System

Abstract

A system according to an embodiment aims to make indoor fitness bike exercise more enjoyable.SOLUTION: A system includes a sensor part, an analysis part, a generation part, and a display part. The sensor unit collects the number of rotations or the speed of the pedal. The analysis unit analyzes the data collected by the sensor unit. The generation unit generates a virtual cycling world based on the data analyzed by the analysis unit. The display unit displays the virtual world generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, indoor exercise on a fitness bike can be monotonous and people can easily get bored of it.

[0005] The system according to the embodiment aims to make indoor exercise on a fitness bike more enjoyable. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor unit, an analysis unit, a generation unit, and a display unit. The sensor unit collects pedal rotation speed or speed. The analysis unit analyzes the data collected by the sensor unit. The generation unit generates a virtual cycling world based on the data analyzed by the analysis unit. The display unit displays the virtual world generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can make indoor exercise on a fitness bike more enjoyable. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A fitness bike system according to an embodiment of the present invention uses a generation AI to create a virtual cycling world and allow users to enjoy various courses while cycling indoors. When a user rides a fitness bike and begins pedaling, sensors collect data such as pedal rotations and speed. This data is input into the generation AI, which then generates a virtual cycling world in real time. The generated virtual world is displayed on a monitor, allowing the user to experience riding outdoors as if they were actually riding. For example, a user rides a fitness bike and begins pedaling. Sensors collect data such as pedal rotations and speed. For example, if the pedal rotations are fast, the scenery in the virtual world is set to move quickly. The collected data is then input into the generation AI. The generation AI analyzes the input data and generates a virtual cycling world in real time. For example, if a user wants to ride a mountain trail, the generation AI generates a mountain trail landscape, providing the user with the experience of actually riding a mountain trail. The generated virtual world is displayed on a monitor. By pedaling a bike while viewing the scenery displayed on the monitor, users can experience riding outdoors as if they were actually riding. For example, if a user wants to ride along the coast, the generation AI generates coastal scenery, providing the user with the experience of actually riding along the coast. This allows the fitness bike system to enjoy a variety of courses while indoors. This eliminates monotony in exercise, allowing users to continue exercising without getting bored. For example, if a user wants to ride a different course every day, the generation AI can generate a course that matches their mood and physical condition on that day.

[0029] The fitness bike system according to the embodiment includes a sensor unit, an analysis unit, a generation unit, and a display unit. The sensor unit collects pedal rotations or speed. For example, the sensor unit can measure pedal rotations in revolutions per minute (RPM). The sensor unit can also measure speed in kilometers per hour (km / h). The sensor unit can also collect both pedal rotations and speed simultaneously. The analysis unit analyzes the data collected by the sensor unit. For example, the analysis unit can calculate the user's exercise intensity based on the collected rotation data. The analysis unit can also calculate the user's travel distance based on the speed data. The analysis unit can also analyze the user's exercise pattern by combining the rotation data and speed data. The generation unit generates a virtual cycling world based on the data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate a real-time virtual world based on the user's exercise data. The generation AI can use a text generation AI (e.g., LLM) or a multimodal generation AI to generate the virtual world based on the user's exercise data. For example, if a user wants to ride a mountain path, the generation unit can generate a mountain path scenery. Also, if a user wants to ride along the coast, the generation unit can generate a coastal scenery. Furthermore, the generation unit can generate various scenery, such as urban areas and forests, based on the user's exercise data. The display unit displays the virtual world generated by the generation unit. For example, the display unit displays the scenery of the virtual world on a monitor. Furthermore, the display unit can move the scenery of the virtual world according to the user's pedal rotation speed. Furthermore, the display unit can change the viewpoint of the virtual world according to the user's handlebar movement. Thus, the fitness bike system according to the embodiment generates and displays a virtual cycling world based on the user's pedal rotation speed and makes indoor cycling more enjoyable.

[0030] The fitness bike system includes a sensor unit that collects the movement or tilt of the handlebars. The sensor unit collects the movement and tilt of the handlebars. For example, the sensor unit can measure changes in the angle of the handlebars. The sensor unit can also measure the distance traveled by the handlebars. Furthermore, the sensor unit can measure the tilt angle and tilt direction of the handlebars. By collecting the movement and tilt of the handlebars, a more realistic virtual cycling experience can be provided. For example, the sensor unit can collect the movement of the handlebars in real time and change the viewpoint of the virtual world. The sensor unit can also collect the tilt of the handlebars and reflect it in the terrain of the virtual world. Furthermore, the sensor unit can analyze the user's exercise data based on the movement and tilt of the handlebars. By collecting the movement and tilt of the handlebars, the fitness bike system can provide a more realistic virtual cycling experience.

[0031] The fitness bike system includes a generator that generates a course tailored to the user's physical condition or mood. The generator generates a course tailored to the user's physical condition or mood. For example, the generator can generate a course based on data such as the user's heart rate, blood pressure, and fatigue level. The generator can also generate a course based on data such as the user's questionnaire results and facial expression analysis. The generator can also generate various courses, such as mountain trails, coastal routes, and urban areas, based on the user's physical condition and mood. This allows for a more personalized exercise experience by generating a course tailored to the user's physical condition and mood. For example, the generator can generate a relaxing course if the user wants to relax. The generator can also generate a challenging course if the user wants to exercise more challengingly. The generator can also adjust the difficulty level and scenery of the course based on the user's physical condition and mood. This allows the fitness bike system to generate a course tailored to the user's physical condition and mood, allowing for a more personalized exercise experience.

[0032] The generation unit can generate scenery based on geographic data or meteorological data. The generation unit generates scenery based on geographic data or meteorological data. For example, the generation unit can generate scenery based on GPS data or map information. The generation unit can also generate scenery based on meteorological data such as temperature, humidity, and wind speed. The generation unit can also combine geographic data and meteorological data to generate more realistic scenery. In this way, by generating scenery based on geographic data and meteorological data, a more realistic virtual cycling experience can be provided. For example, the generation unit can generate a mountain trail scenery based on geographic data of a mountainous area. In addition, the generation unit can generate a coastal scenery based on geographic data of a coastal area. In addition, the generation unit can generate an urban scenery based on geographic data of an urban area. In this way, the fitness bike system can provide a more realistic virtual cycling experience by generating scenery based on geographic data and meteorological data.

[0033] The display unit can move the scenery in the virtual world in accordance with the user's pedal rotation speed. The display unit moves the scenery in the virtual world in accordance with the user's pedal rotation speed. For example, the display unit can move the scenery in the virtual world faster if the user pedals quickly. Conversely, the display unit can move the scenery in the virtual world slower if the user pedals slowly. Furthermore, the display unit can adjust the speed of the scenery movement in real time in accordance with the pedal rotation speed. This allows a more realistic exercise experience to be provided by moving the scenery in accordance with the user's pedal rotation speed. For example, the display unit can move a mountain trail scenery in accordance with the user pedal rotation speed. Conversely, the display unit can move a coastal scenery in accordance with the user pedal rotation speed. Furthermore, the display unit can move an urban area scenery in accordance with the pedal rotation speed. This allows the fitness bike system to move the scenery in accordance with the user's pedal rotation speed in accordance with the user's pedal rotation speed, providing a more realistic exercise experience.

[0034] The display unit can change the viewpoint of the virtual world in response to the user's handlebar movement. The display unit changes the viewpoint of the virtual world in response to the user's handlebar movement. For example, when the user moves the handlebar left, the display unit can also move the viewpoint of the virtual world to the left. Similarly, when the user moves the handlebar right, the display unit can also move the viewpoint of the virtual world to the right. Furthermore, the display unit can adjust the speed of the viewpoint change in real time in response to the handlebar movement. This allows for a more realistic exercise experience by changing the viewpoint in response to the user's handlebar movement. For example, when the user moves the handlebar left, the display unit can change the viewpoint of a mountain road scenery to the left. Similarly, when the user moves the handlebar right, the display unit can change the viewpoint of a coastal scenery to the right. Furthermore, the display unit can change the viewpoint of an urban scenery in response to the handlebar movement. This allows the fitness bike system to change the viewpoint in response to the user's handlebar movement in response to a more realistic exercise experience.

[0035] When collecting pedaling speed or revolutions per minute, the sensor unit can select an appropriate collection method by referring to the user's past exercise data. When collecting pedaling speed or revolutions per minute, the sensor unit can select the optimal collection method by referring to the user's past exercise data. For example, if the user has previously performed high-intensity exercise, the sensor unit can collect detailed data to track the progress of the exercise. Alternatively, if the user has previously performed low-intensity exercise, the sensor unit can select a simplified data collection method. Furthermore, the sensor unit can analyze the user's past exercise data and set optimal collection frequency and accuracy. This allows the optimal data collection method to be selected by referring to the user's past exercise data. For example, the sensor unit can adjust the collection method for current exercise data based on the user's past exercise history. The sensor unit can also refer to the user's past training records to improve the accuracy of the collected data. Furthermore, the sensor unit can filter the collected data based on the user's past exercise data. This allows the fitness bike system to select the optimal data collection method by referring to the user's past exercise data.

[0036] When collecting the pedaling speed or revolutions per minute, the sensor unit can adjust the accuracy of the collected data according to the user's physical fitness level. When collecting the pedaling speed or revolutions per minute, the sensor unit adjusts the accuracy of the collected data according to the user's physical fitness level. For example, if the user's physical fitness level is high, the sensor unit collects detailed data to maximize the effect of the exercise. Alternatively, if the user's physical fitness level is low, the sensor unit can collect simplified data to reduce the strain of the exercise. Furthermore, the sensor unit can dynamically adjust the accuracy of the collected data based on the user's physical fitness level. This allows for more appropriate data collection by adjusting the accuracy of the collected data according to the user's physical fitness level. For example, the sensor unit adjusts the accuracy of the collected data based on the user's maximum oxygen uptake (VO2max). The sensor unit can also adjust the accuracy of the collected data based on the results of a muscle strength test. Furthermore, the sensor unit can filter the collected data based on the user's physical fitness level. This allows the fitness bike system to adjust the accuracy of the collected data according to the user's physical fitness level, allowing for more appropriate data collection.

[0037] When collecting the pedaling speed or revolutions per minute, the sensor unit can filter the collected data based on the user's exercise goal. When collecting the pedaling speed or revolutions per minute, the sensor unit can filter the collected data based on the user's exercise goal. For example, if the user is aiming to lose weight, the sensor unit can prioritize collecting data related to calorie consumption. Also, if the user is aiming to improve muscle strength, the sensor unit can prioritize collecting data related to muscle training. Furthermore, the sensor unit can filter the collected data based on the user's exercise goal and provide an optimal exercise plan. By filtering the collected data based on the user's exercise goal, a more appropriate exercise plan can be provided. For example, the sensor unit can prioritize collecting calorie consumption data based on the user's weight loss goal. Also, the sensor unit can prioritize collecting endurance training data based on the user's endurance improvement goal. Furthermore, the sensor unit can filter the collected data based on the user's exercise goal and provide an optimal exercise plan. By filtering the collected data based on the user's exercise goal, the fitness bike system can provide a more appropriate exercise plan.

[0038] When collecting the number of revolutions or speed of the pedals, the sensor unit can prioritize collecting more relevant data by taking into account the user's geographical location information. When collecting the number of revolutions or speed of the pedals, the sensor unit prioritizes collecting more relevant data by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the sensor unit can prioritize collecting data related to climbing. Also, if the user is in an urban area, the sensor unit can prioritize collecting data related to traffic conditions. Furthermore, the sensor unit can select an optimal data collection method based on the user's geographical location information. This allows for more relevant data to be collected by taking into account the user's geographical location information. For example, the sensor unit can adjust the collection method for current exercise data based on the user's GPS data. The sensor unit can also improve the accuracy of the collected data based on the user's address information. Furthermore, the sensor unit can filter the collected data based on the user's geographical location information. This allows the exercise bike system to collect more relevant data by taking into account the user's geographical location information.

[0039] The sensor unit can analyze the user's social media activities and collect related data when collecting the pedaling speed or revolutions per minute. The sensor unit can analyze the user's social media activities and collect related data when collecting the pedaling speed or revolutions per minute. For example, the sensor unit selects an optimal data collection method based on exercise data shared by the user on social media. The sensor unit can also collect data from the user's social media activities to increase exercise motivation. The sensor unit can also customize the data collection method by reflecting the user's social media feedback. This enables more appropriate data collection by analyzing the user's social media activities. For example, the sensor unit can adjust the current exercise data collection method based on the content of the user's social media posts. The sensor unit can also improve the accuracy of the collected data based on the number of likes the user has on social media. The sensor unit can also filter the collected data based on the user's social media activities. This enables the fitness bike system to collect more appropriate data by analyzing the user's social media activities.

[0040] The sensor unit can customize the collection method by reflecting the user's past feedback when collecting the pedaling speed or revolutions per minute. The sensor unit customizes the collection method by reflecting the user's past feedback when collecting the pedaling speed or revolutions per minute. For example, the sensor unit selects the optimal data collection method based on feedback provided by the user in the past. The sensor unit can also adjust the accuracy of the collected data based on the user's past feedback. The sensor unit can also dynamically customize the collection method by reflecting the user's feedback. This enables more appropriate data collection by reflecting the user's past feedback. For example, the sensor unit adjusts the collection method for current exercise data based on the results of a user survey. The sensor unit can also improve the accuracy of the collected data based on user reviews. The sensor unit can also filter the collected data based on the user's past feedback. This enables the fitness bike system to collect more appropriate data by reflecting the user's past feedback.

[0041] The analysis unit can improve the accuracy of the analysis by referring to the user's past exercise data during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past exercise data during analysis. For example, the analysis unit can analyze the current exercise data based on the user's past exercise data to improve accuracy. The analysis unit can also analyze the user's past exercise patterns and reflect them in the analysis of the current exercise data. The analysis unit can also optimize the analysis algorithm by referring to the user's past exercise data. In this way, the analysis accuracy can be improved by referring to the user's past exercise data. For example, the analysis unit can adjust the analysis method for the current exercise data based on the user's exercise history. The analysis unit can also improve the accuracy of the analysis data based on the user's training records. Furthermore, the analysis unit can filter the analysis data based on the user's past exercise data. In this way, the fitness bike system can improve the analysis accuracy by referring to the user's past exercise data.

[0042] The analysis unit can customize the analysis results based on the user's exercise goals during analysis. The analysis unit customizes the analysis results based on the user's exercise goals during analysis. For example, if the user is aiming to lose weight, the analysis unit can provide analysis results related to calorie consumption. Furthermore, if the user is aiming to improve muscle strength, the analysis unit can provide analysis results related to muscle training. Furthermore, the analysis unit can customize the analysis results based on the user's exercise goals and propose an optimal exercise plan. By customizing the analysis results based on the user's exercise goals, a more appropriate exercise plan can be provided. For example, the analysis unit can prioritize analysis of calorie consumption data based on the user's weight loss goal. Furthermore, the analysis unit can prioritize analysis of endurance training data based on the user's endurance improvement goal. Furthermore, the analysis unit can filter the analysis data based on the user's exercise goals and propose an optimal exercise plan. Thus, the fitness bike system can customize the analysis results based on the user's exercise goals and propose a more appropriate exercise plan.

[0043] The analysis unit can filter the analysis data according to the user's physical fitness level during analysis. The analysis unit can filter the analysis data according to the user's physical fitness level during analysis. For example, if the user's physical fitness level is high, the analysis unit can provide detailed analysis data to maximize the effect of exercise. Furthermore, if the user's physical fitness level is low, the analysis unit can provide simplified analysis data to reduce the burden of exercise. Furthermore, the analysis unit can filter the analysis data based on the user's physical fitness level and provide an optimal exercise plan. In this way, by filtering the analysis data according to the user's physical fitness level, a more appropriate exercise plan can be provided. For example, the analysis unit can adjust the accuracy of the analysis data based on the user's maximum oxygen uptake (VO2max). In addition, the analysis unit can adjust the accuracy of the analysis data based on the user's muscle strength test results. Furthermore, the analysis unit can filter the analysis data based on the user's physical fitness level. In this way, the fitness bike system can provide a more appropriate exercise plan by filtering the analysis data according to the user's physical fitness level.

[0044] The analysis unit can customize the analysis results by taking into account the user's geographical location information during analysis. The analysis unit customizes the analysis results by taking into account the user's geographical location information during analysis. For example, if the user is in a mountainous area, the analysis unit can provide analysis results related to climbing. Also, if the user is in an urban area, the analysis unit can provide analysis results related to traffic conditions. Furthermore, the analysis unit can provide optimal analysis results based on the user's geographical location information. This allows for more relevant analysis results to be provided by taking the user's geographical location information into account. For example, the analysis unit can adjust the analysis method for the current exercise data based on the user's GPS data. Also, the analysis unit can improve the accuracy of the analysis data based on the user's address information. Furthermore, the analysis unit can filter the analysis data based on the user's geographical location information. This allows the fitness bike system to provide more relevant analysis results by taking the user's geographical location information into account.

[0045] The analysis unit can analyze the user's social media activities and analyze the associated data during the analysis. The analysis unit can analyze the user's social media activities and analyze the associated data during the analysis. For example, the analysis unit can provide optimal analysis results based on the exercise data shared by the user on social media. The analysis unit can also provide analysis results that increase exercise motivation based on the user's social media activities. The analysis unit can also customize the analysis results by reflecting the user's feedback on social media. This allows the analysis of the user's social media activities to provide more appropriate analysis results. For example, the analysis unit can adjust the analysis method for the current exercise data based on the content of the user's social media posts. The analysis unit can also improve the accuracy of the analysis data based on the number of likes the user has on social media. The analysis unit can also filter the analysis data based on the user's social media activities. This allows the fitness bike system to provide more appropriate analysis results by analyzing the user's social media activities.

[0046] The analysis unit can adjust the analysis algorithm during analysis by reflecting the user's past feedback. The analysis unit adjusts the analysis algorithm during analysis by reflecting the user's past feedback. For example, the analysis unit selects an optimal analysis algorithm based on feedback provided by the user in the past. The analysis unit can also adjust the accuracy of the analysis data based on the user's past feedback. Furthermore, the analysis unit can dynamically customize the analysis algorithm by reflecting the user's feedback. This allows for more appropriate analysis results to be provided by reflecting the user's past feedback. For example, the analysis unit adjusts the analysis method for the current exercise data based on the results of a user survey. The analysis unit can also improve the accuracy of the analysis data based on user reviews. Furthermore, the analysis unit can filter the analysis data based on the user's past feedback. This allows the fitness bike system to provide more appropriate analysis results by reflecting the user's past feedback.

[0047] The generation unit can adjust the level of detail of the virtual world by referring to the user's past exercise data during generation. The generation unit can adjust the level of detail of the virtual world by referring to the user's past exercise data during generation. For example, the generation unit adjusts the level of detail of the current virtual world based on the user's past exercise data. The generation unit can also analyze the user's past exercise patterns and reflect them in the level of detail of the current virtual world. Furthermore, the generation unit can optimize the virtual world generation algorithm by referring to the user's past exercise data. In this way, the level of detail of the virtual world can be adjusted by referring to the user's past exercise data. For example, the generation unit adjusts the level of detail of the current virtual world based on the user's exercise history. The generation unit can also improve the level of detail of the virtual world based on the user's training records. Furthermore, the generation unit can filter the virtual world based on the user's past exercise data. In this way, the fitness bike system can adjust the level of detail of the virtual world by referring to the user's past exercise data.

[0048] The generation unit can customize the difficulty of the virtual world based on the user's exercise goal at the time of generation. The generation unit customizes the difficulty of the virtual world based on the user's exercise goal at the time of generation. For example, if the user is aiming to lose weight, the generation unit can set a difficulty level that promotes calorie consumption. Also, if the user is aiming to improve muscle strength, the generation unit can set a difficulty level that is suitable for muscle training. Furthermore, the generation unit can customize the difficulty of the virtual world based on the user's exercise goal and provide an optimal exercise plan. In this way, customizing the difficulty of the virtual world based on the user's exercise goal can provide a more appropriate exercise plan. For example, the generation unit can prioritize analyzing calorie consumption data based on the user's weight loss goal. Also, the generation unit can prioritize analyzing endurance training data based on the user's endurance improvement goal. Furthermore, the generation unit can filter the virtual world based on the user's exercise goal and provide an optimal exercise plan. In this way, the fitness bike system can customize the difficulty of the virtual world based on the user's exercise goal and provide a more appropriate exercise plan.

[0049] The generation unit can adjust the terrain of the virtual world according to the user's physical fitness level during generation. The generation unit can adjust the terrain of the virtual world according to the user's physical fitness level during generation. For example, the generation unit can generate challenging terrain when the user's physical fitness level is high. The generation unit can also generate flat terrain when the user's physical fitness level is low. The generation unit can also dynamically adjust the terrain of the virtual world based on the user's physical fitness level. This allows for a more appropriate exercise plan to be provided by adjusting the terrain of the virtual world according to the user's physical fitness level. For example, the generation unit can adjust the terrain of the virtual world based on the user's maximum oxygen uptake (VO2max). The generation unit can also adjust the terrain of the virtual world based on the results of a muscle strength test for the user. The generation unit can also filter the virtual world based on the user's physical fitness level. This allows the fitness bike system to provide a more appropriate exercise plan by adjusting the terrain of the virtual world according to the user's physical fitness level.

[0050] The generation unit can customize the scenery of the virtual world during generation by taking into account the user's geographical location information. The generation unit customizes the scenery of the virtual world during generation by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the generation unit can generate a mountainous scenery. Also, if the user is in an urban area, the generation unit can generate an urban scenery. Furthermore, the generation unit can generate an optimal scenery based on the user's geographical location information. In this way, by taking into account the user's geographical location information, a more relevant virtual world scenery can be provided. For example, the generation unit can adjust the current virtual world scenery based on the user's GPS data. Also, the generation unit can improve the virtual world scenery based on the user's address information. Furthermore, the generation unit can filter the virtual world based on the user's geographical location information. In this way, the fitness bike system can provide a more relevant virtual world scenery by taking into account the user's geographical location information.

[0051] The generation unit can analyze the user's social media activities and reflect related themes in the virtual world during generation. The generation unit can analyze the user's social media activities and reflect related themes in the virtual world during generation. For example, the generation unit can select an optimal theme based on exercise data shared by the user on social media. The generation unit can also select a theme that will increase exercise motivation from the user's social media activities. The generation unit can also customize the theme by reflecting the user's social media feedback. In this way, a more appropriate virtual world theme can be provided by analyzing the user's social media activities. For example, the generation unit can adjust the current virtual world theme based on the content of the user's social media posts. The generation unit can also improve the virtual world theme based on the number of likes the user has on social media. The generation unit can also filter the virtual world based on the user's social media activities. In this way, the fitness bike system can provide a more appropriate virtual world theme by analyzing the user's social media activities.

[0052] The generation unit can adjust the generation algorithm of the virtual world at the time of generation by reflecting the user's past feedback. The generation unit can adjust the generation algorithm of the virtual world at the time of generation by reflecting the user's past feedback. For example, the generation unit selects an optimal generation algorithm based on feedback provided by the user in the past. The generation unit can also adjust the level of detail of the virtual world based on the user's past feedback. Furthermore, the generation unit can dynamically customize the generation algorithm by reflecting the user's feedback. This allows a more appropriate virtual world to be provided by reflecting the user's past feedback. For example, the generation unit can adjust the current virtual world generation method based on the results of a user survey. The generation unit can also improve the level of detail of the virtual world based on user reviews. Furthermore, the generation unit can filter the virtual world based on the user's past feedback. This allows the fitness bike system to provide a more appropriate virtual world by reflecting the user's past feedback.

[0053] The display unit can customize the display content by referring to the user's past exercise data when displaying the content. The display unit customizes the display content by referring to the user's past exercise data when displaying the content. For example, the display unit customizes the current display content based on the user's past exercise data. The display unit can also analyze the user's past exercise patterns and reflect the results in the current display content. Furthermore, the display unit can optimize the display algorithm by referring to the user's past exercise data. This allows the display content to be customized by referring to the user's past exercise data. For example, the display unit adjusts the current display content based on the user's exercise history. The display unit can also improve the accuracy of the display content based on the user's training records. Furthermore, the display unit can filter the display content based on the user's past exercise data. This allows the fitness bike system to customize the display content by referring to the user's past exercise data.

[0054] The display unit can adjust the level of detail of the display content based on the user's exercise goal when displaying the data. The display unit can adjust the level of detail of the display content based on the user's exercise goal when displaying the data. For example, if the user is aiming to lose weight, the display unit can provide display content related to calorie consumption. Also, if the user is aiming to improve muscle strength, the display unit can provide display content related to muscle training. Furthermore, the display unit can customize the level of detail of the display content based on the user's exercise goal and provide an optimal exercise plan. By adjusting the level of detail of the display content based on the user's exercise goal, a more appropriate exercise plan can be provided. For example, the display unit can prioritize displaying calorie consumption data based on the user's weight loss goal. Also, the display unit can prioritize displaying endurance training data based on the user's endurance improvement goal. Furthermore, the display unit can filter the display content based on the user's exercise goal and provide an optimal exercise plan. By adjusting the level of detail of the display content based on the user's exercise goal, the fitness bike system can provide a more appropriate exercise plan.

[0055] The display unit can filter the display content according to the user's physical fitness level when displaying the content. The display unit can filter the display content according to the user's physical fitness level when displaying the content. For example, if the user's physical fitness level is high, the display unit can provide detailed display content to maximize the effect of exercise. Furthermore, if the user's physical fitness level is low, the display unit can provide simplified display content to reduce the burden of exercise. Furthermore, the display unit can filter the display content based on the user's physical fitness level to provide an optimal exercise plan. In this way, by filtering the display content according to the user's physical fitness level, a more appropriate exercise plan can be provided. For example, the display unit can adjust the accuracy of the display content based on the user's maximum oxygen uptake (VO2max). In addition, the display unit can adjust the accuracy of the display content based on the user's muscle strength test results. Furthermore, the display unit can filter the display content based on the user's physical fitness level. In this way, the fitness bike system can provide a more appropriate exercise plan by filtering the display content according to the user's physical fitness level.

[0056] The display unit can customize the display content by taking into account the user's geographical location information. The display unit customizes the display content by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the display unit can display a mountain view. Also, if the user is in an urban area, the display unit can display a city view. Furthermore, the display unit can provide optimal display content based on the user's geographical location information. This allows for more relevant display content to be provided by taking into account the user's geographical location information. For example, the display unit can adjust the current display content based on the user's GPS data. Also, the display unit can improve the accuracy of the display content based on the user's address information. Furthermore, the display unit can filter the display content based on the user's geographical location information. This allows the fitness bike system to provide more relevant display content by taking into account the user's geographical location information.

[0057] The display unit can analyze the user's social media activity and reflect related themes in the display content when displaying the content. The display unit can analyze the user's social media activity and reflect related themes in the display content when displaying the content. For example, the display unit can select an optimal theme based on exercise data shared by the user on social media. The display unit can also select a theme that will increase exercise motivation from the user's social media activity. The display unit can also customize the theme by reflecting the user's social media feedback. In this way, more appropriate display content can be provided by analyzing the user's social media activity. For example, the display unit can adjust the current display content based on the user's social media posts. The display unit can also improve the accuracy of the display content based on the number of likes the user has on social media. Furthermore, the display unit can filter the display content based on the user's social media activity. In this way, the fitness bike system can provide more appropriate display content by analyzing the user's social media activity.

[0058] The display unit can adjust the display algorithm to reflect the user's past feedback when displaying the information. The display unit adjusts the display algorithm to reflect the user's past feedback when displaying the information. For example, the display unit selects an optimal display algorithm based on feedback provided by the user in the past. The display unit can also adjust the level of detail of the display content based on the user's past feedback. Furthermore, the display unit can dynamically customize the display algorithm to reflect the user's feedback. This allows more appropriate display content to be provided by reflecting the user's past feedback. For example, the display unit adjusts the current display content based on the results of a user survey. The display unit can also improve the accuracy of the display content based on user reviews. Furthermore, the display unit can filter the display content based on the user's past feedback. This allows the fitness bike system to provide more appropriate display content by reflecting the user's past feedback.

[0059] When collecting handlebar movement and tilt, the sensor unit can select the optimal collection method by referring to the user's past exercise data. When collecting handlebar movement and tilt, the sensor unit can select the optimal collection method by referring to the user's past exercise data. For example, if the user has previously performed high-intensity exercise, the sensor unit can collect detailed data to track the progress of the exercise. Furthermore, if the user has previously performed low-intensity exercise, the sensor unit can select a simplified data collection method. Furthermore, the sensor unit can analyze the user's past exercise data and set the optimal collection frequency and accuracy. This allows the optimal data collection method to be selected by referring to the user's past exercise data. For example, the sensor unit can adjust the current exercise data collection method based on the user's exercise history. Furthermore, the sensor unit can improve the accuracy of the collected data based on the user's training records. Furthermore, the sensor unit can filter the collected data based on the user's past exercise data. This allows the fitness bike system to select the optimal data collection method by referring to the user's past exercise data.

[0060] When collecting the movement and tilt of the handlebars, the sensor unit can adjust the accuracy of the collected data according to the user's physical fitness level. When collecting the movement and tilt of the handlebars, the sensor unit adjusts the accuracy of the collected data according to the user's physical fitness level. For example, when the user's physical fitness level is high, the sensor unit collects detailed data to maximize the effect of exercise. When the user's physical fitness level is low, the sensor unit can collect simplified data to reduce the strain of exercise. Furthermore, the sensor unit can dynamically adjust the accuracy of the collected data based on the user's physical fitness level. This allows for more appropriate data collection by adjusting the accuracy of the collected data according to the user's physical fitness level. For example, the sensor unit adjusts the accuracy of the collected data based on the user's maximum oxygen uptake (VO2max). The sensor unit can also adjust the accuracy of the collected data based on the results of a muscle strength test. Furthermore, the sensor unit can filter the collected data based on the user's physical fitness level. This allows the fitness bike system to adjust the accuracy of the collected data according to the user's physical fitness level, allowing for more appropriate data collection.

[0061] When collecting data on the movement and tilt of the handlebars, the sensor unit can filter the collected data based on the user's exercise goals. When collecting data on the movement and tilt of the handlebars, the sensor unit can filter the collected data based on the user's exercise goals. For example, if the user is aiming to lose weight, the sensor unit can prioritize collecting data related to calorie consumption. Also, if the user is aiming to improve muscle strength, the sensor unit can prioritize collecting data related to muscle training. Furthermore, the sensor unit can filter the collected data based on the user's exercise goals and provide an optimal exercise plan. By filtering the collected data based on the user's exercise goals, a more appropriate exercise plan can be provided. For example, the sensor unit can prioritize collecting calorie consumption data based on the user's weight loss goal. Also, the sensor unit can prioritize collecting endurance training data based on the user's endurance improvement goal. Furthermore, the sensor unit can filter the collected data based on the user's exercise goals and provide an optimal exercise plan. By filtering the collected data based on the user's exercise goals, the exercise bike system can provide a more appropriate exercise plan.

[0062] When collecting handlebar movement and tilt, the sensor unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. When collecting handlebar movement and tilt, the sensor unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the sensor unit can prioritize collecting data related to climbing. Also, if the user is in an urban area, the sensor unit can prioritize collecting data related to traffic conditions. Furthermore, the sensor unit can select the optimal data collection method based on the user's geographical location information. This allows for more relevant data to be collected by taking into account the user's geographical location information. For example, the sensor unit can adjust the collection method for current exercise data based on the user's GPS data. The sensor unit can also improve the accuracy of the collected data based on the user's address information. Furthermore, the sensor unit can filter the collected data based on the user's geographical location information. This allows the exercise bike system to collect more relevant data by taking into account the user's geographical location information.

[0063] The sensor unit can analyze the user's social media activities and collect related data when collecting the handlebar movement and tilt. The sensor unit can analyze the user's social media activities and collect related data when collecting the handlebar movement and tilt. For example, the sensor unit selects the optimal data collection method based on exercise data shared by the user on social media. The sensor unit can also collect data from the user's social media activities to increase exercise motivation. The sensor unit can also customize the data collection method by reflecting the user's social media feedback. This enables more appropriate data collection by analyzing the user's social media activities. For example, the sensor unit can adjust the current exercise data collection method based on the content of the user's social media posts. The sensor unit can also improve the accuracy of the collected data based on the number of likes the user has on social media. The sensor unit can also filter the collected data based on the user's social media activities. This enables the fitness bike system to collect more appropriate data by analyzing the user's social media activities.

[0064] The sensor unit can customize the collection method by reflecting the user's past feedback when collecting handlebar movement and tilt. The sensor unit customizes the collection method by reflecting the user's past feedback when collecting handlebar movement and tilt. For example, the sensor unit selects the optimal data collection method based on feedback provided by the user in the past. The sensor unit can also adjust the accuracy of the collected data based on the user's past feedback. Furthermore, the sensor unit can dynamically customize the collection method by reflecting the user's feedback. This enables more appropriate data collection by reflecting the user's past feedback. For example, the sensor unit adjusts the current exercise data collection method based on the user's survey results. The sensor unit can also improve the accuracy of the collected data based on user reviews. Furthermore, the sensor unit can filter the collected data based on the user's past feedback. This enables the fitness bike system to collect more appropriate data by reflecting the user's past feedback.

[0065] The generation unit can adjust the level of detail of the course by referring to the user's past exercise data when generating the course. The generation unit can adjust the level of detail of the course by referring to the user's past exercise data when generating the course. For example, the generation unit adjusts the level of detail of the current course based on the user's past exercise data. The generation unit can also analyze the user's past exercise patterns and reflect them in the level of detail of the current course. Furthermore, the generation unit can optimize the course generation algorithm by referring to the user's past exercise data. In this way, the level of detail of the course can be adjusted by referring to the user's past exercise data. For example, the generation unit adjusts the level of detail of the current course based on the user's exercise history. The generation unit can also improve the level of detail of the course based on the user's training records. Furthermore, the generation unit can filter the course based on the user's past exercise data. In this way, the fitness bike system can adjust the level of detail of the course by referring to the user's past exercise data.

[0066] The generation unit can customize the course theme based on the user's exercise goal during generation. The generation unit customizes the course theme based on the user's exercise goal during generation. For example, if the user is aiming to lose weight, the generation unit can generate a course with a theme that promotes calorie consumption. Also, if the user is aiming to improve muscle strength, the generation unit can generate a course with a theme that is suitable for muscle training. Furthermore, the generation unit can customize the course theme based on the user's exercise goal and provide an optimal exercise plan. By customizing the course theme based on the user's exercise goal, a more appropriate exercise plan can be provided. For example, the generation unit can prioritize analyzing calorie consumption data based on the user's weight loss goal. Also, the generation unit can prioritize analyzing endurance training data based on the user's endurance improvement goal. Furthermore, the generation unit can filter the course based on the user's exercise goal and provide an optimal exercise plan. Thus, the fitness bike system can customize the course theme based on the user's exercise goal and provide a more appropriate exercise plan.

[0067] The generation unit can adjust the terrain of the course according to the user's physical fitness level when generating the course. The generation unit can adjust the terrain of the course according to the user's physical fitness level when generating the course. For example, the generation unit can generate challenging terrain when the user's physical fitness level is high. The generation unit can also generate flat terrain when the user's physical fitness level is low. The generation unit can also dynamically adjust the terrain of the course based on the user's physical fitness level. This allows for a more appropriate exercise plan to be provided by adjusting the terrain of the course according to the user's physical fitness level. For example, the generation unit can adjust the terrain of the course based on the user's maximum oxygen uptake (VO2max). The generation unit can also adjust the terrain of the course based on the results of a muscle strength test. The generation unit can also filter the course based on the user's physical fitness level. This allows the fitness bike system to adjust the terrain of the course according to the user's physical fitness level and provide a more appropriate exercise plan.

[0068] The generation unit can customize the scenery of the course during generation by taking into account the user's geographical location information. The generation unit customizes the scenery of the course during generation by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the generation unit can generate a mountain scenery. Also, if the user is in an urban area, the generation unit can generate an urban scenery. Furthermore, the generation unit can generate an optimal scenery based on the user's geographical location information. In this way, by taking into account the user's geographical location information, it is possible to provide a more relevant scenery of the course. For example, the generation unit can adjust the scenery of the current course based on the user's GPS data. Also, the generation unit can improve the scenery of the course based on the user's address information. Furthermore, the generation unit can filter the course based on the user's geographical location information. In this way, the fitness bike system can provide a more relevant scenery of the course by taking into account the user's geographical location information.

[0069] The generation unit can analyze the user's social media activities and reflect related themes in the course when generating the course. The generation unit can analyze the user's social media activities and reflect related themes in the course when generating the course. For example, the generation unit can select an optimal theme based on exercise data shared by the user on social media. The generation unit can also select a theme that will increase exercise motivation from the user's social media activities. The generation unit can also customize the theme by reflecting the user's feedback on social media. In this way, a more appropriate course theme can be provided by analyzing the user's social media activities. For example, the generation unit can adjust the current course theme based on the content of the user's social media posts. The generation unit can also improve the course theme based on the number of likes the user has on social media. The generation unit can also filter the course based on the user's social media activities. In this way, the fitness bike system can provide a more appropriate course theme by analyzing the user's social media activities.

[0070] The generation unit can adjust the course generation algorithm during generation by reflecting the user's past feedback. The generation unit adjusts the course generation algorithm during generation by reflecting the user's past feedback. For example, the generation unit selects an optimal generation algorithm based on feedback provided by the user in the past. The generation unit can also adjust the level of detail of the course based on the user's past feedback. Furthermore, the generation unit can dynamically customize the generation algorithm by reflecting the user's feedback. This makes it possible to provide a more appropriate course by reflecting the user's past feedback. For example, the generation unit adjusts the current course generation method based on the results of a user survey. The generation unit can also improve the level of detail of the course based on user reviews. Furthermore, the generation unit can filter the course based on the user's past feedback. This makes it possible for the fitness bike system to provide a more appropriate course by reflecting the user's past feedback.

[0071] The generation unit can adjust the scenery theme by referring to the user's past exercise data when generating the scenery. The generation unit can adjust the scenery theme by referring to the user's past exercise data when generating the scenery. For example, the generation unit adjusts the current scenery theme based on the user's past exercise data. The generation unit can also analyze the user's past exercise patterns and reflect them in the current scenery theme. Furthermore, the generation unit can optimize the scenery generation algorithm by referring to the user's past exercise data. In this way, the scenery theme can be adjusted by referring to the user's past exercise data. For example, the generation unit adjusts the current scenery theme based on the user's exercise history. The generation unit can also improve the scenery theme based on the user's training records. Furthermore, the generation unit can filter the scenery based on the user's past exercise data. In this way, the fitness bike system can adjust the scenery theme by referring to the user's past exercise data.

[0072] The generation unit can customize the difficulty level of the scenery based on the user's exercise goal during generation. The generation unit customizes the difficulty level of the scenery based on the user's exercise goal during generation. For example, if the user is aiming to lose weight, the generation unit can set a difficulty level that promotes calorie consumption. Also, if the user is aiming to improve muscle strength, the generation unit can set a difficulty level that is suitable for muscle training. Furthermore, the generation unit can customize the difficulty level of the scenery based on the user's exercise goal and provide an optimal exercise plan. In this way, customizing the difficulty level of the scenery based on the user's exercise goal can provide a more appropriate exercise plan. For example, the generation unit can prioritize analyzing calorie consumption data based on the user's weight loss goal. Also, the generation unit can prioritize analyzing endurance training data based on the user's endurance improvement goal. Furthermore, the generation unit can filter the scenery based on the user's exercise goal and provide an optimal exercise plan. In this way, the fitness bike system can customize the difficulty level of the scenery based on the user's exercise goal and provide a more appropriate exercise plan.

[0073] The generation unit can adjust the landscape terrain according to the user's physical fitness level during generation. The generation unit can adjust the landscape terrain according to the user's physical fitness level during generation. For example, the generation unit can generate challenging terrain when the user's physical fitness level is high. The generation unit can also generate flat terrain when the user's physical fitness level is low. The generation unit can also dynamically adjust the landscape terrain based on the user's physical fitness level. This allows for a more appropriate exercise plan to be provided by adjusting the landscape terrain according to the user's physical fitness level. For example, the generation unit can adjust the landscape terrain based on the user's maximum oxygen uptake (VO2max). The generation unit can also adjust the landscape terrain based on the user's muscle strength test results. The generation unit can also filter the landscape based on the user's physical fitness level. This allows the fitness bike system to adjust the landscape terrain according to the user's physical fitness level and provide a more appropriate exercise plan.

[0074] The generation unit can customize the scenery by taking into account the user's geographical location information when generating the scenery. The generation unit customizes the scenery by taking into account the user's geographical location information when generating the scenery. For example, if the user is in a mountainous area, the generation unit can generate a mountain scenery. Also, if the user is in an urban area, the generation unit can generate an urban scenery. Furthermore, the generation unit can generate an optimal scenery based on the user's geographical location information. This makes it possible to provide a more relevant scenery by taking into account the user's geographical location information. For example, the generation unit can adjust the current scenery based on the user's GPS data. Also, the generation unit can improve the scenery based on the user's address information. Furthermore, the generation unit can filter the scenery based on the user's geographical location information. This makes it possible for the exercise bike system to provide a more relevant scenery by taking into account the user's geographical location information.

[0075] The generation unit can analyze the user's social media activities and reflect related themes in the scenery when generating the scenery. The generation unit can analyze the user's social media activities and reflect related themes in the scenery when generating the scenery. For example, the generation unit can select an optimal theme based on exercise data shared by the user on social media. The generation unit can also select a theme that will increase exercise motivation from the user's social media activities. The generation unit can also customize the theme by reflecting the user's social media feedback. In this way, a more appropriate scenery theme can be provided by analyzing the user's social media activities. For example, the generation unit can adjust the current scenery theme based on the content of the user's social media posts. The generation unit can also improve the scenery theme based on the number of likes the user has on social media. In addition, the generation unit can filter the scenery based on the user's social media activities. In this way, the fitness bike system can provide a more appropriate scenery theme by analyzing the user's social media activities.

[0076] The generation unit can adjust the scenery generation algorithm during generation by reflecting the user's past feedback. The generation unit adjusts the scenery generation algorithm during generation by reflecting the user's past feedback. For example, the generation unit selects an optimal generation algorithm based on feedback provided by the user in the past. The generation unit can also adjust the level of detail of the scenery based on the user's past feedback. Furthermore, the generation unit can dynamically customize the generation algorithm by reflecting the user's feedback. This makes it possible to provide more appropriate scenery by reflecting the user's past feedback. For example, the generation unit adjusts the current scenery generation method based on the results of a user survey. The generation unit can also improve the level of detail of the scenery based on user reviews. Furthermore, the generation unit can filter the scenery based on the user's past feedback. This makes it possible for the exercise bike system to provide more appropriate scenery by reflecting the user's past feedback.

[0077] When adjusting the speed of the scenery movement, the display unit can select an optimal speed by referring to the user's past exercise data. When adjusting the speed of the scenery movement, the display unit can select an optimal speed by referring to the user's past exercise data. For example, the display unit adjusts the current speed of the scenery movement based on the user's past exercise data. The display unit can also analyze the user's past exercise patterns and reflect them in the current speed of the scenery movement. Furthermore, the display unit can optimize the speed of the scenery movement by referring to the user's past exercise data. In this way, the speed of the scenery movement can be optimized by referring to the user's past exercise data. For example, the display unit adjusts the current speed of the scenery movement based on the user's exercise history. The display unit can also improve the speed of the scenery movement based on the user's training record. Furthermore, the display unit can filter the speed of the scenery movement based on the user's past exercise data. In this way, the fitness bike system can optimize the speed of the scenery movement by referring to the user's past exercise data.

[0078] The display unit may adjust the speed of the scenery movement based on the user's physical fitness level when adjusting the speed of the scenery movement. The display unit may adjust the speed of the scenery movement based on the user's physical fitness level when adjusting the speed of the scenery movement. For example, if the user's physical fitness level is high, the display unit may move the scenery at a fast speed. Also, if the user's physical fitness level is low, the display unit may move the scenery at a slower speed. Furthermore, the display unit may dynamically adjust the speed of the scenery movement based on the user's physical fitness level. This allows for a more appropriate exercise plan to be provided by adjusting the speed of the scenery movement based on the user's physical fitness level. For example, the display unit may adjust the speed of the scenery movement based on the user's maximum oxygen uptake (VO2max). Also, the display unit may adjust the speed of the scenery movement based on the results of a muscle strength test. Furthermore, the display unit may filter the speed of the scenery movement based on the user's physical fitness level. This allows the fitness bike system to adjust the speed of the scenery movement based on the user's physical fitness level and provide a more appropriate exercise plan.

[0079] When adjusting the speed of the scenery movement, the display unit can customize the speed based on the user's exercise goal. When adjusting the speed of the scenery movement, the display unit customizes the speed based on the user's exercise goal. For example, if the user is aiming to lose weight, the display unit can set a speed that promotes calorie consumption. Also, if the user is aiming to improve muscle strength, the display unit can set a speed that is suitable for muscle training. Furthermore, the display unit can customize the speed of the scenery movement based on the user's exercise goal and provide an optimal exercise plan. In this way, customizing the speed of the scenery movement based on the user's exercise goal can provide a more appropriate exercise plan. For example, the display unit can prioritize analysis of calorie consumption data based on the user's weight loss goal. Also, the display unit can prioritize analysis of endurance training data based on the user's endurance improvement goal. Furthermore, the display unit can filter the speed of the scenery movement based on the user's exercise goal and provide an optimal exercise plan. In this way, the fitness bike system can customize the speed of the scenery movement based on the user's exercise goal and provide a more appropriate exercise plan.

[0080] The display unit can customize the speed of the scenery movement by taking into account the user's geographical location information when adjusting the speed of the scenery movement. The display unit customizes the speed by taking into account the user's geographical location information when adjusting the speed of the scenery movement. For example, if the user is in a mountainous area, the display unit can move the scenery at a speed suitable for climbing. Also, if the user is in an urban area, the display unit can move the scenery at a speed suitable for urban scenery. Furthermore, the display unit can move the scenery at an optimal speed based on the user's geographical location information. In this way, a more relevant speed can be provided by taking into account the user's geographical location information. For example, the display unit can adjust the current speed of the scenery movement based on the user's GPS data. Also, the display unit can increase the speed of the scenery movement based on the user's address information. Furthermore, the display unit can filter the speed of the scenery movement based on the user's geographical location information. In this way, the exercise bike system can provide a more relevant speed by taking into account the user's geographical location information.

[0081] The display unit can analyze the user's social media activity and reflect related themes in the scenery when adjusting the speed of the scenery movement. The display unit can analyze the user's social media activity and reflect related themes in the scenery when adjusting the speed of the scenery movement. For example, the display unit can select an optimal theme based on exercise data shared by the user on social media. The display unit can also select a theme that will increase exercise motivation from the user's social media activity. The display unit can also customize the theme by reflecting the user's social media feedback. In this way, a more appropriate scenery theme can be provided by analyzing the user's social media activity. For example, the display unit can adjust the current scenery theme based on the content of the user's social media posts. The display unit can also improve the scenery theme based on the number of likes the user has on social media. The display unit can also filter the scenery based on the user's social media activity. In this way, the fitness bike system can provide a more appropriate scenery theme by analyzing the user's social media activity.

[0082] When adjusting the speed of the scenery movement, the display unit can adjust the speed by reflecting the user's past feedback. When adjusting the speed of the scenery movement, the display unit adjusts the speed by reflecting the user's past feedback. For example, the display unit selects an optimal speed based on feedback provided by the user in the past. The display unit can also adjust the speed of the scenery movement based on the user's past feedback. Furthermore, the display unit can dynamically customize the speed by reflecting the user's feedback. This makes it possible to provide a more appropriate speed by reflecting the user's past feedback. For example, the display unit adjusts the current speed of the scenery movement based on the results of a user survey. The display unit can also improve the speed of the scenery movement based on user reviews. Furthermore, the display unit can filter the speed of the scenery movement based on the user's past feedback. This makes it possible for the fitness bike system to provide a more appropriate speed by reflecting the user's past feedback.

[0083] When adjusting the frequency of viewpoint changes, the display unit can select an optimal frequency by referring to the user's past exercise data. When adjusting the frequency of viewpoint changes, the display unit can select an optimal frequency by referring to the user's past exercise data. For example, the display unit adjusts the current frequency of viewpoint changes based on the user's past exercise data. The display unit can also analyze the user's past exercise patterns and reflect them in the current frequency of viewpoint changes. Furthermore, the display unit can optimize the frequency of viewpoint changes by referring to the user's past exercise data. In this way, the frequency of viewpoint changes can be optimized by referring to the user's past exercise data. For example, the display unit adjusts the current frequency of viewpoint changes based on the user's exercise history. The display unit can also improve the frequency of viewpoint changes based on the user's training records. Furthermore, the display unit can filter the frequency of viewpoint changes based on the user's past exercise data. In this way, the fitness bike system can optimize the frequency of viewpoint changes by referring to the user's past exercise data.

[0084] The display unit can adjust the frequency of the viewpoint change based on the user's physical fitness level when adjusting the frequency of the viewpoint change. The display unit can adjust the frequency based on the user's physical fitness level when adjusting the frequency of the viewpoint change. For example, the display unit can change the viewpoint more frequently when the user's physical fitness level is high. The display unit can also reduce the frequency of the viewpoint change when the user's physical fitness level is low. The display unit can also dynamically adjust the frequency of the viewpoint change based on the user's physical fitness level. This allows for a more appropriate exercise plan to be provided by adjusting the frequency of the viewpoint change based on the user's physical fitness level. For example, the display unit can adjust the frequency of the viewpoint change based on the user's maximum oxygen uptake (VO2max). The display unit can also adjust the frequency of the viewpoint change based on the results of a muscle strength test. The display unit can also filter the frequency of the viewpoint change based on the user's physical fitness level. This allows the fitness bike system to adjust the frequency of the viewpoint change based on the user's physical fitness level and provide a more appropriate exercise plan.

[0085] When adjusting the frequency of viewpoint changes, the display unit can customize the frequency based on the user's exercise goals. When adjusting the frequency of viewpoint changes, the display unit customizes the frequency based on the user's exercise goals. For example, if the user is aiming to lose weight, the display unit can set a frequency that promotes calorie consumption. Also, if the user is aiming to improve muscle strength, the display unit can set a frequency that is suitable for strength training. Furthermore, the display unit can customize the frequency of viewpoint changes based on the user's exercise goals and provide an optimal exercise plan. By customizing the frequency of viewpoint changes based on the user's exercise goals, a more appropriate exercise plan can be provided. For example, the display unit can prioritize analysis of calorie consumption data based on the user's weight loss goal. Also, the display unit can prioritize analysis of endurance training data based on the user's endurance improvement goal. Furthermore, the display unit can filter the frequency of viewpoint changes based on the user's exercise goals and provide an optimal exercise plan. Thus, the fitness bike system can customize the frequency of viewpoint changes based on the user's exercise goals and provide a more appropriate exercise plan.

[0086] When adjusting the frequency of viewpoint changes, the display unit can customize the frequency by taking into account the user's geographical location information. When adjusting the frequency of viewpoint changes, the display unit customizes the frequency by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the display unit can change the viewpoint at a frequency appropriate for climbing. Also, if the user is in an urban area, the display unit can change the viewpoint at a frequency appropriate for urban scenery. Furthermore, the display unit can change the viewpoint at an optimal frequency based on the user's geographical location information. In this way, by taking the user's geographical location information into account, a more relevant frequency of viewpoint changes can be provided. For example, the display unit can adjust the current frequency of viewpoint changes based on the user's GPS data. Also, the display unit can improve the frequency of viewpoint changes based on the user's address information. Furthermore, the display unit can filter the frequency of viewpoint changes based on the user's geographical location information. In this way, the exercise bike system can provide a more relevant frequency of viewpoint changes by taking the user's geographical location information into account.

[0087] The display unit can analyze the user's social media activity and reflect related themes in the viewpoint when adjusting the frequency of the viewpoint change. The display unit can analyze the user's social media activity and reflect related themes in the viewpoint when adjusting the frequency of the viewpoint change. For example, the display unit can select an optimal theme based on exercise data shared by the user on social media. The display unit can also select a theme that will increase exercise motivation from the user's social media activity. The display unit can also customize the theme by reflecting the user's social media feedback. In this way, a more appropriate viewpoint theme can be provided by analyzing the user's social media activity. For example, the display unit can adjust the current viewpoint theme based on the content of the user's social media posts. The display unit can also improve the viewpoint theme based on the number of likes the user has on social media. The display unit can also filter the viewpoints based on the user's social media activity. In this way, the fitness bike system can provide a more appropriate viewpoint theme by analyzing the user's social media activity.

[0088] The display unit can adjust the frequency of viewpoint changes by reflecting the user's past feedback. When adjusting the frequency of viewpoint changes, the display unit adjusts the frequency by reflecting the user's past feedback. For example, the display unit selects an optimal frequency based on feedback provided by the user in the past. The display unit can also adjust the frequency of viewpoint changes based on the user's past feedback. Furthermore, the display unit can dynamically customize the frequency by reflecting the user's feedback. This allows a more appropriate frequency of viewpoint changes to be provided by reflecting the user's past feedback. For example, the display unit adjusts the current frequency of viewpoint changes based on the results of a user survey. The display unit can also improve the frequency of viewpoint changes based on user reviews. Furthermore, the display unit can filter the frequency of viewpoint changes based on the user's past feedback. This allows the fitness bike system to provide a more appropriate frequency of viewpoint changes by reflecting the user's past feedback.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The fitness bike system may further include a temperature sensor unit that measures the user's body temperature. The temperature sensor unit measures the user's body temperature in real time and transmits the data to the analysis unit. The analysis unit analyzes the user's physical condition based on the body temperature data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user's body temperature is high, the generation unit may generate a cool scene to encourage the user to lower their body temperature. Alternatively, if the user's body temperature is low, the generation unit may generate a warm scene to encourage the user to raise their body temperature. Furthermore, the body temperature data may be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0091] The fitness bike system can further include an electromyographic sensor unit that measures the user's muscle movements. The electromyographic sensor unit measures the user's muscle movements in real time and transmits the data to the analysis unit. The analysis unit analyzes the user's exercise intensity based on the electromyographic data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user's muscle movements are intense, the generation unit can generate relaxing scenery to encourage muscle relaxation. Alternatively, if the user's muscle movements are low, the generation unit can generate a challenging course to increase exercise intensity. Furthermore, the electromyographic data can be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0092] The fitness bike system may further include a sleep sensor unit that collects the user's sleep data. The sleep sensor unit collects the user's sleep data and transmits the data to the analysis unit. The analysis unit analyzes the user's physical condition based on the sleep data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user has not slept enough, the generation unit may generate relaxing scenery to encourage the user to improve their physical condition. Alternatively, if the user has had enough sleep, the generation unit may generate a challenging course to increase exercise intensity. Furthermore, the sleep data may be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0093] The fitness bike system may further include a food sensor unit that collects the user's food data. The food sensor unit collects the user's food data and transmits the data to the analysis unit. The analysis unit analyzes the user's physical condition based on the food data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user eats a high-calorie meal, the generation unit can generate a challenging course to promote calorie consumption. Alternatively, if the user eats a low-calorie meal, the generation unit can generate relaxing scenery to encourage the user to improve their physical condition. Furthermore, the food data can be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0094] The fitness bike system may further include a moisture sensor unit that measures the user's moisture intake. The moisture sensor unit measures the user's moisture intake in real time and transmits the data to the analysis unit. The analysis unit analyzes the user's physical condition based on the moisture data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user's moisture intake is insufficient, the generation unit can generate relaxing scenery to encourage the user to hydrate. Alternatively, if the user has consumed sufficient moisture, the generation unit can generate a challenging course to increase exercise intensity. Furthermore, the moisture data can be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0095] The fitness bike system may further include a posture sensor unit that measures the user's posture. The posture sensor unit measures the user's posture in real time and transmits the data to the analysis unit. The analysis unit analyzes the user's exercise pattern based on the posture data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user's posture is poor, the generation unit can generate a course to improve posture. Alternatively, if the user's posture is good, the generation unit can generate a challenging course to increase exercise intensity. Furthermore, the posture data can be used to analyze the user's exercise pattern and provide an optimal exercise plan.

[0096] The processing flow of the first embodiment will be briefly explained below.

[0097] Step 1: The sensor unit collects pedal rotations or speed. For example, the sensor unit can measure pedal rotations in revolutions per minute (RPM). The sensor unit can also measure speed in kilometers per hour (km / h). Additionally, the sensor unit can collect both pedal rotations and speed simultaneously. Step 2: The analysis unit analyzes the data collected by the sensor unit. For example, the analysis unit calculates the user's exercise intensity based on the collected rotation count data. The analysis unit can also calculate the user's movement distance based on the speed data. Furthermore, the analysis unit can combine the rotation count and speed data to analyze the user's exercise pattern. Step 3: The generation unit generates a virtual cycling world based on the data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate a real-time virtual world based on the user's exercise data. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate a virtual world based on the user's exercise data. For example, if the user wants to ride on a mountain road, the generation unit generates a mountain road scene. Also, if the user wants to ride along the coast, the generation unit can generate a coastal scene. Furthermore, the generation unit can generate various scenes, such as urban areas and forests, based on the user's exercise data. Step 4: The display unit displays the virtual world generated by the generator. For example, the display unit displays the scenery of the virtual world on a monitor. The display unit can also move the scenery of the virtual world according to the number of revolutions of the pedals by the user. Furthermore, the display unit can change the viewpoint of the virtual world according to the movement of the handlebars by the user.

[0098] (Example 2) A fitness bike system according to an embodiment of the present invention uses a generation AI to create a virtual cycling world and allow users to enjoy various courses while cycling indoors. When a user rides a fitness bike and begins pedaling, sensors collect data such as pedal rotations and speed. This data is input into the generation AI, which then generates a virtual cycling world in real time. The generated virtual world is displayed on a monitor, allowing the user to experience riding outdoors as if they were actually riding. For example, a user rides a fitness bike and begins pedaling. Sensors collect data such as pedal rotations and speed. For example, if the pedal rotations are fast, the scenery in the virtual world is set to move quickly. The collected data is then input into the generation AI. The generation AI analyzes the input data and generates a virtual cycling world in real time. For example, if a user wants to ride a mountain trail, the generation AI generates a mountain trail landscape, providing the user with the experience of actually riding a mountain trail. The generated virtual world is displayed on a monitor. By pedaling a bike while viewing the scenery displayed on the monitor, users can experience riding outdoors as if they were actually riding. For example, if a user wants to ride along the coast, the generation AI generates coastal scenery, providing the user with the experience of actually riding along the coast. This allows the fitness bike system to enjoy a variety of courses while indoors. This eliminates monotony in exercise, allowing users to continue exercising without getting bored. For example, if a user wants to ride a different course every day, the generation AI can generate a course that matches their mood and physical condition on that day.

[0099] The fitness bike system according to the embodiment includes a sensor unit, an analysis unit, a generation unit, and a display unit. The sensor unit collects pedal rotations or speed. For example, the sensor unit can measure pedal rotations in revolutions per minute (RPM). The sensor unit can also measure speed in kilometers per hour (km / h). The sensor unit can also collect both pedal rotations and speed simultaneously. The analysis unit analyzes the data collected by the sensor unit. For example, the analysis unit can calculate the user's exercise intensity based on the collected rotation data. The analysis unit can also calculate the user's travel distance based on the speed data. The analysis unit can also analyze the user's exercise pattern by combining the rotation data and speed data. The generation unit generates a virtual cycling world based on the data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate a real-time virtual world based on the user's exercise data. The generation AI can use a text generation AI (e.g., LLM) or a multimodal generation AI to generate the virtual world based on the user's exercise data. For example, if a user wants to ride a mountain path, the generation unit can generate a mountain path scenery. Also, if a user wants to ride along the coast, the generation unit can generate a coastal scenery. Furthermore, the generation unit can generate various scenery, such as urban areas and forests, based on the user's exercise data. The display unit displays the virtual world generated by the generation unit. For example, the display unit displays the scenery of the virtual world on a monitor. Furthermore, the display unit can move the scenery of the virtual world according to the user's pedal rotation speed. Furthermore, the display unit can change the viewpoint of the virtual world according to the user's handlebar movement. Thus, the fitness bike system according to the embodiment generates and displays a virtual cycling world based on the user's pedal rotation speed and makes indoor cycling more enjoyable.

[0100] The fitness bike system includes a sensor unit that collects the movement or tilt of the handlebars. The sensor unit collects the movement and tilt of the handlebars. For example, the sensor unit can measure changes in the angle of the handlebars. The sensor unit can also measure the distance traveled by the handlebars. Furthermore, the sensor unit can measure the tilt angle and tilt direction of the handlebars. By collecting the movement and tilt of the handlebars, a more realistic virtual cycling experience can be provided. For example, the sensor unit can collect the movement of the handlebars in real time and change the viewpoint of the virtual world. The sensor unit can also collect the tilt of the handlebars and reflect it in the terrain of the virtual world. Furthermore, the sensor unit can analyze the user's exercise data based on the movement and tilt of the handlebars. By collecting the movement and tilt of the handlebars, the fitness bike system can provide a more realistic virtual cycling experience.

[0101] The fitness bike system includes a generator that generates a course tailored to the user's physical condition or mood. The generator generates a course tailored to the user's physical condition or mood. For example, the generator can generate a course based on data such as the user's heart rate, blood pressure, and fatigue level. The generator can also generate a course based on data such as the user's questionnaire results and facial expression analysis. The generator can also generate various courses, such as mountain trails, coastal routes, and urban areas, based on the user's physical condition and mood. This allows for a more personalized exercise experience by generating a course tailored to the user's physical condition and mood. For example, the generator can generate a relaxing course if the user wants to relax. The generator can also generate a challenging course if the user wants to exercise more challengingly. The generator can also adjust the difficulty level and scenery of the course based on the user's physical condition and mood. This allows the fitness bike system to generate a course tailored to the user's physical condition and mood, allowing for a more personalized exercise experience.

[0102] The generation unit can generate scenery based on geographic data or meteorological data. The generation unit generates scenery based on geographic data or meteorological data. For example, the generation unit can generate scenery based on GPS data or map information. The generation unit can also generate scenery based on meteorological data such as temperature, humidity, and wind speed. The generation unit can also combine geographic data and meteorological data to generate more realistic scenery. In this way, by generating scenery based on geographic data and meteorological data, a more realistic virtual cycling experience can be provided. For example, the generation unit can generate a mountain trail scenery based on geographic data of a mountainous area. In addition, the generation unit can generate a coastal scenery based on geographic data of a coastal area. In addition, the generation unit can generate an urban scenery based on geographic data of an urban area. In this way, the fitness bike system can provide a more realistic virtual cycling experience by generating scenery based on geographic data and meteorological data.

[0103] The display unit can move the scenery in the virtual world in accordance with the user's pedal rotation speed. The display unit moves the scenery in the virtual world in accordance with the user's pedal rotation speed. For example, the display unit can move the scenery in the virtual world faster if the user pedals quickly. Conversely, the display unit can move the scenery in the virtual world slower if the user pedals slowly. Furthermore, the display unit can adjust the speed of the scenery movement in real time in accordance with the pedal rotation speed. This allows a more realistic exercise experience to be provided by moving the scenery in accordance with the user's pedal rotation speed. For example, the display unit can move a mountain trail scenery in accordance with the user pedal rotation speed. Conversely, the display unit can move a coastal scenery in accordance with the user pedal rotation speed. Furthermore, the display unit can move an urban area scenery in accordance with the pedal rotation speed. This allows the fitness bike system to move the scenery in accordance with the user's pedal rotation speed in accordance with the user's pedal rotation speed, providing a more realistic exercise experience.

[0104] The display unit can change the viewpoint of the virtual world in response to the user's handlebar movement. The display unit changes the viewpoint of the virtual world in response to the user's handlebar movement. For example, when the user moves the handlebar left, the display unit can also move the viewpoint of the virtual world to the left. Similarly, when the user moves the handlebar right, the display unit can also move the viewpoint of the virtual world to the right. Furthermore, the display unit can adjust the speed of the viewpoint change in real time in response to the handlebar movement. This allows for a more realistic exercise experience by changing the viewpoint in response to the user's handlebar movement. For example, when the user moves the handlebar left, the display unit can change the viewpoint of a mountain road scenery to the left. Similarly, when the user moves the handlebar right, the display unit can change the viewpoint of a coastal scenery to the right. Furthermore, the display unit can change the viewpoint of an urban scenery in response to the handlebar movement. This allows the fitness bike system to change the viewpoint in response to the user's handlebar movement in response to a more realistic exercise experience.

[0105] The sensor unit can estimate the user's emotions and adjust the frequency of collecting data on the pedal rotation speed or speed based on the estimated user emotions. The sensor unit can estimate the user's emotions and adjust the frequency of collecting data on the pedal rotation speed or speed based on the estimated user emotions. For example, if the user is feeling stressed, the sensor unit can increase the collection frequency to obtain detailed data and provide an exercise plan for stress relief. The sensor unit can also reduce the collection frequency to reduce the strain of exercise if the user is relaxed. Furthermore, if the user is excited, the sensor unit can set the collection frequency to a medium level to promote moderate exercise. This enables more appropriate data collection by adjusting the collection frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sensor unit can be performed using, for example, AI, or without AI. For example, the sensor unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0106] When collecting pedaling speed or revolutions per minute, the sensor unit can select an appropriate collection method by referring to the user's past exercise data. When collecting pedaling speed or revolutions per minute, the sensor unit can select the optimal collection method by referring to the user's past exercise data. For example, if the user has previously performed high-intensity exercise, the sensor unit can collect detailed data to track the progress of the exercise. Alternatively, if the user has previously performed low-intensity exercise, the sensor unit can select a simplified data collection method. Furthermore, the sensor unit can analyze the user's past exercise data and set optimal collection frequency and accuracy. This allows the optimal data collection method to be selected by referring to the user's past exercise data. For example, the sensor unit can adjust the collection method for current exercise data based on the user's past exercise history. The sensor unit can also refer to the user's past training records to improve the accuracy of the collected data. Furthermore, the sensor unit can filter the collected data based on the user's past exercise data. This allows the fitness bike system to select the optimal data collection method by referring to the user's past exercise data.

[0107] When collecting the pedaling speed or revolutions per minute, the sensor unit can adjust the accuracy of the collected data according to the user's physical fitness level. When collecting the pedaling speed or revolutions per minute, the sensor unit adjusts the accuracy of the collected data according to the user's physical fitness level. For example, if the user's physical fitness level is high, the sensor unit collects detailed data to maximize the effect of the exercise. Alternatively, if the user's physical fitness level is low, the sensor unit can collect simplified data to reduce the strain of the exercise. Furthermore, the sensor unit can dynamically adjust the accuracy of the collected data based on the user's physical fitness level. This allows for more appropriate data collection by adjusting the accuracy of the collected data according to the user's physical fitness level. For example, the sensor unit adjusts the accuracy of the collected data based on the user's maximum oxygen uptake (VO2max). The sensor unit can also adjust the accuracy of the collected data based on the results of a muscle strength test. Furthermore, the sensor unit can filter the collected data based on the user's physical fitness level. This allows the fitness bike system to adjust the accuracy of the collected data according to the user's physical fitness level, allowing for more appropriate data collection.

[0108] When collecting the pedaling speed or revolutions per minute, the sensor unit can filter the collected data based on the user's exercise goal. When collecting the pedaling speed or revolutions per minute, the sensor unit can filter the collected data based on the user's exercise goal. For example, if the user is aiming to lose weight, the sensor unit can prioritize collecting data related to calorie consumption. Also, if the user is aiming to improve muscle strength, the sensor unit can prioritize collecting data related to muscle training. Furthermore, the sensor unit can filter the collected data based on the user's exercise goal and provide an optimal exercise plan. By filtering the collected data based on the user's exercise goal, a more appropriate exercise plan can be provided. For example, the sensor unit can prioritize collecting calorie consumption data based on the user's weight loss goal. Also, the sensor unit can prioritize collecting endurance training data based on the user's endurance improvement goal. Furthermore, the sensor unit can filter the collected data based on the user's exercise goal and provide an optimal exercise plan. By filtering the collected data based on the user's exercise goal, the fitness bike system can provide a more appropriate exercise plan.

[0109] The sensor unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The sensor unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the sensor unit can prioritize collecting data related to stress relief. Furthermore, if the user is relaxed, the sensor unit can prioritize collecting data that enhances the relaxation effect. Furthermore, if the user is excited, the sensor unit can prioritize collecting data to suppress excitement. This enables more appropriate data collection by prioritizing collected data based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sensor unit can be performed using, for example, an AI. For example, the sensor unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0110] When collecting the number of revolutions or speed of the pedals, the sensor unit can prioritize collecting more relevant data by taking into account the user's geographical location information. When collecting the number of revolutions or speed of the pedals, the sensor unit prioritizes collecting more relevant data by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the sensor unit can prioritize collecting data related to climbing. Also, if the user is in an urban area, the sensor unit can prioritize collecting data related to traffic conditions. Furthermore, the sensor unit can select an optimal data collection method based on the user's geographical location information. This allows for more relevant data to be collected by taking into account the user's geographical location information. For example, the sensor unit can adjust the collection method for current exercise data based on the user's GPS data. The sensor unit can also improve the accuracy of the collected data based on the user's address information. Furthermore, the sensor unit can filter the collected data based on the user's geographical location information. This allows the exercise bike system to collect more relevant data by taking into account the user's geographical location information.

[0111] The sensor unit can analyze the user's social media activities and collect related data when collecting the pedaling speed or revolutions per minute. The sensor unit can analyze the user's social media activities and collect related data when collecting the pedaling speed or revolutions per minute. For example, the sensor unit selects an optimal data collection method based on exercise data shared by the user on social media. The sensor unit can also collect data from the user's social media activities to increase exercise motivation. The sensor unit can also customize the data collection method by reflecting the user's social media feedback. This enables more appropriate data collection by analyzing the user's social media activities. For example, the sensor unit can adjust the current exercise data collection method based on the content of the user's social media posts. The sensor unit can also improve the accuracy of the collected data based on the number of likes the user has on social media. The sensor unit can also filter the collected data based on the user's social media activities. This enables the fitness bike system to collect more appropriate data by analyzing the user's social media activities.

[0112] The sensor unit can customize the collection method by reflecting the user's past feedback when collecting the pedaling speed or revolutions per minute. The sensor unit customizes the collection method by reflecting the user's past feedback when collecting the pedaling speed or revolutions per minute. For example, the sensor unit selects the optimal data collection method based on feedback provided by the user in the past. The sensor unit can also adjust the accuracy of the collected data based on the user's past feedback. The sensor unit can also dynamically customize the collection method by reflecting the user's feedback. This enables more appropriate data collection by reflecting the user's past feedback. For example, the sensor unit adjusts the collection method for current exercise data based on the results of a user survey. The sensor unit can also improve the accuracy of the collected data based on user reviews. The sensor unit can also filter the collected data based on the user's past feedback. This enables the fitness bike system to collect more appropriate data by reflecting the user's past feedback.

[0113] The analysis unit can estimate the user's emotions and adjust the data analysis algorithm based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the data analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can adjust the data analysis algorithm to suggest an exercise plan that is effective for stress relief. Furthermore, if the user is relaxed, the analysis unit can adjust the data analysis algorithm to suggest an exercise plan that enhances relaxation. Furthermore, if the user is excited, the analysis unit can adjust the data analysis algorithm to suggest an exercise plan to reduce excitement. In this way, by adjusting the data analysis algorithm based on the user's emotions, a more appropriate exercise plan can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0114] The analysis unit can improve the accuracy of the analysis by referring to the user's past exercise data during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past exercise data during analysis. For example, the analysis unit can analyze the current exercise data based on the user's past exercise data to improve accuracy. The analysis unit can also analyze the user's past exercise patterns and reflect them in the analysis of the current exercise data. The analysis unit can also optimize the analysis algorithm by referring to the user's past exercise data. In this way, the analysis accuracy can be improved by referring to the user's past exercise data. For example, the analysis unit can adjust the analysis method for the current exercise data based on the user's exercise history. The analysis unit can also improve the accuracy of the analysis data based on the user's training records. Furthermore, the analysis unit can filter the analysis data based on the user's past exercise data. In this way, the fitness bike system can improve the analysis accuracy by referring to the user's past exercise data.

[0115] The analysis unit can customize the analysis results based on the user's exercise goals during analysis. The analysis unit customizes the analysis results based on the user's exercise goals during analysis. For example, if the user is aiming to lose weight, the analysis unit can provide analysis results related to calorie consumption. Furthermore, if the user is aiming to improve muscle strength, the analysis unit can provide analysis results related to muscle training. Furthermore, the analysis unit can customize the analysis results based on the user's exercise goals and propose an optimal exercise plan. By customizing the analysis results based on the user's exercise goals, a more appropriate exercise plan can be provided. For example, the analysis unit can prioritize analysis of calorie consumption data based on the user's weight loss goal. Furthermore, the analysis unit can prioritize analysis of endurance training data based on the user's endurance improvement goal. Furthermore, the analysis unit can filter the analysis data based on the user's exercise goals and propose an optimal exercise plan. Thus, the fitness bike system can customize the analysis results based on the user's exercise goals and propose a more appropriate exercise plan.

[0116] The analysis unit can filter the analysis data according to the user's physical fitness level during analysis. The analysis unit can filter the analysis data according to the user's physical fitness level during analysis. For example, if the user's physical fitness level is high, the analysis unit can provide detailed analysis data to maximize the effect of exercise. Furthermore, if the user's physical fitness level is low, the analysis unit can provide simplified analysis data to reduce the burden of exercise. Furthermore, the analysis unit can filter the analysis data based on the user's physical fitness level and provide an optimal exercise plan. In this way, by filtering the analysis data according to the user's physical fitness level, a more appropriate exercise plan can be provided. For example, the analysis unit can adjust the accuracy of the analysis data based on the user's maximum oxygen uptake (VO2max). In addition, the analysis unit can adjust the accuracy of the analysis data based on the user's muscle strength test results. Furthermore, the analysis unit can filter the analysis data based on the user's physical fitness level. In this way, the fitness bike system can provide a more appropriate exercise plan by filtering the analysis data according to the user's physical fitness level.

[0117] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user emotion. The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user emotion. For example, if the user is stressed, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating display method. This enables more appropriate information to be provided by adjusting the display method of the analysis results based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0118] The analysis unit can customize the analysis results by taking into account the user's geographical location information during analysis. The analysis unit customizes the analysis results by taking into account the user's geographical location information during analysis. For example, if the user is in a mountainous area, the analysis unit can provide analysis results related to climbing. Also, if the user is in an urban area, the analysis unit can provide analysis results related to traffic conditions. Furthermore, the analysis unit can provide optimal analysis results based on the user's geographical location information. This allows for more relevant analysis results to be provided by taking the user's geographical location information into account. For example, the analysis unit can adjust the analysis method for the current exercise data based on the user's GPS data. Also, the analysis unit can improve the accuracy of the analysis data based on the user's address information. Furthermore, the analysis unit can filter the analysis data based on the user's geographical location information. This allows the fitness bike system to provide more relevant analysis results by taking the user's geographical location information into account.

[0119] The analysis unit can analyze the user's social media activities and analyze the associated data during the analysis. The analysis unit can analyze the user's social media activities and analyze the associated data during the analysis. For example, the analysis unit can provide optimal analysis results based on the exercise data shared by the user on social media. The analysis unit can also provide analysis results that increase exercise motivation based on the user's social media activities. The analysis unit can also customize the analysis results by reflecting the user's feedback on social media. This allows the analysis of the user's social media activities to provide more appropriate analysis results. For example, the analysis unit can adjust the analysis method for the current exercise data based on the content of the user's social media posts. The analysis unit can also improve the accuracy of the analysis data based on the number of likes the user has on social media. The analysis unit can also filter the analysis data based on the user's social media activities. This allows the fitness bike system to provide more appropriate analysis results by analyzing the user's social media activities.

[0120] The analysis unit can adjust the analysis algorithm during analysis by reflecting the user's past feedback. The analysis unit adjusts the analysis algorithm during analysis by reflecting the user's past feedback. For example, the analysis unit selects an optimal analysis algorithm based on feedback provided by the user in the past. The analysis unit can also adjust the accuracy of the analysis data based on the user's past feedback. Furthermore, the analysis unit can dynamically customize the analysis algorithm by reflecting the user's feedback. This allows for more appropriate analysis results to be provided by reflecting the user's past feedback. For example, the analysis unit adjusts the analysis method for the current exercise data based on the results of a user survey. The analysis unit can also improve the accuracy of the analysis data based on user reviews. Furthermore, the analysis unit can filter the analysis data based on the user's past feedback. This allows the fitness bike system to provide more appropriate analysis results by reflecting the user's past feedback.

[0121] The generation unit can estimate the user's emotions and adjust the theme of the virtual world to be generated based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the theme of the virtual world to be generated based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate a relaxing natural scene. If the user is relaxed, the generation unit can generate a virtual world with an adventurous theme. Furthermore, if the user is excited, the generation unit can generate a virtual world with a visually stimulating theme. This allows for a more appropriate exercise experience by adjusting the theme of the virtual world based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0122] The generation unit can adjust the level of detail of the virtual world by referring to the user's past exercise data during generation. The generation unit can adjust the level of detail of the virtual world by referring to the user's past exercise data during generation. For example, the generation unit adjusts the level of detail of the current virtual world based on the user's past exercise data. The generation unit can also analyze the user's past exercise patterns and reflect them in the level of detail of the current virtual world. Furthermore, the generation unit can optimize the virtual world generation algorithm by referring to the user's past exercise data. In this way, the level of detail of the virtual world can be adjusted by referring to the user's past exercise data. For example, the generation unit adjusts the level of detail of the current virtual world based on the user's exercise history. The generation unit can also improve the level of detail of the virtual world based on the user's training records. Furthermore, the generation unit can filter the virtual world based on the user's past exercise data. In this way, the fitness bike system can adjust the level of detail of the virtual world by referring to the user's past exercise data.

[0123] The generation unit can customize the difficulty of the virtual world based on the user's exercise goal at the time of generation. The generation unit customizes the difficulty of the virtual world based on the user's exercise goal at the time of generation. For example, if the user is aiming to lose weight, the generation unit can set a difficulty level that promotes calorie consumption. Also, if the user is aiming to improve muscle strength, the generation unit can set a difficulty level that is suitable for muscle training. Furthermore, the generation unit can customize the difficulty of the virtual world based on the user's exercise goal and provide an optimal exercise plan. In this way, customizing the difficulty of the virtual world based on the user's exercise goal can provide a more appropriate exercise plan. For example, the generation unit can prioritize analyzing calorie consumption data based on the user's weight loss goal. Also, the generation unit can prioritize analyzing endurance training data based on the user's endurance improvement goal. Furthermore, the generation unit can filter the virtual world based on the user's exercise goal and provide an optimal exercise plan. In this way, the fitness bike system can customize the difficulty of the virtual world based on the user's exercise goal and provide a more appropriate exercise plan.

[0124] The generation unit can adjust the terrain of the virtual world according to the user's physical fitness level during generation. The generation unit can adjust the terrain of the virtual world according to the user's physical fitness level during generation. For example, the generation unit can generate challenging terrain when the user's physical fitness level is high. The generation unit can also generate flat terrain when the user's physical fitness level is low. The generation unit can also dynamically adjust the terrain of the virtual world based on the user's physical fitness level. This allows for a more appropriate exercise plan to be provided by adjusting the terrain of the virtual world according to the user's physical fitness level. For example, the generation unit can adjust the terrain of the virtual world based on the user's maximum oxygen uptake (VO2max). The generation unit can also adjust the terrain of the virtual world based on the results of a muscle strength test for the user. The generation unit can also filter the virtual world based on the user's physical fitness level. This allows the fitness bike system to provide a more appropriate exercise plan by adjusting the terrain of the virtual world according to the user's physical fitness level.

[0125] The generation unit can estimate the user's emotions and adjust the colors of the virtual world to be generated based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the colors of the virtual world to be generated based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can use relaxing colors. If the user is relaxed, the generation unit can also use bright and cheerful colors. If the user is excited, the generation unit can also use visually stimulating colors. This allows for adjusting the colors of the virtual world based on the user's emotions to provide a more appropriate exercise experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0126] The generation unit can customize the scenery of the virtual world during generation by taking into account the user's geographical location information. The generation unit customizes the scenery of the virtual world during generation by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the generation unit can generate a mountainous scenery. Also, if the user is in an urban area, the generation unit can generate an urban scenery. Furthermore, the generation unit can generate an optimal scenery based on the user's geographical location information. In this way, by taking into account the user's geographical location information, a more relevant virtual world scenery can be provided. For example, the generation unit can adjust the current virtual world scenery based on the user's GPS data. Also, the generation unit can improve the virtual world scenery based on the user's address information. Furthermore, the generation unit can filter the virtual world based on the user's geographical location information. In this way, the fitness bike system can provide a more relevant virtual world scenery by taking into account the user's geographical location information.

[0127] The generation unit can analyze the user's social media activities and reflect related themes in the virtual world during generation. The generation unit can analyze the user's social media activities and reflect related themes in the virtual world during generation. For example, the generation unit can select an optimal theme based on exercise data shared by the user on social media. The generation unit can also select a theme that will increase exercise motivation from the user's social media activities. The generation unit can also customize the theme by reflecting the user's social media feedback. In this way, a more appropriate virtual world theme can be provided by analyzing the user's social media activities. For example, the generation unit can adjust the current virtual world theme based on the content of the user's social media posts. The generation unit can also improve the virtual world theme based on the number of likes the user has on social media. The generation unit can also filter the virtual world based on the user's social media activities. In this way, the fitness bike system can provide a more appropriate virtual world theme by analyzing the user's social media activities.

[0128] The generation unit can adjust the generation algorithm of the virtual world at the time of generation by reflecting the user's past feedback. The generation unit can adjust the generation algorithm of the virtual world at the time of generation by reflecting the user's past feedback. For example, the generation unit selects an optimal generation algorithm based on feedback provided by the user in the past. The generation unit can also adjust the level of detail of the virtual world based on the user's past feedback. Furthermore, the generation unit can dynamically customize the generation algorithm by reflecting the user's feedback. This allows a more appropriate virtual world to be provided by reflecting the user's past feedback. For example, the generation unit can adjust the current virtual world generation method based on the results of a user survey. The generation unit can also improve the level of detail of the virtual world based on user reviews. Furthermore, the generation unit can filter the virtual world based on the user's past feedback. This allows the fitness bike system to provide a more appropriate virtual world by reflecting the user's past feedback.

[0129] The display unit can estimate the user's emotions and adjust the viewpoint of the displayed virtual world based on the estimated user emotions. The display unit can estimate the user's emotions and adjust the viewpoint of the displayed virtual world based on the estimated user emotions. For example, if the user is feeling stressed, the display unit can provide a relaxing viewpoint. If the user is relaxed, the display unit can also provide an adventurous viewpoint. Furthermore, if the user is excited, the display unit can also provide a visually stimulating viewpoint. This allows for a more appropriate exercise experience by adjusting the viewpoint of the virtual world based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0130] The display unit can customize the display content by referring to the user's past exercise data when displaying the content. The display unit customizes the display content by referring to the user's past exercise data when displaying the content. For example, the display unit customizes the current display content based on the user's past exercise data. The display unit can also analyze the user's past exercise patterns and reflect the results in the current display content. Furthermore, the display unit can optimize the display algorithm by referring to the user's past exercise data. This allows the display content to be customized by referring to the user's past exercise data. For example, the display unit adjusts the current display content based on the user's exercise history. The display unit can also improve the accuracy of the display content based on the user's training records. Furthermore, the display unit can filter the display content based on the user's past exercise data. This allows the fitness bike system to customize the display content by referring to the user's past exercise data.

[0131] The display unit can adjust the level of detail of the display content based on the user's exercise goal when displaying the data. The display unit can adjust the level of detail of the display content based on the user's exercise goal when displaying the data. For example, if the user is aiming to lose weight, the display unit can provide display content related to calorie consumption. Also, if the user is aiming to improve muscle strength, the display unit can provide display content related to muscle training. Furthermore, the display unit can customize the level of detail of the display content based on the user's exercise goal and provide an optimal exercise plan. By adjusting the level of detail of the display content based on the user's exercise goal, a more appropriate exercise plan can be provided. For example, the display unit can prioritize displaying calorie consumption data based on the user's weight loss goal. Also, the display unit can prioritize displaying endurance training data based on the user's endurance improvement goal. Furthermore, the display unit can filter the display content based on the user's exercise goal and provide an optimal exercise plan. By adjusting the level of detail of the display content based on the user's exercise goal, the fitness bike system can provide a more appropriate exercise plan.

[0132] The display unit can filter the display content according to the user's physical fitness level when displaying the content. The display unit can filter the display content according to the user's physical fitness level when displaying the content. For example, if the user's physical fitness level is high, the display unit can provide detailed display content to maximize the effect of exercise. Furthermore, if the user's physical fitness level is low, the display unit can provide simplified display content to reduce the burden of exercise. Furthermore, the display unit can filter the display content based on the user's physical fitness level to provide an optimal exercise plan. In this way, by filtering the display content according to the user's physical fitness level, a more appropriate exercise plan can be provided. For example, the display unit can adjust the accuracy of the display content based on the user's maximum oxygen uptake (VO2max). In addition, the display unit can adjust the accuracy of the display content based on the user's muscle strength test results. Furthermore, the display unit can filter the display content based on the user's physical fitness level. In this way, the fitness bike system can provide a more appropriate exercise plan by filtering the display content according to the user's physical fitness level.

[0133] The display unit can estimate the user's emotions and adjust the brightness of the displayed virtual world based on the estimated user emotions. The display unit can estimate the user's emotions and adjust the brightness of the displayed virtual world based on the estimated user emotions. For example, if the user is feeling stressed, the display unit can provide a relaxing brightness. If the user is relaxed, the display unit can also provide a bright and cheerful brightness. Furthermore, if the user is excited, the display unit can also provide a visually stimulating brightness. This allows for a more appropriate exercise experience by adjusting the brightness of the virtual world based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0134] The display unit can customize the display content by taking into account the user's geographical location information. The display unit customizes the display content by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the display unit can display a mountain view. Also, if the user is in an urban area, the display unit can display a city view. Furthermore, the display unit can provide optimal display content based on the user's geographical location information. This allows for more relevant display content to be provided by taking into account the user's geographical location information. For example, the display unit can adjust the current display content based on the user's GPS data. Also, the display unit can improve the accuracy of the display content based on the user's address information. Furthermore, the display unit can filter the display content based on the user's geographical location information. This allows the fitness bike system to provide more relevant display content by taking into account the user's geographical location information.

[0135] The display unit can analyze the user's social media activity and reflect related themes in the display content when displaying the content. The display unit can analyze the user's social media activity and reflect related themes in the display content when displaying the content. For example, the display unit can select an optimal theme based on exercise data shared by the user on social media. The display unit can also select a theme that will increase exercise motivation from the user's social media activity. The display unit can also customize the theme by reflecting the user's social media feedback. In this way, more appropriate display content can be provided by analyzing the user's social media activity. For example, the display unit can adjust the current display content based on the user's social media posts. The display unit can also improve the accuracy of the display content based on the number of likes the user has on social media. Furthermore, the display unit can filter the display content based on the user's social media activity. In this way, the fitness bike system can provide more appropriate display content by analyzing the user's social media activity.

[0136] The display unit can adjust the display algorithm to reflect the user's past feedback when displaying the information. The display unit adjusts the display algorithm to reflect the user's past feedback when displaying the information. For example, the display unit selects an optimal display algorithm based on feedback provided by the user in the past. The display unit can also adjust the level of detail of the display content based on the user's past feedback. Furthermore, the display unit can dynamically customize the display algorithm to reflect the user's feedback. This allows more appropriate display content to be provided by reflecting the user's past feedback. For example, the display unit adjusts the current display content based on the results of a user survey. The display unit can also improve the accuracy of the display content based on user reviews. Furthermore, the display unit can filter the display content based on the user's past feedback. This allows the fitness bike system to provide more appropriate display content by reflecting the user's past feedback.

[0137] The sensor unit can estimate the user's emotions and adjust the frequency of collecting steering wheel movement and tilt based on the estimated user emotions. The sensor unit can estimate the user's emotions and adjust the frequency of collecting steering wheel movement and tilt based on the estimated user emotions. For example, if the user is feeling stressed, the sensor unit can increase the collection frequency to obtain detailed data and provide an exercise plan for stress relief. The sensor unit can also reduce the collection frequency to reduce the strain of exercise if the user is relaxed. Furthermore, if the user is excited, the sensor unit can set the collection frequency to a medium level to promote moderate exercise. This enables more appropriate data collection by adjusting the collection frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the sensor unit can be performed, for example, using AI or without AI. For example, the sensor unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0138] When collecting handlebar movement and tilt, the sensor unit can select the optimal collection method by referring to the user's past exercise data. When collecting handlebar movement and tilt, the sensor unit can select the optimal collection method by referring to the user's past exercise data. For example, if the user has previously performed high-intensity exercise, the sensor unit can collect detailed data to track the progress of the exercise. Furthermore, if the user has previously performed low-intensity exercise, the sensor unit can select a simplified data collection method. Furthermore, the sensor unit can analyze the user's past exercise data and set the optimal collection frequency and accuracy. This allows the optimal data collection method to be selected by referring to the user's past exercise data. For example, the sensor unit can adjust the current exercise data collection method based on the user's exercise history. Furthermore, the sensor unit can improve the accuracy of the collected data based on the user's training records. Furthermore, the sensor unit can filter the collected data based on the user's past exercise data. This allows the fitness bike system to select the optimal data collection method by referring to the user's past exercise data.

[0139] When collecting the movement and tilt of the handlebars, the sensor unit can adjust the accuracy of the collected data according to the user's physical fitness level. When collecting the movement and tilt of the handlebars, the sensor unit adjusts the accuracy of the collected data according to the user's physical fitness level. For example, when the user's physical fitness level is high, the sensor unit collects detailed data to maximize the effect of exercise. When the user's physical fitness level is low, the sensor unit can collect simplified data to reduce the strain of exercise. Furthermore, the sensor unit can dynamically adjust the accuracy of the collected data based on the user's physical fitness level. This allows for more appropriate data collection by adjusting the accuracy of the collected data according to the user's physical fitness level. For example, the sensor unit adjusts the accuracy of the collected data based on the user's maximum oxygen uptake (VO2max). The sensor unit can also adjust the accuracy of the collected data based on the results of a muscle strength test. Furthermore, the sensor unit can filter the collected data based on the user's physical fitness level. This allows the fitness bike system to adjust the accuracy of the collected data according to the user's physical fitness level, allowing for more appropriate data collection.

[0140] When collecting data on the movement and tilt of the handlebars, the sensor unit can filter the collected data based on the user's exercise goals. When collecting data on the movement and tilt of the handlebars, the sensor unit can filter the collected data based on the user's exercise goals. For example, if the user is aiming to lose weight, the sensor unit can prioritize collecting data related to calorie consumption. Also, if the user is aiming to improve muscle strength, the sensor unit can prioritize collecting data related to muscle training. Furthermore, the sensor unit can filter the collected data based on the user's exercise goals and provide an optimal exercise plan. By filtering the collected data based on the user's exercise goals, a more appropriate exercise plan can be provided. For example, the sensor unit can prioritize collecting calorie consumption data based on the user's weight loss goal. Also, the sensor unit can prioritize collecting endurance training data based on the user's endurance improvement goal. Furthermore, the sensor unit can filter the collected data based on the user's exercise goals and provide an optimal exercise plan. By filtering the collected data based on the user's exercise goals, the exercise bike system can provide a more appropriate exercise plan.

[0141] The sensor unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The sensor unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the sensor unit can prioritize collecting data related to stress relief. Furthermore, if the user is relaxed, the sensor unit can prioritize collecting data that enhances the relaxation effect. Furthermore, if the user is excited, the sensor unit can prioritize collecting data to suppress excitement. This enables more appropriate data collection by prioritizing collected data based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sensor unit can be performed using, for example, an AI. For example, the sensor unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0142] When collecting handlebar movement and tilt, the sensor unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. When collecting handlebar movement and tilt, the sensor unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the sensor unit can prioritize collecting data related to climbing. Also, if the user is in an urban area, the sensor unit can prioritize collecting data related to traffic conditions. Furthermore, the sensor unit can select the optimal data collection method based on the user's geographical location information. This allows for more relevant data to be collected by taking into account the user's geographical location information. For example, the sensor unit can adjust the collection method for current exercise data based on the user's GPS data. The sensor unit can also improve the accuracy of the collected data based on the user's address information. Furthermore, the sensor unit can filter the collected data based on the user's geographical location information. This allows the exercise bike system to collect more relevant data by taking into account the user's geographical location information.

[0143] The sensor unit can analyze the user's social media activities and collect related data when collecting the handlebar movement and tilt. The sensor unit can analyze the user's social media activities and collect related data when collecting the handlebar movement and tilt. For example, the sensor unit selects the optimal data collection method based on exercise data shared by the user on social media. The sensor unit can also collect data from the user's social media activities to increase exercise motivation. The sensor unit can also customize the data collection method by reflecting the user's social media feedback. This enables more appropriate data collection by analyzing the user's social media activities. For example, the sensor unit can adjust the current exercise data collection method based on the content of the user's social media posts. The sensor unit can also improve the accuracy of the collected data based on the number of likes the user has on social media. The sensor unit can also filter the collected data based on the user's social media activities. This enables the fitness bike system to collect more appropriate data by analyzing the user's social media activities.

[0144] The sensor unit can customize the collection method by reflecting the user's past feedback when collecting handlebar movement and tilt. The sensor unit customizes the collection method by reflecting the user's past feedback when collecting handlebar movement and tilt. For example, the sensor unit selects the optimal data collection method based on feedback provided by the user in the past. The sensor unit can also adjust the accuracy of the collected data based on the user's past feedback. Furthermore, the sensor unit can dynamically customize the collection method by reflecting the user's feedback. This enables more appropriate data collection by reflecting the user's past feedback. For example, the sensor unit adjusts the current exercise data collection method based on the user's survey results. The sensor unit can also improve the accuracy of the collected data based on user reviews. Furthermore, the sensor unit can filter the collected data based on the user's past feedback. This enables the fitness bike system to collect more appropriate data by reflecting the user's past feedback.

[0145] The generation unit can estimate the user's emotions and adjust the difficulty of the generated course based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the difficulty of the generated course based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate a relaxing, low-difficulty course. If the user is relaxed, the generation unit can also generate a moderately difficult course. Furthermore, if the user is excited, the generation unit can also generate a challenging, high-difficulty course. This allows for adjusting the difficulty of the course based on the user's emotions to provide a more appropriate exercise experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0146] The generation unit can adjust the level of detail of the course by referring to the user's past exercise data when generating the course. The generation unit can adjust the level of detail of the course by referring to the user's past exercise data when generating the course. For example, the generation unit adjusts the level of detail of the current course based on the user's past exercise data. The generation unit can also analyze the user's past exercise patterns and reflect them in the level of detail of the current course. Furthermore, the generation unit can optimize the course generation algorithm by referring to the user's past exercise data. In this way, the level of detail of the course can be adjusted by referring to the user's past exercise data. For example, the generation unit adjusts the level of detail of the current course based on the user's exercise history. The generation unit can also improve the level of detail of the course based on the user's training records. Furthermore, the generation unit can filter the course based on the user's past exercise data. In this way, the fitness bike system can adjust the level of detail of the course by referring to the user's past exercise data.

[0147] The generation unit can customize the course theme based on the user's exercise goal during generation. The generation unit customizes the course theme based on the user's exercise goal during generation. For example, if the user is aiming to lose weight, the generation unit can generate a course with a theme that promotes calorie consumption. Also, if the user is aiming to improve muscle strength, the generation unit can generate a course with a theme that is suitable for muscle training. Furthermore, the generation unit can customize the course theme based on the user's exercise goal and provide an optimal exercise plan. By customizing the course theme based on the user's exercise goal, a more appropriate exercise plan can be provided. For example, the generation unit can prioritize analyzing calorie consumption data based on the user's weight loss goal. Also, the generation unit can prioritize analyzing endurance training data based on the user's endurance improvement goal. Furthermore, the generation unit can filter the course based on the user's exercise goal and provide an optimal exercise plan. Thus, the fitness bike system can customize the course theme based on the user's exercise goal and provide a more appropriate exercise plan.

[0148] The generation unit can adjust the terrain of the course according to the user's physical fitness level when generating the course. The generation unit can adjust the terrain of the course according to the user's physical fitness level when generating the course. For example, the generation unit can generate challenging terrain when the user's physical fitness level is high. The generation unit can also generate flat terrain when the user's physical fitness level is low. The generation unit can also dynamically adjust the terrain of the course based on the user's physical fitness level. This allows for a more appropriate exercise plan to be provided by adjusting the terrain of the course according to the user's physical fitness level. For example, the generation unit can adjust the terrain of the course based on the user's maximum oxygen uptake (VO2max). The generation unit can also adjust the terrain of the course based on the results of a muscle strength test. The generation unit can also filter the course based on the user's physical fitness level. This allows the fitness bike system to adjust the terrain of the course according to the user's physical fitness level and provide a more appropriate exercise plan.

[0149] The generation unit can estimate the user's emotion and adjust the color of the generated course based on the estimated user emotion. The generation unit can estimate the user's emotion and adjust the color of the generated course based on the estimated user emotion. For example, if the user is stressed, the generation unit can use relaxing colors. If the user is relaxed, the generation unit can also use bright and cheerful colors. Furthermore, if the user is excited, the generation unit can use visually stimulating colors. This allows for adjusting the color of the course based on the user's emotion to provide a more appropriate exercise experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0150] The generation unit can customize the scenery of the course during generation by taking into account the user's geographical location information. The generation unit customizes the scenery of the course during generation by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the generation unit can generate a mountain scenery. Also, if the user is in an urban area, the generation unit can generate an urban scenery. Furthermore, the generation unit can generate an optimal scenery based on the user's geographical location information. In this way, by taking into account the user's geographical location information, it is possible to provide a more relevant scenery of the course. For example, the generation unit can adjust the scenery of the current course based on the user's GPS data. Also, the generation unit can improve the scenery of the course based on the user's address information. Furthermore, the generation unit can filter the course based on the user's geographical location information. In this way, the fitness bike system can provide a more relevant scenery of the course by taking into account the user's geographical location information.

[0151] The generation unit can analyze the user's social media activities and reflect related themes in the course when generating the course. The generation unit can analyze the user's social media activities and reflect related themes in the course when generating the course. For example, the generation unit can select an optimal theme based on exercise data shared by the user on social media. The generation unit can also select a theme that will increase exercise motivation from the user's social media activities. The generation unit can also customize the theme by reflecting the user's feedback on social media. In this way, a more appropriate course theme can be provided by analyzing the user's social media activities. For example, the generation unit can adjust the current course theme based on the content of the user's social media posts. The generation unit can also improve the course theme based on the number of likes the user has on social media. The generation unit can also filter the course based on the user's social media activities. In this way, the fitness bike system can provide a more appropriate course theme by analyzing the user's social media activities.

[0152] The generation unit can adjust the course generation algorithm during generation by reflecting the user's past feedback. The generation unit adjusts the course generation algorithm during generation by reflecting the user's past feedback. For example, the generation unit selects an optimal generation algorithm based on feedback provided by the user in the past. The generation unit can also adjust the level of detail of the course based on the user's past feedback. Furthermore, the generation unit can dynamically customize the generation algorithm by reflecting the user's feedback. This makes it possible to provide a more appropriate course by reflecting the user's past feedback. For example, the generation unit adjusts the current course generation method based on the results of a user survey. The generation unit can also improve the level of detail of the course based on user reviews. Furthermore, the generation unit can filter the course based on the user's past feedback. This makes it possible for the fitness bike system to provide a more appropriate course by reflecting the user's past feedback.

[0153] The generation unit can estimate the user's emotion and adjust the level of detail of the generated scenery based on the estimated user's emotion. The generation unit can estimate the user's emotion and adjust the level of detail of the generated scenery based on the estimated user's emotion. For example, if the user is feeling stressed, the generation unit can generate a relaxing, detailed scenery. If the user is relaxed, the generation unit can also generate a scenery with an appropriate level of detail. Furthermore, if the user is excited, the generation unit can generate a visually stimulating level of detail. This allows for adjusting the level of detail of the scenery based on the user's emotion, thereby providing a more appropriate exercise experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0154] The generation unit can adjust the scenery theme by referring to the user's past exercise data when generating the scenery. The generation unit can adjust the scenery theme by referring to the user's past exercise data when generating the scenery. For example, the generation unit adjusts the current scenery theme based on the user's past exercise data. The generation unit can also analyze the user's past exercise patterns and reflect them in the current scenery theme. Furthermore, the generation unit can optimize the scenery generation algorithm by referring to the user's past exercise data. In this way, the scenery theme can be adjusted by referring to the user's past exercise data. For example, the generation unit adjusts the current scenery theme based on the user's exercise history. The generation unit can also improve the scenery theme based on the user's training records. Furthermore, the generation unit can filter the scenery based on the user's past exercise data. In this way, the fitness bike system can adjust the scenery theme by referring to the user's past exercise data.

[0155] The generation unit can customize the difficulty level of the scenery based on the user's exercise goal during generation. The generation unit customizes the difficulty level of the scenery based on the user's exercise goal during generation. For example, if the user is aiming to lose weight, the generation unit can set a difficulty level that promotes calorie consumption. Also, if the user is aiming to improve muscle strength, the generation unit can set a difficulty level that is suitable for muscle training. Furthermore, the generation unit can customize the difficulty level of the scenery based on the user's exercise goal and provide an optimal exercise plan. In this way, customizing the difficulty level of the scenery based on the user's exercise goal can provide a more appropriate exercise plan. For example, the generation unit can prioritize analyzing calorie consumption data based on the user's weight loss goal. Also, the generation unit can prioritize analyzing endurance training data based on the user's endurance improvement goal. Furthermore, the generation unit can filter the scenery based on the user's exercise goal and provide an optimal exercise plan. In this way, the fitness bike system can customize the difficulty level of the scenery based on the user's exercise goal and provide a more appropriate exercise plan.

[0156] The generation unit can adjust the landscape terrain according to the user's physical fitness level during generation. The generation unit can adjust the landscape terrain according to the user's physical fitness level during generation. For example, the generation unit can generate challenging terrain when the user's physical fitness level is high. The generation unit can also generate flat terrain when the user's physical fitness level is low. The generation unit can also dynamically adjust the landscape terrain based on the user's physical fitness level. This allows for a more appropriate exercise plan to be provided by adjusting the landscape terrain according to the user's physical fitness level. For example, the generation unit can adjust the landscape terrain based on the user's maximum oxygen uptake (VO2max). The generation unit can also adjust the landscape terrain based on the user's muscle strength test results. The generation unit can also filter the landscape based on the user's physical fitness level. This allows the fitness bike system to adjust the landscape terrain according to the user's physical fitness level and provide a more appropriate exercise plan.

[0157] The generation unit can estimate the user's emotion and adjust the color of the generated scenery based on the estimated user emotion. The generation unit can estimate the user's emotion and adjust the color of the generated scenery based on the estimated user emotion. For example, if the user is feeling stressed, the generation unit can use relaxing colors. If the user is relaxed, the generation unit can also use bright and cheerful colors. If the user is excited, the generation unit can also use visually stimulating colors. This allows for adjusting the color of the scenery based on the user's emotion to provide a more appropriate exercise experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0158] The generation unit can customize the scenery by taking into account the user's geographical location information when generating the scenery. The generation unit customizes the scenery by taking into account the user's geographical location information when generating the scenery. For example, if the user is in a mountainous area, the generation unit can generate a mountain scenery. Also, if the user is in an urban area, the generation unit can generate an urban scenery. Furthermore, the generation unit can generate an optimal scenery based on the user's geographical location information. This makes it possible to provide a more relevant scenery by taking into account the user's geographical location information. For example, the generation unit can adjust the current scenery based on the user's GPS data. Also, the generation unit can improve the scenery based on the user's address information. Furthermore, the generation unit can filter the scenery based on the user's geographical location information. This makes it possible for the exercise bike system to provide a more relevant scenery by taking into account the user's geographical location information.

[0159] The generation unit can analyze the user's social media activities and reflect related themes in the scenery when generating the scenery. The generation unit can analyze the user's social media activities and reflect related themes in the scenery when generating the scenery. For example, the generation unit can select an optimal theme based on exercise data shared by the user on social media. The generation unit can also select a theme that will increase exercise motivation from the user's social media activities. The generation unit can also customize the theme by reflecting the user's social media feedback. In this way, a more appropriate scenery theme can be provided by analyzing the user's social media activities. For example, the generation unit can adjust the current scenery theme based on the content of the user's social media posts. The generation unit can also improve the scenery theme based on the number of likes the user has on social media. In addition, the generation unit can filter the scenery based on the user's social media activities. In this way, the fitness bike system can provide a more appropriate scenery theme by analyzing the user's social media activities.

[0160] The generation unit can adjust the scenery generation algorithm during generation by reflecting the user's past feedback. The generation unit adjusts the scenery generation algorithm during generation by reflecting the user's past feedback. For example, the generation unit selects an optimal generation algorithm based on feedback provided by the user in the past. The generation unit can also adjust the level of detail of the scenery based on the user's past feedback. Furthermore, the generation unit can dynamically customize the generation algorithm by reflecting the user's feedback. This makes it possible to provide more appropriate scenery by reflecting the user's past feedback. For example, the generation unit adjusts the current scenery generation method based on the results of a user survey. The generation unit can also improve the level of detail of the scenery based on user reviews. Furthermore, the generation unit can filter the scenery based on the user's past feedback. This makes it possible for the exercise bike system to provide more appropriate scenery by reflecting the user's past feedback.

[0161] The display unit can estimate the user's emotion and adjust the speed of the scenery movement based on the estimated user's emotion. The display unit can estimate the user's emotion and adjust the speed of the scenery movement based on the estimated user's emotion. For example, if the user is feeling stressed, the display unit can move the scenery at a speed that allows the user to relax. Furthermore, if the user is relaxed, the display unit can move the scenery at a moderate speed. Furthermore, if the user is excited, the display unit can move the scenery at a visually stimulating speed. This allows for adjusting the speed of the scenery movement based on the user's emotion to provide a more appropriate exercise experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0162] When adjusting the speed of the scenery movement, the display unit can select an optimal speed by referring to the user's past exercise data. When adjusting the speed of the scenery movement, the display unit can select an optimal speed by referring to the user's past exercise data. For example, the display unit adjusts the current speed of the scenery movement based on the user's past exercise data. The display unit can also analyze the user's past exercise patterns and reflect them in the current speed of the scenery movement. Furthermore, the display unit can optimize the speed of the scenery movement by referring to the user's past exercise data. In this way, the speed of the scenery movement can be optimized by referring to the user's past exercise data. For example, the display unit adjusts the current speed of the scenery movement based on the user's exercise history. The display unit can also improve the speed of the scenery movement based on the user's training record. Furthermore, the display unit can filter the speed of the scenery movement based on the user's past exercise data. In this way, the fitness bike system can optimize the speed of the scenery movement by referring to the user's past exercise data.

[0163] The display unit may adjust the speed of the scenery movement based on the user's physical fitness level when adjusting the speed of the scenery movement. The display unit may adjust the speed of the scenery movement based on the user's physical fitness level when adjusting the speed of the scenery movement. For example, if the user's physical fitness level is high, the display unit may move the scenery at a fast speed. Also, if the user's physical fitness level is low, the display unit may move the scenery at a slower speed. Furthermore, the display unit may dynamically adjust the speed of the scenery movement based on the user's physical fitness level. This allows for a more appropriate exercise plan to be provided by adjusting the speed of the scenery movement based on the user's physical fitness level. For example, the display unit may adjust the speed of the scenery movement based on the user's maximum oxygen uptake (VO2max). Also, the display unit may adjust the speed of the scenery movement based on the results of a muscle strength test. Furthermore, the display unit may filter the speed of the scenery movement based on the user's physical fitness level. This allows the fitness bike system to adjust the speed of the scenery movement based on the user's physical fitness level and provide a more appropriate exercise plan.

[0164] When adjusting the speed of the scenery movement, the display unit can customize the speed based on the user's exercise goal. When adjusting the speed of the scenery movement, the display unit customizes the speed based on the user's exercise goal. For example, if the user is aiming to lose weight, the display unit can set a speed that promotes calorie consumption. Also, if the user is aiming to improve muscle strength, the display unit can set a speed that is suitable for muscle training. Furthermore, the display unit can customize the speed of the scenery movement based on the user's exercise goal and provide an optimal exercise plan. In this way, customizing the speed of the scenery movement based on the user's exercise goal can provide a more appropriate exercise plan. For example, the display unit can prioritize analysis of calorie consumption data based on the user's weight loss goal. Also, the display unit can prioritize analysis of endurance training data based on the user's endurance improvement goal. Furthermore, the display unit can filter the speed of the scenery movement based on the user's exercise goal and provide an optimal exercise plan. In this way, the fitness bike system can customize the speed of the scenery movement based on the user's exercise goal and provide a more appropriate exercise plan.

[0165] The display unit can estimate the user's emotion and adjust the direction of the scenery movement based on the estimated user's emotion. The display unit can estimate the user's emotion and adjust the direction of the scenery movement based on the estimated user's emotion. For example, if the user is feeling stressed, the display unit can move the scenery in a relaxing direction. Furthermore, if the user is relaxed, the display unit can move the scenery in a moderate direction. Furthermore, if the user is excited, the display unit can move the scenery in a visually stimulating direction. This allows for a more appropriate exercise experience by adjusting the direction of the scenery movement based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, an AI, or without an AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0166] The display unit can customize the speed of the scenery movement by taking into account the user's geographical location information when adjusting the speed of the scenery movement. The display unit customizes the speed by taking into account the user's geographical location information when adjusting the speed of the scenery movement. For example, if the user is in a mountainous area, the display unit can move the scenery at a speed suitable for climbing. Also, if the user is in an urban area, the display unit can move the scenery at a speed suitable for urban scenery. Furthermore, the display unit can move the scenery at an optimal speed based on the user's geographical location information. In this way, a more relevant speed can be provided by taking into account the user's geographical location information. For example, the display unit can adjust the current speed of the scenery movement based on the user's GPS data. Also, the display unit can increase the speed of the scenery movement based on the user's address information. Furthermore, the display unit can filter the speed of the scenery movement based on the user's geographical location information. In this way, the exercise bike system can provide a more relevant speed by taking into account the user's geographical location information.

[0167] The display unit can analyze the user's social media activity and reflect related themes in the scenery when adjusting the speed of the scenery movement. The display unit can analyze the user's social media activity and reflect related themes in the scenery when adjusting the speed of the scenery movement. For example, the display unit can select an optimal theme based on exercise data shared by the user on social media. The display unit can also select a theme that will increase exercise motivation from the user's social media activity. The display unit can also customize the theme by reflecting the user's social media feedback. In this way, a more appropriate scenery theme can be provided by analyzing the user's social media activity. For example, the display unit can adjust the current scenery theme based on the content of the user's social media posts. The display unit can also improve the scenery theme based on the number of likes the user has on social media. The display unit can also filter the scenery based on the user's social media activity. In this way, the fitness bike system can provide a more appropriate scenery theme by analyzing the user's social media activity.

[0168] When adjusting the speed of the scenery movement, the display unit can adjust the speed by reflecting the user's past feedback. When adjusting the speed of the scenery movement, the display unit adjusts the speed by reflecting the user's past feedback. For example, the display unit selects an optimal speed based on feedback provided by the user in the past. The display unit can also adjust the speed of the scenery movement based on the user's past feedback. Furthermore, the display unit can dynamically customize the speed by reflecting the user's feedback. This makes it possible to provide a more appropriate speed by reflecting the user's past feedback. For example, the display unit adjusts the current speed of the scenery movement based on the results of a user survey. The display unit can also improve the speed of the scenery movement based on user reviews. Furthermore, the display unit can filter the speed of the scenery movement based on the user's past feedback. This makes it possible for the fitness bike system to provide a more appropriate speed by reflecting the user's past feedback.

[0169] The display unit can estimate the user's emotion and adjust the frequency of viewpoint changes based on the estimated user emotion. The display unit can estimate the user's emotion and adjust the frequency of viewpoint changes based on the estimated user emotion. For example, if the user is feeling stressed, the display unit can change the viewpoint at a frequency that allows the user to relax. Furthermore, if the user is relaxed, the display unit can also change the viewpoint at a moderate frequency. Furthermore, if the user is excited, the display unit can also change the viewpoint at a visually stimulating frequency. This allows for a more appropriate exercise experience by adjusting the frequency of viewpoint changes based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, an AI, or without an AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0170] When adjusting the frequency of viewpoint changes, the display unit can select an optimal frequency by referring to the user's past exercise data. When adjusting the frequency of viewpoint changes, the display unit can select an optimal frequency by referring to the user's past exercise data. For example, the display unit adjusts the current frequency of viewpoint changes based on the user's past exercise data. The display unit can also analyze the user's past exercise patterns and reflect them in the current frequency of viewpoint changes. Furthermore, the display unit can optimize the frequency of viewpoint changes by referring to the user's past exercise data. In this way, the frequency of viewpoint changes can be optimized by referring to the user's past exercise data. For example, the display unit adjusts the current frequency of viewpoint changes based on the user's exercise history. The display unit can also improve the frequency of viewpoint changes based on the user's training records. Furthermore, the display unit can filter the frequency of viewpoint changes based on the user's past exercise data. In this way, the fitness bike system can optimize the frequency of viewpoint changes by referring to the user's past exercise data.

[0171] The display unit can adjust the frequency of the viewpoint change based on the user's physical fitness level when adjusting the frequency of the viewpoint change. The display unit can adjust the frequency based on the user's physical fitness level when adjusting the frequency of the viewpoint change. For example, the display unit can change the viewpoint more frequently when the user's physical fitness level is high. The display unit can also reduce the frequency of the viewpoint change when the user's physical fitness level is low. The display unit can also dynamically adjust the frequency of the viewpoint change based on the user's physical fitness level. This allows for a more appropriate exercise plan to be provided by adjusting the frequency of the viewpoint change based on the user's physical fitness level. For example, the display unit can adjust the frequency of the viewpoint change based on the user's maximum oxygen uptake (VO2max). The display unit can also adjust the frequency of the viewpoint change based on the results of a muscle strength test. The display unit can also filter the frequency of the viewpoint change based on the user's physical fitness level. This allows the fitness bike system to adjust the frequency of the viewpoint change based on the user's physical fitness level and provide a more appropriate exercise plan.

[0172] When adjusting the frequency of viewpoint changes, the display unit can customize the frequency based on the user's exercise goals. When adjusting the frequency of viewpoint changes, the display unit customizes the frequency based on the user's exercise goals. For example, if the user is aiming to lose weight, the display unit can set a frequency that promotes calorie consumption. Also, if the user is aiming to improve muscle strength, the display unit can set a frequency that is suitable for strength training. Furthermore, the display unit can customize the frequency of viewpoint changes based on the user's exercise goals and provide an optimal exercise plan. By customizing the frequency of viewpoint changes based on the user's exercise goals, a more appropriate exercise plan can be provided. For example, the display unit can prioritize analysis of calorie consumption data based on the user's weight loss goal. Also, the display unit can prioritize analysis of endurance training data based on the user's endurance improvement goal. Furthermore, the display unit can filter the frequency of viewpoint changes based on the user's exercise goals and provide an optimal exercise plan. Thus, the fitness bike system can customize the frequency of viewpoint changes based on the user's exercise goals and provide a more appropriate exercise plan.

[0173] The display unit can estimate the user's emotion and adjust the direction of change of the viewpoint based on the estimated user's emotion. The display unit can estimate the user's emotion and adjust the direction of change of the viewpoint based on the estimated user's emotion. For example, if the user is feeling stressed, the display unit can change the viewpoint in a relaxing direction. If the user is relaxed, the display unit can also change the viewpoint in a moderate direction. Furthermore, if the user is excited, the display unit can change the viewpoint in a visually stimulating direction. This allows for a more appropriate exercise experience by adjusting the direction of change of the viewpoint based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, an AI, or without an AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0174] When adjusting the frequency of viewpoint changes, the display unit can customize the frequency by taking into account the user's geographical location information. When adjusting the frequency of viewpoint changes, the display unit customizes the frequency by taking into account the user's geographical location information. For example, if the user is in a mountainous area, the display unit can change the viewpoint at a frequency appropriate for climbing. Also, if the user is in an urban area, the display unit can change the viewpoint at a frequency appropriate for urban scenery. Furthermore, the display unit can change the viewpoint at an optimal frequency based on the user's geographical location information. In this way, by taking the user's geographical location information into account, a more relevant frequency of viewpoint changes can be provided. For example, the display unit can adjust the current frequency of viewpoint changes based on the user's GPS data. Also, the display unit can improve the frequency of viewpoint changes based on the user's address information. Furthermore, the display unit can filter the frequency of viewpoint changes based on the user's geographical location information. In this way, the exercise bike system can provide a more relevant frequency of viewpoint changes by taking the user's geographical location information into account.

[0175] The display unit can analyze the user's social media activity and reflect related themes in the viewpoint when adjusting the frequency of the viewpoint change. The display unit can analyze the user's social media activity and reflect related themes in the viewpoint when adjusting the frequency of the viewpoint change. For example, the display unit can select an optimal theme based on exercise data shared by the user on social media. The display unit can also select a theme that will increase exercise motivation from the user's social media activity. The display unit can also customize the theme by reflecting the user's social media feedback. In this way, a more appropriate viewpoint theme can be provided by analyzing the user's social media activity. For example, the display unit can adjust the current viewpoint theme based on the content of the user's social media posts. The display unit can also improve the viewpoint theme based on the number of likes the user has on social media. The display unit can also filter the viewpoints based on the user's social media activity. In this way, the fitness bike system can provide a more appropriate viewpoint theme by analyzing the user's social media activity.

[0176] The display unit can adjust the frequency of viewpoint changes by reflecting the user's past feedback. When adjusting the frequency of viewpoint changes, the display unit adjusts the frequency by reflecting the user's past feedback. For example, the display unit selects an optimal frequency based on feedback provided by the user in the past. The display unit can also adjust the frequency of viewpoint changes based on the user's past feedback. Furthermore, the display unit can dynamically customize the frequency by reflecting the user's feedback. This allows a more appropriate frequency of viewpoint changes to be provided by reflecting the user's past feedback. For example, the display unit adjusts the current frequency of viewpoint changes based on the results of a user survey. The display unit can also improve the frequency of viewpoint changes based on user reviews. Furthermore, the display unit can filter the frequency of viewpoint changes based on the user's past feedback. This allows the fitness bike system to provide a more appropriate frequency of viewpoint changes by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the sensor unit, analysis unit, generation unit, and display unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the sensor unit collects pedal rotation count and speed using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual cycling world using a generation AI. The display unit displays the generated virtual world on the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the sensor unit, analysis unit, generation unit, and display unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sensor unit collects pedal rotation count and speed using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual cycling world using a generation AI. The display unit displays the generated virtual world on the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the sensor unit, analysis unit, generation unit, and display unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the sensor unit collects pedal rotation count and speed using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual cycling world using a generation AI. The display unit displays the generated virtual world on the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the sensor unit, analysis unit, generation unit, and display unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sensor unit collects pedal rotation count and speed using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a virtual cycling world using a generative AI. The display unit displays the generated virtual world on the display of the robot 414.

[0177] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0178] The fitness bike system may further include a heart rate sensor unit that monitors the user's heart rate in real time. The heart rate sensor unit measures the user's heart rate and transmits the data to the analysis unit. The analysis unit can more accurately calculate the user's exercise intensity based on the heart rate data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user's heart rate is high, the generation unit can generate relaxing scenery to encourage the user to lower their heart rate. Alternatively, if the heart rate is low, the generation unit can generate a challenging course to increase the exercise intensity. Furthermore, the heart rate data can be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0179] The fitness bike system may further include a temperature sensor unit that measures the user's body temperature. The temperature sensor unit measures the user's body temperature in real time and transmits the data to the analysis unit. The analysis unit analyzes the user's physical condition based on the body temperature data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user's body temperature is high, the generation unit may generate a cool scene to encourage the user to lower their body temperature. Alternatively, if the user's body temperature is low, the generation unit may generate a warm scene to encourage the user to raise their body temperature. Furthermore, the body temperature data may be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0180] The fitness bike system may further include a respiratory sensor unit that measures the user's respiratory rate. The respiratory sensor unit measures the user's respiratory rate in real time and transmits the data to the analysis unit. The analysis unit analyzes the user's exercise intensity based on the respiratory rate data, and the generation unit uses the data to adjust the virtual cycling world. For example, if the user's respiratory rate is high, the generation unit may generate a relaxing scene to encourage the user to lower their breathing rate. Alternatively, if the user's breathing rate is low, the generation unit may generate a challenging course to increase the exercise intensity. Furthermore, the respiratory rate data may be used to analyze the user's exercise pattern and provide an optimal exercise plan.

[0181] The fitness bike system can further include an electromyographic sensor unit that measures the user's muscle movements. The electromyographic sensor unit measures the user's muscle movements in real time and transmits the data to the analysis unit. The analysis unit analyzes the user's exercise intensity based on the electromyographic data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user's muscle movements are intense, the generation unit can generate relaxing scenery to encourage muscle relaxation. Alternatively, if the user's muscle movements are low, the generation unit can generate a challenging course to increase exercise intensity. Furthermore, the electromyographic data can be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0182] The fitness bike system may further include a stress sensor unit that measures the user's stress level. The stress sensor unit measures the user's stress level in real time and transmits the data to the analysis unit. The analysis unit estimates the user's emotions based on the stress data, and the generation unit uses the data to adjust the virtual cycling world. For example, if the user's stress level is high, the generation unit may generate relaxing scenery to encourage stress reduction. Alternatively, if the stress level is low, the generation unit may generate a challenging course to increase exercise intensity. Furthermore, the stress data may be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0183] The fitness bike system may further include a sleep sensor unit that collects the user's sleep data. The sleep sensor unit collects the user's sleep data and transmits the data to the analysis unit. The analysis unit analyzes the user's physical condition based on the sleep data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user has not slept enough, the generation unit may generate relaxing scenery to encourage the user to improve their physical condition. Alternatively, if the user has had enough sleep, the generation unit may generate a challenging course to increase exercise intensity. Furthermore, the sleep data may be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0184] The fitness bike system may further include a food sensor unit that collects the user's food data. The food sensor unit collects the user's food data and transmits the data to the analysis unit. The analysis unit analyzes the user's physical condition based on the food data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user eats a high-calorie meal, the generation unit can generate a challenging course to promote calorie consumption. Alternatively, if the user eats a low-calorie meal, the generation unit can generate relaxing scenery to encourage the user to improve their physical condition. Furthermore, the food data can be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0185] The fitness bike system may further include a moisture sensor unit that measures the user's moisture intake. The moisture sensor unit measures the user's moisture intake in real time and transmits the data to the analysis unit. The analysis unit analyzes the user's physical condition based on the moisture data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user's moisture intake is insufficient, the generation unit can generate relaxing scenery to encourage the user to hydrate. Alternatively, if the user has consumed sufficient moisture, the generation unit can generate a challenging course to increase exercise intensity. Furthermore, the moisture data can be used to analyze the user's exercise patterns and provide an optimal exercise plan.

[0186] The fitness bike system may further include a posture sensor unit that measures the user's posture. The posture sensor unit measures the user's posture in real time and transmits the data to the analysis unit. The analysis unit analyzes the user's exercise pattern based on the posture data, and the generation unit can use the data to adjust the virtual cycling world. For example, if the user's posture is poor, the generation unit can generate a course to improve posture. Alternatively, if the user's posture is good, the generation unit can generate a challenging course to increase exercise intensity. Furthermore, the posture data can be used to analyze the user's exercise pattern and provide an optimal exercise plan.

[0187] The fitness bike system can further estimate the user's emotions and adjust the music in the virtual cycling world based on the estimated user emotions. For example, if the user is feeling stressed, relaxing music can be played. Alternatively, if the user is relaxed, energetic music can be played. Furthermore, if the user is excited, visually stimulating music can be played. By adjusting the music based on the user's emotions, a more appropriate exercise experience can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0188] The processing flow of the second embodiment will be briefly explained below.

[0189] Step 1: The sensor unit collects pedal rotations or speed. For example, the sensor unit can measure pedal rotations in revolutions per minute (RPM). The sensor unit can also measure speed in kilometers per hour (km / h). Additionally, the sensor unit can collect both pedal rotations and speed simultaneously. Step 2: The analysis unit analyzes the data collected by the sensor unit. For example, the analysis unit calculates the user's exercise intensity based on the collected rotation count data. The analysis unit can also calculate the user's movement distance based on the speed data. Furthermore, the analysis unit can combine the rotation count and speed data to analyze the user's exercise pattern. Step 3: The generation unit generates a virtual cycling world based on the data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate a real-time virtual world based on the user's exercise data. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate a virtual world based on the user's exercise data. For example, if the user wants to ride on a mountain road, the generation unit generates a mountain road scene. Also, if the user wants to ride along the coast, the generation unit can generate a coastal scene. Furthermore, the generation unit can generate various scenes, such as urban areas and forests, based on the user's exercise data. Step 4: The display unit displays the virtual world generated by the generator. For example, the display unit displays the scenery of the virtual world on a monitor. The display unit can also move the scenery of the virtual world according to the number of revolutions of the pedals by the user. Furthermore, the display unit can change the viewpoint of the virtual world according to the movement of the handlebars by the user.

[0190] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0191] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0192] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0193] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0194] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0195] 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.

[0196] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0197] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0198] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0199] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0200] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0201] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0202] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0203] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0204] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0205] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0206] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0207] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0208] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0209] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0210] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0211] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0212] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0213] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0214] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0215] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0216] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0217] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0218] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0220] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0221] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0222] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0223] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0224] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0225] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0226] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0227] 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.

[0228] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0229] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0230] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0231] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0232] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0233] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0234] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0235] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0236] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0237] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0238] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0239] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0240] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0241] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0242] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0243] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0244] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0245] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0246] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0247] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0248] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0249] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0250] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0251] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0252] 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.

[0253] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0254] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0255] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0256] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0257] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0258] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0259] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0260] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0261] [Explanation of symbols]

[0262] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a sensor unit that collects pedal rotation speed; an analysis unit that analyzes the data collected by the sensor unit; a generator for generating a virtual cycling world based on the data analyzed by the analyzer; a display unit that displays the virtual world generated by the generation unit. A system characterized by:

2. Equipped with a sensor unit that collects the movement or tilt of the handle The system of claim 1 .

3. A generator is provided that generates a course that matches the user's physical condition or mood. The system of claim 1 .

4. The generation unit Generate scenery based on geographic or meteorological data The system of claim 1 .

5. The display unit The scenery in the virtual world moves according to the user's pedal rotation speed. The system of claim 1 .

6. The display unit Change the viewpoint of the virtual world according to the user's steering wheel movements The system of claim 1 .

7. The sensor unit The user's emotion is estimated, and the frequency of collecting the pedal rotation number or speed is adjusted based on the estimated user's emotion. The system of claim 1 .

8. The sensor unit When collecting pedal rotation speed or speed, the appropriate collection method is selected by referring to the user's past exercise data. The system of claim 1 .

9. The sensor unit When collecting pedaling speed or revolutions, adjust the accuracy of the collected data according to the user's fitness level. The system of claim 1 .

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