system

Abstract

The system according to this embodiment aims to provide personal training in an easy and inexpensive way. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, a progress provision unit, a motivation maintenance unit, and an adjustment unit. The reception unit inputs the user's body type, goals, and progress. The generation unit automatically creates a training plan based on the information entered by the reception unit. The provision unit provides the training plan generated by the generation unit in the form of videos and illustrations. The progress provision unit provides progress status based on the training plan provided by the provision unit using a dashboard and progress reports. The motivation maintenance unit maintains motivation based on the progress status provided by the progress provision unit. The adjustment unit flexibly adjusts the training content based on the motivation maintained by the motivation maintenance unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is costly to receive personal training and it cannot be easily used.

[0005] The system according to the embodiment aims to provide personal training easily and at a low cost.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a progress provision unit, a motivation maintenance unit, and an adjustment unit. The reception unit inputs the user's body type, goals, and progress. The generation unit automatically creates a training plan based on the information entered by the reception unit. The provision unit provides the training plan generated by the generation unit in the form of videos and illustrations. The progress provision unit provides progress status based on the training plan provided by the provision unit using a dashboard and progress reports. The motivation maintenance unit maintains motivation based on the progress status provided by the progress provision unit. The adjustment unit flexibly adjusts the training content based on the motivation maintained by the motivation maintenance unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personal training in an easy and inexpensive way. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The personal training system according to an embodiment of the present invention is a system that provides a more convenient and affordable service than traditional human personal trainers to people aiming to improve their health, increase their physical fitness, lose weight, or improve their physique. This system begins with the user inputting their body type, goals, and daily progress. Next, the AI ​​automatically creates a training plan based on this information. The training plan is explained clearly using videos and illustrations. The AI ​​also provides the user's progress through a dashboard and progress reports, and maintains motivation using gamification and reminders. Furthermore, the AI ​​flexibly adjusts the training content according to the user's health and fitness level. For example, it proposes menus that can be used by beginners, athletes, and the elderly, and provides training content that takes injuries and pre-existing conditions into consideration. With this system, users can train at home or on the go, eliminating time constraints and cost problems. In addition, because the AI ​​automatically creates a training plan tailored to the goals and fitness level, the problem of not knowing the correct training method is also solved. For example, if a user sets a goal of "losing 5 kg," the AI ​​will create a training plan tailored to that goal and allow the user to check their progress on the dashboard. Furthermore, the system flexibly adjusts the plan according to the training progress and provides effective reminders. In this way, the AI-powered personal training system aims to provide an accessible and affordable service to people seeking to improve their health, increase their physical fitness, lose weight, or improve their physique, thereby raising users' health awareness, closing disparities, and transforming their lifestyles. As a result, the personal training system can efficiently support users in improving their health, increasing their physical fitness, losing weight, and improving their physique.

[0029] The personal training system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a progress provision unit, a motivation maintenance unit, and an adjustment unit. The reception unit inputs the user's body type, goals, and progress. For example, the reception unit provides an interface for the user to input their body type and goals. The reception unit allows the user to input information such as weight, height, and body fat percentage. The reception unit also allows the user to input goals they have set (e.g., weight loss, muscle gain). Furthermore, the reception unit provides an interface for the user to input their training progress. The generation unit automatically creates a training plan based on the information input by the reception unit. For example, the generation unit generates an appropriate training menu based on the user's body type and goals. For example, the generation unit creates a plan that combines cardio exercises and strength training for the user's weight loss goal. For example, the generation unit creates a plan that includes weight training and resistance training for the user's muscle gain goal. The provision unit provides the training plan generated by the generation unit in the form of videos and illustrations. The service provider provides users with, for example, videos and illustrations demonstrating training movements. The service provider provides videos demonstrating the correct form for exercises such as squats and planks. The service provider provides illustrations demonstrating stretching and warm-up methods. The progress provider provides progress information on dashboards and progress reports based on the training plan provided by the service provider. The progress provider visually displays the user's training progress using graphs and charts. The progress provider provides a meter that shows the user's training achievement level. The progress provider generates and provides weekly and monthly progress reports to users. The motivation maintenance unit maintains motivation based on the progress information provided by the progress provider. The motivation maintenance unit provides users with, for example, encouraging messages and rewards. The motivation maintenance unit awards users badges and points when they achieve their training goals. The motivation maintenance unit sends users reminders to encourage them to continue training.The adjustment unit flexibly adjusts the training content based on the motivation maintained by the motivation maintenance unit. For example, the adjustment unit adjusts the training menu according to the user's training progress and fitness level. For example, the adjustment unit increases the training intensity when the user becomes accustomed to the training. For example, the adjustment unit reduces the training content when the user is injured. As a result, the personal training system according to the embodiment can efficiently input, generate, provide, manage progress, maintain motivation, and adjust the user's body type, goals, and progress.

[0030] The reception desk inputs the user's body type, goals, and progress. For example, the reception desk provides an interface for users to input their body type and goals. The reception desk allows users to input information such as weight, height, and body fat percentage. It also allows users to input the goals they have set (e.g., weight loss, muscle gain). Furthermore, the reception desk provides an interface for users to input their training progress. The reception desk has an intuitive user interface to make it easy for users to input information. For example, it uses sliders and drop-down menus to reduce the effort required for users to input weight and height. The reception desk also allows users to select specific goals when setting goals. For example, it provides options such as weight loss, muscle gain, and endurance improvement, allowing users to choose the option that best suits their goals. Furthermore, the reception desk provides an interface for users to input their training progress. For example, it allows users to input their daily training content and achievement level. This allows users to easily record their progress and review it later. The reception desk securely stores the information entered by users and makes it available for use in collaboration with other departments. For example, information about the user's body type and goals is used by the generation unit when creating a training plan. Information about the user's progress is used by the progress reporting unit and the motivation maintenance unit. This allows the reception unit to efficiently manage user information and improve the overall functionality of the system.

[0031] The generation unit automatically creates training plans based on information entered by the reception unit. For example, the generation unit generates appropriate training menus based on the user's body type and goals. For example, for a user's weight loss goal, the generation unit creates a plan that combines cardio exercises and strength training. For example, for a user's muscle-building goal, the generation unit creates a plan that includes weight training and resistance training. The generation unit utilizes AI to generate training plans optimized for the user's individual needs. For example, the AI ​​analyzes the user's body type, goals, and progress to suggest the optimal training menu. The AI ​​creates effective training plans by referring to past data and the success stories of other users. The generation unit continuously improves training plans based on user feedback. For example, when a user provides feedback on a training plan, the generation unit adjusts the plan to reflect that feedback. This allows users to receive training plans optimized for their needs. The generation unit considers the user's health condition and limitations when generating training plans. For example, if a user has a specific health problem, the generation unit creates a training menu that takes that problem into account. This allows users to train safely and effectively. The generation unit employs an approach based on the latest fitness theories and scientific evidence in generating training plans. This allows users to receive scientifically supported and effective training.

[0032] The provider unit provides training plans generated by the generator unit in the form of videos and illustrations. For example, the provider unit provides users with videos and illustrations demonstrating training movements. For example, the provider unit provides videos demonstrating the correct form for exercises such as squats and planks. For example, the provider unit provides illustrations demonstrating stretching and warm-up methods. The provider unit provides detailed guides to help users perform training correctly. For example, in videos, a trainer demonstrates the exercises while explaining the correct form and points to watch out for. This allows users to train correctly and reduce the risk of injury. The provider unit organizes videos and illustrations in an easy-to-understand manner so that users can refer to them when training. For example, videos and illustrations are categorized by type of exercise and purpose so that users can easily find the information they need. The provider unit provides encouraging messages and rewards to help users maintain their motivation while training. For example, encouraging messages are displayed or points and badges are awarded when users complete training. This helps users maintain their motivation to continue training. The provider unit provides users with the information they need when training in real time. For example, the system can provide real-time feedback on form corrections and instructions for the next exercise while the user is training. This allows the user to train more efficiently.

[0033] The progress delivery department provides progress information via dashboards and progress reports based on the training plan provided by the department. For example, the progress delivery department visually displays the user's training progress using graphs and charts. For example, the progress delivery department provides a meter that shows the user's training achievement level. For example, the progress delivery department generates and provides weekly and monthly progress reports to users. The progress delivery department provides information in a visually easy-to-understand format so that users can grasp their progress at a glance. For example, it visually displays the user's training progress using graphs and charts. This allows users to easily check their progress and maintain their motivation. The progress delivery department analyzes the user's training data and reports on the progress in detail. For example, it provides a meter that shows the user's training achievement level and goal achievement rate. This allows users to concretely understand the effectiveness of their training. Based on the user's training data, the progress delivery department generates weekly and monthly progress reports. For example, it reports in detail the training content and progress that the user achieved in a week or a month. This allows users to regularly check their training progress and plan towards achieving their goals. The progress reporting department improves its progress reporting methods based on user feedback. For example, when users provide feedback on progress reports, the progress reporting department adjusts the reporting method to reflect that feedback. This allows users to receive progress reports optimized to their needs.

[0034] The Motivation Maintenance Unit maintains motivation based on the progress provided by the Progress Provision Unit. For example, the Motivation Maintenance Unit provides encouraging messages and rewards to users. For example, the Motivation Maintenance Unit awards badges or points when users achieve their training goals. For example, the Motivation Maintenance Unit sends reminders to users to encourage continued training. The Motivation Maintenance Unit provides various ways for users to continue training. For example, it displays encouraging messages or awards points or badges when users complete training. This helps users maintain their motivation to continue training. The Motivation Maintenance Unit sends reminders to users to continue training. For example, it sends regular reminders to users so they don't forget to train. This helps users continue training. The Motivation Maintenance Unit provides a reward system to encourage users to continue training. For example, it awards badges or points when users achieve their training goals. This helps users maintain their motivation to continue training. The Motivation Maintenance Unit provides a community to encourage users to continue training. For example, it provides a community where users can interact with and encourage other users. This helps users maintain their motivation to continue training.

[0035] The adjustment unit flexibly adjusts the training content based on the motivation maintained by the motivation maintenance unit. For example, the adjustment unit adjusts the training menu according to the user's training progress and fitness level. For example, the adjustment unit increases the training intensity when the user becomes accustomed to training. For example, the adjustment unit reduces the training content when the user is injured. The adjustment unit flexibly adjusts the training menu according to the user's training progress and fitness level. For example, when the user becomes accustomed to training, increasing the training intensity can yield further results. Also, if the user is injured, reducing the training content can prevent the injury from worsening. The adjustment unit adjusts the training menu based on user feedback. For example, when the user provides feedback on the training menu, the adjustment unit adjusts the menu to reflect that feedback. This allows the user to receive a training menu optimized for their needs. The adjustment unit adjusts the training menu considering the user's health condition and limitations. For example, if the user has a specific health problem, a training menu that takes that problem into consideration is created. This allows the user to train safely and effectively. The adjustment unit adjusts the training menu by adopting the latest fitness theories and scientifically-based approaches. This allows users to receive scientifically-backed and effective training.

[0036] The generation unit includes a health status consideration unit that takes into account the user's health status and fitness level. For example, the generation unit collects medical data and self-reported data to assess the user's health status. For example, the generation unit conducts cardiopulmonary function tests and muscle strength tests to assess the user's fitness level. For example, the generation unit adjusts the training plan based on the user's health status and fitness level. This allows the generation unit to provide a training plan that is appropriate for the user's health status and fitness level.

[0037] The reception desk analyzes the user's past training history and suggests the optimal input method. For example, the reception desk automatically displays as suggestions the body type and goals the user has frequently entered in the past. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) the user has used in the past. For example, the reception desk predicts and suggests the body type and goals to be used at a specific time of day based on the user's past training history. This allows the reception desk to provide the optimal input method based on the user's past training history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0038] The reception desk collects data to create a more detailed training plan by having the user input additional information about their lifestyle and diet. For example, the reception desk may have the user input their diet and create a training plan that takes nutritional balance into consideration. For example, the reception desk may have the user input their sleep patterns and suggest the optimal training time. For example, the reception desk may have the user input their daily rhythm and create a training plan that can be easily incorporated into their daily life. This allows the reception desk to provide a detailed training plan based on the user's lifestyle and diet information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0039] The reception desk takes the user's geographical location into consideration and prompts them to input information to propose a region-specific training plan. For example, the reception desk proposes a training plan tailored to the climate of the user's area. For example, the reception desk proposes a training plan tailored to the topography of the user's area. For example, the reception desk proposes a training plan tailored to the culture and customs of the user's area. This allows the reception desk to provide a region-specific training plan based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0040] The reception desk analyzes the user's social media activity and suggests relevant training goals. For example, the reception desk suggests a training plan based on fitness goals the user has shared on social media. For example, the reception desk suggests a training plan based on the training methods of fitness influencers the user follows. For example, the reception desk suggests a training plan based on the trends of online fitness communities the user participates in. This allows the reception desk to provide relevant training goals based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0041] The generation unit creates an optimal training plan by referring to the user's past training data when generating a training plan. For example, the generation unit creates an optimal plan based on the results of the user's past training. For example, the generation unit selects effective exercises from the user's past training data. For example, the generation unit analyzes the user's past training data and creates a plan according to the user's progress. In this way, the generation unit can provide an optimal training plan based on the user's past training data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without using AI.

[0042] The generation unit applies different algorithms depending on the user's health condition and fitness level when generating a training plan. For example, the generation unit considers the user's health condition and suggests low-impact exercises. For example, the generation unit suggests training of appropriate intensity according to the user's fitness level. For example, the generation unit adjusts the training frequency based on the user's health condition and fitness level. In this way, the generation unit can provide a training plan that is tailored to the user's health condition and fitness level. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI.

[0043] The generation unit adjusts the schedule of the training plan based on the user's lifestyle when generating the training plan. For example, the generation unit adjusts the training time to match the user's work or school schedule. For example, the generation unit suggests the optimal training time based on the user's sleep pattern. For example, the generation unit adjusts the training plan to match the user's meal times. In this way, the generation unit can provide an optimal training plan based on the user's lifestyle. Some or all of the above processes in the generation unit may be performed using AI, for example, or without using AI.

[0044] The generation unit customizes the training plan by taking into account the user's dietary information when generating the training plan. For example, the generation unit creates a training plan that considers nutritional balance based on the user's diet. For example, the generation unit adjusts the training time to match the user's meal times. For example, the generation unit suggests appropriate exercises according to the user's dietary restrictions. In this way, the generation unit can provide an optimal training plan based on the user's dietary information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0045] The service provider selects the optimal delivery method when providing a training plan by referring to the user's past training history. For example, the service provider selects the optimal method based on the delivery methods the user has used in the past. For example, the service provider selects an effective delivery method from the user's past training history. For example, the service provider analyzes the user's past training history and selects a delivery method according to their progress. This allows the service provider to provide the optimal delivery method based on the user's past training history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0046] The service provider selects the optimal display method when providing a training plan, taking into account the user's device information. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. For example, if the user is using a tablet, the service provider provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider provides a concise and highly visible display method. This allows the service provider to provide the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0047] The service provider, when providing a training plan, provides region-specific training information that takes into account the user's geographical location. For example, the service provider provides training information tailored to the climate of the user's area of ​​residence. For example, the service provider provides training information tailored to the topography of the user's area of ​​residence. For example, the service provider provides training information tailored to the culture and customs of the user's area of ​​residence. This allows the service provider to provide region-specific training information based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0048] The service provider analyzes the user's social media activity and provides relevant training information when providing a training plan. For example, the service provider provides training information based on fitness goals shared by the user on social media. For example, the service provider provides training information based on training methods of fitness influencers followed by the user. For example, the service provider provides training information based on trends in online fitness communities in which the user participates. This allows the service provider to provide relevant training information based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0049] The progress delivery unit selects the optimal display method by referring to the user's past progress data when providing progress. For example, the progress delivery unit selects the optimal method based on the display methods the user has used in the past. For example, the progress delivery unit selects an effective display method from the user's past progress data. For example, the progress delivery unit analyzes the user's past progress data and selects a display method appropriate to the progress. In this way, the progress delivery unit can provide the optimal display method based on the user's past progress data. Some or all of the above processing in the progress delivery unit may be performed using AI, for example, or without using AI.

[0050] The progress delivery unit selects the optimal display method when providing progress, taking into account the user's device information. For example, if the user is using a smartphone, the progress delivery unit provides a display method that matches the screen size. For example, if the user is using a tablet, the progress delivery unit provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the progress delivery unit provides a concise and highly visible display method. In this way, the progress delivery unit can provide the optimal display method based on the user's device information. Some or all of the above processing in the progress delivery unit may be performed using AI, for example, or without using AI.

[0051] The progress delivery unit provides region-specific progress information, taking into account the user's geographical location when providing progress. For example, the progress delivery unit provides progress information tailored to the climate of the user's region. For example, the progress delivery unit provides progress information tailored to the topography of the user's region. For example, the progress delivery unit provides progress information tailored to the culture and customs of the user's region. This enables the progress delivery unit to provide region-specific progress information based on the user's geographical location. Some or all of the above processing in the progress delivery unit may be performed using AI, for example, or without using AI.

[0052] The progress delivery unit analyzes the user's social media activity and provides relevant progress information when providing progress. For example, the progress delivery unit provides progress information based on fitness goals shared by the user on social media. For example, the progress delivery unit provides progress information based on the progress methods of fitness influencers followed by the user. For example, the progress delivery unit provides progress information based on trends in online fitness communities in which the user participates. This allows the progress delivery unit to provide relevant progress information based on the user's social media activity. Some or all of the above processing in the progress delivery unit may be performed using AI, for example, or without AI.

[0053] The motivation maintenance unit selects the optimal method when maintaining motivation by referring to the user's past motivation data. For example, the motivation maintenance unit selects the optimal method based on the motivation maintenance methods the user has used in the past. For example, the motivation maintenance unit selects an effective method from the user's past motivation data. For example, the motivation maintenance unit analyzes the user's past motivation data and selects a method according to the progress. In this way, the motivation maintenance unit can provide the optimal method based on the user's past motivation data. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without using AI.

[0054] The motivation maintenance unit selects the optimal method for maintaining motivation, taking into account the user's device information. For example, if the user is using a smartphone, the motivation maintenance unit provides a method adapted to the screen size. For example, if the user is using a tablet, the motivation maintenance unit provides a method optimized for a large screen. For example, if the user is using a smartwatch, the motivation maintenance unit provides a concise and highly visible method. This allows the motivation maintenance unit to provide the optimal method based on the user's device information. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without using AI.

[0055] The motivation maintenance unit provides region-specific motivation maintenance methods, taking into account the user's geographical location information when maintaining motivation. For example, the motivation maintenance unit provides motivation maintenance methods tailored to the climate of the area where the user lives. For example, the motivation maintenance unit provides motivation maintenance methods tailored to the topography of the area where the user lives. For example, the motivation maintenance unit provides motivation maintenance methods tailored to the culture and customs of the area where the user lives. In this way, the motivation maintenance unit can provide region-specific motivation maintenance methods based on the user's geographical location information. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without using AI.

[0056] The motivation maintenance unit analyzes the user's social media activity and provides relevant motivation maintenance methods when it comes to maintaining motivation. For example, the motivation maintenance unit provides motivation maintenance methods based on fitness goals shared by the user on social media. For example, the motivation maintenance unit provides methods by referencing the motivation maintenance methods of fitness influencers followed by the user. For example, the motivation maintenance unit provides motivation maintenance methods based on trends in online fitness communities in which the user participates. In this way, the motivation maintenance unit can provide relevant motivation maintenance methods based on the user's social media activity. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without using AI.

[0057] The adjustment unit selects the optimal adjustment method when adjusting training content by referring to the user's past training data. For example, the adjustment unit selects the optimal adjustment method based on the results of the user's past training. For example, the adjustment unit selects an effective adjustment method from the user's past training data. For example, the adjustment unit analyzes the user's past training data and selects an adjustment method according to the progress. In this way, the adjustment unit can provide the optimal adjustment method based on the user's past training data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI.

[0058] The adjustment unit applies different adjustment methods depending on the user's health condition and fitness level when adjusting training content. For example, the adjustment unit may suggest low-intensity exercises considering the user's health condition. For example, the adjustment unit may suggest training of appropriate intensity according to the user's fitness level. For example, the adjustment unit may adjust the training frequency based on the user's health condition and fitness level. In this way, the adjustment unit can provide a method for adjusting training content according to the user's health condition and fitness level. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI.

[0059] The adjustment unit, when adjusting training content, takes into account the user's geographical location information to provide region-specific training content. For example, the adjustment unit provides training content tailored to the climate of the area where the user lives. For example, the adjustment unit provides training content tailored to the topography of the area where the user lives. For example, the adjustment unit provides training content tailored to the culture and customs of the area where the user lives. In this way, the adjustment unit can provide region-specific training content based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI.

[0060] The adjustment unit analyzes the user's social media activity and provides relevant training content when adjusting training content. For example, the adjustment unit provides training content based on fitness goals shared by the user on social media. For example, the adjustment unit provides training content based on training methods of fitness influencers followed by the user. For example, the adjustment unit provides training content based on trends in online fitness communities in which the user participates. This allows the adjustment unit to provide relevant training content based on the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The generation unit may also include a health status consideration unit that takes into account the user's health status and fitness level. For example, it can collect medical data or self-reported data to assess the user's health status. It can also conduct cardiopulmonary function tests and muscle strength tests to assess the user's fitness level. Furthermore, it can adjust the training plan based on the user's health status and fitness level. As a result, the generation unit can provide a training plan that is tailored to the user's health status and fitness level.

[0063] The reception desk can analyze the user's past training history and suggest the optimal input method. For example, it can automatically display as suggestions the body type and goals the user has frequently entered in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest the body type and goals to be used at specific times based on the user's past training history. In this way, the reception desk can provide the optimal input method based on the user's past training history.

[0064] The reception desk can also collect additional data on users' lifestyles and dietary information to create more detailed training plans. For example, it can ask users to input their meal details to create a training plan that takes nutritional balance into account. It can also ask users to input their sleep patterns to suggest optimal training times. Furthermore, it can ask users to input their daily routines to create training plans that can be easily incorporated into their daily lives. This allows the reception desk to provide detailed training plans based on the user's lifestyle and dietary information.

[0065] The reception desk can also take into account the user's geographical location and request information to suggest region-specific training plans. For example, it can suggest training plans tailored to the climate of the user's area. It can also suggest training plans tailored to the topography of the user's area. Furthermore, it can suggest training plans tailored to the culture and customs of the user's area. This allows the reception desk to provide region-specific training plans based on the user's geographical location.

[0066] The reception desk can also analyze users' social media activity and suggest relevant training goals. For example, it can suggest a training plan based on fitness goals shared by users on social media. It can also suggest a training plan based on the training methods of fitness influencers that users follow. Furthermore, it can suggest a training plan based on trends in the online fitness communities that users participate in. In this way, the reception desk can provide relevant training goals based on users' social media activity.

[0067] The generation unit can also create an optimal training plan by referring to the user's past training data. For example, it can create an optimal plan based on the results of the user's past training. It can also select effective exercises from the user's past training data. Furthermore, it can analyze the user's past training data and create a plan that is appropriate to their progress. As a result, the generation unit can provide an optimal training plan based on the user's past training data.

[0068] The generation unit can also apply different algorithms to the user's health condition and fitness level when generating a training plan. For example, it can suggest low-impact exercises considering the user's health condition. It can also suggest training of appropriate intensity according to the user's fitness level. Furthermore, it can adjust the training frequency based on the user's health condition and fitness level. As a result, the generation unit can provide a training plan tailored to the user's health condition and fitness level.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk inputs the user's body type, goals, and progress. For example, the reception desk provides an interface for the user to input their body type and goals. The user can input information such as weight, height, and body fat percentage, and set goals such as weight loss or muscle gain. An interface is also provided for the user to input their training progress. Step 2: The generation unit automatically creates a training plan based on the information entered by the reception unit. For example, it generates an appropriate training menu based on the user's body type and goals. For weight loss goals, it creates a plan that combines cardio exercises and strength training, and for muscle strengthening goals, it creates a plan that includes weight training and resistance training. Step 3: The providing unit provides the training plan generated by the generating unit in the form of videos and illustrations. For example, it provides the user with videos and illustrations showing the movements of the training, demonstrating the correct form for exercises such as squats and planks, as well as stretching and warm-up methods. Step 4: The progress delivery department provides progress information via dashboards and progress reports based on the training plan provided by the department. For example, it visually displays the user's training progress using graphs and charts, and generates and provides meters indicating training achievement and weekly / monthly progress reports. Step 5: The Motivation Maintenance Department maintains motivation based on the progress provided by the Progress Delivery Department. For example, it provides encouraging messages and rewards to users, awards badges and points upon achieving training goals, and sends reminders to encourage continued training. Step 6: The adjustment unit flexibly adjusts the training content based on the motivation maintained by the motivation maintenance unit. For example, it adjusts the training menu according to the user's training progress and fitness level, increasing the training intensity when the user becomes accustomed to the training and reducing the training content when the user is injured.

[0071] (Example of form 2) The personal training system according to an embodiment of the present invention is a system that provides a more convenient and affordable service than traditional human personal trainers to people aiming to improve their health, increase their physical fitness, lose weight, or improve their physique. This system begins with the user inputting their body type, goals, and daily progress. Next, the AI ​​automatically creates a training plan based on this information. The training plan is explained clearly using videos and illustrations. The AI ​​also provides the user's progress through a dashboard and progress reports, and maintains motivation using gamification and reminders. Furthermore, the AI ​​flexibly adjusts the training content according to the user's health and fitness level. For example, it proposes menus that can be used by beginners, athletes, and the elderly, and provides training content that takes injuries and pre-existing conditions into consideration. With this system, users can train at home or on the go, eliminating time constraints and cost problems. In addition, because the AI ​​automatically creates a training plan tailored to the goals and fitness level, the problem of not knowing the correct training method is also solved. For example, if a user sets a goal of "losing 5 kg," the AI ​​will create a training plan tailored to that goal and allow the user to check their progress on the dashboard. Furthermore, the system flexibly adjusts the plan according to the training progress and provides effective reminders. In this way, the AI-powered personal training system aims to provide an accessible and affordable service to people seeking to improve their health, increase their physical fitness, lose weight, or improve their physique, thereby raising users' health awareness, closing disparities, and transforming their lifestyles. As a result, the personal training system can efficiently support users in improving their health, increasing their physical fitness, losing weight, and improving their physique.

[0072] The personal training system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a progress provision unit, a motivation maintenance unit, and an adjustment unit. The reception unit inputs the user's body type, goals, and progress. For example, the reception unit provides an interface for the user to input their body type and goals. The reception unit allows the user to input information such as weight, height, and body fat percentage. The reception unit also allows the user to input goals they have set (e.g., weight loss, muscle gain). Furthermore, the reception unit provides an interface for the user to input their training progress. The generation unit automatically creates a training plan based on the information input by the reception unit. For example, the generation unit generates an appropriate training menu based on the user's body type and goals. For example, the generation unit creates a plan that combines cardio exercises and strength training for the user's weight loss goal. For example, the generation unit creates a plan that includes weight training and resistance training for the user's muscle gain goal. The provision unit provides the training plan generated by the generation unit in the form of videos and illustrations. The service provider provides users with, for example, videos and illustrations demonstrating training movements. The service provider provides videos demonstrating the correct form for exercises such as squats and planks. The service provider provides illustrations demonstrating stretching and warm-up methods. The progress provider provides progress information on dashboards and progress reports based on the training plan provided by the service provider. The progress provider visually displays the user's training progress using graphs and charts. The progress provider provides a meter that shows the user's training achievement level. The progress provider generates and provides weekly and monthly progress reports to users. The motivation maintenance unit maintains motivation based on the progress information provided by the progress provider. The motivation maintenance unit provides users with, for example, encouraging messages and rewards. The motivation maintenance unit awards users badges and points when they achieve their training goals. The motivation maintenance unit sends users reminders to encourage them to continue training.The adjustment unit flexibly adjusts the training content based on the motivation maintained by the motivation maintenance unit. For example, the adjustment unit adjusts the training menu according to the user's training progress and fitness level. For example, the adjustment unit increases the training intensity when the user becomes accustomed to the training. For example, the adjustment unit reduces the training content when the user is injured. As a result, the personal training system according to the embodiment can efficiently input, generate, provide, manage progress, maintain motivation, and adjust the user's body type, goals, and progress.

[0073] The reception desk inputs the user's body type, goals, and progress. For example, the reception desk provides an interface for users to input their body type and goals. The reception desk allows users to input information such as weight, height, and body fat percentage. It also allows users to input the goals they have set (e.g., weight loss, muscle gain). Furthermore, the reception desk provides an interface for users to input their training progress. The reception desk has an intuitive user interface to make it easy for users to input information. For example, it uses sliders and drop-down menus to reduce the effort required for users to input weight and height. The reception desk also allows users to select specific goals when setting goals. For example, it provides options such as weight loss, muscle gain, and endurance improvement, allowing users to choose the option that best suits their goals. Furthermore, the reception desk provides an interface for users to input their training progress. For example, it allows users to input their daily training content and achievement level. This allows users to easily record their progress and review it later. The reception desk securely stores the information entered by users and makes it available for use in collaboration with other departments. For example, information about the user's body type and goals is used by the generation unit when creating a training plan. Information about the user's progress is used by the progress reporting unit and the motivation maintenance unit. This allows the reception unit to efficiently manage user information and improve the overall functionality of the system.

[0074] The generation unit automatically creates training plans based on information entered by the reception unit. For example, the generation unit generates appropriate training menus based on the user's body type and goals. For example, for a user's weight loss goal, the generation unit creates a plan that combines cardio exercises and strength training. For example, for a user's muscle-building goal, the generation unit creates a plan that includes weight training and resistance training. The generation unit utilizes AI to generate training plans optimized for the user's individual needs. For example, the AI ​​analyzes the user's body type, goals, and progress to suggest the optimal training menu. The AI ​​creates effective training plans by referring to past data and the success stories of other users. The generation unit continuously improves training plans based on user feedback. For example, when a user provides feedback on a training plan, the generation unit adjusts the plan to reflect that feedback. This allows users to receive training plans optimized for their needs. The generation unit considers the user's health condition and limitations when generating training plans. For example, if a user has a specific health problem, the generation unit creates a training menu that takes that problem into account. This allows users to train safely and effectively. The generation unit employs an approach based on the latest fitness theories and scientific evidence in generating training plans. This allows users to receive scientifically supported and effective training.

[0075] The provider unit provides training plans generated by the generator unit in the form of videos and illustrations. For example, the provider unit provides users with videos and illustrations demonstrating training movements. For example, the provider unit provides videos demonstrating the correct form for exercises such as squats and planks. For example, the provider unit provides illustrations demonstrating stretching and warm-up methods. The provider unit provides detailed guides to help users perform training correctly. For example, in videos, a trainer demonstrates the exercises while explaining the correct form and points to watch out for. This allows users to train correctly and reduce the risk of injury. The provider unit organizes videos and illustrations in an easy-to-understand manner so that users can refer to them when training. For example, videos and illustrations are categorized by type of exercise and purpose so that users can easily find the information they need. The provider unit provides encouraging messages and rewards to help users maintain their motivation while training. For example, encouraging messages are displayed or points and badges are awarded when users complete training. This helps users maintain their motivation to continue training. The provider unit provides users with the information they need when training in real time. For example, the system can provide real-time feedback on form corrections and instructions for the next exercise while the user is training. This allows the user to train more efficiently.

[0076] The progress delivery department provides progress information via dashboards and progress reports based on the training plan provided by the department. For example, the progress delivery department visually displays the user's training progress using graphs and charts. For example, the progress delivery department provides a meter that shows the user's training achievement level. For example, the progress delivery department generates and provides weekly and monthly progress reports to users. The progress delivery department provides information in a visually easy-to-understand format so that users can grasp their progress at a glance. For example, it visually displays the user's training progress using graphs and charts. This allows users to easily check their progress and maintain their motivation. The progress delivery department analyzes the user's training data and reports on the progress in detail. For example, it provides a meter that shows the user's training achievement level and goal achievement rate. This allows users to concretely understand the effectiveness of their training. Based on the user's training data, the progress delivery department generates weekly and monthly progress reports. For example, it reports in detail the training content and progress that the user achieved in a week or a month. This allows users to regularly check their training progress and plan towards achieving their goals. The progress reporting department improves its progress reporting methods based on user feedback. For example, when users provide feedback on progress reports, the progress reporting department adjusts the reporting method to reflect that feedback. This allows users to receive progress reports optimized to their needs.

[0077] The Motivation Maintenance Unit maintains motivation based on the progress provided by the Progress Provision Unit. For example, the Motivation Maintenance Unit provides encouraging messages and rewards to users. For example, the Motivation Maintenance Unit awards badges or points when users achieve their training goals. For example, the Motivation Maintenance Unit sends reminders to users to encourage continued training. The Motivation Maintenance Unit provides various ways for users to continue training. For example, it displays encouraging messages or awards points or badges when users complete training. This helps users maintain their motivation to continue training. The Motivation Maintenance Unit sends reminders to users to continue training. For example, it sends regular reminders to users so they don't forget to train. This helps users continue training. The Motivation Maintenance Unit provides a reward system to encourage users to continue training. For example, it awards badges or points when users achieve their training goals. This helps users maintain their motivation to continue training. The Motivation Maintenance Unit provides a community to encourage users to continue training. For example, it provides a community where users can interact with and encourage other users. This helps users maintain their motivation to continue training.

[0078] The adjustment unit flexibly adjusts the training content based on the motivation maintained by the motivation maintenance unit. For example, the adjustment unit adjusts the training menu according to the user's training progress and fitness level. For example, the adjustment unit increases the training intensity when the user becomes accustomed to training. For example, the adjustment unit reduces the training content when the user is injured. The adjustment unit flexibly adjusts the training menu according to the user's training progress and fitness level. For example, when the user becomes accustomed to training, increasing the training intensity can yield further results. Also, if the user is injured, reducing the training content can prevent the injury from worsening. The adjustment unit adjusts the training menu based on user feedback. For example, when the user provides feedback on the training menu, the adjustment unit adjusts the menu to reflect that feedback. This allows the user to receive a training menu optimized for their needs. The adjustment unit adjusts the training menu considering the user's health condition and limitations. For example, if the user has a specific health problem, a training menu that takes that problem into consideration is created. This allows the user to train safely and effectively. The adjustment unit adjusts the training menu by adopting the latest fitness theories and scientifically-based approaches. This allows users to receive scientifically-backed and effective training.

[0079] The generation unit includes a health status consideration unit that takes into account the user's health status and fitness level. For example, the generation unit collects medical data and self-reported data to assess the user's health status. For example, the generation unit conducts cardiopulmonary function tests and muscle strength tests to assess the user's fitness level. For example, the generation unit adjusts the training plan based on the user's health status and fitness level. This allows the generation unit to provide a training plan that is appropriate for the user's health status and fitness level.

[0080] The reception desk estimates the user's emotions and adjusts the input interface design based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. If the user is relaxed, the reception desk provides detailed input options and suggests a customizable input method. If the user is in a hurry, the reception desk prioritizes voice input, allowing for quick input of body type, goals, and progress. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the reception desk to provide an input interface that responds to the user's emotions.

[0081] The reception desk analyzes the user's past training history and suggests the optimal input method. For example, the reception desk automatically displays as suggestions the body type and goals the user has frequently entered in the past. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) the user has used in the past. For example, the reception desk predicts and suggests the body type and goals to be used at a specific time of day based on the user's past training history. This allows the reception desk to provide the optimal input method based on the user's past training history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0082] The reception desk collects data to create a more detailed training plan by having the user input additional information about their lifestyle and diet. For example, the reception desk may have the user input their diet and create a training plan that takes nutritional balance into consideration. For example, the reception desk may have the user input their sleep patterns and suggest the optimal training time. For example, the reception desk may have the user input their daily rhythm and create a training plan that can be easily incorporated into their daily life. This allows the reception desk to provide a detailed training plan based on the user's lifestyle and diet information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0083] The reception desk estimates the user's emotions and determines the priority of input based on the estimated emotions. For example, if the user is stressed, the reception desk prioritizes inputting only the most important information. For example, if the user is relaxed, the reception desk prioritizes inputting detailed information. For example, if the user is in a hurry, the reception desk prioritizes inputting only the minimum necessary information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the reception desk to provide input priorities according to the user's emotions.

[0084] The reception desk takes the user's geographical location into consideration and prompts them to input information to propose a region-specific training plan. For example, the reception desk proposes a training plan tailored to the climate of the user's area. For example, the reception desk proposes a training plan tailored to the topography of the user's area. For example, the reception desk proposes a training plan tailored to the culture and customs of the user's area. This allows the reception desk to provide a region-specific training plan based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0085] The reception desk analyzes the user's social media activity and suggests relevant training goals. For example, the reception desk suggests a training plan based on fitness goals the user has shared on social media. For example, the reception desk suggests a training plan based on the training methods of fitness influencers the user follows. For example, the reception desk suggests a training plan based on the trends of online fitness communities the user participates in. This allows the reception desk to provide relevant training goals based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0086] The generation unit estimates the user's emotions and adjusts the difficulty level of the training plan based on the estimated emotions. For example, if the user is stressed, the generation unit creates a training plan with a lower difficulty level. For example, if the user is relaxed, the generation unit creates a training plan with a higher difficulty level. For example, if the user is in a hurry, the generation unit creates a short and effective training plan. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the generation unit to provide a training plan with an appropriate difficulty level based on the user's emotions.

[0087] The generation unit creates an optimal training plan by referring to the user's past training data when generating a training plan. For example, the generation unit creates an optimal plan based on the results of the user's past training. For example, the generation unit selects effective exercises from the user's past training data. For example, the generation unit analyzes the user's past training data and creates a plan according to the user's progress. In this way, the generation unit can provide an optimal training plan based on the user's past training data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without using AI.

[0088] The generation unit applies different algorithms depending on the user's health condition and fitness level when generating a training plan. For example, the generation unit considers the user's health condition and suggests low-impact exercises. For example, the generation unit suggests training of appropriate intensity according to the user's fitness level. For example, the generation unit adjusts the training frequency based on the user's health condition and fitness level. In this way, the generation unit can provide a training plan that is tailored to the user's health condition and fitness level. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI.

[0089] The generation unit estimates the user's emotions and adjusts the length of the training plan based on the estimated emotions. For example, if the user is stressed, the generation unit creates a short, effective training plan. For example, if the user is relaxed, the generation unit creates a longer training plan. For example, if the user is in a hurry, the generation unit creates a short, focused training plan. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the generation unit to provide a training plan length that is appropriate for the user's emotions.

[0090] The generation unit adjusts the schedule of the training plan based on the user's lifestyle when generating the training plan. For example, the generation unit adjusts the training time to match the user's work or school schedule. For example, the generation unit suggests the optimal training time based on the user's sleep pattern. For example, the generation unit adjusts the training plan to match the user's meal times. In this way, the generation unit can provide an optimal training plan based on the user's lifestyle. Some or all of the above processes in the generation unit may be performed using AI, for example, or without using AI.

[0091] The generation unit customizes the training plan by taking into account the user's dietary information when generating the training plan. For example, the generation unit creates a training plan that considers nutritional balance based on the user's diet. For example, the generation unit adjusts the training time to match the user's meal times. For example, the generation unit suggests appropriate exercises according to the user's dietary restrictions. In this way, the generation unit can provide an optimal training plan based on the user's dietary information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0092] The service provider estimates the user's emotions and adjusts the delivery method of the training plan based on the estimated emotions. For example, if the user is stressed, the service provider selects a simple and highly visual delivery method. For example, if the user is relaxed, the service provider selects a delivery method that includes detailed information. For example, if the user is in a hurry, the service provider selects a delivery method that gets straight to the point. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the service provider to deliver training plans in a way that is appropriate to the user's emotions.

[0093] The service provider selects the optimal delivery method when providing a training plan by referring to the user's past training history. For example, the service provider selects the optimal method based on the delivery methods the user has used in the past. For example, the service provider selects an effective delivery method from the user's past training history. For example, the service provider analyzes the user's past training history and selects a delivery method according to their progress. This allows the service provider to provide the optimal delivery method based on the user's past training history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0094] The service provider selects the optimal display method when providing a training plan, taking into account the user's device information. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. For example, if the user is using a tablet, the service provider provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider provides a concise and highly visible display method. This allows the service provider to provide the optimal display method based on the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0095] The service provider estimates the user's emotions and adjusts the order in which the training plan is delivered based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing the most important information. For example, if the user is relaxed, the service provider will provide detailed information. For example, if the user is in a hurry, the service provider will prioritize providing only the essential information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the service provider to deliver the training plan in an order that suits the user's emotions.

[0096] The service provider, when providing a training plan, provides region-specific training information that takes into account the user's geographical location. For example, the service provider provides training information tailored to the climate of the user's area of ​​residence. For example, the service provider provides training information tailored to the topography of the user's area of ​​residence. For example, the service provider provides training information tailored to the culture and customs of the user's area of ​​residence. This allows the service provider to provide region-specific training information based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0097] The service provider analyzes the user's social media activity and provides relevant training information when providing a training plan. For example, the service provider provides training information based on fitness goals shared by the user on social media. For example, the service provider provides training information based on training methods of fitness influencers followed by the user. For example, the service provider provides training information based on trends in online fitness communities in which the user participates. This allows the service provider to provide relevant training information based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0098] The progress reporting unit estimates the user's emotions and adjusts the display method of the progress report based on the estimated emotions. For example, if the user is stressed, the progress reporting unit provides a simple and highly visible display method. For example, if the user is relaxed, the progress reporting unit provides a display method that includes detailed information. For example, if the user is in a hurry, the progress reporting unit provides a display method that gets straight to the point. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the progress reporting unit to provide a progress report display method that is appropriate to the user's emotions.

[0099] The progress delivery unit selects the optimal display method by referring to the user's past progress data when providing progress. For example, the progress delivery unit selects the optimal method based on the display methods the user has used in the past. For example, the progress delivery unit selects an effective display method from the user's past progress data. For example, the progress delivery unit analyzes the user's past progress data and selects a display method appropriate to the progress. In this way, the progress delivery unit can provide the optimal display method based on the user's past progress data. Some or all of the above processing in the progress delivery unit may be performed using AI, for example, or without using AI.

[0100] The progress delivery unit selects the optimal display method when providing progress, taking into account the user's device information. For example, if the user is using a smartphone, the progress delivery unit provides a display method that matches the screen size. For example, if the user is using a tablet, the progress delivery unit provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the progress delivery unit provides a concise and highly visible display method. In this way, the progress delivery unit can provide the optimal display method based on the user's device information. Some or all of the above processing in the progress delivery unit may be performed using AI, for example, or without using AI.

[0101] The progress delivery unit estimates the user's emotions and prioritizes progress reports based on the estimated emotions. For example, if the user is stressed, the progress delivery unit will prioritize displaying the most important information. For example, if the user is relaxed, the progress delivery unit will display detailed information. For example, if the user is in a hurry, the progress delivery unit will prioritize displaying only the essential information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the progress delivery unit to provide progress report priorities according to the user's emotions.

[0102] The progress delivery unit provides region-specific progress information, taking into account the user's geographical location when providing progress. For example, the progress delivery unit provides progress information tailored to the climate of the user's region. For example, the progress delivery unit provides progress information tailored to the topography of the user's region. For example, the progress delivery unit provides progress information tailored to the culture and customs of the user's region. This enables the progress delivery unit to provide region-specific progress information based on the user's geographical location. Some or all of the above processing in the progress delivery unit may be performed using AI, for example, or without using AI.

[0103] The progress delivery unit analyzes the user's social media activity and provides relevant progress information when providing progress. For example, the progress delivery unit provides progress information based on fitness goals shared by the user on social media. For example, the progress delivery unit provides progress information based on the progress methods of fitness influencers followed by the user. For example, the progress delivery unit provides progress information based on trends in online fitness communities in which the user participates. This allows the progress delivery unit to provide relevant progress information based on the user's social media activity. Some or all of the above processing in the progress delivery unit may be performed using AI, for example, or without AI.

[0104] The motivation maintenance unit estimates the user's emotions and adjusts its motivation maintenance methods based on the estimated emotions. For example, if the user is stressed, the motivation maintenance unit provides relaxing music or messages. For example, if the user is relaxed, the motivation maintenance unit provides encouraging messages or challenges. For example, if the user is in a hurry, the motivation maintenance unit provides tasks that allow for a sense of accomplishment in a short time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the motivation maintenance unit to provide motivation maintenance methods that are tailored to the user's emotions.

[0105] The motivation maintenance unit selects the optimal method when maintaining motivation by referring to the user's past motivation data. For example, the motivation maintenance unit selects the optimal method based on the motivation maintenance methods the user has used in the past. For example, the motivation maintenance unit selects an effective method from the user's past motivation data. For example, the motivation maintenance unit analyzes the user's past motivation data and selects a method according to the progress. In this way, the motivation maintenance unit can provide the optimal method based on the user's past motivation data. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without using AI.

[0106] The motivation maintenance unit selects the optimal method for maintaining motivation, taking into account the user's device information. For example, if the user is using a smartphone, the motivation maintenance unit provides a method adapted to the screen size. For example, if the user is using a tablet, the motivation maintenance unit provides a method optimized for a large screen. For example, if the user is using a smartwatch, the motivation maintenance unit provides a concise and highly visible method. This allows the motivation maintenance unit to provide the optimal method based on the user's device information. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without using AI.

[0107] The motivation maintenance unit estimates the user's emotions and determines motivation maintenance priorities based on the estimated emotions. For example, if the user is stressed, the motivation maintenance unit will prioritize providing the most important tasks. For example, if the user is relaxed, the motivation maintenance unit will provide detailed tasks. For example, if the user is in a hurry, the motivation maintenance unit will prioritize providing the minimum necessary tasks. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the motivation maintenance unit to provide motivation maintenance priorities according to the user's emotions.

[0108] The motivation maintenance unit provides region-specific motivation maintenance methods, taking into account the user's geographical location information when maintaining motivation. For example, the motivation maintenance unit provides motivation maintenance methods tailored to the climate of the area where the user lives. For example, the motivation maintenance unit provides motivation maintenance methods tailored to the topography of the area where the user lives. For example, the motivation maintenance unit provides motivation maintenance methods tailored to the culture and customs of the area where the user lives. In this way, the motivation maintenance unit can provide region-specific motivation maintenance methods based on the user's geographical location information. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without using AI.

[0109] The motivation maintenance unit analyzes the user's social media activity and provides relevant motivation maintenance methods when it comes to maintaining motivation. For example, the motivation maintenance unit provides motivation maintenance methods based on fitness goals shared by the user on social media. For example, the motivation maintenance unit provides methods by referencing the motivation maintenance methods of fitness influencers followed by the user. For example, the motivation maintenance unit provides motivation maintenance methods based on trends in online fitness communities in which the user participates. In this way, the motivation maintenance unit can provide relevant motivation maintenance methods based on the user's social media activity. Some or all of the above processing in the motivation maintenance unit may be performed using AI, for example, or without using AI.

[0110] The adjustment unit estimates the user's emotions and determines how to adjust the training content based on the estimated emotions. For example, if the user is feeling stressed, the adjustment unit suggests a low-intensity training program. For example, if the user is relaxed, the adjustment unit suggests a high-intensity training program. For example, if the user is in a hurry, the adjustment unit suggests a short and effective training program. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the adjustment unit to provide a method for adjusting the training content according to the user's emotions.

[0111] The adjustment unit selects the optimal adjustment method when adjusting training content by referring to the user's past training data. For example, the adjustment unit selects the optimal adjustment method based on the results of the user's past training. For example, the adjustment unit selects an effective adjustment method from the user's past training data. For example, the adjustment unit analyzes the user's past training data and selects an adjustment method according to the progress. In this way, the adjustment unit can provide the optimal adjustment method based on the user's past training data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI.

[0112] The adjustment unit applies different adjustment methods depending on the user's health condition and fitness level when adjusting training content. For example, the adjustment unit may suggest low-intensity exercises considering the user's health condition. For example, the adjustment unit may suggest training of appropriate intensity according to the user's fitness level. For example, the adjustment unit may adjust the training frequency based on the user's health condition and fitness level. In this way, the adjustment unit can provide a method for adjusting training content according to the user's health condition and fitness level. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI.

[0113] The adjustment unit estimates the user's emotions and prioritizes training content based on the estimated emotions. For example, if the user is stressed, the adjustment unit will prioritize providing the most important exercises. For example, if the user is relaxed, the adjustment unit will provide detailed exercises. For example, if the user is in a hurry, the adjustment unit will prioritize providing the minimum necessary exercises. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the adjustment unit to provide training content priorities according to the user's emotions.

[0114] The adjustment unit, when adjusting training content, takes into account the user's geographical location information to provide region-specific training content. For example, the adjustment unit provides training content tailored to the climate of the area where the user lives. For example, the adjustment unit provides training content tailored to the topography of the area where the user lives. For example, the adjustment unit provides training content tailored to the culture and customs of the area where the user lives. In this way, the adjustment unit can provide region-specific training content based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI.

[0115] The adjustment unit analyzes the user's social media activity and provides relevant training content when adjusting training content. For example, the adjustment unit provides training content based on fitness goals shared by the user on social media. For example, the adjustment unit provides training content based on training methods of fitness influencers followed by the user. For example, the adjustment unit provides training content based on trends in online fitness communities in which the user participates. This allows the adjustment unit to provide relevant training content based on the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The reception desk can also estimate the user's emotions and adjust the input interface design based on those emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be offered, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing for quick input of body type, goals, and progress. In this way, the reception desk can provide an input interface that responds to the user's emotions.

[0118] The generation unit may also include a health status consideration unit that takes into account the user's health status and fitness level. For example, it can collect medical data or self-reported data to assess the user's health status. It can also conduct cardiopulmonary function tests and muscle strength tests to assess the user's fitness level. Furthermore, it can adjust the training plan based on the user's health status and fitness level. As a result, the generation unit can provide a training plan that is tailored to the user's health status and fitness level.

[0119] The reception desk can analyze the user's past training history and suggest the optimal input method. For example, it can automatically display as suggestions the body type and goals the user has frequently entered in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest the body type and goals to be used at specific times based on the user's past training history. In this way, the reception desk can provide the optimal input method based on the user's past training history.

[0120] The reception desk can also collect additional data on users' lifestyles and dietary information to create more detailed training plans. For example, it can ask users to input their meal details to create a training plan that takes nutritional balance into account. It can also ask users to input their sleep patterns to suggest optimal training times. Furthermore, it can ask users to input their daily routines to create training plans that can be easily incorporated into their daily lives. This allows the reception desk to provide detailed training plans based on the user's lifestyle and dietary information.

[0121] The reception desk can also estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, it can prioritize inputting only the most important information. If the user is relaxed, it can prioritize inputting detailed information. Furthermore, if the user is in a hurry, it can prioritize inputting only the bare minimum necessary information. In this way, the reception desk can provide input priorities that are tailored to the user's emotions.

[0122] The reception desk can also take into account the user's geographical location and request information to suggest region-specific training plans. For example, it can suggest training plans tailored to the climate of the user's area. It can also suggest training plans tailored to the topography of the user's area. Furthermore, it can suggest training plans tailored to the culture and customs of the user's area. This allows the reception desk to provide region-specific training plans based on the user's geographical location.

[0123] The reception desk can also analyze users' social media activity and suggest relevant training goals. For example, it can suggest a training plan based on fitness goals shared by users on social media. It can also suggest a training plan based on the training methods of fitness influencers that users follow. Furthermore, it can suggest a training plan based on trends in the online fitness communities that users participate in. In this way, the reception desk can provide relevant training goals based on users' social media activity.

[0124] The generation unit can also estimate the user's emotions and adjust the difficulty level of the training plan based on those emotions. For example, if the user is stressed, it can create a training plan with a lower difficulty level. Conversely, if the user is relaxed, it can create a training plan with a higher difficulty level. Furthermore, if the user is in a hurry, it can create a short and effective training plan. In this way, the generation unit can provide training plans with difficulty levels that are appropriate for the user's emotions.

[0125] The generation unit can also create an optimal training plan by referring to the user's past training data. For example, it can create an optimal plan based on the results of the user's past training. It can also select effective exercises from the user's past training data. Furthermore, it can analyze the user's past training data and create a plan that is appropriate to their progress. As a result, the generation unit can provide an optimal training plan based on the user's past training data.

[0126] The generation unit can also apply different algorithms to the user's health condition and fitness level when generating a training plan. For example, it can suggest low-impact exercises considering the user's health condition. It can also suggest training of appropriate intensity according to the user's fitness level. Furthermore, it can adjust the training frequency based on the user's health condition and fitness level. As a result, the generation unit can provide a training plan tailored to the user's health condition and fitness level.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The reception desk inputs the user's body type, goals, and progress. For example, the reception desk provides an interface for the user to input their body type and goals. The user can input information such as weight, height, and body fat percentage, and set goals such as weight loss or muscle gain. An interface is also provided for the user to input their training progress. Step 2: The generation unit automatically creates a training plan based on the information entered by the reception unit. For example, it generates an appropriate training menu based on the user's body type and goals. For weight loss goals, it creates a plan that combines cardio exercises and strength training, and for muscle strengthening goals, it creates a plan that includes weight training and resistance training. Step 3: The providing unit provides the training plan generated by the generating unit in the form of videos and illustrations. For example, it provides the user with videos and illustrations showing the movements of the training, demonstrating the correct form for exercises such as squats and planks, as well as stretching and warm-up methods. Step 4: The progress delivery department provides progress information via dashboards and progress reports based on the training plan provided by the department. For example, it visually displays the user's training progress using graphs and charts, and generates and provides meters indicating training achievement and weekly / monthly progress reports. Step 5: The Motivation Maintenance Department maintains motivation based on the progress provided by the Progress Delivery Department. For example, it provides encouraging messages and rewards to users, awards badges and points upon achieving training goals, and sends reminders to encourage continued training. Step 6: The adjustment unit flexibly adjusts the training content based on the motivation maintained by the motivation maintenance unit. For example, it adjusts the training menu according to the user's training progress and fitness level, increasing the training intensity when the user becomes accustomed to the training and reducing the training content when the user is injured.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, progress provision unit, motivation maintenance unit, and adjustment unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for inputting the user's body type, goals, and progress. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates a training plan based on the user's information. The provision unit is implemented by, for example, the control unit 46A of the smart device 14 and provides the generated training plan in the form of videos and illustrations. The progress provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides the user's progress status on a dashboard and in progress reports. The motivation maintenance unit is implemented by, for example, the control unit 46A of the smart device 14 and provides the user with encouraging messages and rewards. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and flexibly adjusts the training content according to the user's progress and fitness level. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, progress provision unit, motivation maintenance unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for inputting the user's body type, goals, and progress. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automatically creates a training plan based on the user's information. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated training plan in the form of videos and illustrations. The progress provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides the user's progress status on a dashboard and in progress reports. The motivation maintenance unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the user with encouraging messages and rewards. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and flexibly adjusts the training content according to the user's progress and fitness level. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, progress provision unit, motivation maintenance unit, and adjustment unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for inputting the user's body type, goals, and progress. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates a training plan based on the user's information. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides the generated training plan as a video or illustration. The progress provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides the user's progress status on a dashboard or in progress reports. The motivation maintenance unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides the user with encouraging messages and rewards. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and flexibly adjusts the training content according to the user's progress and fitness level. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, progress provision unit, motivation maintenance unit, and adjustment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for inputting the user's body type, goals, and progress. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates a training plan based on the user's information. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the generated training plan in the form of videos and illustrations. The progress provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides the user's progress status on a dashboard and in progress reports. The motivation maintenance unit is implemented by, for example, the control unit 46A of the robot 414 and provides the user with encouraging messages and rewards. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and flexibly adjusts the training content according to the user's progress and fitness level. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A reception area where users input their body type, goals, and progress, A generation unit that automatically creates a training plan based on the information entered by the reception unit, A providing unit that provides the training plan generated by the generation unit in the form of videos and illustrations, A progress provision unit provides progress information via a dashboard and progress reports based on the training plan provided by the aforementioned provision unit, A motivation maintenance unit that maintains motivation based on the progress status provided by the aforementioned progress provision unit, The system includes an adjustment unit that flexibly adjusts the training content based on the motivation maintained by the aforementioned motivation maintenance unit. A system characterized by the following features. (Note 2) The generating unit is It includes a health status consideration unit that takes into account the user's health condition and fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It analyzes the user's past training history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system collects additional data on users' lifestyles and dietary information to create more detailed training plans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system will ask users to input information that takes their geographical location into account in order to suggest region-specific training plans. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze users' social media activity and suggest relevant training goals. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is It estimates the user's emotions and adjusts the difficulty level of the training plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating a training plan, the system references the user's past training data to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating a training plan, different algorithms are applied depending on the user's health condition and fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the length of the training plan based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a training plan, the schedule is adjusted based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a training plan, the plan is customized by taking into account the user's dietary information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the training plan is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing a training plan, the system selects the optimal delivery method by referring to the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing a training plan, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order in which the training plan is delivered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing training plans, we take the user's geographical location into consideration and provide region-specific training information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing training plans, we analyze users' social media activity to provide relevant training information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned progress provision unit, It estimates the user's emotions and adjusts how progress reports are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned progress provision unit, When providing progress updates, the system will refer to the user's past progress data to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned progress provision unit, When providing progress updates, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned progress provision unit, The system estimates user sentiment and prioritizes progress reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned progress provision unit, When providing progress updates, region-specific progress information will be provided, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned progress provision unit, When providing progress updates, we analyze the user's social media activity and provide relevant progress information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned motivation maintenance unit is It estimates the user's emotions and adjusts methods for maintaining motivation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned motivation maintenance unit is When maintaining motivation, the optimal method is selected by referring to the user's past motivation data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned motivation maintenance unit is When maintaining motivation, the optimal method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned motivation maintenance unit is It estimates the user's emotions and determines the priority for maintaining motivation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned motivation maintenance unit is To maintain motivation, the system provides region-specific motivation maintenance methods that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned motivation maintenance unit is To help users maintain motivation, we analyze their social media activity and provide relevant motivation-maintaining methods. The system described in Appendix 1, characterized by the features described herein. (Note 33) The adjustment unit is, The system estimates the user's emotions and determines how to adjust the training content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, When adjusting training content, the system selects the optimal adjustment method by referring to the user's past training data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The adjustment unit is, When adjusting training content, different adjustment methods are applied depending on the user's health condition and fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 36) The adjustment unit is, It estimates the user's emotions and prioritizes training content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The adjustment unit is, When adjusting training content, we take the user's geographical location into consideration to provide region-specific training. The system described in Appendix 1, characterized by the features described herein. (Note 38) The adjustment unit is, When adjusting training content, we analyze users' social media activity to provide relevant training material. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their body type, goals, and progress, A generation unit that automatically creates a training plan based on the information entered by the reception unit, A providing unit that provides the training plan generated by the generation unit in the form of videos and illustrations, A progress provision unit provides progress information via a dashboard and progress reports based on the training plan provided by the aforementioned provision unit, A motivation maintenance unit that maintains motivation based on the progress status provided by the aforementioned progress provision unit, The system includes an adjustment unit that flexibly adjusts the training content based on the motivation maintained by the aforementioned motivation maintenance unit. A system characterized by the following features.

2. The generating unit is It includes a health status consideration unit that takes into account the user's health condition and fitness level. The system according to feature 1.

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

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

5. The aforementioned reception unit is The system collects additional data on users' lifestyles and dietary information to create more detailed training plans. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is The system will ask users to input information that takes their geographical location into account in order to suggest region-specific training plans. The system according to feature 1.

8. The aforementioned reception unit is Analyze users' social media activity and suggest relevant training goals. The system according to feature 1.

9. The generating unit is It estimates the user's emotions and adjusts the difficulty level of the training plan based on those emotions. The system according to feature 1.

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