system

Abstract

The system according to this embodiment aims to provide an optimal training plan based on the user's health condition and fitness goals. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a feedback unit, a reward unit, and a community unit. The reception unit inputs the user's health status and fitness goals. The generation unit analyzes the information input by the reception unit and generates an optimal training plan. The feedback unit provides real-time feedback based on the training plan generated by the generation unit. The reward unit awards reward points based on the feedback provided by the feedback unit. The community unit promotes interaction among users based on the reward points awarded by the reward unit.

Description

Technical Field

[0004] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide an optimal training plan based on the user's health condition and fitness goal.

[0005] The system according to the embodiment aims to provide an optimal training plan based on the user's health condition and fitness goal.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a feedback unit, a reward unit, and a community unit. The reception unit inputs the user's health status and fitness goals. The generation unit analyzes the information input by the reception unit and generates an optimal training plan. The feedback unit provides real-time feedback based on the training plan generated by the generation unit. The reward unit awards reward points based on the feedback provided by the feedback unit. The community unit facilitates interaction among users based on the reward points awarded by the reward unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal training plan based on the user's health condition and fitness goals. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The training plan provision system according to an embodiment of the present invention is a system that provides an optimal training plan based on the user's health condition and fitness goals by utilizing a generative AI. This training plan provision system creates a personalized fitness plan based on the user's physical fitness level and exercise history, and supports daily training. It also includes functions to provide real-time feedback and enhance motivation. For example, the user inputs their health condition and fitness goals. At this time, the user inputs their physical fitness level and exercise history. For example, they input their past exercise history and current health condition. This information is input into the generative AI. Next, the generative AI analyzes the input information and creates an optimal training plan for the user. The generative AI generates a fitness plan that is optimal for each individual user based on the user's physical fitness level and exercise history. For example, it provides plans that match the user's needs, from light exercise for beginners to hard training for advanced users. Based on the generated fitness plan, it supports daily training. For example, when the user starts training, the generative AI provides real-time feedback. It analyzes the user's movements and supports effective training by instructing them on the correct form and appropriate load. Furthermore, it is equipped with functions to enhance motivation. For example, it maintains user motivation by awarding reward points according to training progress and providing point redemption. Furthermore, the system supports continued training by allowing users to interact with other users and share information through an online community. This system enables users to receive optimal training plans tailored to their health condition and fitness goals. The system incorporates features that provide real-time feedback and motivational enhancements through a generating AI, supporting effective training. This allows users to lead a healthier lifestyle. Thus, the training plan provision system can support effective training by providing optimal training plans based on the user's health condition and fitness goals, and by incorporating real-time feedback and motivational enhancement features.

[0029] The training plan provision system according to this embodiment comprises a reception unit, a generation unit, a feedback unit, a reward unit, and a community unit. The reception unit receives input from the user regarding their health status and fitness goals. The user's health status includes, but is not limited to, heart rate, blood pressure, and weight. Fitness goals include, but are not limited to, weight loss, muscle strengthening, and endurance improvement. The reception unit allows the user to input their fitness level and exercise history, for example. The generation unit uses a generation AI to analyze the information input by the reception unit and generate an optimal training plan. The generation AI analyzes the user's fitness level and exercise history using algorithms such as deep learning and reinforcement learning. The generation unit provides plans tailored to the user's needs, ranging from light exercises for beginners to hard training for advanced users. The feedback unit provides real-time feedback based on the training plan generated by the generation unit. The feedback unit analyzes the user's movements and provides instructions for correct form and appropriate load, for example. The feedback unit provides feedback in the form of voice instructions, visual guides, vibration feedback, etc. The rewards unit awards reward points based on the feedback provided by the feedback unit. The rewards unit awards reward points, for example, according to the progress of training. The rewards unit can set conditions for earning points and the benefits that can be used. The community unit promotes interaction among users based on the reward points awarded by the rewards unit. The community unit promotes interaction and information sharing with other users, for example, through online communities. The community unit promotes interaction among users through methods such as chat functions, forums, and event participation. As a result, the training plan provision system according to the embodiment can support effective training by providing an optimal training plan based on the user's health condition and fitness goals, and by incorporating real-time feedback and motivation-enhancing functions.

[0030] The reception desk inputs the user's health status and fitness goals. The user's health status includes, but is not limited to, heart rate, blood pressure, and weight. This data can be entered manually by the user or automatically retrieved from wearable devices such as smartwatches and fitness trackers. Fitness goals include, but are not limited to, weight loss, muscle gain, and endurance improvement. Users can enter specific goals, such as losing 5 kg in one month or completing a 10 km marathon in three months. The reception desk also allows users to input their fitness level and exercise history. This includes information such as past exercise experience and current exercise habits, including how many times a week they exercise and what types of exercise they do. Furthermore, the reception desk can collect information about the user's eating habits and lifestyle. For example, users can input information such as their daily calorie intake, meal content, sleep duration, and stress level. This allows the reception desk to comprehensively understand the user's overall health status and use this information to generate more accurate training plans.

[0031] The generation unit uses a generation AI to analyze information entered by the reception unit and generate an optimal training plan. The generation AI analyzes the user's fitness level and exercise history using algorithms such as deep learning and reinforcement learning. Specifically, it uses deep learning to analyze the user's health data and generates an optimal training plan based on past training data and success stories. Using reinforcement learning, it can learn from user feedback in real time and continuously improve the training plan. The generation unit provides plans tailored to the user's needs, ranging from light exercise for beginners to hard training for advanced users. Specifically, it suggests exercises such as walking, light jogging, and stretching for beginners, and hard exercises such as high-intensity interval training (HIIT) and weight training for advanced users. Furthermore, the generation unit can dynamically adjust the training plan according to the user's goals and progress. For example, if the user achieves their goals, or conversely, if they do not reach their goals, it regenerates the training plan to provide the user with the optimal plan. This allows the generation unit to respond to the individual needs of users and support effective training.

[0032] The feedback unit provides real-time feedback based on the training plan generated by the generation unit. For example, the feedback unit analyzes the user's movements and instructs them on correct form and appropriate load. Specifically, while the user is exercising, the unit captures their movements using cameras and motion sensors, and AI analyzes those movements. The AI ​​determines whether the user's movements are performed with correct form and issues corrective instructions as needed. For example, if the knee position is incorrect while performing squats, the AI ​​will instruct the user on the correct form through voice instructions or visual guidance. The feedback unit also monitors data such as the user's heart rate and calories burned in real time and instructs them on appropriate load. For example, if the heart rate exceeds the target range, it will instruct the user to reduce the exercise intensity, and conversely, if the heart rate is too low, it will instruct the user to increase the exercise intensity. The feedback unit provides feedback in various ways, such as voice instructions, visual guidance, and vibration feedback. This allows the user to receive appropriate feedback in real time and train effectively and safely. Furthermore, the feedback unit accumulates the user's training data to support long-term performance improvement. For example, it can evaluate the user's progress based on past training data and reflect this in the next training plan. This allows the feedback system to maximize the user's training effectiveness and maintain sustained motivation.

[0033] The rewards department awards reward points based on feedback provided by the feedback department. For example, the rewards department awards reward points according to training progress. Specifically, it awards reward points when the user achieves their set goals or completes a certain number of training sessions. The rewards department can set conditions for earning points and the rewards that can be used. For example, users can use the points they earn to purchase fitness-related products and services. The rewards department can also set special challenges and events to increase user motivation. For example, it can offer challenges where users can earn additional reward points by completing a certain amount of training within a specific period. Furthermore, the rewards department can analyze users' training data and provide individualized reward plans. For example, it can propose customized reward plans based on the user's training history and goals to maintain user motivation. In this way, the rewards department can increase users' motivation to train and support sustained training.

[0034] The Community Department facilitates interaction among users based on reward points awarded by the Rewards Department. For example, the Community Department allows users to interact and share information through online communities. Specifically, users can communicate with other users in real time using the chat function. They can also share training tips and success stories through forums, encouraging each other. Furthermore, the Community Department regularly holds online events and challenges to promote competition and cooperation among users. For example, they might set a monthly training challenge and offer special rewards to the user who earns the most points. The Community Department can also collect user feedback to improve the system. For instance, they might incorporate new features and improvements suggested by users to enhance the system's usability and effectiveness. In this way, the Community Department can not only promote interaction among users and increase training motivation, but also contribute to improving the overall quality of the system.

[0035] The reception desk can input the user's fitness level and exercise history. The reception desk provides, for example, an interface for the user to input their fitness level and exercise history. The reception desk allows the user to input, for example, their past exercise history and current health status. This allows for the provision of a more personalized training plan by inputting the user's fitness level and exercise history. Fitness level includes, but is not limited to, maximum oxygen uptake (VO2max) and muscle strength test results. Exercise history includes, but is not limited to, past training data, exercise frequency, and exercise type. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's input data into AI, which can analyze the data and automatically classify the fitness level and exercise history.

[0036] The generation unit can analyze the user's fitness level and exercise history using a generation AI and generate an optimal training plan. For example, the generation unit uses a generation AI to analyze the user's fitness level and exercise history. The generation AI uses algorithms such as deep learning and reinforcement learning to analyze the user's fitness level and exercise history. The generation unit provides plans tailored to the user's needs, ranging from light exercises for beginners to hard training for advanced users. This allows the generation AI to generate the optimal training plan for the user. For example, the generation AI receives user input data as a prompt and generates the optimal training plan. For example, the generation AI generates an optimal fitness plan for each individual user based on the user's fitness level and exercise history. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs user input data into the generation AI, which analyzes the data and generates the optimal training plan.

[0037] The feedback unit can analyze the user's movements and provide instructions for correct form and appropriate load. For example, the feedback unit can analyze the user's movements and provide instructions for correct form and appropriate load. The feedback unit provides feedback in methods such as voice instructions, visual guidance, and vibration feedback. This allows for effective training by analyzing the user's movements and providing instructions for correct form and appropriate load. Correct form includes, but is not limited to, posture checkpoints and range of motion. Appropriate load includes, but is not limited to, weight settings and adjustments to the number of repetitions and sets. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can input user movement data into an AI, which can analyze the data and provide instructions for correct form and appropriate load.

[0038] The rewards unit can award reward points according to training progress. For example, the rewards unit can award reward points according to training progress. The rewards unit can set conditions for earning points and the benefits that can be used. This can increase user motivation by awarding reward points according to training progress. Training progress includes, but is not limited to, achievement level, number of days continued, and performance improvement. Some or all of the above processing in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the user's training data into AI, and the AI ​​can analyze the data and award reward points.

[0039] The Community Department allows users to interact and share information with other users through online communities. For example, the Community Department facilitates user interaction through methods such as chat functions, forums, and event participation. This supports continued training by allowing users to interact and share information with others through online communities. Online communities include, but are not limited to, social networking services (SNS), dedicated apps, and forums. Some or all of the above-described processes in the Community Department may be performed using AI, or not. For example, the Community Department can input user interaction data into an AI, which can analyze the data and suggest the optimal method of interaction.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions health statuses and fitness goals that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest health statuses and fitness goals to be used at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Input history includes, but is not limited to, past input data, frequency, and content. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past input data into AI, and the AI ​​can analyze the data and suggest the optimal input method.

[0041] The reception unit can simplify input by automatically acquiring the user's current location information when they input their health status or fitness goals. For example, when a user opens the app, the reception unit automatically acquires their current location and simplifies the input of their health status and fitness goals. For example, when a user enters their fitness goals, the reception unit suggests optimal candidate locations considering the distance from their current location. For example, when a user uses the app while on the move, the reception unit updates their current location in real time and simplifies the input of their health status and fitness goals. This simplifies the input process by automatically acquiring the current location information. Location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's location data into AI, which can analyze the data and suggest optimal candidate locations.

[0042] The reception desk can automatically suggest potential destinations based on the user's past travel history when the user inputs their health status and fitness goals. For example, the reception desk can automatically display places the user has frequently visited in the past as potential destinations. For example, the reception desk can predict places the user will visit on specific days of the week or times of day and suggest them as potential destinations. For example, the reception desk can analyze the user's past travel patterns and suggest the most suitable potential destinations. In this way, the reception desk can suggest the most suitable potential destinations for the user by referring to their past travel history. Travel history includes, but is not limited to, past travel data, frequency, and destinations. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's travel data into AI, which can then analyze the data and suggest the most suitable potential destinations.

[0043] The reception desk can refer to the user's calendar information when they input their health status and fitness goals, and make suggestions based on their schedule. For example, the reception desk can refer to the appointments registered in the user's calendar and automatically set their health status and fitness goals. For example, the reception desk can suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, the reception desk can suggest the optimal route based on the user's calendar information. This makes it possible to make optimal suggestions based on the schedule by referring to the calendar information. Calendar information includes, but is not limited to, the type of appointment, priority, and reminder functions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's calendar information into AI, and the AI ​​can analyze the data and make optimal suggestions.

[0044] The generation unit can propose an optimal training plan by referring to the user's past exercise history when generating a training plan. For example, the generation unit proposes an optimal training plan based on the user's past training. For example, the generation unit proposes an effective training plan based on the user's past exercise history. For example, the generation unit analyzes the user's past exercise history and proposes the most efficient training plan. In this way, by referring to past exercise history, it is possible to propose an optimal training plan to the user. Exercise history includes, but is not limited to, past training data, exercise frequency, and exercise type. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's exercise history data into the generation AI, which can analyze the data and propose an optimal training plan.

[0045] The generation unit can optimize training plans by considering real-time health data when generating them. For example, the generation unit can propose an optimal training plan based on real-time heart rate data. For example, the generation unit can propose an optimal training plan by considering real-time calorie consumption data. For example, the generation unit can propose an appropriate training plan based on real-time body temperature data. This allows for the provision of more appropriate training plans by considering real-time health data. Real-time health data includes, but is not limited to, data from wearable devices, sensors, and app integrations. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's real-time health data into the generation AI, which can then analyze the data and propose an optimal training plan.

[0046] The generation unit can propose a training plan while considering the user's current weather information. For example, on rainy days, the generation unit proposes a training plan that can be done indoors. On sunny days, for example, the generation unit proposes a training plan that can be done outdoors. On snowy days, for example, the generation unit proposes a training plan that can be done in a non-slippery location. In this way, by considering the current weather information, the generation unit can propose the most suitable training plan for the user. Weather information includes, but is not limited to, meteorological data and weather forecast services. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's weather data into the generation AI, which can then analyze the data and propose the optimal training plan.

[0047] The generation unit can propose an optimal training plan by considering the user's health condition when generating a training plan. For example, if the user is tired, the generation unit will propose a lighter training plan. For example, if the user is seeking healthy exercise, the generation unit will propose a slightly harder training plan. For example, if the user is feeling unwell, the generation unit will propose a training plan that includes rest. In this way, by considering the user's health condition, a more appropriate training plan can be provided. Health conditions include, but are not limited to, heart rate, blood pressure, and weight. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's health data into the generation AI, which can then analyze the data and propose an optimal training plan.

[0048] The feedback unit can provide optimal feedback by referring to the user's past training data. For example, the feedback unit provides optimal feedback based on the user's past training. For example, the feedback unit provides effective feedback from the user's past training data. For example, the feedback unit analyzes the user's past training data to provide the most efficient feedback. This allows the feedback unit to provide the user with optimal feedback by referring to past training data. Training data includes, but is not limited to, past training content, frequency, and performance. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the user's training data into AI, which can then analyze the data and provide optimal feedback.

[0049] The feedback unit can optimize feedback by considering real-time health data. For example, the feedback unit can provide optimal feedback based on real-time heart rate data. For example, the feedback unit can provide optimal feedback by considering real-time calorie consumption data. For example, the feedback unit can provide appropriate feedback based on real-time body temperature data. This allows for more appropriate feedback to be provided by considering real-time health data. Real-time health data includes, but is not limited to, data from wearable devices, sensors, and app integrations. Some or all of the processing described above in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the user's real-time health data into an AI, which can then analyze the data to provide optimal feedback.

[0050] The feedback unit can select the optimal display method when providing feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit provides feedback that is adapted to the screen size. For example, if the user is using a tablet, the feedback unit provides feedback optimized for a larger screen. For example, if the user is using a smartwatch, the feedback unit provides concise and highly visible feedback. In this way, by considering device information, the feedback unit can provide the user with the most optimal display method for feedback. Device information includes, but is not limited to, the device type, OS, and screen size. Some or all of the processing described above in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the user's device information into AI, and the AI ​​can analyze the data to select the optimal display method.

[0051] The feedback unit can provide multilingual feedback according to the user's language settings when providing feedback. For example, the feedback unit automatically sets the language of the feedback based on the language settings of the user's device. For example, the feedback unit provides a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the feedback unit provides feedback in that language. This allows the user to receive appropriate feedback by providing multilingual feedback according to their language settings. Language settings include, but are not limited to, the user's language selection and the device's language settings. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's language setting data into AI, and the AI ​​can analyze the data to provide multilingual feedback.

[0052] The rewards unit can analyze training progress in real time and award reward points at the appropriate time. For example, the rewards unit can award reward points immediately after a user completes training. For example, the rewards unit can award reward points when a user achieves a goal during training. For example, the rewards unit can award reward points for setting goals before a user starts training. This allows for the awarding of reward points at the appropriate time by analyzing training progress in real time. Training progress includes, but is not limited to, achievement level, number of days continued, and performance improvement. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit can input user training progress data into AI, which can analyze the data and award reward points at the appropriate time.

[0053] The rewards unit can adjust reward points by referring to the user's past training history when awarding reward points. For example, the rewards unit can adjust reward points based on goals the user has achieved in the past. For example, the rewards unit can award reward points commensurate with the user's effort based on the user's past training history. For example, the rewards unit can analyze the user's past training history and propose the most effective method for awarding reward points. This allows the rewards unit to award the user the optimal amount of reward points by referring to their past training history. Training history includes, but is not limited to, past training data, exercise frequency, and exercise type. Some or all of the above processing in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the user's training history data into AI, which can then analyze the data and adjust the reward points.

[0054] The rewards unit can provide region-specific reward points by considering the user's geographical location when awarding reward points. For example, if a user trains in a specific region, the rewards unit will award region-specific reward points if the user trains while traveling. For example, if a user participates in a local fitness event, the rewards unit will award event-specific reward points. This allows for the provision of region-specific reward points by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the user's geographical location information into an AI, which can then analyze the data to provide region-specific reward points.

[0055] The rewards department can analyze a user's social media activity when awarding reward points and provide relevant reward points. For example, the rewards department may award reward points if a user shares their training progress on social media. For example, the rewards department may award reward points if a user posts fitness-related information on social media. For example, the rewards department may award reward points if a user interacts with other users on social media. In this way, by analyzing social media activity, the rewards department can provide reward points relevant to the user. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the rewards department may be performed using, for example, AI, or not using AI. For example, the rewards department can input user social media activity data into AI, which can then analyze the data and provide relevant reward points.

[0056] The Community Department can suggest the most suitable way to interact with users during community activities by referring to their past community activity history. For example, the Community Department can suggest the most suitable way to interact based on the community events the user has previously participated in. For example, the Community Department can suggest effective ways to interact based on the user's past community activity history. For example, the Community Department can suggest the most efficient way to interact by analyzing the user's past community activity history. In this way, the Community Department can suggest the most suitable way to interact with users by referring to their past community activity history. Community activity history includes, but is not limited to, past events participated in, posts made, and interaction frequency. Some or all of the above processing in the Community Department may be performed using AI, for example, or not using AI. For example, the Community Department can input the user's community activity history data into AI, which can then analyze the data and suggest the most suitable way to interact.

[0057] The community department can provide real-time feedback during community activities and facilitate interaction among users. For example, the community department can provide real-time feedback when a user participates in a community event. For example, the community department can provide real-time feedback when a user interacts in an online community. For example, the community department can provide real-time feedback when a user is engaged in community activities. This facilitates interaction among users by providing real-time feedback. Real-time feedback includes, but is not limited to, voice instructions, visual guides, and vibration feedback. Some or all of the above processing in the community department may be performed using, for example, AI, or not using AI. For example, the community department can input user interaction data into an AI, which can then analyze the data and provide real-time feedback.

[0058] The community department can suggest region-specific communities when a user participates in community activities, taking into account the user's geographical location. For example, if a user participates in community activities in a specific region, the community department will suggest region-specific communities. For example, if a user participates in community activities while traveling, the community department will suggest region-specific communities in the travel destination. For example, if a user participates in a local community event, the community department will suggest communities specific to that event. In this way, region-specific communities can be suggested by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the community department may be performed using, for example, AI, or not using AI. For example, the community department can input the user's geographical location information into AI, and the AI ​​can analyze the data and suggest region-specific communities.

[0059] The Community Department can analyze a user's social media activity during community activities and suggest relevant communities. For example, if a user posts fitness-related information on social media, the Community Department can suggest relevant communities. For example, if a user interacts with other users on social media, the Community Department can suggest relevant communities. For example, if a user shares their training progress on social media, the Community Department can suggest relevant communities. In this way, by analyzing social media activity, it is possible to suggest communities relevant to the user. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the Community Department may be performed using AI, for example, or not using AI. For example, the Community Department can input user social media activity data into AI, which can analyze the data and suggest relevant communities.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The training plan provision system can also acquire the user's dietary data and incorporate it into the training plan. For example, by inputting the user's daily meal content, the generating AI analyzes that data and provides a training plan that takes nutritional balance into consideration. If the user has consumed a high-calorie meal, the generating AI can suggest exercises that promote calorie burning. Furthermore, if the user is deficient in a particular nutrient, the system can provide a training plan to supplement that nutrient. This allows for the provision of more personalized training plans by considering the user's dietary data.

[0062] The training plan provision system can also acquire user sleep data and incorporate it into the training plan. For example, by inputting sleep duration and sleep quality, the generating AI analyzes the data and provides an appropriate training plan. If the user is not getting enough sleep, the generating AI can suggest a lighter training plan. Conversely, if the user is getting deep sleep, it can provide a more intense training plan. In this way, by taking the user's sleep data into consideration, a more effective training plan can be provided.

[0063] The training plan provision system can also acquire data on the user's fitness equipment usage and incorporate it into the training plan. For example, by inputting data on the fitness equipment the user uses, the generating AI analyzes that data and provides a training plan optimized for that equipment. If a user frequently uses a particular piece of equipment, the system can suggest a training plan that utilizes that equipment. Furthermore, if a user introduces new equipment, the system can provide a training plan to help them use that equipment effectively. In this way, by considering the user's fitness equipment usage data, a more effective training plan can be provided.

[0064] The training plan provision system can also monitor the user's progress toward their fitness goals and reflect this in the training plan. For example, by inputting the user's progress toward their set goals, the generating AI analyzes this data and provides a training plan aimed at achieving those goals. If the user is approaching their goal, the generating AI can suggest a training plan to support their goal achievement. Conversely, if the user is falling further behind, it can provide a training plan to help them get closer to their goal. This allows for the provision of more effective training plans by considering the user's progress toward their fitness goals.

[0065] The training plan provision system can further reflect changes in the user's fitness goals in real time and adjust the training plan accordingly. For example, if a user sets a new fitness goal, the generating AI analyzes that data and provides a training plan tailored to that goal. If the user achieves their goal, the system can suggest a training plan for a new goal. Furthermore, if the user changes their goal, the system can provide a training plan that reflects that change. This allows for the provision of more effective training plans by reflecting changes in the user's fitness goals in real time.

[0066] The training plan provision system can further monitor the user's progress toward fitness goals and adjust how reward points are awarded. For example, if a user achieves their goal, the generating AI will award more reward points. If a user is approaching their goal, reward points can be provided according to their progress. If a user is falling further away from their goal, reward points can be provided to maintain their motivation. This allows for more effective reward point distribution by considering the user's progress toward fitness goals.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk inputs the user's health status and fitness goals. The user's health status includes, but is not limited to, heart rate, blood pressure, and weight. Fitness goals include, but are not limited to, weight loss, muscle strengthening, and endurance improvement. The reception desk allows the user to input their fitness level and exercise history, for example. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and generate an optimal training plan. The generation AI analyzes the user's fitness level and exercise history using algorithms such as deep learning and reinforcement learning. The generation unit provides plans tailored to the user's needs, ranging from light exercises for beginners to hard training for advanced users. Step 3: The feedback unit provides real-time feedback based on the training plan generated by the generation unit. The feedback unit, for example, analyzes the user's movements and instructs on the correct form and appropriate load. The feedback unit provides feedback in methods such as voice instructions, visual guidance, and vibration feedback. Step 4: The rewards department awards reward points based on the feedback provided by the feedback department. For example, the rewards department awards reward points according to training progress. The rewards department can set conditions for earning points and the rewards that can be used. Step 5: The Community Department facilitates interaction among users based on reward points awarded by the Rewards Department. The Community Department facilitates interaction and information sharing among users, for example, through online communities. The Community Department facilitates interaction among users through methods such as chat functions, forums, and event participation.

[0069] (Example of form 2) The training plan provision system according to an embodiment of the present invention is a system that provides an optimal training plan based on the user's health condition and fitness goals by utilizing a generative AI. This training plan provision system creates a personalized fitness plan based on the user's physical fitness level and exercise history, and supports daily training. It also includes functions to provide real-time feedback and enhance motivation. For example, the user inputs their health condition and fitness goals. At this time, the user inputs their physical fitness level and exercise history. For example, they input their past exercise history and current health condition. This information is input into the generative AI. Next, the generative AI analyzes the input information and creates an optimal training plan for the user. The generative AI generates a fitness plan that is optimal for each individual user based on the user's physical fitness level and exercise history. For example, it provides plans that match the user's needs, from light exercise for beginners to hard training for advanced users. Based on the generated fitness plan, it supports daily training. For example, when the user starts training, the generative AI provides real-time feedback. It analyzes the user's movements and supports effective training by instructing them on the correct form and appropriate load. Furthermore, it is equipped with functions to enhance motivation. For example, it maintains user motivation by awarding reward points according to training progress and providing point redemption. Furthermore, the system supports continued training by allowing users to interact with other users and share information through an online community. This system enables users to receive optimal training plans tailored to their health condition and fitness goals. The system incorporates features that provide real-time feedback and motivational enhancements through a generating AI, supporting effective training. This allows users to lead a healthier lifestyle. Thus, the training plan provision system can support effective training by providing optimal training plans based on the user's health condition and fitness goals, and by incorporating real-time feedback and motivational enhancement features.

[0070] The training plan provision system according to this embodiment comprises a reception unit, a generation unit, a feedback unit, a reward unit, and a community unit. The reception unit receives input from the user regarding their health status and fitness goals. The user's health status includes, but is not limited to, heart rate, blood pressure, and weight. Fitness goals include, but are not limited to, weight loss, muscle strengthening, and endurance improvement. The reception unit allows the user to input their fitness level and exercise history, for example. The generation unit uses a generation AI to analyze the information input by the reception unit and generate an optimal training plan. The generation AI analyzes the user's fitness level and exercise history using algorithms such as deep learning and reinforcement learning. The generation unit provides plans tailored to the user's needs, ranging from light exercises for beginners to hard training for advanced users. The feedback unit provides real-time feedback based on the training plan generated by the generation unit. The feedback unit analyzes the user's movements and provides instructions for correct form and appropriate load, for example. The feedback unit provides feedback in the form of voice instructions, visual guides, vibration feedback, etc. The rewards unit awards reward points based on the feedback provided by the feedback unit. The rewards unit awards reward points, for example, according to the progress of training. The rewards unit can set conditions for earning points and the benefits that can be used. The community unit promotes interaction among users based on the reward points awarded by the rewards unit. The community unit promotes interaction and information sharing with other users, for example, through online communities. The community unit promotes interaction among users through methods such as chat functions, forums, and event participation. As a result, the training plan provision system according to the embodiment can support effective training by providing an optimal training plan based on the user's health condition and fitness goals, and by incorporating real-time feedback and motivation-enhancing functions.

[0071] The reception desk inputs the user's health status and fitness goals. The user's health status includes, but is not limited to, heart rate, blood pressure, and weight. This data can be entered manually by the user or automatically retrieved from wearable devices such as smartwatches and fitness trackers. Fitness goals include, but are not limited to, weight loss, muscle gain, and endurance improvement. Users can enter specific goals, such as losing 5 kg in one month or completing a 10 km marathon in three months. The reception desk also allows users to input their fitness level and exercise history. This includes information such as past exercise experience and current exercise habits, including how many times a week they exercise and what types of exercise they do. Furthermore, the reception desk can collect information about the user's eating habits and lifestyle. For example, users can input information such as their daily calorie intake, meal content, sleep duration, and stress level. This allows the reception desk to comprehensively understand the user's overall health status and use this information to generate more accurate training plans.

[0072] The generation unit uses a generation AI to analyze information entered by the reception unit and generate an optimal training plan. The generation AI analyzes the user's fitness level and exercise history using algorithms such as deep learning and reinforcement learning. Specifically, it uses deep learning to analyze the user's health data and generates an optimal training plan based on past training data and success stories. Using reinforcement learning, it can learn from user feedback in real time and continuously improve the training plan. The generation unit provides plans tailored to the user's needs, ranging from light exercise for beginners to hard training for advanced users. Specifically, it suggests exercises such as walking, light jogging, and stretching for beginners, and hard exercises such as high-intensity interval training (HIIT) and weight training for advanced users. Furthermore, the generation unit can dynamically adjust the training plan according to the user's goals and progress. For example, if the user achieves their goals, or conversely, if they do not reach their goals, it regenerates the training plan to provide the user with the optimal plan. This allows the generation unit to respond to the individual needs of users and support effective training.

[0073] The feedback unit provides real-time feedback based on the training plan generated by the generation unit. For example, the feedback unit analyzes the user's movements and instructs them on correct form and appropriate load. Specifically, while the user is exercising, the unit captures their movements using cameras and motion sensors, and AI analyzes those movements. The AI ​​determines whether the user's movements are performed with correct form and issues corrective instructions as needed. For example, if the knee position is incorrect while performing squats, the AI ​​will instruct the user on the correct form through voice instructions or visual guidance. The feedback unit also monitors data such as the user's heart rate and calories burned in real time and instructs them on appropriate load. For example, if the heart rate exceeds the target range, it will instruct the user to reduce the exercise intensity, and conversely, if the heart rate is too low, it will instruct the user to increase the exercise intensity. The feedback unit provides feedback in various ways, such as voice instructions, visual guidance, and vibration feedback. This allows the user to receive appropriate feedback in real time and train effectively and safely. Furthermore, the feedback unit accumulates the user's training data to support long-term performance improvement. For example, it can evaluate the user's progress based on past training data and reflect this in the next training plan. This allows the feedback system to maximize the user's training effectiveness and maintain sustained motivation.

[0074] The rewards department awards reward points based on feedback provided by the feedback department. For example, the rewards department awards reward points according to training progress. Specifically, it awards reward points when the user achieves their set goals or completes a certain number of training sessions. The rewards department can set conditions for earning points and the rewards that can be used. For example, users can use the points they earn to purchase fitness-related products and services. The rewards department can also set special challenges and events to increase user motivation. For example, it can offer challenges where users can earn additional reward points by completing a certain amount of training within a specific period. Furthermore, the rewards department can analyze users' training data and provide individualized reward plans. For example, it can propose customized reward plans based on the user's training history and goals to maintain user motivation. In this way, the rewards department can increase users' motivation to train and support sustained training.

[0075] The Community Department facilitates interaction among users based on reward points awarded by the Rewards Department. For example, the Community Department allows users to interact and share information through online communities. Specifically, users can communicate with other users in real time using the chat function. They can also share training tips and success stories through forums, encouraging each other. Furthermore, the Community Department regularly holds online events and challenges to promote competition and cooperation among users. For example, they might set a monthly training challenge and offer special rewards to the user who earns the most points. The Community Department can also collect user feedback to improve the system. For instance, they might incorporate new features and improvements suggested by users to enhance the system's usability and effectiveness. In this way, the Community Department can not only promote interaction among users and increase training motivation, but also contribute to improving the overall quality of the system.

[0076] The reception desk can input the user's fitness level and exercise history. The reception desk provides, for example, an interface for the user to input their fitness level and exercise history. The reception desk allows the user to input, for example, their past exercise history and current health status. This allows for the provision of a more personalized training plan by inputting the user's fitness level and exercise history. Fitness level includes, but is not limited to, maximum oxygen uptake (VO2max) and muscle strength test results. Exercise history includes, but is not limited to, past training data, exercise frequency, and exercise type. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's input data into AI, which can analyze the data and automatically classify the fitness level and exercise history.

[0077] The generation unit can analyze the user's fitness level and exercise history using a generation AI and generate an optimal training plan. For example, the generation unit uses a generation AI to analyze the user's fitness level and exercise history. The generation AI uses algorithms such as deep learning and reinforcement learning to analyze the user's fitness level and exercise history. The generation unit provides plans tailored to the user's needs, ranging from light exercises for beginners to hard training for advanced users. This allows the generation AI to generate the optimal training plan for the user. For example, the generation AI receives user input data as a prompt and generates the optimal training plan. For example, the generation AI generates an optimal fitness plan for each individual user based on the user's fitness level and exercise history. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs user input data into the generation AI, which analyzes the data and generates the optimal training plan.

[0078] The feedback unit can analyze the user's movements and provide instructions for correct form and appropriate load. For example, the feedback unit can analyze the user's movements and provide instructions for correct form and appropriate load. The feedback unit provides feedback in methods such as voice instructions, visual guidance, and vibration feedback. This allows for effective training by analyzing the user's movements and providing instructions for correct form and appropriate load. Correct form includes, but is not limited to, posture checkpoints and range of motion. Appropriate load includes, but is not limited to, weight settings and adjustments to the number of repetitions and sets. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can input user movement data into an AI, which can analyze the data and provide instructions for correct form and appropriate load.

[0079] The rewards unit can award reward points according to training progress. For example, the rewards unit can award reward points according to training progress. The rewards unit can set conditions for earning points and the benefits that can be used. This can increase user motivation by awarding reward points according to training progress. Training progress includes, but is not limited to, achievement level, number of days continued, and performance improvement. Some or all of the above processing in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the user's training data into AI, and the AI ​​can analyze the data and award reward points.

[0080] The Community Department allows users to interact and share information with other users through online communities. For example, the Community Department facilitates user interaction through methods such as chat functions, forums, and event participation. This supports continued training by allowing users to interact and share information with others through online communities. Online communities include, but are not limited to, social networking services (SNS), dedicated apps, and forums. Some or all of the above-described processes in the Community Department may be performed using AI, or not. For example, the Community Department can input user interaction data into an AI, which can analyze the data and suggest the optimal method of interaction.

[0081] The reception desk can estimate the user's emotions and adjust the input method for health status and fitness goals based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of health status and fitness goals. This allows for more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the input method.

[0082] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions health statuses and fitness goals that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest health statuses and fitness goals to be used at a specific time of day based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Input history includes, but is not limited to, past input data, frequency, and content. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's past input data into AI, and the AI ​​can analyze the data and suggest the optimal input method.

[0083] The reception unit can simplify input by automatically acquiring the user's current location information when they input their health status or fitness goals. For example, when a user opens the app, the reception unit automatically acquires their current location and simplifies the input of their health status and fitness goals. For example, when a user enters their fitness goals, the reception unit suggests optimal candidate locations considering the distance from their current location. For example, when a user uses the app while on the move, the reception unit updates their current location in real time and simplifies the input of their health status and fitness goals. This simplifies the input process by automatically acquiring the current location information. Location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's location data into AI, which can analyze the data and suggest optimal candidate locations.

[0084] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the reception unit can provide an interface with bright colors to make the input process enjoyable. For example, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. In this way, a more comfortable input environment can be provided by adjusting the design of the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generative AI, which can analyze the data to estimate emotions and adjust the interface design.

[0085] The reception desk can automatically suggest potential destinations based on the user's past travel history when the user inputs their health status and fitness goals. For example, the reception desk can automatically display places the user has frequently visited in the past as potential destinations. For example, the reception desk can predict places the user will visit on specific days of the week or times of day and suggest them as potential destinations. For example, the reception desk can analyze the user's past travel patterns and suggest the most suitable potential destinations. In this way, the reception desk can suggest the most suitable potential destinations for the user by referring to their past travel history. Travel history includes, but is not limited to, past travel data, frequency, and destinations. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's travel data into AI, which can then analyze the data and suggest the most suitable potential destinations.

[0086] The reception desk can refer to the user's calendar information when they input their health status and fitness goals, and make suggestions based on their schedule. For example, the reception desk can refer to the appointments registered in the user's calendar and automatically set their health status and fitness goals. For example, the reception desk can suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, the reception desk can suggest the optimal route based on the user's calendar information. This makes it possible to make optimal suggestions based on the schedule by referring to the calendar information. Calendar information includes, but is not limited to, the type of appointment, priority, and reminder functions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's calendar information into AI, and the AI ​​can analyze the data and make optimal suggestions.

[0087] The generation unit can estimate the user's emotions and adjust the method of generating training plans using the generation AI based on the estimated user emotions. For example, if the user is relaxed, the generation AI will generate a training plan that proceeds at a relaxed pace. If the user is in a hurry, the generation AI will generate a training plan that produces results in the shortest possible time. If the user is excited, the generation AI will generate a training plan that includes visually stimulating effects. In this way, by adjusting the method of generating training plans according to the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI, and the generation AI can analyze the data and adjust the method of generating training plans.

[0088] The generation unit can propose an optimal training plan by referring to the user's past exercise history when generating a training plan. For example, the generation unit proposes an optimal training plan based on the user's past training. For example, the generation unit proposes an effective training plan based on the user's past exercise history. For example, the generation unit analyzes the user's past exercise history and proposes the most efficient training plan. In this way, by referring to past exercise history, it is possible to propose an optimal training plan to the user. Exercise history includes, but is not limited to, past training data, exercise frequency, and exercise type. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's exercise history data into the generation AI, which can analyze the data and propose an optimal training plan.

[0089] The generation unit can optimize training plans by considering real-time health data when generating them. For example, the generation unit can propose an optimal training plan based on real-time heart rate data. For example, the generation unit can propose an optimal training plan by considering real-time calorie consumption data. For example, the generation unit can propose an appropriate training plan based on real-time body temperature data. This allows for the provision of more appropriate training plans by considering real-time health data. Real-time health data includes, but is not limited to, data from wearable devices, sensors, and app integrations. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's real-time health data into the generation AI, which can then analyze the data and propose an optimal training plan.

[0090] The generation unit estimates the user's emotions, and the generating AI can adjust the length and level of detail of the training plan based on the estimated emotions. For example, if the user is in a hurry, the generating AI will generate a short, concise training plan. If the user is relaxed, the generating AI will generate a longer training plan with detailed explanations. If the user is excited, the generating AI will generate a training plan with visually stimulating effects. This allows for the provision of a more appropriate plan by adjusting the length and level of detail of the training plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generating AI. For example, the generation unit inputs user emotion data into the generating AI, which analyzes the data and adjusts the length and level of detail of the training plan.

[0091] The generation unit can propose a training plan while considering the user's current weather information. For example, on rainy days, the generation unit proposes a training plan that can be done indoors. On sunny days, for example, the generation unit proposes a training plan that can be done outdoors. On snowy days, for example, the generation unit proposes a training plan that can be done in a non-slippery location. In this way, by considering the current weather information, the generation unit can propose the most suitable training plan for the user. Weather information includes, but is not limited to, meteorological data and weather forecast services. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's weather data into the generation AI, which can then analyze the data and propose the optimal training plan.

[0092] The generation unit can propose an optimal training plan by considering the user's health condition when generating a training plan. For example, if the user is tired, the generation unit will propose a lighter training plan. For example, if the user is seeking healthy exercise, the generation unit will propose a slightly harder training plan. For example, if the user is feeling unwell, the generation unit will propose a training plan that includes rest. In this way, by considering the user's health condition, a more appropriate training plan can be provided. Health conditions include, but are not limited to, heart rate, blood pressure, and weight. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's health data into the generation AI, which can then analyze the data and propose an optimal training plan.

[0093] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit provides simple and easy-to-understand feedback. For example, if the user is relaxed, the feedback unit provides detailed feedback. For example, if the user is in a hurry, the feedback unit provides concise feedback. This allows for more appropriate feedback to be provided by adjusting the content of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into a generative AI, which can analyze the data and adjust the content of the feedback.

[0094] The feedback unit can provide optimal feedback by referring to the user's past training data. For example, the feedback unit provides optimal feedback based on the user's past training. For example, the feedback unit provides effective feedback from the user's past training data. For example, the feedback unit analyzes the user's past training data to provide the most efficient feedback. This allows the feedback unit to provide the user with optimal feedback by referring to past training data. Training data includes, but is not limited to, past training content, frequency, and performance. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the user's training data into AI, which can then analyze the data and provide optimal feedback.

[0095] The feedback unit can optimize feedback by considering real-time health data. For example, the feedback unit can provide optimal feedback based on real-time heart rate data. For example, the feedback unit can provide optimal feedback by considering real-time calorie consumption data. For example, the feedback unit can provide appropriate feedback based on real-time body temperature data. This allows for more appropriate feedback to be provided by considering real-time health data. Real-time health data includes, but is not limited to, data from wearable devices, sensors, and app integrations. Some or all of the processing described above in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the user's real-time health data into an AI, which can then analyze the data to provide optimal feedback.

[0096] The feedback unit can estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit will provide frequent feedback during training. For example, if the user is relaxed, the feedback unit will provide detailed feedback after the training is complete. For example, if the user is in a hurry, the feedback unit will provide concise feedback that summarizes the key points of the training. By adjusting the timing of feedback according to the user's emotions, feedback can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user emotion data into a generative AI, which can analyze the data and adjust the timing of feedback.

[0097] The feedback unit can select the optimal display method when providing feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit provides feedback that is adapted to the screen size. For example, if the user is using a tablet, the feedback unit provides feedback optimized for a larger screen. For example, if the user is using a smartwatch, the feedback unit provides concise and highly visible feedback. In this way, by considering device information, the feedback unit can provide the user with the most optimal display method for feedback. Device information includes, but is not limited to, the device type, OS, and screen size. Some or all of the processing described above in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the user's device information into AI, and the AI ​​can analyze the data to select the optimal display method.

[0098] The feedback unit can provide multilingual feedback according to the user's language settings when providing feedback. For example, the feedback unit automatically sets the language of the feedback based on the language settings of the user's device. For example, the feedback unit provides a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the feedback unit provides feedback in that language. This allows the user to receive appropriate feedback by providing multilingual feedback according to their language settings. Language settings include, but are not limited to, the user's language selection and the device's language settings. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's language setting data into AI, and the AI ​​can analyze the data to provide multilingual feedback.

[0099] The rewards unit can estimate the user's emotions and adjust the method of awarding reward points based on the estimated emotions. For example, if the user is unmotivated, the rewards unit will award more reward points for even small efforts. For example, if the user is highly motivated, the rewards unit will award more reward points for achieving challenging goals. For example, if the user is tired, the rewards unit will also award reward points for taking rest. By adjusting the method of awarding reward points according to the user's emotions, it becomes possible to maintain motivation more effectively. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the rewards unit may be performed using AI, or not using AI. For example, the rewards unit can input user emotion data into a generative AI, which can analyze the data and adjust the method of awarding reward points.

[0100] The rewards unit can analyze training progress in real time and award reward points at the appropriate time. For example, the rewards unit can award reward points immediately after a user completes training. For example, the rewards unit can award reward points when a user achieves a goal during training. For example, the rewards unit can award reward points for setting goals before a user starts training. This allows for the awarding of reward points at the appropriate time by analyzing training progress in real time. Training progress includes, but is not limited to, achievement level, number of days continued, and performance improvement. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit can input user training progress data into AI, which can analyze the data and award reward points at the appropriate time.

[0101] The rewards unit can adjust reward points by referring to the user's past training history when awarding reward points. For example, the rewards unit can adjust reward points based on goals the user has achieved in the past. For example, the rewards unit can award reward points commensurate with the user's effort based on the user's past training history. For example, the rewards unit can analyze the user's past training history and propose the most effective method for awarding reward points. This allows the rewards unit to award the user the optimal amount of reward points by referring to their past training history. Training history includes, but is not limited to, past training data, exercise frequency, and exercise type. Some or all of the above processing in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the user's training history data into AI, which can then analyze the data and adjust the reward points.

[0102] The reward unit can estimate the user's emotions and adjust the value of reward points based on the estimated emotions. For example, if the user is unmotivated, the reward unit will set the value of reward points higher. For example, if the user is highly motivated, the reward unit will set the value of reward points to normal. For example, if the user is tired, the reward unit will set the value of reward points slightly higher. This allows for more effective motivation maintenance by adjusting the value of reward points according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reward unit may be performed using AI or not using AI. For example, the reward unit can input user emotion data into a generative AI, which can analyze the data and adjust the value of reward points.

[0103] The rewards unit can provide region-specific reward points by considering the user's geographical location when awarding reward points. For example, if a user trains in a specific region, the rewards unit will award region-specific reward points if the user trains while traveling. For example, if a user participates in a local fitness event, the rewards unit will award event-specific reward points. This allows for the provision of region-specific reward points by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the user's geographical location information into an AI, which can then analyze the data to provide region-specific reward points.

[0104] The rewards department can analyze a user's social media activity when awarding reward points and provide relevant reward points. For example, the rewards department may award reward points if a user shares their training progress on social media. For example, the rewards department may award reward points if a user posts fitness-related information on social media. For example, the rewards department may award reward points if a user interacts with other users on social media. In this way, by analyzing social media activity, the rewards department can provide reward points relevant to the user. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the rewards department may be performed using, for example, AI, or not using AI. For example, the rewards department can input user social media activity data into AI, which can then analyze the data and provide relevant reward points.

[0105] The community section can estimate the user's emotions and adjust how the community is displayed based on those emotions. For example, if the user is stressed, the community section provides a simple and highly visible community display. If the user is relaxed, the community section provides a community display with detailed information. If the user is in a hurry, the community section provides a community display that gets straight to the point. By adjusting how the community is displayed according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the community section may be performed using AI, or not using AI. For example, the community section can input user emotion data into a generative AI, which can analyze the data and adjust how the community is displayed.

[0106] The Community Department can suggest the most suitable way to interact with users during community activities by referring to their past community activity history. For example, the Community Department can suggest the most suitable way to interact based on the community events the user has previously participated in. For example, the Community Department can suggest effective ways to interact based on the user's past community activity history. For example, the Community Department can suggest the most efficient way to interact by analyzing the user's past community activity history. In this way, the Community Department can suggest the most suitable way to interact with users by referring to their past community activity history. Community activity history includes, but is not limited to, past events participated in, posts made, and interaction frequency. Some or all of the above processing in the Community Department may be performed using AI, for example, or not using AI. For example, the Community Department can input the user's community activity history data into AI, which can then analyze the data and suggest the most suitable way to interact.

[0107] The community department can provide real-time feedback during community activities and facilitate interaction among users. For example, the community department can provide real-time feedback when a user participates in a community event. For example, the community department can provide real-time feedback when a user interacts in an online community. For example, the community department can provide real-time feedback when a user is engaged in community activities. This facilitates interaction among users by providing real-time feedback. Real-time feedback includes, but is not limited to, voice instructions, visual guides, and vibration feedback. Some or all of the above processing in the community department may be performed using, for example, AI, or not using AI. For example, the community department can input user interaction data into an AI, which can then analyze the data and provide real-time feedback.

[0108] The community section can estimate the user's emotions and prioritize communities based on those emotions. For example, if the user is stressed, the community section will prioritize simple, highly visible communities. If the user is relaxed, the community section will prioritize communities containing detailed information. If the user is in a hurry, the community section will prioritize communities that are to the point. This allows for more appropriate community display by prioritizing communities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the community section may be performed using AI or not. For example, the community section can input user emotion data into a generative AI, which can then analyze the data to determine community priorities.

[0109] The community department can suggest region-specific communities when a user participates in community activities, taking into account the user's geographical location. For example, if a user participates in community activities in a specific region, the community department will suggest region-specific communities. For example, if a user participates in community activities while traveling, the community department will suggest region-specific communities in the travel destination. For example, if a user participates in a local community event, the community department will suggest communities specific to that event. In this way, region-specific communities can be suggested by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the community department may be performed using, for example, AI, or not using AI. For example, the community department can input the user's geographical location information into AI, and the AI ​​can analyze the data and suggest region-specific communities.

[0110] The Community Department can analyze a user's social media activity during community activities and suggest relevant communities. For example, if a user posts fitness-related information on social media, the Community Department can suggest relevant communities. For example, if a user interacts with other users on social media, the Community Department can suggest relevant communities. For example, if a user shares their training progress on social media, the Community Department can suggest relevant communities. In this way, by analyzing social media activity, it is possible to suggest communities relevant to the user. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the Community Department may be performed using AI, for example, or not using AI. For example, the Community Department can input user social media activity data into AI, which can analyze the data and suggest relevant communities.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The training plan provision system can also acquire the user's dietary data and incorporate it into the training plan. For example, by inputting the user's daily meal content, the generating AI analyzes that data and provides a training plan that takes nutritional balance into consideration. If the user has consumed a high-calorie meal, the generating AI can suggest exercises that promote calorie burning. Furthermore, if the user is deficient in a particular nutrient, the system can provide a training plan to supplement that nutrient. This allows for the provision of more personalized training plans by considering the user's dietary data.

[0113] The training plan provision system can also acquire user sleep data and incorporate it into the training plan. For example, by inputting sleep duration and sleep quality, the generating AI analyzes the data and provides an appropriate training plan. If the user is not getting enough sleep, the generating AI can suggest a lighter training plan. Conversely, if the user is getting deep sleep, it can provide a more intense training plan. In this way, by taking the user's sleep data into consideration, a more effective training plan can be provided.

[0114] The training plan provision system can also monitor the user's stress level and reflect it in the training plan. For example, by inputting the user's stress level, the generating AI analyzes the data and provides a training plan aimed at stress reduction. If the user is in a high-stress state, the generating AI can suggest training that promotes relaxation. Conversely, if the user is in a low-stress state, it can provide a more challenging training plan. In this way, by taking the user's stress level into consideration, a more appropriate training plan can be provided.

[0115] The training plan provision system can also acquire data on the user's fitness equipment usage and incorporate it into the training plan. For example, by inputting data on the fitness equipment the user uses, the generating AI analyzes that data and provides a training plan optimized for that equipment. If a user frequently uses a particular piece of equipment, the system can suggest a training plan that utilizes that equipment. Furthermore, if a user introduces new equipment, the system can provide a training plan to help them use that equipment effectively. In this way, by considering the user's fitness equipment usage data, a more effective training plan can be provided.

[0116] The training plan provision system can further estimate the user's emotions and adjust the difficulty of the training plan based on those emotions. For example, if the user is tired, the generating AI can provide an easier training plan. If the user is highly motivated, it can provide a more challenging training plan. Furthermore, if the user is stressed, it can provide a training plan that promotes relaxation. This allows for the provision of more appropriate plans by adjusting the difficulty of the training plan according to the user's emotions.

[0117] The training plan provision system can also monitor the user's progress toward their fitness goals and reflect this in the training plan. For example, by inputting the user's progress toward their set goals, the generating AI analyzes this data and provides a training plan aimed at achieving those goals. If the user is approaching their goal, the generating AI can suggest a training plan to support their goal achievement. Conversely, if the user is falling further behind, it can provide a training plan to help them get closer to their goal. This allows for the provision of more effective training plans by considering the user's progress toward their fitness goals.

[0118] The training plan provision system can further estimate the user's emotions and adjust the content of the training plan based on those emotions. For example, if the user is relaxed, the generating AI can provide a training plan that promotes relaxation. If the user is excited, it can provide an energetic training plan. Furthermore, if the user is feeling anxious, it can provide a training plan that provides a sense of security. This allows for the provision of a more appropriate plan by adjusting the training plan content according to the user's emotions.

[0119] The training plan provision system can further reflect changes in the user's fitness goals in real time and adjust the training plan accordingly. For example, if a user sets a new fitness goal, the generating AI analyzes that data and provides a training plan tailored to that goal. If the user achieves their goal, the system can suggest a training plan for a new goal. Furthermore, if the user changes their goal, the system can provide a training plan that reflects that change. This allows for the provision of more effective training plans by reflecting changes in the user's fitness goals in real time.

[0120] The training plan delivery system can further estimate the user's emotions and adjust the feedback method of the training plan based on those emotions. For example, if the user is nervous, the generating AI can provide simple and easy-to-understand feedback. If the user is relaxed, it can provide detailed feedback. If the user is in a hurry, it can provide concise feedback. In this way, by adjusting the feedback method according to the user's emotions, more appropriate feedback can be provided.

[0121] The training plan provision system can further monitor the user's progress toward fitness goals and adjust how reward points are awarded. For example, if a user achieves their goal, the generating AI will award more reward points. If a user is approaching their goal, reward points can be provided according to their progress. If a user is falling further away from their goal, reward points can be provided to maintain their motivation. This allows for more effective reward point distribution by considering the user's progress toward fitness goals.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk inputs the user's health status and fitness goals. The user's health status includes, but is not limited to, heart rate, blood pressure, and weight. Fitness goals include, but are not limited to, weight loss, muscle strengthening, and endurance improvement. The reception desk allows the user to input their fitness level and exercise history, for example. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and generate an optimal training plan. The generation AI analyzes the user's fitness level and exercise history using algorithms such as deep learning and reinforcement learning. The generation unit provides plans tailored to the user's needs, ranging from light exercises for beginners to hard training for advanced users. Step 3: The feedback unit provides real-time feedback based on the training plan generated by the generation unit. The feedback unit, for example, analyzes the user's movements and instructs on the correct form and appropriate load. The feedback unit provides feedback in methods such as voice instructions, visual guidance, and vibration feedback. Step 4: The rewards department awards reward points based on the feedback provided by the feedback department. For example, the rewards department awards reward points according to training progress. The rewards department can set conditions for earning points and the rewards that can be used. Step 5: The Community Department facilitates interaction among users based on reward points awarded by the Rewards Department. The Community Department facilitates interaction and information sharing among users, for example, through online communities. The Community Department facilitates interaction among users through methods such as chat functions, forums, and event participation.

[0124] 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.

[0125] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the reception unit, generation unit, feedback unit, reward unit, and community unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which inputs the user's health status and fitness goals. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates an optimal training plan using a generation AI. The feedback unit is implemented, for example, by the output device 40 of the smart device 14, which provides real-time feedback. The reward unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which awards reward points according to the progress of training. The community unit facilitates interaction among users through an online community via the communication I / F 44 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0131] 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.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0133] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] 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.

[0135] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, generation unit, feedback unit, reward unit, and community unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which inputs the user's health status and fitness goals. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an optimal training plan using a generation AI. The feedback unit is implemented by the speaker 240 of the smart glasses 214, which provides real-time feedback. The reward unit is implemented by the specific processing unit 290 of the data processing unit 12, which awards reward points according to the progress of training. The community unit facilitates interaction among users through an online community via the communication I / F 44 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0147] 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.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0149] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] 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.

[0151] 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.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, generation unit, feedback unit, reward unit, and community unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and inputs the user's health status and fitness goals. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal training plan using a generation AI. The feedback unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides real-time feedback. The reward unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and awards reward points according to the progress of training. The community unit facilitates interaction among users through an online community via, for example, the communication I / F 44 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0163] 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.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0165] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] 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.

[0167] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] 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.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the reception unit, generation unit, feedback unit, reward unit, and community unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which inputs the user's health status and fitness goals. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an optimal training plan using a generation AI. The feedback unit is implemented by, for example, the speaker 240 of the robot 414, which provides real-time feedback. The reward unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which awards reward points according to the progress of training. The community unit facilitates interaction among users through an online community via the communication I / F 44 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0177] 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.

[0178] Figure 9 shows the 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.

[0179] 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.

[0180] 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.

[0181] 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, and motorcycles, 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 based, for example, 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.

[0182] 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."

[0183] 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.

[0184] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] 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 other things 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.

[0194] 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.

[0195] (Note 1) A reception area where users input their health status and fitness goals, A generation unit analyzes the information input by the reception unit and generates an optimal training plan, A feedback unit provides real-time feedback based on the training plan generated by the generation unit, A reward unit that awards reward points based on the feedback provided by the aforementioned feedback unit, The community unit includes a reward unit that promotes interaction among users based on reward points awarded by the aforementioned reward unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter the user's fitness level and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​analyzes the user's fitness level and exercise history to generate an optimal training plan. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is It analyzes the user's movements and instructs them on the correct form and appropriate load. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned compensation unit is, Reward points will be awarded based on training progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned community department, Interact with other users and share information through online communities. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how health status and fitness goals are entered based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering health status or fitness goals, the system automatically retrieves the user's current location information to simplify the input process. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input their health status or fitness goals, the system automatically suggests potential locations based on their past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input their health status and fitness goals, the system uses their calendar information to provide schedule-based suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts how the generative AI generates training plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a training plan, the system will suggest the optimal plan based on the user's past exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a training plan, the plan is optimized by taking real-time health data into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The AI ​​estimates the user's emotions and then adjusts the length and level of detail of the training plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a training plan, the system will suggest a plan that takes into account the user's current weather information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a training plan, the system takes the user's health condition into consideration and proposes the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is When providing feedback, we refer to the user's past training data to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, we optimize it by taking real-time health data into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is It estimates the user's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is When receiving feedback, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, multilingual feedback is provided according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned compensation unit is, The system estimates the user's emotions and adjusts the reward point distribution method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned compensation unit is, The system analyzes training progress in real time and awards reward points at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned compensation unit is, When awarding reward points, the points will be adjusted based on the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned compensation unit is, The system estimates the user's emotions and adjusts the value of reward points based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned compensation unit is, When awarding reward points, the system takes into account the user's geographical location to provide region-specific reward points. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned compensation unit is, When awarding reward points, the system analyzes the user's social media activity and provides reward points relevant to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned community department, It estimates user sentiment and adjusts how communities are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned community department, During community activities, we suggest the most suitable interaction methods by referring to the user's past community activity history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned community department, During community activities, we provide real-time feedback and promote interaction among users. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned community department, It estimates user sentiment and determines community priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned community department, When engaging in community activities, the system suggests region-specific communities based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned community department, During community activities, we analyze users' social media activity and suggest relevant communities. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their health status and fitness goals, A generation unit analyzes the information input by the reception unit and generates an optimal training plan, A feedback unit provides real-time feedback based on the training plan generated by the generation unit, A reward unit that awards reward points based on the feedback provided by the aforementioned feedback unit, The community unit includes a reward unit that promotes interaction among users based on reward points awarded by the aforementioned reward unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter the user's fitness level and exercise history. The system according to feature 1.

3. The generating unit is The AI ​​generates a training plan based on the user's fitness level and exercise history. The system according to feature 1.

4. The aforementioned feedback unit is It analyzes the user's movements and instructs them on the correct form and appropriate load. The system according to feature 1.

5. The aforementioned compensation unit is, Reward points will be awarded based on training progress. The system according to feature 1.

6. The aforementioned community section, Interact with other users and share information through online communities. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how health status and fitness goals are entered based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering health status or fitness goals, the system automatically retrieves the user's current location information to simplify the input process. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system according to feature 1.

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