system

Abstract

We provide the system. [Solution] Means for collecting individual user data, means for generating an exercise program based on the aforementioned user data, A means of presenting the generated motor program in a game format, A means for collecting data on the performance of exercise in the aforementioned game format, A means of adjusting the exercise program based on collected exercise performance data, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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] For users with limited physical functions, general exercise programs are often not suitable, and symptoms may worsen due to forced exercise. Furthermore, existing exercise programs do not consider the health status and range of motion of individual users, and it is difficult to maintain the motivation for users to continue exercising. There is a need for a system that can improve such a situation and enable users to exercise safely and maintain their health.

Means for Solving the Problems

[0005] This invention provides a system that collects individual user data and generates personalized exercise programs based on that data. This system presents the generated exercise programs in a game format, enabling users to exercise consistently while enjoying the process. Furthermore, based on the exercise data collected within the game, the system continuously adjusts the exercise program and proposes optimal exercises tailored to the user's changing physical function, thereby providing exercise that is not overly strenuous.

[0006] "User data" refers to information about the physical characteristics and health status of individual users.

[0007] An "exercise program" refers to a series of exercises designed according to the user's physical abilities and health condition.

[0008] "Game format" refers to an interactive platform designed to make exercise enjoyable.

[0009] "Exercise performance data" refers to information about the user's progress and results regarding exercise, recorded through a game format.

[0010] "Means of adjusting exercise programs" refers to methods of analyzing collected exercise data and redesigning and improving exercise programs to best suit the user. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5]This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the 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.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] As an embodiment of the present invention, an interactive system that provides a personalized exercise program based on the user's physical data will be described. This system involves interaction between a terminal, a server, and the user.

[0033] The device first receives physical data entered by the user. This physical data includes the user's age, height, weight, medical history, and range of motion. This data later serves as foundational data for generating exercise programs.

[0034] Next, the device sends user data to the server. The server analyzes the received data and generates an optimal exercise program for the user. Specifically, it uses an algorithm to evaluate the user's health condition and range of motion and selects individually optimized exercises. For example, for a user with limited range of motion in their shoulders, a program will be generated that includes shoulder stretches and light-intensity exercises.

[0035] The generated exercise program is presented to the user in a game format. This game format includes interactive elements such as earning points and leveling up by performing exercises. This allows users to enjoy the exercise not just as a form of exercise, but as a game, increasing their likelihood of continuing to exercise.

[0036] As the user exercises, the device provides feedback in real time, matching the progress of the workout. For example, it displays encouraging messages like "Well done!" and provides corrective instructions if the movements are incorrect. Once the workout is complete, the device sends the data to the server.

[0037] The server uses the received exercise data to evaluate the user's progress. If necessary, it readjusts the exercise program and generates a new program tailored to the user's changing health condition. This helps improve the user's physical abilities and enables continuous health maintenance.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The terminal provides the user with a physical data input interface. The user uses this interface to input their age, height, weight, range of motion, etc. This data is used as foundational information to generate a personalized exercise program in a later step.

[0041] Step 2:

[0042] The terminal transmits the entered body data to the server. This transmission is performed using a secure communication protocol to guarantee data integrity.

[0043] Step 3:

[0044] The server analyzes the received user data and generates an individually optimized exercise program. Based on the user's characteristics, the algorithm selects stretches and light exercises to improve shoulder mobility, for example, if the user has limited shoulder range of motion.

[0045] Step 4:

[0046] The server converts the generated exercise program into a game format and sends this data to the terminal. The transmitted game format is then displayed in the user interface.

[0047] Step 5:

[0048] The user performs a game-style exercise program presented through the device. The device monitors the user's movements and progress and provides feedback as needed. For example, if the movements are correct, it displays a message such as "Well done!"

[0049] Step 6:

[0050] After completing an exercise, the device sends the data to the server. This data includes information such as the time taken for the exercise and the accuracy of the movements.

[0051] Step 7:

[0052] The server analyzes the received exercise data and evaluates the user's progress. Based on user feedback, it adjusts the exercise program as needed and generates new programs, continuously providing exercise tailored to the user's changing health condition.

[0053] (Example 1)

[0054] 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."

[0055] In modern times, many people exercise to maintain their health and improve their physical fitness, but there is a problem in that it is difficult to continue exercising effectively and consistently. In particular, each user has a different physical condition and exercise ability, and creating an appropriate exercise program tailored to them is a major challenge. Maintaining motivation to exercise is also difficult.

[0056] 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.

[0057] In this invention, the server includes means for inputting user information, means for generating an exercise plan using an algorithm based on the user information, and means for transmitting exercise performance information to the server, analyzing it, and adjusting the exercise plan. This makes it possible to provide an exercise program optimized for each individual user and to increase motivation through effective feedback.

[0058] "Means for inputting user information" refers to an interface that allows users to input information about their physical data and health status into a terminal.

[0059] "Means of generating exercise plans using algorithms" refers to calculation methods that analyze information provided by users and create exercise programs tailored to individual conditions and goals.

[0060] "Means of presenting in an interactive format" refers to display and operation methods that enable users to perform the generated exercise program through games or activities.

[0061] "Real-time monitoring means" refers to technologies such as sensors and cameras that instantly record and evaluate a user's movements and performance while they are exercising.

[0062] "A means of sending exercise performance information to a server, analyzing it, and adjusting the exercise plan" refers to a process of sending the results of a user's exercise session to a server, re-evaluating the exercise program based on this information, and providing a new plan.

[0063] This invention is a system that provides an optimal exercise program based on the user's individual physical information. This system provides an interactive environment that operates between a terminal, a server, and the user. Specific embodiments are as follows.

[0064] Users input their physical data using a device. This input is done via a touchscreen or keyboard and includes a wide range of information such as age, height, weight, medical history, and range of motion. The interface on the device is designed to allow users to input data accurately and provides feedback prompting the user to check for errors or missing data.

[0065] The device sends the user's entered physical data to the server. The server uses a generative AI model to analyze the received data. This model assesses the user's health status and generates an exercise program that includes the most suitable exercises for the user. The algorithm used here takes into account each user's different limitations and abilities to create a personalized plan.

[0066] The generated exercise program is provided in a gamified format, and users perform it through their device. As users complete the exercise, they are awarded points and level up within the game, incorporating mechanisms to maintain continuous motivation. A feedback function monitors the user's exercise technique in real time and provides corrective instructions as needed. A concrete example of device usage is earning a specific badge for successfully completing 10 squats.

[0067] Once the exercise is complete, the device sends the data back to the server. The server analyzes this data and evaluates the user's progress. Next, based on the generated AI model, it readjusts the exercise program as needed. This enhances the user's physical abilities and helps maintain their health.

[0068] An example of a prompt message is, "If a 50-year-old male user has knee arthritis, please suggest an exercise program that supports the knee while improving overall flexibility." In this way, the system helps generate exercise programs tailored to each individual user.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The terminal receives physical data from the user as input. This data includes information such as age, height, weight, medical history, and range of motion. The terminal converts this data into a format for transmission to the server and completes the preparation for transmission. Specifically, it has functions to verify the integrity of the input data and check for missing values ​​and errors.

[0072] Step 2:

[0073] The server receives physical data transmitted from the terminal as input. Using a generative AI model, this data is analyzed to evaluate the user's health status and exercise capacity. The analysis process involves statistical processing of the data and the use of specialized algorithms to determine the optimal exercise program. As a result, the most suitable exercise program for each user is generated.

[0074] Step 3:

[0075] The generated exercise program is output from the server to the terminal. The terminal presents this exercise program to the user in an interactive format. Specifically, it uses a game-like interface that allows the user to earn points and level up each time they perform an exercise. This helps maintain continuous motivation.

[0076] Step 4:

[0077] When a user performs an exercise program, the device tracks the user's movements in real time using motion sensors and a camera. It receives data from the sensors as input and evaluates the accuracy of the exercise by comparing it to predetermined movements. If the correct movements are confirmed, the device immediately displays feedback such as "Perfect!". It also provides specific instructions if corrections are needed.

[0078] Step 5:

[0079] Once an exercise session ends, the device sends session data to the server. The server receives this data as input and evaluates the user's progress. Next, it uses a generative AI model to determine whether the exercise program is appropriate for the user's current state and readjusts the program if necessary. As a result, the newly adjusted exercise program is output to the device again.

[0080] (Application Example 1)

[0081] 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."

[0082] Traditional fitness programs often provide uniform exercise instruction without adequately considering the individual physical abilities and health conditions of each participant, making effective health management and the formation of consistent exercise habits difficult. Furthermore, they lack systems to maintain motivation, hindering long-term participation. There is a need for a personalized exercise program delivery system that addresses these issues and is applicable even in a work environment.

[0083] 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.

[0084] In this invention, the server includes a device for collecting individual user information, a device for generating an exercise plan based on the user information, and a device for presenting the generated exercise plan in a virtualized format. This enables the provision of exercise programs optimized for each user and the maintenance of sustained motivation.

[0085] "Individual user information" refers to detailed data about individual users, such as age, height, weight, and health status.

[0086] An "exercise plan" refers to an exercise program that is individually tailored to each user based on their physical abilities and health condition.

[0087] "Virtualized format" refers to interactive motion content presented within a virtual environment, separate from the physical reality environment.

[0088] "Exercise performance information" refers to data collected when a user performs exercise, including accuracy of movement, amount of exercise, and heart rate.

[0089] "Analytical techniques" refer to algorithms and tools used to analyze data and extract specific patterns or insights.

[0090] "Interaction-providing media" refers to devices or means that allow users to receive feedback and guidance during exercise.

[0091] The interactive fitness system implementing the present invention provides personalized exercise programs based on the user's physical data, supporting the user's continuous health management. This system involves interaction between a server, a terminal, and the user.

[0092] The server receives information transmitted from terminals that collect physical data such as the user's age, height, weight, medical history, and range of motion. Based on the received data, it performs analysis using a generative AI model to generate an optimal exercise program for the user. This exercise program is then adjusted to optimize the user's individual needs by evaluating the user's health condition using a machine learning model with TENSORFLOW®. For example, a user with limited shoulder range of motion will be provided with a program that includes shoulder stretches and low-impact exercises.

[0093] The device is an interactive medium, similar to smart glasses, that presents the generated exercise program to the user in a virtualized format. The Unity-based system provides real-time feedback and guidance during exercise, playing a role in maintaining user motivation.

[0094] When a user actually exercises, exercise data is collected by the terminal and sent to the server. Based on the collected information, the server evaluates effective progress and continuously optimizes the exercise program. This enables personalized support tailored to the user's health condition, helping them to practice effective fitness.

[0095] As a concrete example, performing a short stretching program using smart glasses daily during factory work hours improves workers' concentration during the afternoon. An example of a prompt message is: "Generate an optimal short exercise program based on the following user physical data: Age 35, Height 170cm, Weight 65kg, Working hours 6 hours, Prone to stiff shoulders."

[0096] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0097] Step 1:

[0098] The device collects physical data from the user (age, height, weight, medical history, range of motion, etc.) as input. This data serves as the basis for generating exercise programs later on. The input data is centrally managed within the device and formatted for transmission to the server.

[0099] Step 2:

[0100] The terminal transmits the collected physical data to the server. By receiving the transmitted data, the server obtains the user's basic physical information. Based on this information, it prepares for subsequent data analysis and program generation.

[0101] Step 3:

[0102] The server begins analyzing the received physical data as input. A generative AI model (e.g., a machine learning model using TensorFlow) is used for the analysis, generating an exercise program tailored to each user's individual health condition. The data calculations performed here determine the program's content using algorithms that reflect the user's range of motion and health constraints.

[0103] Step 4:

[0104] The server sends the generated exercise program to the terminal. Upon receiving this program, the terminal presents it to the user in a virtualized format through an appropriate user interface (e.g., smart glasses). At this time, it prepares to display real-time feedback to the user as the exercise progresses.

[0105] Step 5:

[0106] The user performs physical actions according to the presented exercise program. The device monitors data generated at each stage of the exercise (e.g., exercise intensity, angle, heart rate, etc.) and collects it as a record of the exercise. The collected data is used for subsequent feedback and program adjustments.

[0107] Step 6:

[0108] The terminal sends exercise performance information to the server. Based on the exercise performance information received as input, the server evaluates the user's exercise performance and modifies or regenerates the exercise program as needed. The aforementioned generative AI model is continuously used for regeneration.

[0109] Step 7:

[0110] The server returns the readjusted exercise program to the terminal to provide continuous feedback, and presents it to the user as the program to run in subsequent exercise sessions. This generates a new exercise plan with updated prompts, supporting the user's continued improvement of physical abilities and health management.

[0111] 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.

[0112] As an embodiment of this invention, a system that recognizes a user's emotions and provides an exercise program in accordance with those emotions will be described. This system includes a terminal, a server, and an emotion engine.

[0113] The device first provides the user with a physical data input interface. The user uses this interface to input information such as age, height, weight, and range of motion. This input data serves as the basis for generating an exercise program.

[0114] The device also collects the user's facial expressions and tone of voice in real time. The emotion engine analyzes this data and evaluates the user's emotional state. For example, if the user shows signs of dissatisfaction or stress during exercise, the emotion engine will recognize this.

[0115] The collected data is transmitted to the server via secure communication. The server analyzes the physical and emotional data together to generate an individually optimized exercise program. For example, if the user indicates a relaxed state, it will provide a more challenging workout; if the user is tired, it will provide a program centered on relaxing stretches.

[0116] The generated exercise program is provided to the device in game format and displayed on the user interface. Users can enjoy exercising in a game-like format as they progress. The device provides feedback based on the user's progress and emotions, supporting the user within the game. For example, it can display messages such as "You're making good progress!" or "Take a short break and try again!"

[0117] Once a user completes an exercise, the emotion engine re-evaluates their final emotional state and sends this data, along with the exercise data, to the server. The server uses both the exercise data and the emotional data to adjust the exercise program, continuously providing the most suitable exercise for the user's emotional and physical condition. This creates an environment where users can continue exercising enjoyably and without strain.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] The terminal displays a physical data input interface to the user. Through this interface, the user inputs information about their age, height, weight, and range of motion. This data is used by the system as foundational information to customize exercise programs.

[0121] Step 2:

[0122] The device captures the user's facial expressions and voice tone in real time and sends them to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state in real time. For example, if the user is smiling, it determines that they are experiencing positive emotions.

[0123] Step 3:

[0124] The device transmits collected physical and emotional data to a server. The data is transferred using a secure communication protocol to ensure privacy and data integrity.

[0125] Step 4:

[0126] The server analyzes the user's physical and emotional data and generates an optimal exercise program using a specific algorithm. For example, if the user is feeling stressed, the program will include exercises that promote relaxation, thus taking emotional data into consideration.

[0127] Step 5:

[0128] The server converts the generated exercise program into a game format and sends this game data to the terminal. The game format includes elements such as accumulating points by performing exercises and a level-up system.

[0129] Step 6:

[0130] The user performs a game-style exercise program presented on the device. During the exercise, the device monitors the user's movements and provides real-time feedback. When the user successfully completes the exercise tasks, the device provides positive feedback.

[0131] Step 7:

[0132] Once the exercise is complete, the device uses an emotion engine to re-evaluate the exercise data and final emotional state, and then sends the results to the server. This allows for a comprehensive understanding of the user's progress and emotional changes.

[0133] Step 8:

[0134] The server adjusts the exercise program based on the final data. This makes the user's next exercise experience more personalized and tailored to their feedback and emotional state. It also supports the user in continuing to exercise safely and effectively in subsequent sessions.

[0135] (Example 2)

[0136] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0137] Existing exercise programs often fail to adequately optimize for individual users' emotional states and physical abilities, making it difficult for users to enjoy and continue exercising without undue strain. In particular, programs that lead to decreased motivation or excessive fatigue make long-term participation difficult.

[0138] 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.

[0139] In this invention, the server includes means for collecting individual user information, means for analyzing facial expressions and voice to evaluate emotions, and means for generating exercise programs using a generative AI model. This makes it possible to provide individually optimized exercise programs tailored to the user's emotional state and physical abilities, thereby improving the continuation and enjoyment of exercise.

[0140] "Individual user information" refers to data unique to each user, including information that indicates physical characteristics and emotional state.

[0141] "Means of analyzing facial expressions and voice to evaluate emotions" refers to the process of analyzing a user's facial expressions and voice tone using devices such as cameras and microphones, and then estimating their emotional state from that analysis.

[0142] "Methods for generating exercise programs using generative AI models" refers to the process of creating an optimal exercise program based on user information by utilizing artificial intelligence technologies, including machine learning.

[0143] "Presenting in a game format" refers to the process of providing an exercise program that includes visual and auditory elements in a way that allows users to enjoy it interactively.

[0144] "Information regarding exercise participation" refers to data about the exercise that users actually performed, including information such as the type of exercise, the number of repetitions, and changes in emotions.

[0145] "Having a feedback function" means having the ability to provide appropriate advice and instructions based on the user's actions and progress.

[0146] "Continuous motivation" refers to providing elements and functions that help users maintain their motivation and interest in continuing to exercise.

[0147] This invention relates to a system for recognizing a user's emotional state and providing an individually optimized exercise program. The system comprises a terminal, a server, and an emotion engine.

[0148] The device provides the user with a physical information input interface. The user inputs information about their body, such as age, height, weight, and range of motion. This information is used as basic data for generating exercise programs later. The device also uses a camera and microphone to collect the user's facial expressions and voice tone in real time.

[0149] The emotion engine uses this collected data to analyze and evaluate the user's emotional state. The analysis process employs image processing and speech recognition software, and machine learning algorithms are used to estimate the emotional state. For example, if a user is smiling, the emotion engine evaluates this as joy.

[0150] These analysis results and physical information are transmitted to the server via secure communication. The server uses a generative AI model to generate an exercise program based on the user's physical and emotional state. This AI model is optimized, for example, based on past exercise history and emotional data stored in a database.

[0151] The generated exercise program is provided to the device in a game format. The device presents the program to the user through an interactive screen that incorporates visual and auditory elements. The user can follow the program and enjoy exercising. The device has a feedback function and displays messages such as "Great pace!" or "Let's relax a little!"

[0152] After the user completes their exercise, the emotion engine reassesss their emotional state and sends the results, along with the exercise data, to the server. The server then uses this data to adjust the exercise program, continuously providing the optimal program.

[0153] As a concrete example, if a user inputs information such as being 30 years old, 170 cm tall, and weighing 65 kg, and displays a calm expression, the generating AI model will create a 30-minute yoga program focused on relaxation. An example of a prompt would be: "Create an exercise program for a 30-year-old user, 170 cm tall, and weighing 65 kg, who receives feedback with a calm expression and continues to receive encouraging messages."

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The terminal provides the user with a physical information input interface. The user inputs information such as age, height, weight, and range of motion. Once this data is entered, the terminal saves it to the system as initial setup data and sends it to the server. Accurate data entry is crucial at this stage, and input verification functions are used as needed to prevent errors.

[0157] Step 2:

[0158] The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time. Specifically, the camera tracks the user's facial movements, and the microphone records voice patterns. Based on this input data, the emotion engine performs preprocessing to evaluate the facial and voice data, creating a dataset for emotion estimation.

[0159] Step 3:

[0160] The emotion engine analyzes the facial expression and voice data collected in the previous step. It extracts facial features from the input facial expression data and analyzes the tone and speed of the voice from the voice data. Using machine learning algorithms, it estimates the user's emotional state from these features and outputs an emotion label such as "joy" or "fatigue."

[0161] Step 4:

[0162] The server receives the user's physical information and emotion labels from the emotion engine, and generates an exercise program using a generative AI model. In this step, a specific algorithm calculates the optimal exercise program based on the input information, and the result is constructed as a program. For example, if relaxation is needed, a yoga program will be recommended.

[0163] Step 5:

[0164] The exercise program generated from the server is sent to the terminal. The terminal receives this data and presents it to the user in a game format. Using graphics and sound, it provides an interactive experience and displays instructions to the user in accordance with the progress of the program.

[0165] Step 6:

[0166] While the user progresses through the exercise program, the device continuously monitors the user's progress and emotional changes, providing feedback as needed. This includes messages and advice to boost the user's motivation. The feedback is updated in real time, providing continuous support to the user.

[0167] Step 7:

[0168] After the exercise is completed, the device sends the latest facial and voice data to the emotion engine to re-evaluate the final emotional state. The emotion engine analyzes this data and sends it to the server to create the final dataset. This data forms the basis for future program adjustments.

[0169] (Application Example 2)

[0170] 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".

[0171] Traditional exercise program delivery systems, while capable of incorporating individual physical characteristics, have the problem of being unable to reflect users' changing emotional states in real time, making it difficult to maintain user motivation. Furthermore, the standardization of exercise programs prevents the provision of programs optimized for individual users.

[0172] 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.

[0173] In this invention, the server includes means for collecting data to analyze the user's emotional state, means for generating an exercise program based on the user's data and emotional state, and means for providing real-time feedback to maintain motivation. This enables the provision of an optimal exercise program based on the user's emotions and physical characteristics, allowing for continuous exercise and improved motivation.

[0174] "Data for analyzing the emotional state of users" refers to information including the user's facial expressions, tone of voice, and behavioral indicators, which is used to evaluate the user's emotional tendencies by analyzing this data.

[0175] "Means for generating exercise programs" refers to a processing system that uses algorithms and databases to program personalized exercise content based on collected physical and emotional data of users.

[0176] An "interactive format" refers to a method of presenting exercise programs that offers users an experience in which they can actively participate and receive feedback in real time.

[0177] "Real-time feedback" is a method of communication that aims to increase user motivation by providing immediate information to users as they exercise, based on their progress and emotional state.

[0178] The system for implementing this invention mainly includes a terminal, a server, and an emotion engine. Before the user starts an exercise program, the terminal requests input of physical data such as age, height, weight, and range of motion. This information is transmitted to the server as basic data for generating the exercise program.

[0179] Next, the device uses its built-in camera and microphone to collect the user's real-time facial expressions and voice tone. This data is analyzed by an emotion engine and sent to a server as a basis for evaluating the user's emotional state. The emotion engine utilizes emotion recognition software such as Microsoft® Azure® Face API.

[0180] On the server, an AI model is used to generate individually customized exercise programs based on collected physical and emotional data. This exercise program is sent to the terminal in an interactive format, allowing the user to participate. Here's an example of a user joining a yoga class. For instance, the AI ​​model generates exercise content using a prompt message such as, "What emotion is the user feeling? Suggest a 40-minute yoga routine that is best suited to that emotion."

[0181] The device provides real-time feedback based on the user's emotional state and progress during exercise. This allows users to receive appropriate guidance tailored to their condition during exercise, leading to a more effective workout experience. The feedback includes encouraging messages when the user is progressing at an appropriate pace, as well as messages encouraging rest.

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The device receives physical data from the user. Specifically, the user inputs individual profile data such as age, height, weight, and range of motion into the interface. The input in this step is the user's physical characteristic data, and the output is this data temporarily stored on the device.

[0185] Step 2:

[0186] The device uses its built-in camera and microphone to collect the user's facial expressions and voice tone. This allows for the acquisition of emotional data in real time. The input for this step is the user's facial expressions and voice, and the output is emotional data formatted for analysis.

[0187] Step 3:

[0188] The device collects physical and emotional data and sends it to the server using secure communication. In this step, the input is the formatted physical and emotional data, and the output is the data packets sent to the server.

[0189] Step 4:

[0190] The server uses a generated AI model to customize the exercise program based on the data it receives. Specific prompts, such as "What emotion is the user feeling? Suggest a 40-minute yoga routine best suited to that emotion," are used. The input for this step is integrated physical and emotional data, and the output is a specific exercise program.

[0191] Step 5:

[0192] The server generates an exercise program and sends it to the terminal. The terminal then presents the received program to the user in an interactive format. The input in this step is the created exercise program, and the output is the exercise instructions displayed on the user's screen.

[0193] Step 6:

[0194] During exercise, the device monitors the user's emotional state in real time and provides feedback as needed. It displays encouraging or rest-prompting messages according to the user's pace. The input for this step is emotional data during exercise, and the output is a tailored feedback message.

[0195] Step 7:

[0196] Once the user completes their exercise, the device re-evaluates their final emotional state and sends the exercise data to the server. The input for this step is the completed exercise data and emotional evaluation data, and the output is the final report sent to the server.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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".

[0213] As an embodiment of the present invention, an interactive system that provides a personalized exercise program based on the user's physical data will be described. This system involves interaction between a terminal, a server, and the user.

[0214] The device first receives physical data entered by the user. This physical data includes the user's age, height, weight, medical history, and range of motion. This data later serves as foundational data for generating exercise programs.

[0215] Next, the device sends user data to the server. The server analyzes the received data and generates an optimal exercise program for the user. Specifically, it uses an algorithm to evaluate the user's health condition and range of motion and selects individually optimized exercises. For example, for a user with limited range of motion in their shoulders, a program will be generated that includes shoulder stretches and light-intensity exercises.

[0216] The generated exercise program is presented to the user in a game format. This game format includes interactive elements such as earning points and leveling up by performing exercises. This allows users to enjoy the exercise not just as a form of exercise, but as a game, increasing their likelihood of continuing to exercise.

[0217] As the user exercises, the device provides feedback in real time, matching the progress of the workout. For example, it displays encouraging messages like "Well done!" and provides corrective instructions if the movements are incorrect. Once the workout is complete, the device sends the data to the server.

[0218] The server uses the received exercise data to evaluate the user's progress. If necessary, it readjusts the exercise program and generates a new program tailored to the user's changing health condition. This helps improve the user's physical abilities and enables continuous health maintenance.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The terminal provides the user with a physical data input interface. The user uses this interface to input their age, height, weight, range of motion, etc. This data is used as foundational information to generate a personalized exercise program in a later step.

[0222] Step 2:

[0223] The terminal transmits the entered body data to the server. This transmission is performed using a secure communication protocol to guarantee data integrity.

[0224] Step 3:

[0225] The server analyzes the received user data and generates an individually optimized exercise program. Based on the user's characteristics, the algorithm selects stretches and light exercises to improve shoulder mobility, for example, if the user has limited shoulder range of motion.

[0226] Step 4:

[0227] The server converts the generated exercise program into a game format and sends this data to the terminal. The transmitted game format is then displayed in the user interface.

[0228] Step 5:

[0229] The user performs a game-style exercise program presented through the device. The device monitors the user's movements and progress and provides feedback as needed. For example, if the movements are correct, it displays a message such as "Well done!"

[0230] Step 6:

[0231] After completing an exercise, the device sends the data to the server. This data includes information such as the time taken for the exercise and the accuracy of the movements.

[0232] Step 7:

[0233] The server analyzes the received exercise data and evaluates the user's progress. Based on user feedback, it adjusts the exercise program as needed and generates new programs, continuously providing exercise tailored to the user's changing health condition.

[0234] (Example 1)

[0235] 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."

[0236] In modern times, many people exercise to maintain their health and improve their physical fitness, but there is a problem in that it is difficult to continue exercising effectively and consistently. In particular, each user has a different physical condition and exercise ability, and creating an appropriate exercise program tailored to them is a major challenge. Maintaining motivation to exercise is also difficult.

[0237] 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.

[0238] In this invention, the server includes means for inputting user information, means for generating an exercise plan using an algorithm based on the user information, and means for transmitting exercise performance information to the server, analyzing it, and adjusting the exercise plan. This makes it possible to provide an exercise program optimized for each individual user and to increase motivation through effective feedback.

[0239] "Means for inputting user information" refers to an interface that allows users to input information about their physical data and health status into a terminal.

[0240] "Means of generating exercise plans using algorithms" refers to calculation methods that analyze information provided by users and create exercise programs tailored to individual conditions and goals.

[0241] "Means of presenting in an interactive format" refers to display and operation methods that enable users to perform the generated exercise program through games or activities.

[0242] "Real-time monitoring means" refers to technologies such as sensors and cameras that instantly record and evaluate a user's movements and performance while they are exercising.

[0243] "A means of sending exercise performance information to a server, analyzing it, and adjusting the exercise plan" refers to a process of sending the results of a user's exercise session to a server, re-evaluating the exercise program based on this information, and providing a new plan.

[0244] This invention is a system that provides an optimal exercise program based on the user's individual physical information. This system provides an interactive environment that operates between a terminal, a server, and the user. Specific embodiments are as follows.

[0245] Users input their physical data using a device. This input is done via a touchscreen or keyboard and includes a wide range of information such as age, height, weight, medical history, and range of motion. The interface on the device is designed to allow users to input data accurately and provides feedback prompting the user to check for errors or missing data.

[0246] The device sends the user's entered physical data to the server. The server uses a generative AI model to analyze the received data. This model assesses the user's health status and generates an exercise program that includes the most suitable exercises for the user. The algorithm used here takes into account each user's different limitations and abilities to create a personalized plan.

[0247] The generated exercise program is provided in a gamified format, and users perform it through their device. As users complete the exercise, they are awarded points and level up within the game, incorporating mechanisms to maintain continuous motivation. A feedback function monitors the user's exercise technique in real time and provides corrective instructions as needed. A concrete example of device usage is earning a specific badge for successfully completing 10 squats.

[0248] Once the exercise is complete, the device sends the data back to the server. The server analyzes this data and evaluates the user's progress. Next, based on the generated AI model, it readjusts the exercise program as needed. This enhances the user's physical abilities and helps maintain their health.

[0249] An example of a prompt message is, "If a 50-year-old male user has knee arthritis, please suggest an exercise program that supports the knee while improving overall flexibility." In this way, the system helps generate exercise programs tailored to each individual user.

[0250] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0251] Step 1:

[0252] The terminal receives physical data from the user as input. This data includes information such as age, height, weight, medical history, and range of motion. The terminal converts this data into a format for transmission to the server and completes the preparation for transmission. Specifically, it has functions to verify the integrity of the input data and check for missing values ​​and errors.

[0253] Step 2:

[0254] The server receives physical data transmitted from the terminal as input. Using a generative AI model, this data is analyzed to evaluate the user's health status and exercise capacity. The analysis process involves statistical processing of the data and the use of specialized algorithms to determine the optimal exercise program. As a result, the most suitable exercise program for each user is generated.

[0255] Step 3:

[0256] The generated exercise program is output from the server to the terminal. The terminal presents this exercise program to the user in an interactive format. Specifically, it uses a game-like interface that allows the user to earn points and level up each time they perform an exercise. This helps maintain continuous motivation.

[0257] Step 4:

[0258] When a user performs an exercise program, the device tracks the user's movements in real time using motion sensors and a camera. It receives data from the sensors as input and evaluates the accuracy of the exercise by comparing it to predetermined movements. If the correct movements are confirmed, the device immediately displays feedback such as "Perfect!". It also provides specific instructions if corrections are needed.

[0259] Step 5:

[0260] Once an exercise session ends, the device sends session data to the server. The server receives this data as input and evaluates the user's progress. Next, it uses a generative AI model to determine whether the exercise program is appropriate for the user's current state and readjusts the program if necessary. As a result, the newly adjusted exercise program is output to the device again.

[0261] (Application Example 1)

[0262] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0263] Traditional fitness programs often provide uniform exercise instruction without adequately considering the individual physical abilities and health conditions of each participant, making effective health management and the formation of consistent exercise habits difficult. Furthermore, they lack systems to maintain motivation, hindering long-term participation. There is a need for a personalized exercise program delivery system that addresses these issues and is applicable even in a work environment.

[0264] 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.

[0265] In this invention, the server includes a device for collecting individual user information, a device for generating an exercise plan based on the user information, and a device for presenting the generated exercise plan in a virtualized format. This enables the provision of exercise programs optimized for each user and the maintenance of sustained motivation.

[0266] "Individual user information" refers to detailed data about individual users, such as age, height, weight, and health status.

[0267] An "exercise plan" refers to an exercise program that is individually tailored to each user based on their physical abilities and health condition.

[0268] "Virtualized format" refers to interactive motion content presented within a virtual environment, separate from the physical reality environment.

[0269] "Exercise performance information" refers to data collected when a user performs exercise, including accuracy of movement, amount of exercise, and heart rate.

[0270] "Analytical techniques" refer to algorithms and tools used to analyze data and extract specific patterns or insights.

[0271] "Interaction-providing media" refers to devices or means that allow users to receive feedback and guidance during exercise.

[0272] The interactive fitness system implementing the present invention provides personalized exercise programs based on the user's physical data, supporting the user's continuous health management. This system involves interaction between a server, a terminal, and the user.

[0273] The server receives information transmitted from terminals that collect physical data such as the user's age, height, weight, medical history, and range of motion. Based on the received data, it performs analysis using a generative AI model to generate an optimal exercise program for the user. This exercise program is then adjusted to optimize the user's individual needs by evaluating the user's health condition using a machine learning model based on TensorFlow. For example, a user with limited shoulder range of motion will be provided with a program that includes shoulder stretches and low-impact exercises.

[0274] The device is an interactive medium, similar to smart glasses, that presents the generated exercise program to the user in a virtualized format. The Unity-based system provides real-time feedback and guidance during exercise, playing a role in maintaining user motivation.

[0275] When a user actually exercises, exercise data is collected by the terminal and sent to the server. Based on the collected information, the server evaluates effective progress and continuously optimizes the exercise program. This enables personalized support tailored to the user's health condition, helping them to practice effective fitness.

[0276] As a specific example, by implementing a short stretching program with smart glasses every day during the working hours in a factory, the afternoon work concentration of workers can be improved. As an example of a prompt sentence, there is "Please generate an optimal short exercise program based on the following user's body data: age 35, height 170 cm, weight 65 kg, working hours 6 hours, prone to shoulder stiffness."

[0277] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0278] Step 1:

[0279] The terminal collects body data (age, height, weight, medical history, range of motion, etc.) from the user as input. This data is the basic data used for subsequent exercise program generation. The input data is centrally managed inside the terminal and formatted for transmission to the server.

[0280] Step 2:

[0281] The terminal transmits the collected body data to the server. By receiving the transmitted data, the server obtains the basic body information of the user. Based on this information, preparations for subsequent data analysis and program generation are made.

[0282] Step 3:

[0283] The server starts analyzing the received body data as input. An AI model for generation (e.g., a machine learning model using TensorFlow) is used for the analysis, and an exercise program suitable for the individual health condition of the user is generated. The data operations performed here determine the content of the program by an algorithm that reflects the constraints of the user's range of motion and health condition.

[0284] Step 4:

[0285] The server sends the generated exercise program to the terminal. When the terminal receives this program, it presents it to the user in a virtualized form through an appropriate user interface (e.g., smart glasses). At this time, preparations are made to display real-time feedback to the user as the exercise progresses.

[0286] Step 5:

[0287] The user performs physical actions according to the presented exercise program. The terminal monitors the data generated at each stage of the exercise (e.g., exercise intensity, angle, heart rate, etc.) and collects it as the implementation status. The collected data is used for subsequent feedback and program adjustment.

[0288] Step 6:

[0289] The terminal sends the exercise implementation information to the server. The server evaluates the user's exercise performance based on the received exercise implementation information as input, and modifies or regenerates the exercise program as necessary. The generation AI model described above is continuously used for regeneration.

[0290] Step 7:

[0291] The server returns the readjusted exercise program to the terminal to provide continuous feedback, and presents it to the user as the program to be executed in subsequent exercise sessions. As a result, a new exercise plan with updated prompt text is generated, and continuous support for improving the user's physical ability and health management is provided. \(

[0292] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0293] As an embodiment of this invention, a system that recognizes a user's emotions and provides an exercise program in accordance with those emotions will be described. This system includes a terminal, a server, and an emotion engine.

[0294] The device first provides the user with a physical data input interface. The user uses this interface to input information such as age, height, weight, and range of motion. This input data serves as the basis for generating an exercise program.

[0295] The device also collects the user's facial expressions and tone of voice in real time. The emotion engine analyzes this data and evaluates the user's emotional state. For example, if the user shows signs of dissatisfaction or stress during exercise, the emotion engine will recognize this.

[0296] The collected data is transmitted to the server via secure communication. The server analyzes the physical and emotional data together to generate an individually optimized exercise program. For example, if the user indicates a relaxed state, it will provide a more challenging workout; if the user is tired, it will provide a program centered on relaxing stretches.

[0297] The generated exercise program is provided to the device in game format and displayed on the user interface. Users can enjoy exercising in a game-like format as they progress. The device provides feedback based on the user's progress and emotions, supporting the user within the game. For example, it can display messages such as "You're making good progress!" or "Take a short break and try again!"

[0298] Once a user completes an exercise, the emotion engine re-evaluates their final emotional state and sends this data, along with the exercise data, to the server. The server uses both the exercise data and the emotional data to adjust the exercise program, continuously providing the most suitable exercise for the user's emotional and physical condition. This creates an environment where users can continue exercising enjoyably and without strain.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The terminal displays a physical data input interface to the user. Through this interface, the user inputs information about their age, height, weight, and range of motion. This data is used by the system as foundational information to customize exercise programs.

[0302] Step 2:

[0303] The device captures the user's facial expressions and voice tone in real time and sends them to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state in real time. For example, if the user is smiling, it determines that they are experiencing positive emotions.

[0304] Step 3:

[0305] The device transmits collected physical and emotional data to a server. The data is transferred using a secure communication protocol to ensure privacy and data integrity.

[0306] Step 4:

[0307] The server analyzes the user's physical and emotional data and generates an optimal exercise program using a specific algorithm. For example, if the user is feeling stressed, the program will include exercises that promote relaxation, thus taking emotional data into consideration.

[0308] Step 5:

[0309] The server converts the generated exercise program into a game format and sends this game data to the terminal. The game format includes elements such as accumulating points by performing exercises and a mechanism for level - up.

[0310] Step 6:

[0311] The user executes the exercise program in game format presented from the terminal. During the exercise, the terminal monitors the user's movements and provides real - time feedback. When the user clears the exercise task, the terminal provides positive feedback.

[0312] Step 7:

[0313] When the exercise is completed, the terminal re - evaluates the exercise execution data and the final emotional state with the emotion engine and sends them to the server. This makes it possible to comprehensively understand the user's progress and emotional changes.

[0314] Step 8:

[0315] [[ID=3l]]The server adjusts the exercise program based on the final data. As a result, the user's next exercise experience becomes more individualized and adapted to the user's feedback and emotional state. Support is also provided in subsequent sessions so that the user can continue to exercise in a safe and effective way.

[0316] (Example 2)

[0317] Next, Example 2 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".

[0318] Existing exercise programs often fail to adequately optimize for individual users' emotional states and physical abilities, making it difficult for users to enjoy and continue exercising without undue strain. In particular, programs that lead to decreased motivation or excessive fatigue make long-term participation difficult.

[0319] 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.

[0320] In this invention, the server includes means for collecting individual user information, means for analyzing facial expressions and voice to evaluate emotions, and means for generating exercise programs using a generative AI model. This makes it possible to provide individually optimized exercise programs tailored to the user's emotional state and physical abilities, thereby improving the continuation and enjoyment of exercise.

[0321] "Individual user information" refers to data unique to each user, including information that indicates physical characteristics and emotional state.

[0322] "Means of analyzing facial expressions and voice to evaluate emotions" refers to the process of analyzing a user's facial expressions and voice tone using devices such as cameras and microphones, and then estimating their emotional state from that analysis.

[0323] "Methods for generating exercise programs using generative AI models" refers to the process of creating an optimal exercise program based on user information by utilizing artificial intelligence technologies, including machine learning.

[0324] "Presenting in a game format" refers to the process of providing an exercise program that includes visual and auditory elements in a way that allows users to enjoy it interactively.

[0325] "Information regarding exercise participation" refers to data about the exercise that users actually performed, including information such as the type of exercise, the number of repetitions, and changes in emotions.

[0326] "Having a feedback function" means having the ability to provide appropriate advice and instructions based on the user's actions and progress.

[0327] "Continuous motivation" refers to providing elements and functions that help users maintain their motivation and interest in continuing to exercise.

[0328] This invention relates to a system for recognizing a user's emotional state and providing an individually optimized exercise program. The system comprises a terminal, a server, and an emotion engine.

[0329] The device provides the user with a physical information input interface. The user inputs information about their body, such as age, height, weight, and range of motion. This information is used as basic data for generating exercise programs later. The device also uses a camera and microphone to collect the user's facial expressions and voice tone in real time.

[0330] The emotion engine uses this collected data to analyze and evaluate the user's emotional state. The analysis process employs image processing and speech recognition software, and machine learning algorithms are used to estimate the emotional state. For example, if a user is smiling, the emotion engine evaluates this as joy.

[0331] These analysis results and physical information are transmitted to the server via secure communication. The server uses a generative AI model to generate an exercise program based on the user's physical and emotional state. This AI model is optimized, for example, based on past exercise history and emotional data stored in a database.

[0332] The generated exercise program is provided to the device in a game format. The device presents the program to the user through an interactive screen that incorporates visual and auditory elements. The user can follow the program and enjoy exercising. The device has a feedback function and displays messages such as "Great pace!" or "Let's relax a little!"

[0333] After the user completes their exercise, the emotion engine reassesss their emotional state and sends the results, along with the exercise data, to the server. The server then uses this data to adjust the exercise program, continuously providing the optimal program.

[0334] As a concrete example, if a user inputs information such as being 30 years old, 170 cm tall, and weighing 65 kg, and displays a calm expression, the generating AI model will create a 30-minute yoga program focused on relaxation. An example of a prompt would be: "Create an exercise program for a 30-year-old user, 170 cm tall, and weighing 65 kg, who receives feedback with a calm expression and continues to receive encouraging messages."

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] The terminal provides the user with a physical information input interface. The user inputs information such as age, height, weight, and range of motion. Once this data is entered, the terminal saves it to the system as initial setup data and sends it to the server. Accurate data entry is crucial at this stage, and input verification functions are used as needed to prevent errors.

[0338] Step 2:

[0339] The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time. Specifically, the camera tracks the user's facial movements, and the microphone records voice patterns. Based on this input data, the emotion engine performs preprocessing to evaluate the facial and voice data, creating a dataset for emotion estimation.

[0340] Step 3:

[0341] The emotion engine analyzes the facial expression and voice data collected in the previous step. It extracts facial features from the input facial expression data and analyzes the tone and speed of the voice from the voice data. Using machine learning algorithms, it estimates the user's emotional state from these features and outputs an emotion label such as "joy" or "fatigue."

[0342] Step 4:

[0343] The server receives the user's physical information and emotion labels from the emotion engine, and generates an exercise program using a generative AI model. In this step, a specific algorithm calculates the optimal exercise program based on the input information, and the result is constructed as a program. For example, if relaxation is needed, a yoga program will be recommended.

[0344] Step 5:

[0345] The exercise program generated from the server is sent to the terminal. The terminal receives this data and presents it to the user in a game format. Using graphics and sound, it provides an interactive experience and displays instructions to the user in accordance with the progress of the program.

[0346] Step 6:

[0347] While the user progresses through the exercise program, the device continuously monitors the user's progress and emotional changes, providing feedback as needed. This includes messages and advice to boost the user's motivation. The feedback is updated in real time, providing continuous support to the user.

[0348] Step 7:

[0349] After the exercise is completed, the device sends the latest facial and voice data to the emotion engine to re-evaluate the final emotional state. The emotion engine analyzes this data and sends it to the server to create the final dataset. This data forms the basis for future program adjustments.

[0350] (Application Example 2)

[0351] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0352] Traditional exercise program delivery systems, while capable of incorporating individual physical characteristics, have the problem of being unable to reflect users' changing emotional states in real time, making it difficult to maintain user motivation. Furthermore, the standardization of exercise programs prevents the provision of programs optimized for individual users.

[0353] 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.

[0354] In this invention, the server includes means for collecting data to analyze the user's emotional state, means for generating an exercise program based on the user's data and emotional state, and means for providing real-time feedback to maintain motivation. This enables the provision of an optimal exercise program based on the user's emotions and physical characteristics, allowing for continuous exercise and improved motivation.

[0355] "Data for analyzing the emotional state of users" refers to information including the user's facial expressions, tone of voice, and behavioral indicators, which is used to evaluate the user's emotional tendencies by analyzing this data.

[0356] "Means for generating exercise programs" refers to a processing system that uses algorithms and databases to program personalized exercise content based on collected physical and emotional data of users.

[0357] An "interactive format" refers to a method of presenting exercise programs that offers users an experience in which they can actively participate and receive feedback in real time.

[0358] "Real-time feedback" is a method of communication that aims to increase user motivation by providing immediate information to users as they exercise, based on their progress and emotional state.

[0359] The system for implementing this invention mainly includes a terminal, a server, and an emotion engine. Before the user starts an exercise program, the terminal requests input of physical data such as age, height, weight, and range of motion. This information is transmitted to the server as basic data for generating the exercise program.

[0360] Next, the device uses its built-in camera and microphone to collect the user's real-time facial expressions and voice tone. This data is analyzed by an emotion engine and sent to a server as a basis for evaluating the user's emotional state. The emotion engine utilizes emotion recognition software, such as Microsoft Azure's Face API.

[0361] On the server, an AI model is used to generate individually customized exercise programs based on collected physical and emotional data. This exercise program is sent to the terminal in an interactive format, allowing the user to participate. Here's an example of a user joining a yoga class. For instance, the AI ​​model generates exercise content using a prompt message such as, "What emotion is the user feeling? Suggest a 40-minute yoga routine that is best suited to that emotion."

[0362] The device provides real-time feedback based on the user's emotional state and progress during exercise. This allows users to receive appropriate guidance tailored to their condition during exercise, leading to a more effective workout experience. The feedback includes encouraging messages when the user is progressing at an appropriate pace, as well as messages encouraging rest.

[0363] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0364] Step 1:

[0365] The device receives physical data from the user. Specifically, the user inputs individual profile data such as age, height, weight, and range of motion into the interface. The input in this step is the user's physical characteristic data, and the output is this data temporarily stored on the device.

[0366] Step 2:

[0367] The device uses its built-in camera and microphone to collect the user's facial expressions and voice tone. This allows for the acquisition of emotional data in real time. The input for this step is the user's facial expressions and voice, and the output is emotional data formatted for analysis.

[0368] Step 3:

[0369] The device collects physical and emotional data and sends it to the server using secure communication. In this step, the input is the formatted physical and emotional data, and the output is the data packets sent to the server.

[0370] Step 4:

[0371] The server uses a generated AI model to customize the exercise program based on the data it receives. Specific prompts, such as "What emotion is the user feeling? Suggest a 40-minute yoga routine best suited to that emotion," are used. The input for this step is integrated physical and emotional data, and the output is a specific exercise program.

[0372] Step 5:

[0373] The server generates an exercise program and sends it to the terminal. The terminal then presents the received program to the user in an interactive format. The input in this step is the created exercise program, and the output is the exercise instructions displayed on the user's screen.

[0374] Step 6:

[0375] During exercise, the device monitors the user's emotional state in real time and provides feedback as needed. It displays encouraging or rest-prompting messages according to the user's pace. The input for this step is emotional data during exercise, and the output is a tailored feedback message.

[0376] Step 7:

[0377] Once the user completes their exercise, the device re-evaluates their final emotional state and sends the exercise data to the server. The input for this step is the completed exercise data and emotional evaluation data, and the output is the final report sent to the server.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] [Third Embodiment]

[0382] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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".

[0394] As an embodiment of the present invention, an interactive system that provides a personalized exercise program based on the user's physical data will be described. This system involves interaction between a terminal, a server, and the user.

[0395] The device first receives physical data entered by the user. This physical data includes the user's age, height, weight, medical history, and range of motion. This data later serves as foundational data for generating exercise programs.

[0396] Next, the device sends user data to the server. The server analyzes the received data and generates an optimal exercise program for the user. Specifically, it uses an algorithm to evaluate the user's health condition and range of motion and selects individually optimized exercises. For example, for a user with limited range of motion in their shoulders, a program will be generated that includes shoulder stretches and light-intensity exercises.

[0397] The generated exercise program is presented to the user in a game format. This game format includes interactive elements such as earning points and leveling up by performing exercises. This allows users to enjoy the exercise not just as a form of exercise, but as a game, increasing their likelihood of continuing to exercise.

[0398] As the user exercises, the device provides feedback in real time, matching the progress of the workout. For example, it displays encouraging messages like "Well done!" and provides corrective instructions if the movements are incorrect. Once the workout is complete, the device sends the data to the server.

[0399] The server uses the received exercise data to evaluate the user's progress. If necessary, it readjusts the exercise program and generates a new program tailored to the user's changing health condition. This helps improve the user's physical abilities and enables continuous health maintenance.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] The terminal provides the user with a physical data input interface. The user uses this interface to input their age, height, weight, range of motion, etc. This data is used as foundational information to generate a personalized exercise program in a later step.

[0403] Step 2:

[0404] The terminal transmits the entered body data to the server. This transmission is performed using a secure communication protocol to guarantee data integrity.

[0405] Step 3:

[0406] The server analyzes the received user data and generates an individually optimized exercise program. Based on the user's characteristics, the algorithm selects stretches and light exercises to improve shoulder mobility, for example, if the user has limited shoulder range of motion.

[0407] Step 4:

[0408] The server converts the generated exercise program into a game format and sends this data to the terminal. The transmitted game format is then displayed in the user interface.

[0409] Step 5:

[0410] The user performs a game-style exercise program presented through the device. The device monitors the user's movements and progress and provides feedback as needed. For example, if the movements are correct, it displays a message such as "Well done!"

[0411] Step 6:

[0412] After completing an exercise, the device sends the data to the server. This data includes information such as the time taken for the exercise and the accuracy of the movements.

[0413] Step 7:

[0414] The server analyzes the received exercise data and evaluates the user's progress. Based on user feedback, it adjusts the exercise program as needed and generates new programs, continuously providing exercise tailored to the user's changing health condition.

[0415] (Example 1)

[0416] 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."

[0417] In modern times, many people exercise to maintain their health and improve their physical fitness, but there is a problem in that it is difficult to continue exercising effectively and consistently. In particular, each user has a different physical condition and exercise ability, and creating an appropriate exercise program tailored to them is a major challenge. Maintaining motivation to exercise is also difficult.

[0418] 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.

[0419] In this invention, the server includes means for inputting user information, means for generating an exercise plan using an algorithm based on the user information, and means for transmitting exercise performance information to the server, analyzing it, and adjusting the exercise plan. This makes it possible to provide an exercise program optimized for each individual user and to increase motivation through effective feedback.

[0420] "Means for inputting user information" refers to an interface that allows users to input information about their physical data and health status into a terminal.

[0421] "Means of generating exercise plans using algorithms" refers to calculation methods that analyze information provided by users and create exercise programs tailored to individual conditions and goals.

[0422] "Means of presenting in an interactive format" refers to display and operation methods that enable users to perform the generated exercise program through games or activities.

[0423] "Real-time monitoring means" refers to technologies such as sensors and cameras that instantly record and evaluate a user's movements and performance while they are exercising.

[0424] "A means of sending exercise performance information to a server, analyzing it, and adjusting the exercise plan" refers to a process of sending the results of a user's exercise session to a server, re-evaluating the exercise program based on this information, and providing a new plan.

[0425] This invention is a system that provides an optimal exercise program based on the user's individual physical information. This system provides an interactive environment that operates between a terminal, a server, and the user. Specific embodiments are as follows.

[0426] Users input their physical data using a device. This input is done via a touchscreen or keyboard and includes a wide range of information such as age, height, weight, medical history, and range of motion. The interface on the device is designed to allow users to input data accurately and provides feedback prompting the user to check for errors or missing data.

[0427] The device sends the user's entered physical data to the server. The server uses a generative AI model to analyze the received data. This model assesses the user's health status and generates an exercise program that includes the most suitable exercises for the user. The algorithm used here takes into account each user's different limitations and abilities to create a personalized plan.

[0428] The generated exercise program is provided in a gamified format, and users perform it through their device. As users complete the exercise, they are awarded points and level up within the game, incorporating mechanisms to maintain continuous motivation. A feedback function monitors the user's exercise technique in real time and provides corrective instructions as needed. A concrete example of device usage is earning a specific badge for successfully completing 10 squats.

[0429] Once the exercise is complete, the device sends the data back to the server. The server analyzes this data and evaluates the user's progress. Next, based on the generated AI model, it readjusts the exercise program as needed. This enhances the user's physical abilities and helps maintain their health.

[0430] An example of a prompt message is, "If a 50-year-old male user has knee arthritis, please suggest an exercise program that supports the knee while improving overall flexibility." In this way, the system helps generate exercise programs tailored to each individual user.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The terminal receives physical data from the user as input. This data includes information such as age, height, weight, medical history, and range of motion. The terminal converts this data into a format for transmission to the server and completes the preparation for transmission. Specifically, it has functions to verify the integrity of the input data and check for missing values ​​and errors.

[0434] Step 2:

[0435] The server receives physical data transmitted from the terminal as input. Using a generative AI model, this data is analyzed to evaluate the user's health status and exercise capacity. The analysis process involves statistical processing of the data and the use of specialized algorithms to determine the optimal exercise program. As a result, the most suitable exercise program for each user is generated.

[0436] Step 3:

[0437] The generated exercise program is output from the server to the terminal. The terminal presents this exercise program to the user in an interactive format. Specifically, it uses a game-like interface that allows the user to earn points and level up each time they perform an exercise. This helps maintain continuous motivation.

[0438] Step 4:

[0439] When a user performs an exercise program, the device tracks the user's movements in real time using motion sensors and a camera. It receives data from the sensors as input and evaluates the accuracy of the exercise by comparing it to predetermined movements. If the correct movements are confirmed, the device immediately displays feedback such as "Perfect!". It also provides specific instructions if corrections are needed.

[0440] Step 5:

[0441] Once an exercise session ends, the device sends session data to the server. The server receives this data as input and evaluates the user's progress. Next, it uses a generative AI model to determine whether the exercise program is appropriate for the user's current state and readjusts the program if necessary. As a result, the newly adjusted exercise program is output to the device again.

[0442] (Application Example 1)

[0443] 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."

[0444] Traditional fitness programs often provide uniform exercise instruction without adequately considering the individual physical abilities and health conditions of each participant, making effective health management and the formation of consistent exercise habits difficult. Furthermore, they lack systems to maintain motivation, hindering long-term participation. There is a need for a personalized exercise program delivery system that addresses these issues and is applicable even in a work environment.

[0445] 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.

[0446] In this invention, the server includes a device for collecting individual user information, a device for generating an exercise plan based on the user information, and a device for presenting the generated exercise plan in a virtualized format. This enables the provision of exercise programs optimized for each user and the maintenance of sustained motivation.

[0447] "Individual user information" refers to detailed data about individual users, such as age, height, weight, and health status.

[0448] An "exercise plan" refers to an exercise program that is individually tailored to each user based on their physical abilities and health condition.

[0449] "Virtualized format" refers to interactive motion content presented within a virtual environment, separate from the physical reality environment.

[0450] "Exercise performance information" refers to data collected when a user performs exercise, including accuracy of movement, amount of exercise, and heart rate.

[0451] "Analytical techniques" refer to algorithms and tools used to analyze data and extract specific patterns or insights.

[0452] "Interaction-providing media" refers to devices or means that allow users to receive feedback and guidance during exercise.

[0453] The interactive fitness system implementing the present invention provides personalized exercise programs based on the user's physical data, supporting the user's continuous health management. This system involves interaction between a server, a terminal, and the user.

[0454] The server receives information transmitted from terminals that collect physical data such as the user's age, height, weight, medical history, and range of motion. Based on the received data, it performs analysis using a generative AI model to generate an optimal exercise program for the user. This exercise program is then adjusted to optimize the user's individual needs by evaluating the user's health condition using a machine learning model based on TensorFlow. For example, a user with limited shoulder range of motion will be provided with a program that includes shoulder stretches and low-impact exercises.

[0455] The device is an interactive medium, similar to smart glasses, that presents the generated exercise program to the user in a virtualized format. The Unity-based system provides real-time feedback and guidance during exercise, playing a role in maintaining user motivation.

[0456] When a user actually exercises, exercise data is collected by the terminal and sent to the server. Based on the collected information, the server evaluates effective progress and continuously optimizes the exercise program. This enables personalized support tailored to the user's health condition, helping them to practice effective fitness.

[0457] As a concrete example, performing a short stretching program using smart glasses daily during factory work hours improves workers' concentration during the afternoon. An example of a prompt message is: "Generate an optimal short exercise program based on the following user physical data: Age 35, Height 170cm, Weight 65kg, Working hours 6 hours, Prone to stiff shoulders."

[0458] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0459] Step 1:

[0460] The device collects physical data from the user (age, height, weight, medical history, range of motion, etc.) as input. This data serves as the basis for generating exercise programs later on. The input data is centrally managed within the device and formatted for transmission to the server.

[0461] Step 2:

[0462] The terminal transmits the collected physical data to the server. By receiving the transmitted data, the server obtains the user's basic physical information. Based on this information, it prepares for subsequent data analysis and program generation.

[0463] Step 3:

[0464] The server begins analyzing the received physical data as input. A generative AI model (e.g., a machine learning model using TensorFlow) is used for the analysis, generating an exercise program tailored to each user's individual health condition. The data calculations performed here determine the program's content using algorithms that reflect the user's range of motion and health constraints.

[0465] Step 4:

[0466] The server sends the generated exercise program to the terminal. Upon receiving this program, the terminal presents it to the user in a virtualized format through an appropriate user interface (e.g., smart glasses). At this time, it prepares to display real-time feedback to the user as the exercise progresses.

[0467] Step 5:

[0468] The user performs physical actions according to the presented exercise program. The device monitors data generated at each stage of the exercise (e.g., exercise intensity, angle, heart rate, etc.) and collects it as a record of the exercise. The collected data is used for subsequent feedback and program adjustments.

[0469] Step 6:

[0470] The terminal sends exercise performance information to the server. Based on the exercise performance information received as input, the server evaluates the user's exercise performance and modifies or regenerates the exercise program as needed. The aforementioned generative AI model is continuously used for regeneration.

[0471] Step 7:

[0472] The server returns the readjusted exercise program to the terminal to provide continuous feedback, and presents it to the user as the program to run in subsequent exercise sessions. This generates a new exercise plan with updated prompts, supporting the user's continued improvement of physical abilities and health management.

[0473] 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.

[0474] As an embodiment of this invention, a system that recognizes a user's emotions and provides an exercise program in accordance with those emotions will be described. This system includes a terminal, a server, and an emotion engine.

[0475] The device first provides the user with a physical data input interface. The user uses this interface to input information such as age, height, weight, and range of motion. This input data serves as the basis for generating an exercise program.

[0476] The device also collects the user's facial expressions and tone of voice in real time. The emotion engine analyzes this data and evaluates the user's emotional state. For example, if the user shows signs of dissatisfaction or stress during exercise, the emotion engine will recognize this.

[0477] The collected data is transmitted to the server via secure communication. The server analyzes the physical and emotional data together to generate an individually optimized exercise program. For example, if the user indicates a relaxed state, it will provide a more challenging workout; if the user is tired, it will provide a program centered on relaxing stretches.

[0478] The generated exercise program is provided to the device in game format and displayed on the user interface. Users can enjoy exercising in a game-like format as they progress. The device provides feedback based on the user's progress and emotions, supporting the user within the game. For example, it can display messages such as "You're making good progress!" or "Take a short break and try again!"

[0479] Once a user completes an exercise, the emotion engine re-evaluates their final emotional state and sends this data, along with the exercise data, to the server. The server uses both the exercise data and the emotional data to adjust the exercise program, continuously providing the most suitable exercise for the user's emotional and physical condition. This creates an environment where users can continue exercising enjoyably and without strain.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The terminal displays a physical data input interface to the user. Through this interface, the user inputs information about their age, height, weight, and range of motion. This data is used by the system as foundational information to customize exercise programs.

[0483] Step 2:

[0484] The device captures the user's facial expressions and voice tone in real time and sends them to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state in real time. For example, if the user is smiling, it determines that they are experiencing positive emotions.

[0485] Step 3:

[0486] The device transmits collected physical and emotional data to a server. The data is transferred using a secure communication protocol to ensure privacy and data integrity.

[0487] Step 4:

[0488] The server analyzes the user's physical and emotional data and generates an optimal exercise program using a specific algorithm. For example, if the user is feeling stressed, the program will include exercises that promote relaxation, thus taking emotional data into consideration.

[0489] Step 5:

[0490] The server converts the generated exercise program into a game format and sends this game data to the terminal. The game format includes elements such as accumulating points by performing exercises and a level-up system.

[0491] Step 6:

[0492] The user performs a game-style exercise program presented on the device. During the exercise, the device monitors the user's movements and provides real-time feedback. When the user successfully completes the exercise tasks, the device provides positive feedback.

[0493] Step 7:

[0494] Once the exercise is complete, the device uses an emotion engine to re-evaluate the exercise data and final emotional state, and then sends the results to the server. This allows for a comprehensive understanding of the user's progress and emotional changes.

[0495] Step 8:

[0496] The server adjusts the exercise program based on the final data. This makes the user's next exercise experience more personalized and tailored to their feedback and emotional state. It also supports the user in continuing to exercise safely and effectively in subsequent sessions.

[0497] (Example 2)

[0498] 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."

[0499] Existing exercise programs often fail to adequately optimize for individual users' emotional states and physical abilities, making it difficult for users to enjoy and continue exercising without undue strain. In particular, programs that lead to decreased motivation or excessive fatigue make long-term participation difficult.

[0500] 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.

[0501] In this invention, the server includes means for collecting individual user information, means for analyzing facial expressions and voice to evaluate emotions, and means for generating exercise programs using a generative AI model. This makes it possible to provide individually optimized exercise programs tailored to the user's emotional state and physical abilities, thereby improving the continuation and enjoyment of exercise.

[0502] "Individual user information" refers to data unique to each user, including information that indicates physical characteristics and emotional state.

[0503] "Means of analyzing facial expressions and voice to evaluate emotions" refers to the process of analyzing a user's facial expressions and voice tone using devices such as cameras and microphones, and then estimating their emotional state from that analysis.

[0504] "Methods for generating exercise programs using generative AI models" refers to the process of creating an optimal exercise program based on user information by utilizing artificial intelligence technologies, including machine learning.

[0505] "Presenting in a game format" refers to the process of providing an exercise program that includes visual and auditory elements in a way that allows users to enjoy it interactively.

[0506] "Information regarding exercise participation" refers to data about the exercise that users actually performed, including information such as the type of exercise, the number of repetitions, and changes in emotions.

[0507] "Having a feedback function" means having the ability to provide appropriate advice and instructions based on the user's actions and progress.

[0508] "Continuous motivation" refers to providing elements and functions that help users maintain their motivation and interest in continuing to exercise.

[0509] This invention relates to a system for recognizing a user's emotional state and providing an individually optimized exercise program. The system comprises a terminal, a server, and an emotion engine.

[0510] The device provides the user with a physical information input interface. The user inputs information about their body, such as age, height, weight, and range of motion. This information is used as basic data for generating exercise programs later. The device also uses a camera and microphone to collect the user's facial expressions and voice tone in real time.

[0511] The emotion engine uses this collected data to analyze and evaluate the user's emotional state. The analysis process employs image processing and speech recognition software, and machine learning algorithms are used to estimate the emotional state. For example, if a user is smiling, the emotion engine evaluates this as joy.

[0512] These analysis results and physical information are transmitted to the server via secure communication. The server uses a generative AI model to generate an exercise program based on the user's physical and emotional state. This AI model is optimized, for example, based on past exercise history and emotional data stored in a database.

[0513] The generated exercise program is provided to the device in a game format. The device presents the program to the user through an interactive screen that incorporates visual and auditory elements. The user can follow the program and enjoy exercising. The device has a feedback function and displays messages such as "Great pace!" or "Let's relax a little!"

[0514] After the user completes their exercise, the emotion engine reassesss their emotional state and sends the results, along with the exercise data, to the server. The server then uses this data to adjust the exercise program, continuously providing the optimal program.

[0515] As a concrete example, if a user inputs information such as being 30 years old, 170 cm tall, and weighing 65 kg, and displays a calm expression, the generating AI model will create a 30-minute yoga program focused on relaxation. An example of a prompt would be: "Create an exercise program for a 30-year-old user, 170 cm tall, and weighing 65 kg, who receives feedback with a calm expression and continues to receive encouraging messages."

[0516] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0517] Step 1:

[0518] The terminal provides the user with a physical information input interface. The user inputs information such as age, height, weight, and range of motion. Once this data is entered, the terminal saves it to the system as initial setup data and sends it to the server. Accurate data entry is crucial at this stage, and input verification functions are used as needed to prevent errors.

[0519] Step 2:

[0520] The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time. Specifically, the camera tracks the user's facial movements, and the microphone records voice patterns. Based on this input data, the emotion engine performs preprocessing to evaluate the facial and voice data, creating a dataset for emotion estimation.

[0521] Step 3:

[0522] The emotion engine analyzes the facial expression and voice data collected in the previous step. It extracts facial features from the input facial expression data and analyzes the tone and speed of the voice from the voice data. Using machine learning algorithms, it estimates the user's emotional state from these features and outputs an emotion label such as "joy" or "fatigue."

[0523] Step 4:

[0524] The server receives the user's physical information and emotion labels from the emotion engine, and generates an exercise program using a generative AI model. In this step, a specific algorithm calculates the optimal exercise program based on the input information, and the result is constructed as a program. For example, if relaxation is needed, a yoga program will be recommended.

[0525] Step 5:

[0526] The exercise program generated from the server is sent to the terminal. The terminal receives this data and presents it to the user in a game format. Using graphics and sound, it provides an interactive experience and displays instructions to the user in accordance with the progress of the program.

[0527] Step 6:

[0528] While the user progresses through the exercise program, the device continuously monitors the user's progress and emotional changes, providing feedback as needed. This includes messages and advice to boost the user's motivation. The feedback is updated in real time, providing continuous support to the user.

[0529] Step 7:

[0530] After the exercise is completed, the device sends the latest facial and voice data to the emotion engine to re-evaluate the final emotional state. The emotion engine analyzes this data and sends it to the server to create the final dataset. This data forms the basis for future program adjustments.

[0531] (Application Example 2)

[0532] 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."

[0533] Traditional exercise program delivery systems, while capable of incorporating individual physical characteristics, have the problem of being unable to reflect users' changing emotional states in real time, making it difficult to maintain user motivation. Furthermore, the standardization of exercise programs prevents the provision of programs optimized for individual users.

[0534] 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.

[0535] In this invention, the server includes means for collecting data to analyze the user's emotional state, means for generating an exercise program based on the user's data and emotional state, and means for providing real-time feedback to maintain motivation. This enables the provision of an optimal exercise program based on the user's emotions and physical characteristics, allowing for continuous exercise and improved motivation.

[0536] "Data for analyzing the emotional state of users" refers to information including the user's facial expressions, tone of voice, and behavioral indicators, which is used to evaluate the user's emotional tendencies by analyzing this data.

[0537] "Means for generating exercise programs" refers to a processing system that uses algorithms and databases to program personalized exercise content based on collected physical and emotional data of users.

[0538] An "interactive format" refers to a method of presenting exercise programs that offers users an experience in which they can actively participate and receive feedback in real time.

[0539] "Real-time feedback" is a method of communication that aims to increase user motivation by providing immediate information to users as they exercise, based on their progress and emotional state.

[0540] The system for implementing this invention mainly includes a terminal, a server, and an emotion engine. Before the user starts an exercise program, the terminal requests input of physical data such as age, height, weight, and range of motion. This information is transmitted to the server as basic data for generating the exercise program.

[0541] Next, the device uses its built-in camera and microphone to collect the user's real-time facial expressions and voice tone. This data is analyzed by an emotion engine and sent to a server as a basis for evaluating the user's emotional state. The emotion engine utilizes emotion recognition software, such as Microsoft Azure's Face API.

[0542] On the server, an AI model is used to generate individually customized exercise programs based on collected physical and emotional data. This exercise program is sent to the terminal in an interactive format, allowing the user to participate. Here's an example of a user joining a yoga class. For instance, the AI ​​model generates exercise content using a prompt message such as, "What emotion is the user feeling? Suggest a 40-minute yoga routine that is best suited to that emotion."

[0543] The device provides real-time feedback based on the user's emotional state and progress during exercise. This allows users to receive appropriate guidance tailored to their condition during exercise, leading to a more effective workout experience. The feedback includes encouraging messages when the user is progressing at an appropriate pace, as well as messages encouraging rest.

[0544] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0545] Step 1:

[0546] The device receives physical data from the user. Specifically, the user inputs individual profile data such as age, height, weight, and range of motion into the interface. The input in this step is the user's physical characteristic data, and the output is this data temporarily stored on the device.

[0547] Step 2:

[0548] The device uses its built-in camera and microphone to collect the user's facial expressions and voice tone. This allows for the acquisition of emotional data in real time. The input for this step is the user's facial expressions and voice, and the output is emotional data formatted for analysis.

[0549] Step 3:

[0550] The device collects physical and emotional data and sends it to the server using secure communication. In this step, the input is the formatted physical and emotional data, and the output is the data packets sent to the server.

[0551] Step 4:

[0552] The server uses a generated AI model to customize the exercise program based on the data it receives. Specific prompts, such as "What emotion is the user feeling? Suggest a 40-minute yoga routine best suited to that emotion," are used. The input for this step is integrated physical and emotional data, and the output is a specific exercise program.

[0553] Step 5:

[0554] The server generates an exercise program and sends it to the terminal. The terminal then presents the received program to the user in an interactive format. The input in this step is the created exercise program, and the output is the exercise instructions displayed on the user's screen.

[0555] Step 6:

[0556] During exercise, the device monitors the user's emotional state in real time and provides feedback as needed. It displays encouraging or rest-prompting messages according to the user's pace. The input for this step is emotional data during exercise, and the output is a tailored feedback message.

[0557] Step 7:

[0558] Once the user completes their exercise, the device re-evaluates their final emotional state and sends the exercise data to the server. The input for this step is the completed exercise data and emotional evaluation data, and the output is the final report sent to the server.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] [Fourth Embodiment]

[0563] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0564] 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.

[0565] 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).

[0566] 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.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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".

[0576] As an embodiment of the present invention, an interactive system that provides a personalized exercise program based on the user's physical data will be described. This system involves interaction between a terminal, a server, and the user.

[0577] The device first receives physical data entered by the user. This physical data includes the user's age, height, weight, medical history, and range of motion. This data later serves as foundational data for generating exercise programs.

[0578] Next, the device sends user data to the server. The server analyzes the received data and generates an optimal exercise program for the user. Specifically, it uses an algorithm to evaluate the user's health condition and range of motion and selects individually optimized exercises. For example, for a user with limited range of motion in their shoulders, a program will be generated that includes shoulder stretches and light-intensity exercises.

[0579] The generated exercise program is presented to the user in a game format. This game format includes interactive elements such as earning points and leveling up by performing exercises. This allows users to enjoy the exercise not just as a form of exercise, but as a game, increasing their likelihood of continuing to exercise.

[0580] As the user exercises, the device provides feedback in real time, matching the progress of the workout. For example, it displays encouraging messages like "Well done!" and provides corrective instructions if the movements are incorrect. Once the workout is complete, the device sends the data to the server.

[0581] The server uses the received exercise data to evaluate the user's progress. If necessary, it readjusts the exercise program and generates a new program tailored to the user's changing health condition. This helps improve the user's physical abilities and enables continuous health maintenance.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The terminal provides the user with a physical data input interface. The user uses this interface to input their age, height, weight, range of motion, etc. This data is used as foundational information to generate a personalized exercise program in a later step.

[0585] Step 2:

[0586] The terminal transmits the entered body data to the server. This transmission is performed using a secure communication protocol to guarantee data integrity.

[0587] Step 3:

[0588] The server analyzes the received user data and generates an individually optimized exercise program. Based on the user's characteristics, the algorithm selects stretches and light exercises to improve shoulder mobility, for example, if the user has limited shoulder range of motion.

[0589] Step 4:

[0590] The server converts the generated exercise program into a game format and sends this data to the terminal. The transmitted game format is then displayed in the user interface.

[0591] Step 5:

[0592] The user performs a game-style exercise program presented through the device. The device monitors the user's movements and progress and provides feedback as needed. For example, if the movements are correct, it displays a message such as "Well done!"

[0593] Step 6:

[0594] After completing an exercise, the device sends the data to the server. This data includes information such as the time taken for the exercise and the accuracy of the movements.

[0595] Step 7:

[0596] The server analyzes the received exercise data and evaluates the user's progress. Based on user feedback, it adjusts the exercise program as needed and generates new programs, continuously providing exercise tailored to the user's changing health condition.

[0597] (Example 1)

[0598] 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".

[0599] In modern times, many people exercise to maintain their health and improve their physical fitness, but there is a problem in that it is difficult to continue exercising effectively and consistently. In particular, each user has a different physical condition and exercise ability, and creating an appropriate exercise program tailored to them is a major challenge. Maintaining motivation to exercise is also difficult.

[0600] 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.

[0601] In this invention, the server includes means for inputting user information, means for generating an exercise plan using an algorithm based on the user information, and means for transmitting exercise performance information to the server, analyzing it, and adjusting the exercise plan. This makes it possible to provide an exercise program optimized for each individual user and to increase motivation through effective feedback.

[0602] "Means for inputting user information" refers to an interface that allows users to input information about their physical data and health status into a terminal.

[0603] "Means of generating exercise plans using algorithms" refers to calculation methods that analyze information provided by users and create exercise programs tailored to individual conditions and goals.

[0604] "Means of presenting in an interactive format" refers to display and operation methods that enable users to perform the generated exercise program through games or activities.

[0605] "Real-time monitoring means" refers to technologies such as sensors and cameras that instantly record and evaluate a user's movements and performance while they are exercising.

[0606] "A means of sending exercise performance information to a server, analyzing it, and adjusting the exercise plan" refers to a process of sending the results of a user's exercise session to a server, re-evaluating the exercise program based on this information, and providing a new plan.

[0607] This invention is a system that provides an optimal exercise program based on the user's individual physical information. This system provides an interactive environment that operates between a terminal, a server, and the user. Specific embodiments are as follows.

[0608] Users input their physical data using a device. This input is done via a touchscreen or keyboard and includes a wide range of information such as age, height, weight, medical history, and range of motion. The interface on the device is designed to allow users to input data accurately and provides feedback prompting the user to check for errors or missing data.

[0609] The device sends the user's entered physical data to the server. The server uses a generative AI model to analyze the received data. This model assesses the user's health status and generates an exercise program that includes the most suitable exercises for the user. The algorithm used here takes into account each user's different limitations and abilities to create a personalized plan.

[0610] The generated exercise program is provided in a gamified format, and users perform it through their device. As users complete the exercise, they are awarded points and level up within the game, incorporating mechanisms to maintain continuous motivation. A feedback function monitors the user's exercise technique in real time and provides corrective instructions as needed. A concrete example of device usage is earning a specific badge for successfully completing 10 squats.

[0611] Once the exercise is complete, the device sends the data back to the server. The server analyzes this data and evaluates the user's progress. Next, based on the generated AI model, it readjusts the exercise program as needed. This enhances the user's physical abilities and helps maintain their health.

[0612] An example of a prompt message is, "If a 50-year-old male user has knee arthritis, please suggest an exercise program that supports the knee while improving overall flexibility." In this way, the system helps generate exercise programs tailored to each individual user.

[0613] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0614] Step 1:

[0615] The terminal receives physical data from the user as input. This data includes information such as age, height, weight, medical history, and range of motion. The terminal converts this data into a format for transmission to the server and completes the preparation for transmission. Specifically, it has functions to verify the integrity of the input data and check for missing values ​​and errors.

[0616] Step 2:

[0617] The server receives physical data transmitted from the terminal as input. Using a generative AI model, this data is analyzed to evaluate the user's health status and exercise capacity. The analysis process involves statistical processing of the data and the use of specialized algorithms to determine the optimal exercise program. As a result, the most suitable exercise program for each user is generated.

[0618] Step 3:

[0619] The generated exercise program is output from the server to the terminal. The terminal presents this exercise program to the user in an interactive format. Specifically, it uses a game-like interface that allows the user to earn points and level up each time they perform an exercise. This helps maintain continuous motivation.

[0620] Step 4:

[0621] When a user performs an exercise program, the device tracks the user's movements in real time using motion sensors and a camera. It receives data from the sensors as input and evaluates the accuracy of the exercise by comparing it to predetermined movements. If the correct movements are confirmed, the device immediately displays feedback such as "Perfect!". It also provides specific instructions if corrections are needed.

[0622] Step 5:

[0623] Once an exercise session ends, the device sends session data to the server. The server receives this data as input and evaluates the user's progress. Next, it uses a generative AI model to determine whether the exercise program is appropriate for the user's current state and readjusts the program if necessary. As a result, the newly adjusted exercise program is output to the device again.

[0624] (Application Example 1)

[0625] 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".

[0626] Traditional fitness programs often provide uniform exercise instruction without adequately considering the individual physical abilities and health conditions of each participant, making effective health management and the formation of consistent exercise habits difficult. Furthermore, they lack systems to maintain motivation, hindering long-term participation. There is a need for a personalized exercise program delivery system that addresses these issues and is applicable even in a work environment.

[0627] 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.

[0628] In this invention, the server includes a device for collecting individual user information, a device for generating an exercise plan based on the user information, and a device for presenting the generated exercise plan in a virtualized format. This enables the provision of exercise programs optimized for each user and the maintenance of sustained motivation.

[0629] "Individual user information" refers to detailed data about individual users, such as age, height, weight, and health status.

[0630] An "exercise plan" refers to an exercise program that is individually tailored to each user based on their physical abilities and health condition.

[0631] "Virtualized format" refers to interactive motion content presented within a virtual environment, separate from the physical reality environment.

[0632] "Exercise performance information" refers to data collected when a user performs exercise, including accuracy of movement, amount of exercise, and heart rate.

[0633] "Analytical techniques" refer to algorithms and tools used to analyze data and extract specific patterns or insights.

[0634] "Interaction-providing media" refers to devices or means that allow users to receive feedback and guidance during exercise.

[0635] The interactive fitness system implementing the present invention provides personalized exercise programs based on the user's physical data, supporting the user's continuous health management. This system involves interaction between a server, a terminal, and the user.

[0636] The server receives information transmitted from terminals that collect physical data such as the user's age, height, weight, medical history, and range of motion. Based on the received data, it performs analysis using a generative AI model to generate an optimal exercise program for the user. This exercise program is then adjusted to optimize the user's individual needs by evaluating the user's health condition using a machine learning model based on TensorFlow. For example, a user with limited shoulder range of motion will be provided with a program that includes shoulder stretches and low-impact exercises.

[0637] The device is an interactive medium, similar to smart glasses, that presents the generated exercise program to the user in a virtualized format. The Unity-based system provides real-time feedback and guidance during exercise, playing a role in maintaining user motivation.

[0638] When a user actually exercises, exercise data is collected by the terminal and sent to the server. Based on the collected information, the server evaluates effective progress and continuously optimizes the exercise program. This enables personalized support tailored to the user's health condition, helping them to practice effective fitness.

[0639] As a concrete example, performing a short stretching program using smart glasses daily during factory work hours improves workers' concentration during the afternoon. An example of a prompt message is: "Generate an optimal short exercise program based on the following user physical data: Age 35, Height 170cm, Weight 65kg, Working hours 6 hours, Prone to stiff shoulders."

[0640] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0641] Step 1:

[0642] The device collects physical data from the user (age, height, weight, medical history, range of motion, etc.) as input. This data serves as the basis for generating exercise programs later on. The input data is centrally managed within the device and formatted for transmission to the server.

[0643] Step 2:

[0644] The terminal transmits the collected physical data to the server. By receiving the transmitted data, the server obtains the user's basic physical information. Based on this information, it prepares for subsequent data analysis and program generation.

[0645] Step 3:

[0646] The server begins analyzing the received physical data as input. A generative AI model (e.g., a machine learning model using TensorFlow) is used for the analysis, generating an exercise program tailored to each user's individual health condition. The data calculations performed here determine the program's content using algorithms that reflect the user's range of motion and health constraints.

[0647] Step 4:

[0648] The server sends the generated exercise program to the terminal. Upon receiving this program, the terminal presents it to the user in a virtualized format through an appropriate user interface (e.g., smart glasses). At this time, it prepares to display real-time feedback to the user as the exercise progresses.

[0649] Step 5:

[0650] The user performs physical actions according to the presented exercise program. The device monitors data generated at each stage of the exercise (e.g., exercise intensity, angle, heart rate, etc.) and collects it as a record of the exercise. The collected data is used for subsequent feedback and program adjustments.

[0651] Step 6:

[0652] The terminal sends exercise performance information to the server. Based on the exercise performance information received as input, the server evaluates the user's exercise performance and modifies or regenerates the exercise program as needed. The aforementioned generative AI model is continuously used for regeneration.

[0653] Step 7:

[0654] The server returns the readjusted exercise program to the terminal to provide continuous feedback, and presents it to the user as the program to run in subsequent exercise sessions. This generates a new exercise plan with updated prompts, supporting the user's continued improvement of physical abilities and health management.

[0655] 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.

[0656] As an embodiment of this invention, a system that recognizes a user's emotions and provides an exercise program in accordance with those emotions will be described. This system includes a terminal, a server, and an emotion engine.

[0657] The device first provides the user with a physical data input interface. The user uses this interface to input information such as age, height, weight, and range of motion. This input data serves as the basis for generating an exercise program.

[0658] The device also collects the user's facial expressions and tone of voice in real time. The emotion engine analyzes this data and evaluates the user's emotional state. For example, if the user shows signs of dissatisfaction or stress during exercise, the emotion engine will recognize this.

[0659] The collected data is transmitted to the server via secure communication. The server analyzes the physical and emotional data together to generate an individually optimized exercise program. For example, if the user indicates a relaxed state, it will provide a more challenging workout; if the user is tired, it will provide a program centered on relaxing stretches.

[0660] The generated exercise program is provided to the device in game format and displayed on the user interface. Users can enjoy exercising in a game-like format as they progress. The device provides feedback based on the user's progress and emotions, supporting the user within the game. For example, it can display messages such as "You're making good progress!" or "Take a short break and try again!"

[0661] Once a user completes an exercise, the emotion engine re-evaluates their final emotional state and sends this data, along with the exercise data, to the server. The server uses both the exercise data and the emotional data to adjust the exercise program, continuously providing the most suitable exercise for the user's emotional and physical condition. This creates an environment where users can continue exercising enjoyably and without strain.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The terminal displays a physical data input interface to the user. Through this interface, the user inputs information about their age, height, weight, and range of motion. This data is used by the system as foundational information to customize exercise programs.

[0665] Step 2:

[0666] The device captures the user's facial expressions and voice tone in real time and sends them to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state in real time. For example, if the user is smiling, it determines that they are experiencing positive emotions.

[0667] Step 3:

[0668] The device transmits collected physical and emotional data to a server. The data is transferred using a secure communication protocol to ensure privacy and data integrity.

[0669] Step 4:

[0670] The server analyzes the user's physical and emotional data and generates an optimal exercise program using a specific algorithm. For example, if the user is feeling stressed, the program will include exercises that promote relaxation, thus taking emotional data into consideration.

[0671] Step 5:

[0672] The server converts the generated exercise program into a game format and sends this game data to the terminal. The game format includes elements such as accumulating points by performing exercises and a level-up system.

[0673] Step 6:

[0674] The user performs a game-style exercise program presented on the device. During the exercise, the device monitors the user's movements and provides real-time feedback. When the user successfully completes the exercise tasks, the device provides positive feedback.

[0675] Step 7:

[0676] Once the exercise is complete, the device uses an emotion engine to re-evaluate the exercise data and final emotional state, and then sends the results to the server. This allows for a comprehensive understanding of the user's progress and emotional changes.

[0677] Step 8:

[0678] The server adjusts the exercise program based on the final data. This makes the user's next exercise experience more personalized and tailored to their feedback and emotional state. It also supports the user in continuing to exercise safely and effectively in subsequent sessions.

[0679] (Example 2)

[0680] 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".

[0681] Existing exercise programs often fail to adequately optimize for individual users' emotional states and physical abilities, making it difficult for users to enjoy and continue exercising without undue strain. In particular, programs that lead to decreased motivation or excessive fatigue make long-term participation difficult.

[0682] 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.

[0683] In this invention, the server includes means for collecting individual user information, means for analyzing facial expressions and voice to evaluate emotions, and means for generating exercise programs using a generative AI model. This makes it possible to provide individually optimized exercise programs tailored to the user's emotional state and physical abilities, thereby improving the continuation and enjoyment of exercise.

[0684] "Individual user information" refers to data unique to each user, including information that indicates physical characteristics and emotional state.

[0685] "Means of analyzing facial expressions and voice to evaluate emotions" refers to the process of analyzing a user's facial expressions and voice tone using devices such as cameras and microphones, and then estimating their emotional state from that analysis.

[0686] "Methods for generating exercise programs using generative AI models" refers to the process of creating an optimal exercise program based on user information by utilizing artificial intelligence technologies, including machine learning.

[0687] "Presenting in a game format" refers to the process of providing an exercise program that includes visual and auditory elements in a way that allows users to enjoy it interactively.

[0688] "Information regarding exercise participation" refers to data about the exercise that users actually performed, including information such as the type of exercise, the number of repetitions, and changes in emotions.

[0689] "Having a feedback function" means having the ability to provide appropriate advice and instructions based on the user's actions and progress.

[0690] "Continuous motivation" refers to providing elements and functions that help users maintain their motivation and interest in continuing to exercise.

[0691] This invention relates to a system for recognizing a user's emotional state and providing an individually optimized exercise program. The system comprises a terminal, a server, and an emotion engine.

[0692] The device provides the user with a physical information input interface. The user inputs information about their body, such as age, height, weight, and range of motion. This information is used as basic data for generating exercise programs later. The device also uses a camera and microphone to collect the user's facial expressions and voice tone in real time.

[0693] The emotion engine uses this collected data to analyze and evaluate the user's emotional state. The analysis process employs image processing and speech recognition software, and machine learning algorithms are used to estimate the emotional state. For example, if a user is smiling, the emotion engine evaluates this as joy.

[0694] These analysis results and physical information are transmitted to the server via secure communication. The server uses a generative AI model to generate an exercise program based on the user's physical and emotional state. This AI model is optimized, for example, based on past exercise history and emotional data stored in a database.

[0695] The generated exercise program is provided to the device in a game format. The device presents the program to the user through an interactive screen that incorporates visual and auditory elements. The user can follow the program and enjoy exercising. The device has a feedback function and displays messages such as "Great pace!" or "Let's relax a little!"

[0696] After the user completes their exercise, the emotion engine reassesss their emotional state and sends the results, along with the exercise data, to the server. The server then uses this data to adjust the exercise program, continuously providing the optimal program.

[0697] As a concrete example, if a user inputs information such as being 30 years old, 170 cm tall, and weighing 65 kg, and displays a calm expression, the generating AI model will create a 30-minute yoga program focused on relaxation. An example of a prompt would be: "Create an exercise program for a 30-year-old user, 170 cm tall, and weighing 65 kg, who receives feedback with a calm expression and continues to receive encouraging messages."

[0698] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0699] Step 1:

[0700] The terminal provides the user with a physical information input interface. The user inputs information such as age, height, weight, and range of motion. Once this data is entered, the terminal saves it to the system as initial setup data and sends it to the server. Accurate data entry is crucial at this stage, and input verification functions are used as needed to prevent errors.

[0701] Step 2:

[0702] The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time. Specifically, the camera tracks the user's facial movements, and the microphone records voice patterns. Based on this input data, the emotion engine performs preprocessing to evaluate the facial and voice data, creating a dataset for emotion estimation.

[0703] Step 3:

[0704] The emotion engine analyzes the facial expression and voice data collected in the previous step. It extracts facial features from the input facial expression data and analyzes the tone and speed of the voice from the voice data. Using machine learning algorithms, it estimates the user's emotional state from these features and outputs an emotion label such as "joy" or "fatigue."

[0705] Step 4:

[0706] The server receives the user's physical information and emotion labels from the emotion engine, and generates an exercise program using a generative AI model. In this step, a specific algorithm calculates the optimal exercise program based on the input information, and the result is constructed as a program. For example, if relaxation is needed, a yoga program will be recommended.

[0707] Step 5:

[0708] The exercise program generated from the server is sent to the terminal. The terminal receives this data and presents it to the user in a game format. Using graphics and sound, it provides an interactive experience and displays instructions to the user in accordance with the progress of the program.

[0709] Step 6:

[0710] While the user progresses through the exercise program, the device continuously monitors the user's progress and emotional changes, providing feedback as needed. This includes messages and advice to boost the user's motivation. The feedback is updated in real time, providing continuous support to the user.

[0711] Step 7:

[0712] After the exercise is completed, the device sends the latest facial and voice data to the emotion engine to re-evaluate the final emotional state. The emotion engine analyzes this data and sends it to the server to create the final dataset. This data forms the basis for future program adjustments.

[0713] (Application Example 2)

[0714] 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".

[0715] Traditional exercise program delivery systems, while capable of incorporating individual physical characteristics, have the problem of being unable to reflect users' changing emotional states in real time, making it difficult to maintain user motivation. Furthermore, the standardization of exercise programs prevents the provision of programs optimized for individual users.

[0716] 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.

[0717] In this invention, the server includes means for collecting data to analyze the user's emotional state, means for generating an exercise program based on the user's data and emotional state, and means for providing real-time feedback to maintain motivation. This enables the provision of an optimal exercise program based on the user's emotions and physical characteristics, allowing for continuous exercise and improved motivation.

[0718] "Data for analyzing the emotional state of users" refers to information including the user's facial expressions, tone of voice, and behavioral indicators, which is used to evaluate the user's emotional tendencies by analyzing this data.

[0719] "Means for generating exercise programs" refers to a processing system that uses algorithms and databases to program personalized exercise content based on collected physical and emotional data of users.

[0720] An "interactive format" refers to a method of presenting exercise programs that offers users an experience in which they can actively participate and receive feedback in real time.

[0721] "Real-time feedback" is a method of communication that aims to increase user motivation by providing immediate information to users as they exercise, based on their progress and emotional state.

[0722] The system for implementing this invention mainly includes a terminal, a server, and an emotion engine. Before the user starts an exercise program, the terminal requests input of physical data such as age, height, weight, and range of motion. This information is transmitted to the server as basic data for generating the exercise program.

[0723] Next, the device uses its built-in camera and microphone to collect the user's real-time facial expressions and voice tone. This data is analyzed by an emotion engine and sent to a server as a basis for evaluating the user's emotional state. The emotion engine utilizes emotion recognition software, such as Microsoft Azure's Face API.

[0724] On the server, an AI model is used to generate individually customized exercise programs based on collected physical and emotional data. This exercise program is sent to the terminal in an interactive format, allowing the user to participate. Here's an example of a user joining a yoga class. For instance, the AI ​​model generates exercise content using a prompt message such as, "What emotion is the user feeling? Suggest a 40-minute yoga routine that is best suited to that emotion."

[0725] The device provides real-time feedback based on the user's emotional state and progress during exercise. This allows users to receive appropriate guidance tailored to their condition during exercise, leading to a more effective workout experience. The feedback includes encouraging messages when the user is progressing at an appropriate pace, as well as messages encouraging rest.

[0726] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0727] Step 1:

[0728] The device receives physical data from the user. Specifically, the user inputs individual profile data such as age, height, weight, and range of motion into the interface. The input in this step is the user's physical characteristic data, and the output is this data temporarily stored on the device.

[0729] Step 2:

[0730] The device uses its built-in camera and microphone to collect the user's facial expressions and voice tone. This allows for the acquisition of emotional data in real time. The input for this step is the user's facial expressions and voice, and the output is emotional data formatted for analysis.

[0731] Step 3:

[0732] The device collects physical and emotional data and sends it to the server using secure communication. In this step, the input is the formatted physical and emotional data, and the output is the data packets sent to the server.

[0733] Step 4:

[0734] The server uses a generated AI model to customize the exercise program based on the data it receives. Specific prompts, such as "What emotion is the user feeling? Suggest a 40-minute yoga routine best suited to that emotion," are used. The input for this step is integrated physical and emotional data, and the output is a specific exercise program.

[0735] Step 5:

[0736] The server generates an exercise program and sends it to the terminal. The terminal then presents the received program to the user in an interactive format. The input in this step is the created exercise program, and the output is the exercise instructions displayed on the user's screen.

[0737] Step 6:

[0738] During exercise, the device monitors the user's emotional state in real time and provides feedback as needed. It displays encouraging or rest-prompting messages according to the user's pace. The input for this step is emotional data during exercise, and the output is a tailored feedback message.

[0739] Step 7:

[0740] Once the user completes their exercise, the device re-evaluates their final emotional state and sends the exercise data to the server. The input for this step is the completed exercise data and emotional evaluation data, and the output is the final report sent to the server.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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."

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] The following is further disclosed regarding the embodiments described above.

[0763] (Claim 1)

[0764] Means for collecting individual user data,

[0765] means for generating an exercise program based on the aforementioned user data,

[0766] A means of presenting the generated motor program in a game format,

[0767] A means for collecting data on the performance of exercise in the aforementioned game format,

[0768] A means of adjusting the exercise program based on collected exercise performance data,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, wherein the generated exercise program takes into account limitations related to the user's physical function.

[0772] (Claim 3)

[0773] The system according to claim 1, wherein the game format includes a feedback function for providing continuous motivation to the user.

[0774] "Example 1"

[0775] (Claim 1)

[0776] A means of entering user information,

[0777] Means for generating an exercise plan using an algorithm based on the user information,

[0778] A means of presenting the generated motion plan in an interactive format,

[0779] A real-time monitoring system that provides feedback on exercise performance,

[0780] The means includes transmitting the exercise performance information to a server, analyzing it, and adjusting the exercise plan,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, wherein the generated exercise plan takes into account limitations related to the user's physical functions.

[0784] (Claim 3)

[0785] The system according to claim 1, wherein the interactive format includes a feedback function for providing continuous motivation to the user.

[0786] "Application Example 1"

[0787] (Claim 1)

[0788] A device that collects individual user information,

[0789] A device that generates an exercise plan based on the user information,

[0790] A device that presents the generated motion plan in a virtualized format,

[0791] A device for collecting information relating to the execution of the aforementioned virtualized form of exercise,

[0792] A device that adjusts the exercise plan based on collected exercise performance information,

[0793] The device includes machine learning as part of the analysis techniques used to generate motion plans,

[0794] A medium that provides interaction during the performance of physical activities,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, wherein the generated exercise plan takes into account constraints related to the user's physical abilities.

[0798] (Claim 3)

[0799] The system according to claim 1, wherein the virtualization format includes an evaluation function for providing sustained motivation to the user.

[0800] "Example 2 of combining an emotion engine"

[0801] (Claim 1)

[0802] Means for collecting individual user information,

[0803] A means for analyzing facial expressions and voice and evaluating emotions based on the aforementioned user information,

[0804] A means for generating an exercise program using a generative AI model based on evaluated emotions,

[0805] A means of presenting the generated motor program in a game format,

[0806] A means for collecting information regarding the implementation of exercise in the aforementioned game format,

[0807] A means of adjusting the exercise program based on collected exercise performance information,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, wherein the generated exercise program takes into account constraints related to the user's physical abilities.

[0811] (Claim 3)

[0812] The system according to claim 1, wherein the game format includes a feedback function for providing continuous motivation to the user.

[0813] "Application example 2 when combining with an emotional engine"

[0814] (Claim 1)

[0815] A means of collecting data to analyze the emotional state of users,

[0816] Means for generating an exercise program based on the user data and emotional state,

[0817] A means of presenting the generated exercise program in an interactive format,

[0818] A means for collecting data related to the performance of exercise in the aforementioned interactive format,

[0819] A means of adjusting the exercise program based on collected exercise performance data and emotional state,

[0820] A means of providing real-time feedback to users and maintaining their motivation,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, wherein the generated exercise program takes into account the physical and emotional characteristics of the user.

[0824] (Claim 3)

[0825] The system according to claim 1, wherein the interactive format includes a real-time feedback function for providing continuous motivation to the user. [Explanation of symbols]

[0826] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting individual user data, means for generating an exercise program based on the aforementioned user data, A means of presenting the generated motor program in a game format, A means for collecting data on the performance of exercise in the aforementioned game format, A means of adjusting the exercise program based on collected exercise performance data, A system that includes this.

2. The system according to claim 1, wherein the generated exercise program takes into account limitations related to the user's physical function.

3. The system according to claim 1, wherein the game format includes a feedback function for providing continuous motivation to the user.

Images