887] (Claim 3)

[0888] The system according to claim 1, further comprising means for instructing the user on the type of exercise, repetitions, number of sets, and rest time based on the generated training plan.

[0889] "Example 1"

[0890] (Claim 1)

[0891] A device for measuring the user's physical characteristics,

[0892] A terminal that receives and temporarily stores information transmitted from a device,

[0893] A means of verifying the validity and integrity of received information and transmitting the information using a secure communication path,

[0894] A computing device equipped with artificial intelligence that analyzes stored information and generates a training plan,

[0895] A means of presenting the generated training plan to the user via a terminal,

[0896] A means for collecting user responses after training and transmitting them to a computing device for use in analysis,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, wherein physical characteristics include information such as mass, fat mass, and muscle mass.

[0900] (Claim 3)

[0901] The system according to claim 1, further comprising means for showing the user the classification of exercises, repetitions, sets, and rest time based on the generated training plan.

[0902] "Application Example 1"

[0903] (Claim 1)

[0904] A device for measuring the user's biometric data,

[0905] A device that receives information transmitted from a device,

[0906] A server equipped with a computing device that analyzes received information and generates a motion plan,

[0907] A means of displaying the generated exercise plan to the user,

[0908] A means of sending user feedback to a server for analysis,

[0909] Means of providing individualized exercise guidance to residents in an urban environment,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, wherein the biometric data includes information such as weight, body fat percentage, and muscle mass percentage.

[0913] (Claim 3)

[0914] The system according to claim 1, further comprising means for presenting the user with the type, frequency, number of repetitions and rest intervals of exercise based on the generated exercise plan, and for guiding residents so that they can utilize it at exercise facilities in the city.

[0915] "Example 2 of combining an emotion engine"

[0916] (Claim 1)

[0917] A device that measures the user's physical and emotional data,

[0918] A terminal that receives data transmitted from the device and analyzes the audio and images,

[0919] A server equipped with artificial intelligence that generates training plans and motivational messages based on received physical and emotional data,

[0920] A means of providing the user with the generated training plan and motivational messages,

[0921] A means of sending user feedback to a server for analysis,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, wherein the physical data is data containing various information about body composition, and the emotional data represents various psychological states obtained by voice and facial expression analysis.

[0925] (Claim 3)

[0926] The system according to claim 1, further comprising means for instructing the user on the type, intensity, repetitions, number of sets, and rest time of exercise according to the user's emotional state, based on the generated training plan.

[0927] "Application example 2 when combining with an emotional engine"

[0928] (Claim 1)

[0929] A device for measuring the user's physical data,

[0930] An information processing device that receives data transmitted from a device,

[0931] An information processing device equipped with an intelligent engine that analyzes received data and emotion recognition data to generate a fitness plan,

[0932] A means of providing the user with a generated fitness plan and motivational messages,

[0933] To analyze the user's emotional state, a means of collecting voice and facial expression data,

[0934] A means of adjusting a fitness plan based on emotional state,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, wherein the emotional data includes data obtained by analyzing voice and facial expressions.

[0938] (Claim 3)

[0939] The system according to claim 1, further comprising means for adjusting the type of exercise, repetitions, number of sets, and rest time according to the user's emotional state based on the generated fitness plan. [Explanation of Symbols]

[0940] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for measuring the user's biometric data, A device that receives information transmitted from a device, A server equipped with a computing device that analyzes received information and generates a motion plan, A means of displaying the generated exercise plan to the user, A means of sending user feedback to a server for analysis, Means of providing individualized exercise guidance to residents in an urban environment, A system that includes this.

2. The system according to claim 1, wherein the biometric data includes information such as weight, body fat percentage, and muscle mass percentage.

3. The system according to claim 1, further comprising means for presenting the user with the type, frequency, number of repetitions, and rest intervals of exercise based on the generated exercise plan, and for guiding residents so that they can utilize it at exercise facilities in the city.

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